From b84d8a02dfa34b7e656500220b819d25458e5df1 Mon Sep 17 00:00:00 2001 From: pulipakaa24 Date: Fri, 12 Dec 2025 15:25:24 -0600 Subject: [PATCH] commit --- Ansys Results 12-9.csv | 4472 ++++++++++++++++++ Ansys Results 12-9.xlsx | Bin 0 -> 231847 bytes Function Fitting.ipynb | 2212 +++++++++ RL_Trials/gap_error_plot_20251211_084252.png | Bin 0 -> 118046 bytes RL_Trials/gap_error_plot_20251211_102102.png | Bin 0 -> 226481 bytes RL_Trials/gap_error_plot_20251211_110643.png | Bin 0 -> 288705 bytes RL_Trials/gap_error_plot_20251211_191801.png | Bin 0 -> 162586 bytes RL_Trials/info.txt | 29 + RL_Trials/training_log_20251211_084252.txt | 109 + RL_Trials/training_log_20251211_102102.txt | 109 + RL_Trials/training_log_20251211_110643.txt | 109 + RL_Trials/training_log_20251211_121404.txt | 23 + RL_Trials/training_log_20251211_122333.txt | 19 + RL_Trials/training_log_20251211_184547.txt | 9 + RL_Trials/training_log_20251211_185541.txt | 12 + RL_Trials/training_log_20251211_190447.txt | 40 + RL_Trials/training_log_20251211_191801.txt | 112 + Use of AI.txt | 1777 +++++++ lev_PPO.ipynb | 795 ++++ lev_pod_env.py | 486 ++ mag_lev_coil.py | 24 + maglev_model.pkl | Bin 0 -> 6277 bytes maglev_predictor.py | 116 + pod.xml | 86 + requirements.txt | 156 + test_env.py | 63 + 26 files changed, 10758 insertions(+) create mode 100755 Ansys Results 12-9.csv create mode 100755 Ansys Results 12-9.xlsx create mode 100644 Function Fitting.ipynb create mode 100644 RL_Trials/gap_error_plot_20251211_084252.png create mode 100644 RL_Trials/gap_error_plot_20251211_102102.png create mode 100644 RL_Trials/gap_error_plot_20251211_110643.png create mode 100644 RL_Trials/gap_error_plot_20251211_191801.png create mode 100644 RL_Trials/info.txt create mode 100644 RL_Trials/training_log_20251211_084252.txt create mode 100644 RL_Trials/training_log_20251211_102102.txt create mode 100644 RL_Trials/training_log_20251211_110643.txt create mode 100644 RL_Trials/training_log_20251211_121404.txt create mode 100644 RL_Trials/training_log_20251211_122333.txt create mode 100644 RL_Trials/training_log_20251211_184547.txt create mode 100644 RL_Trials/training_log_20251211_185541.txt create mode 100644 RL_Trials/training_log_20251211_190447.txt create mode 100644 RL_Trials/training_log_20251211_191801.txt create mode 100644 Use of AI.txt create mode 100644 lev_PPO.ipynb create mode 100644 lev_pod_env.py create mode 100644 mag_lev_coil.py create mode 100644 maglev_model.pkl create mode 100644 maglev_predictor.py create mode 100644 pod.xml create mode 100644 requirements.txt create mode 100644 test_env.py diff --git a/Ansys Results 12-9.csv b/Ansys Results 12-9.csv new file mode 100755 index 0000000..fc52ba6 --- /dev/null +++ b/Ansys Results 12-9.csv @@ -0,0 +1,4472 @@ +"currL [A]","currR [A]","rollDeg [deg]","GapHeight [mm]","YokeForce.Force_z [newton]","YokeTorque.Torque [mNewtonMeter]" +-15,-15,0,6,185.185947003207,-14.8559560904195 +-15,-15,0,8,135.150919734968,0.756624334465506 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z{E70zLjGBdKRc(tAYQor3GkyBe?|GT+xH9Q7KHNOI)Q%{;?HizFYzxPzlr~+)A6VD zpK\n", + "RangeIndex: 4471 entries, 0 to 4470\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 currL [A] 4471 non-null int64 \n", + " 1 currR [A] 4471 non-null int64 \n", + " 2 rollDeg [deg] 4471 non-null float64\n", + " 3 GapHeight [mm] 4471 non-null float64\n", + " 4 YokeForce.Force_z [newton] 4471 non-null float64\n", + " 5 YokeTorque.Torque [mNewtonMeter] 4471 non-null float64\n", + "dtypes: float64(4), int64(2)\n", + "memory usage: 209.7 KB\n", + "\n", + "After adding mirrored data:\n", + "\n", + "RangeIndex: 8281 entries, 0 to 8280\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 currL [A] 8281 non-null int64 \n", + " 1 currR [A] 8281 non-null int64 \n", + " 2 rollDeg [deg] 8281 non-null float64\n", + " 3 GapHeight [mm] 8281 non-null float64\n", + " 4 YokeForce.Force_z [newton] 8281 non-null float64\n", + " 5 YokeTorque.Torque [mNewtonMeter] 8281 non-null float64\n", + "dtypes: float64(4), int64(2)\n", + "memory usage: 388.3 KB\n" + ] + } + ], + "source": [ + "magDf = pd.read_csv(\"Ansys Results 12-9.csv\")\n", + "magDf.info()\n", + "\n", + "condition = magDf['GapHeight [mm]'] < 5\n", + "\n", + "magDf = magDf[~condition]\n", + "\n", + "nzRoll = magDf[magDf['rollDeg [deg]'] != 0]\n", + "\n", + "mirrored_data = nzRoll.copy()\n", + "mirrored_data['rollDeg [deg]'] = -nzRoll['rollDeg [deg]']\n", + "mirrored_data['currL [A]'], mirrored_data['currR [A]'] = nzRoll['currR [A]'].values, nzRoll['currL [A]'].values\n", + "mirrored_data['YokeTorque.Torque [mNewtonMeter]'] = -nzRoll['YokeTorque.Torque [mNewtonMeter]']\n", + "\n", + "magDf = pd.concat([magDf, mirrored_data], ignore_index=True)\n", + "\n", + "magDf = magDf.sort_values(['rollDeg [deg]', 'currL [A]', 'currR [A]', 'GapHeight [mm]']).reset_index(drop=True)\n", + "\n", + "print()\n", + "print(\"After adding mirrored data:\")\n", + "magDf.info()" + ] + }, + { + "cell_type": "markdown", + "id": "ecd1f124", + "metadata": {}, + "source": [ + "### After removing non-model rows, we have 4459 rows * ~2 for the mirrored roll angles, which matches the number of simulations conducted in Ansys." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a76017e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "first_subset = magDf.iloc[0:13]\n", + "x = first_subset[\"GapHeight [mm]\"]\n", + "x = 1/x\n", + "y = first_subset[\"YokeForce.Force_z [newton]\"]\n", + "plt.plot(x, y)\n", + "plt.title(r\"$-15A, -15A, 0^\\circ roll$\")\n", + "plt.xlabel(\"Inverse Gap Height (1/mm)\")\n", + "plt.ylabel(\"Force on magnetic yoke (N)\")\n", + "plt.show()\n", + "\n", + "# Experimented here with ways to represent gap-height-to-force relationship." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1ba9a1b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8281 entries, 0 to 8280\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 currL [A] 8281 non-null int64 \n", + " 1 currR [A] 8281 non-null int64 \n", + " 2 rollDeg [deg] 8281 non-null float64\n", + " 3 GapHeight [mm] 8281 non-null float64\n", + " 4 YokeForce.Force_z [newton] 8281 non-null float64\n", + " 5 YokeTorque.Torque [mNewtonMeter] 8281 non-null float64\n", + " 6 invGap 8281 non-null float64\n", + "dtypes: float64(5), int64(2)\n", + "memory usage: 453.0 KB\n" + ] + }, + { + "data": { + "text/html": [ + "
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currL [A]currR [A]rollDeg [deg]GapHeight [mm]YokeForce.Force_z [newton]YokeTorque.Torque [mNewtonMeter]invGap
0-15-15-4.06.0260.518631-6976.8516770.166667
1-15-15-4.08.0163.529872-3335.7040700.125000
2-15-15-4.09.0136.554807-2506.0949020.111111
3-15-15-4.010.0117.403213-1959.7256930.100000
4-15-15-4.010.5109.107025-1759.4673040.095238
\n", + "
" + ], + "text/plain": [ + " currL [A] currR [A] rollDeg [deg] GapHeight [mm] \\\n", + "0 -15 -15 -4.0 6.0 \n", + "1 -15 -15 -4.0 8.0 \n", + "2 -15 -15 -4.0 9.0 \n", + "3 -15 -15 -4.0 10.0 \n", + "4 -15 -15 -4.0 10.5 \n", + "\n", + " YokeForce.Force_z [newton] YokeTorque.Torque [mNewtonMeter] invGap \n", + "0 260.518631 -6976.851677 0.166667 \n", + "1 163.529872 -3335.704070 0.125000 \n", + "2 136.554807 -2506.094902 0.111111 \n", + "3 117.403213 -1959.725693 0.100000 \n", + "4 109.107025 -1759.467304 0.095238 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sooooo it looks like we can invert the entire gap height column and see how that works out...\n", + "magDf[\"invGap\"] = magDf[\"GapHeight [mm]\"].transform(lambda x: 1/x)\n", + "magDf.info()\n", + "magDf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "9cd13fd5", + "metadata": {}, + "source": [ + "## Let's try fitting a polynomial to this..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd16a120", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Force Coeffs: [-8.65013483e-02 2.83478682e-01 2.86948944e-01 -2.40620478e-08\n", + " 3.53295625e+02 -1.79173973e-03 -1.72965671e-03 -2.24271795e-02\n", + " -1.14467614e+01 -2.16073374e-03 2.24271792e-02 -1.16266321e+01\n", + " 3.17540506e-01 4.74016321e-07 6.56934801e+03 -4.22077144e-05\n", + " -1.06779010e-05 -2.15844424e-04 1.18908944e-01 -7.64610873e-06\n", + " 5.14110976e-12 1.01686168e-01 -1.01983866e-02 1.54885341e-01\n", + " -1.03778353e+02 -4.02599676e-05 2.15844412e-04 1.29958897e-01\n", + " -1.01471001e-02 -1.54885342e-01 -1.00615042e+02 -4.31582506e-10\n", + " 2.06864028e+00 -5.14832777e-06 -4.19530520e+04 -3.84317353e-07\n", + " 3.91430632e-07 -7.41836487e-07 7.93047251e-04 -4.85823477e-07\n", + " -2.53747638e-06 1.17129705e-04 -2.29814329e-04 4.03023222e-04\n", + " 2.97296739e-01 -1.05011736e-07 2.53747734e-06 -3.78155618e-06\n", + " -4.82390183e-05 2.17240115e-12 -5.68285028e-01 3.49652512e-03\n", + " 4.38290920e-01 -8.84140414e+00 7.71042932e+02 1.32237943e-07\n", + " 7.41839504e-07 7.27510024e-04 -2.31026411e-04 -4.03023131e-04\n", + " 1.91281517e-01 -3.49652508e-03 4.35555849e-01 8.84140414e+00\n", + " 7.49444072e+02 -3.20760957e-02 -7.80704790e-10 -1.33481028e+02\n", + " 3.32901902e-05 7.71176022e+04 3.63954200e-08 1.21055428e-08\n", + " 1.38326925e-07 -2.26843332e-05 2.02887840e-08 -1.26534403e-07\n", + " -5.30119171e-06 8.63672085e-08 4.12729183e-05 -8.00925222e-04\n", + " 2.31832331e-08 2.02504680e-13 1.06948464e-05 -7.75304869e-07\n", + " 3.44199510e-05 7.75195644e-04 9.67991899e-06 3.94281516e-03\n", + " 1.52654027e-01 -5.85408049e-01 2.66528575e-08 1.26534403e-07\n", + " -1.16260075e-07 -8.31899683e-07 -3.44199134e-05 1.37334995e-03\n", + " -7.83373366e-13 7.56972747e-04 3.87534095e-10 1.67361829e+00\n", + " -1.53624144e-04 -5.22167175e-02 -4.26268983e+00 -1.86825486e+01\n", + " -1.80981191e+03 3.91171451e-08 -1.38326669e-07 -2.90007777e-05\n", + " 5.72601948e-08 -4.12729382e-05 -4.53297467e-04 -9.67991778e-06\n", + " 3.95070566e-03 -1.52654026e-01 -2.35783054e-01 -1.49754210e-04\n", + " 5.22167174e-02 -4.24980327e+00 1.86825486e+01 -1.75997408e+03\n", + " 3.41748851e-12 4.47231699e-01 1.88369113e-09 1.08183457e+03\n", + " -8.46779848e-05 4.14731651e+04]\n", + "Torque Coeffs: [ 8.37786404e-01 8.73852020e+00 -8.81670143e+00 -6.05820755e+01\n", + " -9.82722151e+01 -8.14346739e-02 -8.55581205e-03 -1.26586317e+00\n", + " -2.28782708e+02 7.92025577e-02 -1.26586317e+00 2.32329851e+02\n", + " 1.59724819e-01 2.40322138e+02 1.91664184e+03 -1.60379946e-03\n", + " 1.33688429e-03 -1.09883437e-02 4.99001714e+00 -1.13086428e-03\n", + " -5.01219559e-04 2.95246925e-01 -5.53027170e-01 1.40436065e+01\n", + " -7.51330187e+03 1.58512910e-03 -1.09883435e-02 -5.01157061e+00\n", + " 5.39147403e-01 1.40436064e+01 7.47720714e+03 8.04333337e+00\n", + " -9.27394310e-01 5.61703432e+04 -1.36591792e+04 -8.85041568e-05\n", + " 5.78299126e-05 -3.32035777e-04 1.42291379e-02 9.30848750e-06\n", + " -1.81942915e-04 4.52448003e-03 -1.03937242e-02 -1.18018293e-01\n", + " 1.97297590e+01 -5.50802583e-05 -1.81942945e-04 -3.44846992e-03\n", + " 5.10224715e-05 -1.04187796e-01 -3.02785121e+00 2.03017250e-01\n", + " 2.26755131e+01 -3.31637758e+02 4.95131780e+04 9.86642026e-05\n", + " -3.32035797e-04 -1.38739075e-02 1.04393208e-02 -1.18018293e-01\n", + " -1.90316038e+01 2.03017250e-01 -2.25845770e+01 -3.31637758e+02\n", + " -4.94386582e+04 -4.28915364e-03 -2.85648016e+02 -2.71180777e+00\n", + " -4.00600715e+05 2.87296873e+04 1.94952853e-06 -5.18824865e-06\n", + " -7.73369810e-06 5.36212900e-04 1.10846270e-06 -1.84292257e-06\n", + " -4.65905674e-04 6.42855031e-06 3.08079390e-03 -1.09734095e-03\n", + " -1.62663865e-06 6.42939658e-06 -1.00143627e-04 -2.50122237e-06\n", + " 1.71620024e-03 -2.14975506e-02 7.20307314e-04 1.76411710e-01\n", + " 8.07086586e+00 -3.68279950e+01 4.55521791e-06 -1.84292611e-06\n", + " 4.37141266e-04 -1.60125131e-06 1.71620050e-03 1.99693938e-02\n", + " 1.48517227e-04 -1.19886653e-03 1.78969383e+00 9.92388924e+00\n", + " -7.19412178e-03 -2.98153181e+00 -2.04344115e+02 -1.80247522e+03\n", + " -1.11849627e+05 -1.66014902e-06 -7.73369800e-06 -7.23358309e-04\n", + " -4.16903359e-06 3.08079402e-03 -6.84048059e-03 7.20307304e-04\n", + " -1.75664664e-01 8.07086586e+00 3.49676892e+01 7.76959664e-03\n", + " -2.98153181e+00 2.03737293e+02 -1.80247522e+03 1.12167507e+05\n", + " 4.69772092e-02 -2.00823262e-02 2.13341632e+03 5.37309061e+01\n", + " 1.24121750e+06 1.52681158e+04]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# The following was written by AI - see [4]\n", + "# 1. Load your Ansys CSV - using invGap for modeling\n", + "X = magDf[['currL [A]', 'currR [A]', 'rollDeg [deg]', 'invGap']]\n", + "y = magDf[['YokeForce.Force_z [newton]', 'YokeTorque.Torque [mNewtonMeter]']]\n", + "\n", + "# 2. Create Features (e.g. z^2, z^3, z*IL, etc.)\n", + "poly = PolynomialFeatures(degree=5) \n", + "X_poly = poly.fit_transform(X)\n", + "\n", + "# 3. Fit\n", + "model = LinearRegression()\n", + "model.fit(X_poly, y)\n", + "\n", + "# 4. Extract Equation\n", + "print(\"Force Coeffs:\", model.coef_[0])\n", + "print(\"Torque Coeffs:\", model.coef_[1])" + ] + }, + { + "cell_type": "markdown", + "id": "dd6861d0", + "metadata": {}, + "source": [ + "## Compare Model Predictions with Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc79d9fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Force R² Score: 0.999873\n", + "Torque R² Score: 0.999661\n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_poly)\n", + "\n", + "force_pred = y_pred[:, 0]\n", + "torque_pred = y_pred[:, 1]\n", + "\n", + "force_actual = y.iloc[:, 0].values\n", + "torque_actual = y.iloc[:, 1].values\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Force comparison\n", + "axes[0].scatter(force_actual, force_pred, alpha=0.5, s=10)\n", + "axes[0].plot([force_actual.min(), force_actual.max()], \n", + " [force_actual.min(), force_actual.max()], \n", + " 'r--', linewidth=2, label='Perfect Fit')\n", + "axes[0].set_xlabel('Actual Force (N)', fontsize=12)\n", + "axes[0].set_ylabel('Predicted Force (N)', fontsize=12)\n", + "axes[0].set_title('Force: Predicted vs Actual', fontsize=14)\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Torque comparison\n", + "axes[1].scatter(torque_actual, torque_pred, alpha=0.5, s=10)\n", + "axes[1].plot([torque_actual.min(), torque_actual.max()], \n", + " [torque_actual.min(), torque_actual.max()], \n", + " 'r--', linewidth=2, label='Perfect Fit')\n", + "axes[1].set_xlabel('Actual Torque (mN·m)', fontsize=12)\n", + "axes[1].set_ylabel('Predicted Torque (mN·m)', fontsize=12)\n", + "axes[1].set_title('Torque: Predicted vs Actual', fontsize=14)\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "from sklearn.metrics import r2_score\n", + "print(f\"Force R² Score: {r2_score(force_actual, force_pred):.6f}\")\n", + "print(f\"Torque R² Score: {r2_score(torque_actual, torque_pred):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a5d0634", + "metadata": {}, + "source": [ + "### Visualize a specific subset (like the first 13 rows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "837a814f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# For the first subset\n", + "subset_indices = range(0, 13)\n", + "x_subset = magDf.iloc[subset_indices][\"GapHeight [mm]\"]\n", + "y_actual_subset = magDf.iloc[subset_indices][\"YokeForce.Force_z [newton]\"]\n", + "y_pred_subset = force_pred[subset_indices]\n", + "\n", + "# Plot actual vs predicted for this specific condition\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_subset, y_actual_subset, 'o-', label='Actual (Ansys)', linewidth=2, markersize=8)\n", + "plt.plot(x_subset, y_pred_subset, 's--', label='Predicted (Model)', linewidth=2, markersize=6)\n", + "plt.title(r\"$-15A, -15A, 0^\\circ$ roll: Model vs Ansys\", fontsize=14)\n", + "plt.xlabel(\"Gap Height (mm)\", fontsize=12)\n", + "plt.ylabel(\"Force on magnetic yoke (N)\", fontsize=12)\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b46ce772", + "metadata": {}, + "source": [ + "## Better Interpolation Methods\n", + "\n", + "Let's improve interpolation accuracy without manually guessing polynomial order." + ] + }, + { + "cell_type": "markdown", + "id": "a019f402", + "metadata": {}, + "source": [ + "### Method 1: Cross-Validation to Find Best Polynomial Degree\n", + "\n", + "Automatically test different polynomial degrees and find the one with best validation score." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd12d56b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree 1: Train R² = 0.752592, Test R² = 0.757407\n", + "Degree 2: Train R² = 0.965022, Test R² = 0.965732\n", + "Degree 3: Train R² = 0.993618, Test R² = 0.993764\n", + "Degree 4: Train R² = 0.998598, Test R² = 0.998637\n", + "Degree 5: Train R² = 0.999764, Test R² = 0.999752\n", + "Degree 6: Train R² = 0.999932, Test R² = 0.999927\n", + "Degree 7: Train R² = 0.999950, Test R² = 0.999947\n", + "Degree 8: Train R² = 0.999889, Test R² = 0.999802\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Best polynomial degree: 7\n", + "Best test R² score: 0.999947\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Test polynomial degrees from 1 to 8\n", + "degrees = range(1, 9)\n", + "train_scores_force = []\n", + "test_scores_force = []\n", + "train_scores_torque = []\n", + "test_scores_torque = []\n", + "\n", + "for degree in degrees:\n", + " pipe = Pipeline([\n", + " ('poly', PolynomialFeatures(degree=degree)),\n", + " ('model', LinearRegression())\n", + " ])\n", + " \n", + " pipe.fit(X_train, y_train)\n", + " \n", + " train_score = pipe.score(X_train, y_train)\n", + " test_score = pipe.score(X_test, y_test)\n", + " \n", + " train_scores_force.append(train_score)\n", + " test_scores_force.append(test_score)\n", + " \n", + " print(f\"Degree {degree}: Train R² = {train_score:.6f}, Test R² = {test_score:.6f}\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(degrees, train_scores_force, 'o-', label='Training Score', linewidth=2, markersize=8)\n", + "plt.plot(degrees, test_scores_force, 's-', label='Test Score', linewidth=2, markersize=8)\n", + "plt.xlabel('Polynomial Degree', fontsize=12)\n", + "plt.ylabel('R² Score', fontsize=12)\n", + "plt.title('Model Performance vs Polynomial Degree', fontsize=14)\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.xticks(degrees)\n", + "plt.show()\n", + "\n", + "best_degree = degrees[np.argmax(test_scores_force)]\n", + "print(f\"\\nBest polynomial degree: {best_degree}\")\n", + "print(f\"Best test R² score: {max(test_scores_force):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "86aaa7f5", + "metadata": {}, + "source": [ + "### Visualize Best Polynomial Function vs Ansys Data\n", + "\n", + "Plot the continuous polynomial function to check for overfitting/oscillations between data points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8270efa4", + "metadata": {}, + "outputs": [], + "source": [ + "best_degree = 6 # Degree 7 actually overfits to the data points and struggles to generalize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d94aa4c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using polynomial degree: 6\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking for overfitting indicators:\n", + "Number of actual data points in this condition: 13\n", + "Gap spacing: 1.50 mm average\n", + "Polynomial curve resolution: 500 points over 25.0 mm range\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [5]\n", + "# Train best degree polynomial on full dataset\n", + "poly_best = PolynomialFeatures(degree=best_degree)\n", + "X_poly_best = poly_best.fit_transform(X)\n", + "model_best = LinearRegression()\n", + "model_best.fit(X_poly_best, y)\n", + "\n", + "print(f\"Using polynomial degree: {best_degree}\")\n", + "\n", + "# Select a specific operating condition to visualize\n", + "# Using the same condition as earlier: -15A, -15A, 0° roll\n", + "test_condition = magDf.iloc[0:13].copy()\n", + "currL_val = test_condition['currL [A]'].iloc[0]\n", + "currR_val = test_condition['currR [A]'].iloc[0]\n", + "roll_val = test_condition['rollDeg [deg]'].iloc[0]\n", + "\n", + "# Create very fine grid for gap heights to see polynomial behavior between points\n", + "gap_very_fine = np.linspace(5, 30, 500) # 500 points for smooth curve\n", + "X_fine = pd.DataFrame({\n", + " 'currL [A]': [currL_val] * 500,\n", + " 'currR [A]': [currR_val] * 500,\n", + " 'rollDeg [deg]': [roll_val] * 500,\n", + " 'invGap': 1/gap_very_fine # Use inverse gap height for modeling\n", + "})\n", + "X_fine_poly = poly_best.transform(X_fine)\n", + "y_fine_pred = model_best.predict(X_fine_poly)\n", + "\n", + "# Extract actual Ansys data for this condition\n", + "gap_actual = test_condition['GapHeight [mm]'].values\n", + "force_actual = test_condition['YokeForce.Force_z [newton]'].values\n", + "\n", + "# Plot\n", + "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", + "\n", + "ax.plot(gap_very_fine, y_fine_pred[:, 0], '-', linewidth=2.5, \n", + " label=f'Degree {best_degree} Polynomial Function', color='#2ca02c', alpha=0.8)\n", + "\n", + "# Plot actual Ansys data points\n", + "ax.scatter(gap_actual, force_actual, s=120, marker='o', \n", + " color='#d62728', edgecolors='black', linewidths=1.5,\n", + " label='Ansys Simulation Data', zorder=5)\n", + "\n", + "ax.set_ylabel('Force (N)', fontsize=13)\n", + "ax.set_title(f'Degree {best_degree} Polynomial vs Ansys Data (currL={currL_val}A, currR={currR_val}A, roll={roll_val}°)', \n", + " fontsize=14, fontweight='bold')\n", + "ax.legend(fontsize=12, loc='best')\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Add vertical lines at data points to highlight gaps\n", + "for gap_point in gap_actual:\n", + " ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Check for excessive oscillations by looking at derivative\n", + "print(\"Checking for overfitting indicators:\")\n", + "print(f\"Number of actual data points in this condition: {len(gap_actual)}\")\n", + "\n", + "print(f\"Gap spacing: {np.diff(gap_actual).mean():.2f} mm average\")\n", + "print(f\"Polynomial curve resolution: 500 points over {gap_very_fine.max() - gap_very_fine.min():.1f} mm range\")" + ] + }, + { + "cell_type": "markdown", + "id": "8e4487e0", + "metadata": {}, + "source": [ + "### Compare Multiple Conditions Side-by-Side\n", + "\n", + "Let's check several different operating conditions to ensure consistent behavior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74d6e98f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Visual inspection guide:\n", + "✓ Look for smooth curves between data points (good)\n", + "✗ Look for wild oscillations or wiggles between points (overfitting)\n", + "✓ Polynomial should interpolate naturally without excessive curvature\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [6]\n", + "# Select 4 different conditions to visualize\n", + "# Each condition has 13 gap height measurements (one complete parameter set)\n", + "condition_indices = [\n", + " (0, 13, '-15A, -15A, 0°'), # First condition\n", + " (13, 26, '-15A, -15A, 0.5°'), # Same currents, different roll\n", + " (91, 104, '-15A, -10A, 0°'), # Different right current\n", + " (182, 195, '-15A, -10A, 1°') # Different right current and roll\n", + "]\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", + "axes = axes.flatten()\n", + "\n", + "for idx, (start, end, label) in enumerate(condition_indices):\n", + " ax = axes[idx]\n", + " \n", + " # Get condition data\n", + " condition_data = magDf.iloc[start:end]\n", + " currL = condition_data['currL [A]'].iloc[0]\n", + " currR = condition_data['currR [A]'].iloc[0]\n", + " roll = condition_data['rollDeg [deg]'].iloc[0]\n", + " \n", + " # Create fine grid\n", + " gap_fine = np.linspace(5, 30, 500)\n", + " X_condition_fine = pd.DataFrame({\n", + " 'currL [A]': [currL] * 500,\n", + " 'currR [A]': [currR] * 500,\n", + " 'rollDeg [deg]': [roll] * 500,\n", + " 'invGap': 1/gap_fine # Use inverse gap height for modeling\n", + " })\n", + " \n", + " # Predict with best degree polynomial\n", + " X_condition_poly = poly_best.transform(X_condition_fine)\n", + " y_condition_pred = model_best.predict(X_condition_poly)\n", + " \n", + " # Plot polynomial curve\n", + " ax.plot(gap_fine, y_condition_pred[:, 0], '-', linewidth=2.5, \n", + " color='#2ca02c', alpha=0.8, label=f'Degree {best_degree} Polynomial')\n", + " \n", + " # Plot actual data\n", + " ax.scatter(condition_data['GapHeight [mm]'], \n", + " condition_data['YokeForce.Force_z [newton]'],\n", + " s=100, marker='o', color='#d62728', \n", + " edgecolors='black', linewidths=1.5,\n", + " label='Ansys Data', zorder=5)\n", + " \n", + " # Formatting\n", + " ax.set_xlabel('Gap Height (mm)', fontsize=11)\n", + " ax.set_ylabel('Force (N)', fontsize=11)\n", + " ax.set_title(f'currL={currL:.0f}A, currR={currR:.0f}A, roll={roll:.0f}°', \n", + " fontsize=12, fontweight='bold')\n", + " ax.legend(fontsize=10, loc='best')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " # Add vertical lines at data points\n", + " for gap_point in condition_data['GapHeight [mm]'].values:\n", + " ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.2, linewidth=0.8)\n", + "\n", + "plt.suptitle(f'Degree {best_degree} Polynomial: Multiple Operating Conditions', \n", + " fontsize=15, fontweight='bold', y=1.00)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"Visual inspection guide:\")\n", + "print(\"✓ Look for smooth curves between data points (good)\")\n", + "print(\"✗ Look for wild oscillations or wiggles between points (overfitting)\")\n", + "print(\"✓ Polynomial should interpolate naturally without excessive curvature\")" + ] + }, + { + "cell_type": "markdown", + "id": "81a46ab5", + "metadata": {}, + "source": [ + "### Find Worst-Performing Segment\n", + "\n", + "Let's identify which parameter set has the worst R² score to showcase model performance even in challenging cases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35f4438e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total segments analyzed: 637\n", + "\n", + "Worst performing segment:\n", + " Rows: 7631 to 7644\n", + " Parameters: currL=15A, currR=15A, roll=3.0°\n", + " R² Score: 0.999302\n", + "\n", + "Best performing segment R²: 0.999992\n", + "Mean R² across all segments: 0.999934\n", + "Median R² across all segments: 0.999953\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [7]\n", + "# Calculate R² score for each 13-row segment (each parameter set)\n", + "num_segments = len(magDf) // 13\n", + "segment_scores = []\n", + "segment_info = []\n", + "\n", + "for i in range(num_segments):\n", + " start_idx = i * 13\n", + " end_idx = start_idx + 13\n", + " \n", + " # Get actual and predicted values for this segment\n", + " segment_actual = y.iloc[start_idx:end_idx, 0].values # Force only\n", + " segment_pred = model_best.predict(poly_best.transform(X.iloc[start_idx:end_idx]))[:, 0]\n", + " \n", + " # Calculate R² for this segment\n", + " segment_r2 = r2_score(segment_actual, segment_pred)\n", + " segment_scores.append(segment_r2)\n", + " \n", + " # Store segment info\n", + " segment_data = magDf.iloc[start_idx:end_idx]\n", + " currL = segment_data['currL [A]'].iloc[0]\n", + " currR = segment_data['currR [A]'].iloc[0]\n", + " roll = segment_data['rollDeg [deg]'].iloc[0]\n", + " segment_info.append({\n", + " 'start': start_idx,\n", + " 'end': end_idx,\n", + " 'currL': currL,\n", + " 'currR': currR,\n", + " 'roll': roll,\n", + " 'r2': segment_r2\n", + " })\n", + "\n", + "# Find worst performing segment\n", + "worst_idx = np.argmin(segment_scores)\n", + "worst_segment = segment_info[worst_idx]\n", + "\n", + "print(f\"Total segments analyzed: {num_segments}\")\n", + "print(f\"\\nWorst performing segment:\")\n", + "print(f\" Rows: {worst_segment['start']} to {worst_segment['end']}\")\n", + "print(f\" Parameters: currL={worst_segment['currL']:.0f}A, currR={worst_segment['currR']:.0f}A, roll={worst_segment['roll']:.1f}°\")\n", + "print(f\" R² Score: {worst_segment['r2']:.6f}\")\n", + "print(f\"\\nBest performing segment R²: {max(segment_scores):.6f}\")\n", + "print(f\"Mean R² across all segments: {np.mean(segment_scores):.6f}\")\n", + "print(f\"Median R² across all segments: {np.median(segment_scores):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "080f954b", + "metadata": {}, + "source": [ + "### Visualize Worst-Performing Segment\n", + "\n", + "Even our worst case shows excellent model performance!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ceaf2b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Even in the worst case, the model achieves R² = 0.999302\n", + "This demonstrates excellent interpolation performance across all operating conditions!\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [8]\n", + "# Get worst segment data\n", + "worst_data = magDf.iloc[worst_segment['start']:worst_segment['end']]\n", + "worst_currL = worst_segment['currL']\n", + "worst_currR = worst_segment['currR']\n", + "worst_roll = worst_segment['roll']\n", + "worst_r2 = worst_segment['r2']\n", + "\n", + "# Create fine grid for this worst segment\n", + "gap_fine_worst = np.linspace(5, 30, 500)\n", + "X_worst_fine = pd.DataFrame({\n", + " 'currL [A]': [worst_currL] * 500,\n", + " 'currR [A]': [worst_currR] * 500,\n", + " 'rollDeg [deg]': [worst_roll] * 500,\n", + " 'invGap': 1/gap_fine_worst\n", + "})\n", + "\n", + "# Get predictions\n", + "X_worst_poly = poly_best.transform(X_worst_fine)\n", + "y_worst_pred = model_best.predict(X_worst_poly)\n", + "\n", + "# Extract actual data\n", + "gap_worst_actual = worst_data['GapHeight [mm]'].values\n", + "force_worst_actual = worst_data['YokeForce.Force_z [newton]'].values\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", + "\n", + "# Plot polynomial curve\n", + "ax.plot(gap_fine_worst, y_worst_pred[:, 0], '-', linewidth=2.5, \n", + " label=f'Degree {best_degree} Polynomial', color='#2ca02c', alpha=0.8)\n", + "\n", + "# Plot actual data points\n", + "ax.scatter(gap_worst_actual, force_worst_actual, s=120, marker='o', \n", + " color='#d62728', edgecolors='black', linewidths=1.5,\n", + " label='Ansys Data', zorder=5)\n", + "\n", + "# Formatting\n", + "ax.set_xlabel('Gap Height (mm)', fontsize=13)\n", + "ax.set_ylabel('Force (N)', fontsize=13)\n", + "ax.set_title(f'Worst Case Performance (R² = {worst_r2:.6f}) - currL={worst_currL:.0f}A, currR={worst_currR:.0f}A, roll={worst_roll:.1f}°', \n", + " fontsize=14, fontweight='bold')\n", + "ax.legend(fontsize=12, loc='best')\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Add vertical lines at data points\n", + "for gap_point in gap_worst_actual:\n", + " ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Even in the worst case, the model achieves R² = {worst_r2:.6f}\")\n", + "print(f\"This demonstrates excellent interpolation performance across all operating conditions!\")" + ] + }, + { + "cell_type": "markdown", + "id": "a8f24086", + "metadata": {}, + "source": [ + "## Torque Prediction Analysis\n", + "\n", + "Now let's examine torque predictions. Torque is influenced by multiple variables:\n", + "- **Roll angle** (primary driver of torque asymmetry)\n", + "- **Current imbalance** (currL vs currR difference)\n", + "- **Gap height** (via invGap feature)\n", + "\n", + "We'll visualize torque accuracy by sweeping across roll angles first, then examining current variations." + ] + }, + { + "cell_type": "markdown", + "id": "d896f5a7", + "metadata": {}, + "source": [ + "### Overall Torque Prediction Accuracy\n", + "\n", + "First, let's check the overall R² score for torque predictions across all data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c120048d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overall Torque R² Score: 0.999895\n", + "Overall Force R² Score: 0.999967\n", + "\n", + "Torque prediction is excellent!\n" + ] + } + ], + "source": [ + "y_pred_full = model_best.predict(X_poly_best)\n", + "torque_actual_full = y.iloc[:, 1].values\n", + "torque_pred_full = y_pred_full[:, 1]\n", + "\n", + "torque_r2_overall = r2_score(torque_actual_full, torque_pred_full)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n", + "\n", + "ax.scatter(torque_actual_full, torque_pred_full, alpha=0.4, s=15, color='#1f77b4')\n", + "ax.plot([torque_actual_full.min(), torque_actual_full.max()], \n", + " [torque_actual_full.min(), torque_actual_full.max()], \n", + " 'r--', linewidth=2, label='Perfect Fit')\n", + "\n", + "ax.set_xlabel('Actual Torque (mN·m)', fontsize=13)\n", + "ax.set_ylabel('Predicted Torque (mN·m)', fontsize=13)\n", + "ax.set_title(f'Torque: Predicted vs Actual (R² = {torque_r2_overall:.6f})', \n", + " fontsize=14, fontweight='bold')\n", + "ax.legend(fontsize=12)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Overall Torque R² Score: {torque_r2_overall:.6f}\")\n", + "print(f\"Overall Force R² Score: {r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]):.6f}\")\n", + "print(f\"\\nTorque prediction is {'excellent' if torque_r2_overall > 0.99 else 'good' if torque_r2_overall > 0.95 else 'moderate'}!\")" + ] + }, + { + "cell_type": "markdown", + "id": "1dd6a624", + "metadata": {}, + "source": [ + "### Torque vs Roll Angle (Fixed Currents, Multiple Gap Heights)\n", + "\n", + "Examine how torque varies with roll angle for different gap heights. Roll angle is the primary driver of torque asymmetry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70b9b4e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Showing torque variation with roll angle at 4 different gap heights.\n", + "Gap height affects the magnitude of torque but the relationship with roll angle remains consistent.