Further optimization

7b110766 · Anchal Gupta · 86babaf8 · 7b110766 · 7b110766 · 7b110766
Commit 7b110766 authored Feb 13, 2020 by Anchal Gupta
--- a/data/20200212_FSS_Gain_Span/ChannelList.txt
+++ b/data/20200212_FSS_Gain_Span/ChannelList.txt
+C3:PSL-TEMP_TABLE
+C3:PSL-TEMP_VACCAN_INLOOP
+C3:PSL-PRECAV_BEATNOTE_FREQ
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD
+C3:PSL-SCAV_TRANS_POW
+C3:PSL-NCAV_TRANS_POW
+C3:PSL-SCAV_REFL_POW
+C3:PSL-NCAV_REFL_POW
+C3:PSL-SCAV_MODE_MATCHING
+C3:PSL-NCAV_MODE_MATCHING
+C3:PSL-SCAV_FSS_SLOWOUT
+C3:PSL-NCAV_FSS_SLOWOUT
+C3:PSL-SCAV_FSS_COMGAIN_DB
+C3:PSL-NCAV_FSS_COMGAIN_DB
+C3:PSL-SCAV_FSS_FASTGAIN_DB
+C3:PSL-NCAV_FSS_FASTGAIN_DB
+C3:PSL-HEATER_SHIELD_DIFF_POUT
+C3:PSL-HEATER_SHIELD_COM_POUT
--- a/data/20200212_FSS_Gain_Span/FSS_Gain_Span.ipynb
+++ b/data/20200212_FSS_Gain_Span/FSS_Gain_Span.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt                 #For plotting\n",
+    "from matplotlib import cm\n",
+    "import matplotlib.colors as colors\n",
+    "import seaborn as sns;\n",
+    "from matplotlib.backends.backend_pdf import PdfPages       #For saving figures to single pdf\n",
+    "figlist = []\n",
+    "#*******************************************************************************************************\n",
+    "#Setting RC Parameters for figure size and fontsizes\n",
+    "import matplotlib.pylab as pylab                   \n",
+    "params = {'legend.fontsize': 'xx-large',\n",
+    "          'figure.figsize': (20, 10),\n",
+    "         'axes.labelsize': 'xx-large',\n",
+    "         'axes.titlesize':'xx-large',\n",
+    "         'xtick.labelsize':'xx-large',\n",
+    "         'ytick.labelsize':'xx-large',\n",
+    "         'lines.linewidth': 2.0}\n",
+    "pylab.rcParams.update(params)\n",
+    "#********************************************************************************************************\n",
+    "import os\n",
+    "from noiseBudgetModule import noiseBudget\n",
+    "import uncertainties.unumpy as unp\n",
+    "import yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gainValues(fn, data_dir='./'):\n",
+    "    cwd = os.getcwd()\n",
+    "    os.chdir(data_dir)\n",
+    "    configFile = fn.replace('Spectrum', 'ExpConfig').replace('.txt', '.yml')\n",
+    "    with open(configFile, 'r') as cf:\n",
+    "                    config = yaml.full_load(cf)\n",
+    "    NCOM = config['C3:PSL-NCAV_FSS_COMGAIN_DB']\n",
+    "    NFAST = config['C3:PSL-NCAV_FSS_FASTGAIN_DB']\n",
+    "    SCOM = config['C3:PSL-SCAV_FSS_COMGAIN_DB']\n",
+    "    SFAST = config['C3:PSL-SCAV_FSS_FASTGAIN_DB']\n",
+    "    os.chdir(cwd)\n",
+    "    return {'NCOM' : NCOM,\n",
+    "            'NFAST' : NFAST,\n",
+    "            'SCOM' : SCOM,\n",
+    "            'SFAST' : SFAST}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def createBNDist(files, X, Y, fStart, fStop, y_ax, x_ax, data_dir='./'):\n",
+    "    cwd = os.getcwd()\n",
+    "    os.chdir(data_dir)\n",
+    "    Z = np.ones(np.shape(X))*np.inf\n",
+    "    for fn in files:\n",
+    "        data = np.loadtxt(fn)\n",
+    "        fStartInd = np.argmin(np.abs(data[:, 0] - fStart))\n",
+    "        fStopInd = np.argmin(np.abs(data[:, 0] - fStop))\n",
+    "        xG = gainValues(fn, data_dir)[x_ax]\n",
+    "        yG = gainValues(fn, data_dir)[y_ax]\n",
+    "        for ii in range(np.shape(Y)[0]):\n",
+    "            if Y[ii, 0] == yG:\n",
+    "                break\n",
+    "        for jj in range(np.shape(X)[1]):\n",
