diff --git a/doc/chart_strap_reactance.ipynb b/doc/chart_strap_reactance.ipynb index 9b79eca..b67e497 100644 --- a/doc/chart_strap_reactance.ipynb +++ b/doc/chart_strap_reactance.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -25,18 +25,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\JH218595\\Documents\\scikit-rf\\skrf\\plotting.py:1441: UserWarning: Style includes a parameter, 'interactive', that is not related to style. Ignoring\n", - " mpl.style.use(os.path.join(pwd, style_file))\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -53,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -75,35 +66,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0d85488d882d422a8ebd659f9cfa6d5d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(2, 1, sharex=True)\n", "# real parts\n", @@ -133,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -142,17 +107,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.65682345e-04 -1.52541615e-02 1.04388949e+00 -7.76846926e+00]\n" - ] - } - ], + "outputs": [], "source": [ "# polynomial fit of the strap reactance\n", "coeffs = polyfit(ntw_vacuum.frequency.f/1e6, \n", @@ -179,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -192,34 +149,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2bb96cde53aa4a85b8acc40978564846", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$X_s$ [$\\\\Omega$]')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ntw_vacuum.plot_z_im(m=0, n=0, ax=ax, lw=2, label='simulation')\n", @@ -234,112 +166,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from IPython.core.display import HTML\n", "def _set_css_style(css_file_path):\n", @@ -371,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/doc/coupling_to_ideal_load.ipynb b/doc/coupling_to_ideal_load.ipynb index d08298f..ed88f61 100644 --- a/doc/coupling_to_ideal_load.ipynb +++ b/doc/coupling_to_ideal_load.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -56,18 +56,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\JH218595\\Documents\\scikit-rf\\skrf\\plotting.py:1441: UserWarning: Style includes a parameter, 'interactive', that is not related to style. Ignoring\n", - " mpl.style.use(os.path.join(pwd, style_file))\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -92,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -103,18 +94,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True solution #1: [53.56567365 45.98820709]\n", - "True solution #1: [53.56564882 45.98822849]\n" - ] - } - ], + "outputs": [], "source": [ "# matching left and right sides : note that the solutions are (almost) the same\n", "f_match = 55.5e6\n", @@ -133,24 +115,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c2547cb0b938448dbc0aa8a124cec929", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# dipole excitation\n", "power = [1, 1]\n", @@ -182,66 +149,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0270875b558d43af8df5d4b899955baf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=14.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [52.02785554 50.46880396]\n", - "True solution #1: [52.4097308 50.09882462]\n", - "True solution #1: [52.7552753 49.76713493]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [53.66611397 48.90006135]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [54.2767751 48.3126251]\n", - "True solution #1: [54.73987025 47.84863587]\n", - "True solution #1: [55.102375 47.45603461]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [55.38480972 47.10756983]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [55.59611424 46.78433624]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [55.73868969 46.46927577]\n", - "True solution #1: [55.80947891 46.14276843]\n", - "True solution #1: [55.79864076 45.7759411 ]\n", - "True solution #1: [55.68811645 45.32042321]\n", - "True solution #1: [55.44475928 44.67697131]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "f_match = 55e6\n", - "Rcs = np.r_[0.01, 0.05, np.arange(0.1, 2.5, 0.2)]\n", + "Rcs = np.r_[0.01, 0.05, np.arange(0.1, 3.0, 