Merge branch 'master' of git.ligo.org:IFOsim/aundhaisc

.gitignore

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DRMI_controls/.gitignore

+*.pdf
+*.zip

DRMI_controls/OptimizeDRMI-LSC.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DRMI ISC Designer\n",
+    "## Use Finesse to optimize the LSC error signals for the DRMI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "from pykat import finesse\n",
+    "from pykat.commands import *\n",
+    "import pykat.external.peakdetect as peak\n",
+    "import pykat.ifo.aligo as aligo\n",
+    "import pykat.ifo.aligo.plot as aligoplt\n",
+    "\n",
+    "import gwinc as gwinc\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import pandas as pd\n",
+    "\n",
+    "pykat.init_pykat_plotting(dpi=72)\n",
+    "\n",
+    "#for animations:\n",
+    "from matplotlib import animation, rc\n",
+    "from IPython.display import HTML\n",
+    "plt.style.use('dark_background')\n",
+    "# Update the matplotlib configuration parameters:\n",
+    "plt.rcParams.update({'text.usetex': False,\n",
+    "                     'lines.linewidth': 4,\n",
+    "                     'font.family': 'serif',\n",
+    "                     'font.serif': 'Georgia',\n",
+    "                     'font.size': 14,\n",
+    "                     'xtick.direction': 'in',\n",
+    "                     'ytick.direction': 'in',\n",
+    "                     'xtick.labelsize': 'small',\n",
+    "                     'ytick.labelsize': 'small',\n",
+    "                     'axes.labelsize': 'medium',\n",
+    "                     'axes.titlesize': 'medium',\n",
+    "                     'axes.grid.axis': 'both',\n",
+    "                     'axes.grid.which': 'both',\n",
+    "                     'axes.grid': True,\n",
+    "                     'grid.color': 'xkcd:Sea Green',\n",
+    "                     'grid.alpha': 0.3,\n",
+    "                     'lines.markersize': 12,\n",
+    "                     'legend.borderpad': 0.2,\n",
+    "                     'legend.fancybox': True,\n",
+    "                     'legend.fontsize': 'small',\n",
+    "                     'legend.framealpha': 0.8,\n",
+    "                     'legend.handletextpad': 0.5,\n",
+    "                     'legend.labelspacing': 0.33,\n",
+    "                     'legend.loc': 'best',\n",
+    "                     'figure.figsize': ((14, 8)),\n",
+    "                     'savefig.dpi': 140,\n",
+    "                     'savefig.bbox': 'tight',\n",
+    "                     'pdf.compression': 9})\n",
+    "\n",
+    "\n",
+    "def radar_plot(self, detector_I, detector_Q, DOFs=None, ax=None, title=None, leg=True, autoscale=True):\n",
+    "    \"\"\"Generates an I-Q quadrature radar plot from this sensing matrix.\n",
+    "    \n",
+    "    Each radar plot shows the magnitude and phase of the response in that\n",
+    "    sensor for each of the DOFs. e.g. Watts in POP_f1 per meter of ETMX motion\n",
+    "                    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    detector_I : str\n",
+    "        Detector name in the sensing matrix for I quadrature\n",
+    "        \n",
+    "    detector_Q : str\n",
+    "        Detector name in the sensing matrix for Q quadrature\n",
+    "        \n",
+    "    DOFs : collection[str], optional\n",
+    "        DOFs in the sensing matrix to plot\n",
+    "    \n",
+    "    ax : axis, optional\n",
+    "        Matplotlib axis to put plot into\n",
+    "        \n",
+    "    title: str, optional\n",
+    "        Title of plot\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    I = self[detector_I]\n",
+    "    Q = self[detector_Q]\n",
+    "    \n",
+    "    A = I + 1j*Q\n",
+    "    \n",