+    "            if X[0, jj] == xG:\n",
+    "                break\n",
+    "        #print('Ind', ii, jj)\n",
+    "        #print(X[ii, jj], Y[ii, jj])\n",
+    "        #print(yG, xG)\n",
+    "        '''\n",
+    "        Z[ii, jj] = np.sqrt(np.sum((((data[fStartInd:fStopInd, 1]**2)\n",
+    "                                     * (data[fStartInd+1:fStopInd+1, 0]\n",
+    "                                        - data[fStartInd:fStopInd, 0])\n",
+    "                                    ))))\n",
+    "        '''\n",
+    "        Z[ii, jj] = np.sum(data[fStartInd:fStopInd, 1])\n",
+    "    os.chdir(cwd)\n",
+    "    \n",
+    "    return Z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NFSS_Span_Files = [fn for fn in os.listdir() if (fn.find('.txt')!=-1\n",
+    "                                                 and fn.find('NFSS_Spanned')!=-1) ]\n",
+    "NCOM = np.array(sorted(list(set([gainValues(fn)['NCOM'] for fn in NFSS_Span_Files ]))))\n",
+    "NFAST = np.array(sorted(list(set([gainValues(fn)['NFAST'] for fn in NFSS_Span_Files ]))))\n",
+    "print(NCOM, NFAST)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = np.meshgrid(NCOM, NFAST)\n",
+    "Z = createBNDist(NFSS_Span_Files, X, Y, 300, 600, 'NFAST', 'NCOM')\n",
+    "minInd = np.unravel_index(np.argmin(Z), np.shape(Z))\n",
+    "print(minInd)\n",
+    "print(np.min(Z))\n",
+    "print('Minimum at COM Gain: '\n",
+    "      + str(X[0, minInd[1]]) + ' dB, FAST Gain: '\n",
+    "      + str(Y[minInd[0], 0])+ ' dB')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = sns.heatmap(Z, cmap=cm.jet,\n",
+    "                 cbar_kws = {'label' : 'Summed BN Noise [$Hz/\\sqrt{Hz}$]'},\n",
+    "                 linewidths=0.5, annot=True,vmin=0.9, vmax=1.3)\n",
+    "                 #vmin=np.min(Z[np.nonzero(Z)]), vmax = 3.2)\n",
+    "ax.invert_yaxis()\n",
+    "ax.set_xticklabels(X[0, :])\n",
+    "ax.set_ylim([-0.5, len(Y[:, 0]) + 0.5])\n",
+    "ax.set_yticks(Y[:, 0] - Y[0, 0] + 0.5)\n",
+    "ax.set_yticklabels(Y[:, 0])\n",
+    "plt.setp(ax.get_yticklabels(), rotation=0, ha=\"right\",\n",
+    "         rotation_mode=\"anchor\");\n",
+    "ax.set_xlabel('NFSS COM Gain (dB)', linespacing=4)\n",
+    "ax.set_ylabel('NFSS FAST Gain (dB)', linespacing=4)\n",
+    "ax.set_title('Beatnote frequency noise summed from 300 Hz to 600 Hz\\n'\n",
+    "             'South COM Gain = 23 dB; North Fast Gain = 15 dB')\n",
+    "fig = ax.get_figure()\n",
+    "fig.set_size_inches(11, 8.5, forward=True)\n",
+    "figlist = [fig]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SFSS_Span_Files = [fn for fn in os.listdir() if (fn.find('.txt')!=-1\n",
+    "                                                 and fn.find('SFSS_Spanned')!=-1) ]\n",
+    "SCOM = np.array(sorted(list(set([gainValues(fn)['SCOM'] for fn in SFSS_Span_Files ]))))\n",
+    "SFAST = np.array(sorted(list(set([gainValues(fn)['SFAST'] for fn in SFSS_Span_Files ]))))\n",
+    "print(SCOM, SFAST)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = np.meshgrid(SCOM, SFAST)\n",
+    "Z = createBNDist(SFSS_Span_Files, X, Y, 300, 600, 'SFAST', 'SCOM')\n",
+    "minInd = np.unravel_index(np.argmin(Z), np.shape(Z))\n",
+    "print(minInd)\n",
+    "print(np.min(Z))\n",
+    "print('Minimum at COM Gain: '\n",
+    "      + str(X[0, minInd[1]]) + ' dB, FAST Gain: '\n",
+    "      + str(Y[minInd[0], 0])+ ' dB')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = sns.heatmap(Z, cmap=cm.jet,\n",
+    "                 cbar_kws = {'label' : 'Summed BN Noise [$Hz/\\sqrt{Hz}$]'},\n",