0.2)]\n", "\n", "C_matchs = []\n", "ant = WestIcrhAntenna()\n", @@ -261,34 +174,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eaca400241794b48afe5fd2121afd873", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(Rcs, np.array(C_matchs)[:,0:2], lw=2, marker='o')\n", @@ -308,34 +196,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3755698a7c9b44ba90a612452ef23201", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "delta_C_pos = np.array(C_matchs)[:,0] - C_matchs[0][0]\n", "delta_C_neg = C_matchs[0][1] - np.array(C_matchs)[:,1]\n", @@ -367,55 +230,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "047c16c85f7d41bc8ed2db4e7060c8bf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2d53d04b41b14f4aabe172fa24cca33c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=8.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# create a single frequency point antenna to speed-up calculations\n", "ant = WestIcrhAntenna(frequency=rf.Frequency.from_f(f_match, unit='Hz'))\n", @@ -440,114 +257,97 @@ "ax.legend(Rcs)" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VSWR(C_top, C_bot) plane\n", + "A symetrical Resonant Double Loop (RDL) without mutual coupling between top and bottom strap, has two sets of (symmetrical) solutions. Let's visualize these solution on a $(C_{top}, C_{bot})$ map." + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "C_top, C_bot = np.meshgrid(np.arange(40, 60, .5), np.arange(40, 60, .5))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "Cs_plane = zip(C_top.flatten(), C_bot.flatten(), C_top.flatten(), C_bot.flatten())\n", + "\n", + "power, phase = [1, 1], [0, np.pi] # dipole\n", + "antenna = WestIcrhAntenna(frequency=rf.Frequency(55, 55, npoints=1, unit='MHz'))\n", + "\n", + "# initiate arrays\n", + "results = {}\n", + "loads = [0.2, .5, 1, 2]\n", + "for load in loads:\n", + " results[load] = []\n", + "\n", + "# calculate VSWR for all (C_top, C_bot) and for different loading cases\n", + "for Cs in tqdm(Cs_plane):\n", + " for load in loads:\n", + " antenna.load(load)\n", + " _vswr = antenna.vswr_act(power, phase, Cs=Cs)\n", + " results[load].append(_vswr)\n", + "\n", + "# convert data into arrays\n", + "for load in loads:\n", + " results[load] = np.array(results[load])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "#ax.contourf(C_top, C_bot, np.ones_like(C_top), colors='white')\n", + "ax.set_facecolor('white')\n", + "ax.grid(True, ls='--', color='gray', alpha=0.5)\n", + "\n", + "for idx, load in enumerate(loads[-1::-1]):\n", + " #map = ax.pcolormesh(C_top, C_bot, results[load][:,0].reshape(C_top.shape),\n", + " # vmin=1, vmax=1.25, shading='gouraud', cmap='Blues_r')\n", + " \n", + " cs = ax.contourf(C_top, C_bot, results[load][:,0].reshape(C_top.shape),\n", + " levels=np.arange(1, 2 ,0.1), alpha=0.7)\n", + " #ax.clabel(cs, inline=True, fontsize=7)\n", + "fig.colorbar(cs)\n", + "ax.plot([40, 59], [40, 59], ls='--', color='k')\n", + "ax.axis([40, 60, 40, 60])\n", + "ax.axis('tight')\n", + "ax.plot(np.array(C_matchs)[:,0], np.array(C_matchs)[:,1], color='red', ls=':')\n", + "ax.set_xlabel('$C_{top}$')\n", + "ax.set_ylabel('$C_{bot}$')\n", + "ax.set_title('VSWR Map')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We refer in this package as:\n", + "- \"Solution 1\" the case when $C_{top} > C_{bot}$\n", + "- \"Solution 2\" the case when $C_{top} < C_{bot}$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.core.display import HTML\n", "def _set_css_style(css_file_path):\n", @@ -572,7 +372,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -586,7 +386,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/doc/coupling_to_plasma_from_COMSOL.ipynb b/doc/coupling_to_plasma_from_COMSOL.ipynb index 5412e4b..b8edfc4 100644 --- a/doc/coupling_to_plasma_from_COMSOL.ipynb +++ b/doc/coupling_to_plasma_from_COMSOL.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -32,17 +32,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4-Port Network: 'ORNL_front_face_conventional', 55000000.0-55000000.0 Hz, 1 pts, z0=[50.+0.j 50.+0.j 50.