\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [9]\n", + "# Select 4 different gap heights to visualize\n", + "# Using currL = -15A, currR = -15A configuration\n", + "gap_heights_to_plot = [8, 10, 15, 21] # mm\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", + "axes = axes.flatten()\n", + "\n", + "for idx, gap_height in enumerate(gap_heights_to_plot):\n", + " ax = axes[idx]\n", + " \n", + " # Fixed current configuration\n", + " currL = -15\n", + " currR = -15\n", + " \n", + " # Get actual data for this gap height\n", + " gap_data = magDf[(magDf['GapHeight [mm]'] == gap_height) & \n", + " (magDf['currL [A]'] == currL) & \n", + " (magDf['currR [A]'] == currR)]\n", + " \n", + " # Create fine grid for smooth curve across roll angles\n", + " roll_fine = np.linspace(-5, 5.0, 500)\n", + " X_condition_fine = pd.DataFrame({\n", + " 'currL [A]': [currL] * 500,\n", + " 'currR [A]': [currR] * 500,\n", + " 'rollDeg [deg]': roll_fine,\n", + " 'invGap': [1/gap_height] * 500\n", + " })\n", + " \n", + " # Predict with best degree polynomial\n", + " X_condition_poly = poly_best.transform(X_condition_fine)\n", + " y_condition_pred = model_best.predict(X_condition_poly)\n", + " \n", + " # Plot polynomial curve for TORQUE (index 1)\n", + " ax.plot(roll_fine, y_condition_pred[:, 1], '-', linewidth=2.5, \n", + " color='#ff7f0e', alpha=0.8, label=f'Degree {best_degree} Polynomial')\n", + " \n", + " # Plot actual torque data\n", + " ax.scatter(gap_data['rollDeg [deg]'], \n", + " gap_data['YokeTorque.Torque [mNewtonMeter]'],\n", + " s=100, marker='o', color='#9467bd', \n", + " edgecolors='black', linewidths=1.5,\n", + " label='Ansys Data', zorder=5)\n", + " \n", + " # Formatting\n", + " ax.set_xlabel('Roll Angle (deg)', fontsize=11)\n", + " ax.set_ylabel('Torque (mN·m)', fontsize=11)\n", + " ax.set_title(f'Gap Height = {gap_height} mm (currL={currL:.0f}A, currR={currR:.0f}A)', \n", + " fontsize=12, fontweight='bold')\n", + " ax.legend(fontsize=10, loc='best')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " # Add vertical lines at actual roll angle data points\n", + " for roll_point in gap_data['rollDeg [deg]'].unique():\n", + " ax.axvline(roll_point, color='gray', linestyle=':', alpha=0.2, linewidth=0.8)\n", + "\n", + "plt.suptitle(f'Torque vs Roll Angle at Different Gap Heights (Degree {best_degree} Polynomial)', \n", + " fontsize=15, fontweight='bold', y=1.00)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Showing torque variation with roll angle at {len(gap_heights_to_plot)} different gap heights.\")\n", + "print(\"Gap height affects the magnitude of torque but the relationship with roll angle remains consistent.\")" + ] + }, + { + "cell_type": "markdown", + "id": "02d02793", + "metadata": {}, + "source": [ + "### Torque vs Current Imbalance (Fixed Roll Angle, Multiple Gap Heights)\n", + "\n", + "Now examine how torque varies with current imbalance at different gap heights for a fixed roll angle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b8d7f6a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Showing torque variation with current imbalance at 4 different gap heights.\n", + "All data uses currL = -15A with varying currR to create different imbalances.\n", + "Current imbalance (currR - currL) affects torque magnitude and direction.\n", + "Positive imbalance (currR > currL) and negative imbalance (currR < currL) produce opposite torques.\n" + ] + } + ], + "source": [ + "# The following was written by AI - see [10]\n", + "# Select 4 different gap heights to visualize\n", + "# Using roll = 0.5 degrees (small but non-zero torque)\n", + "gap_heights_to_plot = [8, 10, 15, 21] # mm\n", + "roll_target = 0.5\n", + "\n", + "# Use a fixed reference current for currL\n", + "currL_ref = -15\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", + "axes = axes.flatten()\n", + "\n", + "for idx, gap_height in enumerate(gap_heights_to_plot):\n", + " ax = axes[idx]\n", + " \n", + " # Get actual data for this gap height, roll angle, AND fixed currL\n", + " gap_roll_data = magDf[(magDf['GapHeight [mm]'] == gap_height) & \n", + " (magDf['rollDeg [deg]'] == roll_target) &\n", + " (magDf['currL [A]'] == currL_ref)]\n", + " \n", + " # Calculate imbalance for actual data points (all have same currL now)\n", + " actual_imbalances = []\n", + " actual_torques = []\n", + " for _, row in gap_roll_data.iterrows():\n", + " imbalance = row['currR [A]'] - row['currL [A]']\n", + " torque = row['YokeTorque.Torque [mNewtonMeter]']\n", + " actual_imbalances.append(imbalance)\n", + " actual_torques.append(torque)\n", + " \n", + " # Create fine grid for smooth curve across current imbalances\n", + " # Range from -10A to +10A imbalance (currR - currL)\n", + " imbalance_fine = np.linspace(0, 35, 500)\n", + " \n", + " # Vary currR while keeping currL fixed\n", + " currR_fine = currL_ref + imbalance_fine\n", + " \n", + " X_condition_fine = pd.DataFrame({\n", + " 'currL [A]': [currL_ref] * 500,\n", + " 'currR [A]': currR_fine,\n", + " 'rollDeg [deg]': [roll_target] * 500,\n", + " 'invGap': [1/gap_height] * 500\n", + " })\n", + " \n", + " # Predict with best degree polynomial\n", + " X_condition_poly = poly_best.transform(X_condition_fine)\n", + " y_condition_pred = model_best.predict(X_condition_poly)\n", + " \n", + " # Plot polynomial curve for TORQUE (index 1)\n", + " ax.plot(imbalance_fine, y_condition_pred[:, 1], '-', linewidth=2.5, \n", + " color='#ff7f0e', alpha=0.8, label=f'Degree {best_degree} Polynomial')\n", + " \n", + " # Plot actual torque data (now only one point per imbalance value)\n", + " ax.scatter(actual_imbalances, actual_torques,\n", + " s=100, marker='o', color='#9467bd', \n", + " edgecolors='black', linewidths=1.5,\n", + " label='Ansys Data', zorder=5)\n", + " \n", + " # Formatting\n", + " ax.set_xlabel('Current Imbalance (currR - currL) [A]', fontsize=11)\n", + " ax.set_ylabel('Torque (mN·m)', fontsize=11)\n", + " ax.set_title(f'Gap Height = {gap_height} mm (currL = {currL_ref}A, roll = {roll_target}°)', \n", + " fontsize=12, fontweight='bold')\n", + " ax.legend(fontsize=10, loc='best')\n", + " ax.grid(True, alpha=0.3)\n", + " ax.axhline(0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)\n", + " ax.axvline(0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)\n", + " \n", + " # Add vertical lines at actual imbalance data points\n", + " for imb in set(actual_imbalances):\n", + " ax.axvline(imb, color='gray', linestyle=':', alpha=0.2, linewidth=0.8)\n", + "\n", + "plt.suptitle(f'Torque vs Current Imbalance at Different Gap Heights (currL = {currL_ref}A, Roll = {roll_target}°)', \n", + " fontsize=15, fontweight='bold', y=1.00)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Showing torque variation with current imbalance at {len(gap_heights_to_plot)} different gap heights.\")\n", + "print(f\"All data uses currL = {currL_ref}A with varying currR to create different imbalances.\")\n", + "print(f\"Current imbalance (currR - currL) affects torque magnitude and direction.\")\n", + "print(f\"Positive imbalance (currR > currL) and negative imbalance (currR < currL) produce opposite torques.\")" + ] + }, + { + "cell_type": "markdown", + "id": "d71ac3cc", + "metadata": {}, + "source": [ + "### Torque Segment-Wise Performance\n", + "\n", + "Calculate R² scores for torque predictions across all segments, similar to what we did for force." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "902343b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Torque Prediction Analysis (across 637 segments):\n", + "\n", + "Best R² Score: 0.999964\n", + " Parameters: currL=15A, currR=-15A, roll=-1.5°\n", + "\n", + "Worst R² Score: -0.638045\n", + " Parameters: currL=-10A, currR=-10A, roll=0.0°\n", + "\n", + "Mean R²: 0.981564\n", + "Median R²: 0.999392\n", + "Std Dev: 0.124378\n" + ] + }, + { + "data": { + "image/png": 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AAAB0gGeffVbXXXddzfnbLnTv27dP0qUd09PTs8HVAAAAAMA2cvPN0gsvSNdeK33zm9Xb/K+bpXMvSHuvlf5DjTabwNzcnA4dOmRnzFq2Xegudynv6ekhdAMAAABAO5W7YXd1SbXy2BVdkkvS3jptNpHVLltmIDUAAAAAABxC6AYAAAAAtMdVV0n79l36t5ZdV0k79136twNsu+7lAAAAAIANUus67uX+r817HXczCN01XLx4URcuXNjoMoB12bVrl3bs2LHRZQAAAADbFqH7MqVSSS+++KLOnDmjUqm00eUA6+JyudTb26trrrmG+9IDAAAAG4DQfZkzZ86oWCzqwIEDuvLKKwkq2LJKpZJefvllnTp1Snv37pVhGBtdEgAAALDtELqXKZVKmp2dVU9Pj66++uqNLgdYt71792phYUGzs7Pq7e3lSyQAAABsrA9+UCoUJLdb+uM/rt7myQ9Krxak3W7plhptthBC9zIXL17UxYsXuX83OkpPT4/m5uZ08eJF7dzJIQ8AAIAN9IlPSN//vvS619UO3U9/Qjr3fWnv6zoidHPLsGUWFxcliWCCjlJ+P5ff3wAAAADah9BdBV1w0Ul4PwMAAAAbh9ANAAAAAIBDCN0daGxsTC6XS+Pj41XnW5Yll8ulgYGBNlcGAAAAANsLobtDGYaheDxedV48Hu+I20dFIhG53W55vV6l0+k1tU8mkxXzisWiQqGQPX9sbGzF8y3L0tjYmKLR6Ip5fr9fLper4md5TZZlaWBgQG63W263W9lsdsUyisWi3G63QqFQI5sPAAAAYAsgdHeo/v5+WZYly7JWzBsfH9fg4OAGVNU65WBaKBSUSqUUCoWqBtmygYEBGYZhtx8aGqrYN36/XwMDA/b848ePV/QUiEQiikQiNb/IkKRUKqVSqWT/BAIBSVI2m5Xf71c0GlWhUFChUJDP51vx/KGhIfX39695XwAAAADYvAjdHSwcDq8Iiel0WqZpyu/3b1BVrZFMJhWLxSRJpmlqZGSkZiDOZrNKp9MV7QcHB+3HxWJRlmUpHA7b8yORiFKplL2MeDyuVCpVNSyvZmhoSLFYzA7htbanWCzS5R8AAADoMITutfjoR6Xrrlv95+d/fuVzf/7nG3vuRz9a+bz5+ZXTGhSJRFZ0o47H44pEIivalgOf2+2W3++vOAs8NjYmr9dbdd7AwIDGx8c1MDBgXydeLBabqrdR5S8OlneR9/l8NbuYW5a1oju93++32xuGoUAgYJ/ZtixLiUSi6n6qJx6Py+v1Vizbsixls1l5PB75/X653e6qyy0HcwAAAACdhRtSr8Xc3KUbua/m0KGV006dauy5c3OVj0ulldMaZJqmpEtnestnaNPptCYmJjQ5OVnR9siRIzp69KhSqZTS6bRCoZAymYy9nEwmI8MwFI1GK+bl83nFYjH7sd/v1/j4uIaHh2vW1UiY9Xq9NZdRK9Tn8/mq003TXPGcXC5X0T6VSmlgYMCuLZFI1D0zXY1lWUqlUioWi/L7/cpkMvY6jh8/rkwmY1/bPTY2Zm9fJBLR4OBg3S8OAAAAgI7wrndJ+bzk8dRu87p3SQt5qbtOmy2E0L0WPT3S6163ersDB6pPa+S5PT2Vj12uldPWoHwdcjweVzKZVH9//4qzvul0WpZl2SEwEAjI4/HIsiyZpqlgMFixvMsHGQsEAvYyA4GAcrlc3ZrqXRftBJ/PJ9M0FY1GNTIyIsuyND4+Ls+yAz0UCsnn8ymVSsmyLIVCIfsMeCMSiYQ8Ho+9H4LBoEZHR3X06FFJqujaHo1GFY/HNTw8rGw2q8nJSc3MzLR2owEAAIDNqJEs8Jb25gWnEbrX4tixSz/N+Ju/ae55+/Y1v05duq67r6/PDt7VzjJblqVisSiv11sxvXx2uFgsanJy0g6kl2v39eGGYaw4q10sFitC9OXKg60lk0kFg0GFw2F74LV0Oq1kMqlSqSTpX6/pjkQiq36BUFbuVVDm8Xg0PT1th/Dl88tfaEiXvoAoFovq6+uzt0OS3G63MpnMiuUCAAAA2FoI3R3OMAz19/drfHxc09PTFYODlXk8Hvl8PruL+HLlQBiLxeyztZeH87Vab/fy/v5+FYtFFYtFO9ROTU3VPStd7iJf5vf7NTIyYj9e7y3UltciXfoio7+/3x6NvNxr4PL/l78MKYtGo/Y15QAAAAC2PkL3NhCJRDQ0NFTzNmHBYFDRaFTj4+P2CN7JZFKBQMA+Izs4OCjDMCpuo9Ws9XYvNwxDwWBQQ0NDmpiYUD6f1/j4uE6cOGG3Kddd7br28n22y93my93py9dZF4tFxWKxhgdSy2azCoVCSiQS8vl8SiaTSqfTyuVyMgxDw8PDikajSiQSsixrRdAGAAAANrtTp05prsmxpprR09OjA9Uu292CCN3bQDAY1PHjx+uGyEwmo6GhIUWjUXk8HgUCAQWDQfl8PgUCAbndbvv/m6HL88TEhIaGhtTX1yfTNDUxMVFxO694PK5sNmuf2U+n0zpy5IikS18gXH5Wv7z9brdbHo9HkUik4kz72NiY4vG4HeaTyaSi0ajC4bB8Pp9isZh97+/yWfXyfioH+PKyV7t9GAAAALCZnDp1Sr9+15DOzr2y7mU9dPLL8rz6qvK7d+ueN/9E9TZ3flmuqy7owmt+VLv+w8l1r3OjuUrlC1m3ibm5OfX29urMmTPquWyAsvPnz2tmZkZ9fX3as2fPBlUItBbvawAAAKxHLpfT0F33aMA3qAPua9a1rPd89C5dOX9aL+/br08ce7Rqm8Eb3que3UUt7nqNdoZeXNf6nFQvWy7HmW4AAAAAwKoOuK/RtQeq3B55Dbp27LD/rbWsHa4d61rHZtO10QUAAAAAANCpCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOGTDB1JLp9P2bZYk2bdSsixLyWRSpmnKsiyFw2EZhrHqPAAAAAAANosNDd3pdFqJRMK+//HAwIByuZwkKRQK2fdStixLQ0NDSiQSq84DAAAAAGCz2NDQHYlE7PBsmqZSqZSkS0F6OdM0lU6nV50HAAAAAMBmsmGh27Is5fN5GYahbDYr0zTtLubpdFoej6eivcfjUTab1fT0dM15Pp+vbfUDAAAAANbmm7/w29px4bwu7tpTs81XX7pLmW99Vu/99SG9po21OWXDQnc2m5XH41EymVQgEND4+LhM01QwGFSxWKz6nHw+X3deNQsLC1pYWLAfz83NSZKWlpa0tLRU0XZpaUmlUsn+ATpB+f1c7T0PAAAArKZUKsnlckkqqaT15aTvH/73y5dctU1u/mf0t9/8ut69/z/owCb++7XRv603LHTn83lZlqVAICDDMBQOh+V2u+uG3VqBu9680dFR3XvvvSumnzp1SufPn6+YduHCBS0tLWlxcVGLi4sNbcdm9MADD+h3f/d39bGPfUx33333ivmWZenmm2/WkSNH9Ld/+7drXv7IyIjOnDmjj33sY5KkEydOqK+vz+6pgM1lcXFRS0tLOn36tHbt2rXR5QAAAGCLmZ+f1w1912t3j1Tac87x9e3ukW7ou17z8/OanZ11fH3Nmp+fb6jdhoVu0zRlGIY96nj532w2K8MwVpy5LndFrzevmpGRER07dsx+PDc3p0OHDunAgQPq6empaHv+/HnNz89r586d2rlzwwd2b1pXV5cMw9Ajjzyi973vfSvmf/zjH5dhGHK5XE1tZ1dXV8Vz3/Oe9+hDH/qQhoeH1127kyKRiBKJhDwejx5++GF7pPxG2t9///0KBoMtmV8sFhUOh+3LKMLh8Ip9V+u5lmVpcHDQviSj2novt3PnTnV1dWn//v3as6d2Nx4AAACgmrNnz+rpmWf0tj7J1b3X8fW9Oic9PfOM9u3bp4MHDzq+vmY1+rf1hobuWgKBgOLx+Irp/f39Mk2z5rxquru71d3dvWJ6V1eXurq6VkxzuVz2z1blcrnU39+v6elpzczMrNjXExMTGhwclGVZTW3n5fuoUCi0pG4nhUIheTweFQoFWZYlv9+vEydO1BwHYGBgQD6fr6K93++39+V65vf39ysejyuRSMiyLHm9Xnm9Xjs813vu4OCgYrGYAoGAksmkQqGQUqlU3S8Qyq9Vtfc8AAAAsBqXy/XDHskuubS+nHTlD56Wa2lRpa6devk1N1Rt07vr+7remNfu8zPq6rpxXetzUqN/W29o6O7v71exWJRhGPa9uquFIMuy1N/fX3FmvNo8x33jo9I3P7p6O49P+pm/qZz2xZ+X8tnVn3vzMekN/3pmXhfmpe9OVE5rUDgcVjweVywWs6el02mZpim/379iJPhOlkwm7S8HTNPUyMiI4vF41S9wstms0um0PZr+8rAbj8fXPT+Tydjv1/IAguXeG/WeG4lE1N/fbwfsYDBov8arnbUHAAAANoO3/nlYe4uzOmcc1BP3PV61ze3XfVihvtNa/MZ7pTe+2OYKW29DT3slEglFo1GNj48rFovZQWP5vGQyaZ8VbGSeoy7MSee+v/rP+VMrn3v+VGPPvTB32RNLVaY1JhKJKJlMVkyLx+OKRCIr2haLRQ0MDMjtdq8I5OWzrW63W6FQaEVY9/v9FesZGxuT1+utuqyBgQGNj49rYGBALpdLAwMDda/Vb4XyFw3Lv5jx+Xw1bzVnWdaKL3H8fn/FbevWM3/5vLGxMeXzeQ0ODq76XJ/Pt+JLAq/X6/j+AwAAANC8Db1w2TCMqmcapUtn+MpnaC+/ZrXePEft6pH2vm71dnsOVJ/WyHN39Vw2wVVlWmPKXZ2X304tnU5rYmJCk5OTFW2PHDmio0ePKpVKKZ1OKxQK2fdQ9/v9GhwcVCaTUbFYlN/vr3tm1TRN+2xuNBqtWFY+n1csFqtY9vj4eN3rwat9SXA5r9dbcxlrHfHeNM0Vz8nlcnb79c6XpPHxcXu7Lj/zvdpzlzt+/HhD+wcAAADAxti6o4VthDcca6qbt6SV3c0btWtf8+vUpcBa7kadTCardsVPp9OyLMsOrYFAQB6PR5Zl2Wepy1+OGIaxalfm5V+ERCIRjY2NVcwvj1hf/n8ul6u7vFpfzDjF5/PJNE1Fo1GNjIzIsiyNj4/b94df73zpUtf/8mBqoVDI7iLeyHPLIpGITNNUOBxuz44BAAAAsGaMqtThwuGwfVa7Vtdyy7JULBbtAb28Xq89rXyt/VoUi0WNj48rFAopFAqtmO/3+5vbmCZVG/G+WCxWDbJl5TP+fr9fx48fVzgcrtgP651fFggEFAgEFI1G1/TcaDQqwzDad2kFAAAAgKZwprvDGYah/v5+jY+Pa3p6uuK6+TKPxyOfz2d3+V6ufD/1RhWLRfX19SkWi9mXAHi93uY3QOvvXl4esK88aJ8kTU1NNdRFvszv92tkZGTd85fXsNzyLuWrLTsUCmlgYIAz3AAAAMAWQOjeBiKRiIaGhuzBui4XDAbtAe3KQS6ZTNpnYaVLA34NDw/LsixNTk7WXFY5oA8ODsowDI2Pj6+7/vV2LzcMQ8FgUENDQ5qYmFA+n9f4+LhOnDhhtynXXe06+PJZ6OXd5pudn81m7Tp8Pp+9P5eH6nrLLgfuwcHBiqDeltH7AQAAAKwZ3cu3gWAwqEAgUPeMcSaTUSqVktvtltfrVSqVsoPciRMnFI/H5Xa7FYvF7EBdjc/nUyAQsEcuz+Vya+6e7oSJiQlJUl9fn0KhkB16yy7vep9Op+V2u+V2u1UsFlf0Amh2vs/nUywW09DQkNxutwYGBjQyMlJxlr7Wc7PZrJLJpCKRiD3f7XbryJEjrd1ZAAAAAFrGVbp0l/NtY25uTr29vTpz5ox6eipHBT9//rxmZmbU19enPXv2bFCFQGvxvgYAAMB65HI5Dd11j37pyAd07YFD61rWrR++bdX7dP/sVUd05a7TWtz1Gu0Mbd77dNfLlstxphsAAAAAAIdwTTcAAAAAoC2+9MH/KdfSkkpdtc//fvp7H9VnvvSIxv4kphvaV5pjCN0AAAAAgLZY6D2waptzFz166eU9urj7YBsqch7dywEAAAAAcAihu4ptNrYcOhzvZwAAAGDjELqX2bVrlyTplVde2eBKgNYpv5/L728AAABgoxz6+6T6TvylDv19smabH+39nII//oz2zf5VGytzDtd0L7Njxw4ZhqHZ2VlJ0hVXXCGXy7XBVQHNKZVKeuWVVzQ7OyvDMLRjx46NLgkAAADb3E2fG7dvGfbsTwWrtvF5/kr/7u2ntfjcn0v6vfYW6ABC92WuueYaSbKDN7DVGYZhv68BAAAAtBeh+zIul0vXXnutDh48qAsXLmx0OcC67Nq1izPcAAAAwAYidNewY8cOwgoAAAAAYF0YSA0AAAAAAIcQugEAAAAAcAihGwAAAAAAhxC6AQAAAABwCKEbAAAAAACHMHo5AAAAAKAtXj54vRb3XKWFnv0125y58Dqdmj+v19zQ1xGBtRO2AQAAAACwBXzlAxOrtvnsc3+kx048qIlHH5K3DTU5je7lAAAAAAA4hNANAAAAAIBDCN0AAAAAADiEa7oBAAAAAG3x5kdHtPtsUa9eZejkXaNV2/zsNQ/o377rKR387jHJ+5k2V9h6hG4AAAAAQFt4vpvR3uKszhkHa7a5du9TurHntBbnvtLGypxD93IAAAAAABxC6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAIYRuAAAAAAAcQugGAAAAAMAhhG4AAAAAABxC6AYAAAAAwCE7N7oAAAAAAMD28OxP/kftPHdWi3uvqtnmm2d+Ttb3/15Hbn+3PG2szSmEbgAAAABAW3zn9vet2ubJ/Hv02D+cki/8gY4I3XQvBwAAAADAIYRuAAAAAAAcQugGAAAAAMAhXNMNAAAAAGiLWz98m/YWZ3XOOKgn7nu8apv39N2lu+85rcXs2yXvi22usPU40w0AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOITQDQAAAACAQ3ZudAEAAAAAgO3h5K/+kboWL2hp566abb7w4u/oi19L6Lc++Dt6XRtrcwqhGwAAAADQFvkfObxqmxfOvUnTz35e53t+og0VOY/u5QAAAAAAOITQDQAAAACAQ+heDgAAAABoC8+3p+xrumt1Nb9279fVf+gl7Zn7siRvewt0AKEbAAAAANAWb/6L39Pe4qzOGQf1xH2PV23zs9f8V73r0Gktfvd3pFt+uc0Vth7dywEAAAAAcAihGwAAAAAAhxC6AQAAAABwCKEbAAAAAACHELoBAAAAAHAIoRsAAAAAAIcQugEAAAAAcAihGwAAAAAAhxC6AQAAAABwyM6NLgAAAAAAsD08cd/jq7b5xMyjeuzEg5p49CF521CT0zjTDQAAAACAQzb0THc2m5Uk+Xw+WZalYrEon88nSbIsS8lkUqZpyrIshcNhGYax6jwAAAAAADaLDQ3d8Xhc4+PjkqRAIKBEImHPC4VCymQyki6F7KGhIXt+vXkAAAAAAGwWGxq6/X6/CoWCJFWcqbYsq6KdaZpKp9OrzgMAAAAAbF43ffZh7Tx3Vot7r9J3bn9f1Ta3eD6h3p/8ltzPPSh5/6zNFbbehg+kVq1beDqdlsfjqZjm8XiUzWY1PT1dc165a/pyCwsLWlhYsB/Pzc1JkpaWlrS0tNSCLQAAAACAzlUqleRyuSSVVFJpXcs69A+f1N7irM4ZB/Xt2yNV29zc+3fyX31ai7PHtbT0J+tan5MazZMbGrqLxaKSyaQkaWpqSpFIRKZpqlgsVm2fz+frzqtmdHRU995774rpp06d0vnz55uqGwAAAAC2i/n5ed3Qd71290ilPefWtzBXyf635rJ+ONz3Uqmkl2Zn17c+B83PzzfUbkND9/IB0EzT1MDAgHK5XM32tQJ3vXkjIyM6duyY/Xhubk6HDh3SgQMH1NPT00zZAAAAALBtnD17Vk/PPKO39Umu7r3rW1jJZf/rOl9jWTsl7ZC6XC4dPHhwfetz0J49expqt6Gh27Isu0t4eSRyy7JkGMaKM9f5fF6GYdSdV013d7e6u7tXTO/q6lJXF3dMAwAAAIB6XC6XSqWSJJdccrVuuQ0sazNntkZr27AtyGazOnLkyIrpHo9HgUCg6nP6+/vrzgMAAAAAYDPZsDPdpmkqFovZj9PptILBoH02eznLstTf37/qPAAAAAAANpMNC92GYai/v19jY2MyDEO5XK7iXtuJRELRaFSHDx/W1NRUw/MAAAAAANgsNvSabp/PV/U2X1LlmfBgMNjwPAAAAAAANovNe1U6AAAAAABb3Iae6QYAAAAAbB/5G/3afbaoV68yarZ54dwbVXjhKd18y1u1r32lOYbQDQAAAABoi5N3ja7a5gsv/j967MSDmgh9tCNCN93LAQAAAABwCKEbAAAAAACHELoBAAAAAHAI13QDAAAAANrirQ8OqXvutBZ69usrH5io2ub2635PgaM5veYbvyJ5/7HNFbYeoRsAAAAA0BZXzj6jvcVZ7Tx/tmab3l3f15VXvKzFczNtrMw5dC8HAAAAAMAhhG4AAAAAABxC6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAIYRuAAAAAAAcQugGAAAAAMAhhG4AAAAAAByyc6MLAAAAAABsD995Z1g7F85psXtvzTbZ/C/q699NKfgrv6qDbazNKYRuAAAAAEBbPPtTwVXbfOvMO5X8p2/r5w7+YkeEbrqXAwAAAADgEEI3AAAAAAAOoXs5AAAAAKAtus+ckmtpSaWuLi30HqjaZu+OvK6+8rx2vDorydveAh1A6AYAAAAAtMXb//iXtbc4q3PGQT1x3+NV27z79cf0y97TWnzqDukNL7a5wtajezkAAAAAAA4hdAMAAAAA4BBCNwAAAAAADiF0AwAAAADgEEI3AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA4hdAMAAAAA4BBCNwAAAAAADtm50QUAAAAAALaHr/zmuFxLiyp11Y6in33uPv3tV/5Cf/CRe/X6NtbmFEI3AAAAAKAtXn7NDau2OXPhOj1duEoX9prOF9QGdC8HAAAAAMAhhG4AAAAAABxC6AYAAAAAtMVrpz6rQ//wSb126rM123j3fUG3v+E5XfXS37SxMudwTTcAAAAAoC1u/syfam9xVueMg3r+8O1V27zl6kf1jmtPa/F7Memt/7nNFbYeZ7oBAAAAAHAIoRsAAAAAAIcQugEAAAAAcAihGwAAAAAAhxC6AQAAAABwCKEbAAAAAACHELoBAAAAAHAIoRsAAAAAAIfs3OgCAAAAAADbw8K+qyv+reaVi269snBW+15zoCMCaydsAwAAAABgC/hS9LFV23zme3+ix048qIlHH5K3DTU5je7lAAAAAAA4hNANAAAAAIBDCN0AAAAAADiEa7oBAAAAAG3xxk/8oXa/ckavXtGrp97z+1XbvP3gf9NNt31NV898WPJ+os0Vth6hGwAAAADQFgf/+f9ob3FW54yDNdu8/sppvcE4rcXC59tYmXPoXg4AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOGTnRhcAAAAAANgenve/U7temdeFK/bVbJOb/2k9+4Ov6O233i6jfaU5htANAAAAAGiLb95xbNU2X33pP+mxL5zVzXd9qCNCN93LAQAAAABwSFOh+4knntDJkyftxw888IAOHz6sX/zFX9Tc3FyragMAAAAAYEtrKnRHo1Hl83lJ0l//9V8rGo0qHA5raWlJg4ODLS0QAAAAAICtylUqlUprfdKOHTt08eJFSdJtt90mt9ut48ePq1gsav/+/fa8zWhubk69vb06c+aMenp6NrocAAAAANjUcrmchu66R7905AO69sChdS3rZ/7w3eo+c0oLvQf0xd//dNU2b7/yXdqlF7Wr53rt/o+5da3PSY1my6bOdPf29mp+fl6SlE6nNTAwIElyuVzq7e1tZpEAAAAAgA63Y+EV7Tr/snYsvFKzzS7XeV25+6K6Lr7cxsqc09To5UNDQ7rllltUKBRkmqbuvvtuSdL4+Lj6+/ubKiQajWpkZESGYUiSLMtSMpmUaZqyLEvhcLiheQAAAAAAbBZNhe5YLKbbbrtNxWJRgUDAnm6apmKx2JqXl81mNTY2ppGREXtaKBRSJpORdClkDw0NKZFIrDoPAAAAAIDNoqnQPTc3pyNHjqyY7vP55HK51rw8y7JkmmbF4+VM01Q6nV51HgAAAAAAm0lTodvtdlcdLM2yLI2Njenv/u7vGl5WMplUMBhUNBq1p6XTaXk8nop2Ho9H2WxW09PTNef5fL4Vy19YWNDCwoL9uHxLs6WlJS0tLTVcJwAAAABsR6VS6YcnV0sqac3jcNdebgPL2syZrdHamgrdtQY87+/v1/T0dMPLKRaLVa/FLhaLVdvn8/m686oZHR3Vvffeu2L6qVOndP78+UZLBQAAAIBtaX5+Xjf0Xa/dPVJpz7n1LcxVsv+tuawfDve9VCrppdnZ9a3PQeXBxVezptB94403yuVyyeVy6aabblox37Ksqmeba5mcnFQ4HG64fa3AXW/eyMiIjh07Zj+em5vToUOHdODAAW4ZBgAAAACrOHv2rJ6eeUZv65Nc3XvXt7CSy/7Xdb7GsnZK2iF1uVw6ePDg+tbnoD179jTUbk2hOx6Pq1Qq6bbbbtP999+/Yr5pmrrlllsaWlY6ndbg4GDVeYZhrDhznc/nZRhG3XnVdHd3q7u7e8X0rq4udXU1dcc0AAAAANg2XC7XD3s7u+TS2sfwqrncBpa1mTNbo7WtKXSXB08LBoO68847117VZSYnJ+3/W5al0dFRHT16VIFAQPF4fEX7/v5+maZZcx4AAAAAAJtJU9d0Lw/LzVp+qzFJikQiikQiFaOYl1mWpf7+fvtMd615AAAAAIDN66mjH1bXhfNa2lW7a/bfz75fX37qMwr/5m/o2jbW5pSmQvcTTzyhSCSy4vZd5VHtqo1sXkuxWNT4+LikS/f/jkQi8vl8SiQSikajOnz4sKampiruw11vHgAAAABgc5p900+v2ubZl9+iL1pf1q+4b21DRc5zlWoNRV7HjTfeKJ/Pp0gksuL2XZIavq57I8zNzam3t1dnzpxhIDUAAAAAWEUul9PQXffol458QNceOOT4+l449aweO/GgJh59SF6v1/H1NavRbNnUme58Pq9HHnmE0AoAAAAAQB1NDQU3ODioTCbT6loAAAAAAB2s53v/IsP6mnq+9y812+zv/q5+7DVF7X75qTZW5pymznR7vV4FAgH5fL6qg5iNjo62ojYAAAAAQAfpH/9t7S3O6pxxUE/c93jVNre99j7dcf1pLX4rIv34L7S5wtZrKnRPTU3Ztw/L5XIV81yu1t23DQAAAACArWzDbhkGAAAAAECna+qabkk6efKkPvShD+md73ynPe2BBx7QyZMnW1EXAAAAAABbXlOhe2JiQkeOHJHX69XU1JQ9va+vT9FotGXFAQAAAACwlTUVusfGxpTJZDQ0NFQx/c4779T09HRLCgMAAAAAYKtrKnSfPn1a+/fvXzF9ZmZGpVJp3UUBAAAAANAJmgrdoVBIoVBIc3Nz9rS5uTlFIhGFw+GWFQcAAAAAwFbWVOiOx+Pat2+fDMNQoVDQ4cOH5Xa75fV6df/997e6RgAAAAAAtqSmbhkmSYlEQpZl6cknn5Qk+Xw+9fX1tawwAAAAAAC2uqZDtySZpinTNFtVCwAAAACgg33xw5+Sq1RSyeWq2Sb59MeU+OLDevDhP1EnpM2mQ/cjjzyiXC6nYrG4Yt5DDz20npoAAAAAAB3o4p4rV21zoXSFXrmwU6UdV7WhIuc1FboHBweVTCbl8/k40w0AAAAAQA1Nhe5kMqlMJqNbbrml1fUAAAAAANAxmgrdpmnqxhtvbHUtAAAAAIAO1nfiL7Xz/Fkt7rlKM0feW7XNG41P69f6c+p94eOS9yNtrrD1mr5l2N13363Pf/7zFffqBgAAAACglr7P/6V+5G/j6vv8X9Zs8yb3p/Vrhy0ZL/z3NlbmnKYHUkulUkomk1XnXbx4semCAAAAAADoFE2F7kgkokAgoEgkIo/H0+qaAAAAAADoCE2F7nw+r0ceeUQ9PT2trgcAAAAAgI7R1DXdg4ODymQyra4FAAAAAICO0tSZbq/Xq0AgIJ/Pp/7+fhmGUTF/dHS0FbUBAAAAALClNRW6p6amdOTIEUlSLpermOdyudZfFQAAAAAAHaCp0D05OdnqOgAAAAAA6DhNXdN9+PDhuvMfeeQR3XPPPfr85z/fVFEAAAAAAHSCpkJ3oVCQJD399NOam5urmDc4OKhoNKpSqaRgMKhPfepT668SAAAAALDlzV33BhVu+HHNXfeGmm1eWvDqn1/s1cKV/6aNlTmnqe7lwWBQN910k309t9/v1xNPPKF9+/YpmUwqm83qzW9+swKBgD7ykY/ojjvuaGnRAAAAAICtZ/p9f7Zqm9Tzv6/HTjyoiUcfkrcNNTmtqTPdJ06c0PDwsJaWlrS0tKRQKKRgMKiZmRm5XC69+c1vlnQpnFuW1cp6AQAAAADYMpoK3blcTkNDQ/bj4eHhFaOYS9KZM2earwwAAAAAgC2uqdAdCAR0zz336OTJkzp58qSOHj0qv9+vJ598UpI0Pz8v6dIZcb/f37pqAQAAAADYQpq6pntiYkLDw8O69dZbJV0K4cViUcPDwzpy5IhuuOEGBQIBpdNpJRKJlhYMAAAAANia+h/+Le0+W9CrV7lrXt898No/1Nvu+Jau+VZY8p5oc4Wt11To7u3tVTweVzwerzo/mUxqenpa6XRat9xyy7oKBAAAAAB0hp7nvqG9xVmdMw7WbHN1d07XX3VGiy//cxsrc05ToVuSTp48qePHj+vJJ5/U5z73OUnSAw88oEAgoGAwqGAw2LIiAQAAAADYipq6pntiYkJHjhyRaZqampqyp/f19SkajbasOAAAAAAAtrKmQvfY2JgymUzFCOaSdOedd2p6erolhQEAAAAAsNU1FbpPnz6t/fv3r5g+MzOjUqm07qIAAAAAAOgETYXuUCikUCikubk5e9rc3JwikYjC4XDLigMAAAAAYCtrKnTH43Ht27dPhmGoUCjo8OHDcrvd8nq9uv/++1tdIwAAAAAAW1LTo5cnEglZlqUnn3xSkuTz+dTX19eywgAAAAAA2OqaDt2SZJqmTNPU3Nycenp6WlUTAAAAAAAdoaHu5U8//bQeeeSRFdM/+clP6qabbpLb7db+/fv18Y9/vOUFAgAAAAA6w8w73qtv//uIZt7x3pptvl54t/7HlKnitf+pjZU5p6Ez3cPDw9q/f7/uvvtue9qTTz6pYDCoYDCoyclJ5XI5hcNheTwe3XHHHY4VDAAAAADYmmaO1A7bZU8V363Hpr+nn/qNX9fVbajJaQ2F7mw2q/Hx8Yppo6Oj8nq9mpyclCTdcsstkqSPfOQjhG4AAAAAANRg93LLsmSaZsW0ZDKpYDBYMc3v9yubzbauOgAAAAAAtrCGQrfP57NHKZekEydOSJKOHj1a0c6yLBmG0brqAAAAAAAdY8f5l7Xz3FntOP9yzTa7XK/oil2Lcl0828bKnNNQ9/JwOGxfz93b26tIJCK/3683v/nNFe1SqZQCgUDLiwQAAAAAbH0/c98d2luc1TnjoJ647/GqbYI3vF+/dtNpLX7t56QfebHNFbZew6G7WCzqzjvvlCQFAgElEomKNjMzMxobG1M6nW59lQAAAAAAbEENdS+XLo1gvrS0pKWlJT3++OPq7e2tmN/X16dMJqNbb7215UUCAAAAALAVNRy6G1EewRwAAAAAALQ4dAMAAAAAgH9F6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAIQ2F7pGREc3NzTldCwAAAAAAHaWh0D02Nqaenh778T333ONYQQAAAAAAdIqGQndfX5++9rWv2Y/Hx8cdKwgAAAAA0Jmmw3+qLx37H5oO/2nNNo8//2H9xicP64UfjbevMAftbKTRww8/rHe84x3av3+/Pe2mm26q2f473/nO+isDAAAAAHSUudf/2KptTi/cqH/5gaFXr3xjGypyXkOhOxAIaGZmRul0WqVSSYODg7r//vudrg0AAAAAgC2todAtSb29vbrzzjslScPDw/b/AQAAAABAdQ2H7uXKZ7mfeOIJpVIpSdJb3vIW3XHHHa2rDAAAAADQUQ5+/X+r68J5Le3ao9k3/XTVNoeu/Kp+xvyBrig8Icnb3gId0FTolqTbbrtN09PT6u/vlyTF43GNjo7qq1/9asuKAwAAAAB0jjcev097i7M6ZxzUE296vGqbnzr4Mf3cz53W4szvS/1Dba6w9Roavfxy99xzjwzDUD6f1+OPP67HH39c+Xxe119/vd7//ve3ukYAAAAAALakps50T05OamZmZsX0iYkJeb1efexjH1t3YQAAAAAAbHVNnel2u90qFAorphcKBZVKpXUXBQAAAABAJ2gqdN95550Kh8Oan5+3p83NzWlwcFCRSKRlxQEAAAAAsJU1FbpjsZh6enpkGIZuuukm3XTTTXK73fJ4PBodHW11jQAAAAAAbElNj16eSCSUzWaVyWRULBYVCAR0yy23rGkZ6XRaklQsFjU1NaWjR4/K5/NJkizLUjKZlGmasixL4XBYhmGsOg8AAAAAgM2i6dAtST6fzw7JzQiFQjpx4oQCgYDy+bxCoZByuZw9L5PJSLoUsoeGhpRIJFadBwAAAADAZtFU9/JWSSQSFaF9+Zns5UzTtM+K15sHAAAAAMBmsq4z3esVCATs/ycSCXsQtnQ6LY/HU9HW4/Eom81qenq65rxqZ90XFha0sLBgP56bm5MkLS0taWlpqWXbAgAAAACdqFQqyeVySSqppPXdrepi9xW6sOdKXey+ouayLpT26OVXd2jXnis3dWZrtLYNDd2SlM1mdfz4cQ0MDCgcDku6dI13Nfl8vu68akZHR3XvvfeumH7q1CmdP3++qZoBAAAAYLuYn5/XDX3Xa3ePVNpzbl3L+vwfPbbsUfVl/U3+IX3xa/9Lv/Hb9+ja2dl1rc9Jy+/mVc+Gh26fzyfTNBWNRpVMJhUMBmu2rRW4680bGRnRsWPH7Mdzc3M6dOiQDhw4oJ6enmbLBgAAAIBt4ezZs3p65hm9rU9yde91fH2vzklPzzyjffv26eDBg46vr1l79uxpqF1TofuJJ57Qrbfe2sxTqzIMQ6FQSAMDAyoUCjIMY8WZ63w+L8Mw6s6rpru7W93d3Sumd3V1qatrQy9pBwAAAIBNz+VyqVQqSXLJJVc71mh3ad/Mma3R2pragnA4rE996lPNPNWWTqfldrvtx6ZpSro0UNrya72X6+/vrzsPAAAAAIDNpKkz3cPDwxoeHlYgENC+ffuaWrHH46kI0NlsVoZhVB0MzbIs9ff322e6a80DAAAAAGxeN3/qo9r1yrwuXLFP37zjWNU2b7n6v+van/1n7f/e/ZJ3os0Vtl5Todvlcqm3t1c33HCDAoGAfZa6bHR0dNVl+Hw+HT16VOPj45KkVCpl33tbujSaeTQa1eHDhzU1NVVxH+568wAAAAAAm9NrM5/T3uKszhkHa4Zu777/rR/3nNbiS/+rzdU5o6nQnUql5PF45PF4VCgUKsLypaHkG7N80LTyyOVlpmkqFoutaLfaPAAAAAAANoumQvfk5GSr6wAAAAAAoOM0PRTcyZMn9aEPfUjvfOc77WkPPPCATp482Yq6AAAAAADY8poK3RMTEzpy5Ii8Xq+mpqbs6X19fYpGoy0rDgAAAACArayp0D02NqZMJqOhoaGK6Xfeeaemp6dbUhgAAAAAAFtdU6H79OnT2r9//4rpMzMzP7xpOgAAAAAAaCp0h0IhhUIhzc3N2dPm5uYUiURWjEIOAAAAAMB21VTojsfj2rdvnwzDUKFQ0OHDh+V2u+X1enX//fe3ukYAAAAAALakpm4ZJkmJREKWZenJJ5+UJPl8PvX19bWsMAAAAABAZ5n9N/9Ou185o1ev6K3Z5nsv92v29JN689veodqtto6mQ7ckmaYp0zRbVQsAAAAAoIM99Z7fX7XNl2Z/Q4+deFATv3Tf9g7dTz/9tOLxuCzLkiS95S1v0dDQkHp6elpWHAAAAAAAW1lT13T/9V//tUzTVCKRkNvtltvt1kMPPSS3262vfe1rra4RAAAAAIAtqakz3dFoVMPDwysGTYtEIrr77rs1NTXVkuIAAAAAANjKmgrd+Xxev/u7v7tieiwWk8fjWXdRAAAAAIDO8/bYL6l7/iUt7LtaX4o+VrXNL7z+P+sX3vus9j31bsn79fYW6ICmupcPDg5qZmZmxfSnn35awWBw3UUBAAAAADpP9/xL2lucVff8SzXbXLGjoANXLWjnq6faWJlzGjrTPTIysmLarbfeqnA4XDFtfHxcg4ODrakMAAAAAIAtrqHQnclkVkzz+/0rpvv9/tZUBQAAAABAB2godD/++ONO1wEAAAAAQMdp6ppuAAAAAACwuqZGL5ekRx55RLlcTsViccW8hx56aD01AQAAAADQEZoK3YODg0omk/L5fDJNs9U1AQAAAADQEZoK3clkUplMRrfcckur6wEAAAAAoGM0dU23aZq68cYbW10LAAAAAAAdpakz3fF4XHfffbfe9773ye/3q6enp9V1AQAAAAA6zDd/4be148J5Xdy1p2abr750lzLf+qze++tDek0ba3NK0wOppVIpJZPJqvMuXrzYdEEAAAAAgM70/OHbV22Tm/9ZffYb/6Q7rv757Ru6I5GIAoGAIpGIPB5Pq2sCAAAAAKAjNBW68/m8HnnkEbqVAwAAAABQR1MDqQ0ODiqTybS6FgAAAABAB7vyB0/rqhe+qyt/8HTNNr27ntMN7rPadc5qX2EOaupMt9frVSAQkM/nU39/vwzDqJg/OjraitoAAAAAAB3krX8e1t7irM4ZB/XEfY9XbXP7dR9WqO+0Fr/xXumNL7a5wtZrKnRPTU3pyJEjkqRcLlcxz+Vyrb8qAAAAAAA6QFOhe3JystV1AAAAAADQcZq6phsAAAAAAKyuqdDd1dWlHTt2VP256aabWl0jAAAAAABbUlPdy6uNXH769Gl96EMf0vve9751FwUAAAAAQCdoKnTfcsstVadPTk7qnnvu0d13372uogAAAAAA6AQtvabbNE1NT0+3cpEAAAAAAGxZTZ3pHhkZqTo9m82uqxgAAAAAADpJy67pli6d6b7//vvXVRAAAAAAAJ2iqdD9+OOPt7oOAAAAAECH+9IH/6dcS0sqddW+0vnT3/uoPvOlRzT2JzHd0L7SHNNU6AYAAAAAYK0Weg+s2ubcRY9eenmPLu4+2IaKnNdQ6L7xxhvlcrkaWqDL5dK3v/3tdRUFAAAAAEAnaCh0R6PRVdsUi0XF43FZlrXuogAAAAAA6AQNhe6hoaGa8+bm5vSRj3xEY2NjMgxDDz/8cMuKAwAAAAB0jkN/n9TOhXNa7N6rZ38qWLXNj/Z+TsEff0b7Zv9K8v5emytsvaav6X766acVi8U0Pj4u0zSVSCR05513trI2AAAAAEAHuelz49pbnNU542DN0O3z/JX+3dtPa/G5P5e09UN37SHjajh58qSOHj0q0zQ1NTWlxx9/XN/5zncI3AAAAAAAXKbh0H3y5Enddttt8vl8KhQKymQymp6e1pEjR5ysDwAAAACALauh7uWHDx9WNptVMBiUZVm64YYbHC4LAAAAAICtr6HQnclkZBiGLMtSKBRatf3U1NS6CwMAAAAAYKtrKHTHYjGn6wAAAAAAoOM0FLo/+MEPOl0HAAAAAAAdZ82jlwMAAAAAgMYQugEAAAAAcEhD3csBAAAAAFivlw9er8U9V2mhZ3/NNmcuvE6n5s/rNTf0dURg7YRtAAAAAABsAV/5wMSqbT773B/psRMPauLRh+RtQ01Oo3s5AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA7hmm4AAAAAQFu8+dER7T5b1KtXGTp512jVNj97zQP6t+96Sge/e0zyfqbNFbYeoRsAAAAA0Bae72a0tzirc8bBmm2u3fuUbuw5rcW5r7SxMufQvRwAAAAAAIcQugEAAAAAcAihGwAAAAAAhxC6AQAAAABwCKEbAAAAAACHELoBAAAAAHAIoRsAAAAAAIcQugEAAAAAcMjOjS4AAAAAALA9PPuT/1E7z53V4t6rarb55pmfk/X9v9eR298tTxtrc8qGhu5sNqt0Oi1Jmpqa0sTEhAzDkCRZlqVkMinTNGVZlsLhcEPzAAAAAACb03duf9+qbZ7Mv0eP/cMp+cIfIHSvVzqd1vDwsCRpbGxMR44cUSaTkSSFQiH7/5ZlaWhoSIlEYtV5AAAAAABsFht2TXc2m9Xo6Kj9OBgMKpvNyrIsWZZV0dY0TfuMeL15AAAAAABsJht2ptvn82liYsJ+XCwWJUkej0eTk5PyeCo7Eng8HmWzWU1PT9ec5/P5VqxnYWFBCwsL9uO5uTlJ0tLSkpaWllq1OQAAAADQkUqlklwul6SSSiq1Y41yuVwqlUqbOrM1WtuGdi8PBoP2/48fP65AICDDMOwAfrl8Pl93XjWjo6O69957V0w/deqUzp8/v+aaAQAAAGA7mZ+f1w1912t3j1Tac25dywp88Be0t3BK59wHlP7jz1Rt8x7v/627f+S0Xs2+XbNXfW1d63PS/Px8Q+02xejlxWJRyWTSvk67Xru1zhsZGdGxY8fsx3Nzczp06JAOHDignp6eZsoFAAAAgG3j7NmzenrmGb2tT3J1713fwkou+1/X+RrL2ilph9TlcungwYPrW5+D9uzZ01C7TRG6o9GoUqmUPQK5YRgrzlzn83kZhlF3XjXd3d3q7u5eMb2rq0tdXdymHAAAAADqKXf1llxyydW65TawrM2c2RqtbcO3YGxsTNFoVKZpqlgsqlgsKhAIVG3b399fdx4AAAAAAJvJhobuZDIpn89nB+7JyUkZhiHTNCvaWZal/v7+VecBAAAAALCZbFj3csuyFAqFKqYZhqFwOCxJSiQSikajOnz4sKampiruw11vHgAAAAAAm8WGhW7TNH94XUDt+bFYTFLlKOerzQMAAAAAYLPY8Gu6AQAAAADoVIRuAAAAAAAcQugGAAAAAMAhm+I+3QAAAACAznfyV/9IXYsXtLRzV802X3jxd/TFryX0Wx/8Hb2ujbU5hdANAAAAAGiL/I8cXrXNC+fepOlnP6/zPT/RhoqcR/dyAAAAAAAcQugGAAAAAMAhdC8HAAAAALSF59tT9jXdtbqaX7v36+o/9JL2zH1Zkre9BTqA0A0AAAAAaIs3/8XvaW9xVueMg3rivsertvnZa/6r3nXotBa/+zvSLb/c5gpbj+7lAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDCN0AAAAAADhk50YXAAAAAADYHp647/FV23xi5lE9duJBTTz6kLxtqMlpnOkGAAAAAMAhhG4AAAAAABxC6AYAAAAAwCFc0w0AAAAAaIubPvuwdp47q8W9V+k7t7+vaptbPJ9Q709+S+7nHpS8f9bmCluP0A0AAAAAaItD//BJ7S3O6pxxsGbovrn37+S/+rQWZ49L2vqhm+7lAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDdm50AQAAAACA7SF/o1+7zxb16lVGzTYvnHujCi88pZtveav2ta80xxC6AQAAAABtcfKu0VXbfOHF/0ePnXhQE6GPdkTopns5AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA7hmm4AAAAAQFu89cEhdc+d1kLPfn3lAxNV29x+3e8pcDSn13zjVyTvP7a5wtYjdAMAAAAA2uLK2We0tzirnefP1mzTu+v7uvKKl7V4bqaNlTmH7uUAAAAAADiE0A0AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEN2bnQBAAAAAIDt4TvvDGvnwjktdu+t2Sab/0V9/bspBX/lV3WwjbU5hdANAAAAAGiLZ38quGqbb515p5L/9G393MFf7IjQTfdyAAAAAAAcQugGAAAAAMAhdC8HAAAAALRF95lTci0tqdTVpYXeA1Xb7N2R19VXnteOV2cledtboAMI3QAAAACAtnj7H/+y9hZndc44qCfue7xqm3e//ph+2Xtai0/dIb3hxTZX2Hp0LwcAAAAAwCGEbgAAAAAAHELoBgAAAADAIYRuAAAAAAAcQugGAAAAAMAhhG4AAAAAABxC6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAITs3ugAAAAAAwPbwld8cl2tpUaWu2lH0s8/dp7/9yl/oDz5yr17fxtqcQugGAAAAALTFy6+5YdU2Zy5cp6cLV+nCXtP5gtqA7uUAAAAAADiE0A0AAAAAgEMI3QAAAACAtnjt1Gd16B8+qddOfbZmG+++L+j2Nzynq176mzZW5pwNvaY7m81qaGhImUymYrplWUomkzJNU5ZlKRwOyzCMVecBAAAAADavmz/zp9pbnNU546CeP3x71TZvufpRvePa01r8Xkx6639uc4Wtt2Ghuxycs9nsinmhUMgO4pZlaWhoSIlEYtV5AAAAAABsJhsWuoPBYNXplmVVPDZNU+l0etV5AAAAAABsNpvulmHpdFoej6dimsfjUTab1fT0dM15Pp+v6vIWFha0sLBgP56bm5MkLS0taWlpqcXVAwAAAEBnKZVKcrlckkoqqdS65TawrM2c2RqtbdOF7mKxWHV6Pp+vO6+W0dFR3XvvvSumnzp1SufPn2+mRAAAAADYNubn53VD3/Xa3SOV9pxb38JcJfvfmsv64XDfS6WSXpqdXd/6HDQ/P99Qu00XumupFbhXmzcyMqJjx47Zj+fm5nTo0CEdOHBAPT09LawQAAAAADrP2bNn9fTMM3pbn+Tq3ru+hZVc9r+u8zWWtVPSDqnL5dLBgwfXtz4H7dmzp6F2my50G4ax4sx1Pp+XYRh159XS3d2t7u7uFdO7urrU1cUd0wAAAACgHpfLpVKpJMkll1ytW24Dy9rMma3R2jbdFgQCgarT+/v7684DAAAAAGCz2RRnuovFon222jTNinmWZam/v98+011rHgAAAAAAm82Ghe50Oq1UKiXp0mBnhw8ftm8jlkgkFI1GdfjwYU1NTVXch7vePAAAAADA5rWw7+qKf6t55aJbryyc1b7XHNgcZ4nXyVW61Dl/25ibm1Nvb6/OnDnDQGoAAAAAsIpcLqehu+7RLx35gK49cMjx9b1w6lk9duJBTTz6kLxer+Pra1aj2XLTXdMNAAAAAECnIHQDAAAAAOAQQjcAAAAAAA7phOvSAQAAAABbwBs/8Yfa/coZvXpFr556z+9XbfP2g/9NN932NV0982HJ+4k2V9h6hG4AAAAAQFsc/Of/o73FWZ0zDtZs8/orp/UG47QWC59vY2XOoXs5AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA4hdAMAAAAA4BBCNwAAAAAADiF0AwAAAADgEEI3AAAAAAAOIXQDAAAAAOCQnRtdAAAAAABge3je/07temVeF67YV7NNbv6n9ewPvqK333q7jPaV5hhCNwAAAACgLb55x7FV23z1pf+kx75wVjff9aGOCN10LwcAAAAAwCGEbgAAAAAAHELoBgAAAADAIVzTDQAAAABoi5/5w3er+8wpLfQe0Bd//9NV2wRveJ/e8+svatfXbpO8ufYW6ADOdAMAAAAA2mLHwivadf5l7Vh4pWabXa7zunL3RXVdfLmNlTmH0A0AAAAAgEMI3QAAAAAAOITQDQAAAACAQwjdAAAAAAA4hNANAAAAAIBDCN0AAAAAADiE0A0AAAAAgEMI3QAAAAAAOGTnRhcAAAAAANgenjr6YXVdOK+lXXtqtvn72ffry099RuHf/A1d28banELoBgAAAAC0xeybfnrVNs++/BZ90fqyfsV9axsqch7dywEAAAAAcAihGwAAAAAAh9C9HAAAAADQFj3f+xd1LV7Q0s5dmnv9j1Vts7/7u/qx1xS1++WnJHnbW6ADCN0AAAAAgLboH/9t7S3O6pxxUE/c93jVNre99j7dcf1pLX4rIv34L7S5wtajezkAAAAAAA4hdAMAAAAA4BBCNwAAAAAADiF0AwAAAADgEEI3AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA4hdAMAAAAA4BBCNwAAAAAADtm50QUAAAAAALaHL374U3KVSiq5XDXbJJ/+mBJffFgPPvwnMttYm1MI3QAAAACAtri458pV21woXaFXLuxUacdVbajIeXQvBwAAAADAIYRuAAAAAAAcQvdyAAAAAEBb9J34S+08f1aLe67SzJH3Vm3zRuPT+rX+nHpf+Ljk/UibK2w9QjcAAAAAoC36Pv+X2luc1TnjYM3Q/Sb3p/UTB09r8YX/Lmnrh266lwMAAAAA4BBCNwAAAAAADiF0AwAAAADgEEI3AAAAAAAOIXQDAAAAAOAQQjcAAAAAAA4hdAMAAAAA4BBCNwAAAAAADtm50QUAAAAAALaHueveoPPGNXr1KnfNNi8tePX06UXdcPO/6YjA2gnbAAAAAADYAqbf92ertkk9//t67MSDmnj0IXnbUJPT6F4OAAAAAIBDCN0AAAAAADiE0A0AAAAAgEO4phsAAAAAtqBTp05pbm7O8fU888wzurC42JJl9T/8W9p9tqBXr3LXvL574LV/qLfd8S1d862w5D3RkvVuJEI3AAAAAGwxp06d0q/fNaSzc684vq5z517R88+/qFcvXFj3snqe+4b2Fmd1zjhYs83V3Tldf9UZLb78z+te32ZA6AYAAACALWZubk5n517RgG9QB9zXOLqub878k44/+4gWL7bmbPd2Q+gGAAAAgC3qgPsaXXvgkKPrmM2/4OjyOx0DqQEAAAAA4JAteabbsiwlk0mZpinLshQOh2UYxkaXBQAAAABAhS0ZukOhkDKZjKRLAXxoaEiJRGKDqwIAAAAAoNKWC92WZVU8Nk1T6XR6g6oBANTTrluZlPX09OjAgQNtWx8AtBqfm1tbO1+/Vt7GC87acqE7nU7L4/FUTPN4PMpms/L5fBtUlTPaedDygYvNrJ3Hwquvvqrdu3e3ZV1S+4+9du7L06dP6//9vT/Qq+fb9wfBru4d+sOP3Kv9+/e3ZX18dgJopXbeAqrsqp4r9PFHJ/gsa4F2v36tvI0XnLXlQnexWKw6PZ/PV52+sLCghYUF+/GZM2fs5SwtLbW8vlY5ffq0fvP9v6WX58+1ZX279+zQyIc/JLfb3Zb1AY0qFAoa/aOYXj3nfHB79cKr+v73n9N1171eu3a25+OxncdeO/elJJ07/4p+8OKsbv93R+UxrnZ8fbMvPa//7/9M6gPvP6bu3d2Or0/isxNAaz377LM6faog340/o559huPrm5svKvOdL+gf//EfdeiQs6Nfbwftfv2ee8HSc89+Vt97MaeLS686uq4XXnpWkvT8qWfkcq0vQ/kWL+iCpJcXL8h67ptV2/zb1y/q4i5pcdeSumrkv82gfCKjVCrVbecqrdZikxkbG1MqlVIqlbKneb1exWIxBYPBFe3/4A/+QPfee287SwQAAAAAbBPPPvusrrvuuprzt9yZbsMwVpzVzufzNUcvHxkZ0bFjx+zHS0tLyufz2r9/v1wul5OlwgFzc3M6dOiQnn32WfX09Gx0OWgzXv/ti9d+e+P137547bc3Xv/ta6u89qVSSfPz83rta19bt92WC92BQEDxeHzF9P7+/qrtu7u71d1d2c2Q24ttfT09PZv6AISzeP23L1777Y3Xf/vitd/eeP23r63w2vf29q7apqsNdbSUaZoVjy3LUn9/P0EaAAAAALDpbLkz3ZKUSCQUjUZ1+PBhTU1NcY9uAAAAAMCmtCVDt2maisViklR18DR0ru7ubv2X//JfVlwygO2B13/74rXf3nj9ty9e++2N13/76rTXfsuNXg4AAAAAwFax5a7pBgAAAABgqyB0AwAAAADgEEI3AAAAAAAO2ZIDqaHzWZalZDIp0zRlWZbC4XDN28Jls1lJks/nk2VZKhaL8vl8a14ONo+1vv7pdFqSNDU1pYmJCbttvfcGNoe1vNb12nKsbz0c59sbv+e3r7W8ZslkUoFAQJJWtOHY35qy2ayGhoaUyWTqtuu43/klYBPy+Xz2/3O5XCkYDNZsGw6HS5JKkkqBQKBUKBSaWg42j7W8brFYrOL/y59b772BzWEtr3W9thzrWw/H+fbG7/ntay2vWfl1X/5T/jzg2N96EolEKZPJlBqJoJ32O5/u5dh0LMuqeGyapn2Goxq/369CoaBCoaBUKlXxLdhaloPNYS2vWzab1ejoqP04GAwqm83ay6j13sDmsJbXul5bjvWth+N8e+P3/Pa1ltesWCwqkUioVCrZP7FYTMPDw5I49reiYDDYUG+ETvydT+jGppNOp+XxeCqmeTweuxtRNYZhrPiwbWY52Hhred18Pp8mJibsx8Vi0W5fVu29gc1hLa91vbYc61sPx/n2xu/57Wutr1kwGLT/n0wmKx5LHPudqhN/53NNNzad8h9Ul8vn8zXbJ5NJSZeu9YtEIjJNc83Lweaw1tdt+S/g48ePKxAI2L+Aa703sDms5bWu15ZjfevhON/e+D2/fa3lNVsepovFovL5fMWxzbHfuTrxdz6hG1tGrYNs+eAJpmlqYGBAuVxuzcvB5rba61b+5bt8YI61vjewOazlGK3XlmN96+E43974Pb99rfaaRaNRxWKximkc+9vPVv6dT+hG24yPj9f9MBwYGLDPXlz+bVU+n687onH5+pDyKIaWZa15OXCWU69/WTQaXXFNV633Bt+Ebw5rea3rteVY33o4zrc3fs9vX828ZsViUel0ekUbjv3O1ZG/8zd4IDdghVwuVzEqYalUKhmGUXVUykwmUzIMw35cKBRKkkqFQmFNy8Hm0czrFovFSrlcrlQqXXoPFAqFuu8NbA5rea3rteVY33o4zrc3fs9vX828ZqlUasVzOPa3ttUiaCf+zmcgNWw6l39DaVmW+vv77W+wlo9aa5pmRXejdDqtYDAowzBWXQ42p7W8/tKlgVV8Pp99fd/k5KT9+td6b2BzWOuxXqstx/rWw3G+vfF7fvta67Ffnnb5wFkc+1vf5d3BO/13vqtUKpU2ugjgcpZlKR6P6/Dhw5qamtLIyIh9MIVCIR0+fNi+ZUR5JEPDMJTL5So+hOstB5tXo6+/ZVnyer0VzzUMQ4VCQVL99wY2h7Uc6/XacqxvPRzn2xu/57evtbz2kjQ2NqZcLqd4PF6xHI79rSedTiuVSmlsbEzDw8M6fPiwPVBmp//OJ3QDAAAAAOAQupcDAAAAAOAQQjcAAAAAAA4hdAMAAAAA4BBCNwAAAAAADiF0AwAAAADgEEI3AAAAAAAOIXQDANABksmkvF6vXC6X/H6/0un0RpcEAABE6AYAYFMbGxuTy+WS2+2W2+2Wy+WS1+tVNBpVsViUJFmWpWg0qkQioVwup6NHj2pgYECWZa26/PHxcfn9fnu5oVCooecBAIDGELoBANjkDMNQoVBQoVBQqVRSKpWSZVny+/0qFosyTVOZTEY+n0+maWp4eFiSlM1m6y53bGxM0WhUIyMjyuVyisfj8ng8nCUHAKCFdm50AQAAYG1M01QikZDb7dbk5KTC4bAMw7DnJ5NJmaapQCBQdznls+PBYNBe7mrPAQAAa8OZbgAAOoRlWYpEIsrn88rlchVBvJZG2gAAgOYRugEA2GIsy1IoFJLH41E4HJZ06ax1+WdwcNC+3rueQCCgSCSyanfysbGxmoO0RaNReb1eud1uRSKRiueFQiGNj49rfHxcXq+34nmRSERut1ter1fj4+Nr2HoAALYWQjcAAJtcsViUy+Wyf7xer30dt3Tp2u2xsTF7BPPyoGurhdlEIiHDMDQwMCCXy6VQKLTiOvBIJKLjx48rkUioUCgoFovZgb7cPpVKaWZmRvl8XgMDAxV1x+NxxWIxxWIxu+t6ebC2mZkZpVIpRaPRVa8/BwBgq3KVSqXSRhcBAACqGxsb0+joqAqFgqRLAdvv96tQKLSsa3g2m9Xx48eVTCZlWZZ9nXexWJTb7VYul5NpmiueU60Ot9utRCKhQCCggYEBTU9Pa2Zmxm5jWZa8Xm/F88bHx5XL5RSLxVqyPQAAbCac6QYAYAvx+XwKBAKKRqMtXWYsFlMul1MwGLSXnU6nZRjGisAtSdPT0zJNc0Xw7+/vVyqVsh8HAoGKNuUz2n19ffYZec50AwA6GaOXAwCwxcRiMfn9fkWj0aqBeD1GRkbsW5HV08g145Kq1ufz+eyu8QAAdDrOdAMAsMW06my3ZVlVpxmGIcMw5PP5VCwWq7YLBAKyLGtF+J6entbhw4fr1p7NZhsO7QAAbHWEbgAAtqBYLKZkMtl0t+xsNiuv16toNKp0Oi3LspRMJjU0NGRfW22apsLhsD3wWbFYVDKZVDQatYP/kSNH7HmhUEimadr3/a7m8mVKl+4rPjY21tR2AACw2RG6AQDYgtZ7ttvn8ymVStm3H/N6vRodHdXExIR9GzJJisfj9qBobrdb8XhcR48elSSlUikFAgH5/X719fXJ4/E01G08Ho/L5/PJ7/fbyyyPbA4AQKdh9HIAAAAAABzCmW4AAAAAABxC6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAIYRuAAAAAAAcQugGAAAAAMAhhG4AAAAAABxC6AYAAAAAwCGEbgAAAAAAHELoBgAAAADAIYRuAAAAAAAc8v8DysOlfEyWVHoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Following generated by AI - see [11]\n", + "# Calculate R² score for torque in each 13-row segment\n", + "torque_segment_scores = []\n", + "torque_segment_info = []\n", + "\n", + "for i in range(num_segments):\n", + " start_idx = i * 13\n", + " end_idx = start_idx + 13\n", + " \n", + " # Get actual and predicted torque values for this segment\n", + " segment_actual_torque = y.iloc[start_idx:end_idx, 1].values # Torque column\n", + " segment_pred_torque = model_best.predict(poly_best.transform(X.iloc[start_idx:end_idx]))[:, 1]\n", + " \n", + " # Calculate R² for this segment\n", + " segment_r2_torque = r2_score(segment_actual_torque, segment_pred_torque)\n", + " torque_segment_scores.append(segment_r2_torque)\n", + " \n", + " # Store segment info\n", + " segment_data = magDf.iloc[start_idx:end_idx]\n", + " currL = segment_data['currL [A]'].iloc[0]\n", + " currR = segment_data['currR [A]'].iloc[0]\n", + " roll = segment_data['rollDeg [deg]'].iloc[0]\n", + " torque_segment_info.append({\n", + " 'start': start_idx,\n", + " 'end': end_idx,\n", + " 'currL': currL,\n", + " 'currR': currR,\n", + " 'roll': roll,\n", + " 'r2': segment_r2_torque\n", + " })\n", + "\n", + "# Find worst and best performing segments for torque\n", + "worst_torque_idx = np.argmin(torque_segment_scores)\n", + "best_torque_idx = np.argmax(torque_segment_scores)\n", + "worst_torque_segment = torque_segment_info[worst_torque_idx]\n", + "best_torque_segment = torque_segment_info[best_torque_idx]\n", + "\n", + "print(f\"Torque Prediction Analysis (across {num_segments} segments):\")\n", + "print(f\"\\nBest R² Score: {max(torque_segment_scores):.6f}\")\n", + "print(f\" Parameters: currL={best_torque_segment['currL']:.0f}A, currR={best_torque_segment['currR']:.0f}A, roll={best_torque_segment['roll']:.1f}°\")\n", + "print(f\"\\nWorst R² Score: {min(torque_segment_scores):.6f}\")\n", + "print(f\" Parameters: currL={worst_torque_segment['currL']:.0f}A, currR={worst_torque_segment['currR']:.0f}A, roll={worst_torque_segment['roll']:.1f}°\")\n", + "print(f\"\\nMean R²: {np.mean(torque_segment_scores):.6f}\")\n", + "print(f\"Median R²: {np.median(torque_segment_scores):.6f}\")\n", + "print(f\"Std Dev: {np.std(torque_segment_scores):.6f}\")\n", + "\n", + "# Create histogram of torque R² scores\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", + "ax.hist(torque_segment_scores, bins=30, edgecolor='black', alpha=0.7, color='#9467bd')\n", + "ax.axvline(np.mean(torque_segment_scores), color='red', linestyle='--', linewidth=2, label=f'Mean = {np.mean(torque_segment_scores):.6f}')\n", + "ax.axvline(np.median(torque_segment_scores), color='orange', linestyle='--', linewidth=2, label=f'Median = {np.median(torque_segment_scores):.6f}')\n", + "ax.set_xlabel('R² Score', fontsize=12)\n", + "ax.set_ylabel('Number of Segments', fontsize=12)\n", + "ax.set_title('Distribution of Torque R² Scores Across All Parameter Segments', fontsize=14, fontweight='bold')\n", + "ax.legend(fontsize=11)\n", + "ax.grid(True, alpha=0.3, axis='y')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5bffc9a9", + "metadata": {}, + "source": [ + "### Worst-Case Torque Prediction Visualization\n", + "\n", + "Let's visualize the worst-performing torque segment to assess model robustness." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d5580f3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Worst-case torque prediction achieves R² = -0.638045\n" + ] + } + ], + "source": [ + "# Following generated by AI - see [11]\n", + "# Get worst torque segment data\n", + "worst_torque_data = magDf.iloc[worst_torque_segment['start']:worst_torque_segment['end']]\n", + "worst_torque_currL = worst_torque_segment['currL']\n", + "worst_torque_currR = worst_torque_segment['currR']\n", + "worst_torque_roll = worst_torque_segment['roll']\n", + "worst_torque_r2 = worst_torque_segment['r2']\n", + "\n", + "# Create fine grid for this worst segment\n", + "gap_fine_worst_torque = np.linspace(2, 30, 500)\n", + "X_worst_torque_fine = pd.DataFrame({\n", + " 'currL [A]': [worst_torque_currL] * 500,\n", + " 'currR [A]': [worst_torque_currR] * 500,\n", + " 'rollDeg [deg]': [worst_torque_roll] * 500,\n", + " 'invGap': 1/gap_fine_worst_torque\n", + "})\n", + "\n", + "# Get predictions\n", + "X_worst_torque_poly = poly_best.transform(X_worst_torque_fine)\n", + "y_worst_torque_pred = model_best.predict(X_worst_torque_poly)\n", + "\n", + "# Extract actual data\n", + "gap_worst_torque_actual = worst_torque_data['GapHeight [mm]'].values\n", + "torque_worst_actual = worst_torque_data['YokeTorque.Torque [mNewtonMeter]'].values\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", + "\n", + "# Plot polynomial curve for TORQUE\n", + "ax.plot(gap_fine_worst_torque, y_worst_torque_pred[:, 1], '-', linewidth=2.5, \n", + " label=f'Degree {best_degree} Polynomial', color='#ff7f0e', alpha=0.8)\n", + "\n", + "# Plot actual data points\n", + "ax.scatter(gap_worst_torque_actual, torque_worst_actual, s=120, marker='o', \n", + " color='#9467bd', edgecolors='black', linewidths=1.5,\n", + " label='Ansys Data', zorder=5)\n", + "\n", + "# Formatting\n", + "ax.set_xlabel('Gap Height (mm)', fontsize=13)\n", + "ax.set_ylabel('Torque (mN·m)', fontsize=13)\n", + "ax.set_title(f'Worst Torque Case (R² = {worst_torque_r2:.6f}) - currL={worst_torque_currL:.0f}A, currR={worst_torque_currR:.0f}A, roll={worst_torque_roll:.1f}°', \n", + " fontsize=14, fontweight='bold')\n", + "ax.legend(fontsize=12, loc='best')\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Add vertical lines at data points\n", + "for gap_point in gap_worst_torque_actual:\n", + " ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Worst-case torque prediction achieves R² = {worst_torque_r2:.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c63aca1e", + "metadata": {}, + "source": [ + "### We can see here that at roll angle and current differential of 0 (when torque will be very close to 0):\n", + "\n", + "There is significant overfitting to noise, but within the operating range of 6 - 24 mm, there is never more variation than there would be with simple linear interpolation.\n", + "\n", + "Hopefully this means we are ok" + ] + }, + { + "cell_type": "markdown", + "id": "1e1c384f", + "metadata": {}, + "source": [ + "### Export Model Summary\n", + "\n", + "Display key model information before exporting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "778f58df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n", + "MODEL EXPORT SUMMARY\n", + "============================================================\n", + "\n", + "Model Type: Polynomial Regression\n", + "Polynomial Degree: 6\n", + "Number of Features: 4\n", + "Number of Polynomial Terms: 210\n", + "\n", + "Input Features:\n", + " - currL [A]\n", + " - currR [A]\n", + " - rollDeg [deg]\n", + " - invGap (1/GapHeight)\n", + "\n", + "Outputs:\n", + " - YokeForce.Force_z [newton]\n", + " - YokeTorque.Torque [mNewtonMeter]\n", + "\n", + "Model Performance:\n", + " Force R² Score: 0.999967\n", + " Torque R² Score: 0.999895\n", + "\n", + "Model Coefficients Shape:\n", + " Force coefficients: (210,)\n", + " Torque coefficients: (210,)\n", + "============================================================\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [12]\n", + "print(\"=\" * 60)\n", + "print(\"MODEL EXPORT SUMMARY\")\n", + "print(\"=\" * 60)\n", + "print(f\"\\nModel Type: Polynomial Regression\")\n", + "print(f\"Polynomial Degree: {best_degree}\")\n", + "print(f\"Number of Features: {poly_best.n_features_in_}\")\n", + "print(f\"Number of Polynomial Terms: {poly_best.n_output_features_}\")\n", + "print(f\"\\nInput Features:\")\n", + "print(\" - currL [A]\")\n", + "print(\" - currR [A]\")\n", + "print(\" - rollDeg [deg]\")\n", + "print(\" - invGap (1/GapHeight)\")\n", + "print(f\"\\nOutputs:\")\n", + "print(\" - YokeForce.Force_z [newton]\")\n", + "print(\" - YokeTorque.Torque [mNewtonMeter]\")\n", + "print(f\"\\nModel Performance:\")\n", + "print(f\" Force R² Score: {r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]):.6f}\")\n", + "print(f\" Torque R² Score: {torque_r2_overall:.6f}\")\n", + "print(f\"\\nModel Coefficients Shape:\")\n", + "print(f\" Force coefficients: {model_best.coef_[0].shape}\")\n", + "print(f\" Torque coefficients: {model_best.coef_[1].shape}\")\n", + "print(\"=\" * 60)" + ] + }, + { + "cell_type": "markdown", + "id": "88208b13", + "metadata": {}, + "source": [ + "### Create Standalone Python Predictor Class\n", + "\n", + "Generate a self-contained Python module that can be imported into your simulator without scikit-learn dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edb239e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved standalone predictor: maglev_predictor.py\n", + "\n", + "This module:\n", + " - Has NO scikit-learn dependency (only numpy)\n", + " - Can be imported directly into your simulator\n", + " - Includes both single and batch prediction methods\n", + " - Implements polynomial feature generation internally\n", + "\n", + "To use:\n", + "```python\n", + "from maglev_predictor import MaglevPredictor\n", + "predictor = MaglevPredictor()\n", + "force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0)\n", + "```\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [12]\n", + "# Generate standalone predictor class\n", + "predictor_code = f'''\"\"\"\n", + "Magnetic Levitation Force and Torque Predictor\n", + "Generated from polynomial regression model (degree {best_degree})\n", + "\n", + "Performance:\n", + " - Force R²: {r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]):.6f}\n", + " - Torque R²: {torque_r2_overall:.6f}\n", + "\n", + "Usage:\n", + " predictor = MaglevPredictor()\n", + " force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0)\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "from itertools import combinations_with_replacement\n", + "\n", + "class MaglevPredictor:\n", + " def __init__(self):\n", + " \"\"\"Initialize the magnetic levitation force/torque predictor.\"\"\"\n", + " self.degree = {best_degree}\n", + " self.n_features = 4 # currL, currR, roll, invGap\n", + " \n", + " # Force model coefficients\n", + " self.force_intercept = {model_best.intercept_[0]}\n", + " self.force_coef = np.array({model_best.coef_[0].tolist()})\n", + " \n", + " # Torque model coefficients \n", + " self.torque_intercept = {model_best.intercept_[1]}\n", + " self.torque_coef = np.array({model_best.coef_[1].tolist()})\n", + " \n", + " def _polynomial_features(self, X):\n", + " \"\"\"\n", + " Generate polynomial features up to specified degree.\n", + " Mimics sklearn's PolynomialFeatures with include_bias=True.\n", + " \n", + " Args:\n", + " X: numpy array of shape (n_samples, 4) with [currL, currR, roll, invGap]\n", + " \n", + " Returns:\n", + " Polynomial features array\n", + " \"\"\"\n", + " n_samples = X.shape[0]\n", + " \n", + " # Start with bias term (column of ones)\n", + " features = [np.ones(n_samples)]\n", + " \n", + " # Add original features\n", + " for i in range(self.n_features):\n", + " features.append(X[:, i])\n", + " \n", + " # Add polynomial combinations\n", + " for deg in range(2, self.degree + 1):\n", + " for combo in combinations_with_replacement(range(self.n_features), deg):\n", + " term = np.ones(n_samples)\n", + " for idx in combo:\n", + " term *= X[:, idx]\n", + " features.append(term)\n", + " \n", + " return np.column_stack(features)\n", + " \n", + " def predict(self, currL, currR, roll, gap_height):\n", + " \"\"\"\n", + " Predict force and torque for given operating conditions.\n", + " \n", + " Args:\n", + " currL: Left coil current in Amps\n", + " currR: Right coil current in Amps\n", + " roll: Roll angle in degrees\n", + " gap_height: Gap height in mm\n", + " \n", + " Returns:\n", + " tuple: (force [N], torque [mN·m])\n", + " \"\"\"\n", + " # Compute inverse gap (critical transformation!)\n", + " invGap = 1.0 / gap_height\n", + " \n", + " # Create input array\n", + " X = np.array([[currL, currR, roll, invGap]])\n", + " \n", + " # Generate polynomial features\n", + " X_poly = self._polynomial_features(X)\n", + " \n", + " # Compute predictions\n", + " force = self.force_intercept + np.dot(X_poly, self.force_coef)[0]\n", + " torque = self.torque_intercept + np.dot(X_poly, self.torque_coef)[0]\n", + " \n", + " return force, torque\n", + " \n", + " def predict_batch(self, currL_array, currR_array, roll_array, gap_height_array):\n", + " \"\"\"\n", + " Predict force and torque for multiple operating conditions.\n", + " \n", + " Args:\n", + " currL_array: Array of left coil currents [A]\n", + " currR_array: Array of right coil currents [A]\n", + " roll_array: Array of roll angles [deg]\n", + " gap_height_array: Array of gap heights [mm]\n", + " \n", + " Returns:\n", + " tuple: (force_array [N], torque_array [mN·m])\n", + " \"\"\"\n", + " # Convert to numpy arrays\n", + " currL_array = np.asarray(currL_array)\n", + " currR_array = np.asarray(currR_array)\n", + " roll_array = np.asarray(roll_array)\n", + " gap_height_array = np.asarray(gap_height_array)\n", + " \n", + " # Compute inverse gaps\n", + " invGap_array = 1.0 / gap_height_array\n", + " \n", + " # Stack into feature matrix\n", + " X = np.column_stack([currL_array, currR_array, roll_array, invGap_array])\n", + " \n", + " # Generate polynomial features\n", + " X_poly = self._polynomial_features(X)\n", + " \n", + " # Compute predictions\n", + " force_array = self.force_intercept + np.dot(X_poly, self.force_coef)\n", + " torque_array = self.torque_intercept + np.dot(X_poly, self.torque_coef)\n", + " \n", + " return force_array, torque_array\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " # Example usage\n", + " predictor = MaglevPredictor()\n", + " \n", + " # Single prediction\n", + " force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0)\n", + " print(f\"Single prediction:\")\n", + " print(f\" Force: {{force:.2f}} N\")\n", + " print(f\" Torque: {{torque:.2f}} mN·m\")\n", + " \n", + " # Batch prediction\n", + " currL = np.array([-15, -15, -10])\n", + " currR = np.array([-15, -10, -10])\n", + " roll = np.array([0, 0.5, 1.0])\n", + " gap = np.array([10, 12, 15])\n", + " \n", + " forces, torques = predictor.predict_batch(currL, currR, roll, gap)\n", + " print(f\"\\\\nBatch prediction:\")\n", + " for i in range(len(forces)):\n", + " print(f\" Condition {{i+1}}: Force={{forces[i]:.2f}} N, Torque={{torques[i]:.2f}} mN·m\")\n", + "'''\n", + "\n", + "# Save to file\n", + "predictor_filename = 'maglev_predictor.py'\n", + "with open(predictor_filename, 'w') as f:\n", + " f.write(predictor_code)\n", + "\n", + "print(f\"✓ Saved standalone predictor: {predictor_filename}\")\n", + "print(f\"\\nThis module:\")\n", + "print(f\" - Has NO scikit-learn dependency (only numpy)\")\n", + "print(f\" - Can be imported directly into your simulator\")\n", + "print(f\" - Includes both single and batch prediction methods\")\n", + "print(f\" - Implements polynomial feature generation internally\")\n", + "print(f\"\\nTo use:\")\n", + "print(f\"```python\")\n", + "print(f\"from maglev_predictor import MaglevPredictor\")\n", + "print(f\"predictor = MaglevPredictor()\")\n", + "print(f\"force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0)\")\n", + "print(f\"```\")" + ] + }, + { + "cell_type": "markdown", + "id": "e4f40fb4", + "metadata": {}, + "source": [ + "### Test the Exported Predictor\n", + "\n", + "Verify the standalone predictor produces identical results to the original model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a1ddacd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation: Comparing Standalone Predictor vs Original Model\n", + "================================================================================\n", + "\n", + "Test Case 1: currL=-15A, currR=-15A, roll=0.0°, gap=10.0mm\n", + " Force: Standalone= 103.3304 N | Original= 103.3304 N | Diff=2.84e-14\n", + " Torque: Standalone= -0.8561 mN·m | Original= -0.8561 mN·m | Diff=1.02e-12\n", + "\n", + "Test Case 2: currL=-15A, currR=-15A, roll=1.0°, gap=10.0mm\n", + " Force: Standalone= 104.0520 N | Original= 104.0520 N | Diff=2.84e-14\n", + " Torque: Standalone= 450.3916 mN·m | Original= 450.3916 mN·m | Diff=3.41e-13\n", + "\n", + "Test Case 3: currL=-15A, currR=-10A, roll=0.5°, gap=12.0mm\n", + " Force: Standalone= 75.0401 N | Original= 75.0401 N | Diff=5.68e-14\n", + " Torque: Standalone= 390.7023 mN·m | Original= 390.7023 mN·m | Diff=3.98e-13\n", + "\n", + "Test Case 4: currL=-10A, currR=-10A, roll=2.0°, gap=15.0mm\n", + " Force: Standalone= 50.4344 N | Original= 50.4344 N | Diff=1.42e-14\n", + " Torque: Standalone= 303.7400 mN·m | Original= 303.7400 mN·m | Diff=1.71e-13\n", + "\n", + "================================================================================\n", + "✓ All tests passed! Standalone predictor matches original model perfectly.\n", + "\n", + "The standalone predictor is ready for integration into your simulator!\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [12]\n", + "# Import the standalone predictor (reload to get latest version)\n", + "import importlib\n", + "import maglev_predictor\n", + "importlib.reload(maglev_predictor)\n", + "from maglev_predictor import MaglevPredictor\n", + "\n", + "# Create predictor instance\n", + "standalone_predictor = MaglevPredictor()\n", + "\n", + "# Test cases\n", + "test_cases = [\n", + " {'currL': -15, 'currR': -15, 'roll': 0.0, 'gap': 10.0},\n", + " {'currL': -15, 'currR': -15, 'roll': 1.0, 'gap': 10.0},\n", + " {'currL': -15, 'currR': -10, 'roll': 0.5, 'gap': 12.0},\n", + " {'currL': -10, 'currR': -10, 'roll': 2.0, 'gap': 15.0},\n", + "]\n", + "\n", + "print(\"Validation: Comparing Standalone Predictor vs Original Model\")\n", + "print(\"=\" * 80)\n", + "\n", + "for i, case in enumerate(test_cases):\n", + " # Standalone predictor\n", + " force_standalone, torque_standalone = standalone_predictor.predict(\n", + " case['currL'], case['currR'], case['roll'], case['gap']\n", + " )\n", + " \n", + " # Original model\n", + " X_test = pd.DataFrame({\n", + " 'currL [A]': [case['currL']],\n", + " 'currR [A]': [case['currR']],\n", + " 'rollDeg [deg]': [case['roll']],\n", + " 'invGap': [1/case['gap']]\n", + " })\n", + " X_test_poly = poly_best.transform(X_test)\n", + " y_test_pred = model_best.predict(X_test_poly)\n", + " force_original = y_test_pred[0, 0]\n", + " torque_original = y_test_pred[0, 1]\n", + " \n", + " # Compare\n", + " force_diff = abs(force_standalone - force_original)\n", + " torque_diff = abs(torque_standalone - torque_original)\n", + " \n", + " print(f\"\\nTest Case {i+1}: currL={case['currL']}A, currR={case['currR']}A, \" + \n", + " f\"roll={case['roll']}°, gap={case['gap']}mm\")\n", + " print(f\" Force: Standalone={force_standalone:9.4f} N | Original={force_original:9.4f} N | Diff={force_diff:.2e}\")\n", + " print(f\" Torque: Standalone={torque_standalone:9.4f} mN·m | Original={torque_original:9.4f} mN·m | Diff={torque_diff:.2e}\")\n", + " \n", + " # Verify match (should be essentially identical, accounting for floating point)\n", + " assert force_diff < 1e-8, f\"Force mismatch in test case {i+1}\"\n", + " assert torque_diff < 1e-8, f\"Torque mismatch in test case {i+1}\"\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"✓ All tests passed! Standalone predictor matches original model perfectly.\")\n", + "print(\"\\nThe standalone predictor is ready for integration into your simulator!\")" + ] + }, + { + "cell_type": "markdown", + "id": "d473d5fa", + "metadata": {}, + "source": [ + "### Export Sklearn Model (Lossless & Faster)\n", + "\n", + "Save the actual sklearn model using joblib for lossless serialization. This is also faster than the standalone predictor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fd33470", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved sklearn model: maglev_model.pkl\n", + "\n", + "File size: 6.13 KB\n", + "\n", + "Contents:\n", + " - PolynomialFeatures transformer (degree 6)\n", + " - LinearRegression model\n", + " - Feature names and metadata\n", + " - Performance metrics\n", + "\n", + "To use:\n", + "```python\n", + "import joblib\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Load model\n", + "data = joblib.load('maglev_model.pkl')\n", + "poly = data['poly_features']\n", + "model = data['model']\n", + "\n", + "# Make prediction\n", + "X = pd.DataFrame({'currL [A]': [-15], 'currR [A]': [-15], \n", + " 'rollDeg [deg]': [1.0], 'invGap': [1/10.0]})\n", + "X_poly = poly.transform(X)\n", + "force, torque = model.predict(X_poly)[0]\n", + "```\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [12]\n", + "import joblib\n", + "import pickle\n", + "\n", + "# Save the polynomial transformer and model\n", + "model_data = {\n", + " 'poly_features': poly_best,\n", + " 'model': model_best,\n", + " 'degree': best_degree,\n", + " 'feature_names': ['currL [A]', 'currR [A]', 'rollDeg [deg]', 'invGap'],\n", + " 'performance': {\n", + " 'force_r2': r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]),\n", + " 'torque_r2': torque_r2_overall\n", + " }\n", + "}\n", + "\n", + "# Save using joblib (preferred for sklearn models)\n", + "joblib.dump(model_data, 'maglev_model.pkl')\n", + "\n", + "print(\"✓ Saved sklearn model: maglev_model.pkl\")\n", + "print(f\"\\nFile size: {os.path.getsize('maglev_model.pkl') / 1024:.2f} KB\")\n", + "print(f\"\\nContents:\")\n", + "print(f\" - PolynomialFeatures transformer (degree {best_degree})\")\n", + "print(f\" - LinearRegression model\")\n", + "print(f\" - Feature names and metadata\")\n", + "print(f\" - Performance metrics\")\n", + "print(f\"\\nTo use:\")\n", + "print(f\"```python\")\n", + "print(f\"import joblib\")\n", + "print(f\"import numpy as np\")\n", + "print(f\"import pandas as pd\")\n", + "print(f\"\")\n", + "print(f\"# Load model\")\n", + "print(f\"data = joblib.load('maglev_model.pkl')\")\n", + "print(f\"poly = data['poly_features']\")\n", + "print(f\"model = data['model']\")\n", + "print(f\"\")\n", + "print(f\"# Make prediction\")\n", + "print(f\"X = pd.DataFrame({{'currL [A]': [-15], 'currR [A]': [-15], \")\n", + "print(f\" 'rollDeg [deg]': [1.0], 'invGap': [1/10.0]}})\")\n", + "print(f\"X_poly = poly.transform(X)\")\n", + "print(f\"force, torque = model.predict(X_poly)[0]\")\n", + "print(f\"```\")" + ] + }, + { + "cell_type": "markdown", + "id": "99b93e74", + "metadata": {}, + "source": [ + "### Compare Performance: Sklearn Model vs Standalone Predictor\n", + "\n", + "Benchmark both approaches to show the speed advantage of using sklearn directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "889df820", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performance Comparison (1000 predictions)\n", + "============================================================\n", + "Sklearn Model (pickled): 2.48 ms\n", + "Standalone Predictor: 4.07 ms\n", + "\n", + "Speedup: 1.64x faster with sklearn model\n", + "\n", + "Result verification:\n", + " Max force difference: 2.27e-13 N\n", + " Max torque difference: 5.46e-12 mN·m\n", + " ✓ Results match!\n", + "\n", + "Per-prediction time:\n", + " Sklearn: 2.48 μs\n", + " Standalone: 4.07 μs\n", + "\n", + "============================================================\n", + "Recommendation:\n", + " Use sklearn model (maglev_model.pkl) - 1.6x faster!\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [12]\n", + "import time\n", + "\n", + "# Load the sklearn model\n", + "loaded_data = joblib.load('maglev_model.pkl')\n", + "loaded_poly = loaded_data['poly_features']\n", + "loaded_model = loaded_data['model']\n", + "\n", + "# Create test data (1000 predictions)\n", + "n_tests = 1000\n", + "test_currL = np.random.uniform(-15, 0, n_tests)\n", + "test_currR = np.random.uniform(-15, 0, n_tests)\n", + "test_roll = np.random.uniform(-5, 5, n_tests)\n", + "test_gap = np.random.uniform(6, 24, n_tests)\n", + "\n", + "print(\"Performance Comparison (1000 predictions)\")\n", + "print(\"=\" * 60)\n", + "\n", + "# Benchmark 1: Sklearn model (loaded from pickle)\n", + "start = time.perf_counter()\n", + "X_test = pd.DataFrame({\n", + " 'currL [A]': test_currL,\n", + " 'currR [A]': test_currR,\n", + " 'rollDeg [deg]': test_roll,\n", + " 'invGap': 1/test_gap\n", + "})\n", + "X_test_poly = loaded_poly.transform(X_test)\n", + "sklearn_results = loaded_model.predict(X_test_poly)\n", + "sklearn_time = time.perf_counter() - start\n", + "\n", + "print(f\"Sklearn Model (pickled): {sklearn_time*1000:.2f} ms\")\n", + "\n", + "# Benchmark 2: Standalone predictor\n", + "start = time.perf_counter()\n", + "standalone_results = standalone_predictor.predict_batch(test_currL, test_currR, test_roll, test_gap)\n", + "standalone_time = time.perf_counter() - start\n", + "\n", + "print(f\"Standalone Predictor: {standalone_time*1000:.2f} ms\")\n", + "\n", + "# Speedup\n", + "speedup = standalone_time / sklearn_time\n", + "print(f\"\\nSpeedup: {speedup:.2f}x {'faster' if speedup > 1 else 'slower'} with sklearn model\")\n", + "\n", + "# Verify results match\n", + "force_diff = np.abs(sklearn_results[:, 0] - standalone_results[0]).max()\n", + "torque_diff = np.abs(sklearn_results[:, 1] - standalone_results[1]).max()\n", + "\n", + "print(f\"\\nResult verification:\")\n", + "print(f\" Max force difference: {force_diff:.2e} N\")\n", + "print(f\" Max torque difference: {torque_diff:.2e} mN·m\")\n", + "print(f\" ✓ Results match!\" if (force_diff < 1e-8 and torque_diff < 1e-8) else \" ✗ Results differ!\")\n", + "\n", + "# Per-prediction timing\n", + "print(f\"\\nPer-prediction time:\")\n", + "print(f\" Sklearn: {sklearn_time/n_tests*1e6:.2f} μs\")\n", + "print(f\" Standalone: {standalone_time/n_tests*1e6:.2f} μs\")\n", + "\n", + "print(f\"\\n{'='*60}\")\n", + "print(f\"Recommendation:\")\n", + "if speedup > 1.2:\n", + " print(f\" Use sklearn model (maglev_model.pkl) - {speedup:.1f}x faster!\")\n", + "elif speedup < 0.8:\n", + " print(f\" Use standalone predictor - {1/speedup:.1f}x faster!\")\n", + "else:\n", + " print(f\" Performance is similar - choose based on dependencies:\")\n", + " print(f\" • Sklearn model: Faster, requires sklearn+joblib\")\n", + " print(f\" • Standalone: No sklearn dependency, portable\")" + ] + }, + { + "cell_type": "markdown", + "id": "21babeb3", + "metadata": {}, + "source": [ + "### Now, let's find the equilibrium height for our pod, given mass of 5.8 kg. \n", + "\n", + "5.8 kg * 9.81 $m/s^2$ = 56.898 N" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "badbc379", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "EQUILIBRIUM GAP HEIGHT FINDER (Analytical Solution)\n", + "======================================================================\n", + "Pod mass: 5.8 kg\n", + "Total weight: 56.898 N\n", + "Target force per yoke: 28.449 N\n", + "Parameters: currL = 0 A, currR = 0 A, roll = 0°\n", + "\n", + "using scipy.optimize.fsolve:\n", + " Gap: 16.491741 mm → Force: 28.449000 N\n", + "\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [13]\n", + "# Find equilibrium gap height for 5.8 kg pod using polynomial root finding\n", + "import numpy as np\n", + "from maglev_predictor import MaglevPredictor\n", + "from scipy.optimize import fsolve\n", + "\n", + "# Initialize predictor\n", + "predictor = MaglevPredictor()\n", + "\n", + "# Target force for 5.8 kg pod (total force = weight)\n", + "# Since we have TWO yokes (front and back), each produces this force\n", + "target_force_per_yoke = 5.8 * 9.81 / 2 # 28.449 N per yoke\n", + "\n", + "print(\"=\" * 70)\n", + "print(\"EQUILIBRIUM GAP HEIGHT FINDER (Analytical Solution)\")\n", + "print(\"=\" * 70)\n", + "print(f\"Pod mass: 5.8 kg\")\n", + "print(f\"Total weight: {5.8 * 9.81:.3f} N\")\n", + "print(f\"Target force per yoke: {target_force_per_yoke:.3f} N\")\n", + "print(f\"Parameters: currL = 0 A, currR = 0 A, roll = 0°\")\n", + "print()\n", + "\n", + "# Method 2: Use scipy.optimize.fsolve for verification\n", + "def force_error(gap_height):\n", + " # Handle array input from fsolve (convert to scalar)\n", + " gap_height = float(np.atleast_1d(gap_height)[0])\n", + " force, _ = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap_height)\n", + " return force - target_force_per_yoke\n", + "\n", + "# Try multiple initial guesses to find all solutions\n", + "initial_guesses = [8, 10, 15, 20, 25]\n", + "scipy_solutions = []\n", + "\n", + "print(\"using scipy.optimize.fsolve:\")\n", + "for guess in initial_guesses:\n", + " solution = fsolve(force_error, guess)[0]\n", + " if 5 <= solution <= 30: # Valid range\n", + " force, torque = predictor.predict(currL=0, currR=0, roll=0, gap_height=solution)\n", + " error = abs(force - target_force_per_yoke)\n", + " if error < 0.01: # Valid solution (within 10 mN)\n", + " scipy_solutions.append((solution, force))\n", + "\n", + "# Remove duplicates (solutions within 0.1 mm)\n", + "unique_solutions = []\n", + "for sol, force in scipy_solutions:\n", + " is_duplicate = False\n", + " for unique_sol, _ in unique_solutions:\n", + " if abs(sol - unique_sol) < 0.1:\n", + " is_duplicate = True\n", + " break\n", + " if not is_duplicate:\n", + " unique_solutions.append((sol, force))\n", + "\n", + "for gap_val, force in unique_solutions:\n", + " print(f\" Gap: {gap_val:.6f} mm → Force: {force:.6f} N\")\n", + "print()" + ] + } + ], + "metadata": { + "kernelspec": { + 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zg|?m2;R{b6h^*ML2~19YPfUJ2PMlE@KL*OvdaABgz3=;{z>LO>Xh*<70(rF4=D7GW zN{Y#<4?mF*1BDTmk2fn0k#wA`{{U0fc#~2k`{$p3=IiT7epYpX%o5p5cppG8H8;0Y zn9b6hdVm6IX@;*|#7^<=c>-Wx;I(xRhcW>%l=*};cpTF-LIvEiWlh7WwSO)WT)KEM z!GE{~Cwr5+mZ-@;#8V*1n@|Z0rASm#wUI&A0Hj|vgKQWb(zP~0U#?hmRuSSZYB$At zsNIIvr>P?2aB541uZ6(~rxZXi#diXobAn+bIZvH^;75Sv6!}S0Uk{Pxs7y1#%Q(E} z%mlRqpb8gcAKL@&bI@tppqN1hF7r$tI02Ykm z#-Esec2`+IEUZQk?S7nHU0|vXL`vm7ppe^d3kNgz4YGan$QSoR$?4l#`+W`JuWizRxv* 0 + + # Don't terminate on contact. + # Instead, penalize it, but allow the episode to continue so it can try to fix it. + # if has_contact: + # 5.0 is painful, but surviving 100 steps of pain is better than immediate death (-50) + reward -= len(contact_points) + +# at this point, we still either stick or fall, no hovering training has been achieved. + +# Tried increasing lambda value and starting at optimal all the time. + +#Tried reducing entropy and resetting all params but allowing for full range of motion without bolts - 7 pm ish \ No newline at end of file diff --git a/RL_Trials/training_log_20251211_084252.txt b/RL_Trials/training_log_20251211_084252.txt new file mode 100644 index 0000000..5e324cc --- /dev/null +++ b/RL_Trials/training_log_20251211_084252.txt @@ -0,0 +1,109 @@ +Training Started: 20251211_084252 +Number of Episodes: 1000 +Print Frequency: 10 +Target Gap Height: 16.491741 mm +====================================================================== + +Ep 10 | Reward: 339.6 | Length: 2000 | R/step: 0.170 ( 17.0% of max) | Gap Error: 11.386 mm +Ep 20 | Reward: 582.7 | Length: 2000 | R/step: 0.291 ( 29.1% of max) | Gap Error: 12.576 mm +Ep 30 | Reward: 1572.2 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.500 mm +Ep 40 | Reward: 1574.1 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.505 mm +Ep 50 | Reward: 1272.2 | Length: 2000 | R/step: 0.636 ( 63.6% of max) | Gap Error: 16.009 mm +Ep 60 | Reward: 1874.6 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 70 | Reward: 1570.4 | Length: 2000 | R/step: 0.785 ( 78.5% of max) | Gap Error: 17.495 mm +Ep 80 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 90 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 100 | Reward: 1571.3 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.498 mm +Ep 110 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 120 | Reward: 1874.0 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 19.000 mm +Ep 130 | Reward: 1874.1 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.998 mm +Ep 140 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.997 mm +Ep 150 | Reward: 1874.1 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.998 mm +Ep 160 | Reward: 1873.8 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 19.002 mm +Ep 170 | Reward: 1874.4 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 180 | Reward: 1874.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.987 mm +Ep 190 | Reward: 1875.5 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.927 mm +Ep 200 | Reward: 1876.3 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.949 mm +Ep 210 | Reward: 1273.8 | Length: 2000 | R/step: 0.637 ( 63.7% of max) | Gap Error: 16.011 mm +Ep 220 | Reward: 1571.1 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.494 mm +Ep 230 | Reward: 1874.6 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 240 | Reward: 1877.3 | Length: 2000 | R/step: 0.939 ( 93.9% of max) | Gap Error: 18.912 mm +Ep 250 | Reward: 1584.6 | Length: 2000 | R/step: 0.792 ( 79.2% of max) | Gap Error: 17.495 mm +Ep 260 | Reward: 1876.5 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.948 mm +Ep 270 | Reward: 1876.6 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.955 mm +Ep 280 | Reward: 1576.3 | Length: 2000 | R/step: 0.788 ( 78.8% of max) | Gap Error: 17.433 mm +Ep 290 | Reward: 1878.4 | Length: 2000 | R/step: 0.939 ( 93.9% of max) | Gap Error: 18.920 mm +Ep 300 | Reward: 1877.9 | Length: 2000 | R/step: 0.939 ( 93.9% of max) | Gap Error: 18.884 mm +Ep 310 | Reward: 1878.5 | Length: 2000 | R/step: 0.939 ( 93.9% of max) | Gap Error: 18.880 mm +Ep 320 | Reward: 1878.0 | Length: 2000 | R/step: 0.939 ( 93.9% of max) | Gap Error: 18.899 mm +Ep 330 | Reward: 1575.8 | Length: 2000 | R/step: 0.788 ( 78.8% of max) | Gap Error: 17.475 mm +Ep 340 | Reward: 1874.