{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `pyCactus` Demo\n", "This notebook demonstrates the core functionality of the `pyCactus` post-processing module.\n", "\n", "Author: [Phillip Chiu](mailto:pchiu@sandia.gov)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# enable autoreload\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "# set up plotting backend\n", "# %matplotlib nbagg\n", "%matplotlib inline\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "# matplotlib.rcParams['figure.figsize'] = (14.0, 10.5) # large figures\n", "matplotlib.rcParams['figure.figsize'] = (8, 6) # medium figures\n", "matplotlib.rcParams.update({'font.size': 14}) # label size\n", "\n", "# set up paths\n", "import os\n", "import sys\n", "\n", "nb_root_dir = os.path.expanduser('~/Notebooks/') # notebook root dir\n", "nb_dir_name = 'demo1' # notebook work dir name\n", "nb_dir = os.path.abspath(os.path.join(nb_root_dir,\n", " nb_dir_name)) # notebook work dir\n", "\n", "# create notebook dir if it doesn't exist\n", "if not os.path.exists(nb_dir):\n", " os.makedirs(nb_dir)\n", " \n", "sys.path.append(nb_dir) # append notebook work dir to pythonpath\n", "os.chdir(nb_dir) # change path to notebook work dir\n", "\n", "# import commonly-used modules\n", "import math\n", "import numpy as np\n", "import pandas as pd\n", "import time as pytime\n", "\n", "# apply style (from external notebook)\n", "%run '~/Notebooks/style.ipynb'\n", "clean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the path to pyCactus and import." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sys.path.append(nb_dir + '/CACTUS-tools/')\n", "import pyCactus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a `CactusRun` instance\n", "The `CactusRun` reads and stores information about a single run. The path to the data files is specified when initializing the `CactusRun` instance. It can be instantiated as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case_name = 'NREL-5MW'\n", "run_dir = os.path.join(nb_dir,'NREL-5MW-CACTUS')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read input namelist in 0.01 s\n", "Read geometry file in 0.01 s\n", "Read parameter data in 0.00 s\n", "Read revolution-averaged data in 0.00 s\n", "Read blade element data in 0.06 s\n", "Read time data in 0.01 s\n", "Read 20 wake element data headers in 0.00 s\n", "Read 20 wake element data headers in 0.00 s\n", "Read grid dimensions in 0.05 s\n", "Read 6 probe data file headers in 0.00 s\n", "\n", "Success: Loaded case `NREL-5MW` from path `/home/phil/Notebooks/demo1/NREL-5MW-CACTUS`\n", "\n", "Time to instantiante CactusRun class: 0.17 s\n" ] } ], "source": [ "tic = pytime.time()\n", "geom_fname = 'NREL-5MW.geom'\n", "run = pyCactus.CactusRun(run_dir, case_name,\n", " geom_fname=geom_fname,\n", " wakeelem_fnames_pattern='*WakeData_*.csv',\n", " field_fnames_pattern='*WakeDefData_*.csv')\n", "print 'Time to instantiante CactusRun class: %2.2f s' % (pytime.time() - tic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On instantiation, the `CactusRun` class loads the geometry file along with the time data, blade element data, and revolution-averaged data. It also parses the first line of each of the wake grid and wake node `.csv` files to determine the output times.\n", "\n", "The time, element, and revolution data are stored as Pandas dataframes. In order to reduce both memory usage and the time for class instantiation, data is only read from wake element and field data files (which may be very large) when needed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Geometry data\n", "The geometry data can be accessed easily by calling the `CactusRun.geom` attribute, which is an instance of the `CactusGeom` class.\n", "\n", "The geometry data is stored in the `globalvars`, `blades`, and `struts` attributes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'NBlade': [3],\n", " 'NStrut': [0],\n", " 'RefAR': array([ 3.14159]),\n", " 'RefR': array([ 206.693]),\n", " 'RotN': array([ 1., 0., 0.]),\n", " 'RotP': array([ 0., 0., 0.]),\n", " 'Type': ' HAWT'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.geom.globalvars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `blades` and `struts` are lists of variable dictionaries, enumerated by blade number and strut number." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['ECtoR',\n", " 'nEz',\n", " 'nEx',\n", " 'nEy',\n", " 'tz',\n", " 'PEy',\n", " 'PEz',\n", " 'ty',\n", " 'QCz',\n", " 'QCy',\n", " 'QCx',\n", " 'iSect',\n", " 'EAreaR',\n", " 'NElem',\n", " 'dr_over_R',\n", " 'r_over_R_elem',\n", " 'FlipN',\n", " 'PEx',\n", " 'CtoR',\n", " 'tx',\n", " 'Blade 1',\n", " 'sEz',\n", " 'sEy',\n", " 'sEx',\n", " 'tEx',\n", " 'tEy',\n", " 'tEz']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "blade_num = 0\n", "run.geom.blades[blade_num].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing time, revolution-averaged, and blade element data\n", "Time, blade element, and revolution data can be accessed using the appropriate variables names:\n", "\n", "- `CactusRun.time_data`\n", "- `CactusRun.elem_data`\n", "- `CactusRun.rev_data`\n", "\n", "Each returns a Pandas dataframe whose column names are shown below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The column names for the time data are shown below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'Normalized Time (-)', u'Theta (rad)', u'Rev', u'Torque Coeff. (-)',\n", " u'Power Coeff. (-)', u'Fx Coeff. (-)', u'Fy Coeff. (-)',\n", " u'Fz Coeff. (-)', u'Blade Fx Coeff. (-)', u'Blade Fy Coeff. (-)',\n", " u'Blade Fz Coeff. (-)', u'Blade Torque Coeff. (-)',\n", " u'Blade Fx Coeff. (-).1', u'Blade Fy Coeff. (-).1',\n", " u'Blade Fz Coeff. (-).1', u'Blade Torque Coeff. (-).1',\n", " u'Blade Fx Coeff. (-).2', u'Blade Fy Coeff. (-).2',\n", " u'Blade Fz Coeff. (-).2', u'Blade Torque Coeff. (-).2'],\n", " dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.time_data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easy to plot this data. Here we use the built-in plotting feature of the Pandas dataframe, but this could also be done with NumPy." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAGJCAYAAACw8/t+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCRg/j/2Oqq7xKlLigAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "run.time_data.plot(x='Normalized Time (-)',y='Power Coeff. (-)',\n", " marker='o')\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Revolution-averaged data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The column names for the revolution-averaged data are shown below." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index([u'Rev', u'Power Coeff. (-)', u'Tip Power Coeff. (-)',\n", " u'Torque Coeff. (-)', u'Fx Coeff. (-)', u'Fy Coeff. (-)',\n", " u'Fz Coeff. (-)', u'Power (kW)', u'Torque (ft-lbs)'],\n", " dtype='object')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.rev_data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the time data, this can be easily plotted." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGJCAYAAACNYZoYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSTgVexERkYRTsRcREUm4/w+GoKAfhrIOvgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "run.rev_data.plot(x='Rev',y='Fx Coeff. (-)',\n", " marker='o')\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blade element data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The column names for the blade element data are shown below." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'Normalized Time (-)', u'Theta (rad)', u'Blade', u'Element', u'Rev',\n", " u'x/R (-)', u'y/R (-)', u'z/R (-)', u'AOA25 (deg)', u'AOA50 (deg)',\n", " u'AOA75 (deg)', u'AdotNorm (-)', u'Re (-)', u'Mach (-)', u'Ur (-)',\n", " u'IndU (-)', u'IndV (-)', u'IndW (-)', u'GB (?)', u'CL (-)', u'CD (-)',\n", " u'CM25 (-)', u'CLCirc (-)', u'CN (-)', u'CT (-)', u'Fx (-)', u'Fy (-)',\n", " u'Fz (-)', u'te (-)'],\n", " dtype='object')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.bladeelem_data.data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This data can also be plotted easily. We can extract the blade data at a particular time index by calling `CactusRun.blade_data_at_time_index()`.\n", "\n", "For horizontal axis wind turbines, it is often desirable to plot a blade quantity against the distance from the rotation axis. This is demonstrated here using data from the `CactusRun.geom` instance." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAGJCAYAAADbgQqfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blade_num = 0\n", "\n", "# get the blade data at the final time index\n", "time, dfs_blade = run.bladeelem_data.data_at_time_index(time_index=-1)\n", "\n", "plt.figure()\n", "plt.plot(run.geom.blades[blade_num]['r_over_R_elem'],\n", " dfs_blade[blade_num]['CT (-)'])\n", "plt.grid(True)\n", "plt.xlabel('r_over_R_elem')\n", "plt.ylabel('CT (-)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The blade data can also be time-averaged over specified timesteps. In the following example, we average over the last 5 timesteps." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-5, -4, -3, -2, -1]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timesteps = range(-5,0)\n", "CT_averaged = run.bladeelem_data.data_time_average('CT (-)', timesteps, 1)\n", "\n", "timesteps" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAGJCAYAAADbgQqfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERKZgKCBERESmYCggREREp2P8DVFnugQb0tKsAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(run.geom.blades[blade_num]['r_over_R_elem'],\n", " CT_averaged)\n", "plt.grid(True)\n", "plt.xlabel('r_over_R_elem')\n", "plt.ylabel('CT (-)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing wake element and field data\n", "Due to their potentially large file sizes, wake element and field data are accessed differently than the above data -- the entire data file is never loaded into memory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wake element data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Viewing times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The times at which we have wake element data can be easily viewed." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.wakeelems.num_times" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.2080525, 0.416105 , 0.6241575, 0.83221 ,\n", " 1.040262 , 1.248315 , 1.456367 , 1.66442 , 1.872472 ,\n", " 2.080525 , 2.288577 , 2.49663 , 2.704682 , 2.912735 ,\n", " 3.120787 , 3.32884 , 3.536892 , 3.744945 , 3.952997 ])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.wakeelems.times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Extracting data to NumPy arrays\n", "A dataframe containing data at a particular timestep can be extracted using the method `CactusWakeElem.get_df_inst()`. This returns a dictionary containing variables names and their values as NumPy arrays." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get dataframe for a specific time\n", "ti = 5\n", "wake_df_inst = run.wakeelems.get_df_inst(time=run.wakeelems.times[ti])\n", "wake_df_inst.head()\n", "\n", "# extract variables\n", "wakeelemdata, has_node_ids = run.wakeelems.wakedata_from_df(wake_df_inst)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['node_ids', 'elems', 'u', 'w', 'v', 'y', 'x', 'z']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wakeelemdata.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second return is a flag which indicates whether the wake element data files have a `node_id` column. (For compatibility with data generated by versions of CACTUS)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "has_node_ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Writing to VTK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The wake element data can be written to a VTK file to facilitate post-processing with ParaView or other tools. To do this, we can use the `CactusWakeElems.write_vtk_series()` method. This writes a series of VTK files, and a `.pvd` collection file which can be read into ParaView directly as a time series." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(['/home/phil/Notebooks/demo1/vtk/wakenode_name_0.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_1.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_2.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_3.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_4.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_5.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_6.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_7.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_8.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_9.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_10.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_11.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_12.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_13.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_14.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_15.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_16.