+    "                 linewidths=0.5, annot=True,vmin=0.9, vmax=1.3)\n",
+    "                 #vmin=np.min(Z[np.nonzero(Z)]), vmax = 3.2)\n",
+    "ax.invert_yaxis()\n",
+    "ax.set_xticklabels(X[0, :])\n",
+    "ax.set_ylim([-0.5, len(Y[:, 0]) + 0.5])\n",
+    "ax.set_yticks(Y[:, 0] - Y[0, 0] + 0.5)\n",
+    "ax.set_yticklabels(Y[:, 0])\n",
+    "plt.setp(ax.get_yticklabels(), rotation=0, ha=\"right\",\n",
+    "         rotation_mode=\"anchor\");\n",
+    "ax.set_xlabel('SFSS COM Gain (dB)', linespacing=4)\n",
+    "ax.set_ylabel('SFSS FAST Gain (dB)', linespacing=4)\n",
+    "ax.set_title('Beatnote frequency noise summed from 300 Hz to 600 Hz\\n'\n",
+    "             'North COM Gain = 12 dB; North Fast Gain = 10 dB')\n",
+    "fig = ax.get_figure()\n",
+    "fig.set_size_inches(11, 8.5, forward=True)\n",
+    "figlist = [fig]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp = PdfPages('FSS_Gain_Span.pdf')\n",
+    "for fig in figlist:\n",
+    "    pp.savefig(fig,bbox_inches='tight')\n",
+    "pp.close()"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import matplotlib.pyplot as plt                 #For plotting
+from matplotlib import cm
+import matplotlib.colors as colors
+import seaborn as sns;
+from matplotlib.backends.backend_pdf import PdfPages       #For saving figures to single pdf
+figlist = []
+#*******************************************************************************************************
+#Setting RC Parameters for figure size and fontsizes
+import matplotlib.pylab as pylab
+params = {'legend.fontsize': 'xx-large',
+          'figure.figsize': (20, 10),
+         'axes.labelsize': 'xx-large',
+         'axes.titlesize':'xx-large',
+         'xtick.labelsize':'xx-large',
+         'ytick.labelsize':'xx-large',
+         'lines.linewidth': 2.0}
+pylab.rcParams.update(params)
+#********************************************************************************************************
+import os
+from noiseBudgetModule import noiseBudget
+import uncertainties.unumpy as unp
+import yaml
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def gainValues(fn, data_dir='./'):
+    cwd = os.getcwd()
+    os.chdir(data_dir)
+    configFile = fn.replace('Spectrum', 'ExpConfig').replace('.txt', '.yml')
+    with open(configFile, 'r') as cf:
+                    config = yaml.full_load(cf)
+    NCOM = config['C3:PSL-NCAV_FSS_COMGAIN_DB']
+    NFAST = config['C3:PSL-NCAV_FSS_FASTGAIN_DB']
+    SCOM = config['C3:PSL-SCAV_FSS_COMGAIN_DB']
+    SFAST = config['C3:PSL-SCAV_FSS_FASTGAIN_DB']
+    os.chdir(cwd)
+    return {'NCOM' : NCOM,
+            'NFAST' : NFAST,
+            'SCOM' : SCOM,
+            'SFAST' : SFAST}
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def createBNDist(files, X, Y, fStart, fStop, y_ax, x_ax, data_dir='./'):
+    cwd = os.getcwd()
+    os.chdir(data_dir)
+    Z = np.ones(np.shape(X))*np.inf
+    for fn in files:
+        data = np.loadtxt(fn)
+        fStartInd = np.argmin(np.abs(data[:, 0] - fStart))
+        fStopInd = np.argmin(np.abs(data[:, 0] - fStop))
+        xG = gainValues(fn, data_dir)[x_ax]
+        yG = gainValues(fn, data_dir)[y_ax]
+        for ii in range(np.shape(Y)[0]):
+            if Y[ii, 0] == yG:
+                break
+        for jj in range(np.shape(X)[1]):
+            if X[0, jj] == xG:
+                break
+        #print('Ind', ii, jj)
+        #print(X[ii, jj], Y[ii, jj])
+        #print(yG, xG)
+        '''
+        Z[ii, jj] = np.sqrt(np.sum((((data[fStartInd:fStopInd, 1]**2)