+    "    # FFS, we need to standardize on the colors for the DOFs with an iron fist\n",
+    "    # DARM = DODGER BLUE, CARM = CARNATION, PRCL=PURPLE, MICH = MIDNIGHT, SRCL = Cyan\n",
+    "    clrs = ['xkcd:Dodger Blue', 'xkcd:Red', 'xkcd:Lavender', 'xkcd:Cyan', 'xkcd:Grey']\n",
+    "    \n",
+    "    _ax = ax or plt.subplot(111, projection='polar')\n",
+    "    _ax.set_theta_zero_location('E')\n",
+    "    r_lim = (np.log10(np.abs(A)).min()-1, np.log10(np.abs(A)).max())\n",
+    "    \n",
+    "    if DOFs and any((_ not in A.keys() for _ in DOFs)):\n",
+    "        raise Exception(\"Sensing matrix is missing one of DOFs ({0}) requested\".format(DOFs))\n",
+    "        \n",
+    "    if DOFs:\n",
+    "        keys = tuple(_ for _ in A.keys() if _ in DOFs)\n",
+    "    else:\n",
+    "        keys = A.keys()\n",
+    "        \n",
+    "    scaling = np.linspace(7, 4, len(keys))\n",
+    "    \n",
+    "    for _, s, cc in zip(keys, scaling, clrs):\n",
+    "        theta = np.angle(A[_])\n",
+    "        r     = np.log10(np.abs(A[_]))\n",
+    "        _ax.plot((theta,theta), (r_lim[0], r), lw=s, label=_, alpha=0.6, color=cc)\n",
+    "    ttl = _ax.set_title(title, fontsize=11)\n",
+    "    ttl.set_position([.5, 1.12])\n",
+    "    \n",
+    "    if autoscale == True:\n",
+    "        _ax.set_ylim(r_lim[0], r_lim[1])\n",
+    "    else:\n",
+    "        _ax.set_ylim(6, 11)\n",
+    "    \n",
+    "    if leg==True:\n",
+    "        _ax.legend(bbox_to_anchor=(0.5, -0.1), ncol=5)\n",
+    "    \n",
+    "    _ax.set_rticks(np.arange(*np.round(r_lim)))\n",
+    "    _ax.set_yticklabels(tuple( \"$10^{%s}$\" % _ for _ in np.arange(*np.round(r_lim), dtype=int)))\n",
+    "    _ax.grid(True, zorder = -10, lw = 2)\n",
+    "    if ax is None:\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()\n",
+    "        \n",
+    "def prettySensingMatrix(self, cmap = 'jet'):\n",
+    "    # https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n",
+    "    # Set colormap equal to seaborns light green color palette\n",
+    "    cmap = plt.get_cmap(cmap)\n",
+    "\n",
+    "    # Set CSS properties for th elements in dataframe\n",
+    "    th_props = [\n",
+    "      ('font-size', '24'),\n",
+    "      ('text-align', 'center'),\n",
+    "      ('font-weight', 'bold'),\n",
+    "      ('color', 'xkcd:White'),\n",
+    "      ('background-color', 'xkcd:Black')\n",
+    "      ]\n",
+    "\n",
+    "    # Set CSS properties for td elements in dataframe\n",
+    "    td_props = [\n",
+    "      ('font-size', '24')\n",
+    "      ]\n",
+    "\n",
+    "    # Set table styles\n",
+    "    styles = [\n",
+    "      dict(selector=\"th\", props=th_props),\n",
+    "      dict(selector=\"td\", props=td_props)\n",
+    "      ]\n",
+    "        \n",
+    "    self = (self.style\n",
+    "      .background_gradient(cmap=cmap, subset=list(self))\n",
+    "      .set_caption('Interferometer Sensing Matrix')\n",
+    "      .format(\"{:0.3g}\")\n",
+    "      .set_table_styles(styles))\n",
+    "    \n",
+    "    return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "1. Get IFO params, build Finesse model\n",
+    "1. Get the LSC sensing matrix\n",
+    "1. Make a radar plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aLIGO = aligo.make_kat()\n",
+    "aLIGO.maxtem = -1 #USE PLANE WAVES MODEL FIRST (speed, complexity...)\n",
+    "TT    = 0.325 #better match to sites\n",
+    "aLIGO.SRM.setRTL(1 - TT-aLIGO.SRM.L, TT, aLIGO.SRM.L)\n",
+    "\n",
+    "# DRMI Only\n",
+    "#aLIGO.ETMX.T = 1 - aLIGO.ETMX.L\n",
+    "#aLIGO.ETMY.T = 1 - aLIGO.ETMY.T\n",
+    "\n",
+    "aLIGO = aligo.setup(aLIGO)\n",
+    "\n",
+    "kat   = aLIGO.deepcopy() # Make a copy of the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DOFs = [\"DARM\", \"CARM\", \"PRCL\", \"SRCL\", \"MICH\"]\n",