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_17.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_18.vtu',\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name_19.vtu'],\n", " '/home/phil/Notebooks/demo1/vtk/wakenode_name.pvd')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wakeelem_output_path = os.path.join(nb_dir,'vtk')\n", "run.wakeelems.write_vtk_series(wakeelem_output_path,\n", " 'wakenode_name',\n", " print_status=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Field data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Viewing times" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.wakeelems.num_times" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.2080525, 0.416105 , 0.6241575, 0.83221 ,\n", " 1.040262 , 1.248315 , 1.456367 , 1.66442 , 1.872472 ,\n", " 2.080525 , 2.288577 , 2.49663 , 2.704682 , 2.912735 ,\n", " 3.120787 , 3.32884 , 3.536892 , 3.744945 , 3.952997 ])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.field.times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Extracting data to NumPy arrays\n", "A dataframe containing data at a particular timestep can be extracted using the method `CactusField.get_df_inst()`. This returns a dictionary containing variables names and their values as NumPy arrays." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get dataframe for a specific time\n", "ti = 5\n", "field_df_inst = run.field.get_df_inst(time=run.wakeelems.times[ti])\n", "field_df_inst.head()\n", "\n", "# extract variables\n", "fielddata, fielddims = run.field.fielddata_from_df(field_df_inst)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Ufs', 'Wfs', 'U', 'W', 'V', 'Y', 'X', 'Z', 'Vfs']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fielddata.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second return is a dictionary of the field dimensions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'dx': 0.0,\n", " 'dy': 0.040000000000000001,\n", " 'dz': 0.040000000000000001,\n", " 'nx': 1,\n", " 'ny': 100,\n", " 'nz': 100,\n", " 'xlim': [0.0, 0.0],\n", " 'ylim': [-2.0, 2.0],\n", " 'zlim': [-2.0, 2.0]}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fielddims" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dimensions of the returned arrays match the grid dimensions." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(100, 100, 1)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fielddata['U'].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Time series at a specific location on the Cartesian grid\n", "Data at a specific location can be extracted. This operation may take some time, since it loops through all the data files." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to produce series: 0.70 s\n", "[(0.0, 0.50505048, 0.50505048), (0.0, -0.02020202, -0.02020202)]\n" ] } ], "source": [ "# enter a list of point coordinates\n", "point_list = [(0.0, 0.5, 0.5),\n", " (1.0, 0.0, 0.0)]\n", "\n", "tic = pytime.time()\n", "timeseries_dict, nearest_points = run.field.pointdata_time_series(point_list, ti_start=0, ti_end=-1)\n", "print 'Time to produce series: %2.2f s' % (pytime.time() - tic)\n", "\n", "print nearest_points" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# extract the data\n", "t = timeseries_dict['t']\n", "u = timeseries_dict['u']\n", "v = timeseries_dict['v']\n", "w = timeseries_dict['w']\n", "ufs = timeseries_dict['ufs']\n", "vfs = timeseries_dict['vfs']\n", "wfs = timeseries_dict['wfs']" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.2080525 0.416105 0.6241575 0.83221 1.040262 1.248315\n", " 1.456367 1.66442 1.872472 2.080525 2.288577 2.49663 2.704682\n", " 2.912735 3.120787 3.32884 3.536892 3.744945 ]\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGhCAYAAAB/I44UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKErI8f8DZMpEGXrja9sAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "point_id = 1\n", "\n", "print t\n", "# plot the data at the point_id'th point\n", "plt.figure()\n", "plt.plot(t, u[point_id,:], label='$u$', lw=2)\n", "plt.plot(t, v[point_id,:], label='$v$', lw=2)\n", "plt.plot(t, w[point_id,:], label='$w$', lw=2)\n", "\n", "plt.title('Time series')\n", "plt.xlabel('non-dimensionalized time, $t^*$')\n", "plt.ylabel('non-dimensionalized velocity')\n", "plt.legend(loc='best')\n", "\n", "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Field averaging\n", "We can time average the field data using the `CactusField.field_time_average()` method. This returns a dictionary containing the averaged data." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to average fields: 0.19 s\n" ] } ], "source": [ "tic = pytime.time()\n", "\n", "averaged_data = run.field.field_time_average(ti_start=-5,\n", " ti_end=-1)\n", "\n", "print 'Time to average fields: %2.2f s' % (pytime.time() - tic)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ufs', 'U', 't', 'W', 'V', 'Y', 'X', 'Z', 'Vfs', 'Wfs']\n" ] } ], "source": [ "print averaged_data.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `t` field gives the normalized times which were averaged." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 3.120787 3.32884 3.536892 3.744945]\n" ] } ], "source": [ "print averaged_data['t']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The averaged data may be plotted." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGbCAYAAAAoSIKLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.cm as cm\n", "\n", "# Get the data\n", "X = averaged_data['X']\n", "Y = averaged_data['Y']\n", "Z = averaged_data['Z']\n", "U = averaged_data['U']\n", "V = averaged_data['V']\n", "W = averaged_data['W']\n", "Ufs = averaged_data['Ufs']\n", "Vfs = averaged_data['Vfs']\n", "Wfs = averaged_data['Wfs']\n", "\n", "# Reshape to 2D\n", "ny = run.field.grid_dims['ny']\n", "nz = run.field.grid_dims['nz']\n", "\n", "X = np.reshape(X, [ny, nz])\n", "Y = np.reshape(Y, [ny, nz])\n", "Z = np.reshape(Z, [ny, nz])\n", "U = np.reshape(U, [ny, nz])\n", "V = np.reshape(V, [ny, nz])\n", "W = np.reshape(W, [ny, nz])\n", "Ufs = np.reshape(Ufs, [ny, nz])\n", "Vfs = np.reshape(Vfs, [ny, nz])\n", "Wfs = np.reshape(Wfs, [ny, nz])\n", "\n", "# u\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "cax = ax.contourf(Y,Z,U+Ufs,128,cmap=cm.coolwarm)\n", "fig.colorbar(cax)\n", "ax.axis('image')\n", "ax.set_title('$U/U_{\\infty}$, time-averaged')\n", "ax.set_xlabel('$x/R$')\n", "ax.set_ylabel('$y/R$')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Writing to VTK file\n", "As with the wake element data, the field data can also be written to VTK files, using the `CactusField.write_vtk_series()` method. This writes a series of structured VTK files (`.vts`), and a `.pvd` collection file which can be read into ParaView directly as a time series." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(['/home/phil/Notebooks/demo1/vtk/field_0.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_1.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_2.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_3.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_4.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_5.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_6.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_7.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_8.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_9.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_10.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_11.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_12.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_13.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_14.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_15.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_16.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_17.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_18.vts',\n", " '/home/phil/Notebooks/demo1/vtk/field_19.vts'],\n", " '/home/phil/Notebooks/demo1/vtk/field.pvd')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "field_output_path = os.path.join(nb_dir,'vtk')\n", "run.field.write_vtk_series(field_output_path,\n", " 'field',\n", " print_status=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probe data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `CactusRun.probe` attribute is an instance of the `CactusProbe` class. We can check the number of probes and their locations." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.probes.num_probes" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: (0.0, 0.0, 0.0),\n", " 1: (1.0, 0.0, 0.0),\n", " 2: (2.0, 0.0, 0.0),\n", " 3: (3.0, 0.0, 0.0),\n", " 4: (4.0, 0.0, 0.0),\n", " 5: (5.0, 0.0, 0.0)}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.probes.locations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also extract the probe data using `get_probe_data_by_id()`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Normalized Time (-)U/Uinf (-)V/Uinf (-)W/Uinf (-)Ufs/Uinf (-)Vfs/Uinf (-)Wfs/Uinf (-)