+                                     * (data[fStartInd+1:fStopInd+1, 0]
+                                        - data[fStartInd:fStopInd, 0])
+                                    ))))
+        '''
+        Z[ii, jj] = np.sum(data[fStartInd:fStopInd, 1])
+    os.chdir(cwd)
+
+    return Z
+```
+
+%% Cell type:code id: tags:
+
+``` python
+NFSS_Span_Files = [fn for fn in os.listdir() if (fn.find('.txt')!=-1
+                                                 and fn.find('NFSS_Spanned')!=-1) ]
+NCOM = np.array(sorted(list(set([gainValues(fn)['NCOM'] for fn in NFSS_Span_Files ]))))
+NFAST = np.array(sorted(list(set([gainValues(fn)['NFAST'] for fn in NFSS_Span_Files ]))))
+print(NCOM, NFAST)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+X, Y = np.meshgrid(NCOM, NFAST)
+Z = createBNDist(NFSS_Span_Files, X, Y, 300, 600, 'NFAST', 'NCOM')
+minInd = np.unravel_index(np.argmin(Z), np.shape(Z))
+print(minInd)
+print(np.min(Z))
+print('Minimum at COM Gain: '
+      + str(X[0, minInd[1]]) + ' dB, FAST Gain: '
+      + str(Y[minInd[0], 0])+ ' dB')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ax = sns.heatmap(Z, cmap=cm.jet,
+                 cbar_kws = {'label' : 'Summed BN Noise [$Hz/\sqrt{Hz}$]'},
+                 linewidths=0.5, annot=True,vmin=0.9, vmax=1.3)
+                 #vmin=np.min(Z[np.nonzero(Z)]), vmax = 3.2)
+ax.invert_yaxis()
+ax.set_xticklabels(X[0, :])
+ax.set_ylim([-0.5, len(Y[:, 0]) + 0.5])
+ax.set_yticks(Y[:, 0] - Y[0, 0] + 0.5)
+ax.set_yticklabels(Y[:, 0])
+plt.setp(ax.get_yticklabels(), rotation=0, ha="right",
+         rotation_mode="anchor");
+ax.set_xlabel('NFSS COM Gain (dB)', linespacing=4)
+ax.set_ylabel('NFSS FAST Gain (dB)', linespacing=4)
+ax.set_title('Beatnote frequency noise summed from 300 Hz to 600 Hz\n'
+             'South COM Gain = 23 dB; North Fast Gain = 15 dB')
+fig = ax.get_figure()
+fig.set_size_inches(11, 8.5, forward=True)
+figlist = [fig]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+SFSS_Span_Files = [fn for fn in os.listdir() if (fn.find('.txt')!=-1
+                                                 and fn.find('SFSS_Spanned')!=-1) ]
+SCOM = np.array(sorted(list(set([gainValues(fn)['SCOM'] for fn in SFSS_Span_Files ]))))
+SFAST = np.array(sorted(list(set([gainValues(fn)['SFAST'] for fn in SFSS_Span_Files ]))))
+print(SCOM, SFAST)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+X, Y = np.meshgrid(SCOM, SFAST)
+Z = createBNDist(SFSS_Span_Files, X, Y, 300, 600, 'SFAST', 'SCOM')
+minInd = np.unravel_index(np.argmin(Z), np.shape(Z))
+print(minInd)
+print(np.min(Z))
+print('Minimum at COM Gain: '
+      + str(X[0, minInd[1]]) + ' dB, FAST Gain: '
+      + str(Y[minInd[0], 0])+ ' dB')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ax = sns.heatmap(Z, cmap=cm.jet,
+                 cbar_kws = {'label' : 'Summed BN Noise [$Hz/\sqrt{Hz}$]'},
+                 linewidths=0.5, annot=True,vmin=0.9, vmax=1.3)
+                 #vmin=np.min(Z[np.nonzero(Z)]), vmax = 3.2)
+ax.invert_yaxis()
+ax.set_xticklabels(X[0, :])
+ax.set_ylim([-0.5, len(Y[:, 0]) + 0.5])
+ax.set_yticks(Y[:, 0] - Y[0, 0] + 0.5)
+ax.set_yticklabels(Y[:, 0])
+plt.setp(ax.get_yticklabels(), rotation=0, ha="right",
+         rotation_mode="anchor");
+ax.set_xlabel('SFSS COM Gain (dB)', linespacing=4)
+ax.set_ylabel('SFSS FAST Gain (dB)', linespacing=4)
+ax.set_title('Beatnote frequency noise summed from 300 Hz to 600 Hz\n'
+             'North COM Gain = 12 dB; North Fast Gain = 10 dB')
+fig = ax.get_figure()
+fig.set_size_inches(11, 8.5, forward=True)
+figlist = [fig]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+pp = PdfPages('FSS_Gain_Span.pdf')
+for fig in figlist:
+    pp.savefig(fig,bbox_inches='tight')
+pp.close()
+```
--- a/data/20200212_FSS_Gain_Span/FSS_Gain_Span.pdf
+++ b/data/20200212_FSS_Gain_Span/FSS_Gain_Span.pdf
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_155926.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_155926.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12511
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: -1.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.920692702579768
+C3:PSL-NCAV_MODE_MATCHING: 68.87319526355358
+C3:PSL-NCAV_REFL_POW: 2.562527031768
+C3:PSL-NCAV_TRANS_POW: 5.511283463109001
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.330584169659662
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.340926
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.2684230369509075
+C3:PSL-SCAV_MODE_MATCHING: 77.52343146234584
+C3:PSL-SCAV_REFL_POW: 1.798154960607
+C3:PSL-SCAV_TRANS_POW: 6.210823990049999
+C3:PSL-TEMP_TABLE: 18.994165397260673
+C3:PSL-TEMP_VACCAN_INLOOP: 34.36323221692307
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160152.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160152.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.10985
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 0.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.9202595953303194
+C3:PSL-NCAV_MODE_MATCHING: 69.31963979748339
+C3:PSL-NCAV_REFL_POW: 2.554728314184
+C3:PSL-NCAV_TRANS_POW: 5.772189288219001
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.33637697765964
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.336738
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.26874916201885
+C3:PSL-SCAV_MODE_MATCHING: 77.49815443086085
+C3:PSL-SCAV_REFL_POW: 1.8042446000789998
+C3:PSL-SCAV_TRANS_POW: 6.21396259335
+C3:PSL-TEMP_TABLE: 18.993549544015725
+C3:PSL-TEMP_VACCAN_INLOOP: 34.38228548461538
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160418.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160418.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.13685
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 1.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.9203445257731184
+C3:PSL-NCAV_MODE_MATCHING: 69.3996853532704
+C3:PSL-NCAV_REFL_POW: 2.55559483836
+C3:PSL-NCAV_TRANS_POW: 5.795936405235
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.344357036259623
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.344212
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.268878465487645
+C3:PSL-SCAV_MODE_MATCHING: 77.60828825355192
+C3:PSL-SCAV_REFL_POW: 1.797393755673
+C3:PSL-SCAV_TRANS_POW: 6.229655609849999
+C3:PSL-TEMP_TABLE: 18.975689799912132
+C3:PSL-TEMP_VACCAN_INLOOP: 34.37982699846154
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160644.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160644.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12186
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 2.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.9203917529077246
+C3:PSL-NCAV_MODE_MATCHING: 69.34410696131138
+C3:PSL-NCAV_REFL_POW: 2.557327886712
+C3:PSL-NCAV_TRANS_POW: 5.819995984317
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.341106106259613
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.340176
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.268846906653796
+C3:PSL-SCAV_MODE_MATCHING: 77.59356668146043
+C3:PSL-SCAV_REFL_POW: 1.792065321135