+0.j 50.+0.j]\n" - ] - } - ], + "outputs": [], "source": [ "front_face_conventional = rf.Network(\n", " '../west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_conventional.s4p')\n", @@ -69,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -139,24 +131,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking for individual solutions separately for 1st guess...\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [52.57986227 45.88696785]\n", - "True solution #1: [52.30894839 46.0700872 ]\n", - "Searching for the active match point solution...\n", - "Reducing search range to +/- 5pF around individual solutions\n", - "True solution #1: [53.67807308 46.12207788 53.62800637 46.30935881]\n" - ] - } - ], + "outputs": [], "source": [ "Cs = ant.match_both_sides(f_match=55e6)" ] @@ -170,20 +147,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.70081638, 0.6878438 ])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "power = [1, 1]\n", "phase = [0, np.pi]\n", @@ -201,22 +167,11 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[22.16126386, 23.75535708, 20.56902439, 25.21223565]])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "power = [1, 1] # MW, to adjust to fit with experiment\n", + "power = [1.6/2, 1.6/2] # MW, to adjust to fit with experiment\n", "phase = [0, np.pi] # rad\n", "\n", "abs(ant.voltages(power, phase)) # results in kV" @@ -224,20 +179,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.70248687, 0.75556107, 0.65298786, 0.80405425]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "abs(ant.currents(power, phase)) # results in kA" ] diff --git a/doc/coupling_to_plasma_from_TOPICA.ipynb b/doc/coupling_to_plasma_from_TOPICA.ipynb index bedf090..2fcf927 100644 --- a/doc/coupling_to_plasma_from_TOPICA.ipynb +++ b/doc/coupling_to_plasma_from_TOPICA.ipynb @@ -2,16 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,6 +21,7 @@ "import skrf as rf\n", "from tqdm.notebook import tqdm\n", "\n", + "import sys; sys.path.append('..')\n", "from west_ic_antenna import WestIcrhAntenna\n", "from west_ic_antenna.topica import TopicaResult" ] @@ -54,23 +46,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4-Port Network: 'S_TSproto12_55MHz_Profile1', 55000000.0-55000000.0 Hz, 1 pts, z0=[46.7+0.j 46.7+0.j 46.7+0.j 46.7+0.j]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# generates a 2 Ohm coupling resistance front-face\n", - "WestIcrhAntenna.TOPICA_front_face(Rc=2, mode='L')" + "WestIcrhAntenna.interpolate_front_face(Rc=2, source='TOPICA-L-mode')" ] }, { @@ -82,37 +63,17 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking for individual solutions separately for 1st guess...\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [56.56365762 45.75301262]\n", - "True solution #1: [55.98256028 45.75805833]\n", - "Searching for the active match point solution...\n", - "Reducing search range to +/- 2pF around individual solutions\n", - "True solution #1: [55.2314647 44.22049285 54.91290648 43.75805833]\n" - ] - } - ], + "outputs": [], "source": [ "# Antenna Frequency band defined as single point\n", "f0 = 55e6\n", "freq = rf.Frequency(f0, f0, npoints=1, unit='Hz')\n", "# generate antenna front face\n", - "TOPICA_front_face = WestIcrhAntenna.TOPICA_front_face(Rc=2, mode='L')\n", - "#TOPICA_front_face = rf.Network('../west_ic_antenna/data/Sparameters/front_faces/TOPICA/S_TSproto12_55MHz_Profile1.s4p')\n", + "TOPICA_front_face = WestIcrhAntenna.interpolate_front_face(Rc=2, source='TOPICA-L-mode')\n", "# build the antenna model\n", - "antenna = WestIcrhAntenna(frequency=freq, front_face=ff)\n", + "antenna = WestIcrhAntenna(frequency=freq, front_face=TOPICA_front_face)\n", "# match the antenna in dipole\n", "power = [1, 1]\n", "phase = [0, np.pi]\n", @@ -122,107 +83,18 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[55.23146469964239, 44.220492846794556, 54.912906479915755, 43.75805833440046]" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "C_match" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[57.24348474 42.38029181 