+    "# Add some detectors in to measure the transfer function\n",
+    "# from each degree of freedom specified above to each one\n",
+    "# of these outputs. Here we pick either the I or Q quadrature\n",
+    "detectors = [\n",
+    "    kat.IFO.REFL_f1.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f1.add_transfer('Q'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f1.add_transfer('I'),\n",
+    "    kat.IFO.POP_f1.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f2.add_transfer('I'),\n",
+    "    kat.IFO.POP_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_f2.add_transfer('I'),\n",
+    "    kat.IFO.AS_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_DC.add_transfer()\n",
+    "]\n",
+    "\n",
+    "# what frequency is this measured at ???\n",
+    "sens = kat.IFO.sensing_matrix(DOFs, detectors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set the demod phases\n",
+    "#### put CARM out of the Q phase everywhere\n",
+    "##### this doesn't make much sense until the CARM loop is closed with high gain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aLIGO.IFO.REFL_f1.phase += 180/np.pi * np.arctan2(sens['REFL_f1_Q_TF']['CARM'], sens['REFL_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.REFL_f2.phase += 180/np.pi * np.arctan2(sens['REFL_f2_Q_TF']['CARM'], sens['REFL_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f1.phase  += 180/np.pi * np.arctan2( sens['POP_f1_Q_TF']['CARM'],  sens['POP_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f2.phase  += 180/np.pi * np.arctan2( sens['POP_f2_Q_TF']['CARM'],  sens['POP_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.AS_f2.phase   += 180/np.pi * np.arctan2(  sens['AS_f2_Q_TF']['CARM'],   sens['AS_f2_I_TF']['CARM'])\n",
+    "\n",
+    "aLIGO = aligo.setup(aLIGO)\n",
+    "\n",
+    "kat   = aLIGO.deepcopy() # Make a copy of the model\n",
+    "\n",
+    "detectors = [\n",
+    "    kat.IFO.REFL_f1.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f1.add_transfer('Q'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f1.add_transfer('I'),\n",
+    "    kat.IFO.POP_f1.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f2.add_transfer('I'),\n",
+    "    kat.IFO.POP_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_f2.add_transfer('I'),\n",
+    "    kat.IFO.AS_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_DC.add_transfer()\n",
+    "]\n",
+    "\n",
+    "sens = kat.IFO.sensing_matrix(DOFs, detectors)\n",
+    "prettySensingMatrix(sens, cmap = 'twilight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = len(detectors)\n",
+    "M = N\n",
+    "if np.mod(N,2) > 0:\n",
+    "    M    = N - 1\n",
+    "    ASDC = detectors[-1]\n",
+    "\n",
+    "#fig, ax = plt.subplots(nrows=1, ncols=M // 2, projection='polar')\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=M//2, subplot_kw=dict(polar=True), figsize=(22,9))\n",
+    "leg     = False\n",
+    "for b in range(M//2):\n",
+    "\n",
+    "    if b == (M // 2) - 1:\n",
+    "        leg = True\n",
+    "    radar_plot(sens, detectors[2*b], detectors[2*b+1], ax=ax[b], \n",
+    "               leg=leg, title=detectors[2*b][:-5], autoscale=True)\n",
+    "\n",
+    "#fig.tight_layout(pad=5.0)\n",
+    "plt.savefig('radar-ALL.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aLIGO.IFO.REFL_f1.phase += 180/np.pi * np.arctan2(sens['REFL_f1_Q_TF']['CARM'], sens['REFL_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.REFL_f2.phase += 180/np.pi * np.arctan2(sens['REFL_f2_Q_TF']['CARM'], sens['REFL_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f1.phase  += 180/np.pi * np.arctan2( sens['POP_f1_Q_TF']['CARM'],  sens['POP_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f2.phase  += 180/np.pi * np.arctan2( sens['POP_f2_Q_TF']['CARM'],  sens['POP_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.AS_f2.phase   += 180/np.pi * np.arctan2(  sens['AS_f2_Q_TF']['CARM'],   sens['AS_f2_I_TF']['CARM'])\n",