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.986 mm +Ep 350 | Reward: 1873.8 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 19.001 mm +Ep 360 | Reward: 1874.0 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.999 mm +Ep 370 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 380 | Reward: 1874.1 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 390 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 400 | Reward: 1873.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.998 mm +Ep 410 | Reward: 1873.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 19.002 mm +Ep 420 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 430 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.998 mm +Ep 440 | Reward: 1874.0 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.999 mm +Ep 450 | Reward: 1874.0 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 19.000 mm +Ep 460 | Reward: 1874.1 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.997 mm +Ep 470 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 480 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 490 | Reward: 1874.5 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.992 mm +Ep 500 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 510 | Reward: 1874.7 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.991 mm +Ep 520 | Reward: 1874.5 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.989 mm +Ep 530 | Reward: 1874.7 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 540 | Reward: 1874.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.987 mm +Ep 550 | Reward: 1875.1 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.985 mm +Ep 560 | Reward: 1572.3 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.487 mm +Ep 570 | Reward: 1874.4 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 580 | Reward: 1274.7 | Length: 2000 | R/step: 0.637 ( 63.7% of max) | Gap Error: 16.011 mm +Ep 590 | Reward: 1875.5 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.979 mm +Ep 600 | Reward: 1876.6 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.964 mm +Ep 610 | Reward: 1875.6 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.977 mm +Ep 620 | Reward: 1875.2 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.981 mm +Ep 630 | Reward: 1875.4 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.976 mm +Ep 640 | Reward: 1874.8 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.988 mm +Ep 650 | Reward: 1574.9 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.495 mm +Ep 660 | Reward: 1875.1 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.983 mm +Ep 670 | Reward: 1875.4 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.981 mm +Ep 680 | Reward: 1875.2 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.983 mm +Ep 690 | Reward: 1574.9 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.504 mm +Ep 700 | Reward: 1874.6 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 710 | Reward: 1874.4 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 720 | Reward: 1572.4 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.500 mm +Ep 730 | Reward: 1874.2 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 740 | Reward: 1874.7 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.987 mm +Ep 750 | Reward: 1874.7 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.988 mm +Ep 760 | Reward: 1874.8 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.989 mm +Ep 770 | Reward: 1874.6 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.993 mm +Ep 780 | Reward: 1874.8 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.988 mm +Ep 790 | Reward: 1574.2 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.502 mm +Ep 800 | Reward: 1271.4 | Length: 2000 | R/step: 0.636 ( 63.6% of max) | Gap Error: 16.010 mm +Ep 810 | Reward: 1874.5 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.993 mm +Ep 820 | Reward: 1576.0 | Length: 2000 | R/step: 0.788 ( 78.8% of max) | Gap Error: 17.509 mm +Ep 830 | Reward: 1573.7 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.502 mm +Ep 840 | Reward: 1874.4 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.995 mm +Ep 850 | Reward: 1875.2 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.981 mm +Ep 860 | Reward: 971.6 | Length: 2000 | R/step: 0.486 ( 48.6% of max) | Gap Error: 14.520 mm +Ep 870 | Reward: 1574.9 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.502 mm +Ep 880 | Reward: 1874.4 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.992 mm +Ep 890 | Reward: 1874.1 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.997 mm +Ep 900 | Reward: 1873.9 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.999 mm +Ep 910 | Reward: 1572.2 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.495 mm +Ep 920 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 930 | Reward: 1874.3 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.996 mm +Ep 940 | Reward: 1874.6 | Length: 2000 | R/step: 0.937 ( 93.7% of max) | Gap Error: 18.990 mm +Ep 950 | Reward: 1573.6 | Length: 2000 | R/step: 0.787 ( 78.7% of max) | Gap Error: 17.497 mm +Ep 960 | Reward: 1571.9 | Length: 2000 | R/step: 0.786 ( 78.6% of max) | Gap Error: 17.484 mm +Ep 970 | Reward: 1271.5 | Length: 2000 | R/step: 0.636 ( 63.6% of max) | Gap Error: 16.006 mm +Ep 980 | Reward: 1875.0 | Length: 2000 | R/step: 0.938 ( 93.8% of max) | Gap Error: 18.985 mm +Ep 990 | Reward: 1570.5 | Length: 2000 | R/step: 0.785 ( 78.5% of max) | Gap Error: 17.488 mm +Ep 1000 | Reward: 1575.9 | Length: 2000 | R/step: 0.788 ( 78.8% of max) | Gap Error: 17.503 mm + +====================================================================== +Training Completed: 20251211_095328 diff --git a/RL_Trials/training_log_20251211_102102.txt b/RL_Trials/training_log_20251211_102102.txt new file mode 100644 index 0000000..0571ffc --- /dev/null +++ b/RL_Trials/training_log_20251211_102102.txt @@ -0,0 +1,109 @@ +Training Started: 20251211_102102 +Number of Episodes: 1000 +Print Frequency: 10 +Target Gap Height: 16.491741 mm +====================================================================== + +Ep 10 | Reward: -7172.7 | Length: 500 | R/step: -14.345 (-1434.5% of max) | Gap Error: 16.068 mm +Ep 20 | Reward: -7031.0 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.962 mm +Ep 30 | Reward: -7101.5 | Length: 500 | R/step: -14.203 (-1420.3% of max) | Gap Error: 17.537 mm +Ep 40 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.972 mm +Ep 50 | Reward: -7030.8 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.949 mm +Ep 60 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.975 mm +Ep 70 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.974 mm +Ep 80 | Reward: -7031.2 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.979 mm +Ep 90 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.962 mm +Ep 100 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.978 mm +Ep 110 | Reward: -7248.5 | Length: 500 | R/step: -14.497 (-1449.7% of max) | Gap Error: 14.626 mm +Ep 120 | Reward: -7164.7 | Length: 500 | R/step: -14.329 (-1432.9% of max) | Gap Error: 16.227 mm +Ep 130 | Reward: -7239.3 | Length: 500 | R/step: -14.479 (-1447.9% of max) | Gap Error: 14.752 mm +Ep 140 | Reward: -7611.2 | Length: 500 | R/step: -15.222 (-1522.2% of max) | Gap Error: 7.389 mm +Ep 150 | Reward: -7316.7 | Length: 500 | R/step: -14.633 (-1463.3% of max) | Gap Error: 13.257 mm +Ep 160 | Reward: -7657.2 | Length: 500 | R/step: -15.314 (-1531.4% of max) | Gap Error: 6.463 mm +Ep 170 | Reward: -7564.9 | Length: 500 | R/step: -15.130 (-1513.0% of max) | Gap Error: 8.263 mm +Ep 180 | Reward: -7320.2 | Length: 500 | R/step: -14.640 (-1464.0% of max) | Gap Error: 13.172 mm +Ep 190 | Reward: -7601.5 | Length: 500 | R/step: -15.203 (-1520.3% of max) | Gap Error: 7.545 mm +Ep 200 | Reward: -7311.8 | Length: 500 | R/step: -14.624 (-1462.4% of max) | Gap Error: 13.311 mm +Ep 210 | Reward: -7100.9 | Length: 500 | R/step: -14.202 (-1420.2% of max) | Gap Error: 17.572 mm +Ep 220 | Reward: -7170.8 | Length: 500 | R/step: -14.342 (-1434.2% of max) | Gap Error: 16.143 mm +Ep 230 | Reward: -7241.1 | Length: 500 | R/step: -14.482 (-1448.2% of max) | Gap Error: 14.729 mm +Ep 240 | Reward: -7310.3 | Length: 500 | R/step: -14.621 (-1462.1% of max) | Gap Error: 13.338 mm +Ep 250 | Reward: -7244.1 | Length: 500 | R/step: -14.488 (-1448.8% of max) | Gap Error: 14.699 mm +Ep 260 | Reward: -7172.7 | Length: 500 | R/step: -14.345 (-1434.5% of max) | Gap Error: 16.127 mm +Ep 270 | Reward: -7031.2 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.962 mm +Ep 280 | Reward: -7031.2 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.977 mm +Ep 290 | Reward: -7031.3 | Length: 500 | R/step: -14.063 (-1406.3% of max) | Gap Error: 18.967 mm +Ep 300 | Reward: -7031.4 | Length: 500 | R/step: -14.063 (-1406.3% of max) | Gap Error: 18.965 mm +Ep 310 | Reward: -7031.3 | Length: 500 | R/step: -14.063 (-1406.3% of max) | Gap Error: 18.927 mm +Ep 320 | Reward: -7031.3 | Length: 500 | R/step: -14.063 (-1406.3% of max) | Gap Error: 18.980 mm +Ep 330 | Reward: -7031.4 | Length: 500 | R/step: -14.063 (-1406.3% of max) | Gap Error: 18.957 mm +Ep 340 | Reward: -7101.4 | Length: 500 | R/step: -14.203 (-1420.3% of max) | Gap Error: 17.557 mm +Ep 350 | Reward: -7102.1 | Length: 500 | R/step: -14.204 (-1420.4% of max) | Gap Error: 17.547 mm +Ep 360 | Reward: -7312.6 | Length: 500 | R/step: -14.625 (-1462.5% of max) | Gap Error: 13.311 mm +Ep 370 | Reward: -7315.5 | Length: 500 | R/step: -14.631 (-1463.1% of max) | Gap Error: 13.255 mm +Ep 380 | Reward: -7380.4 | Length: 500 | R/step: -14.761 (-1476.1% of max) | Gap Error: 11.917 mm +Ep 390 | Reward: -7171.6 | Length: 500 | R/step: -14.343 (-1434.3% of max) | Gap Error: 16.108 mm +Ep 400 | Reward: -7244.8 | Length: 500 | R/step: -14.490 (-1449.0% of max) | Gap Error: 14.659 mm +Ep 410 | Reward: -7030.6 | Length: 500 | R/step: -14.061 (-1406.1% of max) | Gap Error: 18.943 mm +Ep 420 | Reward: -7169.8 | Length: 500 | R/step: -14.340 (-1434.0% of max) | Gap Error: 16.169 mm +Ep 430 | Reward: -7381.3 | Length: 500 | R/step: -14.763 (-1476.3% of max) | Gap Error: 11.908 mm +Ep 440 | Reward: -7306.4 | Length: 500 | R/step: -14.613 (-1461.3% of max) | Gap Error: 13.356 mm +Ep 450 | Reward: -7099.3 | Length: 500 | R/step: -14.199 (-1419.9% of max) | Gap Error: 17.564 mm +Ep 460 | Reward: -7031.0 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.969 mm +Ep 470 | Reward: -7030.5 | Length: 500 | R/step: -14.061 (-1406.1% of max) | Gap Error: 18.934 mm +Ep 480 | Reward: -7100.7 | Length: 500 | R/step: -14.201 (-1420.1% of max) | Gap Error: 17.549 mm +Ep 490 | Reward: -7169.3 | Length: 500 | R/step: -14.339 (-1433.9% of max) | Gap Error: 16.184 mm +Ep 500 | Reward: -7101.3 | Length: 500 | R/step: -14.203 (-1420.3% of max) | Gap Error: 17.559 mm +Ep 510 | Reward: -7168.6 | Length: 500 | R/step: -14.337 (-1433.7% of max) | Gap Error: 16.185 mm +Ep 520 | Reward: -7385.4 | Length: 500 | R/step: -14.771 (-1477.1% of max) | Gap Error: 11.860 mm +Ep 530 | Reward: -7386.8 | Length: 500 | R/step: -14.774 (-1477.4% of max) | Gap Error: 11.816 mm +Ep 540 | Reward: -7173.0 | Length: 500 | R/step: -14.346 (-1434.6% of max) | Gap Error: 16.121 mm +Ep 550 | Reward: -7244.3 | Length: 500 | R/step: -14.489 (-1448.9% of max) | Gap Error: 14.693 mm +Ep 560 | Reward: -7242.9 | Length: 500 | R/step: -14.486 (-1448.6% of max) | Gap Error: 14.731 mm +Ep 570 | Reward: -7315.1 | Length: 500 | R/step: -14.630 (-1463.0% of max) | Gap Error: 13.236 mm +Ep 580 | Reward: -7243.6 | Length: 500 | R/step: -14.487 (-1448.7% of max) | Gap Error: 14.684 mm +Ep 590 | Reward: -7031.1 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.979 mm +Ep 600 | Reward: -7168.0 | Length: 500 | R/step: -14.336 (-1433.6% of max) | Gap Error: 16.203 mm +Ep 610 | Reward: -7169.4 | Length: 500 | R/step: -14.339 (-1433.9% of max) | Gap Error: 16.183 mm +Ep 620 | Reward: -7102.4 | Length: 500 | R/step: -14.205 (-1420.5% of max) | Gap Error: 17.536 mm +Ep 630 | Reward: -7166.3 | Length: 500 | R/step: -14.333 (-1433.3% of max) | Gap Error: 16.216 mm +Ep 640 | Reward: -7030.7 | Length: 500 | R/step: -14.061 (-1406.1% of max) | Gap Error: 18.949 mm +Ep 650 | Reward: -7031.0 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.966 mm +Ep 660 | Reward: -7098.9 | Length: 500 | R/step: -14.198 (-1419.8% of max) | Gap Error: 17.581 mm +Ep 670 | Reward: -7236.5 | Length: 500 | R/step: -14.473 (-1447.3% of max) | Gap Error: 14.809 mm +Ep 680 | Reward: -7174.4 | Length: 500 | R/step: -14.349 (-1434.9% of max) | Gap Error: 16.106 mm +Ep 690 | Reward: -7314.2 | Length: 500 | R/step: -14.628 (-1462.8% of max) | Gap Error: 13.294 mm +Ep 700 | Reward: -7102.0 | Length: 500 | R/step: -14.204 (-1420.4% of max) | Gap Error: 17.539 mm +Ep 710 | Reward: -7314.9 | Length: 500 | R/step: -14.630 (-1463.0% of max) | Gap Error: 13.277 mm +Ep 720 | Reward: -7310.9 | Length: 500 | R/step: -14.622 (-1462.2% of max) | Gap Error: 13.335 mm +Ep 730 | Reward: -7315.2 | Length: 500 | R/step: -14.630 (-1463.0% of max) | Gap Error: 13.275 mm +Ep 740 | Reward: -7386.0 | Length: 500 | R/step: -14.772 (-1477.2% of max) | Gap Error: 11.842 mm +Ep 750 | Reward: -7300.9 | Length: 500 | R/step: -14.602 (-1460.2% of max) | Gap Error: 13.480 mm +Ep 760 | Reward: -7311.7 | Length: 500 | R/step: -14.623 (-1462.3% of max) | Gap Error: 13.314 mm +Ep 770 | Reward: -7382.8 | Length: 500 | R/step: -14.766 (-1476.6% of max) | Gap Error: 11.885 mm +Ep 780 | Reward: -7529.6 | Length: 500 | R/step: -15.059 (-1505.9% of max) | Gap Error: 8.946 mm +Ep 790 | Reward: -7523.1 | Length: 500 | R/step: -15.046 (-1504.6% of max) | Gap Error: 9.063 mm +Ep 800 | Reward: -7391.4 | Length: 500 | R/step: -14.783 (-1478.3% of max) | Gap Error: 11.758 mm +Ep 810 | Reward: -7531.9 | Length: 500 | R/step: -15.064 (-1506.4% of max) | Gap Error: 8.915 mm +Ep 820 | Reward: -7462.2 | Length: 500 | R/step: -14.924 (-1492.4% of max) | Gap Error: 10.318 mm +Ep 830 | Reward: -7214.0 | Length: 500 | R/step: -14.428 (-1442.8% of max) | Gap Error: 15.251 mm +Ep 840 | Reward: -7426.1 | Length: 500 | R/step: -14.852 (-1485.2% of max) | Gap Error: 10.944 mm +Ep 850 | Reward: -7460.9 | Length: 500 | R/step: -14.922 (-1492.2% of max) | Gap Error: 10.270 mm +Ep 860 | Reward: -7663.9 | Length: 500 | R/step: -15.328 (-1532.8% of max) | Gap Error: 6.237 mm +Ep 870 | Reward: -7437.2 | Length: 500 | R/step: -14.874 (-1487.4% of max) | Gap Error: 10.765 mm +Ep 880 | Reward: -6530.8 | Length: 421 | R/step: -15.524 (-1552.4% of max) | Gap Error: 7.413 mm +Ep 890 | Reward: -7696.3 | Length: 500 | R/step: -15.393 (-1539.3% of max) | Gap Error: 5.736 mm +Ep 900 | Reward: -7732.4 | Length: 500 | R/step: -15.465 (-1546.5% of max) | Gap Error: 5.083 mm +Ep 910 | Reward: -7746.3 | Length: 500 | R/step: -15.493 (-1549.3% of max) | Gap Error: 4.839 mm +Ep 920 | Reward: -7745.4 | Length: 500 | R/step: -15.491 (-1549.1% of max) | Gap Error: 4.833 mm +Ep 930 | Reward: -7749.5 | Length: 500 | R/step: -15.499 (-1549.9% of max) | Gap Error: 4.838 mm +Ep 940 | Reward: -5944.1 | Length: 383 | R/step: -15.536 (-1553.6% of max) | Gap Error: 9.080 mm +Ep 950 | Reward: -4103.1 | Length: 261 | R/step: -15.739 (-1573.9% of max) | Gap Error: 12.558 mm +Ep 960 | Reward: -4697.1 | Length: 303 | R/step: -15.517 (-1551.7% of max) | Gap Error: 12.987 mm +Ep 970 | Reward: -4455.6 | Length: 288 | R/step: -15.455 (-1545.5% of max) | Gap Error: 14.009 mm +Ep 980 | Reward: -3854.4 | Length: 251 | R/step: -15.356 (-1535.6% of max) | Gap Error: 14.805 mm +Ep 990 | Reward: -5979.5 | Length: 415 | R/step: -14.405 (-1440.5% of max) | Gap Error: 17.115 mm +Ep 1000 | Reward: -7031.2 | Length: 500 | R/step: -14.062 (-1406.2% of max) | Gap Error: 18.909 mm + +====================================================================== +Training Completed: 20251211_103854 diff --git a/RL_Trials/training_log_20251211_110643.txt b/RL_Trials/training_log_20251211_110643.txt new file mode 100644 index 0000000..7e5c9fe --- /dev/null +++ b/RL_Trials/training_log_20251211_110643.txt @@ -0,0 +1,109 @@ +Training Started: 20251211_110643 +Number of Episodes: 1000 +Print Frequency: 10 +Target Gap Height: 16.491741 mm +====================================================================== + +Ep 10 | Reward: -7123.0 | Length: 457 | R/step: -15.580 (-1558.0% of max) | Gap Error: 14.704 mm +Ep 20 | Reward: -6697.0 | Length: 418 | R/step: -16.025 (-1602.5% of max) | Gap Error: 11.138 mm +Ep 30 | Reward: -6050.2 | Length: 377 | R/step: -16.031 (-1603.1% of max) | Gap Error: 12.985 mm +Ep 40 | Reward: -7891.0 | Length: 500 | R/step: -15.782 (-1578.2% of max) | Gap Error: 11.726 mm +Ep 50 | Reward: -8254.2 | Length: 500 | R/step: -16.508 (-1650.8% of max) | Gap Error: 4.602 mm +Ep 60 | Reward: -8255.5 | Length: 500 | R/step: -16.511 (-1651.1% of max) | Gap Error: 4.537 mm +Ep 70 | Reward: -8185.8 | Length: 500 | R/step: -16.372 (-1637.2% of max) | Gap Error: 5.910 mm +Ep 80 | Reward: -7920.1 | Length: 500 | R/step: -15.840 (-1584.0% of max) | Gap Error: 11.089 mm +Ep 90 | Reward: -8262.8 | Length: 500 | R/step: -16.526 (-1652.6% of max) | Gap Error: 4.395 mm +Ep 100 | Reward: -8164.6 | Length: 500 | R/step: -16.329 (-1632.9% of max) | Gap Error: 6.340 mm +Ep 110 | Reward: -5294.0 | Length: 332 | R/step: -15.960 (-1596.0% of max) | Gap Error: 14.124 mm +Ep 120 | Reward: -6081.3 | Length: 375 | R/step: -16.230 (-1623.0% of max) | Gap Error: 11.482 mm +Ep 130 | Reward: -7346.6 | Length: 458 | R/step: -16.058 (-1605.8% of max) | Gap Error: 10.391 mm +Ep 140 | Reward: -7065.4 | Length: 458 | R/step: -15.427 (-1542.7% of max) | Gap Error: 15.974 mm +Ep 150 | Reward: -6516.7 | Length: 415 | R/step: -15.695 (-1569.5% of max) | Gap Error: 13.902 mm +Ep 160 | Reward: -6601.1 | Length: 417 | R/step: -15.822 (-1582.2% of max) | Gap Error: 13.542 mm +Ep 170 | Reward: -6440.3 | Length: 416 | R/step: -15.463 (-1546.3% of max) | Gap Error: 16.043 mm +Ep 180 | Reward: -5308.5 | Length: 336 | R/step: -15.799 (-1579.9% of max) | Gap Error: 15.176 mm +Ep 190 | Reward: -5847.7 | Length: 378 | R/step: -15.458 (-1545.8% of max) | Gap Error: 16.860 mm +Ep 200 | Reward: -5325.4 | Length: 333 | R/step: -16.007 (-1600.7% of max) | Gap Error: 14.364 mm +Ep 210 | Reward: -5893.1 | Length: 376 | R/step: -15.652 (-1565.2% of max) | Gap Error: 15.352 mm +Ep 220 | Reward: -7743.7 | Length: 500 | R/step: -15.487 (-1548.7% of max) | Gap Error: 14.608 mm +Ep 230 | Reward: -7180.3 | Length: 460 | R/step: -15.596 (-1559.6% of max) | Gap Error: 14.406 mm +Ep 240 | Reward: -7221.9 | Length: 459 | R/step: -15.741 (-1574.1% of max) | Gap Error: 13.200 mm +Ep 250 | Reward: -5970.0 | Length: 377 | R/step: -15.852 (-1585.2% of max) | Gap Error: 14.499 mm +Ep 260 | Reward: -7481.9 | Length: 462 | R/step: -16.212 (-1621.2% of max) | Gap Error: 8.666 mm +Ep 270 | Reward: -7189.7 | Length: 457 | R/step: -15.732 (-1573.2% of max) | Gap Error: 13.117 mm +Ep 280 | Reward: -6560.4 | Length: 418 | R/step: -15.710 (-1571.0% of max) | Gap Error: 14.139 mm +Ep 290 | Reward: -7423.1 | Length: 459 | R/step: -16.186 (-1618.6% of max) | Gap Error: 9.175 mm +Ep 300 | Reward: -7137.2 | Length: 458 | R/step: -15.594 (-1559.4% of max) | Gap Error: 14.544 mm +Ep 310 | Reward: -6457.1 | Length: 416 | R/step: -15.522 (-1552.2% of max) | Gap Error: 15.529 mm +Ep 320 | Reward: -7154.6 | Length: 459 | R/step: -15.598 (-1559.8% of max) | Gap Error: 14.440 mm +Ep 330 | Reward: -7814.2 | Length: 500 | R/step: -15.628 (-1562.8% of max) | Gap Error: 13.236 mm +Ep 340 | Reward: -7199.5 | Length: 457 | R/step: -15.744 (-1574.4% of max) | Gap Error: 13.286 mm +Ep 350 | Reward: -7234.7 | Length: 460 | R/step: -15.738 (-1573.8% of max) | Gap Error: 13.107 mm +Ep 360 | Reward: -7819.8 | Length: 500 | R/step: -15.640 (-1564.0% of max) | Gap Error: 13.160 mm +Ep 370 | Reward: -7148.2 | Length: 459 | R/step: -15.580 (-1558.0% of max) | Gap Error: 14.240 mm +Ep 380 | Reward: -7888.0 | Length: 500 | R/step: -15.776 (-1577.6% of max) | Gap Error: 11.788 mm +Ep 390 | Reward: -6321.7 | Length: 416 | R/step: -15.186 (-1518.6% of max) | Gap Error: 18.744 mm +Ep 400 | Reward: -7809.0 | Length: 500 | R/step: -15.618 (-1561.8% of max) | Gap Error: 13.359 mm +Ep 410 | Reward: -7004.8 | Length: 459 | R/step: -15.274 (-1527.4% of max) | Gap Error: 17.361 mm +Ep 420 | Reward: -7003.1 | Length: 455 | R/step: -15.391 (-1539.1% of max) | Gap Error: 15.613 mm +Ep 430 | Reward: -7280.0 | Length: 458 | R/step: -15.906 (-1590.6% of max) | Gap Error: 11.408 mm +Ep 440 | Reward: -7168.7 | Length: 460 | R/step: -15.594 (-1559.4% of max) | Gap Error: 13.981 mm +Ep 450 | Reward: -7605.7 | Length: 500 | R/step: -15.211 (-1521.1% of max) | Gap Error: 17.346 mm +Ep 460 | Reward: -6611.7 | Length: 417 | R/step: -15.871 (-1587.1% of max) | Gap Error: 12.581 mm +Ep 470 | Reward: -7086.7 | Length: 459 | R/step: -15.429 (-1542.9% of max) | Gap Error: 15.629 mm +Ep 480 | Reward: -7674.5 | Length: 500 | R/step: -15.349 (-1534.9% of max) | Gap Error: 16.008 mm +Ep 490 | Reward: -6481.8 | Length: 417 | R/step: -15.529 (-1552.9% of max) | Gap Error: 15.462 mm +Ep 500 | Reward: -7959.4 | Length: 500 | R/step: -15.919 (-1591.9% of max) | Gap Error: 10.456 mm +Ep 510 | Reward: -7088.4 | Length: 459 | R/step: -15.436 (-1543.6% of max) | Gap Error: 15.514 mm +Ep 520 | Reward: -7600.0 | Length: 500 | R/step: -15.200 (-1520.0% of max) | Gap Error: 17.460 mm +Ep 530 | Reward: -5979.7 | Length: 378 | R/step: -15.819 (-1581.9% of max) | Gap Error: 14.469 mm +Ep 540 | Reward: -6667.2 | Length: 416 | R/step: -16.038 (-1603.8% of max) | Gap Error: 11.886 mm +Ep 550 | Reward: -7165.7 | Length: 460 | R/step: -15.595 (-1559.5% of max) | Gap Error: 14.504 mm +Ep 560 | Reward: -7209.9 | Length: 458 | R/step: -15.739 (-1573.9% of max) | Gap Error: 12.949 mm +Ep 570 | Reward: -7745.6 | Length: 500 | R/step: -15.491 (-1549.1% of max) | Gap Error: 14.593 mm +Ep 580 | Reward: -7678.6 | Length: 500 | R/step: -15.357 (-1535.7% of max) | Gap Error: 15.895 mm +Ep 590 | Reward: -7107.0 | Length: 456 | R/step: -15.579 (-1557.9% of max) | Gap Error: 14.736 mm +Ep 600 | Reward: -7189.8 | Length: 458 | R/step: -15.712 (-1571.2% of max) | Gap Error: 13.386 mm +Ep 610 | Reward: -6914.9 | Length: 458 | R/step: -15.115 (-1511.5% of max) | Gap Error: 18.915 mm +Ep 620 | Reward: -7748.9 | Length: 500 | R/step: -15.498 (-1549.8% of max) | Gap Error: 14.607 mm +Ep 630 | Reward: -7894.4 | Length: 500 | R/step: -15.789 (-1578.9% of max) | Gap Error: 11.719 mm +Ep 640 | Reward: -6970.0 | Length: 457 | R/step: -15.255 (-1525.5% of max) | Gap Error: 17.182 mm +Ep 650 | Reward: -7746.9 | Length: 500 | R/step: -15.494 (-1549.4% of max) | Gap Error: 14.609 mm +Ep 660 | Reward: -7604.6 | Length: 500 | R/step: -15.209 (-1520.9% of max) | Gap Error: 17.373 mm +Ep 670 | Reward: -7747.7 | Length: 500 | R/step: -15.495 (-1549.5% of max) | Gap Error: 14.560 mm +Ep 680 | Reward: -6720.6 | Length: 419 | R/step: -16.043 (-1604.3% of max) | Gap Error: 11.076 mm +Ep 690 | Reward: -6998.9 | Length: 458 | R/step: -15.275 (-1527.5% of max) | Gap Error: 17.079 mm +Ep 700 | Reward: -7746.6 | Length: 500 | R/step: -15.493 (-1549.3% of max) | Gap Error: 14.625 mm +Ep 710 | Reward: -7894.9 | Length: 500 | R/step: -15.790 (-1579.0% of max) | Gap Error: 11.707 mm +Ep 720 | Reward: -7966.2 | Length: 500 | R/step: -15.932 (-1593.2% of max) | Gap Error: 10.268 mm +Ep 730 | Reward: -7966.1 | Length: 500 | R/step: -15.932 (-1593.2% of max) | Gap Error: 10.285 mm +Ep 740 | Reward: -7604.9 | Length: 500 | R/step: -15.210 (-1521.0% of max) | Gap Error: 17.376 mm +Ep 750 | Reward: -7673.6 | Length: 500 | R/step: -15.347 (-1534.7% of max) | Gap Error: 16.016 mm +Ep 760 | Reward: -7964.8 | Length: 500 | R/step: -15.930 (-1593.0% of max) | Gap Error: 10.311 mm +Ep 770 | Reward: -7747.0 | Length: 500 | R/step: -15.494 (-1549.4% of max) | Gap Error: 14.583 mm +Ep 780 | Reward: -7963.6 | Length: 500 | R/step: -15.927 (-1592.7% of max) | Gap Error: 10.346 mm +Ep 790 | Reward: -7209.1 | Length: 458 | R/step: -15.747 (-1574.7% of max) | Gap Error: 13.169 mm +Ep 800 | Reward: -7879.7 | Length: 500 | R/step: -15.759 (-1575.9% of max) | Gap Error: 11.951 mm +Ep 810 | Reward: -7960.0 | Length: 500 | R/step: -15.920 (-1592.0% of max) | Gap Error: 10.372 mm +Ep 820 | Reward: -7818.1 | Length: 500 | R/step: -15.636 (-1563.6% of max) | Gap Error: 13.177 mm +Ep 830 | Reward: -7965.3 | Length: 500 | R/step: -15.931 (-1593.1% of max) | Gap Error: 10.322 mm +Ep 840 | Reward: -7891.4 | Length: 500 | R/step: -15.783 (-1578.3% of max) | Gap Error: 11.716 mm +Ep 850 | Reward: -7883.5 | Length: 500 | R/step: -15.767 (-1576.7% of max) | Gap Error: 11.861 mm +Ep 860 | Reward: -7531.1 | Length: 500 | R/step: -15.062 (-1506.2% of max) | Gap Error: 18.853 mm +Ep 870 | Reward: -7601.2 | Length: 500 | R/step: -15.202 (-1520.2% of max) | Gap Error: 17.472 mm +Ep 880 | Reward: -7747.0 | Length: 500 | R/step: -15.494 (-1549.4% of max) | Gap Error: 14.594 mm +Ep 890 | Reward: -7600.6 | Length: 500 | R/step: -15.201 (-1520.1% of max) | Gap Error: 17.470 mm +Ep 900 | Reward: -7676.1 | Length: 500 | R/step: -15.352 (-1535.2% of max) | Gap Error: 15.984 mm +Ep 910 | Reward: -7750.4 | Length: 500 | R/step: -15.501 (-1550.1% of max) | Gap Error: 14.519 mm +Ep 920 | Reward: -7820.0 | Length: 500 | R/step: -15.640 (-1564.0% of max) | Gap Error: 13.170 mm +Ep 930 | Reward: -7884.7 | Length: 500 | R/step: -15.769 (-1576.9% of max) | Gap Error: 11.879 mm +Ep 940 | Reward: -7604.0 | Length: 500 | R/step: -15.208 (-1520.8% of max) | Gap Error: 17.410 mm +Ep 950 | Reward: -7677.5 | Length: 500 | R/step: -15.355 (-1535.5% of max) | Gap Error: 15.963 mm +Ep 960 | Reward: -7883.3 | Length: 500 | R/step: -15.767 (-1576.7% of max) | Gap Error: 11.895 mm +Ep 970 | Reward: -7818.1 | Length: 500 | R/step: -15.636 (-1563.6% of max) | Gap Error: 13.216 mm +Ep 980 | Reward: -7743.4 | Length: 500 | R/step: -15.487 (-1548.7% of max) | Gap Error: 14.655 mm +Ep 990 | Reward: -7960.6 | Length: 500 | R/step: -15.921 (-1592.1% of max) | Gap Error: 10.375 mm +Ep 1000 | Reward: -7601.5 | Length: 500 | R/step: -15.203 (-1520.3% of max) | Gap Error: 17.483 mm + +====================================================================== +Training Completed: 20251211_112339 diff --git a/RL_Trials/training_log_20251211_121404.txt b/RL_Trials/training_log_20251211_121404.txt new file mode 100644 index 0000000..aafa3d7 --- /dev/null +++ b/RL_Trials/training_log_20251211_121404.txt @@ -0,0 +1,23 @@ +Training Started: 20251211_121404 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: -16131.1 | Len: 500 | R/s: -32.26 (-4032.8%) | Gap: 4.41mm (min: 4.26) | Best: 4.26mm +Ep 40 | R: -16140.1 | Len: 500 | R/s: -32.28 (-4035.0%) | Gap: 4.36mm (min: 4.25) | Best: 4.25mm +Ep 60 | R: -16140.8 | Len: 500 | R/s: -32.28 (-4035.2%) | Gap: 4.35mm (min: 4.22) | Best: 4.22mm +Ep 80 | R: -16143.7 | Len: 500 | R/s: -32.29 (-4035.9%) | Gap: 4.33mm (min: 4.22) | Best: 4.22mm +Ep 100 | R: -16142.5 | Len: 500 | R/s: -32.29 (-4035.6%) | Gap: 4.35mm (min: 4.26) | Best: 4.22mm +Ep 120 | R: -16142.2 | Len: 500 | R/s: -32.28 (-4035.5%) | Gap: 4.35mm (min: 4.26) | Best: 4.22mm +Ep 140 | R: -16142.9 | Len: 500 | R/s: -32.29 (-4035.7%) | Gap: 4.33mm (min: 4.23) | Best: 4.22mm +Ep 160 | R: -16144.1 | Len: 500 | R/s: -32.29 (-4036.0%) | Gap: 4.32mm (min: 4.21) | Best: 4.21mm +Ep 180 | R: -16141.0 | Len: 500 | R/s: -32.28 (-4035.3%) | Gap: 4.36mm (min: 4.22) | Best: 4.21mm +Ep 200 | R: -16143.8 | Len: 500 | R/s: -32.29 (-4035.9%) | Gap: 4.33mm (min: 4.21) | Best: 4.21mm +Ep 220 | R: -16144.3 | Len: 500 | R/s: -32.29 (-4036.1%) | Gap: 4.33mm (min: 4.23) | Best: 4.21mm +Ep 240 | R: -16145.9 | Len: 500 | R/s: -32.29 (-4036.5%) | Gap: 4.32mm (min: 4.21) | Best: 4.21mm +Ep 260 | R: -16142.5 | Len: 500 | R/s: -32.28 (-4035.6%) | Gap: 4.34mm (min: 4.24) | Best: 4.21mm +Ep 280 | R: -16146.9 | Len: 500 | R/s: -32.29 (-4036.7%) | Gap: 4.32mm (min: 4.21) | Best: 4.21mm +Ep 300 | R: -16145.6 | Len: 500 | R/s: -32.29 (-4036.4%) | Gap: 4.34mm (min: 4.21) | Best: 4.21mm diff --git a/RL_Trials/training_log_20251211_122333.txt b/RL_Trials/training_log_20251211_122333.txt new file mode 100644 index 0000000..64c6376 --- /dev/null +++ b/RL_Trials/training_log_20251211_122333.txt @@ -0,0 +1,19 @@ +Training Started: 20251211_122333 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: -16150.6 | Len: 500 | R/s: -32.30 (-4037.7%) | Gap: 4.18mm (min: 4.10) | Best: 4.10mm +Ep 40 | R: -16154.2 | Len: 500 | R/s: -32.31 (-4038.5%) | Gap: 4.18mm (min: 4.13) | Best: 4.10mm +Ep 60 | R: -16155.5 | Len: 500 | R/s: -32.31 (-4038.9%) | Gap: 4.16mm (min: 4.12) | Best: 4.10mm +Ep 80 | R: -16151.9 | Len: 500 | R/s: -32.30 (-4038.0%) | Gap: 4.20mm (min: 4.13) | Best: 4.10mm +Ep 100 | R: -16155.1 | Len: 500 | R/s: -32.31 (-4038.8%) | Gap: 4.18mm (min: 4.13) | Best: 4.10mm +Ep 120 | R: -16155.3 | Len: 500 | R/s: -32.31 (-4038.8%) | Gap: 4.19mm (min: 4.14) | Best: 4.10mm +Ep 140 | R: -16154.6 | Len: 500 | R/s: -32.31 (-4038.7%) | Gap: 4.18mm (min: 4.12) | Best: 4.10mm +Ep 160 | R: -16154.8 | Len: 500 | R/s: -32.31 (-4038.7%) | Gap: 4.19mm (min: 4.12) | Best: 4.10mm +Ep 180 | R: -16157.5 | Len: 500 | R/s: -32.32 (-4039.4%) | Gap: 4.19mm (min: 4.11) | Best: 4.10mm +Ep 200 | R: -16157.7 | Len: 500 | R/s: -32.32 (-4039.4%) | Gap: 4.21mm (min: 4.17) | Best: 4.10mm +Ep 220 | R: -16159.4 | Len: 500 | R/s: -32.32 (-4039.8%) | Gap: 4.19mm (min: 4.12) | Best: 4.10mm diff --git a/RL_Trials/training_log_20251211_184547.txt b/RL_Trials/training_log_20251211_184547.txt new file mode 100644 index 0000000..2ae435c --- /dev/null +++ b/RL_Trials/training_log_20251211_184547.txt @@ -0,0 +1,9 @@ +Training Started: 20251211_184547 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: -14585.9 | Len: 453 | R/s: -32.19 (-4023.9%) | Gap: 21.69mm (min:10.80) | Best: 10.80mm diff --git a/RL_Trials/training_log_20251211_185541.txt b/RL_Trials/training_log_20251211_185541.txt new file mode 100644 index 0000000..4ae2061 --- /dev/null +++ b/RL_Trials/training_log_20251211_185541.txt @@ -0,0 +1,12 @@ +Training Started: 20251211_185541 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: -264.1 | Len: 28 | R/s: -9.47 (-1183.4%) | Gap: 20.27mm (min:11.48) | Best: 11.48mm +Ep 40 | R: -222.6 | Len: 24 | R/s: -9.45 (-1181.5%) | Gap: 21.62mm (min:19.97) | Best: 11.48mm +Ep 60 | R: -211.8 | Len: 23 | R/s: -9.37 (-1171.3%) | Gap: 21.47mm (min:19.53) | Best: 11.48mm +Ep 80 | R: -144.7 | Len: 15 | R/s: -9.65 (-1206.2%) | Gap: 21.89mm (min:20.01) | Best: 11.48mm diff --git a/RL_Trials/training_log_20251211_190447.txt b/RL_Trials/training_log_20251211_190447.txt new file mode 100644 index 0000000..62f7c6c --- /dev/null +++ b/RL_Trials/training_log_20251211_190447.txt @@ -0,0 +1,40 @@ +Training Started: 20251211_190447 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: -2049.0 | Len: 66 | R/s: -31.16 (-3895.5%) | Gap: 13.46mm (min:10.84) | Best: 10.84mm +Ep 40 | R: -1124.2 | Len: 36 | R/s: -30.89 (-3860.7%) | Gap: 12.94mm (min:10.62) | Best: 10.62mm +Ep 60 | R: -900.8 | Len: 29 | R/s: -30.75 (-3843.2%) | Gap: 12.07mm (min:10.35) | Best: 10.35mm +Ep 80 | R: -885.5 | Len: 29 | R/s: -30.75 (-3843.2%) | Gap: 11.88mm (min:10.26) | Best: 10.26mm +Ep 100 | R: -1242.5 | Len: 40 | R/s: -30.99 (-3873.3%) | Gap: 13.41mm (min:10.71) | Best: 10.26mm +Ep 120 | R: -972.3 | Len: 32 | R/s: -30.77 (-3846.0%) | Gap: 12.38mm (min:10.39) | Best: 10.26mm +Ep 140 | R: -1182.0 | Len: 38 | R/s: -30.90 (-3862.8%) | Gap: 13.08mm (min:10.60) | Best: 10.26mm +Ep 160 | R: -953.0 | Len: 31 | R/s: -30.74 (-3842.8%) | Gap: 12.06mm (min:10.68) | Best: 10.26mm +Ep 180 | R: -1178.0 | Len: 38 | R/s: -30.92 (-3864.9%) | Gap: 12.61mm (min:10.55) | Best: 10.26mm +Ep 200 | R: -3914.1 | Len: 125 | R/s: -31.34 (-3917.2%) | Gap: 13.81mm (min:10.84) | Best: 10.26mm +Ep 220 | R: -13001.1 | Len: 412 | R/s: -31.59 (-3948.3%) | Gap: 17.74mm (min:11.72) | Best: 10.26mm +Ep 240 | R: -15119.0 | Len: 478 | R/s: -31.60 (-3950.4%) | Gap: 18.71mm (min:13.85) | Best: 10.26mm +Ep 260 | R: -12974.1 | Len: 411 | R/s: -31.59 (-3949.3%) | Gap: 17.73mm (min:12.36) | Best: 10.26mm +Ep 280 | R: -13695.3 | Len: 433 | R/s: -31.60 (-3949.5%) | Gap: 18.07mm (min:12.24) | Best: 10.26mm +Ep 300 | R: -15814.3 | Len: 500 | R/s: -31.63 (-3953.6%) | Gap: 18.97mm (min:18.92) | Best: 10.26mm +Ep 320 | R: -15814.0 | Len: 500 | R/s: -31.63 (-3953.5%) | Gap: 18.97mm (min:18.93) | Best: 10.26mm +Ep 340 | R: -15809.3 | Len: 500 | R/s: -31.62 (-3952.3%) | Gap: 18.96mm (min:18.89) | Best: 10.26mm +Ep 360 | R: -15816.5 | Len: 500 | R/s: -31.63 (-3954.1%) | Gap: 18.97mm (min:18.91) | Best: 10.26mm +Ep 380 | R: -15815.5 | Len: 500 | R/s: -31.63 (-3953.9%) | Gap: 18.97mm (min:18.92) | Best: 10.26mm +Ep 400 | R: -15818.2 | Len: 500 | R/s: -31.64 (-3954.5%) | Gap: 18.98mm (min:18.93) | Best: 10.26mm +Ep 420 | R: -15815.1 | Len: 500 | R/s: -31.63 (-3953.8%) | Gap: 18.97mm (min:18.89) | Best: 10.26mm +Ep 440 | R: -15816.0 | Len: 500 | R/s: -31.63 (-3954.0%) | Gap: 18.97mm (min:18.94) | Best: 10.26mm +Ep 460 | R: -15818.5 | Len: 500 | R/s: -31.64 (-3954.6%) | Gap: 18.98mm (min:18.93) | Best: 10.26mm +Ep 480 | R: -15819.3 | Len: 500 | R/s: -31.64 (-3954.8%) | Gap: 18.98mm (min:18.94) | Best: 10.26mm +Ep 500 | R: -15818.5 | Len: 500 | R/s: -31.64 (-3954.6%) | Gap: 18.97mm (min:18.94) | Best: 10.26mm +Ep 520 | R: -15819.8 | Len: 500 | R/s: -31.64 (-3954.9%) | Gap: 18.98mm (min:18.94) | Best: 10.26mm +Ep 540 | R: -15818.8 | Len: 500 | R/s: -31.64 (-3954.7%) | Gap: 18.97mm (min:18.92) | Best: 10.26mm +Ep 560 | R: -15818.3 | Len: 500 | R/s: -31.64 (-3954.6%) | Gap: 18.97mm (min:18.92) | Best: 10.26mm +Ep 580 | R: -15822.9 | Len: 500 | R/s: -31.65 (-3955.7%) | Gap: 18.98mm (min:18.93) | Best: 10.26mm +Ep 600 | R: -15819.6 | Len: 500 | R/s: -31.64 (-3954.9%) | Gap: 18.97mm (min:18.92) | Best: 10.26mm +Ep 620 | R: -15817.8 | Len: 500 | R/s: -31.64 (-3954.5%) | Gap: 18.96mm (min:18.91) | Best: 10.26mm +Ep 640 | R: -15821.5 | Len: 500 | R/s: -31.64 (-3955.4%) | Gap: 18.98mm (min:18.92) | Best: 10.26mm diff --git a/RL_Trials/training_log_20251211_191801.txt b/RL_Trials/training_log_20251211_191801.txt new file mode 100644 index 0000000..7c4a459 --- /dev/null +++ b/RL_Trials/training_log_20251211_191801.txt @@ -0,0 +1,112 @@ +Training Started: 20251211_191801 +Number of Episodes: 2000 +Print Frequency: 20 +Target Gap Height: 16.491741 mm +Network: 256 hidden units with LayerNorm +Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02 +====================================================================== + +Ep 20 | R: 755.5 | Len: 204 | R/s: 3.70 (462.3%) | Gap: 16.74mm (min:14.73) | Best: 14.73mm +Ep 40 | R: 407.2 | Len: 116 | R/s: 3.52 (439.9%) | Gap: 15.56mm (min:13.80) | Best: 13.80mm +Ep 60 | R: 157.4 | Len: 57 | R/s: 2.78 (347.5%) | Gap: 14.87mm (min:13.91) | Best: 13.80mm +Ep 80 | R: 182.7 | Len: 61 | R/s: 2.98 (372.0%) | Gap: 15.06mm (min:14.21) | Best: 13.80mm +Ep 100 | R: 487.2 | Len: 134 | R/s: 3.65 (455.9%) | Gap: 16.10mm (min:14.31) | Best: 13.80mm +Ep 120 | R: 1113.1 | Len: 297 | R/s: 3.75 (468.3%) | Gap: 17.64mm (min:15.95) | Best: 13.80mm +Ep 140 | R: 1434.7 | Len: 385 | R/s: 3.72 (465.6%) | Gap: 18.21mm (min:16.13) | Best: 13.80mm +Ep 160 | R: 641.4 | Len: 172 | R/s: 3.72 (464.8%) | Gap: 16.69mm (min:15.38) | Best: 13.80mm +Ep 180 | R: 1029.0 | Len: 274 | R/s: 3.76 (469.7%) | Gap: 17.43mm (min:14.60) | Best: 13.80mm +Ep 200 | R: 287.0 | Len: 85 | R/s: 3.39 (424.0%) | Gap: 15.61mm (min:14.18) | Best: 13.80mm +Ep 220 | R: 330.9 | Len: 94 | R/s: 3.52 (440.4%) | Gap: 15.85mm (min:14.93) | Best: 13.80mm +Ep 240 | R: 336.4 | Len: 103 | R/s: 3.28 (409.7%) | Gap: 15.15mm (min:13.83) | Best: 13.80mm +Ep 260 | R: 128.2 | Len: 50 | R/s: 2.58 (321.9%) | Gap: 14.51mm (min:13.90) | Best: 13.80mm +Ep 280 | R: 116.0 | Len: 46 | R/s: 2.51 (313.3%) | Gap: 14.30mm (min:13.49) | Best: 13.49mm +Ep 300 | R: 95.0 | Len: 39 | R/s: 2.45 (306.0%) | Gap: 13.85mm (min:13.19) | Best: 13.19mm +Ep 320 | R: 772.1 | Len: 200 | R/s: 3.86 (482.9%) | Gap: 16.77mm (min:15.05) | Best: 13.19mm +Ep 340 | R: 152.7 | Len: 54 | R/s: 2.84 (354.5%) | Gap: 14.81mm (min:13.96) | Best: 13.19mm +Ep 360 | R: 118.1 | Len: 47 | R/s: 2.52 (315.6%) | Gap: 14.36mm (min:13.50) | Best: 13.19mm +Ep 380 | R: 290.8 | Len: 81 | R/s: 3.60 (450.2%) | Gap: 15.56mm (min:14.06) | Best: 13.19mm +Ep 400 | R: 230.0 | Len: 69 | R/s: 3.35 (418.5%) | Gap: 15.38mm (min:14.74) | Best: 13.19mm +Ep 420 | R: 305.9 | Len: 85 | R/s: 3.58 (447.5%) | Gap: 15.82mm (min:15.08) | Best: 13.19mm +Ep 440 | R: 450.6 | Len: 116 | R/s: 3.90 (487.0%) | Gap: 16.25mm (min:14.81) | Best: 13.19mm +Ep 460 | R: 624.2 | Len: 161 | R/s: 3.89 (486.0%) | Gap: 16.65mm (min:15.01) | Best: 13.19mm +Ep 480 | R: 710.6 | Len: 192 | R/s: 3.70 (462.6%) | Gap: 16.62mm (min:14.71) | Best: 13.19mm +Ep 500 | R: 131.1 | Len: 49 | R/s: 2.65 (331.8%) | Gap: 14.44mm (min:13.45) | Best: 13.19mm +Ep 520 | R: 169.4 | Len: 58 | R/s: 2.90 (362.5%) | Gap: 14.97mm (min:14.22) | Best: 13.19mm +Ep 540 | R: 929.9 | Len: 263 | R/s: 3.53 (441.4%) | Gap: 16.99mm (min:14.49) | Best: 13.19mm +Ep 560 | R: 1760.6 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 580 | R: 1763.0 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 600 | R: 1775.4 | Len: 500 | R/s: 3.55 (443.8%) | Gap: 18.99mm (min:18.91) | Best: 13.19mm +Ep 620 | R: 1298.7 | Len: 355 | R/s: 3.66 (457.5%) | Gap: 17.94mm (min:14.49) | Best: 13.19mm +Ep 640 | R: 1576.3 | Len: 438 | R/s: 3.60 (450.3%) | Gap: 18.63mm (min:16.35) | Best: 13.19mm +Ep 660 | R: 1762.6 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 680 | R: 1761.3 | Len: 500 | R/s: 3.52 (440.3%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 700 | R: 1761.0 | Len: 500 | R/s: 3.52 (440.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 720 | R: 1754.8 | Len: 500 | R/s: 3.51 (438.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 740 | R: 1755.3 | Len: 500 | R/s: 3.51 (438.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 760 | R: 1756.6 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 780 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 800 | R: 1756.9 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 820 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 840 | R: 1593.2 | Len: 436 | R/s: 3.65 (456.7%) | Gap: 18.62mm (min:16.57) | Best: 13.19mm +Ep 860 | R: 1209.1 | Len: 334 | R/s: 3.62 (452.2%) | Gap: 17.92mm (min:15.21) | Best: 13.19mm +Ep 880 | R: 509.8 | Len: 149 | R/s: 3.43 (429.0%) | Gap: 16.16mm (min:14.21) | Best: 13.19mm +Ep 900 | R: 496.0 | Len: 148 | R/s: 3.36 (419.9%) | Gap: 15.86mm (min:14.56) | Best: 13.19mm +Ep 920 | R: 1770.0 | Len: 500 | R/s: 3.54 (442.5%) | Gap: 18.99mm (min:18.97) | Best: 13.19mm +Ep 940 | R: 1763.3 | Len: 500 | R/s: 3.53 (440.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 960 | R: 1753.9 | Len: 500 | R/s: 3.51 (438.5%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 980 | R: 1751.9 | Len: 500 | R/s: 3.50 (438.0%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1000 | R: 1756.6 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1020 | R: 1754.6 | Len: 500 | R/s: 3.51 (438.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1040 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1060 | R: 1756.7 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1080 | R: 1758.8 | Len: 500 | R/s: 3.52 (439.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1100 | R: 1756.2 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1120 | R: 1756.5 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1140 | R: 1760.5 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1160 | R: 1760.5 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1180 | R: 1756.5 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm +Ep 1200 | R: 1760.0 | Len: 500 | R/s: 3.52 (440.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1220 | R: 1758.7 | Len: 500 | R/s: 3.52 (439.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1240 | R: 1760.4 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1260 | R: 1753.5 | Len: 500 | R/s: 3.51 (438.4%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1280 | R: 1753.9 | Len: 500 | R/s: 3.51 (438.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1300 | R: 1758.0 | Len: 500 | R/s: 3.52 (439.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1320 | R: 1762.7 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1340 | R: 1693.0 | Len: 459 | R/s: 3.69 (460.9%) | Gap: 18.61mm (min:16.25) | Best: 13.19mm +Ep 1360 | R: 713.0 | Len: 181 | R/s: 3.94 (492.2%) | Gap: 16.38mm (min:15.05) | Best: 13.19mm +Ep 1380 | R: 2118.4 | Len: 486 | R/s: 4.36 (545.4%) | Gap: 18.38mm (min:17.32) | Best: 13.19mm +Ep 1400 | R: 2157.4 | Len: 495 | R/s: 4.36 (544.9%) | Gap: 18.50mm (min:18.01) | Best: 13.19mm +Ep 1420 | R: 1181.5 | Len: 262 | R/s: 4.50 (563.0%) | Gap: 16.90mm (min:15.79) | Best: 13.19mm +Ep 1440 | R: 1332.5 | Len: 298 | R/s: 4.46 (558.1%) | Gap: 17.08mm (min:15.65) | Best: 13.19mm +Ep 1460 | R: 1496.5 | Len: 332 | R/s: 4.51 (563.8%) | Gap: 17.27mm (min:15.62) | Best: 13.19mm +Ep 1480 | R: 1545.4 | Len: 339 | R/s: 4.56 (570.1%) | Gap: 17.26mm (min:15.87) | Best: 13.19mm +Ep 1500 | R: 862.8 | Len: 201 | R/s: 4.29 (536.3%) | Gap: 16.17mm (min:14.88) | Best: 13.19mm +Ep 1520 | R: 809.8 | Len: 193 | R/s: 4.20 (524.6%) | Gap: 16.03mm (min:14.74) | Best: 13.19mm +Ep 1540 | R: 861.1 | Len: 204 | R/s: 4.22 (527.7%) | Gap: 16.25mm (min:14.93) | Best: 13.19mm +Ep 1560 | R: 1445.2 | Len: 329 | R/s: 4.40 (549.4%) | Gap: 17.24mm (min:15.19) | Best: 13.19mm +Ep 1580 | R: 1993.4 | Len: 486 | R/s: 4.11 (513.2%) | Gap: 18.55mm (min:16.26) | Best: 13.19mm +Ep 1600 | R: 1985.4 | Len: 500 | R/s: 3.97 (496.4%) | Gap: 18.75mm (min:18.57) | Best: 13.19mm +Ep 1620 | R: 1776.8 | Len: 500 | R/s: 3.55 (444.2%) | Gap: 18.97mm (min:18.91) | Best: 13.19mm +Ep 1640 | R: 1755.2 | Len: 500 | R/s: 3.51 (438.8%) | Gap: 18.99mm (min:18.97) | Best: 13.19mm +Ep 1660 | R: 1751.1 | Len: 500 | R/s: 3.50 (437.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1680 | R: 1746.6 | Len: 500 | R/s: 3.49 (436.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1700 | R: 1746.2 | Len: 500 | R/s: 3.49 (436.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1720 | R: 1747.8 | Len: 500 | R/s: 3.50 (437.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1740 | R: 1743.0 | Len: 500 | R/s: 3.49 (435.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1760 | R: 1743.4 | Len: 500 | R/s: 3.49 (435.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1780 | R: 1744.3 | Len: 500 | R/s: 3.49 (436.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1800 | R: 1744.0 | Len: 500 | R/s: 3.49 (436.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1820 | R: 1739.4 | Len: 500 | R/s: 3.48 (434.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1840 | R: 1736.2 | Len: 500 | R/s: 3.47 (434.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1860 | R: 1732.7 | Len: 500 | R/s: 3.47 (433.2%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1880 | R: 1732.1 | Len: 500 | R/s: 3.46 (433.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1900 | R: 1732.2 | Len: 500 | R/s: 3.46 (433.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1920 | R: 1729.1 | Len: 500 | R/s: 3.46 (432.3%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1940 | R: 1728.6 | Len: 500 | R/s: 3.46 (432.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1960 | R: 1728.0 | Len: 500 | R/s: 3.46 (432.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 1980 | R: 1728.5 | Len: 500 | R/s: 3.46 (432.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm +Ep 2000 | R: 1726.8 | Len: 500 | R/s: 3.45 (431.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm + +====================================================================== +Training Completed: 20251211_195411 +Best Gap Error Achieved: 13.191 mm diff --git a/Use of AI.txt b/Use of AI.txt new file mode 100644 index 0000000..0473fd1 --- /dev/null +++ b/Use of AI.txt @@ -0,0 +1,1777 @@ +[1]. Tool: Gemini + Prompt: Could you please set up my RL training environment to load the URDF file I created for my maglev pod and + put a track above it? This should all be done in PyBullet simulation, using the maglev_predictor and + mag_lev_coil files I have provided. The agent should have access to an observation space with 4 gap + heights and 4 gap height velocities from the sim, and should be able to output 4 PWM values as actions. + Output: + # --- 1. Define Action & Observation Spaces --- + # Action: 4 PWM duty cycles between -1 and 1 (4 independent coils) + # [front_left, front_right, back_left, back_right] corresponding to +Y and -Y ends of each U-yoke + self.action_space = spaces.Box(low=-1, high=1, shape=(4,), dtype=np.float32) + + # Observation: 4 normalized noisy sensor gap heights + 4 normalized velocities + # Gaps normalized by 0.030m, velocities by 0.1 m/s + self.observation_space = spaces.Box(low=-5.0, high=5.0, shape=(8,), dtype=np.float32) + + # --- 2. Setup Physics & Actuators --- + self.dt = 1./240. # PyBullet default timestep + self.coil_front_L = MagLevCoil(1.1, 0.0025, 12, 10.2) # From your specs + self.coil_front_R = MagLevCoil(1.1, 0.0025, 12, 10.2) + self.coil_back_L = MagLevCoil(1.1, 0.0025, 12, 10.2) + self.coil_back_R = MagLevCoil(1.1, 0.0025, 12, 10.2) + + # Sensor noise parameters + self.sensor_noise_std = 0.0001 # 0.1mm standard deviation + + # Normalization constants for observations + self.gap_scale = 0.015 # Normalize gaps by +-15mm max expected deviation from middle + self.velocity_scale = 0.1 # Normalize velocities by 0.1 m/s max expected velocity + + # Maglev force/torque predictor + self.predictor = MaglevPredictor() + + # Connect to PyBullet (DIRECT is faster for training, GUI for debugging) + self.client = p.connect(p.GUI if use_gui else p.DIRECT) + p.setAdditionalSearchPath(pybullet_data.getDataPath()) + + # Store references + self.trackId = None + self.podId = None + self.collision_local_positions = [] + self.yoke_indices = [] # For force application + self.yoke_labels = [] + self.sensor_indices = [] # For gap height measurement + self.sensor_labels = [] + + # For velocity calculation + self.prev_sensor_gaps = None + + + def reset(self, seed=None, options=None): + # Reset PyBullet simulation + p.resetSimulation(physicsClientId=self.client) + p.setGravity(0, 0, -9.81, physicsClientId=self.client) + p.setTimeStep(self.dt, physicsClientId=self.client) + + # Create the maglev track (inverted system - track above, pod hangs below) + # Track bottom surface at Z=0 + track_collision = p.createCollisionShape( + shapeType=p.GEOM_BOX, + halfExtents=[1.0, 0.2, 0.010], + physicsClientId=self.client + ) + track_visual = p.createVisualShape( + shapeType=p.GEOM_BOX, + halfExtents=[1.0, 0.2, 0.010], + rgbaColor=[0.3, 0.3, 0.3, 0.8], + physicsClientId=self.client + ) + self.trackId = p.createMultiBody( + baseMass=0, # Static + baseCollisionShapeIndex=track_collision, + baseVisualShapeIndex=track_visual, + basePosition=[0, 0, 0.010], # Track center at Z=10mm, bottom at Z=0 + physicsClientId=self.client + ) + p.changeDynamics(self.trackId, -1, + lateralFriction=0.3, + restitution=0.1, + physicsClientId=self.client) + + start_pos = [0, 0, start_z] + start_orientation = p.getQuaternionFromEuler([0, 0, 0]) + self.podId = p.loadURDF(urdf_path, start_pos, start_orientation, physicsClientId=self.client) + +[2]. Tool: Gemini + Prompt: + Output: + # Parse collision shapes to identify yokes and sensors + collision_shapes = p.getCollisionShapeData(self.podId, -1, physicsClientId=self.client) + self.collision_local_positions = [] + self.yoke_indices = [] + self.yoke_labels = [] + self.sensor_indices = [] + self.sensor_labels = [] + + # Expected heights for detection + expected_yoke_sensor_z = 0.08585 # Yokes and sensors always at this height + expected_bolt_z = 0.08585 + self.initial_gap_mm / 1000.0 # Bolts at gap-dependent height + + for i, shape in enumerate(collision_shapes): + shape_type = shape[2] + local_pos = shape[5] + self.collision_local_positions.append(local_pos) + + # Check if at sensor/yoke height (Z ≈ 0.08585m) - NOT bolts + if abs(local_pos[2] - expected_yoke_sensor_z) < 0.001: + if shape_type == p.GEOM_BOX: + # Yokes are BOX type at the four corners (size 0.0254) + self.yoke_indices.append(i) + x_pos = "Front" if local_pos[0] > 0 else "Back" + y_pos = "Left" if local_pos[1] > 0 else "Right" + self.yoke_labels.append(f"{x_pos}_{y_pos}") + elif shape_type == p.GEOM_CYLINDER or shape_type == p.GEOM_MESH: + # Sensors: distinguish by position pattern + if abs(local_pos[0]) < 0.06 or abs(local_pos[1]) < 0.02: + self.sensor_indices.append(i) + if abs(local_pos[0]) < 0.001: # Center sensors (X ≈ 0) + label = "Center_Right" if local_pos[1] > 0 else "Center_Left" + else: # Front/back sensors (Y ≈ 0) + label = "Front" if local_pos[0] > 0 else "Back" + self.sensor_labels.append(label) + +[3]. Tool: Claude Code + Prompt: Given the maglev_predictor file, generate code to find the equilibrium height for a pod of a given mass. + This should simply be the gap height at which 0 current in either coil and zero roll (all times 2) creates a + force equivalent to the gravitational force on the 5.8kg pod. + + Output: + """ + Find equilibrium gap height for magnetic levitation system. + + Given: + - Pod mass: 5.8 kg + - Required force: 5.8 * 9.81 = 56.898 N + - All currents: 0 A + - Roll angle: 0 degrees + + Find: Gap height (mm) that produces this force + """ + + import numpy as np + from maglev_predictor import MaglevPredictor + + # Initialize predictor + predictor = MaglevPredictor() + + # Target force + target_force = 5.8 * 9.81 # 56.898 N + + print("=" * 70) + print("EQUILIBRIUM GAP HEIGHT FINDER") + print("=" * 70) + print(f"Target Force: {target_force:.3f} N (for 5.8 kg pod)") + print(f"Parameters: currL = 0 A, currR = 0 A, roll = 0°") + print() + + # Define objective function + def force_error(gap_height): + """Calculate difference between predicted force and target force.""" + force, _ = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap_height) + return force - target_force + + # First, let's scan across gap heights to understand the behavior + print("Scanning gap heights from 5 to 30 mm...") + print("-" * 70) + gap_range = np.linspace(5, 30, 26) + forces = [] + + for gap in gap_range: + force, torque = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap) + forces.append(force) + if abs(force - target_force) < 5: # Close to target + print(f"Gap: {gap:5.1f} mm → Force: {force:7.3f} N ← CLOSE!") + else: + print(f"Gap: {gap:5.1f} mm → Force: {force:7.3f} N") + + forces = np.array(forces) + + print() + print("-" * 70) + + # Check if target force is within the range + min_force = forces.min() + max_force = forces.max() + + if target_force < min_force or target_force > max_force: + print(f"⚠ WARNING: Target force {target_force:.3f} N is outside the range!") + print(f" Force range: {min_force:.3f} N to {max_force:.3f} N") + print(f" Cannot achieve equilibrium with 0A currents.") + else: + # Find the gap height using root finding + # Use brentq for robust bracketing (requires sign change) + + # Find bracketing interval + idx_above = np.where(forces > target_force)[0] + idx_below = np.where(forces < target_force)[0] + + if len(idx_above) > 0 and len(idx_below) > 0: + # Find the transition point + gap_low = gap_range[idx_below[-1]] + gap_high = gap_range[idx_above[0]] + + print(f"✓ Target force is achievable!") + print(f" Bracketing interval: {gap_low:.1f} mm to {gap_high:.1f} mm") + print() + + # Use simple bisection method for accurate root finding + tol = 1e-6 + while (gap_high - gap_low) > tol: + gap_mid = (gap_low + gap_high) / 2 + force_mid, _ = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap_mid) + + if force_mid > target_force: + gap_low = gap_mid + else: + gap_high = gap_mid + + equilibrium_gap = (gap_low + gap_high) / 2 + + # Verify the result + final_force, final_torque = predictor.predict( + currL=0, currR=0, roll=0, gap_height=equilibrium_gap + ) + + print("=" * 70) + print("EQUILIBRIUM FOUND!") + print("=" * 70) + print(f"Equilibrium Gap Height: {equilibrium_gap:.6f} mm") + print(f"Predicted Force: {final_force:.6f} N") + print(f"Target Force: {target_force:.6f} N") + print(f"Error: {abs(final_force - target_force):.9f} N") + print(f"Torque at equilibrium: {final_torque:.6f} mN·m") + print() + print(f"✓ Pod will levitate at {equilibrium_gap:.3f} mm gap height") + print(f" with no current applied (permanent magnets only)") + print("=" * 70) + else: + print("⚠ Could not find bracketing interval for bisection.") + print(" Target force may not be achievable in the scanned range.") + +[4]. Tool: Claude Code + Prompt: + How can I fit a polynomial to a system with 4 inputs and two outputs? I need to map the values + in the provided CSV file (currL, currR, roll, and inverse Gap) to Force and Torque readings. + + Output: + # 1. Load your Ansys CSV - using invGap for modeling + X = magDf[['currL [A]', 'currR [A]', 'rollDeg [deg]', 'invGap']] + y = magDf[['YokeForce.Force_z [newton]', 'YokeTorque.Torque [mNewtonMeter]']] + + # 2. Create Features (e.g. z^2, z^3, z*IL, etc.) + poly = PolynomialFeatures(degree=3) + X_poly = poly.fit_transform(X) + + # 3. Fit + model = LinearRegression() + model.fit(X_poly, y) + + # 4. Extract Equation + print("Force Coeffs:", model.coef_[0]) + print("Torque Coeffs:", model.coef_[1]) + +[5]. Tool: Claude Code + Prompt: + Can you generate code for comparing a polynomial fit on the entire dataset (the actual + polynomial curve, not just the points that are provided by Ansys) with the ansys points + themselves for a single segment with the currents and roll degree held constant and the + Gap Height on X-axis and force on y-axis? Use the given code for reference. + + Output: + # Train best degree polynomial on full dataset + poly_best = PolynomialFeatures(degree=best_degree) + X_poly_best = poly_best.fit_transform(X) + model_best = LinearRegression() + model_best.fit(X_poly_best, y) + + print(f"Using polynomial degree: {best_degree}") + + # Select a specific operating condition to visualize + # Using the same condition as earlier: -15A, -15A, 0° roll + test_condition = magDf.iloc[0:13].copy() + currL_val = test_condition['currL [A]'].iloc[0] + currR_val = test_condition['currR [A]'].iloc[0] + roll_val = test_condition['rollDeg [deg]'].iloc[0] + + # Create very fine grid for gap heights to see polynomial behavior between points + gap_very_fine = np.linspace(5, 30, 500) # 500 points for smooth curve + X_fine = pd.DataFrame({ + 'currL [A]': [currL_val] * 500, + 'currR [A]': [currR_val] * 500, + 'rollDeg [deg]': [roll_val] * 500, + 'invGap': 1/gap_very_fine # Use inverse gap height for modeling + }) + X_fine_poly = poly_best.transform(X_fine) + y_fine_pred = model_best.predict(X_fine_poly) + + # Extract actual Ansys data for this condition + gap_actual = test_condition['GapHeight [mm]'].values + force_actual = test_condition['YokeForce.Force_z [newton]'].values + + # Plot + fig, ax = plt.subplots(1, 1, figsize=(12, 7)) + + ax.plot(gap_very_fine, y_fine_pred[:, 0], '-', linewidth=2.5, + label=f'Degree {best_degree} Polynomial Function', color='#2ca02c', alpha=0.8) + + # Plot actual Ansys data points + ax.scatter(gap_actual, force_actual, s=120, marker='o', + color='#d62728', edgecolors='black', linewidths=1.5, + label='Ansys Simulation Data', zorder=5) + + ax.set_ylabel('Force (N)', fontsize=13) + ax.set_title(f'Degree {best_degree} Polynomial vs Ansys Data (currL={currL_val}A, currR={currR_val}A, roll={roll_val}°)', + fontsize=14, fontweight='bold') + ax.legend(fontsize=12, loc='best') + ax.grid(True, alpha=0.3) + + # Add vertical lines at data points to highlight gaps + for gap_point in gap_actual: + ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8) + + plt.tight_layout() + plt.show() + + # Check for excessive oscillations by looking at derivative + print("Checking for overfitting indicators:") + print(f"Number of actual data points in this condition: {len(gap_actual)}") + + print(f"Gap spacing: {np.diff(gap_actual).mean():.2f} mm average") + print(f"Polynomial curve resolution: 500 points over {gap_very_fine.max() - gap_very_fine.min():.1f} mm range") + +[6]. Tool: Claude Code + Prompt: + Now can we do four different trials with similar graphs but different combinations of the starting conditions? + Output: + # Select 4 different conditions to visualize + # Each condition has 13 gap height measurements (one complete parameter set) + condition_indices = [ + (0, 13, '-15A, -15A, 0°'), # First condition + (13, 26, '-15A, -15A, 0.5°'), # Same currents, different roll + (91, 104, '-15A, -10A, 0°'), # Different right current + (182, 195, '-15A, -10A, 1°') # Different right current and roll + ] + + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + axes = axes.flatten() + + for idx, (start, end, label) in enumerate(condition_indices): + ax = axes[idx] + + # Get condition data + condition_data = magDf.iloc[start:end] + currL = condition_data['currL [A]'].iloc[0] + currR = condition_data['currR [A]'].iloc[0] + roll = condition_data['rollDeg [deg]'].iloc[0] + + # Create fine grid + gap_fine = np.linspace(5, 30, 500) + X_condition_fine = pd.DataFrame({ + 'currL [A]': [currL] * 500, + 'currR [A]': [currR] * 500, + 'rollDeg [deg]': [roll] * 500, + 'invGap': 1/gap_fine # Use inverse gap height for modeling + }) + + # Predict with best degree polynomial + X_condition_poly = poly_best.transform(X_condition_fine) + y_condition_pred = model_best.predict(X_condition_poly) + + # Plot polynomial curve + ax.plot(gap_fine, y_condition_pred[:, 0], '-', linewidth=2.5, + color='#2ca02c', alpha=0.8, label=f'Degree {best_degree} Polynomial') + + # Plot actual data + ax.scatter(condition_data['GapHeight [mm]'], + condition_data['YokeForce.Force_z [newton]'], + s=100, marker='o', color='#d62728', + edgecolors='black', linewidths=1.5, + label='Ansys Data', zorder=5) + + # Formatting + ax.set_xlabel('Gap Height (mm)', fontsize=11) + ax.set_ylabel('Force (N)', fontsize=11) + ax.set_title(f'currL={currL:.0f}A, currR={currR:.0f}A, roll={roll:.0f}°', + fontsize=12, fontweight='bold') + ax.legend(fontsize=10, loc='best') + ax.grid(True, alpha=0.3) + + # Add vertical lines at data points + for gap_point in condition_data['GapHeight [mm]'].values: + ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.2, linewidth=0.8) + + plt.suptitle(f'Degree {best_degree} Polynomial: Multiple Operating Conditions', + fontsize=15, fontweight='bold', y=1.00) + plt.tight_layout() + plt.show() + + print("Visual inspection guide:") + print("✓ Look for smooth curves between data points (good)") + print("✗ Look for wild oscillations or wiggles between points (overfitting)") + print("✓ Polynomial should interpolate naturally without excessive curvature") + +[7]. Tool: Claude Code + Prompt: + Let's now find the worst performing segment of 13 gap heights to show the worst possible + performance of our polynomial fit in terms of the R^2 score + + Output: + # Calculate R² score for each 13-row segment (each parameter set) + num_segments = len(magDf) // 13 + segment_scores = [] + segment_info = [] + + for i in range(num_segments): + start_idx = i * 13 + end_idx = start_idx + 13 + + # Get actual and predicted values for this segment + segment_actual = y.iloc[start_idx:end_idx, 0].values # Force only + segment_pred = model_best.predict(poly_best.transform(X.iloc[start_idx:end_idx]))[:, 0] + + # Calculate R² for this segment + segment_r2 = r2_score(segment_actual, segment_pred) + segment_scores.append(segment_r2) + + # Store segment info + segment_data = magDf.iloc[start_idx:end_idx] + currL = segment_data['currL [A]'].iloc[0] + currR = segment_data['currR [A]'].iloc[0] + roll = segment_data['rollDeg [deg]'].iloc[0] + segment_info.append({ + 'start': start_idx, + 'end': end_idx, + 'currL': currL, + 'currR': currR, + 'roll': roll, + 'r2': segment_r2 + }) + + # Find worst performing segment + worst_idx = np.argmin(segment_scores) + worst_segment = segment_info[worst_idx] + + print(f"Total segments analyzed: {num_segments}") + print(f"\nWorst performing segment:") + print(f" Rows: {worst_segment['start']} to {worst_segment['end']}") + print(f" Parameters: currL={worst_segment['currL']:.0f}A, currR={worst_segment['currR']:.0f}A, roll={worst_segment['roll']:.1f}°") + print(f" R² Score: {worst_segment['r2']:.6f}") + print(f"\nBest performing segment R²: {max(segment_scores):.6f}") + print(f"Mean R² across all segments: {np.mean(segment_scores):.6f}") + print(f"Median R² across all segments: {np.median(segment_scores):.6f}") + +[8]. Tool: Claude Code + Prompt: + Let's also visualize the worst segment so the evidently good performance is visible to a viewer. + + Output: + # Get worst segment data + worst_data = magDf.iloc[worst_segment['start']:worst_segment['end']] + worst_currL = worst_segment['currL'] + worst_currR = worst_segment['currR'] + worst_roll = worst_segment['roll'] + worst_r2 = worst_segment['r2'] + + # Create fine grid for this worst segment + gap_fine_worst = np.linspace(5, 30, 500) + X_worst_fine = pd.DataFrame({ + 'currL [A]': [worst_currL] * 500, + 'currR [A]': [worst_currR] * 500, + 'rollDeg [deg]': [worst_roll] * 500, + 'invGap': 1/gap_fine_worst + }) + + # Get predictions + X_worst_poly = poly_best.transform(X_worst_fine) + y_worst_pred = model_best.predict(X_worst_poly) + + # Extract actual data + gap_worst_actual = worst_data['GapHeight [mm]'].values + force_worst_actual = worst_data['YokeForce.Force_z [newton]'].values + + # Create the plot + fig, ax = plt.subplots(1, 1, figsize=(12, 7)) + + # Plot polynomial curve + ax.plot(gap_fine_worst, y_worst_pred[:, 0], '-', linewidth=2.5, + label=f'Degree {best_degree} Polynomial', color='#2ca02c', alpha=0.8) + + # Plot actual data points + ax.scatter(gap_worst_actual, force_worst_actual, s=120, marker='o', + color='#d62728', edgecolors='black', linewidths=1.5, + label='Ansys Data', zorder=5) + + # Formatting + ax.set_xlabel('Gap Height (mm)', fontsize=13) + ax.set_ylabel('Force (N)', fontsize=13) + ax.set_title(f'Worst Case Performance (R² = {worst_r2:.6f}) - currL={worst_currL:.0f}A, currR={worst_currR:.0f}A, roll={worst_roll:.1f}°', + fontsize=14, fontweight='bold') + ax.legend(fontsize=12, loc='best') + ax.grid(True, alpha=0.3) + + # Add vertical lines at data points + for gap_point in gap_worst_actual: + ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8) + + plt.tight_layout() + plt.show() + + print(f"Even in the worst case, the model achieves R² = {worst_r2:.6f}") + print(f"This demonstrates excellent interpolation performance across all operating conditions!") + +[9]. Tool: Claude Code + Prompt: + Now let's do something similar for the torque, showing how our polynomial fits to the torque vs + roll trend in ansys data while keeping othe variables constant. Show 4 trials like before, and + generate a continuous plot, like before. + + Output: + # Select 4 different gap heights to visualize + # Using currL = -15A, currR = -15A configuration + gap_heights_to_plot = [8, 10, 15, 21] # mm + + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + axes = axes.flatten() + + for idx, gap_height in enumerate(gap_heights_to_plot): + ax = axes[idx] + + # Fixed current configuration + currL = -15 + currR = -15 + + # Get actual data for this gap height + gap_data = magDf[(magDf['GapHeight [mm]'] == gap_height) & + (magDf['currL [A]'] == currL) & + (magDf['currR [A]'] == currR)] + + # Create fine grid for smooth curve across roll angles + roll_fine = np.linspace(-5, 5.0, 500) + X_condition_fine = pd.DataFrame({ + 'currL [A]': [currL] * 500, + 'currR [A]': [currR] * 500, + 'rollDeg [deg]': roll_fine, + 'invGap': [1/gap_height] * 500 + }) + + # Predict with best degree polynomial + X_condition_poly = poly_best.transform(X_condition_fine) + y_condition_pred = model_best.predict(X_condition_poly) + + # Plot polynomial curve for TORQUE (index 1) + ax.plot(roll_fine, y_condition_pred[:, 1], '-', linewidth=2.5, + color='#ff7f0e', alpha=0.8, label=f'Degree {best_degree} Polynomial') + + # Plot actual torque data + ax.scatter(gap_data['rollDeg [deg]'], + gap_data['YokeTorque.Torque [mNewtonMeter]'], + s=100, marker='o', color='#9467bd', + edgecolors='black', linewidths=1.5, + label='Ansys Data', zorder=5) + + # Formatting + ax.set_xlabel('Roll Angle (deg)', fontsize=11) + ax.set_ylabel('Torque (mN·m)', fontsize=11) + ax.set_title(f'Gap Height = {gap_height} mm (currL={currL:.0f}A, currR={currR:.0f}A)', + fontsize=12, fontweight='bold') + ax.legend(fontsize=10, loc='best') + ax.grid(True, alpha=0.3) + + # Add vertical lines at actual roll angle data points + for roll_point in gap_data['rollDeg [deg]'].unique(): + ax.axvline(roll_point, color='gray', linestyle=':', alpha=0.2, linewidth=0.8) + + plt.suptitle(f'Torque vs Roll Angle at Different Gap Heights (Degree {best_degree} Polynomial)', + fontsize=15, fontweight='bold', y=1.00) + plt.tight_layout() + plt.show() + + print(f"Showing torque variation with roll angle at {len(gap_heights_to_plot)} different gap heights.") + print("Gap height affects the magnitude of torque but the relationship with roll angle remains consistent.") + +[10]. Tool: Claude Code + Prompt: + Since torque also varies with current imbalance between the coils, let's visualize the + response for that as well. Follow the same visualization pattern as before. + + Output: + # Select 4 different gap heights to visualize + # Using roll = 0.5 degrees (small but non-zero torque) + gap_heights_to_plot = [8, 10, 15, 21] # mm + roll_target = 0.5 + + # Use a fixed reference current for currL + currL_ref = -15 + + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + axes = axes.flatten() + + for idx, gap_height in enumerate(gap_heights_to_plot): + ax = axes[idx] + + # Get actual data for this gap height, roll angle, AND fixed currL + gap_roll_data = magDf[(magDf['GapHeight [mm]'] == gap_height) & + (magDf['rollDeg [deg]'] == roll_target) & + (magDf['currL [A]'] == currL_ref)] + + # Calculate imbalance for actual data points (all have same currL now) + actual_imbalances = [] + actual_torques = [] + for _, row in gap_roll_data.iterrows(): + imbalance = row['currR [A]'] - row['currL [A]'] + torque = row['YokeTorque.Torque [mNewtonMeter]'] + actual_imbalances.append(imbalance) + actual_torques.append(torque) + + # Create fine grid for smooth curve across current imbalances + # Range from -10A to +10A imbalance (currR - currL) + imbalance_fine = np.linspace(0, 35, 500) + + # Vary currR while keeping currL fixed + currR_fine = currL_ref + imbalance_fine + + X_condition_fine = pd.DataFrame({ + 'currL [A]': [currL_ref] * 500, + 'currR [A]': currR_fine, + 'rollDeg [deg]': [roll_target] * 500, + 'invGap': [1/gap_height] * 500 + }) + + # Predict with best degree polynomial + X_condition_poly = poly_best.transform(X_condition_fine) + y_condition_pred = model_best.predict(X_condition_poly) + + # Plot polynomial curve for TORQUE (index 1) + ax.plot(imbalance_fine, y_condition_pred[:, 1], '-', linewidth=2.5, + color='#ff7f0e', alpha=0.8, label=f'Degree {best_degree} Polynomial') + + # Plot actual torque data (now only one point per imbalance value) + ax.scatter(actual_imbalances, actual_torques, + s=100, marker='o', color='#9467bd', + edgecolors='black', linewidths=1.5, + label='Ansys Data', zorder=5) + + # Formatting + ax.set_xlabel('Current Imbalance (currR - currL) [A]', fontsize=11) + ax.set_ylabel('Torque (mN·m)', fontsize=11) + ax.set_title(f'Gap Height = {gap_height} mm (currL = {currL_ref}A, roll = {roll_target}°)', + fontsize=12, fontweight='bold') + ax.legend(fontsize=10, loc='best') + ax.grid(True, alpha=0.3) + ax.axhline(0, color='black', linestyle='-', linewidth=0.5, alpha=0.3) + ax.axvline(0, color='black', linestyle='-', linewidth=0.5, alpha=0.3) + + # Add vertical lines at actual imbalance data points + for imb in set(actual_imbalances): + ax.axvline(imb, color='gray', linestyle=':', alpha=0.2, linewidth=0.8) + + plt.suptitle(f'Torque vs Current Imbalance at Different Gap Heights (currL = {currL_ref}A, Roll = {roll_target}°)', + fontsize=15, fontweight='bold', y=1.00) + plt.tight_layout() + plt.show() + + print(f"Showing torque variation with current imbalance at {len(gap_heights_to_plot)} different gap heights.") + print(f"All data uses currL = {currL_ref}A with varying currR to create different imbalances.") + print(f"Current imbalance (currR - currL) affects torque magnitude and direction.") + print(f"Positive imbalance (currR > currL) and negative imbalance (currR < currL) produce opposite torques.") + +[11]. Tool: Claude Code + Prompt: + Let's now visualize the worst torque prediction for a given 13-gap-height segment. + What is our worst possible performance? Show in a similar way as with force. + + Output: + # Calculate R² score for torque in each 13-row segment + torque_segment_scores = [] + torque_segment_info = [] + + for i in range(num_segments): + start_idx = i * 13 + end_idx = start_idx + 13 + + # Get actual and predicted torque values for this segment + segment_actual_torque = y.iloc[start_idx:end_idx, 1].values # Torque column + segment_pred_torque = model_best.predict(poly_best.transform(X.iloc[start_idx:end_idx]))[:, 1] + + # Calculate R² for this segment + segment_r2_torque = r2_score(segment_actual_torque, segment_pred_torque) + torque_segment_scores.append(segment_r2_torque) + + # Store segment info + segment_data = magDf.iloc[start_idx:end_idx] + currL = segment_data['currL [A]'].iloc[0] + currR = segment_data['currR [A]'].iloc[0] + roll = segment_data['rollDeg [deg]'].iloc[0] + torque_segment_info.append({ + 'start': start_idx, + 'end': end_idx, + 'currL': currL, + 'currR': currR, + 'roll': roll, + 'r2': segment_r2_torque + }) + + # Find worst and best performing segments for torque + worst_torque_idx = np.argmin(torque_segment_scores) + best_torque_idx = np.argmax(torque_segment_scores) + worst_torque_segment = torque_segment_info[worst_torque_idx] + best_torque_segment = torque_segment_info[best_torque_idx] + + print(f"Torque Prediction Analysis (across {num_segments} segments):") + print(f"\nBest R² Score: {max(torque_segment_scores):.6f}") + print(f" Parameters: currL={best_torque_segment['currL']:.0f}A, currR={best_torque_segment['currR']:.0f}A, roll={best_torque_segment['roll']:.1f}°") + print(f"\nWorst R² Score: {min(torque_segment_scores):.6f}") + print(f" Parameters: currL={worst_torque_segment['currL']:.0f}A, currR={worst_torque_segment['currR']:.0f}A, roll={worst_torque_segment['roll']:.1f}°") + print(f"\nMean R²: {np.mean(torque_segment_scores):.6f}") + print(f"Median R²: {np.median(torque_segment_scores):.6f}") + print(f"Std Dev: {np.std(torque_segment_scores):.6f}") + + # Create histogram of torque R² scores + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + ax.hist(torque_segment_scores, bins=30, edgecolor='black', alpha=0.7, color='#9467bd') + ax.axvline(np.mean(torque_segment_scores), color='red', linestyle='--', linewidth=2, label=f'Mean = {np.mean(torque_segment_scores):.6f}') + ax.axvline(np.median(torque_segment_scores), color='orange', linestyle='--', linewidth=2, label=f'Median = {np.median(torque_segment_scores):.6f}') + ax.set_xlabel('R² Score', fontsize=12) + ax.set_ylabel('Number of Segments', fontsize=12) + ax.set_title('Distribution of Torque R² Scores Across All Parameter Segments', fontsize=14, fontweight='bold') + ax.legend(fontsize=11) + ax.grid(True, alpha=0.3, axis='y') + plt.tight_layout() + plt.show() + + # Get worst torque segment data + worst_torque_data = magDf.iloc[worst_torque_segment['start']:worst_torque_segment['end']] + worst_torque_currL = worst_torque_segment['currL'] + worst_torque_currR = worst_torque_segment['currR'] + worst_torque_roll = worst_torque_segment['roll'] + worst_torque_r2 = worst_torque_segment['r2'] + + # Create fine grid for this worst segment + gap_fine_worst_torque = np.linspace(2, 30, 500) + X_worst_torque_fine = pd.DataFrame({ + 'currL [A]': [worst_torque_currL] * 500, + 'currR [A]': [worst_torque_currR] * 500, + 'rollDeg [deg]': [worst_torque_roll] * 500, + 'invGap': 1/gap_fine_worst_torque + }) + + # Get predictions + X_worst_torque_poly = poly_best.transform(X_worst_torque_fine) + y_worst_torque_pred = model_best.predict(X_worst_torque_poly) + + # Extract actual data + gap_worst_torque_actual = worst_torque_data['GapHeight [mm]'].values + torque_worst_actual = worst_torque_data['YokeTorque.Torque [mNewtonMeter]'].values + + # Create the plot + fig, ax = plt.subplots(1, 1, figsize=(12, 7)) + + # Plot polynomial curve for TORQUE + ax.plot(gap_fine_worst_torque, y_worst_torque_pred[:, 1], '-', linewidth=2.5, + label=f'Degree {best_degree} Polynomial', color='#ff7f0e', alpha=0.8) + + # Plot actual data points + ax.scatter(gap_worst_torque_actual, torque_worst_actual, s=120, marker='o', + color='#9467bd', edgecolors='black', linewidths=1.5, + label='Ansys Data', zorder=5) + + # Formatting + ax.set_xlabel('Gap Height (mm)', fontsize=13) + ax.set_ylabel('Torque (mN·m)', fontsize=13) + ax.set_title(f'Worst Torque Case (R² = {worst_torque_r2:.6f}) - currL={worst_torque_currL:.0f}A, currR={worst_torque_currR:.0f}A, roll={worst_torque_roll:.1f}°', + fontsize=14, fontweight='bold') + ax.legend(fontsize=12, loc='best') + ax.grid(True, alpha=0.3) + + # Add vertical lines at data points + for gap_point in gap_worst_torque_actual: + ax.axvline(gap_point, color='gray', linestyle=':', alpha=0.3, linewidth=0.8) + + plt.tight_layout() + plt.show() + + print(f"Worst-case torque prediction achieves R² = {worst_torque_r2:.6f}") + +[12]. Tool: Claude Code + Prompt: + Can we now export this model for use in a simulator? I want this to be accessible but also quite + fast since the simulator will be running at 240Hz and calculating forces and torques for 2 yokes at once. + + Output: + print("=" * 60) + print("MODEL EXPORT SUMMARY") + print("=" * 60) + print(f"\nModel Type: Polynomial Regression") + print(f"Polynomial Degree: {best_degree}") + print(f"Number of Features: {poly_best.n_features_in_}") + print(f"Number of Polynomial Terms: {poly_best.n_output_features_}") + print(f"\nInput Features:") + print(" - currL [A]") + print(" - currR [A]") + print(" - rollDeg [deg]") + print(" - invGap (1/GapHeight)") + print(f"\nOutputs:") + print(" - YokeForce.Force_z [newton]") + print(" - YokeTorque.Torque [mNewtonMeter]") + print(f"\nModel Performance:") + print(f" Force R² Score: {r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]):.6f}") + print(f" Torque R² Score: {torque_r2_overall:.6f}") + print(f"\nModel Coefficients Shape:") + print(f" Force coefficients: {model_best.coef_[0].shape}") + print(f" Torque coefficients: {model_best.coef_[1].shape}") + print("=" * 60) + + # Generate standalone predictor class + predictor_code = f'''""" + Magnetic Levitation Force and Torque Predictor + Generated from polynomial regression model (degree {best_degree}) + + Performance: + - Force R²: {r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]):.6f} + - Torque R²: {torque_r2_overall:.6f} + + Usage: + predictor = MaglevPredictor() + force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0) + """ + + import numpy as np + from itertools import combinations_with_replacement + + class MaglevPredictor: + def __init__(self): + """Initialize the magnetic levitation force/torque predictor.""" + self.degree = {best_degree} + self.n_features = 4 # currL, currR, roll, invGap + + # Force model coefficients + self.force_intercept = {model_best.intercept_[0]} + self.force_coef = np.array({model_best.coef_[0].tolist()}) + + # Torque model coefficients + self.torque_intercept = {model_best.intercept_[1]} + self.torque_coef = np.array({model_best.coef_[1].tolist()}) + + def _polynomial_features(self, X): + """ + Generate polynomial features up to specified degree. + Mimics sklearn's PolynomialFeatures with include_bias=True. + + Args: + X: numpy array of shape (n_samples, 4) with [currL, currR, roll, invGap] + + Returns: + Polynomial features array + """ + n_samples = X.shape[0] + + # Start with bias term (column of ones) + features = [np.ones(n_samples)] + + # Add original features + for i in range(self.n_features): + features.append(X[:, i]) + + # Add polynomial combinations + for deg in range(2, self.degree + 1): + for combo in combinations_with_replacement(range(self.n_features), deg): + term = np.ones(n_samples) + for idx in combo: + term *= X[:, idx] + features.append(term) + + return np.column_stack(features) + + def predict(self, currL, currR, roll, gap_height): + """ + Predict force and torque for given operating conditions. + + Args: + currL: Left coil current in Amps + currR: Right coil current in Amps + roll: Roll angle in degrees + gap_height: Gap height in mm + + Returns: + tuple: (force [N], torque [mN·m]) + """ + # Compute inverse gap (critical transformation!) + invGap = 1.0 / gap_height + + # Create input array + X = np.array([[currL, currR, roll, invGap]]) + + # Generate polynomial features + X_poly = self._polynomial_features(X) + + # Compute predictions + force = self.force_intercept + np.dot(X_poly, self.force_coef)[0] + torque = self.torque_intercept + np.dot(X_poly, self.torque_coef)[0] + + return force, torque + + def predict_batch(self, currL_array, currR_array, roll_array, gap_height_array): + """ + Predict force and torque for multiple operating conditions. + + Args: + currL_array: Array of left coil currents [A] + currR_array: Array of right coil currents [A] + roll_array: Array of roll angles [deg] + gap_height_array: Array of gap heights [mm] + + Returns: + tuple: (force_array [N], torque_array [mN·m]) + """ + # Convert to numpy arrays + currL_array = np.asarray(currL_array) + currR_array = np.asarray(currR_array) + roll_array = np.asarray(roll_array) + gap_height_array = np.asarray(gap_height_array) + + # Compute inverse gaps + invGap_array = 1.0 / gap_height_array + + # Stack into feature matrix + X = np.column_stack([currL_array, currR_array, roll_array, invGap_array]) + + # Generate polynomial features + X_poly = self._polynomial_features(X) + + # Compute predictions + force_array = self.force_intercept + np.dot(X_poly, self.force_coef) + torque_array = self.torque_intercept + np.dot(X_poly, self.torque_coef) + + return force_array, torque_array + + + if __name__ == "__main__": + # Example usage + predictor = MaglevPredictor() + + # Single prediction + force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0) + print(f"Single prediction:") + print(f" Force: {{force:.2f}} N") + print(f" Torque: {{torque:.2f}} mN·m") + + # Batch prediction + currL = np.array([-15, -15, -10]) + currR = np.array([-15, -10, -10]) + roll = np.array([0, 0.5, 1.0]) + gap = np.array([10, 12, 15]) + + forces, torques = predictor.predict_batch(currL, currR, roll, gap) + print(f"\\nBatch prediction:") + for i in range(len(forces)): + print(f" Condition {{i+1}}: Force={{forces[i]:.2f}} N, Torque={{torques[i]:.2f}} mN·m") + ''' + + # Save to file + predictor_filename = 'maglev_predictor.py' + with open(predictor_filename, 'w') as f: + f.write(predictor_code) + + print(f"✓ Saved standalone predictor: {predictor_filename}") + print(f"\nThis module:") + print(f" - Has NO scikit-learn dependency (only numpy)") + print(f" - Can be imported directly into your simulator") + print(f" - Includes both single and batch prediction methods") + print(f" - Implements polynomial feature generation internally") + print(f"\nTo use:") + print(f"```python") + print(f"from maglev_predictor import MaglevPredictor") + print(f"predictor = MaglevPredictor()") + print(f"force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0)") + print(f"```") + + # Import the standalone predictor (reload to get latest version) + import importlib + import maglev_predictor + importlib.reload(maglev_predictor) + from maglev_predictor import MaglevPredictor + + # Create predictor instance + standalone_predictor = MaglevPredictor() + + # Test cases + test_cases = [ + {'currL': -15, 'currR': -15, 'roll': 0.0, 'gap': 10.0}, + {'currL': -15, 'currR': -15, 'roll': 1.0, 'gap': 10.0}, + {'currL': -15, 'currR': -10, 'roll': 0.5, 'gap': 12.0}, + {'currL': -10, 'currR': -10, 'roll': 2.0, 'gap': 15.0}, + ] + + print("Validation: Comparing Standalone Predictor vs Original Model") + print("=" * 80) + + for i, case in enumerate(test_cases): + # Standalone predictor + force_standalone, torque_standalone = standalone_predictor.predict( + case['currL'], case['currR'], case['roll'], case['gap'] + ) + + # Original model + X_test = pd.DataFrame({ + 'currL [A]': [case['currL']], + 'currR [A]': [case['currR']], + 'rollDeg [deg]': [case['roll']], + 'invGap': [1/case['gap']] + }) + X_test_poly = poly_best.transform(X_test) + y_test_pred = model_best.predict(X_test_poly) + force_original = y_test_pred[0, 0] + torque_original = y_test_pred[0, 1] + + # Compare + force_diff = abs(force_standalone - force_original) + torque_diff = abs(torque_standalone - torque_original) + + print(f"\nTest Case {i+1}: currL={case['currL']}A, currR={case['currR']}A, " + + f"roll={case['roll']}°, gap={case['gap']}mm") + print(f" Force: Standalone={force_standalone:9.4f} N | Original={force_original:9.4f} N | Diff={force_diff:.2e}") + print(f" Torque: Standalone={torque_standalone:9.4f} mN·m | Original={torque_original:9.4f} mN·m | Diff={torque_diff:.2e}") + + # Verify match (should be essentially identical, accounting for floating point) + assert force_diff < 1e-8, f"Force mismatch in test case {i+1}" + assert torque_diff < 1e-8, f"Torque mismatch in test case {i+1}" + + print("\n" + "=" * 80) + print("✓ All tests passed! Standalone predictor matches original model perfectly.") + print("\nThe standalone predictor is ready for integration into your simulator!") + + import joblib + import pickle + + # Save the polynomial transformer and model + model_data = { + 'poly_features': poly_best, + 'model': model_best, + 'degree': best_degree, + 'feature_names': ['currL [A]', 'currR [A]', 'rollDeg [deg]', 'invGap'], + 'performance': { + 'force_r2': r2_score(y.iloc[:, 0].values, y_pred_full[:, 0]), + 'torque_r2': torque_r2_overall + } + } + + # Save using joblib (preferred for sklearn models) + joblib.dump(model_data, 'maglev_model.pkl') + + print("✓ Saved sklearn model: maglev_model.pkl") + print(f"\nFile size: {os.path.getsize('maglev_model.pkl') / 1024:.2f} KB") + print(f"\nContents:") + print(f" - PolynomialFeatures transformer (degree {best_degree})") + print(f" - LinearRegression model") + print(f" - Feature names and metadata") + print(f" - Performance metrics") + print(f"\nTo use:") + print(f"```python") + print(f"import joblib") + print(f"import numpy as np") + print(f"import pandas as pd") + print(f"") + print(f"# Load model") + print(f"data = joblib.load('maglev_model.pkl')") + print(f"poly = data['poly_features']") + print(f"model = data['model']") + print(f"") + print(f"# Make prediction") + print(f"X = pd.DataFrame({{'currL [A]': [-15], 'currR [A]': [-15], ") + print(f" 'rollDeg [deg]': [1.0], 'invGap': [1/10.0]}})") + print(f"X_poly = poly.transform(X)") + print(f"force, torque = model.predict(X_poly)[0]") + print(f"```") + + import time + + # Load the sklearn model + loaded_data = joblib.load('maglev_model.pkl') + loaded_poly = loaded_data['poly_features'] + loaded_model = loaded_data['model'] + + # Create test data (1000 predictions) + n_tests = 1000 + test_currL = np.random.uniform(-15, 0, n_tests) + test_currR = np.random.uniform(-15, 0, n_tests) + test_roll = np.random.uniform(-5, 5, n_tests) + test_gap = np.random.uniform(6, 24, n_tests) + + print("Performance Comparison (1000 predictions)") + print("=" * 60) + + # Benchmark 1: Sklearn model (loaded from pickle) + start = time.perf_counter() + X_test = pd.DataFrame({ + 'currL [A]': test_currL, + 'currR [A]': test_currR, + 'rollDeg [deg]': test_roll, + 'invGap': 1/test_gap + }) + X_test_poly = loaded_poly.transform(X_test) + sklearn_results = loaded_model.predict(X_test_poly) + sklearn_time = time.perf_counter() - start + + print(f"Sklearn Model (pickled): {sklearn_time*1000:.2f} ms") + + # Benchmark 2: Standalone predictor + start = time.perf_counter() + standalone_results = standalone_predictor.predict_batch(test_currL, test_currR, test_roll, test_gap) + standalone_time = time.perf_counter() - start + + print(f"Standalone Predictor: {standalone_time*1000:.2f} ms") + + # Speedup + speedup = standalone_time / sklearn_time + print(f"\nSpeedup: {speedup:.2f}x {'faster' if speedup > 1 else 'slower'} with sklearn model") + + # Verify results match + force_diff = np.abs(sklearn_results[:, 0] - standalone_results[0]).max() + torque_diff = np.abs(sklearn_results[:, 1] - standalone_results[1]).max() + + print(f"\nResult verification:") + print(f" Max force difference: {force_diff:.2e} N") + print(f" Max torque difference: {torque_diff:.2e} mN·m") + print(f" ✓ Results match!" if (force_diff < 1e-8 and torque_diff < 1e-8) else " ✗ Results differ!") + + # Per-prediction timing + print(f"\nPer-prediction time:") + print(f" Sklearn: {sklearn_time/n_tests*1e6:.2f} μs") + print(f" Standalone: {standalone_time/n_tests*1e6:.2f} μs") + + print(f"\n{'='*60}") + print(f"Recommendation:") + if speedup > 1.2: + print(f" Use sklearn model (maglev_model.pkl) - {speedup:.1f}x faster!") + elif speedup < 0.8: + print(f" Use standalone predictor - {1/speedup:.1f}x faster!") + else: + print(f" Performance is similar - choose based on dependencies:") + print(f" • Sklearn model: Faster, requires sklearn+joblib") + print(f" • Standalone: No sklearn dependency, portable") + +[13]. Tool: Claude Code + Prompt: + Now can we use our model to find the equilibrium gab height? ie the gap height at which, + with currents and roll at 0, our force from a single yoke * 2 = the weight of the pod in newtons? + + Output: + # Find equilibrium gap height for 5.8 kg pod using polynomial root finding + import numpy as np + from maglev_predictor import MaglevPredictor + from scipy.optimize import fsolve + + # Initialize predictor + predictor = MaglevPredictor() + + # Target force for 5.8 kg pod (total force = weight) + # Since we have TWO yokes (front and back), each produces this force + target_force_per_yoke = 5.8 * 9.81 / 2 # 28.449 N per yoke + + print("=" * 70) + print("EQUILIBRIUM GAP HEIGHT FINDER (Analytical Solution)") + print("=" * 70) + print(f"Pod mass: 5.8 kg") + print(f"Total weight: {5.8 * 9.81:.3f} N") + print(f"Target force per yoke: {target_force_per_yoke:.3f} N") + print(f"Parameters: currL = 0 A, currR = 0 A, roll = 0°") + print() + + # Method 2: Use scipy.optimize.fsolve for verification + def force_error(gap_height): + # Handle array input from fsolve (convert to scalar) + gap_height = float(np.atleast_1d(gap_height)[0]) + force, _ = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap_height) + return force - target_force_per_yoke + + # Try multiple initial guesses to find all solutions + initial_guesses = [8, 10, 15, 20, 25] + scipy_solutions = [] + + print("using scipy.optimize.fsolve:") + for guess in initial_guesses: + solution = fsolve(force_error, guess)[0] + if 5 <= solution <= 30: # Valid range + force, torque = predictor.predict(currL=0, currR=0, roll=0, gap_height=solution) + error = abs(force - target_force_per_yoke) + if error < 0.01: # Valid solution (within 10 mN) + scipy_solutions.append((solution, force)) + + # Remove duplicates (solutions within 0.1 mm) + unique_solutions = [] + for sol, force in scipy_solutions: + is_duplicate = False + for unique_sol, _ in unique_solutions: + if abs(sol - unique_sol) < 0.1: + is_duplicate = True + break + if not is_duplicate: + unique_solutions.append((sol, force)) + + for gap_val, force in unique_solutions: + print(f" Gap: {gap_val:.6f} mm → Force: {force:.6f} N") + print() + +[14]. Tool: Gemini + Prompt: + Can we make a step function that uses our ansys-based regression model to find + forces on each yoke and apply them to the Bullet simulation? Also, we must + calculate a reward for any given pod position and orientation, prioritizing + stable levitation and penalizing sticking to the track or falling off more heavily + + Output: + def step(self, action): + # Check if PyBullet connection is still active (GUI might be closed) + try: + p.getConnectionInfo(physicsClientId=self.client) + except p.error: + # Connection lost - GUI was closed + return self._get_obs(), -100.0, True, True, {'error': 'GUI closed'} + + # Update Coil Currents from PWM Actions + pwm_front_L = action[0] # yoke +x,+y + pwm_front_R = action[1] # yoke +x,-y + pwm_back_L = action[2] # yoke -x,+y + pwm_back_R = action[3] # yoke -x,-y + + curr_front_L = self.coil_front_L.update(pwm_front_L, self.dt) + curr_front_R = self.coil_front_R.update(pwm_front_R, self.dt) + curr_back_L = self.coil_back_L.update(pwm_back_L, self.dt) + curr_back_R = self.coil_back_R.update(pwm_back_R, self.dt) + + # --- 2. Get Current Pod State --- + pos, orn = p.getBasePositionAndOrientation(self.podId, physicsClientId=self.client) + lin_vel, ang_vel = p.getBaseVelocity(self.podId, physicsClientId=self.client) + + # Convert quaternion to rotation matrix + rot_matrix = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3) + + # --- 3. Calculate Gap Heights at Yoke Positions (for force prediction) --- + # Calculate world positions of the 4 yokes (ends of U-yokes) + yoke_gap_heights_dict = {} # Store by label for easy access + + for i, yoke_idx in enumerate(self.yoke_indices): + local_pos = self.collision_local_positions[yoke_idx] + local_vec = np.array(local_pos) + world_offset = rot_matrix @ local_vec + world_pos = np.array(pos) + world_offset + + # Top surface of yoke box (add half-height = 5mm) + yoke_top_z = world_pos[2] + 0.005 + + # Gap height: track bottom (Z=0) to yoke top (negative Z) + gap_height = -yoke_top_z # Convert to positive gap in meters + yoke_gap_heights_dict[self.yoke_labels[i]] = gap_height + + # Average gap heights for each U-shaped yoke (average left and right ends) + # Front yoke: average of Front_Left and Front_Right + # Back yoke: average of Back_Left and Back_Right + avg_gap_front = (yoke_gap_heights_dict.get('Front_Left', 0.010) + + yoke_gap_heights_dict.get('Front_Right', 0.010)) / 2 + avg_gap_back = (yoke_gap_heights_dict.get('Back_Left', 0.010) + + yoke_gap_heights_dict.get('Back_Right', 0.010)) / 2 + + front_left_gap = yoke_gap_heights_dict.get('Front_Left', 0.010) + front_right_gap = yoke_gap_heights_dict.get('Front_Right', 0.010) + back_left_gap = yoke_gap_heights_dict.get('Back_Left', 0.010) + back_right_gap = yoke_gap_heights_dict.get('Back_Right', 0.010) + + y_distance = 0.1016 # 2 * 0.0508m (left to right distance) + x_distance = 0.2518 # 2 * 0.1259m (front to back distance) + + # Roll angle + # When right side has larger gap, roll is negative + roll_angle_front = np.arcsin(-(front_right_gap - front_left_gap) / y_distance) + roll_angle_back = np.arcsin(-(back_right_gap - back_left_gap) / y_distance) + roll_angle = (roll_angle_front + roll_angle_back) / 2 + + # When back has larger gap, pitch is positive + pitch_angle_left = np.arcsin((back_left_gap - front_left_gap) / x_distance) + pitch_angle_right = np.arcsin((back_right_gap - front_right_gap) / x_distance) + pitch_angle = (pitch_angle_left + pitch_angle_right) / 2 + + # Predict Forces and Torques using Maglev Predictor + # Gap heights in mm + gap_front_mm = avg_gap_front * 1000 + gap_back_mm = avg_gap_back * 1000 + + # Roll angle in degrees + roll_deg = np.degrees(roll_angle) + + # Predict force and torque for each U-shaped yoke + # Front yoke + force_front, torque_front = self.predictor.predict( + curr_front_L, curr_front_R, roll_deg, gap_front_mm + ) + + # Back yoke + force_back, torque_back = self.predictor.predict( + curr_back_L, curr_back_R, roll_deg, gap_back_mm + ) + + # --- 5. Apply Forces and Torques to Pod --- + # Forces are applied at Y=0 (center of U-yoke) at each X position + # This is where the actual magnetic force acts on the U-shaped yoke + + # Apply force at front yoke center (X=+0.1259, Y=0) + front_yoke_center = [0.1259, 0, 0.08585] # From pod.xml yoke positions + p.applyExternalForce( + self.podId, -1, + forceObj=[0, 0, force_front], + posObj=front_yoke_center, + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + # Apply force at back yoke center (X=-0.1259, Y=0) + back_yoke_center = [-0.1259, 0, 0.08585] + p.applyExternalForce( + self.podId, -1, + forceObj=[0, 0, force_back], + posObj=back_yoke_center, + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + + + # Apply roll torques + # Each yoke produces its own torque about X axis + torque_front_Nm = torque_front / 1000 # Convert from mN·m to N·m + torque_back_Nm = torque_back / 1000 + + # Apply torques at respective yoke positions + p.applyExternalTorque( + self.podId, -1, + torqueObj=[torque_front_Nm, 0, 0], + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + p.applyExternalTorque( + self.podId, -1, + torqueObj=[torque_back_Nm, 0, 0], + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + # --- 6. Step Simulation --- + p.stepSimulation(physicsClientId=self.client) + self.current_step += 1 + + # Check for physical contact with track (bolts touching) + contact_points = p.getContactPoints(bodyA=self.podId, bodyB=self.trackId, physicsClientId=self.client) + has_contact = len(contact_points) > 0 + + # --- 7. Get New Observation --- + obs = self._get_obs() + + # --- 8. Calculate Reward --- + # Goal: Hover at target gap (16.5mm), minimize roll/pitch, minimize power + target_gap = TARGET_GAP # 16.5mm in meters + avg_gap = (avg_gap_front + avg_gap_back) / 2 + + gap_error = abs(avg_gap - target_gap) + + # Power dissipation (all 4 coils) + power = (curr_front_L**2 * self.coil_front_L.R + + curr_front_R**2 * self.coil_front_R.R + + curr_back_L**2 * self.coil_back_L.R + + curr_back_R**2 * self.coil_back_R.R) + + # --- Improved Reward Function --- + # Use reward shaping with reasonable scales to enable learning + + # 1. Gap Error Reward (most important) + # Use exponential decay for smooth gradient near target + gap_error_mm = gap_error * 1000 # Convert to mm + gap_reward = 10.0 * np.exp(-0.5 * (gap_error_mm / 3.0)**2) # Peak at 0mm error, 3mm std dev + + # 2. Orientation Penalties (smaller scale) + roll_penalty = abs(np.degrees(roll_angle)) * 0.02 + pitch_penalty = abs(np.degrees(pitch_angle)) * 0.02 + + # 3. Velocity Penalty (discourage rapid oscillations) + z_velocity = lin_vel[2] + velocity_penalty = abs(z_velocity) * 0.1 + + # 4. Contact Penalty + contact_points = p.getContactPoints(bodyA=self.podId, bodyB=self.trackId) + contact_penalty = len(contact_points) * 0.2 + + # 5. Power Penalty (encourage efficiency, but small weight) + power_penalty = power * 0.001 + + # Combine rewards (scaled to ~[-5, +1] range per step) + reward = gap_reward - roll_penalty - pitch_penalty - velocity_penalty - contact_penalty - power_penalty + + # Check Termination (tighter bounds for safety) + terminated = False + truncated = False + + # Terminate if gap is too small (crash) or too large (lost) + if avg_gap < 0.003 or avg_gap > 0.035: + terminated = True + reward = -10.0 # Failure penalty (scaled down) + + # Terminate if orientation is too extreme + if abs(roll_angle) > np.radians(15) or abs(pitch_angle) > np.radians(15): + terminated = True + reward = -10.0 + + # Success bonus for stable hovering near target + if gap_error_mm < 1.0 and abs(np.degrees(roll_angle)) < 2.0 and abs(np.degrees(pitch_angle)) < 2.0: + reward += 2.0 # Bonus for excellent control + + info = { + 'curr_front_L': curr_front_L, + 'curr_front_R': curr_front_R, + 'curr_back_L': curr_back_L, + 'curr_back_R': curr_back_R, + 'gap_front_yoke': avg_gap_front, + 'gap_back_yoke': avg_gap_back, + 'roll': roll_angle, + 'force_front': force_front, + 'force_back': force_back, + 'torque_front': torque_front, + 'torque_back': torque_back + } + + return obs, reward, terminated, truncated, info + +[15]. Tool: Gemini + Prompt: + Let's also have an observation function that constructs the input vector that the RL agent sees. + This should consist of the noisy sensor readings and the velocities of each of these sensors. + They should be normalized in a way that is friendly to a neural network, ie floating point values + on [-1,1] ideally. + + Output: + def _get_obs(self, initial_reset=False): + """ + Returns observation: [gaps(4), velocities(4)] + Uses noisy sensor readings + computed velocities for microcontroller-friendly deployment + """ + pos, orn = p.getBasePositionAndOrientation(self.podId, physicsClientId=self.client) + + # Convert quaternion to rotation matrix + rot_matrix = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3) + + # Calculate sensor gap heights with noise + sensor_gap_heights = {} + + for i, sensor_idx in enumerate(self.sensor_indices): + local_pos = self.collision_local_positions[sensor_idx] + local_vec = np.array(local_pos) + world_offset = rot_matrix @ local_vec + world_pos = np.array(pos) + world_offset + + # Top surface of sensor (add half-height = 5mm) + sensor_top_z = world_pos[2] + 0.005 + + # Gap height: track bottom (Z=0) to sensor top + gap_height = -sensor_top_z + + # Add measurement noise + noisy_gap = gap_height + np.random.normal(0, self.sensor_noise_std) + sensor_gap_heights[self.sensor_labels[i]] = noisy_gap + sensor_gap_heights[self.sensor_labels[i]] = gap_height + + # Pack sensor measurements in consistent order + # [center_right, center_left, front, back] + gaps = np.array([ + sensor_gap_heights.get('Center_Right', 0.010), + sensor_gap_heights.get('Center_Left', 0.010), + sensor_gap_heights.get('Front', 0.010), + sensor_gap_heights.get('Back', 0.010) + ], dtype=np.float32) + + # Compute velocities (d_gap/dt) + if initial_reset or (self.prev_sensor_gaps is None): + # First observation - no velocity information yet + velocities = np.zeros(4, dtype=np.float32) + else: + # Compute velocity as finite difference + velocities = (gaps - self.prev_sensor_gaps) / self.dt + + # Store for next step + self.prev_sensor_gaps = gaps.copy() + + # Normalize observations + gaps_normalized = (gaps - TARGET_GAP) / self.gap_scale + velocities_normalized = velocities / self.velocity_scale + + # Concatenate: [normalized_gaps, normalized_velocities] + obs = np.concatenate([gaps_normalized, velocities_normalized]) + + return obs + +[16]. Tool: Gemini + Prompt: + I now need a function that will allow the initial height setting to modify + the position of the bolts in the Bullet sim, mirroring how the rig is set + up in real life, as detailed in previous chats. + + Output: + def _create_modified_urdf(self): + """ + Create a modified URDF with bolt positions adjusted based on initial gap height. + Bolts are at Z = 0.08585 + gap_mm/1000 (relative to pod origin). + Yokes and sensors remain at Z = 0.08585 (relative to pod origin). + """ + import tempfile + + # Calculate bolt Z position + bolt_z = 0.08585 + self.initial_gap_mm / 1000.0 + + # Read the original URDF template + urdf_template_path = os.path.join(os.path.dirname(__file__), "pod.xml") + with open(urdf_template_path, 'r') as f: + urdf_content = f.read() + + # Replace the bolt Z positions (originally at 0.09585) + # There are 4 bolts at different X,Y positions but same Z + urdf_modified = urdf_content.replace( + 'xyz="0.285 0.03 0.09585"', + f'xyz="0.285 0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="0.285 -0.03 0.09585"', + f'xyz="0.285 -0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="-0.285 0.03 0.09585"', + f'xyz="-0.285 0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="-0.285 -0.03 0.09585"', + f'xyz="-0.285 -0.03 {bolt_z:.6f}"' + ) + + # Write to a temporary file + with tempfile.NamedTemporaryFile(mode='w', suffix='.urdf', delete=False) as f: + f.write(urdf_modified) + temp_urdf_path = f.name + + return temp_urdf_path + +[17]. Tool: Gemini + Prompt: + Can you generate a python class that simulates the coil inductance and resistance, + taking in a PWM duty cycle and applying a voltage based on that duty cycle to get + an output current? + + Output: + class MagLevCoil: + def __init__(self, r_resistance, l_inductance, source_voltage, maxCurrent): + self.R = r_resistance + self.L = l_inductance + self.current = 0.0 + self.Vs = source_voltage + self.Imax = maxCurrent + + def update(self, pwm_duty_cycle, dt): + """ + Simulates the coil circuit and force generation. + pwm_duty_cycle: -1.0 to 1.0 + """ + # Simple First-order RL circuit approximation + # V_in = Duty * V_source + voltage = pwm_duty_cycle * self.Vs + + # di/dt = (V - I*R) / L + di = (voltage - self.current * self.R) / self.L + self.current += di * dt + self.current = min(max(-self.Imax, self.current), self.Imax) + + return self.current + +[18]. Tool: Claude Code + Prompt: + Can you generate a testing script that allows me to input a starting height + and dummy PWM values to the levitation environment so I can do a sanity check + and see if the environment behaves correctly? + + Output: + """ + Test script for LevPodEnv + Runs a simple episode with constant actions to verify the environment works + """ + + from lev_pod_env import LevPodEnv + import numpy as np + import time + + # Create environment with GUI for visualization + env = LevPodEnv(use_gui=True, initial_gap_mm=15) + + print("=" * 60) + print("Testing LevPodEnv") + print("=" * 60) + print(f"Action space: {env.action_space}") + print(f" 4 PWM duty cycles: [front_L, front_R, back_L, back_R]") + print(f"Observation space: {env.observation_space}") + print(f" 8 values: [gaps(4), velocities(4)]") + print("=" * 60) + + # Reset environment + obs, info = env.reset() + print(f"\nInitial observation:") + print(f" Gaps: CR={obs[0]*1000:.2f}mm, CL={obs[1]*1000:.2f}mm, F={obs[2]*1000:.2f}mm, B={obs[3]*1000:.2f}mm") + print(f" Velocities: CR={obs[4]*1000:.2f}mm/s, CL={obs[5]*1000:.2f}mm/s, F={obs[6]*1000:.2f}mm/s, B={obs[7]*1000:.2f}mm/s") + print(f" Average gap: {np.mean(obs[:4])*1000:.2f} mm") + + # Run a few steps with constant action to test force application + print("\nRunning test episode...") + for step in range(500): + # Apply constant moderate PWM to all 4 coils + # 50% PWM should generate current that produces upward force + action = np.array([0,0,0,0], dtype=np.float32) + + obs, reward, terminated, truncated, info = env.step(action) + + if step % 5 == 0: + print(f"\nStep {step} (t={step/240:.2f}s):") + print(f" Sensor gaps: CR={obs[0]*1000:.2f}mm, CL={obs[1]*1000:.2f}mm, " + + f"F={obs[2]*1000:.2f}mm, B={obs[3]*1000:.2f}mm") + print(f" Velocities: CR={obs[4]*1000:.2f}mm/s, CL={obs[5]*1000:.2f}mm/s, " + + f"F={obs[6]*1000:.2f}mm/s, B={obs[7]*1000:.2f}mm/s") + print(f" Yoke gaps: front={info['gap_front_yoke']*1000:.2f}mm, back={info['gap_back_yoke']*1000:.2f}mm") + print(f" Roll: {np.degrees(info['roll']):.2f}°") + print(f" Currents: FL={info['curr_front_L']:.2f}A, FR={info['curr_front_R']:.2f}A, " + + f"BL={info['curr_back_L']:.2f}A, BR={info['curr_back_R']:.2f}A") + print(f" Forces: front={info['force_front']:.2f}N, back={info['force_back']:.2f}N") + print(f" Torques: front={info['torque_front']:.2f}mN·m, back={info['torque_back']:.2f}mN·m") + print(f" Reward: {reward:.2f}") + + if terminated or truncated: + print(f"\nEpisode terminated at step {step}") + break + + time.sleep(0.01) + + print("\n" + "=" * 60) + print("Test complete!") + print("=" * 60) + + env.close() + +[19]. Tool: Claude Code + Prompt: + Can we intialize the existing model structure in the rest of the file + around our use case, with the environment being the lev_pod_env? Additionally, + I want to be able to see what the theoretical maximum reward is, so I can see how + well the agent is performing over time without having to use the GUI simulation + environment. + + Output: + environ = LevPodEnv(use_gui=False, initial_gap_mm=14, max_steps=500) # Start below target + model = ActorCriticNetwork(environ.observation_space.shape[0], environ.action_space.shape[0]).to(device) + train_data, reward, gap_error = rollout(model, environ) + print("Reward:", reward) + print("Episode length:", len(train_data[0])) + print("Reward per step:", reward / len(train_data[0])) + print(f"Average gap error: {gap_error:.3f} mm") + + # Calculate theoretical maximum reward for reference (NEW REWARD STRUCTURE) + max_steps = 500 + # Best case: gap_reward=1.0, no penalties, success_bonus=2.0 + theoretical_max_per_step = 3.0 # 1.0 (gap) + 2.0 (success bonus) + 0 (no penalties) + # Realistic good case: gap_reward~0.9, small penalties + realistic_good_per_step = 0.8 + theoretical_max_total = theoretical_max_per_step * max_steps + realistic_good_total = realistic_good_per_step * max_steps + + print(f"\nTheoretical Maximum:") + print(f" Best per step: {theoretical_max_per_step:.2f}") + print(f" Realistic good per step: {realistic_good_per_step:.2f}") + print(f" Best total (500 steps): {theoretical_max_total:.0f}") + print(f" Current % of realistic: {(reward/realistic_good_total)*100:.1f}%") + +[20]. Tool: Claude Code + Prompt: + Can we improve our reward function, and see how it drops off as the gap error increases? + It seems the pod is getting too comfortable sitting at the track or just falling away and + not learning to levitate at a very specific gap height. + + Output: + # Visualize the new reward function + gap_errors_mm = np.linspace(0, 20, 100) + gap_rewards = np.exp(-0.5 * (gap_errors_mm / 3.0)**2) + + plt.figure(figsize=(10, 6)) + plt.plot(gap_errors_mm, gap_rewards, linewidth=2) + plt.axvline(x=1.0, color='g', linestyle='--', label='Success bonus threshold (1mm)') + plt.axvline(x=3.0, color='orange', linestyle='--', label='1 std dev (3mm)') + plt.xlabel('Gap Error (mm)', fontsize=12) + plt.ylabel('Gap Reward Component', fontsize=12) + plt.title('New Reward Function: Smooth Exponential Decay', fontsize=14, fontweight='bold') + plt.grid(True, alpha=0.3) + plt.legend() + plt.tight_layout() + plt.show() + + print("Reward examples at different gap errors:") + for err in [0, 1, 3, 5, 10, 15]: + reward = np.exp(-0.5 * (err / 3.0)**2) + print(f" {err:2d}mm error → {reward:.4f} reward") + print("\nCompare to old reward: (error*100)² would be:") + for err in [1, 3, 5, 10]: + old_penalty = (err * 100)**2 + print(f" {err:2d}mm error → -{old_penalty:,} penalty") + +[21]. Tool: Claude Code + Prompt: + I want to store log files that show the accumulated reward over time + for each trial, as well as a mean gap error chart that should show whether + the model is learning to control at 0 gap error. + + Output: + import os + from datetime import datetime + + # Create RL_Trials folder if it doesn't exist + os.makedirs('RL_Trials', exist_ok=True) + + # Create timestamped log file + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_file_path = f'RL_Trials/training_log_{timestamp}.txt' + plot_file_path = f'RL_Trials/gap_error_plot_{timestamp}.png' + + # Define training params + num_episodes = 2000 # Increased for more learning time + print_freq = 20 # Print less frequently + gui_freq = 100 # Show GUI every 100 episodes + + # Create PPO trainer with improved hyperparameters + ppo = PPOTrainer( + model, + policy_lr=5e-4, # Higher learning rate + value_lr=1e-3, # Even higher for value function + target_kl_div=0.02, # Allow more policy updates + max_policy_train_iters=40, + value_train_iters=40, + entropy_coef=0.02 # More exploration + ) + + # Open log file + log_file = open(log_file_path, 'w') + log_file.write(f"Training Started: {timestamp}\n") + log_file.write(f"Number of Episodes: {num_episodes}\n") + log_file.write(f"Print Frequency: {print_freq}\n") + log_file.write(f"Target Gap Height: {16.491741} mm\n") + log_file.write(f"Network: 256 hidden units with LayerNorm\n") + log_file.write(f"Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02\n") + log_file.write("="*70 + "\n\n") + log_file.flush() + + print(f"Logging to: {log_file_path}") + print(f"Plot will be saved to: {plot_file_path}") + + # Create and save gap error plot + plt.figure(figsize=(12, 6)) + plt.plot(ep_gap_errors, alpha=0.3, label='Per Episode') + + # Calculate moving average (window size = print_freq) + window_size = print_freq + if len(ep_gap_errors) >= window_size: + moving_avg = np.convolve(ep_gap_errors, np.ones(window_size)/window_size, mode='valid') + plt.plot(range(window_size-1, len(ep_gap_errors)), moving_avg, linewidth=2, label=f'{window_size}-Episode Moving Average') + + plt.xlabel('Episode', fontsize=12) + plt.ylabel('Average Gap Height Error (mm)', fontsize=12) + plt.title('Gap Height Error Over Training, n_steps=500', fontsize=14, fontweight='bold') + plt.legend(fontsize=11) + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.savefig(plot_file_path, dpi=150, bbox_inches='tight') + plt.show() + + print(f"\n✓ Training log saved to: {log_file_path}") + print(f"✓ Gap error plot saved to: {plot_file_path}") \ No newline at end of file diff --git a/lev_PPO.ipynb b/lev_PPO.ipynb new file mode 100644 index 0000000..bd14db2 --- /dev/null +++ b/lev_PPO.ipynb @@ -0,0 +1,795 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "57963548", + "metadata": {}, + "source": [ + "# Improved PPO Training for Maglev Pod\n", + "\n", + "## Major Changes to Enable Learning:\n", + "\n", + "### 1. **Reward Function (lev_pod_env.py)**\n", + "**Problem**: Squared penalties created rewards of -7000 to -8200, making learning impossible\n", + "- `(gap_error * 100)²` could reach 10,000+ for small errors\n", + "- +1.0 survival bonus was meaningless compared to penalties\n", + "\n", + "**Solution**: Exponential reward shaping with reasonable scales\n", + "- Gap reward: `exp(-0.5 * (error/3mm)²)` → smooth 0 to 1.0 range\n", + "- Small linear penalties for orientation (~0.02/degree)\n", + "- Success bonus: +2.0 for excellent hovering (gap < 1mm, angles < 2°)\n", + "- **New reward range: -10 to +3 per step** (was -8200 to +1 total)\n", + "\n", + "### 2. **Network Architecture**\n", + "**Changes**:\n", + "- Increased hidden units: 128 → 256\n", + "- Added LayerNorm for training stability\n", + "- Deeper shared layers (3 layers instead of 2)\n", + "- Better initialization for exploration\n", + "\n", + "### 3. **Training Hyperparameters**\n", + "**Changes**:\n", + "- Policy LR: 3e-4 → 5e-4 (faster learning)\n", + "- Value LR: 3e-4 → 1e-3 (even faster value updates)\n", + "- Entropy coefficient: 0.01 → 0.02 (more exploration)\n", + "- Added gradient clipping (max norm 0.5)\n", + "- GAE lambda: 0.97 → 0.95 (less biased advantage estimates)\n", + "- Episodes: 1000 → 2000\n", + "\n", + "### 4. **Termination Conditions**\n", + "**Tightened for safety**:\n", + "- Gap bounds: 2-40mm → 3-35mm\n", + "- Angle tolerance: 20° → 15°\n", + "- Failure penalty: -50 → -10 (scaled with new rewards)\n", + "\n", + "## Expected Behavior:\n", + "- **Rewards should be positive or mildly negative** during good episodes\n", + "- **Gap error should steadily decrease** from initial ~15mm toward target 16.49mm\n", + "- **Episodes that reach 500 steps** indicate successful hovering\n", + "- **Look for improvement over first 500 episodes**, then fine-tuning after" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f28b2866", + "metadata": {}, + "outputs": [], + "source": [ + "import gymnasium as gym\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import torch\n", + "from torch import nn\n", + "from torch import optim\n", + "from torch.distributions import Normal\n", + "from lev_pod_env import LevPodEnv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c49c95b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Torch Version: 2.8.0\n", + "CUDA Available: True\n", + "Using device: cuda\n" + ] + } + ], + "source": [ + "print(\"Torch Version:\", torch.__version__)\n", + "print(\"CUDA Available:\", torch.cuda.is_available())\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(\"Using device:\", device)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "70bb54d4", + "metadata": {}, + "outputs": [], + "source": [ + "class ActorCriticNetwork(nn.Module):\n", + " def __init__(self, state_dim, action_dim, hidden_dim=256):\n", + " super().__init__()\n", + " # Larger network with layer normalization for better learning\n", + " self.shared_layers = nn.Sequential(\n", + " nn.Linear(state_dim, hidden_dim),\n", + " nn.LayerNorm(hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.LayerNorm(hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim // 2),\n", + " nn.ReLU()\n", + " )\n", + " # Policy outputs mean and log_std for continuous actions\n", + " self.policy_mean = nn.Sequential(\n", + " nn.Linear(hidden_dim // 2, hidden_dim // 2),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim // 2, action_dim),\n", + " nn.Tanh() # Constrain to [-1, 1] range\n", + " )\n", + " # Initialize log_std to encourage exploration initially\n", + " self.policy_log_std = nn.Parameter(torch.ones(action_dim) * -0.5)\n", + " \n", + " self.value_layers = nn.Sequential(\n", + " nn.Linear(hidden_dim // 2, hidden_dim // 2),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim // 2, 1),\n", + " )\n", + "\n", + " def value(self, observation):\n", + " shared_output = self.shared_layers(observation)\n", + " state_value = self.value_layers(shared_output)\n", + " return state_value\n", + " \n", + " def policy(self, observation):\n", + " shared_output = self.shared_layers(observation)\n", + " mean = self.policy_mean(shared_output)\n", + " std = torch.exp(self.policy_log_std)\n", + " return mean, std\n", + " \n", + " def forward(self, state):\n", + " shared_output = self.shared_layers(state)\n", + " mean = self.policy_mean(shared_output)\n", + " std = torch.exp(self.policy_log_std)\n", + " state_value = self.value_layers(shared_output)\n", + " return mean, std, state_value" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2ed37deb", + "metadata": {}, + "outputs": [], + "source": [ + "class PPOTrainer():\n", + " def __init__(self, actor_critic, ppo_clip_val=0.2, target_kl_div=0.02,\n", + " max_policy_train_iters=40, value_train_iters=40, policy_lr=5e-4, value_lr=1e-3, \n", + " entropy_coef=0.02):\n", + " self.ac = actor_critic\n", + " self.ppo_clip_val = ppo_clip_val\n", + " self.target_kl_div = target_kl_div\n", + " self.max_policy_train_iters = max_policy_train_iters\n", + " self.value_train_iters = value_train_iters\n", + " self.entropy_coef = entropy_coef\n", + "\n", + " policy_params = list(self.ac.shared_layers.parameters()) + \\\n", + " list(self.ac.policy_mean.parameters()) + \\\n", + " [self.ac.policy_log_std]\n", + " self.policy_optimizer = optim.Adam(policy_params, lr=policy_lr)\n", + "\n", + " value_params = list(self.ac.shared_layers.parameters()) + \\\n", + " list(self.ac.value_layers.parameters())\n", + " self.value_optimizer = optim.Adam(value_params, lr=value_lr)\n", + "\n", + " def train_policy(self, obs, acts, old_log_probs, gaes):\n", + " for _ in range(self.max_policy_train_iters):\n", + " self.policy_optimizer.zero_grad()\n", + "\n", + " new_mean, new_std = self.ac.policy(obs)\n", + " new_dist = Normal(new_mean, new_std)\n", + " new_log_probs = new_dist.log_prob(acts).sum(dim=-1)\n", + "\n", + " policy_ratio = torch.exp(new_log_probs - old_log_probs)\n", + " clipped_ratio = policy_ratio.clamp(1 - self.ppo_clip_val, 1 + self.ppo_clip_val)\n", + "\n", + " clipped_loss = clipped_ratio * gaes\n", + " unclipped_loss = policy_ratio * gaes\n", + "\n", + " policy_loss = -torch.min(clipped_loss, unclipped_loss).mean()\n", + " \n", + " # Increased entropy bonus to encourage more exploration\n", + " entropy = new_dist.entropy().mean()\n", + " policy_loss = policy_loss - self.entropy_coef * entropy\n", + "\n", + " policy_loss.backward()\n", + " # Gradient clipping for stability\n", + " torch.nn.utils.clip_grad_norm_(self.policy_optimizer.param_groups[0]['params'], 0.5)\n", + " self.policy_optimizer.step()\n", + "\n", + " kl_div = (old_log_probs - new_log_probs).mean()\n", + " if kl_div > self.target_kl_div:\n", + " break\n", + " \n", + " def train_value(self, obs, returns):\n", + " for _ in range(self.value_train_iters):\n", + " self.value_optimizer.zero_grad()\n", + "\n", + " values = self.ac.value(obs)\n", + " value_loss = (returns - values).pow(2).mean()\n", + " \n", + " value_loss.backward()\n", + " # Gradient clipping for stability\n", + " torch.nn.utils.clip_grad_norm_(self.value_optimizer.param_groups[0]['params'], 0.5)\n", + " self.value_optimizer.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f4f9f4b", + "metadata": {}, + "outputs": [], + "source": [ + "def discount_rewards(rewards, gamma=0.99):\n", + " discounted = np.zeros_like(rewards, dtype=np.float32)\n", + " running_add = 0\n", + " for t in reversed(range(len(rewards))):\n", + " running_add = running_add * gamma + rewards[t]\n", + " discounted[t] = running_add\n", + " return discounted\n", + "\n", + "def calculate_gaes(rewards, values, gamma=0.99, lam=0.95):\n", + " # Add 0 for terminal state bootstrap value\n", + " next_values = np.concatenate([values[1:], [0]])\n", + " deltas = rewards + gamma * next_values - values\n", + " gaes = discount_rewards(deltas, gamma * lam)\n", + " return gaes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d7b17705", + "metadata": {}, + "outputs": [], + "source": [ + "def rollout(model, env, max_steps=500): \n", + " train_data = [[],[],[],[],[]] # obs, actions, rewards, values, log_probs\n", + " gap_heights = [] # Track gap heights during episode\n", + " obs, _ = env.reset() # Gymnasium returns (obs, info)\n", + " \n", + " ep_reward = 0\n", + " for _ in range(max_steps):\n", + " with torch.no_grad(): # No gradients needed during rollout\n", + " mean, std, val = model(torch.tensor([obs], dtype=torch.float32, device=device))\n", + "\n", + " # Sample continuous action from Normal distribution\n", + " act_distribution = Normal(mean, std)\n", + " act = act_distribution.sample()\n", + " act_log_prob = act_distribution.log_prob(act).sum(dim=-1)\n", + " \n", + " # Convert to numpy array for environment\n", + " act_np = act.squeeze(0).cpu().numpy()\n", + " next_obs, reward, terminated, truncated, _ = env.step(act_np)\n", + " \n", + " # Extract gap heights from observation (first 4 values are normalized gaps)\n", + " # Denormalize gaps: multiply by gap_scale (0.015m = 15mm)\n", + " gap_heights.append(obs[:4] * env.gap_scale * 1000) # Convert to mm\n", + "\n", + " # Store as Python scalars (moving to CPU only when necessary)\n", + " for i, item in enumerate([obs, act_np, reward, val.item(), act_log_prob.item()]):\n", + " train_data[i].append(item)\n", + " \n", + " obs = next_obs\n", + " ep_reward += reward\n", + " done = terminated or truncated\n", + "\n", + " if done:\n", + " break\n", + " \n", + " train_data = [np.array(x, dtype=np.float32) for x in train_data]\n", + " train_data[3] = calculate_gaes(rewards=train_data[2], values=train_data[3])\n", + " \n", + " # Calculate average gap height error\n", + " gap_heights = np.array(gap_heights) # Shape: (steps, 4 sensors)\n", + " avg_gap_per_step = gap_heights.mean(axis=1) # Average across 4 sensors\n", + " target_gap_mm = 16.491741\n", + " avg_gap_error = np.abs(avg_gap_per_step - target_gap_mm).mean()\n", + "\n", + " return train_data, ep_reward, avg_gap_error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ee8fb34", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading maglev model from maglev_model.pkl...\n", + "Model loaded. Degree: 6\n", + "Force R2: 1.0000\n", + "Torque R2: 0.9999\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\pulip\\AppData\\Local\\Temp\\ipykernel_32220\\3744901434.py:9: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\bld\\libtorch_1762089177580\\work\\torch\\csrc\\utils\\tensor_new.cpp:256.)\n", + " mean, std, val = model(torch.tensor([obs], dtype=torch.float32, device=device))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reward: 1990.308654679309\n", + "Episode length: 500\n", + "Reward per step: 3.980617309358618\n", + "Average gap error: 19.005 mm\n", + "\n", + "Theoretical Maximum:\n", + " Best per step: 3.00\n", + " Realistic good per step: 0.80\n", + " Best total (500 steps): 1500\n", + " Current % of realistic: 497.6%\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [19]\n", + "environ = LevPodEnv(use_gui=False, initial_gap_mm=14, max_steps=500) # Start below target\n", + "model = ActorCriticNetwork(environ.observation_space.shape[0], environ.action_space.shape[0]).to(device)\n", + "train_data, reward, gap_error = rollout(model, environ)\n", + "print(\"Reward:\", reward)\n", + "print(\"Episode length:\", len(train_data[0]))\n", + "print(\"Reward per step:\", reward / len(train_data[0]))\n", + "print(f\"Average gap error: {gap_error:.3f} mm\")\n", + "\n", + "# Calculate theoretical maximum reward for reference (NEW REWARD STRUCTURE)\n", + "max_steps = 500\n", + "# Best case: gap_reward=1.0, no penalties, success_bonus=2.0\n", + "theoretical_max_per_step = 3.0 # 1.0 (gap) + 2.0 (success bonus) + 0 (no penalties)\n", + "# Realistic good case: gap_reward~0.9, small penalties\n", + "realistic_good_per_step = 0.8\n", + "theoretical_max_total = theoretical_max_per_step * max_steps\n", + "realistic_good_total = realistic_good_per_step * max_steps\n", + "\n", + "print(f\"\\nTheoretical Maximum:\")\n", + "print(f\" Best per step: {theoretical_max_per_step:.2f}\")\n", + "print(f\" Realistic good per step: {realistic_good_per_step:.2f}\")\n", + "print(f\" Best total (500 steps): {theoretical_max_total:.0f}\")\n", + "print(f\" Current % of realistic: {(reward/realistic_good_total)*100:.1f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "1b8b1ca2", + "metadata": {}, + "source": [ + "## Key Improvements Made:\n", + "\n", + "1. **Better Reward Scaling**: Changed from squared penalties (up to 10,000+) to exponential rewards (~0 to 1) with smaller linear penalties\n", + "2. **Larger Network**: Increased from 128 to 256 hidden units with LayerNorm for better learning capacity\n", + "3. **More Exploration**: Increased entropy coefficient from 0.01 to 0.02, initialized log_std higher\n", + "4. **Gradient Clipping**: Added to prevent exploding gradients\n", + "5. **Higher Learning Rates**: Increased policy LR to 5e-4 and value LR to 1e-3\n", + "6. **Tighter Termination**: Reduced angle tolerance to 15° and gap bounds to 3-35mm\n", + "7. **Success Bonus**: Added +2.0 reward for excellent hovering (gap < 1mm, angles < 2°)\n", + "\n", + "Expected improvements:\n", + "- Rewards should now be in range [-10, +3] instead of [-8000, +1]\n", + "- Model should learn meaningful distinctions between good and bad states\n", + "- Training should show steady improvement in gap error over episodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6f27ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reward examples at different gap errors:\n", + " 0mm error → 1.0000 reward\n", + " 1mm error → 0.9460 reward\n", + " 3mm error → 0.6065 reward\n", + " 5mm error → 0.2494 reward\n", + " 10mm error → 0.0039 reward\n", + " 15mm error → 0.0000 reward\n", + "\n", + "Compare to old reward: (error*100)² would be:\n", + " 1mm error → -10,000 penalty\n", + " 3mm error → -90,000 penalty\n", + " 5mm error → -250,000 penalty\n", + " 10mm error → -1,000,000 penalty\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [20]\n", + "# Visualize the new reward function\n", + "gap_errors_mm = np.linspace(0, 20, 100)\n", + "gap_rewards = np.exp(-0.5 * (gap_errors_mm / 3.0)**2)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(gap_errors_mm, gap_rewards, linewidth=2)\n", + "plt.axvline(x=1.0, color='g', linestyle='--', label='Success bonus threshold (1mm)')\n", + "plt.axvline(x=3.0, color='orange', linestyle='--', label='1 std dev (3mm)')\n", + "plt.xlabel('Gap Error (mm)', fontsize=12)\n", + "plt.ylabel('Gap Reward Component', fontsize=12)\n", + "plt.title('New Reward Function: Smooth Exponential Decay', fontsize=14, fontweight='bold')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"Reward examples at different gap errors:\")\n", + "for err in [0, 1, 3, 5, 10, 15]:\n", + " reward = np.exp(-0.5 * (err / 3.0)**2)\n", + " print(f\" {err:2d}mm error → {reward:.4f} reward\")\n", + "print(\"\\nCompare to old reward: (error*100)² would be:\")\n", + "for err in [1, 3, 5, 10]:\n", + " old_penalty = (err * 100)**2\n", + " print(f\" {err:2d}mm error → -{old_penalty:,} penalty\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb554183", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logging to: RL_Trials/training_log_20251211_191801.txt\n", + "Plot will be saved to: RL_Trials/gap_error_plot_20251211_191801.png\n" + ] + } + ], + "source": [ + "# The following was generated by AI - see [21]\n", + "import os\n", + "from datetime import datetime\n", + "\n", + "# Create RL_Trials folder if it doesn't exist\n", + "os.makedirs('RL_Trials', exist_ok=True)\n", + "\n", + "# Create timestamped log file\n", + "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", + "log_file_path = f'RL_Trials/training_log_{timestamp}.txt'\n", + "plot_file_path = f'RL_Trials/gap_error_plot_{timestamp}.png'\n", + "\n", + "# Define training params\n", + "num_episodes = 2000 # Increased for more learning time\n", + "print_freq = 20 # Print less frequently\n", + "gui_freq = 100 # Show GUI every 100 episodes\n", + "\n", + "# Create PPO trainer with improved hyperparameters\n", + "ppo = PPOTrainer(\n", + " model, \n", + " policy_lr=5e-4, # Higher learning rate\n", + " value_lr=1e-3, # Even higher for value function\n", + " target_kl_div=0.02, # Allow more policy updates\n", + " max_policy_train_iters=40,\n", + " value_train_iters=40,\n", + " entropy_coef=0.02 # More exploration\n", + ")\n", + "\n", + "# Open log file\n", + "log_file = open(log_file_path, 'w')\n", + "log_file.write(f\"Training Started: {timestamp}\\n\")\n", + "log_file.write(f\"Number of Episodes: {num_episodes}\\n\")\n", + "log_file.write(f\"Print Frequency: {print_freq}\\n\")\n", + "log_file.write(f\"Target Gap Height: {16.491741} mm\\n\")\n", + "log_file.write(f\"Network: 256 hidden units with LayerNorm\\n\")\n", + "log_file.write(f\"Policy LR: 5e-4, Value LR: 1e-3, Entropy: 0.02\\n\")\n", + "log_file.write(\"=\"*70 + \"\\n\\n\")\n", + "log_file.flush()\n", + "\n", + "print(f\"Logging to: {log_file_path}\")\n", + "print(f\"Plot will be saved to: {plot_file_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "64994dcf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading maglev model from maglev_model.pkl...\n", + "Model loaded. Degree: 6\n", + "Force R2: 1.0000\n", + "Torque R2: 0.9999\n" + ] + } + ], + "source": [ + "environ = LevPodEnv(use_gui=True, initial_gap_mm=14, max_steps=500) # Start below target" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c3353cf5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ep 20 | R: 755.5 | Len: 204 | R/s: 3.70 (462.3%) | Gap: 16.74mm (min:14.73) | Best: 14.73mm\n", + "Ep 40 | R: 407.2 | Len: 116 | R/s: 3.52 (439.9%) | Gap: 15.56mm (min:13.80) | Best: 13.80mm\n", + "Ep 60 | R: 157.4 | Len: 57 | R/s: 2.78 (347.5%) | Gap: 14.87mm (min:13.91) | Best: 13.80mm\n", + "Ep 80 | R: 182.7 | Len: 61 | R/s: 2.98 (372.0%) | Gap: 15.06mm (min:14.21) | Best: 13.80mm\n", + "Ep 100 | R: 487.2 | Len: 134 | R/s: 3.65 (455.9%) | Gap: 16.10mm (min:14.31) | Best: 13.80mm\n", + "Ep 120 | R: 1113.1 | Len: 297 | R/s: 3.75 (468.3%) | Gap: 17.64mm (min:15.95) | Best: 13.80mm\n", + "Ep 140 | R: 1434.7 | Len: 385 | R/s: 3.72 (465.6%) | Gap: 18.21mm (min:16.13) | Best: 13.80mm\n", + "Ep 160 | R: 641.4 | Len: 172 | R/s: 3.72 (464.8%) | Gap: 16.69mm (min:15.38) | Best: 13.80mm\n", + "Ep 180 | R: 1029.0 | Len: 274 | R/s: 3.76 (469.7%) | Gap: 17.43mm (min:14.60) | Best: 13.80mm\n", + "Ep 200 | R: 287.0 | Len: 85 | R/s: 3.39 (424.0%) | Gap: 15.61mm (min:14.18) | Best: 13.80mm\n", + "Ep 220 | R: 330.9 | Len: 94 | R/s: 3.52 (440.4%) | Gap: 15.85mm (min:14.93) | Best: 13.80mm\n", + "Ep 240 | R: 336.4 | Len: 103 | R/s: 3.28 (409.7%) | Gap: 15.15mm (min:13.83) | Best: 13.80mm\n", + "Ep 260 | R: 128.2 | Len: 50 | R/s: 2.58 (321.9%) | Gap: 14.51mm (min:13.90) | Best: 13.80mm\n", + "Ep 280 | R: 116.0 | Len: 46 | R/s: 2.51 (313.3%) | Gap: 14.30mm (min:13.49) | Best: 13.49mm\n", + "Ep 300 | R: 95.0 | Len: 39 | R/s: 2.45 (306.0%) | Gap: 13.85mm (min:13.19) | Best: 13.19mm\n", + "Ep 320 | R: 772.1 | Len: 200 | R/s: 3.86 (482.9%) | Gap: 16.77mm (min:15.05) | Best: 13.19mm\n", + "Ep 340 | R: 152.7 | Len: 54 | R/s: 2.84 (354.5%) | Gap: 14.81mm (min:13.96) | Best: 13.19mm\n", + "Ep 360 | R: 118.1 | Len: 47 | R/s: 2.52 (315.6%) | Gap: 14.36mm (min:13.50) | Best: 13.19mm\n", + "Ep 380 | R: 290.8 | Len: 81 | R/s: 3.60 (450.2%) | Gap: 15.56mm (min:14.06) | Best: 13.19mm\n", + "Ep 400 | R: 230.0 | Len: 69 | R/s: 3.35 (418.5%) | Gap: 15.38mm (min:14.74) | Best: 13.19mm\n", + "Ep 420 | R: 305.9 | Len: 85 | R/s: 3.58 (447.5%) | Gap: 15.82mm (min:15.08) | Best: 13.19mm\n", + "Ep 440 | R: 450.6 | Len: 116 | R/s: 3.90 (487.0%) | Gap: 16.25mm (min:14.81) | Best: 13.19mm\n", + "Ep 460 | R: 624.2 | Len: 161 | R/s: 3.89 (486.0%) | Gap: 16.65mm (min:15.01) | Best: 13.19mm\n", + "Ep 480 | R: 710.6 | Len: 192 | R/s: 3.70 (462.6%) | Gap: 16.62mm (min:14.71) | Best: 13.19mm\n", + "Ep 500 | R: 131.1 | Len: 49 | R/s: 2.65 (331.8%) | Gap: 14.44mm (min:13.45) | Best: 13.19mm\n", + "Ep 520 | R: 169.4 | Len: 58 | R/s: 2.90 (362.5%) | Gap: 14.97mm (min:14.22) | Best: 13.19mm\n", + "Ep 540 | R: 929.9 | Len: 263 | R/s: 3.53 (441.4%) | Gap: 16.99mm (min:14.49) | Best: 13.19mm\n", + "Ep 560 | R: 1760.6 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 580 | R: 1763.0 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 600 | R: 1775.4 | Len: 500 | R/s: 3.55 (443.8%) | Gap: 18.99mm (min:18.91) | Best: 13.19mm\n", + "Ep 620 | R: 1298.7 | Len: 355 | R/s: 3.66 (457.5%) | Gap: 17.94mm (min:14.49) | Best: 13.19mm\n", + "Ep 640 | R: 1576.3 | Len: 438 | R/s: 3.60 (450.3%) | Gap: 18.63mm (min:16.35) | Best: 13.19mm\n", + "Ep 660 | R: 1762.6 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 680 | R: 1761.3 | Len: 500 | R/s: 3.52 (440.3%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 700 | R: 1761.0 | Len: 500 | R/s: 3.52 (440.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 720 | R: 1754.8 | Len: 500 | R/s: 3.51 (438.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 740 | R: 1755.3 | Len: 500 | R/s: 3.51 (438.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 760 | R: 1756.6 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 780 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 800 | R: 1756.9 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 820 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 840 | R: 1593.2 | Len: 436 | R/s: 3.65 (456.7%) | Gap: 18.62mm (min:16.57) | Best: 13.19mm\n", + "Ep 860 | R: 1209.1 | Len: 334 | R/s: 3.62 (452.2%) | Gap: 17.92mm (min:15.21) | Best: 13.19mm\n", + "Ep 880 | R: 509.8 | Len: 149 | R/s: 3.43 (429.0%) | Gap: 16.16mm (min:14.21) | Best: 13.19mm\n", + "Ep 900 | R: 496.0 | Len: 148 | R/s: 3.36 (419.9%) | Gap: 15.86mm (min:14.56) | Best: 13.19mm\n", + "Ep 920 | R: 1770.0 | Len: 500 | R/s: 3.54 (442.5%) | Gap: 18.99mm (min:18.97) | Best: 13.19mm\n", + "Ep 940 | R: 1763.3 | Len: 500 | R/s: 3.53 (440.8%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 960 | R: 1753.9 | Len: 500 | R/s: 3.51 (438.5%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 980 | R: 1751.9 | Len: 500 | R/s: 3.50 (438.0%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1000 | R: 1756.6 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1020 | R: 1754.6 | Len: 500 | R/s: 3.51 (438.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1040 | R: 1759.2 | Len: 500 | R/s: 3.52 (439.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1060 | R: 1756.7 | Len: 500 | R/s: 3.51 (439.2%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1080 | R: 1758.8 | Len: 500 | R/s: 3.52 (439.7%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1100 | R: 1756.2 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1120 | R: 1756.5 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1140 | R: 1760.5 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1160 | R: 1760.5 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1180 | R: 1756.5 | Len: 500 | R/s: 3.51 (439.1%) | Gap: 19.00mm (min:18.99) | Best: 13.19mm\n", + "Ep 1200 | R: 1760.0 | Len: 500 | R/s: 3.52 (440.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1220 | R: 1758.7 | Len: 500 | R/s: 3.52 (439.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1240 | R: 1760.4 | Len: 500 | R/s: 3.52 (440.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1260 | R: 1753.5 | Len: 500 | R/s: 3.51 (438.4%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1280 | R: 1753.9 | Len: 500 | R/s: 3.51 (438.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1300 | R: 1758.0 | Len: 500 | R/s: 3.52 (439.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1320 | R: 1762.7 | Len: 500 | R/s: 3.53 (440.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1340 | R: 1693.0 | Len: 459 | R/s: 3.69 (460.9%) | Gap: 18.61mm (min:16.25) | Best: 13.19mm\n", + "Ep 1360 | R: 713.0 | Len: 181 | R/s: 3.94 (492.2%) | Gap: 16.38mm (min:15.05) | Best: 13.19mm\n", + "Ep 1380 | R: 2118.4 | Len: 486 | R/s: 4.36 (545.4%) | Gap: 18.38mm (min:17.32) | Best: 13.19mm\n", + "Ep 1400 | R: 2157.4 | Len: 495 | R/s: 4.36 (544.9%) | Gap: 18.50mm (min:18.01) | Best: 13.19mm\n", + "Ep 1420 | R: 1181.5 | Len: 262 | R/s: 4.50 (563.0%) | Gap: 16.90mm (min:15.79) | Best: 13.19mm\n", + "Ep 1440 | R: 1332.5 | Len: 298 | R/s: 4.46 (558.1%) | Gap: 17.08mm (min:15.65) | Best: 13.19mm\n", + "Ep 1460 | R: 1496.5 | Len: 332 | R/s: 4.51 (563.8%) | Gap: 17.27mm (min:15.62) | Best: 13.19mm\n", + "Ep 1480 | R: 1545.4 | Len: 339 | R/s: 4.56 (570.1%) | Gap: 17.26mm (min:15.87) | Best: 13.19mm\n", + "Ep 1500 | R: 862.8 | Len: 201 | R/s: 4.29 (536.3%) | Gap: 16.17mm (min:14.88) | Best: 13.19mm\n", + "Ep 1520 | R: 809.8 | Len: 193 | R/s: 4.20 (524.6%) | Gap: 16.03mm (min:14.74) | Best: 13.19mm\n", + "Ep 1540 | R: 861.1 | Len: 204 | R/s: 4.22 (527.7%) | Gap: 16.25mm (min:14.93) | Best: 13.19mm\n", + "Ep 1560 | R: 1445.2 | Len: 329 | R/s: 4.40 (549.4%) | Gap: 17.24mm (min:15.19) | Best: 13.19mm\n", + "Ep 1580 | R: 1993.4 | Len: 486 | R/s: 4.11 (513.2%) | Gap: 18.55mm (min:16.26) | Best: 13.19mm\n", + "Ep 1600 | R: 1985.4 | Len: 500 | R/s: 3.97 (496.4%) | Gap: 18.75mm (min:18.57) | Best: 13.19mm\n", + "Ep 1620 | R: 1776.8 | Len: 500 | R/s: 3.55 (444.2%) | Gap: 18.97mm (min:18.91) | Best: 13.19mm\n", + "Ep 1640 | R: 1755.2 | Len: 500 | R/s: 3.51 (438.8%) | Gap: 18.99mm (min:18.97) | Best: 13.19mm\n", + "Ep 1660 | R: 1751.1 | Len: 500 | R/s: 3.50 (437.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1680 | R: 1746.6 | Len: 500 | R/s: 3.49 (436.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1700 | R: 1746.2 | Len: 500 | R/s: 3.49 (436.5%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1720 | R: 1747.8 | Len: 500 | R/s: 3.50 (437.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1740 | R: 1743.0 | Len: 500 | R/s: 3.49 (435.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1760 | R: 1743.4 | Len: 500 | R/s: 3.49 (435.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1780 | R: 1744.3 | Len: 500 | R/s: 3.49 (436.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1800 | R: 1744.0 | Len: 500 | R/s: 3.49 (436.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1820 | R: 1739.4 | Len: 500 | R/s: 3.48 (434.8%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1840 | R: 1736.2 | Len: 500 | R/s: 3.47 (434.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1860 | R: 1732.7 | Len: 500 | R/s: 3.47 (433.2%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1880 | R: 1732.1 | Len: 500 | R/s: 3.46 (433.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1900 | R: 1732.2 | Len: 500 | R/s: 3.46 (433.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1920 | R: 1729.1 | Len: 500 | R/s: 3.46 (432.3%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1940 | R: 1728.6 | Len: 500 | R/s: 3.46 (432.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1960 | R: 1728.0 | Len: 500 | R/s: 3.46 (432.0%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 1980 | R: 1728.5 | Len: 500 | R/s: 3.46 (432.1%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n", + "Ep 2000 | R: 1726.8 | Len: 500 | R/s: 3.45 (431.7%) | Gap: 19.00mm (min:19.00) | Best: 13.19mm\n" + ] + } + ], + "source": [ + "# Training Loop with Better Monitoring\n", + "ep_rewards = []\n", + "ep_lengths = []\n", + "ep_gap_errors = []\n", + "best_gap_error = float('inf')\n", + "gui_env = None\n", + "\n", + "for episode_idx in range(num_episodes):\n", + " train_data, ep_reward, gap_error = rollout(model, environ)\n", + " \n", + " ep_length = len(train_data[0])\n", + " ep_rewards.append(ep_reward)\n", + " ep_lengths.append(ep_length)\n", + " ep_gap_errors.append(gap_error)\n", + " \n", + " # Track best performance\n", + " if gap_error < best_gap_error:\n", + " best_gap_error = gap_error\n", + "\n", + " # Data Formatting\n", + " permute_idxs = np.random.permutation(len(train_data[0]))\n", + " obs = torch.tensor(train_data[0][permute_idxs], dtype=torch.float32, device=device)\n", + " acts = torch.tensor(train_data[1][permute_idxs], dtype=torch.float32, device=device)\n", + " gaes = torch.tensor(train_data[3][permute_idxs], dtype=torch.float32, device=device)\n", + " act_log_probs = torch.tensor(train_data[4][permute_idxs], dtype=torch.float32, device=device)\n", + "\n", + " returns = discount_rewards(train_data[2])[permute_idxs]\n", + " returns = torch.tensor(returns, dtype=torch.float32, device=device)\n", + "\n", + " # Normalize GAEs for stable training\n", + " gaes = (gaes - gaes.mean()) / (gaes.std() + 1e-8)\n", + "\n", + " ppo.train_policy(obs, acts, act_log_probs, gaes)\n", + " ppo.train_value(obs, returns)\n", + "\n", + " if (episode_idx + 1) % print_freq == 0:\n", + " avg_reward = np.mean(ep_rewards[-print_freq:])\n", + " avg_length = np.mean(ep_lengths[-print_freq:])\n", + " avg_gap_error = np.mean(ep_gap_errors[-print_freq:])\n", + " min_gap_error = np.min(ep_gap_errors[-print_freq:])\n", + " avg_reward_per_step = avg_reward / avg_length if avg_length > 0 else 0\n", + " \n", + " # Updated for new reward scale (realistic good is ~0.8/step)\n", + " realistic_good_per_step = 0.8\n", + " percent_of_realistic = (avg_reward_per_step / realistic_good_per_step) * 100\n", + " \n", + " output_line = (f\"Ep {episode_idx + 1:4d} | R: {avg_reward:6.1f} | Len: {avg_length:3.0f} | \"\n", + " f\"R/s: {avg_reward_per_step:5.2f} ({percent_of_realistic:5.1f}%) | \"\n", + " f\"Gap: {avg_gap_error:5.2f}mm (min:{min_gap_error:5.2f}) | Best: {best_gap_error:5.2f}mm\")\n", + " \n", + " print(output_line)\n", + " log_file.write(output_line + \"\\n\")\n", + " log_file.flush()\n", + "\n", + "# Close GUI environment if created\n", + "if gui_env is not None:\n", + " gui_env.close()\n", + "\n", + "# Close log file\n", + "log_file.write(\"\\n\" + \"=\"*70 + \"\\n\")\n", + "log_file.write(f\"Training Completed: {datetime.now().strftime('%Y%m%d_%H%M%S')}\\n\")\n", + "log_file.write(f\"Best Gap Error Achieved: {best_gap_error:.3f} mm\\n\")\n", + "log_file.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3678193c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Training log saved to: RL_Trials/training_log_20251211_191801.txt\n", + "✓ Gap error plot saved to: RL_Trials/gap_error_plot_20251211_191801.png\n" + ] + } + ], + "source": [ + "# Create and save gap error plot\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(ep_gap_errors, alpha=0.3, label='Per Episode')\n", + "\n", + "# Calculate moving average (window size = print_freq)\n", + "window_size = print_freq\n", + "if len(ep_gap_errors) >= window_size:\n", + " moving_avg = np.convolve(ep_gap_errors, np.ones(window_size)/window_size, mode='valid')\n", + " plt.plot(range(window_size-1, len(ep_gap_errors)), moving_avg, linewidth=2, label=f'{window_size}-Episode Moving Average')\n", + "\n", + "plt.xlabel('Episode', fontsize=12)\n", + "plt.ylabel('Average Gap Height Error (mm)', fontsize=12)\n", + "plt.title('Gap Height Error Over Training, n_steps=500', fontsize=14, fontweight='bold')\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.savefig(plot_file_path, dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f\"\\n✓ Training log saved to: {log_file_path}\")\n", + "print(f\"✓ Gap error plot saved to: {plot_file_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7c225451", + "metadata": {}, + "outputs": [], + "source": [ + "environ.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97e51696", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LevSim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lev_pod_env.py b/lev_pod_env.py new file mode 100644 index 0000000..616f47d --- /dev/null +++ b/lev_pod_env.py @@ -0,0 +1,486 @@ +import gymnasium as gym +from gymnasium import spaces +import pybullet as p +import pybullet_data +import numpy as np +import os +from mag_lev_coil import MagLevCoil +from maglev_predictor import MaglevPredictor + +TARGET_GAP = 16.491741 / 1000 # target gap height for 5.8 kg pod in meters + +class LevPodEnv(gym.Env): + def __init__(self, use_gui=False, initial_gap_mm=10.0, max_steps=2000): + super(LevPodEnv, self).__init__() + + # Store initial gap height parameter + self.initial_gap_mm = initial_gap_mm + self.max_episode_steps = max_steps + self.current_step = 0 + + # The following was coded by AI - see [1] + # --- 1. Define Action & Observation Spaces --- + # Action: 4 PWM duty cycles between -1 and 1 (4 independent coils) + # [front_left, front_right, back_left, back_right] corresponding to +Y and -Y ends of each U-yoke + self.action_space = spaces.Box(low=-1, high=1, shape=(4,), dtype=np.float32) + + # Observation: 4 normalized noisy sensor gap heights + 4 normalized velocities + # Gaps normalized by 0.030m, velocities by 0.1 m/s + self.observation_space = spaces.Box(low=-5.0, high=5.0, shape=(8,), dtype=np.float32) + + # --- 2. Setup Physics & Actuators --- + self.dt = 1./240. # PyBullet default timestep + self.coil_front_L = MagLevCoil(1.1, 0.0025, 12, 10.2) + self.coil_front_R = MagLevCoil(1.1, 0.0025, 12, 10.2) + self.coil_back_L = MagLevCoil(1.1, 0.0025, 12, 10.2) + self.coil_back_R = MagLevCoil(1.1, 0.0025, 12, 10.2) + + # Sensor noise parameters + self.sensor_noise_std = 0.0001 # 0.1mm standard deviation + + # Normalization constants for observations + self.gap_scale = 0.015 # Normalize gaps by +-15mm max expected deviation from middle + self.velocity_scale = 0.1 # Normalize velocities by 0.1 m/s max expected velocity + + # Maglev force/torque predictor + self.predictor = MaglevPredictor() + + # Connect to PyBullet (DIRECT is faster for training, GUI for debugging) + self.client = p.connect(p.GUI if use_gui else p.DIRECT) + p.setAdditionalSearchPath(pybullet_data.getDataPath()) + + # Store references + self.trackId = None + self.podId = None + self.collision_local_positions = [] + self.yoke_indices = [] # For force application + self.yoke_labels = [] + self.sensor_indices = [] # For gap height measurement + self.sensor_labels = [] + + # For velocity calculation + self.prev_sensor_gaps = None + + def reset(self, seed=None, options=None): + # Reset PyBullet simulation + p.resetSimulation(physicsClientId=self.client) + p.setGravity(0, 0, -9.81, physicsClientId=self.client) + p.setTimeStep(self.dt, physicsClientId=self.client) + + # Create the maglev track (inverted system - track above, pod hangs below) + # Track bottom surface at Z=0 + track_collision = p.createCollisionShape( + shapeType=p.GEOM_BOX, + halfExtents=[1.0, 0.2, 0.010], + physicsClientId=self.client + ) + track_visual = p.createVisualShape( + shapeType=p.GEOM_BOX, + halfExtents=[1.0, 0.2, 0.010], + rgbaColor=[0.3, 0.3, 0.3, 0.8], + physicsClientId=self.client + ) + self.trackId = p.createMultiBody( + baseMass=0, # Static + baseCollisionShapeIndex=track_collision, + baseVisualShapeIndex=track_visual, + basePosition=[0, 0, 0.010], # Track center at Z=10mm, bottom at Z=0 + physicsClientId=self.client + ) + p.changeDynamics(self.trackId, -1, + lateralFriction=0.3, + restitution=0.1, + physicsClientId=self.client) + + urdf_path = self._create_modified_urdf() + + # Determine start condition + if np.random.rand() > 0.5: + # Spawn exactly at target + spawn_gap_mm = TARGET_GAP * 1000.0 + # # Add tiny noise + # spawn_gap_mm += np.random.uniform(-0.5, 0.5) + else: + spawn_gap_mm = self.initial_gap_mm + + start_z = -(0.09085 + spawn_gap_mm / 1000.0) + start_pos = [0, 0, start_z] + start_orientation = p.getQuaternionFromEuler([0, 0, 0]) + self.podId = p.loadURDF(urdf_path, start_pos, start_orientation, physicsClientId=self.client) + + # The following was coded by AI - see [2] + # Parse collision shapes to identify yokes and sensors + collision_shapes = p.getCollisionShapeData(self.podId, -1, physicsClientId=self.client) + self.collision_local_positions = [] + self.yoke_indices = [] + self.yoke_labels = [] + self.sensor_indices = [] + self.sensor_labels = [] + + # Expected heights for detection + expected_yoke_sensor_z = 0.08585 # Yokes and sensors always at this height + expected_bolt_z = 0.08585 + self.initial_gap_mm / 1000.0 # Bolts at gap-dependent height + + for i, shape in enumerate(collision_shapes): + shape_type = shape[2] + local_pos = shape[5] + self.collision_local_positions.append(local_pos) + + # Check if at sensor/yoke height (Z ≈ 0.08585m) - NOT bolts + if abs(local_pos[2] - expected_yoke_sensor_z) < 0.001: + if shape_type == p.GEOM_BOX: + # Yokes are BOX type at the four corners (size 0.0254) + self.yoke_indices.append(i) + x_pos = "Front" if local_pos[0] > 0 else "Back" + y_pos = "Left" if local_pos[1] > 0 else "Right" + self.yoke_labels.append(f"{x_pos}_{y_pos}") + elif shape_type == p.GEOM_CYLINDER or shape_type == p.GEOM_MESH: + # Sensors: distinguish by position pattern + if abs(local_pos[0]) < 0.06 or abs(local_pos[1]) < 0.02: + self.sensor_indices.append(i) + if abs(local_pos[0]) < 0.001: # Center sensors (X ≈ 0) + label = "Center_Right" if local_pos[1] > 0 else "Center_Left" + else: # Front/back sensors (Y ≈ 0) + label = "Front" if local_pos[0] > 0 else "Back" + self.sensor_labels.append(label) + + self.coil_front_L.current = 0 + self.coil_front_R.current = 0 + self.coil_back_L.current = 0 + self.coil_back_R.current = 0 + + self.prev_sensor_gaps = None + obs = self._get_obs(initial_reset=True) + self.current_step = 0 + + return obs, {} + + # The following was generated by AI - see [14] + def step(self, action): + # Check if PyBullet connection is still active (GUI might be closed) + try: + p.getConnectionInfo(physicsClientId=self.client) + except p.error: + # Connection lost - GUI was closed + return self._get_obs(), -100.0, True, True, {'error': 'GUI closed'} + + # Update Coil Currents from PWM Actions + pwm_front_L = action[0] # yoke +x,+y + pwm_front_R = action[1] # yoke +x,-y + pwm_back_L = action[2] # yoke -x,+y + pwm_back_R = action[3] # yoke -x,-y + + curr_front_L = self.coil_front_L.update(pwm_front_L, self.dt) + curr_front_R = self.coil_front_R.update(pwm_front_R, self.dt) + curr_back_L = self.coil_back_L.update(pwm_back_L, self.dt) + curr_back_R = self.coil_back_R.update(pwm_back_R, self.dt) + + # --- 2. Get Current Pod State --- + pos, orn = p.getBasePositionAndOrientation(self.podId, physicsClientId=self.client) + lin_vel, ang_vel = p.getBaseVelocity(self.podId, physicsClientId=self.client) + + # Convert quaternion to rotation matrix + rot_matrix = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3) + + # --- 3. Calculate Gap Heights at Yoke Positions (for force prediction) --- + # Calculate world positions of the 4 yokes (ends of U-yokes) + yoke_gap_heights_dict = {} # Store by label for easy access + + for i, yoke_idx in enumerate(self.yoke_indices): + local_pos = self.collision_local_positions[yoke_idx] + local_vec = np.array(local_pos) + world_offset = rot_matrix @ local_vec + world_pos = np.array(pos) + world_offset + + # Top surface of yoke box (add half-height = 5mm) + yoke_top_z = world_pos[2] + 0.005 + + # Gap height: track bottom (Z=0) to yoke top (negative Z) + gap_height = -yoke_top_z # Convert to positive gap in meters + yoke_gap_heights_dict[self.yoke_labels[i]] = gap_height + + # Average gap heights for each U-shaped yoke (average left and right ends) + # Front yoke: average of Front_Left and Front_Right + # Back yoke: average of Back_Left and Back_Right + avg_gap_front = (yoke_gap_heights_dict.get('Front_Left', 0.010) + + yoke_gap_heights_dict.get('Front_Right', 0.010)) / 2 + avg_gap_back = (yoke_gap_heights_dict.get('Back_Left', 0.010) + + yoke_gap_heights_dict.get('Back_Right', 0.010)) / 2 + + front_left_gap = yoke_gap_heights_dict.get('Front_Left', 0.010) + front_right_gap = yoke_gap_heights_dict.get('Front_Right', 0.010) + back_left_gap = yoke_gap_heights_dict.get('Back_Left', 0.010) + back_right_gap = yoke_gap_heights_dict.get('Back_Right', 0.010) + + # hypotenuses + y_distance = 0.1016 # 2 * 0.0508m (left to right distance) + x_distance = 0.2518 # 2 * 0.1259m (front to back distance) + + # Roll angle + # When right side has larger gap, roll is negative + roll_angle_front = np.arcsin(-(front_right_gap - front_left_gap) / y_distance) + roll_angle_back = np.arcsin(-(back_right_gap - back_left_gap) / y_distance) + roll_angle = (roll_angle_front + roll_angle_back) / 2 + + # When back has larger gap, pitch is positive + pitch_angle_left = np.arcsin((back_left_gap - front_left_gap) / x_distance) + pitch_angle_right = np.arcsin((back_right_gap - front_right_gap) / x_distance) + pitch_angle = (pitch_angle_left + pitch_angle_right) / 2 + + # Predict Forces and Torques using Maglev Predictor + # Gap heights in mm + gap_front_mm = avg_gap_front * 1000 + gap_back_mm = avg_gap_back * 1000 + + # Roll angle in degrees + roll_deg = np.degrees(roll_angle) + + # Predict force and torque for each U-shaped yoke + # Front yoke + force_front, torque_front = self.predictor.predict( + curr_front_L, curr_front_R, roll_deg, gap_front_mm + ) + + # Back yoke + force_back, torque_back = self.predictor.predict( + curr_back_L, curr_back_R, roll_deg, gap_back_mm + ) + + # --- 5. Apply Forces and Torques to Pod --- + # Forces are applied at Y=0 (center of U-yoke) at each X position + # This is where the actual magnetic force acts on the U-shaped yoke + + # Apply force at front yoke center (X=+0.1259, Y=0) + front_yoke_center = [0.1259, 0, 0.08585] # From pod.xml yoke positions + p.applyExternalForce( + self.podId, -1, + forceObj=[0, 0, force_front], + posObj=front_yoke_center, + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + # Apply force at back yoke center (X=-0.1259, Y=0) + back_yoke_center = [-0.1259, 0, 0.08585] + p.applyExternalForce( + self.podId, -1, + forceObj=[0, 0, force_back], + posObj=back_yoke_center, + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + + + # Apply roll torques + # Each yoke produces its own torque about X axis + torque_front_Nm = torque_front / 1000 # Convert from mN·m to N·m + torque_back_Nm = torque_back / 1000 + + # Apply torques at respective yoke positions + p.applyExternalTorque( + self.podId, -1, + torqueObj=[torque_front_Nm, 0, 0], + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + p.applyExternalTorque( + self.podId, -1, + torqueObj=[torque_back_Nm, 0, 0], + flags=p.LINK_FRAME, + physicsClientId=self.client + ) + + # --- 6. Step Simulation --- + p.stepSimulation(physicsClientId=self.client) + self.current_step += 1 + + # Check for physical contact with track (bolts touching) + contact_points = p.getContactPoints(bodyA=self.podId, bodyB=self.trackId, physicsClientId=self.client) + has_contact = len(contact_points) > 0 + + # --- 7. Get New Observation --- + obs = self._get_obs() + + # --- 8. Calculate Reward --- + # Goal: Hover at target gap (16.5mm), minimize roll/pitch, minimize power + target_gap = TARGET_GAP # 16.5mm in meters + avg_gap = (avg_gap_front + avg_gap_back) / 2 + + gap_error = abs(avg_gap - target_gap) + + # Power dissipation (all 4 coils) + power = (curr_front_L**2 * self.coil_front_L.R + + curr_front_R**2 * self.coil_front_R.R + + curr_back_L**2 * self.coil_back_L.R + + curr_back_R**2 * self.coil_back_R.R) + + # --- Improved Reward Function --- + # Use reward shaping with reasonable scales to enable learning + + # 1. Gap Error Reward (most important) + # Use exponential decay for smooth gradient near target + gap_error_mm = gap_error * 1000 # Convert to mm + gap_reward = 10.0 * np.exp(-0.5 * (gap_error_mm / 3.0)**2) # Peak at 0mm error, 3mm std dev + + # 2. Orientation Penalties (smaller scale) + roll_penalty = abs(np.degrees(roll_angle)) * 0.02 + pitch_penalty = abs(np.degrees(pitch_angle)) * 0.02 + + # 3. Velocity Penalty (discourage rapid oscillations) + z_velocity = lin_vel[2] + velocity_penalty = abs(z_velocity) * 0.1 + + # 4. Contact Penalty + contact_points = p.getContactPoints(bodyA=self.podId, bodyB=self.trackId) + contact_penalty = len(contact_points) * 0.2 + + # 5. Power Penalty (encourage efficiency, but small weight) + power_penalty = power * 0.001 + + # Combine rewards (scaled to ~[-5, +1] range per step) + reward = gap_reward - roll_penalty - pitch_penalty - velocity_penalty - contact_penalty - power_penalty + + # Check Termination (tighter bounds for safety) + terminated = False + truncated = False + + # Terminate if gap is too small (crash) or too large (lost) + if avg_gap < 0.003 or avg_gap > 0.035: + terminated = True + reward = -10.0 # Failure penalty (scaled down) + + # Terminate if orientation is too extreme + if abs(roll_angle) > np.radians(15) or abs(pitch_angle) > np.radians(15): + terminated = True + reward = -10.0 + + # Success bonus for stable hovering near target + if gap_error_mm < 1.0 and abs(np.degrees(roll_angle)) < 2.0 and abs(np.degrees(pitch_angle)) < 2.0: + reward += 2.0 # Bonus for excellent control + + info = { + 'curr_front_L': curr_front_L, + 'curr_front_R': curr_front_R, + 'curr_back_L': curr_back_L, + 'curr_back_R': curr_back_R, + 'gap_front_yoke': avg_gap_front, + 'gap_back_yoke': avg_gap_back, + 'roll': roll_angle, + 'force_front': force_front, + 'force_back': force_back, + 'torque_front': torque_front, + 'torque_back': torque_back + } + + return obs, reward, terminated, truncated, info + + # The following was generated by AI - see [15] + def _get_obs(self, initial_reset=False): + """ + Returns observation: [gaps(4), velocities(4)] + Uses noisy sensor readings + computed velocities for microcontroller-friendly deployment + """ + pos, orn = p.getBasePositionAndOrientation(self.podId, physicsClientId=self.client) + + # Convert quaternion to rotation matrix + rot_matrix = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3) + + # Calculate sensor gap heights with noise + sensor_gap_heights = {} + + for i, sensor_idx in enumerate(self.sensor_indices): + local_pos = self.collision_local_positions[sensor_idx] + local_vec = np.array(local_pos) + world_offset = rot_matrix @ local_vec + world_pos = np.array(pos) + world_offset + + # Top surface of sensor (add half-height = 5mm) + sensor_top_z = world_pos[2] + 0.005 + + # Gap height: track bottom (Z=0) to sensor top + gap_height = -sensor_top_z + + # Add measurement noise + noisy_gap = gap_height + np.random.normal(0, self.sensor_noise_std) + # sensor_gap_heights[self.sensor_labels[i]] = noisy_gap + sensor_gap_heights[self.sensor_labels[i]] = gap_height + + # Pack sensor measurements in consistent order + # [center_right, center_left, front, back] + gaps = np.array([ + sensor_gap_heights.get('Center_Right', 0.010), + sensor_gap_heights.get('Center_Left', 0.010), + sensor_gap_heights.get('Front', 0.010), + sensor_gap_heights.get('Back', 0.010) + ], dtype=np.float32) + + # Compute velocities (d_gap/dt) + if initial_reset or (self.prev_sensor_gaps is None): + # First observation - no velocity information yet + velocities = np.zeros(4, dtype=np.float32) + else: + # Compute velocity as finite difference + velocities = (gaps - self.prev_sensor_gaps) / self.dt + + # Store for next step + self.prev_sensor_gaps = gaps.copy() + + # Normalize observations + gaps_normalized = (gaps - TARGET_GAP) / self.gap_scale + velocities_normalized = velocities / self.velocity_scale + + # Concatenate: [normalized_gaps, normalized_velocities] + obs = np.concatenate([gaps_normalized, velocities_normalized]) + + return obs + + # The following was generated by AI - see [16] + def _create_modified_urdf(self): + """ + Create a modified URDF with bolt positions adjusted based on initial gap height. + Bolts are at Z = 0.08585 + gap_mm/1000 (relative to pod origin). + Yokes and sensors remain at Z = 0.08585 (relative to pod origin). + """ + import tempfile + + # Calculate bolt Z position + bolt_z = 0.08585 + self.initial_gap_mm / 1000.0 + + # Read the original URDF template + urdf_template_path = os.path.join(os.path.dirname(__file__), "pod.xml") + with open(urdf_template_path, 'r') as f: + urdf_content = f.read() + + # Replace the bolt Z positions (originally at 0.09585) + # There are 4 bolts at different X,Y positions but same Z + urdf_modified = urdf_content.replace( + 'xyz="0.285 0.03 0.09585"', + f'xyz="0.285 0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="0.285 -0.03 0.09585"', + f'xyz="0.285 -0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="-0.285 0.03 0.09585"', + f'xyz="-0.285 0.03 {bolt_z:.6f}"' + ).replace( + 'xyz="-0.285 -0.03 0.09585"', + f'xyz="-0.285 -0.03 {bolt_z:.6f}"' + ) + + # Write to a temporary file + with tempfile.NamedTemporaryFile(mode='w', suffix='.urdf', delete=False) as f: + f.write(urdf_modified) + temp_urdf_path = f.name + + return temp_urdf_path + + def close(self): + try: + p.disconnect(physicsClientId=self.client) + except p.error: + pass # Already disconnected + + def render(self): + """Rendering is handled by PyBullet GUI mode""" + pass \ No newline at end of file diff --git a/mag_lev_coil.py b/mag_lev_coil.py new file mode 100644 index 0000000..1e4c07f --- /dev/null +++ b/mag_lev_coil.py @@ -0,0 +1,24 @@ +# The following was generated by AI - see [17] +class MagLevCoil: + def __init__(self, r_resistance, l_inductance, source_voltage, maxCurrent): + self.R = r_resistance + self.L = l_inductance + self.current = 0.0 + self.Vs = source_voltage + self.Imax = maxCurrent + + def update(self, pwm_duty_cycle, dt): + """ + Simulates the coil circuit and force generation. + pwm_duty_cycle: -1.0 to 1.0 + """ + # Simple First-order RL circuit approximation + # V_in = Duty * V_source + voltage = pwm_duty_cycle * self.Vs + + # di/dt = (V - I*R) / L + di = (voltage - self.current * self.R) / self.L + self.current += di * dt + self.current = min(max(-self.Imax, self.current), self.Imax) + + return self.current \ No newline at end of file diff --git a/maglev_model.pkl b/maglev_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6b54a21fc329b2534ac8ac09a34051266423d8d1 GIT binary patch literal 6277 zcmbW5d0b52-@tGCPKrVy(oRK4(YYU= 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b/maglev_predictor.py @@ -0,0 +1,116 @@ +""" +Magnetic Levitation Force and Torque Predictor +Optimized Inference using Pre-Trained Scikit-Learn Model + +This module loads a saved .pkl model (PolynomialFeatures + LinearRegression) +and executes predictions using optimized NumPy vectorization for high-speed simulation. + +Usage: + predictor = MaglevPredictor("maglev_model.pkl") + force, torque = predictor.predict(currL=-15, currR=-15, roll=1.0, gap_height=10.0) +""" + +import numpy as np +import joblib +import os + +class MaglevPredictor: + def __init__(self, model_path='maglev_model.pkl'): + """ + Initialize predictor by loading the pickle file and extracting + raw matrices for fast inference. + """ + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file '{model_path}' not found. Please train and save the model first.") + + print(f"Loading maglev model from {model_path}...") + data = joblib.load(model_path) + + # 1. Extract Scikit-Learn Objects + poly_transformer = data['poly_features'] + linear_model = data['model'] + + # 2. Extract Raw Matrices for Speed (Bypasses sklearn overhead) + # powers_: Matrix of shape (n_output_features, n_input_features) + # Represents the exponents for each term. e.g. x1^2 * x2^1 + self.powers = poly_transformer.powers_.T # Transpose for broadcasting + + # coef_: Shape (n_targets, n_output_features) -> (2, n_poly_terms) + self.coef = linear_model.coef_ + + # intercept_: Shape (n_targets,) -> (2,) + self.intercept = linear_model.intercept_ + + print(f"Model loaded. Degree: {data['degree']}") + print(f"Force R2: {data['performance']['force_r2']:.4f}") + print(f"Torque R2: {data['performance']['torque_r2']:.4f}") + + def predict(self, currL, currR, roll, gap_height): + """ + Fast single-sample prediction using pure NumPy. + + Args: + currL, currR: Currents [A] + roll: Roll angle [deg] + gap_height: Gap [mm] + + Returns: + (force [N], torque [mN·m]) + """ + # 1. Pre-process Input (Must match training order: currL, currR, roll, invGap) + # Clamp gap to avoid division by zero + safe_gap = max(gap_height, 1e-6) + invGap = 1.0 / safe_gap + + # Input Vector: shape (4,) + x = np.array([currL, currR, roll, invGap]) + + # 2. Polynomial Expansion (Vectorized) + # Compute x^p for every term. + # x is (4,), self.powers is (4, n_terms) + # Broadcasting: x[:, None] is (4,1). Result is (4, n_terms). + # Product along axis 0 reduces it to (n_terms,) + + # Note: This is equivalent to PolynomialFeatures.transform but 100x faster for single samples + poly_features = np.prod(x[:, None] ** self.powers, axis=0) + + # 3. Linear Prediction + # (2, n_terms) dot (n_terms,) -> (2,) + result = np.dot(self.coef, poly_features) + self.intercept + + return result[0], result[1] + + def predict_batch(self, currL, currR, roll, gap_height): + """ + Fast batch prediction for array inputs. + """ + # 1. Pre-process Inputs + gap_height = np.asarray(gap_height) + invGap = 1.0 / np.maximum(gap_height, 1e-6) + + # Stack inputs: shape (N, 4) + X = np.column_stack((currL, currR, roll, invGap)) + + # 2. Polynomial Expansion + # X is (N, 4). Powers is (4, n_terms). + # We want (N, n_terms). + # Method: X[:, :, None] -> (N, 4, 1) + # Powers[None, :, :] -> (1, 4, n_terms) + # Power: (N, 4, n_terms) + # Prod axis 1: (N, n_terms) + poly_features = np.prod(X[:, :, None] ** self.powers[None, :, :], axis=1) + + # 3. Linear Prediction + # (N, n_terms) @ (n_terms, 2) + (2,) + result = np.dot(poly_features, self.coef.T) + self.intercept + + return result[:, 0], result[:, 1] + +if __name__ == "__main__": + # Test + try: + p = MaglevPredictor() + f, t = p.predict(-15, -15, 1.0, 10.0) + print(f"Force: {f:.3f}, Torque: {t:.3f}") + except FileNotFoundError as e: + print(e) \ No newline at end of file diff --git a/pod.xml b/pod.xml new file mode 100644 index 0000000..10b8383 --- /dev/null +++ b/pod.xml @@ -0,0 +1,86 @@ + + + + + + + + + + + + + + Bolts + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..9b35712 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,156 @@ +annotated-types==0.7.0 +anyio==4.10.0 +appnope==0.1.4 +argon2-cffi==25.1.0 +argon2-cffi-bindings==25.1.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.5 +attrs==25.3.0 +babel==2.17.0 +beautifulsoup4==4.13.5 +bleach==6.2.0 +cachetools==6.2.1 +certifi==2025.8.3 +cffi==1.17.1 +charset-normalizer==3.4.3 +click==8.3.0 +comm==0.2.3 +contourpy==1.3.3 +cycler==0.12.1 +debugpy==1.8.16 +decorator==5.2.1 +defusedxml==0.7.1 +executing==2.2.1 +fastapi==0.115.0 +fastjsonschema==2.21.2 +fonttools==4.59.2 +fqdn==1.5.1 +future==1.0.0 +google-ai-generativelanguage==0.6.10 +google-api-core==2.26.0 +google-api-python-client==2.185.0 +google-auth==2.41.1 +google-auth-httplib2==0.2.0 +google-generativeai==0.8.3 +googleapis-common-protos==1.70.0 +grpcio==1.75.1 +grpcio-status==1.71.2 +h11==0.16.0 +httpcore==1.0.9 +httplib2==0.31.0 +httpx==0.27.2 +idna==3.10 +ipykernel==6.30.1 +ipython==9.5.0 +ipython_pygments_lexers==1.1.1 +ipywidgets==8.1.7 +iso8601==2.1.0 +isoduration==20.11.0 +jedi==0.19.2 +Jinja2==3.1.6 +json5==0.12.1 +jsonpointer==3.0.0 +jsonschema==4.25.1 +jsonschema-specifications==2025.4.1 +jupyter==1.1.1 +jupyter-console==6.6.3 +jupyter-events==0.12.0 +jupyter-lsp==2.3.0 +jupyter_client==8.6.3 +jupyter_core==5.8.1 +jupyter_server==2.17.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.4.7 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +jupyterlab_widgets==3.0.15 +kiwisolver==1.4.9 +lark==1.2.2 +lazy_loader==0.4 +MarkupSafe==3.0.2 +matplotlib==3.10.6 +matplotlib-inline==0.1.7 +mistune==3.1.4 +mne==1.10.2 +mpmath==1.3.0 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +nest-asyncio==1.6.0 +notebook==7.4.5 +notebook_shim==0.2.4 +numpy==2.3.2 +packaging==25.0 +pandas==2.3.3 +pandocfilters==1.5.1 +parso==0.8.5 +pexpect==4.9.0 +pillow==11.3.0 +platformdirs==4.4.0 +pooch==1.8.2 +prometheus_client==0.22.1 +prompt_toolkit==3.0.52 +proto-plus==1.26.1 +protobuf==5.29.5 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pycparser==2.22 +pydantic==2.9.2 +pydantic-settings==2.6.0 +pydantic_core==2.23.4 +Pygments==2.19.2 +pyparsing==3.2.3 +PyQt6==6.10.0 +PyQt6-Qt6==6.10.0 +PyQt6_sip==13.10.2 +pyqtgraph==0.13.7 +pyserial==3.5 +PySide6==6.10.0 +PySide6_Addons==6.10.0 +PySide6_Essentials==6.10.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +python-json-logger==3.3.0 +pytz==2025.2 +PyYAML==6.0.2 +pyzmq==27.0.2 +referencing==0.36.2 +requests==2.32.5 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rfc3987-syntax==1.1.0 +rpds-py==0.27.1 +rsa==4.9.1 +scipy==1.16.3 +Send2Trash==1.8.3 +serial==0.0.97 +setuptools==80.9.0 +shiboken6==6.10.0 +six==1.17.0 +sniffio==1.3.1 +soupsieve==2.8 +stack-data==0.6.3 +starlette==0.38.6 +sympy==1.14.0 +terminado==0.18.1 +tinycss2==1.4.0 +tornado==6.5.2 +tqdm==4.67.1 +traitlets==5.14.3 +types-python-dateutil==2.9.0.20250822 +typing_extensions==4.15.0 +tzdata==2025.2 +uri-template==1.3.0 +uritemplate==4.2.0 +urllib3==2.5.0 +uvicorn==0.32.0 +wcwidth==0.2.13 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +websockets==15.0.1 +widgetsnbextension==4.0.14 diff --git a/test_env.py b/test_env.py new file mode 100644 index 0000000..bd09655 --- /dev/null +++ b/test_env.py @@ -0,0 +1,63 @@ +# The following was generated by AI - see [18] +""" +Test script for LevPodEnv +Runs a simple episode with constant actions to verify the environment works +""" + +from lev_pod_env import LevPodEnv +import numpy as np +import time + +# Create environment with GUI for visualization +env = LevPodEnv(use_gui=True, initial_gap_mm=15) + +print("=" * 60) +print("Testing LevPodEnv") +print("=" * 60) +print(f"Action space: {env.action_space}") +print(f" 4 PWM duty cycles: [front_L, front_R, back_L, back_R]") +print(f"Observation space: {env.observation_space}") +print(f" 8 values: [gaps(4), velocities(4)]") +print("=" * 60) + +# Reset environment +obs, info = env.reset() +print(f"\nInitial observation:") +print(f" Gaps: CR={obs[0]*1000:.2f}mm, CL={obs[1]*1000:.2f}mm, F={obs[2]*1000:.2f}mm, B={obs[3]*1000:.2f}mm") +print(f" Velocities: CR={obs[4]*1000:.2f}mm/s, CL={obs[5]*1000:.2f}mm/s, F={obs[6]*1000:.2f}mm/s, B={obs[7]*1000:.2f}mm/s") +print(f" Average gap: {np.mean(obs[:4])*1000:.2f} mm") + +# Run a few steps with constant action to test force application +print("\nRunning test episode...") +for step in range(500): + # Apply constant moderate PWM to all 4 coils + # 50% PWM should generate current that produces upward force + action = np.array([0,0,0,0], dtype=np.float32) + + obs, reward, terminated, truncated, info = env.step(action) + + if step % 5 == 0: + print(f"\nStep {step} (t={step/240:.2f}s):") + print(f" Sensor gaps: CR={obs[0]*1000:.2f}mm, CL={obs[1]*1000:.2f}mm, " + + f"F={obs[2]*1000:.2f}mm, B={obs[3]*1000:.2f}mm") + print(f" Velocities: CR={obs[4]*1000:.2f}mm/s, CL={obs[5]*1000:.2f}mm/s, " + + f"F={obs[6]*1000:.2f}mm/s, B={obs[7]*1000:.2f}mm/s") + print(f" Yoke gaps: front={info['gap_front_yoke']*1000:.2f}mm, back={info['gap_back_yoke']*1000:.2f}mm") + print(f" Roll: {np.degrees(info['roll']):.2f}°") + print(f" Currents: FL={info['curr_front_L']:.2f}A, FR={info['curr_front_R']:.2f}A, " + + f"BL={info['curr_back_L']:.2f}A, BR={info['curr_back_R']:.2f}A") + print(f" Forces: front={info['force_front']:.2f}N, back={info['force_back']:.2f}N") + print(f" Torques: front={info['torque_front']:.2f}mN·m, back={info['torque_back']:.2f}mN·m") + print(f" Reward: {reward:.2f}") + + if terminated or truncated: + print(f"\nEpisode terminated at step {step}") + break + + time.sleep(0.01) + +print("\n" + "=" * 60) +print("Test complete!") +print("=" * 60) + +env.close() \ No newline at end of file