56.93196223 41.90864916] -> [[1.48517091 1.45864571]]\n", - "[57.25209586 42.53750054 56.94858509 42.05796714] -> [[1.46777711 1.44865192]]\n", - "[57.25731492 42.69184824 56.96273956 42.20584768] -> [[1.44991143 1.43816891]]\n", - "[57.25917622 42.84306716 56.97439555 42.35212453] -> [[1.431667 1.42723053]]\n", - "[57.25773226 42.99089509 56.98352863 42.49663022] -> [[1.4131395 1.41587262]]\n", - "[57.25305416 43.13507815 56.99012035 42.63919654] -> [[1.39442594 1.40413279]]\n", - "[57.24523179 43.27537368 56.99415851 42.77965509] -> [[1.37562351 1.3920502 ]]\n", - "[57.2343735 43.41155324 56.99563751 42.91783781] -> [[1.35682821 1.3796654 ]]\n", - "[57.22060539 43.54340568 56.9945586 43.05357762] -> [[1.33813368 1.36702014]]\n", - "[57.20407022 43.67073996 56.99093018 43.18670913] -> [[1.31962997 1.35415721]]\n", - "[57.18492582 43.79338789 56.98476811 43.31706931] -> [[1.30140239 1.34112035]]\n", - "[57.16334311 43.91120647 56.97609592 43.4444984 ] -> [[1.28353055 1.32795406]]\n", - "[57.13950372 44.02407986 56.96494506 43.56884069] -> [[1.26608753 1.31470355]]\n", - "[57.11359737 44.13192078 56.95135507 43.68994554] -> [[1.24913924 1.30141454]]\n", - "[57.08581903 44.23467136 56.93537369 43.80766838] -> [[1.23274398 1.28813319]]\n", - "[57.05636593 44.33230336 56.91705687 43.92187179] -> [[1.21695228 1.27490597]]\n", - "[57.02543463 44.42481772 56.89646865 44.03242658] -> [[1.20180694 1.26177945]]\n", - "[56.99321819 44.51224357 56.87368104 44.13921296] -> [[1.18734324 1.24880024]]\n", - "[56.95990358 44.59463661 56.84877359 44.2421216 ] -> [[1.17358946 1.23601474]]\n", - "[56.92566931 44.672077 56.82183294 44.34105473] -> [[1.16056751 1.22346906]]\n", - "[56.89068354 44.74466696 56.79295223 44.43592711] -> [[1.14829372 1.21120887]]\n", - "[56.85510251 44.81252795 56.7622303 44.52666687] -> [[1.13677976 1.19927927]]\n", - "[56.81906942 44.87579782 56.72977081 44.61321628] -> [[1.12603362 1.18772476]]\n", - "[56.78271371 44.93462777 56.6956813 44.69553219] -> [[1.1160606 1.17658922]]\n", - "[56.74615077 44.98917946 56.66007208 44.77358642] -> [[1.10686426 1.1659159 ]]\n", - "[56.70948197 45.03962219 56.62305513 44.84736586] -> [[1.0984472 1.15574751]]\n", - "[56.67279496 45.08613024 56.58474304 44.91687231] -> [[1.09081159 1.14612625]]\n", - "[56.63616433 45.12888052 56.54524781 44.98212213] -> [[1.08395927 1.13709378]]\n", - "[56.59965233 45.16805046 56.50467989 45.04314573] -> [[1.07789135 1.12869112]]\n", - "[56.56330982 45.20381614 56.46314714 45.09998673] -> [[1.07260696 1.12095821]]\n", - "[56.52717726 45.23635079 56.42075399 45.1527011 ] -> [[1.06810134 1.11393316]]\n", - "[56.49128575 45.26582349 56.37760065 45.20135607] -> [[1.06436318 1.10765091]]\n", - "[56.45565811 45.2923982 56.33378249 45.24602894] -> [[1.06137176 1.10214138]]\n", - "[56.42030988 45.31623298 56.28938956 45.28680588] -> [[1.05909447 1.09742695]]\n", - "[56.38525033 45.33747945 56.24450618 45.32378061] -> [[1.05748568 1.09351959]]\n", - "[56.35048336 45.35628238 56.19921074 45.35705314] -> [[1.05648743 1.09041803]]\n", - "[56.31600837 45.37277955 56.15357554 45.38672849] -> [[1.05603204 1.08810559]]\n", - "[56.28182104 45.38710161 56.1076668 45.41291549] -> [[1.05604624 1.08654927]]\n", - "[56.24791399 45.39937211 56.06154473 45.43572563] -> [[1.05645567 1.08570065]]\n", - "[56.21427742 45.40970766 56.01526366 45.45527194] -> [[1.05718892 1.08549842]]\n", - "[56.18089966 45.41821804 55.9688723 45.47166806] -> [[1.05818054 1.08587235]]\n", - "[56.14776761 45.42500646 55.92241396 45.48502733] -> [[1.05937274 1.08674769]]\n", - "[56.11486716 45.43016979 55.87592687 45.495462 ] -> [[1.06071604 1.08804949]]\n", - "[56.08218352 45.43379888 55.82944452 45.50308255] -> [[1.06216909 1.08970608]]\n", - "[56.04970152 45.4359788 55.78299593 45.5079971 ] -> [[1.06369799 1.09165152]]\n", - "[56.01740585 45.43678918 55.73660606 45.51031089] -> [[1.06527543 1.09382704]]\n", - "[55.98528124 45.43630451 55.69029611 45.5101259 ] -> [[1.06687967 1.09618162]]\n", - "[55.95331264 45.43459438 55.6440838 45.50754048] -> [[1.06849365 1.09867185]]\n", - "[55.92148535 45.43172384 55.59798378 45.50264915] -> [[1.07010414 1.10126155]]\n", - "[55.8897851 45.42775363 55.55200779 45.49554231] -> [[1.07170102 1.10392097]]\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0m_Cs_left\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_Cs_right\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mantenna\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapacitor_predictor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_Cs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.3\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0m_Cs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_Cs_left\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_Cs_right\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_Cs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'->'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mantenna\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvswr_act\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_Cs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Documents\\WEST_IC_antenna\\west_ic_antenna\\antenna.py\u001b[0m in \u001b[0;36mvswr_act\u001b[1;34m(self, power, phase, Cs)\u001b[0m\n\u001b[0;32m 1044\u001b[0m \"\"\"\n\u001b[0;32m 1045\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1046\u001b[1;33m \u001b[0ms_act\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ms_act\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1047\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1048\u001b[0m \u001b[0mvswr_left\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_act\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_act\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\WEST_IC_antenna\\west_ic_antenna\\antenna.py\u001b[0m in \u001b[0;36ms_act\u001b[1;34m(self, power, phase, Cs)\u001b[0m\n\u001b[0;32m 983\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 984\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_a_external\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 985\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ms_active\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 986\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 987\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mz_act\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpower\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\WEST_IC_antenna\\west_ic_antenna\\antenna.py\u001b[0m in \u001b[0;36mcircuit\u001b[1;34m(self, Cs)\u001b[0m\n\u001b[0;32m 338\u001b[0m \"\"\"\n\u001b[0;32m 339\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 340\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_circuit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_antenna_circuit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 341\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_circuit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\WEST_IC_antenna\\west_ic_antenna\\antenna.py\u001b[0m in \u001b[0;36m_antenna_circuit\u001b[1;34m(self, Cs)\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[0mcapa_C1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"C1\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 256\u001b[0m capa_C2 = self.capa(\n\u001b[1;32m--> 257\u001b[1;33m \u001b[0mC2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz0_bridge\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbridge_left\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mz0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz0_antenna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mantenna\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mz0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 258\u001b[0m )\n\u001b[0;32m 259\u001b[0m \u001b[0mcapa_C2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"C2\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\WEST_IC_antenna\\west_ic_antenna\\antenna.py\u001b[0m in \u001b[0;36mcapa\u001b[1;34m(self, C, R, L, R1, C1, L1, z0_bridge, z0_antenna)\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[1;31m# the 50 Ohm characteristic impedance is artifical. However, the R,L,R1,L1,C1 values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;31m# have been fitted to full-wave solutions using this 50 ohm value, so it should not be modified\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedia\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDefinedGammaZ0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrequency\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 214\u001b[0m pre = (\n", - "\u001b[1;32m~\\Documents\\scikit-rf\\skrf\\media\\media.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, frequency, z0, gamma, Z0)\u001b[0m\n\u001b[0;32m 1252\u001b[0m '''\n\u001b[0;32m 1253\u001b[0m super(DefinedGammaZ0, self).__init__(frequency=frequency, \n\u001b[1;32m-> 1254\u001b[1;33m z0=z0)\n\u001b[0m\u001b[0;32m 1255\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1256\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mZ0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mZ0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\Documents\\scikit-rf\\skrf\\frequency.py\u001b[0m in \u001b[0;36mfrom_f\u001b[1;34m(cls, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mtemp_freq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[0mtemp_freq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mtemp_freq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmultiplier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mtemp_freq\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\scikit-rf\\skrf\\frequency.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(self, new_f)\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[0msets\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfrequency\u001b[0m \u001b[0mobject\u001b[0m \u001b[0mby\u001b[0m \u001b[0mpassing\u001b[0m \u001b[0ma\u001b[0m \u001b[0mvector\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mHz\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 425\u001b[0m '''\n\u001b[1;32m--> 426\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_f\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_f\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 428\u001b[0m if npy.allclose( self._f, \n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "_Cs = C_match.copy()\n", "_Cs[0] += 2\n", @@ -230,97 +102,30 @@ "_Cs[2] += 2\n", "_Cs[3] -= 2\n", "\n", - "for idx in np.arange(100):\n", - " _Cs_left, _Cs_right = antenna.capacitor_predictor(power, phase, Cs=list(_Cs), K=0.3 )\n", + "for idx in np.arange(30):\n", + " _Cs_left, _Cs_right, _ = antenna.capacitor_predictor(power, phase, Cs=list(_Cs), K=0.3 )\n", " _Cs = np.array([_Cs_left, _Cs_right]).reshape(4)\n", " print(_Cs, '->', antenna.vswr_act(power, phase, Cs=list(_Cs)))" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[53.91040479346856, 45.824412581816475, 53.50659913492343, 45.858742134766054]" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "C_match" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[53.80174503, 45.2457775 ]]), array([[53.17680873, 45.15503712]]))" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ " antenna.capacitor_predictor(power, phase, Cs=list(_Cs) )" ] }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([53.8615444 , 45.51829412, 53.34862839, 45.50401679])" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -336,1173 +141,48 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking for individual solutions separately for 1st guess...\n", - "True solution #1: [53.87182182 45.7934624 ]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [53.47959704 45.9637657 ]\n", - "Searching for the active match point solution...