+    "\n",
+    "aLIGO = aligo.setup(aLIGO)\n",
+    "\n",
+    "kat   = aLIGO.deepcopy() # Make a copy of the model\n",
+    "\n",
+    "detectors = [\n",
+    "    kat.IFO.REFL_f1.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f1.add_transfer('Q'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f1.add_transfer('I'),\n",
+    "    kat.IFO.POP_f1.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f2.add_transfer('I'),\n",
+    "    kat.IFO.POP_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_f2.add_transfer('I'),\n",
+    "    kat.IFO.AS_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_DC.add_transfer()\n",
+    "]\n",
+    "\n",
+    "sens = kat.IFO.sensing_matrix(DOFs, detectors)\n",
+    "prettySensingMatrix(sens, cmap = 'Spectral')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = len(detectors)\n",
+    "M = N\n",
+    "if np.mod(N,2) > 0:\n",
+    "    M    = N - 1\n",
+    "    ASDC = detectors[-1]\n",
+    "\n",
+    "#fig, ax = plt.subplots(nrows=1, ncols=M // 2, projection='polar')\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=M//2, subplot_kw=dict(polar=True), figsize=(22,9))\n",
+    "leg     = False\n",
+    "for b in range(M//2):\n",
+    "\n",
+    "    if b == (M // 2) - 1:\n",
+    "        leg = True\n",
+    "    radar_plot(sens, detectors[2*b], detectors[2*b+1], ax=ax[b], \n",
+    "               leg=leg, title=detectors[2*b][:-5], autoscale=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DRMI_controls/control-loops.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DRMI ISC Designer\n",
+    "## Use Finesse to optimize the LSC error signals for the DRMI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "from pykat import finesse\n",
+    "from pykat.commands import *\n",
+    "import pykat.external.peakdetect as peak\n",
+    "import pykat.ifo.aligo as aligo\n",
+    "import pykat.ifo.aligo.plot as aligoplt\n",
+    "\n",
+    "import gwinc as gwinc\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import pandas as pd\n",
+    "\n",
+    "pykat.init_pykat_plotting(dpi=72)\n",
+    "\n",
+    "#for animations:\n",
+    "from matplotlib import animation, rc\n",
+    "from IPython.display import HTML\n",
+    "plt.style.use('dark_background')\n",
+    "# Update the matplotlib configuration parameters:\n",
+    "plt.rcParams.update({'text.usetex': False,\n",
+    "                     'lines.linewidth': 4,\n",
+    "                     'font.family': 'sans-serif',\n",
+    "                     'font.sans-serif': 'Verdana',\n",
+    "                     'font.size': 14,\n",
+    "                     'xtick.direction': 'in',\n",
+    "                     'ytick.direction': 'in',\n",
+    "                     'xtick.labelsize': 'small',\n",
+    "                     'ytick.labelsize': 'small',\n",
+    "                     'axes.labelsize': 'medium',\n",
+    "                     'axes.titlesize': 'medium',\n",
+    "                     'axes.grid.axis': 'both',\n",
+    "                     'axes.grid.which': 'both',\n",
+    "                     'axes.grid': True,\n",
+    "                     'grid.color': 'xkcd:Sea Green',\n",
+    "                     'grid.alpha': 0.3,\n",
+    "                     'lines.markersize': 12,\n",
+    "                     'legend.borderpad': 0.2,\n",
+    "                     'legend.fancybox': True,\n",
+    "                     'legend.fontsize': 'small',\n",
+    "                     'legend.framealpha': 0.8,\n",
