From 261de52a1abd05e229dd447b0acdb6051b11a487 Mon Sep 17 00:00:00 2001 From: Ameyanagi <77273474+Ameyanagi@users.noreply.github.com> Date: Fri, 25 Aug 2023 15:09:15 -0400 Subject: [PATCH] add correction to V_average --- .../notebook/calculation_of_V_average.ipynb | 526 ++++++++++++++++++ examples/notebook/plotting_hollow_shell.ipynb | 77 ++- 2 files changed, 581 insertions(+), 22 deletions(-) create mode 100644 examples/notebook/calculation_of_V_average.ipynb diff --git a/examples/notebook/calculation_of_V_average.ipynb b/examples/notebook/calculation_of_V_average.ipynb new file mode 100644 index 0000000..9e3065e --- /dev/null +++ b/examples/notebook/calculation_of_V_average.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "D = np.linspace(0, 50, 1000)\n", + "mu = 17\n", + "sigma = 6\n", + "PD = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-(D-mu)**2/(2*sigma**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "V = 1/6*np.pi*D**3\n", + "PV = PD*V\n", + "plt.plot(D, PV)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3533.7681365129183\n", + "18.89788242747988\n" + ] + } + ], + "source": [ + "V_mu = 1/6 * mu * np.pi * (mu**2 + 3*sigma**2)\n", + "DV_mu = (6/np.pi*V_mu)**(1/3)\n", + "print(V_mu)\n", + "print(DV_mu)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "# (mu^4 + 6 mu^2 sigma^2 + \n", + "# 3 sigma^4)/(mu Sqrt[1/sigma^2] sigma (mu^2 + 3 sigma^2))\n", + "\n", + "M = (mu**4 + 6*mu**2*sigma**2 + 3*sigma**4)/(mu*(mu**2 + 3*sigma**2))\n", + "\n", + "# (sigma^2 (mu^6 + 3 mu^4 sigma^2 + 9 mu^2 sigma^4 - \n", + "# 9 sigma^6))/(mu (mu^2 + 3 sigma^2) (mu^3 + 3 mu sigma^2))\n", + "S = (mu**4 + 10 * mu**2 * sigma**2 + 15 * sigma**4)/(mu**2 + 3*sigma**2) - M**2" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "def mu_vol(mu, sigma):\n", + " return (mu**4 + 6*mu**2*sigma**2 + 3*sigma**4)/(mu*(mu**2 + 3*sigma**2))\n", + "\n", + "def sigma_vol(mu, sigma):\n", + " return np.sqrt((mu**4 + 10 * mu**2 * sigma**2 + 15 * sigma**4)/(mu**2 + 3*sigma**2) - mu_vol(mu, sigma)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.342180091555236" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sqrt(S)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [], + "source": [ + "PV = D**3 / (mu*(mu**2 + 3*sigma**2)) * PD" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "theortical_PD = 1/sigma_vol(mu, sigma)/np.sqrt(2*np.pi) * np.exp(-(D-mu_vol(mu, sigma))**2/(2*sigma_vol(mu, sigma)**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.342180091555236" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigma_vol(mu, sigma) " + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "22.200770484516223" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu_vol(mu, sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(D, PV)\n", + "plt.plot(D, PD)\n", + "plt.plot(D, theortical_PD)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28.538888130609056" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "22.200770484516223" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.025541462954478088" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.std(PV/V_mu)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (1,) and (1000,)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[39m.\u001b[39;49mplot(D, PV\u001b[39m/\u001b[39;49mV_mu)\n\u001b[1;32m 2\u001b[0m plt\u001b[39m.\u001b[39mplot(D, PD)\n", + "File \u001b[0;32m~/mambaforge/envs/decomnano/lib/python3.11/site-packages/matplotlib/pyplot.py:2812\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2810\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mplot)\n\u001b[1;32m 2811\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, scaley\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 2812\u001b[0m \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49mplot(\n\u001b[1;32m 2813\u001b[0m \u001b[39m*\u001b[39;49margs, scalex\u001b[39m=\u001b[39;49mscalex, scaley\u001b[39m=\u001b[39;49mscaley,\n\u001b[1;32m 2814\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m~/mambaforge/envs/decomnano/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1447\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m 1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m 1690\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n", + "File \u001b[0;32m~/mambaforge/envs/decomnano/lib/python3.11/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m 310\u001b[0m args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m 312\u001b[0m this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n", + "File \u001b[0;32m~/mambaforge/envs/decomnano/lib/python3.11/site-packages/matplotlib/axes/_base.py:504\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n\u001b[1;32m 503\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 504\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y must have same first dimension, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 505\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhave shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 506\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m y\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m 507\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y can be no greater than 2D, but have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 508\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (1,) and (1000,)" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(D, PV/V_mu)\n", + "plt.plot(D, PD)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.integrate as integrate" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3533.8001506043684" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate.trapz(PV, D)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0000090594770832" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate.trapz(PV/V_mu, D)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9976965964837604" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate.trapz(PD, D)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2249.6666666666665" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mu * (mu**2 + 3*sigma**2))**1/3" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.666666666666667" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu**1/3" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.034523633130835" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mu**4 + 6 * mu**2 * sigma*2 + \n", + " 3 *sigma**4)/ (mu**3 + 3 * mu * sigma**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "decomnano", + "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": "3.11.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebook/plotting_hollow_shell.ipynb b/examples/notebook/plotting_hollow_shell.ipynb index 07066b2..66cb685 100644 --- a/examples/notebook/plotting_hollow_shell.ipynb +++ b/examples/notebook/plotting_hollow_shell.