\n", - "True solution #1: [53.91040479 45.82441258 53.50659913 45.85874213]\n" - ] - } - ], - "source": [ - "antenna = WestIcrhAntenna(frequency=freq, front_face=s_dir+s_files[0])\n", - "# matches the antenna in dipole excitation\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the antenna is tuned let's vary the coupling case and check the resulting VSWR and coupling resistance Rc" - ] - }, - { - "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "f0 = 55e6\n", - "freq = rf.Frequency(f0, f0, npoints=1, unit='Hz')\n", - "antenna = WestIcrhAntenna(frequency=freq, front_face=s_dir+s_files[0])\n", - "antenna.Cs = C_match" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0000112 , 1.00002663]])" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "antenna.vswr_act(power, phase, Cs=C_match)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.1251504903562153" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ + "# dipole excitation\n", "power = [1, 1]\n", "phase = [0, np.pi] \n", - "antenna.front_face_Rc()[freq.center_idx,0]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9fddfcf2561a4fe6bf0711e9ef9a118d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=8.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "freq = rf.Frequency(54, 56, npoints=1001, unit='MHz')\n", "\n", - "Rcs, Rcs_ideal, VSWR = [], [], []\n", - "for s_file in tqdm(s_files):\n", - " antenna = WestIcrhAntenna(frequency=freq, front_face=s_dir+s_file)\n", - " antenna.Cs = C_match\n", - " \n", - " # ideal coupling resistance\n", - " Rcs_ideal.append(antenna.front_face_Rc()[freq.center_idx,0])\n", - " # achieved coupling resistance\n", - " Rcs.append(antenna.Rc_WEST(power, phase)[freq.center_idx,:])\n", - " # VSWR\n", - " VSWR.append(antenna.vswr_act(power, phase)[freq.center_idx])\n", + "# create a reference case\n", + "ff = WestIcrhAntenna.interpolate_front_face(Rc=2, source='TOPICA-L-mode')\n", + "antenna = WestIcrhAntenna(frequency=freq, front_face=ff)\n", "\n", - "Rcs_ideal = np.array(Rcs_ideal)\n", - "Rcs = np.array(Rcs)\n", - "VSWR = np.array(VSWR)" + "# tune the antenna\n", + "antenna.C_match = antenna.match_both_sides()" ] }, { - "cell_type": "code", - "execution_count": 59, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(1.0, 1.4695424096552265)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(Rcs, VSWR[:,0], '.')\n", - "ax.plot(Rcs, VSWR[:,1], 'x')\n", - "ax.set_ylim(bottom=1)" + "Now that the antenna is tuned let's vary the coupling case and check the resulting VSWR and coupling resistance Rc" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d22be356e2654599b73856a7fcf24ffb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=10.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "freq = rf.Frequency(54, 56, npoints=1001, unit='MHz')\n", - "Rcs_ = np.arange(1, 3, .2)\n", "\n", - "Rcs, Rcs_ideal, VSWR = [], [], []\n", - "for Rc_ in tqdm(Rcs_):\n", - " ff = antenna.TOPICA_front_face(Rc_)\n", + "Rcs_ideal, Rcs, VSWR = [], [], []\n", + "\n", + "Rcs_target = np.arange(start=1, stop=2.9, step=0.2)\n", + "for Rc in tqdm(Rcs_target):\n", + " ff = WestIcrhAntenna.interpolate_front_face(Rc=Rc, source='TOPICA-L-mode')\n", " antenna = WestIcrhAntenna(frequency=freq, front_face=ff)\n", - " antenna.Cs = C_match\n", + " antenna.Cs = C_match # use match point calculated previously\n", " \n", - " # ideal coupling resistance\n", + " # plasma ideal coupling resistance\n", " Rcs_ideal.append(antenna.front_face_Rc()[freq.center_idx,0])\n", - " # achieved coupling resistance\n", + " # achieved antenna coupling resistance\n", " Rcs.append(antenna.Rc_WEST(power, phase)[freq.center_idx,:])\n", " # VSWR\n", " VSWR.append(antenna.vswr_act(power, phase)[freq.center_idx])\n", @@ -1514,1108 +194,22 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - 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Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(1.0, 1.4517023162815283)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", - "ax.plot(Rcs, VSWR[:,0], '.')\n", - "ax.plot(Rcs, VSWR[:,1], 'x')\n", - "ax.set_ylim(bottom=1)" + "ax.plot(Rcs[:,0], VSWR[:,0], '.', label='left')\n", + "ax.plot(Rcs[:,1], VSWR[:,1], 'x', label='right')\n", + "ax.set_ylim(bottom=1)\n", + "ax.legend()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from IPython.core.display import HTML\n", "def _set_css_style(css_file_path):\n", @@ -2640,7 +234,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2654,7 +248,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/doc/phase_scan.ipynb b/doc/phase_scan.ipynb index 3d03ebd..96d6c29 100644 --- a/doc/phase_scan.ipynb +++ b/doc/phase_scan.