From 7fb1b9467ff75c96e3e62ebb108a4d0dc346d3a9 Mon Sep 17 00:00:00 2001 From: Craig Warren Date: Thu, 12 May 2016 18:08:51 +0100 Subject: [PATCH] Directory for Python notebooks. --- tools/Jupyter notebooks/plot_Ascan.ipynb | 284 ++++++++++++++++++ tools/Jupyter notebooks/plot_Bscan.ipynb | 94 ++++++ .../plot_antenna_params.ipynb | 79 +++++ 3 files changed, 457 insertions(+) create mode 100644 tools/Jupyter notebooks/plot_Ascan.ipynb create mode 100644 tools/Jupyter notebooks/plot_Bscan.ipynb create mode 100644 tools/Jupyter notebooks/plot_antenna_params.ipynb diff --git a/tools/Jupyter notebooks/plot_Ascan.ipynb b/tools/Jupyter notebooks/plot_Ascan.ipynb new file mode 100644 index 00000000..130a9948 --- /dev/null +++ b/tools/Jupyter notebooks/plot_Ascan.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import sys\n", + "\n", + "# Change to your path to gprMax\n", + "path = '/Users/cwarren/Documents/Git-projects/gprMax/'\n", + "sys.path.append(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting an A-scan\n", + "\n", + "In the ``tools`` sub-package is a module called ``plot_Ascan`` which can be used to plot A-scans or single traces from models. The module will plot electric and magnetic field components and currents from any outputs (defined using the ``#rx`` command) in a model. When a single field component or current is specified, a FFT can also be plotted. The module takes the arguments:\n", + "\n", + "* ``--outputs`` which can be any field component or current, i.e. ``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``, ``Ix``, ``Iy``, or ``Iz``, so long as those components or currents were specified in the output in the model (by default all components and currents are output)\n", + "* ``-fft`` a switch to turn on the FFT plotting for a single field component or current\n", + "\n", + "Each output (``#rx``) from a model will be plotted in a separate figure window.\n", + "\n", + "For example (to use the module outside this notebook) to plot the ``Ez`` component of an output with its FFT:\n", + "\n", + " python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out --outputs Ez -fft" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the following code to experiment (in this notebook) with plotting different field/current components and FFTs." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\n", + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "\n", + "from gprMax.receivers import Rx\n", + "from tools.plot_Ascan import make_plot\n", + "\n", + "filename = os.path.join(path, 'user_models', 'cylinder_Ascan_2D.out')\n", + "outputs = ['Ez']\n", + "make_plot(filename, outputs, fft=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tools/Jupyter notebooks/plot_Bscan.ipynb b/tools/Jupyter notebooks/plot_Bscan.ipynb new file mode 100644 index 00000000..df0f0d1f --- /dev/null +++ b/tools/Jupyter notebooks/plot_Bscan.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import sys\n", + "\n", + "# Change to your path to gprMax\n", + "path = '/Users/cwarren/Documents/Git-projects/gprMax/'\n", + "sys.path.append(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting a B-scan\n", + "\n", + "In the ``tools`` sub-package is a module called ``plot_Bscan`` which can be used to plot a B-scan (collection of A-scans) from models. The module takes a single argument which can be any field component or current, i.e. ``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``, ``Ix``, ``Iy``, or ``Iz``, so long as those components or currents were specified in the output in the model (by default all components and currents are output)\n", + "\n", + "For example (to use the module outside this notebook) to plot the B-scan of the ``Ez`` component of an output:\n", + "\n", + " python -m tools.plot_Bscan user_models/cylinder_Bscan_2D_merged.out Ez\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the following code to experiment (in this notebook) with plotting different field/current components." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gjUzBEnmCNTIFpI6OjXB4VdigYwOWCgsLyRRMkSlYI1OwRJ5gjUwByBVeFTZ2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "from tools.plot_Bscan import get_output_data, mpl_plot\n", + "\n", + "filename = os.path.join(path, 'user_models', 'cylinder_Bscan_2D_merged.out')\n", + "rxnumber = 1\n", + "rxcomponent = 'Ez'\n", + "outputdata, dt = get_output_data(filename, rxnumber, rxcomponent)\n", + "plt = mpl_plot(outputdata, dt, rxnumber, rxcomponent)\n", + "\n", + "# Change from the default 'seismic' colormap\n", + "plt.set_cmap('gray')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tools/Jupyter notebooks/plot_antenna_params.ipynb b/tools/Jupyter notebooks/plot_antenna_params.ipynb new file mode 100644 index 00000000..c6a2968d --- /dev/null +++ b/tools/Jupyter notebooks/plot_antenna_params.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting antenna parameters\n", + "\n", + "In the ``tools`` sub-package is a module called ``plot_antenna_params`` which can be used to plot parameters from a model containing an antenna. The antenna in the model must be fed using the ``#transmission_line`` command and, optionally, a receiver antenna in the model The module will plot electric and magnetic field components and currents from any outputs (defined using the ``#rx`` command) in a model. When a single field component or current is specified, a FFT can also be plotted. The module takes the arguments:\n", + "\n", + "* ``--outputs`` which can be any field component or current, i.e. ``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``, ``Ix``, ``Iy``, or ``Iz``, so long as those components or currents were specified in the output in the model (by default all components and currents are output)\n", + "* ``-fft`` a switch to turn on the FFT plotting for a single field component or current\n", + "\n", + "Each output (``#rx``) from a model will be plotted in a separate figure window.\n", + "\n", + "For example (to use the module outside this notebook) to plot the ``Ez`` component of an output with its FFT:\n", + "\n", + " python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out --outputs Ez -fft\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the following code to experiment (in this notebook) with plotting different field/current components and FFTs." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KS0tzHQLiZds26YILpIEDbXD58OG2Oqpw4fw/dvPmUnq6rZgaPdqu0voEOe5x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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import os\n", + "from gprMax.receivers import Rx\n", + "from tools.plot_Ascan import make_plot\n", + "\n", + "filename = os.path.join('user_models', 'cylinder_Ascan_2D.out')\n", + "outputs = ['Ez']\n", + "make_plot(filename, outputs, fft=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}