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -45,7 +45,10 @@ "Requirement already satisfied: pytz>=2020.1 in /Users/ryuichi/mambaforge/envs/decomnano/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n", "Requirement already satisfied: tzdata>=2022.1 in /Users/ryuichi/mambaforge/envs/decomnano/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n", "Requirement already satisfied: XlsxWriter>=0.5.7 in /Users/ryuichi/mambaforge/envs/decomnano/lib/python3.11/site-packages (from python-pptx->pptemp) (3.1.0)\n", - "Requirement already satisfied: six>=1.5 in /Users/ryuichi/mambaforge/envs/decomnano/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n" + "Requirement already satisfied: six>=1.5 in /Users/ryuichi/mambaforge/envs/decomnano/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -63,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -206,6 +209,10 @@ " digits = 1\n", " table = pd.concat([table,\n", " pd.DataFrame([[column,\n", + " # str(df[column].min()),\n", + " # str(df[column].max()),\n", + " # str(df[column].mean()),\n", + " # str(df[column].std())]],columns = [\"Parameter\", \"Min\", \"Max\", \"Mean\", \"Standard Deviation\"])])\n", " df[column].min().round(digits).astype(\"str\"),\n", " df[column].max().round(digits).astype(\"str\"),\n", " df[column].mean().round(digits).astype(\"str\"),\n", @@ -359,21 +366,21 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 66.84it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 100.29it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 85.16it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 217.25it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 180.60it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 284.09it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 144.53it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 130.71it/s]\n" + "100%|██████████| 1/1 [00:00<00:00, 98.64it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 108.90it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 81.92it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 197.83it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 230.89it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 272.48it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 190.26it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 104.50it/s]\n" ] }, { @@ -394,21 +401,21 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 94.71it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 99.77it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 83.24it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 178.35it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 199.09it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 259.81it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 212.00it/s]\n", - "100%|██████████| 1/1 [00:00<00:00, 150.44it/s]\n" + "100%|██████████| 1/1 [00:00<00:00, 91.31it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 120.05it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 82.25it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 192.53it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 209.87it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 332.22it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 186.12it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 163.90it/s]\n" ] }, { @@ -421,6 +428,32 @@ "output_type": "display_data" } ], + "source": [ + "#Plot the histogram of the input data and save to pptx slides\n", + "adn = Analyze(\"../sweep/Pt20Au80_AgBP1_hollow_shell_results.csv\")\n", + "adn.df = adn.df[adn.df[\"XA\"] > adn.df[\"XAP\"]]\n", + "adn.analyze_all(\"Pt20Au80_AgBP1_hollow_shell_XA>XAP\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'float' object has no attribute 'round'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m#Plot the histogram of the input data and save to pptx slides\u001b[39;00m\n\u001b[1;32m 2\u001b[0m adn \u001b[39m=\u001b[39m Analyze(\u001b[39m\"\u001b[39m\u001b[39m../sweep/Pt40Au60_AgBP1_hollow_shell_results.csv\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m adn\u001b[39m.\u001b[39;49manalyze_all(\u001b[39m\"\u001b[39;49m\u001b[39mPt40Au60_AgBP1_hollow_shell\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", + "Cell \u001b[0;32mIn[6], line 166\u001b[0m, in \u001b[0;36mAnalyze.analyze_all\u001b[0;34m(self, title)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf\u001b[39m.\u001b[39mdropna(inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 163\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf[\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m]\n\u001b[0;32m--> 166\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_table_drop \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgen_df_table(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_df_input())\n\u001b[1;32m 167\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_drop_len \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_df_len()\n\u001b[1;32m 168\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_table \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgen_df_table(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_df_output())\n", + "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mAnalyze.gen_df_table\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 91\u001b[0m digits \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 92\u001b[0m table \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([table,\n\u001b[1;32m 93\u001b[0m pd\u001b[39m.\u001b[39mDataFrame([[column,\n\u001b[1;32m 94\u001b[0m \u001b[39m# str(df[column].min()),\u001b[39;00m\n\u001b[1;32m 95\u001b[0m \u001b[39m# str(df[column].max()),\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[39m# str(df[column].mean()),\u001b[39;00m\n\u001b[1;32m 97\u001b[0m \u001b[39m# str(df[column].std())]],columns = [\"Parameter\", \"Min\", \"Max\", \"Mean\", \"Standard Deviation\"])])\u001b[39;00m\n\u001b[0;32m---> 98\u001b[0m df[column]\u001b[39m.\u001b[39;49mmin()\u001b[39m.\u001b[39;49mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m 99\u001b[0m df[column]\u001b[39m.\u001b[39mmax()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m 100\u001b[0m df[column]\u001b[39m.\u001b[39mmean()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m 101\u001b[0m df[column]\u001b[39m.\u001b[39mstd()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m)]],columns \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mParameter\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMin\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMax\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMean\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mStandard Deviation\u001b[39m\u001b[39m\"\u001b[39m])])\n\u001b[1;32m 103\u001b[0m \u001b[39mreturn\u001b[39;00m table\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'round'" + ] + } + ], "source": [ "#Plot the histogram of the input data and save to pptx slides\n", "adn = Analyze(\"../sweep/Pt40Au60_AgBP1_hollow_shell_results.csv\")\n",