+C3:PSL-SCAV_TRANS_POW: 6.229655609849999
+C3:PSL-TEMP_TABLE: 18.98431174534145
+C3:PSL-TEMP_VACCAN_INLOOP: 34.37644658
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160911.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_160911.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12174
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 3.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.920325446569629
+C3:PSL-NCAV_MODE_MATCHING: 69.2565131436531
+C3:PSL-NCAV_REFL_POW: 2.56426008012
+C3:PSL-NCAV_TRANS_POW: 5.776563757143
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.340672644419612
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.340128
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.268746970696589
+C3:PSL-SCAV_MODE_MATCHING: 77.62978776540712
+C3:PSL-SCAV_REFL_POW: 1.789020501399
+C3:PSL-SCAV_TRANS_POW: 6.2083131074099995
+C3:PSL-TEMP_TABLE: 18.989854424546017
+C3:PSL-TEMP_VACCAN_INLOOP: 34.36691994615384
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161137.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161137.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12181
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 4.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.9203841992276756
+C3:PSL-NCAV_MODE_MATCHING: 69.3200317457405
+C3:PSL-NCAV_REFL_POW: 2.563393555944
+C3:PSL-NCAV_TRANS_POW: 5.791874398377
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.340209219779588
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.339816
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.2689169691507125
+C3:PSL-SCAV_MODE_MATCHING: 77.65572345941628
+C3:PSL-SCAV_REFL_POW: 1.7791248372569999
+C3:PSL-SCAV_TRANS_POW: 6.183204281009999
+C3:PSL-TEMP_TABLE: 18.998476369975336
+C3:PSL-TEMP_VACCAN_INLOOP: 34.3884317
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161403.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161403.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12167
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 5.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.920356925786669
+C3:PSL-NCAV_MODE_MATCHING: 69.36805493260513
+C3:PSL-NCAV_REFL_POW: 2.55126221748
+C3:PSL-NCAV_TRANS_POW: 5.775626370945001
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.340602731219555
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.33989
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.268912163578876
+C3:PSL-SCAV_MODE_MATCHING: 77.49554114035578
+C3:PSL-SCAV_REFL_POW: 1.787498091531
+C3:PSL-SCAV_TRANS_POW: 6.166883543849999
+C3:PSL-TEMP_TABLE: 18.993549544015725
+C3:PSL-TEMP_VACCAN_INLOOP: 34.36784187846153
+detector: SN101
+instrument: moku
--- a/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161630.yml
+++ b/data/20200212_FSS_Gain_Span/NFSS_SpannedExpConfig_20200212_161630.yml
+C3:PSL-HEATER_SHIELD_COM_POUT: 0.5
+C3:PSL-HEATER_SHIELD_DIFF_POUT: -0.12114
+C3:PSL-NCAV_FSS_COMGAIN_DB: 10.0
+C3:PSL-NCAV_FSS_FASTGAIN_DB: 6.0
+C3:PSL-NCAV_FSS_SLOWOUT: 2.9200333973236683
+C3:PSL-NCAV_MODE_MATCHING: 69.21405102923725
+C3:PSL-NCAV_REFL_POW: 2.574658370232
+C3:PSL-NCAV_TRANS_POW: 5.788437315651
+C3:PSL-PLL_AUTOLOCKER_BEATNOTE_FREQ: 27.340221204899542
+C3:PSL-PLL_AUTOLOCKER_FREQ_MOD: 2.0
+C3:PSL-PRECAV_BEATNOTE_FREQ: 27.339902
+C3:PSL-SCAV_FSS_COMGAIN_DB: 23.0
+C3:PSL-SCAV_FSS_FASTGAIN_DB: 15.0
+C3:PSL-SCAV_FSS_SLOWOUT: -6.269003167095315
+C3:PSL-SCAV_MODE_MATCHING: 77.50670577497385
+C3:PSL-SCAV_REFL_POW: 1.792065321135
+C3:PSL-SCAV_TRANS_POW: 6.175043912429999
+C3:PSL-TEMP_TABLE: 18.995397103750577