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,17 +53,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal coupling resistance expected: 1.6095500081638645\n" - ] - } - ], + "outputs": [], "source": [ "freq = rf.Frequency(start=50, stop=60, npoints=1001, unit='MHz')\n", "plasma_TOPICA = '../west_ic_antenna/data/Sparameters/front_faces/TOPICA/S_TSproto12_55MHz_Hmode_LAD6-2.5cm.s4p'\n", @@ -81,23 +73,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking for individual solutions separately for 1st guess...\n", - "True solution #1: [53.55296435 46.40953715]\n", - "Wrong solution (out of range capacitor) ! Re-doing...\n", - "False solution #1: [150. 150.]\n", - "True solution #1: [53.17919956 46.56525553]\n", - "Searching for the active match point solution...\n", - "True solution #1: [54.14662724 46.73409217 53.85622982 46.9009773 ]\n" - ] - } - ], + "outputs": [], "source": [ "# antenna excitation to match for\n", "power = [1, 1] # W\n", @@ -116,30 +94,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "s_act = antenna.s_act(power=power, phase=phase, Cs=Cs)\n", "\n", @@ -160,30 +117,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(antenna.f_scaled, np.abs(antenna.voltages(power, phase, Cs)), lw=2)\n", @@ -202,20 +138,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, (ax1,ax2) = plt.subplots(2, 1, sharex=True)\n", "ax1.plot(antenna.f_scaled, np.abs(antenna.voltages(power, phase, Cs)), lw=2)\n", @@ -238,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -248,31 +173,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9ca0b02d1c7546b3a025d147859d559e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=36.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "antenna_single_freq = WestIcrhAntenna(frequency=rf.Frequency(55, 55, 1, unit='MHz'),\n", " front_face=plasma_TOPICA, Cs=Cs) \n", @@ -289,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -300,30 +203,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 50.0)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(toroidal_phases), np.abs(voltages)/1e3, lw=2)\n", @@ -350,30 +232,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(toroidal_phases), np.rad2deg(np.angle(voltages[:,2]) - np.angle(voltages[:,0]))%360, lw=2)\n", @@ -394,30 +255,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 1.3)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(toroidal_phases), Rcs, lw=2)\n", @@ -430,112 +270,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from IPython.core.display import HTML\n", "def _set_css_style(css_file_path):\n", @@ -560,7 +297,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -574,7 +311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/doc/tutorial_matching_manual.ipynb b/doc/tutorial_matching_manual.ipynb index 933dd80..f114be2 100644 --- a/doc/tutorial_matching_manual.ipynb +++ b/doc/tutorial_matching_manual.ipynb @@ -413,7 +413,7 @@ "for Rc in tqdm(Rcs1):\n", " _plasma = WestIcrhAntenna.interpolate_front_face(Rc=Rc, source='TOPICA-H-mode')\n", " _ant = WestIcrhAntenna(frequency=freq, front_face=_plasma)\n", - " _C_match = _ant.match_both_sides_iterative(f0, power=powers, phase=phases, C0=C_opt_vacuum_dipole)\n", + " _C_match = _ant.match_both_sides_iterative(f0, power=powers, phase=phases, Cs=C_opt_vacuum_dipole)\n", " C_matchs.append(_C_match)\n", "\n", "\n", @@ -421,7 +421,7 @@ "for Rc in tqdm(Rcs2):\n", " _plasma = WestIcrhAntenna.interpolate_front_face(Rc=Rc, source='TOPICA-L-mode')\n", " _ant = WestIcrhAntenna(frequency=freq, front_face=_plasma)\n", - " _C_match = _ant.match_both_sides_iterative(f0, power=powers, phase=phases, C0=C0)\n", + " _C_match = _ant.match_both_sides_iterative(f0, power=powers, phase=phases, Cs=C0)\n", " C_matchs.append(_C_match)" ] },