+    "                     'legend.handletextpad': 0.5,\n",
+    "                     'legend.labelspacing': 0.33,\n",
+    "                     'legend.loc': 'best',\n",
+    "                     'figure.figsize': ((14, 8)),\n",
+    "                     'savefig.dpi': 140,\n",
+    "                     'savefig.bbox': 'tight',\n",
+    "                     'pdf.compression': 9})\n",
+    "\n",
+    "\n",
+    "\n",
+    "        \n",
+    "def prettySensingMatrix(self, cmap = 'jet'):\n",
+    "    # https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n",
+    "    # Set colormap equal to seaborns light green color palette\n",
+    "    cmap = plt.get_cmap(cmap)\n",
+    "\n",
+    "    # Set CSS properties for th elements in dataframe\n",
+    "    th_props = [\n",
+    "      ('font-size', '24'),\n",
+    "      ('text-align', 'center'),\n",
+    "      ('font-weight', 'bold'),\n",
+    "      ('color', 'xkcd:White'),\n",
+    "      ('background-color', 'xkcd:Black')\n",
+    "      ]\n",
+    "\n",
+    "    # Set CSS properties for td elements in dataframe\n",
+    "    td_props = [\n",
+    "      ('font-size', '24')\n",
+    "      ]\n",
+    "\n",
+    "    # Set table styles\n",
+    "    styles = [\n",
+    "      dict(selector=\"th\", props=th_props),\n",
+    "      dict(selector=\"td\", props=td_props)\n",
+    "      ]\n",
+    "        \n",
+    "    self = (self.style\n",
+    "      .background_gradient(cmap=cmap, subset=list(self))\n",
+    "      .set_caption('Interferometer Sensing Matrix')\n",
+    "      .format(\"{:0.3g}\")\n",
+    "      .set_table_styles(styles))\n",
+    "    \n",
+    "    return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "1. Get IFO params, build Finesse model\n",
+    "1. Get the LSC sensing matrix\n",
+    "1. Make a radar plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aLIGO = aligo.make_kat()\n",
+    "aLIGO.maxtem = -1 #USE PLANE WAVES MODEL FIRST (speed, complexity...)\n",
+    "TT    = 0.325 #better match to sites\n",
+    "aLIGO.SRM.setRTL(1 - TT-aLIGO.SRM.L, TT, aLIGO.SRM.L)\n",
+    "\n",
+    "# DRMI Only\n",
+    "#aLIGO.ETMX.T = 1 - aLIGO.ETMX.L\n",
+    "#aLIGO.ETMY.T = 1 - aLIGO.ETMY.T\n",
+    "\n",
+    "aLIGO = aligo.setup(aLIGO)\n",
+    "\n",
+    "kat   = aLIGO.deepcopy() # Make a copy of the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DOFs = [\"DARM\", \"CARM\", \"PRCL\", \"SRCL\", \"MICH\"]\n",
+    "# Add some detectors in to measure the transfer function\n",
+    "# from each degree of freedom specified above to each one\n",
+    "# of these outputs. Here we pick either the I or Q quadrature\n",
+    "detectors = [\n",
+    "    kat.IFO.REFL_f1.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f1.add_transfer('Q'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f1.add_transfer('I'),\n",
+    "    kat.IFO.POP_f1.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f2.add_transfer('I'),\n",
+    "    kat.IFO.POP_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_f2.add_transfer('I'),\n",
+    "    kat.IFO.AS_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_DC.add_transfer()\n",
+    "]\n",
+    "\n",
+    "# what frequency is this measured at ???\n",
+    "sens = kat.IFO.sensing_matrix(DOFs, detectors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set the demod phases\n",
+    "#### put CARM out of the Q phase everywhere\n",
+    "##### this doesn't make much sense until the CARM loop is closed with high gain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aLIGO.IFO.REFL_f1.phase += 180/np.pi * np.arctan2(sens['REFL_f1_Q_TF']['CARM'], sens['REFL_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.REFL_f2.phase += 180/np.pi * np.arctan2(sens['REFL_f2_Q_TF']['CARM'], sens['REFL_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f1.phase  += 180/np.pi * np.arctan2( sens['POP_f1_Q_TF']['CARM'],  sens['POP_f1_I_TF']['CARM'])\n",
+    "aLIGO.IFO.POP_f2.phase  += 180/np.pi * np.arctan2( sens['POP_f2_Q_TF']['CARM'],  sens['POP_f2_I_TF']['CARM'])\n",
+    "aLIGO.IFO.AS_f2.phase   += 180/np.pi * np.arctan2(  sens['AS_f2_Q_TF']['CARM'],   sens['AS_f2_I_TF']['CARM'])\n",
+    "\n",
+    "aLIGO = aligo.setup(aLIGO)\n",
+    "\n",
+    "kat   = aLIGO.deepcopy() # Make a copy of the model\n",
+    "\n",
+    "detectors = [\n",
+    "    kat.IFO.REFL_f1.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f1.add_transfer('Q'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('I'),\n",
+    "    kat.IFO.REFL_f2.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f1.add_transfer('I'),\n",
+    "    kat.IFO.POP_f1.add_transfer('Q'),\n",
+    "    kat.IFO.POP_f2.add_transfer('I'),\n",
+    "    kat.IFO.POP_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_f2.add_transfer('I'),\n",
+    "    kat.IFO.AS_f2.add_transfer('Q'),\n",
+    "    kat.IFO.AS_DC.add_transfer()\n",
+    "]\n",
+    "\n",
+    "sens = kat.IFO.sensing_matrix(DOFs, detectors)\n",
+    "prettySensingMatrix(sens, cmap = 'Spectral')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get the AC Sensing Matrix:\n",
+    "### Drive the DoFs, get the complex demod outputs\n",
+    "#### make some Bode plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kat = aLIGO.deepcopy()\n",
+    "\n",
+    "peedees = []\n",
+    "for n in detectors:\n",
+    "    peedees.append(n[:-3])\n",
+    "\n",
+    "kat.parse(\"\"\" \n",
+    "\n",
+    "const f1 9099471.0\n",
+    "const f2 5 * $f1\n",
+    "\n",
+    "%%% FTblock errsigs\n",
+    "pd0 AS_DC nAS\n",
+    "pd1 REFL_f1_I  $f1   96.27427245747704 nREFL\n",
+    "pd1 REFL_f1_Q  $f1    6.27427245747704 nREFL\n",
+    "pd1 REFL_f2_I  $f2   96.27427245747704 nREFL\n",
+    "pd1 REFL_f2_Q  $f2    6.27427245747704 nREFL\n",
+    "pd1 POP_f1_I   $f1  -57.86313647329267 nPOP\n",
+    "pd1 POP_f1_Q   $f1  211.11868103763894 nPOP\n",
+    "pd1 POP_f2_I   $f2  -57.86313647329267 nPOP\n",
+    "pd1 POP_f2_Q   $f2  211.11868103763894 nPOP\n",
+    "pd1 AS_f1_I    $f1  -57.86313647329267 nAS\n",
+    "pd1 AS_f1_Q    $f1  211.11868103763894 nAS\n",
+    "pd1 AS_f2_I    $f2  -57.86313647329267 nAS\n",
+    "pd1 AS_f2_Q    $f2  211.11868103763894 nAS\n",
+    "\n",
+    "fsig sig1 LX 1 0 1\n",
+    "fsig sig1 LY 1 180 1\n",
+    "\n",
+    "xaxis sig1 f log 1 100k 1000\n",
+    "scale meter\n",
+    "\"\"\")\n",
+    "\n",
+    "out = kat.run()\n",
+    "\n",
+    "# make Bode plots of the LSC Sensing Matrix\n",
+    "fig, ax = plt.subplots(2, 1, sharex=True, figsize=(10, 9))\n",
+    "for d in range(len(peedees)):\n",
+    "    v = peedees[d]\n",
+    "    ax[0].loglog(out.x,     np.abs(out[v]),           label=v)\n",
+    "    ax[1].semilogx(out.x, np.angle(out[v], deg=True), label=v)\n",
+    "    \n",
+    "#ax[0].loglog(f_a, np.abs(Em), color='xkcd:Purple Blue', label='Lower Sideband', ls='--')\n",
+    "#ax[0].set_xlabel(r'Frequency [Hz]')\n",
+    "ax[0].set_ylabel(r'Response [W/m ?]')\n",
+    "ax[0].legend(ncol=3)\n",
+    "#ax[0].grid(True, which='Both')\n",
+    "ax[0].set_title('Bode Plot: Transfer function for modulation of the end mirror')\n",
+    "\n",
+    "#ax[1].semilogx(f_a, np.angle(Em, deg=True), color='xkcd:Purple Blue', ls='--')\n",
+    "\n",
+    "ax[1].set_xlabel(r'Frequency [Hz]')\n",
+    "ax[1].set_ylabel(r'Phase [deg]')\n",
+    "ax[1].set_yticks(np.arange(-180, 181, 60))\n",
+    "#ax[1].grid()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aLIGO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "    \n",
+    "peedees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}