diff --git a/docs/tutorial.ipynb b/docs/tutorial.ipynb index 10468bd..2aadbc0 100644 --- a/docs/tutorial.ipynb +++ b/docs/tutorial.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Tutorial \n","\n"," \"Open\n","\n","\n","This notebook provides an introductory tutorial for echoSMs."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10231,"status":"ok","timestamp":1724374592133,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"vvvd0_LuowDN","outputId":"6ebbda45-32d1-4c4b-9ea2-85dbf1326efd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting echosms\n"," Downloading echosms-0.1.3-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: matplotlib in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from echosms) (3.9.1)\n","Requirement already satisfied: numpy in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from echosms) (2.0.1)\n","Requirement already satisfied: pandas in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from echosms) (2.2.2)\n","Requirement already satisfied: scipy in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from echosms) (1.14.0)\n","Collecting toml (from echosms)\n"," Downloading toml-0.10.2-py2.py3-none-any.whl.metadata (7.1 kB)\n","Requirement already satisfied: xarray in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from echosms) (2024.7.0)\n","Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (4.53.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (24.1)\n","Requirement already satisfied: pillow>=8 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (10.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (3.1.2)\n","Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from matplotlib->echosms) (2.9.0)\n","Requirement already satisfied: pytz>=2020.1 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from pandas->echosms) (2024.1)\n","Requirement already satisfied: tzdata>=2022.7 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from pandas->echosms) (2024.1)\n","Requirement already satisfied: six>=1.5 in c:\\users\\gavinmacaulay\\miniforge3\\envs\\echosms_test\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->echosms) (1.16.0)\n","Downloading echosms-0.1.3-py3-none-any.whl (29 kB)\n","Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n","Installing collected packages: toml, echosms\n","Successfully installed echosms-0.1.3 toml-0.10.2\n"]}],"source":["!pip install echosms"]},{"cell_type":"markdown","metadata":{"id":"K9izbBdpuj30"},"source":["## Imports\n","\n","We import the modal series solution model from echoSMs and the benchmark data and reference models."]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":2521,"status":"ok","timestamp":1724374603926,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"yzcGKsBuo2Hj"},"outputs":[],"source":["from echosms import MSSModel, BenchmarkData, ReferenceModels\n","import matplotlib.pyplot as plt\n","import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"V1uNxsWfraXn"},"source":["## Reference models\n","The reference models in the Jech et al (2015) paper are available in the echoSMs package:"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1724374610285,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"o2J4jrvvo_OL","outputId":"1506a818-16c5-4892-ac2d-311bd4f69eb2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Available reference models are:\n","\n","fixed rigid sphere\n","pressure release sphere\n","gas filled sphere\n","weakly scattering sphere\n","spherical fluid shell with pressure release interior\n","spherical fluid shell with gas interior\n","spherical fluid shell with weakly scattering interior\n","fixed rigid prolate spheroid\n","pressure release prolate spheroid\n","gas filled prolate spheroid\n","weakly scattering prolate spheroid\n","fixed rigid finite cylinder\n","pressure release finite cylinder\n","gas filled finite cylinder\n","weakly scattering finite cylinder\n","WC38.1 calibration sphere\n","Cu60 calibration sphere\n"]}],"source":["rm = ReferenceModels()\n","print('Available reference models are:\\n')\n","print('\\n'.join(rm.names()))"]},{"cell_type":"markdown","metadata":{"id":"uz4JhAIArmZA"},"source":["## Benchmark results\n","Likewise, the results from the benchmark model runs in the Jech et al (2015) paper are available in the echoSMs package."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1724374618002,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"HV8YqRUDpDfF","outputId":"4e851e6c-2280-4f4a-cc5a-41052994e26b"},"outputs":[{"data":{"text/html":["
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Frequency_kHzSphere_RigidSphere_PressureReleaseSphere_GasSphere_WeaklyScatteringShellSphere_PressureReleaseShellSphere_GasShellSphere_WeaklyScatteringProlateSpheroid_RigidProlateSpheroid_PressureReleaseProlateSpheroid_GasProlateSpheroid_WeaklyScatteringCylinder_RigidCylinder_PressureReleaseCylinder_GasCylinder_WeaklyScattering
012-54.44-42.29-42.34-103.95-42.83-42.80-99.15-35.98-30.16NaN-87.05-38.75-35.29-35.30-89.79
114-52.20-42.92-42.93-101.62-43.40-43.44-96.79-33.83-30.02NaN-84.71-36.83-34.93-34.93-87.54
216-50.40-43.52-43.52-99.69-43.96-43.99-94.83-32.20-29.87NaN-82.78-35.45-34.56-34.56-85.73
318-48.96-44.02-44.03-98.10-44.49-44.51-93.20-30.97-29.70NaN-81.19-34.51-34.17-34.18-84.27
420-47.85-44.39-44.40-96.79-44.94-44.97-91.85-30.08-29.54NaN-79.87-33.95-33.80-33.81-83.12
...................................................
190392-45.96-46.00-46.03-103.61-46.91-46.88-108.29NaNNaNNaN-86.94-21.74-21.86-21.92-70.78
191394-45.86-46.00-45.96-100.25-46.91-46.78-107.19NaNNaNNaN-83.45-21.72-21.84-21.83-70.14
192396-45.80-46.00-46.07-98.03-46.91-46.75-106.49NaNNaNNaN-81.16-21.70-21.82-21.82-69.78
193398-45.80-46.00-45.96-96.47-46.91-46.99-106.14NaNNaNNaN-79.55-21.68-21.80-21.79-69.68
194400-45.84-46.00-46.05-95.37-46.91-46.94-106.15NaNNaNNaN-78.41-21.65-21.77-21.79-69.83
\n","

195 rows × 16 columns

\n","
"],"text/plain":[" Frequency_kHz Sphere_Rigid Sphere_PressureRelease Sphere_Gas \\\n","0 12 -54.44 -42.29 -42.34 \n","1 14 -52.20 -42.92 -42.93 \n","2 16 -50.40 -43.52 -43.52 \n","3 18 -48.96 -44.02 -44.03 \n","4 20 -47.85 -44.39 -44.40 \n",".. ... ... ... ... \n","190 392 -45.96 -46.00 -46.03 \n","191 394 -45.86 -46.00 -45.96 \n","192 396 -45.80 -46.00 -46.07 \n","193 398 -45.80 -46.00 -45.96 \n","194 400 -45.84 -46.00 -46.05 \n","\n"," Sphere_WeaklyScattering ShellSphere_PressureRelease ShellSphere_Gas \\\n","0 -103.95 -42.83 -42.80 \n","1 -101.62 -43.40 -43.44 \n","2 -99.69 -43.96 -43.99 \n","3 -98.10 -44.49 -44.51 \n","4 -96.79 -44.94 -44.97 \n",".. ... ... ... \n","190 -103.61 -46.91 -46.88 \n","191 -100.25 -46.91 -46.78 \n","192 -98.03 -46.91 -46.75 \n","193 -96.47 -46.91 -46.99 \n","194 -95.37 -46.91 -46.94 \n","\n"," ShellSphere_WeaklyScattering ProlateSpheroid_Rigid \\\n","0 -99.15 -35.98 \n","1 -96.79 -33.83 \n","2 -94.83 -32.20 \n","3 -93.20 -30.97 \n","4 -91.85 -30.08 \n",".. ... ... \n","190 -108.29 NaN \n","191 -107.19 NaN \n","192 -106.49 NaN \n","193 -106.14 NaN \n","194 -106.15 NaN \n","\n"," ProlateSpheroid_PressureRelease ProlateSpheroid_Gas \\\n","0 -30.16 NaN \n","1 -30.02 NaN \n","2 -29.87 NaN \n","3 -29.70 NaN \n","4 -29.54 NaN \n",".. ... ... \n","190 NaN NaN \n","191 NaN NaN \n","192 NaN NaN \n","193 NaN NaN \n","194 NaN NaN \n","\n"," ProlateSpheroid_WeaklyScattering Cylinder_Rigid \\\n","0 -87.05 -38.75 \n","1 -84.71 -36.83 \n","2 -82.78 -35.45 \n","3 -81.19 -34.51 \n","4 -79.87 -33.95 \n",".. ... ... \n","190 -86.94 -21.74 \n","191 -83.45 -21.72 \n","192 -81.16 -21.70 \n","193 -79.55 -21.68 \n","194 -78.41 -21.65 \n","\n"," Cylinder_PressureRelease Cylinder_Gas Cylinder_WeaklyScattering \n","0 -35.29 -35.30 -89.79 \n","1 -34.93 -34.93 -87.54 \n","2 -34.56 -34.56 -85.73 \n","3 -34.17 -34.18 -84.27 \n","4 -33.80 -33.81 -83.12 \n",".. ... ... ... \n","190 -21.86 -21.92 -70.78 \n","191 -21.84 -21.83 -70.14 \n","192 -21.82 -21.82 -69.78 \n","193 -21.80 -21.79 -69.68 \n","194 -21.77 -21.79 -69.83 \n","\n","[195 rows x 16 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["bm = BenchmarkData()\n","bmf = bm.freq_dataset # this is a Pandas DataFrame\n","bmf"]},{"cell_type":"markdown","metadata":{"id":"SreX_FuOr9US"},"source":["## Creating the model parameters\n","\n","We can now get the model parameters and results for a given model in the Jech et al (2015) paper and run the same model using the echoSMs package and compare them. First step is to get the model parameters for a model - we choose the weakly scattering sphere for this example:"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":364,"status":"ok","timestamp":1724374636656,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"jynjQr3lpIBp","outputId":"c0307411-2e0c-48d0-bdb9-8b052e5e8239"},"outputs":[{"data":{"text/plain":["{'boundary_type': 'fluid filled',\n"," 'a': 0.01,\n"," 'medium_rho': 1026.8,\n"," 'medium_c': 1477.4,\n"," 'target_rho': 1028.9,\n"," 'target_c': 1480.3}"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["\n","m = rm.parameters('weakly scattering sphere')\n","m"]},{"cell_type":"markdown","metadata":{"id":"wUh0d2QTrUpq"},"source":["These parameters need to have an angle and frequency range added. We will use the frequencies from the Jech et al (2015) paper to make comparisons simplier."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":571,"status":"ok","timestamp":1724373610677,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"RTXejojmpQqC","outputId":"1130042a-0942-4d05-c3aa-80429019f1b6"},"outputs":[{"data":{"text/plain":["{'boundary_type': 'fluid filled',\n"," 'a': 0.01,\n"," 'medium_rho': 1026.8,\n"," 'medium_c': 1477.4,\n"," 'target_rho': 1028.9,\n"," 'target_c': 1480.3,\n"," 'f': 0 12000.0\n"," 1 14000.0\n"," 2 16000.0\n"," 3 18000.0\n"," 4 20000.0\n"," ... \n"," 190 392000.0\n"," 191 394000.0\n"," 192 396000.0\n"," 193 398000.0\n"," 194 400000.0\n"," Name: Frequency_kHz, Length: 195, dtype: float64,\n"," 'theta': 90}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["m['f'] = bm.freq_dataset['Frequency_kHz']*1e3\n","m['theta'] = 90\n","m"]},{"cell_type":"markdown","metadata":{"id":"FL-vhBY2qStT"},"source":["## Calculating target strength\n","\n","The reference model for a weakly scattering sphere was the model series solution, so we create an instance of that model in echoSMs and get it to calculate the target strength as per the parameters in ``m``.\n"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"Di4CFovupSGx"},"outputs":[],"source":["mod = MSSModel()\n","ts = mod.calculate_ts(m)"]},{"cell_type":"markdown","metadata":{"id":"bZP9vgKAqqwI"},"source":["## Comparison to existing target strength\n","\n","These results can be compared to those from the Jech et al (2015) paper. We can also calculate the mean difference between the Jech values and those from the echoSMs calculations."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":497},"executionInfo":{"elapsed":2206,"status":"ok","timestamp":1724374342091,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"cXTE9u-TpumE","outputId":"46e575e0-361a-4f1e-eebe-acc8f90dbbef"},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["jech_index = np.mean(np.abs(ts - bmf['Sphere_WeaklyScattering']))\n","\n","fig, axs = plt.subplots(2, 1, sharex=True)\n","\n","axs[0].plot(m['f']/1e3, ts, label='echoSMs')\n","axs[0].plot(bmf['Frequency_kHz'], bmf['Sphere_WeaklyScattering'], label='Benchmark')\n","axs[0].set_ylabel('TS re 1 m$^2$ [dB]')\n","axs[0].legend(frameon=False, fontsize=6)\n","\n","axs[1].plot(m['f']*1e-3, ts-bmf['Sphere_WeaklyScattering'])\n","axs[1].set_xlabel('Frequency [kHz]')\n","axs[1].set_ylabel(r'$\\Delta$ TS [dB]')\n","axs[1].annotate(f'{jech_index:.2f} dB', (0.05, 0.80), xycoords='axes fraction',\n"," backgroundcolor=[.8, .8, .8])\n","_ = plt.suptitle('Weakly scattering sphere')"]},{"cell_type":"markdown","metadata":{"id":"QOHyiEE-vkbr"},"source":["There is a 0.15 dB difference between the echoSMs results and those from the Jech et al (2015) paper. We don't know why (comparisons of other models and parameters give near identical results - it is just the weakly scattering models that don't agree)."]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOLhNe1NqPbGufGbKTfWjEp","provenance":[{"file_id":"1EPUlnNihQmkFtk5OvXHN0B0MUKSTvMkX","timestamp":1724374399220}]},"kernelspec":{"display_name":"Python 3","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":"3.11.9"}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Tutorial \n","\n"," \"Open\n","\n","\n","This notebook provides an introductory tutorial for echoSMs."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10231,"status":"ok","timestamp":1724374592133,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"vvvd0_LuowDN","outputId":"6ebbda45-32d1-4c4b-9ea2-85dbf1326efd"},"outputs":[],"source":["!pip install echosms"]},{"cell_type":"markdown","metadata":{"id":"K9izbBdpuj30"},"source":["## Imports\n","\n","We import the modal series solution model from echoSMs and the benchmark data and reference models."]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":2521,"status":"ok","timestamp":1724374603926,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"yzcGKsBuo2Hj"},"outputs":[],"source":["from echosms import MSSModel, BenchmarkData, ReferenceModels\n","import matplotlib.pyplot as plt\n","import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"V1uNxsWfraXn"},"source":["## Reference models\n","The reference models in the Jech et al (2015) paper are available in the echoSMs package:"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1724374610285,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"o2J4jrvvo_OL","outputId":"1506a818-16c5-4892-ac2d-311bd4f69eb2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Available reference models are:\n","\n","fixed rigid sphere\n","pressure release sphere\n","gas filled sphere\n","weakly scattering sphere\n","spherical fluid shell with pressure release interior\n","spherical fluid shell with gas interior\n","spherical fluid shell with weakly scattering interior\n","fixed rigid prolate spheroid\n","pressure release prolate spheroid\n","gas filled prolate spheroid\n","weakly scattering prolate spheroid\n","fixed rigid finite cylinder\n","pressure release finite cylinder\n","gas filled finite cylinder\n","weakly scattering finite cylinder\n","WC38.1 calibration sphere\n","Cu60 calibration sphere\n"]}],"source":["rm = ReferenceModels()\n","print('Available reference models are:\\n')\n","print('\\n'.join(rm.names()))"]},{"cell_type":"markdown","metadata":{"id":"uz4JhAIArmZA"},"source":["## Benchmark results\n","Likewise, the results from the benchmark model runs in the Jech et al (2015) paper are available in the echoSMs package."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1724374618002,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"HV8YqRUDpDfF","outputId":"4e851e6c-2280-4f4a-cc5a-41052994e26b"},"outputs":[{"data":{"text/html":["
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Frequency_kHzSphere_RigidSphere_PressureReleaseSphere_GasSphere_WeaklyScatteringShellSphere_PressureReleaseShellSphere_GasShellSphere_WeaklyScatteringProlateSpheroid_RigidProlateSpheroid_PressureReleaseProlateSpheroid_GasProlateSpheroid_WeaklyScatteringCylinder_RigidCylinder_PressureReleaseCylinder_GasCylinder_WeaklyScattering
012-54.44-42.29-42.34-103.95-42.83-42.80-99.15-35.98-30.16NaN-87.05-38.75-35.29-35.30-89.79
114-52.20-42.92-42.93-101.62-43.40-43.44-96.79-33.83-30.02NaN-84.71-36.83-34.93-34.93-87.54
216-50.40-43.52-43.52-99.69-43.96-43.99-94.83-32.20-29.87NaN-82.78-35.45-34.56-34.56-85.73
318-48.96-44.02-44.03-98.10-44.49-44.51-93.20-30.97-29.70NaN-81.19-34.51-34.17-34.18-84.27
420-47.85-44.39-44.40-96.79-44.94-44.97-91.85-30.08-29.54NaN-79.87-33.95-33.80-33.81-83.12
...................................................
190392-45.96-46.00-46.03-103.61-46.91-46.88-108.29NaNNaNNaN-86.94-21.74-21.86-21.92-70.78
191394-45.86-46.00-45.96-100.25-46.91-46.78-107.19NaNNaNNaN-83.45-21.72-21.84-21.83-70.14
192396-45.80-46.00-46.07-98.03-46.91-46.75-106.49NaNNaNNaN-81.16-21.70-21.82-21.82-69.78
193398-45.80-46.00-45.96-96.47-46.91-46.99-106.14NaNNaNNaN-79.55-21.68-21.80-21.79-69.68
194400-45.84-46.00-46.05-95.37-46.91-46.94-106.15NaNNaNNaN-78.41-21.65-21.77-21.79-69.83
\n","

195 rows × 16 columns

\n","
"],"text/plain":[" Frequency_kHz Sphere_Rigid Sphere_PressureRelease Sphere_Gas \\\n","0 12 -54.44 -42.29 -42.34 \n","1 14 -52.20 -42.92 -42.93 \n","2 16 -50.40 -43.52 -43.52 \n","3 18 -48.96 -44.02 -44.03 \n","4 20 -47.85 -44.39 -44.40 \n",".. ... ... ... ... \n","190 392 -45.96 -46.00 -46.03 \n","191 394 -45.86 -46.00 -45.96 \n","192 396 -45.80 -46.00 -46.07 \n","193 398 -45.80 -46.00 -45.96 \n","194 400 -45.84 -46.00 -46.05 \n","\n"," Sphere_WeaklyScattering ShellSphere_PressureRelease ShellSphere_Gas \\\n","0 -103.95 -42.83 -42.80 \n","1 -101.62 -43.40 -43.44 \n","2 -99.69 -43.96 -43.99 \n","3 -98.10 -44.49 -44.51 \n","4 -96.79 -44.94 -44.97 \n",".. ... ... ... \n","190 -103.61 -46.91 -46.88 \n","191 -100.25 -46.91 -46.78 \n","192 -98.03 -46.91 -46.75 \n","193 -96.47 -46.91 -46.99 \n","194 -95.37 -46.91 -46.94 \n","\n"," ShellSphere_WeaklyScattering ProlateSpheroid_Rigid \\\n","0 -99.15 -35.98 \n","1 -96.79 -33.83 \n","2 -94.83 -32.20 \n","3 -93.20 -30.97 \n","4 -91.85 -30.08 \n",".. ... ... \n","190 -108.29 NaN \n","191 -107.19 NaN \n","192 -106.49 NaN \n","193 -106.14 NaN \n","194 -106.15 NaN \n","\n"," ProlateSpheroid_PressureRelease ProlateSpheroid_Gas \\\n","0 -30.16 NaN \n","1 -30.02 NaN \n","2 -29.87 NaN \n","3 -29.70 NaN \n","4 -29.54 NaN \n",".. ... ... \n","190 NaN NaN \n","191 NaN NaN \n","192 NaN NaN \n","193 NaN NaN \n","194 NaN NaN \n","\n"," ProlateSpheroid_WeaklyScattering Cylinder_Rigid \\\n","0 -87.05 -38.75 \n","1 -84.71 -36.83 \n","2 -82.78 -35.45 \n","3 -81.19 -34.51 \n","4 -79.87 -33.95 \n",".. ... ... \n","190 -86.94 -21.74 \n","191 -83.45 -21.72 \n","192 -81.16 -21.70 \n","193 -79.55 -21.68 \n","194 -78.41 -21.65 \n","\n"," Cylinder_PressureRelease Cylinder_Gas Cylinder_WeaklyScattering \n","0 -35.29 -35.30 -89.79 \n","1 -34.93 -34.93 -87.54 \n","2 -34.56 -34.56 -85.73 \n","3 -34.17 -34.18 -84.27 \n","4 -33.80 -33.81 -83.12 \n",".. ... ... ... \n","190 -21.86 -21.92 -70.78 \n","191 -21.84 -21.83 -70.14 \n","192 -21.82 -21.82 -69.78 \n","193 -21.80 -21.79 -69.68 \n","194 -21.77 -21.79 -69.83 \n","\n","[195 rows x 16 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["bm = BenchmarkData()\n","bmf = bm.freq_dataset # this is a Pandas DataFrame\n","bmf"]},{"cell_type":"markdown","metadata":{"id":"SreX_FuOr9US"},"source":["## Creating the model parameters\n","\n","We can now get the model parameters and results for a given model in the Jech et al (2015) paper and run the same model using the echoSMs package and compare them. First step is to get the model parameters for a model - we choose the weakly scattering sphere for this example:"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":364,"status":"ok","timestamp":1724374636656,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"jynjQr3lpIBp","outputId":"c0307411-2e0c-48d0-bdb9-8b052e5e8239"},"outputs":[{"data":{"text/plain":["{'boundary_type': 'fluid filled',\n"," 'a': 0.01,\n"," 'medium_rho': 1026.8,\n"," 'medium_c': 1477.4,\n"," 'target_rho': 1028.9,\n"," 'target_c': 1480.3}"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["\n","m = rm.parameters('weakly scattering sphere')\n","m"]},{"cell_type":"markdown","metadata":{"id":"wUh0d2QTrUpq"},"source":["These parameters need to have an angle and frequency range added. We will use the frequencies from the Jech et al (2015) paper to make comparisons simplier."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":571,"status":"ok","timestamp":1724373610677,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"RTXejojmpQqC","outputId":"1130042a-0942-4d05-c3aa-80429019f1b6"},"outputs":[{"data":{"text/plain":["{'boundary_type': 'fluid filled',\n"," 'a': 0.01,\n"," 'medium_rho': 1026.8,\n"," 'medium_c': 1477.4,\n"," 'target_rho': 1028.9,\n"," 'target_c': 1480.3,\n"," 'f': 0 12000.0\n"," 1 14000.0\n"," 2 16000.0\n"," 3 18000.0\n"," 4 20000.0\n"," ... \n"," 190 392000.0\n"," 191 394000.0\n"," 192 396000.0\n"," 193 398000.0\n"," 194 400000.0\n"," Name: Frequency_kHz, Length: 195, dtype: float64,\n"," 'theta': 90}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["m['f'] = bm.freq_dataset['Frequency_kHz']*1e3\n","m['theta'] = 90\n","m"]},{"cell_type":"markdown","metadata":{"id":"FL-vhBY2qStT"},"source":["## Calculating target strength\n","\n","The reference model for a weakly scattering sphere was the model series solution, so we create an instance of that model in echoSMs and get it to calculate the target strength as per the parameters in ``m``.\n"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"Di4CFovupSGx"},"outputs":[],"source":["mod = MSSModel()\n","ts = mod.calculate_ts(m)"]},{"cell_type":"markdown","metadata":{"id":"bZP9vgKAqqwI"},"source":["## Comparison to existing target strength\n","\n","These results can be compared to those from the Jech et al (2015) paper. We can also calculate the mean difference between the Jech values and those from the echoSMs calculations."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":497},"executionInfo":{"elapsed":2206,"status":"ok","timestamp":1724374342091,"user":{"displayName":"Gavin Macaulay","userId":"15996873107556149664"},"user_tz":-720},"id":"cXTE9u-TpumE","outputId":"46e575e0-361a-4f1e-eebe-acc8f90dbbef"},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["jech_index = np.mean(np.abs(ts - bmf['Sphere_WeaklyScattering']))\n","\n","fig, axs = plt.subplots(2, 1, sharex=True)\n","\n","axs[0].plot(m['f']/1e3, ts, label='echoSMs')\n","axs[0].plot(bmf['Frequency_kHz'], bmf['Sphere_WeaklyScattering'], label='Benchmark')\n","axs[0].set_ylabel('TS re 1 m$^2$ [dB]')\n","axs[0].legend(frameon=False, fontsize=6)\n","\n","axs[1].plot(m['f']*1e-3, ts-bmf['Sphere_WeaklyScattering'])\n","axs[1].set_xlabel('Frequency [kHz]')\n","axs[1].set_ylabel(r'$\\Delta$ TS [dB]')\n","axs[1].annotate(f'{jech_index:.2f} dB', (0.05, 0.80), xycoords='axes fraction',\n"," backgroundcolor=[.8, .8, .8])\n","_ = plt.suptitle('Weakly scattering sphere')"]},{"cell_type":"markdown","metadata":{"id":"QOHyiEE-vkbr"},"source":["There is a 0.15 dB difference between the echoSMs results and those from the Jech et al (2015) paper. We don't know why (comparisons of other models and parameters give near identical results - it is just the weakly scattering models that don't agree)."]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOLhNe1NqPbGufGbKTfWjEp","provenance":[{"file_id":"1EPUlnNihQmkFtk5OvXHN0B0MUKSTvMkX","timestamp":1724374399220}]},"kernelspec":{"display_name":"Python 3","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":"3.11.9"}},"nbformat":4,"nbformat_minor":0}