diff --git a/examples/ex_004_isotropic_mixtures.ipynb b/examples/ex_004_isotropic_mixtures.ipynb index 155d6d2..225c4cb 100644 --- a/examples/ex_004_isotropic_mixtures.ipynb +++ b/examples/ex_004_isotropic_mixtures.ipynb @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "f00cffe6-9773-4775-9279-ae4c37142c12", "metadata": {}, "outputs": [ @@ -110,7 +110,7 @@ "(100.0, 60.0, 60.0, 0.25)" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "629d9d88-c598-4791-aee5-67c10b57e5ac", "metadata": {}, "outputs": [ @@ -139,7 +139,7 @@ "(4629.100498862757, 8017.837257372731)" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -153,54 +153,429 @@ "id": "9db99ee2-8cfc-4c15-a0a6-9aa9964de93b", "metadata": {}, "source": [ - "You can also specify " + "You can also specify inputs as arrays. In the following, we calculate the density-dependence of shear wave velocity at fixed values for all other parameters by providing " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "a866cb2e-05da-4ab8-b32b-b710f9a458b9", "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "08dc358d-c805-4433-bfcd-0139bbb196d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Vs [m/s]')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "density = np.linspace(3000, 3300, 10) # density \n", + "Gu = np.full((10,), 60*1e9) # unrelaxed shear modulus\n", + "m1 = IsotropicMedium(.25, 60*1e9, 'shear', density=density)\n", + "plt.plot(m1.density, m1.v_s)\n", + "plt.title(\"providing arrays to IsotropicMedium\")\n", + "plt.xlabel(\"density [kg/m3]\")\n", + "plt.ylabel(\"Vs [m/s]\")" + ] + }, + { + "cell_type": "markdown", + "id": "812a42a8-09b2-45d7-8952-c5b3327853f0", + "metadata": {}, "source": [ - "import numpy as np" + "Or, a more interesting example, let's calculate the anharmonic temperature dependence at surface pressure. For this calculation, we'll specify how both shear modulus and density vary with temperature with the following constants:" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "08dc358d-c805-4433-bfcd-0139bbb196d5", + "execution_count": 7, + "id": "24513fb9-7511-4dbb-9b01-04483ba6d7c2", "metadata": {}, "outputs": [], "source": [ - "m1 = IsotropicMedium(.25, np.full((10,1), 60*1e9), 'shear', density)" + "dGdT = -13.6*1e6 # anharmonic temperature derivative of shear modulus, Pa/K\n", + "Gu_0_ol = 81 * 1e9 # unrelaxed shear modulus in Pa at reference T, P\n", + "Tref_K = 300 # reference T in K\n", + "thermal_exp = 3*1e-5 # coefficient of thermal expansion" + ] + }, + { + "cell_type": "markdown", + "id": "5cc9a44e-6736-4469-84b7-36c3a262e333", + "metadata": {}, + "source": [ + "and calculate the temperature-dependent modulus and density:" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "fd5673a7-a7a6-4d6d-8ea6-e38bb47fc591", + "execution_count": 8, + "id": "33f2aa91-1e8b-49ce-8334-d082276665d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T_K = np.linspace(800, 1200, 100) + 273\n", + "Gu = Gu_0_ol + dGdT * (T_K - Tref_K)\n", + "density = 3300 * (1 - (T_K - Tref_K)* thermal_exp)\n", + "m1 = IsotropicMedium(.25, Gu, 'shear', density=density)\n", + "plt.plot(T_K, m1.v_s, label='V_s')\n", + "plt.plot(T_K, m1.v_p, label='V_p')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "984d5718-e2bc-4d55-b6a6-647e5b960992", + "metadata": {}, + "source": [ + "## Isotropic Medium from VBRc\n", + "\n", + "You can initialize an `IsotropicMedium` instance using the `load_isotropic_medium` function by providing a `vbr` structure that you've loaded with `VBRCstruct`. \n", + "\n", + "For example, using the small sample file, first load in the structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a347058-b150-472c-ac9a-8e1d299dc407", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pyVBRc : [INFO ] 2023-09-01 14:56:25,308: /Users/chavlin/src/vbr_/pyVBRc/pyVBRc/sample_data/VBRc_sample_LUT.mat loaded.\n" + ] + } + ], + "source": [ + "from pyVBRc.materials import load_isotropic_medium\n", + "from pyVBRc.sample_data import get_sample_filename\n", + "from pyVBRc.vbrc_structure import VBRCstruct\n", + "\n", + "file = get_sample_filename(\"VBRc_sample_LUT.mat\")\n", + "vbr = VBRCstruct(file, lut_dimensions=[\"T_K\", \"phi\", \"dg_um\"])" + ] + }, + { + "cell_type": "markdown", + "id": "c7302a93-b46d-4239-8a0c-129626b03f32", + "metadata": {}, + "source": [ + "and then supply `load_isotropic_medium` both the `vbr` variable as well as the location of the shear modulus you want to use. For example, to use the unrelaxed anharmonic modulus at `vbr.output.elastic.anharmonic.Gu`, you would supply:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "89d7812d-d553-4825-9443-e31e56a64461", + "metadata": {}, + "outputs": [], + "source": [ + "m = load_isotropic_medium(vbr, (\"elastic\", \"anharmonic\", \"Gu\"))" + ] + }, + { + "cell_type": "markdown", + "id": "16ec2894-c730-412c-99fa-6073c6575bb0", + "metadata": {}, + "source": [ + "this is useful for creating `IsotropicMedium` instances that you can then mix together. For example, you can use the VBRc to calculate the the properties of endmembmers, and then use the mixing methods below to combine the endmembers." + ] + }, + { + "cell_type": "markdown", + "id": "f98cef9d-5400-4984-96f4-ed48908c6734", + "metadata": {}, + "source": [ + "## Mixtures of IsotropicMedium instances\n", + "\n", + "The `IsotropicMixture` class will take a list of `IsotropicMedium` instances along with their proportions and calculate properties of an isotropic composite. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "810107f0-f7bc-4dac-93f6-81ebc6a5607b", + "metadata": {}, + "outputs": [], + "source": [ + "from pyVBRc.materials import IsotropicMixture" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e904fbf8-17fd-41ea-a83b-850ce9883545", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m\n", + "\u001b[0mIsotropicMixture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmaterials\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpyVBRc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaterials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaterials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIsotropicMedium\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mproportions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_array_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SupportsArray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nested_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NestedSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_array_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SupportsArray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nested_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NestedSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "\u001b[0;31mFile:\u001b[0m ~/src/vbr_/pyVBRc/pyVBRc/materials/materials.py\n", + "\u001b[0;31mType:\u001b[0m type\n", + "\u001b[0;31mSubclasses:\u001b[0m " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "IsotropicMixture?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d4b2af99-3a0b-419a-9e01-f44a914e8b5a", + "metadata": {}, + "source": [ + "so let's create a couple of endmembers. \n", + "\n", + "Let's create endmembers for forsterite (Fo) and fayalite (Fa). The values for the poisson ratio, shear modulus and density come from Hacker and Abers 2004, for surface conditions:\n", + "\n", + "```\n", + "Subduction Factory 3: An Excel worksheet and macro for calculating the densities, seismic wave speeds, and H2O contents of minerals and rocks at pressure and temperature\n", + "Bradley R. Hacker, Geoffrey A. Abers\n", + "First published: 20 January 2004\n", + "https://doi.org/10.1029/2003GC000614\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6914043d-d57b-4580-b063-922d0195a1e8", + "metadata": {}, + "outputs": [], + "source": [ + "Fo = IsotropicMedium(0.241, 78*1e9, 'shear', density=3100)\n", + "Fa = IsotropicMedium(0.335, 50*1e9, 'shear', density=4300) " + ] + }, + { + "cell_type": "markdown", + "id": "43932534-3658-4fcc-a02c-c1c61f520a2f", + "metadata": {}, + "source": [ + "and now we can create a mixture by providing a list of the endmembers and the mixing proportions:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dee25871-7d5e-4eec-880c-ee33041c45da", + "metadata": {}, + "outputs": [], + "source": [ + "mixture = IsotropicMixture([Fo, Fa], [0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "2fd0721c-fdc5-4bb6-a39a-ed5cc87ceaf0", + "metadata": {}, + "source": [ + "the density is a simple volumetric average:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "136d24b2-2531-4988-9c68-ae81ba9d748a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.25" + "3700.0" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m1.poisson_ratio" + "mixture.density()" + ] + }, + { + "cell_type": "markdown", + "id": "3ebce49d-88f9-48f5-996c-6e9809f4e561", + "metadata": {}, + "source": [ + "while the moduli can be calculated with Voit, Reuss, or Voit-Reuss averaging:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "47fda50d-fb55-4bc4-b860-25b5b0d874dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mmixture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshear_modulus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'voigt-reuss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Parameters\n", + "----------\n", + "method: str\n", + " voigt, reuss or voigt-reuss (the default)\n", + "\n", + "Returns\n", + "-------\n", + "\u001b[0;31mFile:\u001b[0m ~/src/vbr_/pyVBRc/pyVBRc/materials/materials.py\n", + "\u001b[0;31mType:\u001b[0m method" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mixture.shear_modulus?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1fb9c268-049a-411a-937b-c1e364770b0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "62.46875" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mixture.shear_modulus()/1e9" + ] + }, + { + "cell_type": "markdown", + "id": "e365bd46-91c4-4236-9c7d-7eb31c71dfa5", + "metadata": {}, + "source": [ + "To calculate the dependence on the mixing proportions, you can use a loop to vary the volume fraction. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cac6c6e5-f3c4-4da2-8908-e177a9513798", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = []\n", + "density = []\n", + "vs_v = []\n", + "vs_r = []\n", + "vs_vr = []\n", + "fracs_fo = np.linspace(0, 1, 50)\n", + "for frac_fo in fracs_fo:\n", + " mixture = IsotropicMixture([Fo, Fa], [frac_fo, 1.0 - frac_fo])\n", + " G.append(mixture.shear_modulus()/1e9)\n", + " vs_v.append(mixture.shear_velocity(method='voigt')/1e3)\n", + " vs_r.append(mixture.shear_velocity(method='reuss')/1e3)\n", + " vs_vr.append(mixture.shear_velocity(method='voigt-reuss')/1e3)\n", + " density.append(mixture.density())\n", + "\n", + "f, axs = plt.subplots(nrows=1, ncols=3, figsize=(10, 3))\n", + "\n", + "axs[0].plot(fracs_fo, G)\n", + "axs[0].set_ylabel('G [GPa]')\n", + "axs[1].plot(fracs_fo, density)\n", + "axs[1].set_ylabel('density [kg/m3]')\n", + "axs[2].plot(fracs_fo, vs_r, label='reuss')\n", + "axs[2].plot(fracs_fo, vs_v, label='voigt')\n", + "axs[2].plot(fracs_fo, vs_vr, label='voigt-reuss')\n", + "axs[2].set_ylabel('Vs [km/s]')\n", + "\n", + "for ax in axs:\n", + " ax.set_xlabel('Volume fraction Fo')\n", + "f.tight_layout()\n", + " " ] }, { "cell_type": "code", "execution_count": null, - "id": "33f2aa91-1e8b-49ce-8334-d082276665d0", + "id": "20cb3856-c034-493c-b12b-47ade6cd57cc", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/ex_005_anisotropy.ipynb b/examples/ex_005_anisotropy.ipynb index a6e1a1a..b1b874d 100644 --- a/examples/ex_005_anisotropy.ipynb +++ b/examples/ex_005_anisotropy.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "09e2de6e-fa0d-4f12-ad56-22be2a0778bc", "metadata": {}, "outputs": [], @@ -36,41 +36,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "81db62dd-78cb-4c8b-b2a1-67e430a36c29", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "\u001b[0;31mInit signature:\u001b[0m\n", - "\u001b[0mmaterials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIsotropicMedium\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mpoisson_ratio\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_array_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SupportsArray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nested_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NestedSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_array_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SupportsArray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nested_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NestedSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - 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"execution_count": 6, + "execution_count": 3, "id": "f2777b3d-e207-4ac5-95eb-f89d769da91c", "metadata": {}, "outputs": [], @@ -106,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "a2c619aa-50cf-480b-9d01-4e6a7ecb50ec", "metadata": {}, "outputs": [], @@ -128,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "83fd0d5c-abce-464c-a012-40c5c56fd9cb", "metadata": {}, "outputs": [ @@ -143,7 +118,7 @@ " array([10.00910117]))" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -163,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "a618e7b7-9787-4f3a-9dad-e8054645f969", "metadata": {}, "outputs": [ @@ -173,7 +148,7 @@ "Text(0.5, 0, 'volume fraction')" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -229,17 +204,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "019f6997-91fc-4f60-87ee-d77e3441def2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -310,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "6c9800c2-f969-4a41-b53a-131752ac5eea", "metadata": {}, "outputs": [ @@ -372,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "0feb1a67-f4a5-43c2-8a92-b29c2f8e3175", "metadata": {}, "outputs": [ @@ -418,17 +393,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "id": "00306adc-31eb-4ac0-b45d-8666779751fb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -494,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "0507850f-9fe1-4cb5-bd92-bfecbe9b994c", "metadata": {}, "outputs": [ @@ -562,17 +537,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "id": "14b45669-d271-4aaf-b75e-312bf3958138", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -650,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "id": "4c1f6967-5ec4-43de-8494-5d22989ae1f2", "metadata": {}, "outputs": [], @@ -660,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "id": "77cb71d0-d8d8-42e3-8b7b-7d94ea139214", "metadata": {}, "outputs": [