00.0000000.0002883.685064e-09-2.268072e-09100
10.041611-0.004127-1.330602e-091.217035e-09100
20.083221-0.0079501.282547e-095.536929e-10100
30.124832-0.0120124.207945e-103.674458e-10100
40.166442-0.0164456.160830e-105.428379e-10100
\n", "
" ], "text/plain": [ " Normalized Time (-) U/Uinf (-) V/Uinf (-) W/Uinf (-) Ufs/Uinf (-) \\\n", "0 0.000000 0.000288 3.685064e-09 -2.268072e-09 1 \n", "1 0.041611 -0.004127 -1.330602e-09 1.217035e-09 1 \n", "2 0.083221 -0.007950 1.282547e-09 5.536929e-10 1 \n", "3 0.124832 -0.012012 4.207945e-10 3.674458e-10 1 \n", "4 0.166442 -0.016445 6.160830e-10 5.428379e-10 1 \n", "\n", " Vfs/Uinf (-) Wfs/Uinf (-) \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.probes.get_probe_data_by_id(1).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The velocity for any given probe can be plotted quickly by calling `plot_probe_data_by_id()`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAGXCAYAAAAeSEK+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqHeqL/Wzy8cAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = run.probes.plot_probe_data_by_id(0,timestep=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing the input namelist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The input file namelist can be accessed using the `namelist` instance attribute." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('configinputs',\n", " OrderedDict([('gpflag', 0),\n", " ('nr', 5),\n", " ('nti', 20),\n", " ('iut', 1),\n", " ('convrg', -1),\n", " ('ifc', 0),\n", " ('nric', 9),\n", " ('ntif', 30),\n", " ('iutf', 1),\n", " ('convrgf', 0.0001),\n", " ('ixterm', 0),\n", " ('xstop', 5)])),\n", " ('caseinputs',\n", " OrderedDict([('jbtitle', 'NREL 5 MW'),\n", " ('rho', 0.002378),\n", " ('vis', 3.739e-07),\n", " ('tempr', 60.0),\n", " ('slex', 0.0),\n", " ('hblref', 0.0),\n", " ('hag', 295.276),\n", " ('rpm', 12.1),\n", " ('ut', 7.55),\n", " ('geomfilepath', './NREL-5MW.geom'),\n", " ('nsect', 8),\n", " ('afdpath',\n", " ['./airfoils/Cylinder1.dat',\n", " './airfoils/Cylinder2.dat',\n", " './airfoils/DU40_A17.dat',\n", " './airfoils/DU35_A17.dat',\n", " './airfoils/DU30_A17.dat',\n", " './airfoils/DU25_A17.dat',\n", " './airfoils/DU21_A17.dat',\n", " './airfoils/NACA64_A17.dat'])])),\n", " ('configoutputs',\n", " OrderedDict([('diagoutflag', 1),\n", " ('output_elflag', 1),\n", " ('wakeelementoutflag', 1),\n", " ('wakegridoutflag', 1),\n", " ('probeflag', 1),\n", " ('probespecpath', './probes.dat')]))])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run.input.namelist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This namelist can easily be modified and rewritten as a separate file (perhaps facilitating parametric studies of parameters)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "run.input.namelist['configinputs']['nti'] = 30" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import StringIO\n", "\n", "output = StringIO.StringIO()\n", "run.input.namelist.write(output)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "&configinputs\n", " gpflag = 0\n", " nr = 5\n", " nti = 30\n", " iut = 1\n", " convrg = -1\n", " ifc = 0\n", " nric = 9\n", " ntif = 30\n", " iutf = 1\n", " convrgf = 0.0001\n", " ixterm = 0\n", " xstop = 5\n", "/\n", "\n", "&caseinputs\n", " jbtitle = 'NREL 5 MW'\n", " rho = 0.002378\n", " vis = 3.739e-07\n", " tempr = 60.0\n", " slex = 0.0\n", " hblref = 0.0\n", " hag = 295.276\n", " rpm = 12.1\n", " ut = 7.55\n", " geomfilepath = './NREL-5MW.geom'\n", " nsect = 8\n", " afdpath = './airfoils/Cylinder1.dat', './airfoils/Cylinder2.dat', './airfoils/DU40_A17.dat',\n", " './airfoils/DU35_A17.dat', './airfoils/DU30_A17.dat', './airfoils/DU25_A17.dat',\n", " './airfoils/DU21_A17.dat', './airfoils/NACA64_A17.dat'\n", "/\n", "\n", "&configoutputs\n", " diagoutflag = 1\n", " output_elflag = 1\n", " wakeelementoutflag = 1\n", " wakegridoutflag = 1\n", " probeflag = 1\n", " probespecpath = './probes.dat'\n", "/\n", "\n" ] } ], "source": [ "print output.getvalue()" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }