diff --git a/tests/filter.ipynb b/tests/filter.ipynb
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+++ b/tests/filter.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "editorial-primary",
+ "metadata": {},
+ "source": [
+ "# Filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "considered-stroke",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import lisainstrument\n",
+ "import logging\n",
+ "import numpy\n",
+ "import scipy\n",
+ "\n",
+ "import scipy.signal\n",
+ "import matplotlib.pyplot as plt\n",
+ "from numpy import pi, cos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "intelligent-petroleum",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def nuttall4(point):\n",
+ " coeffs = [0.3125, -0.46875, 0.1875, -0.03125]\n",
+ " args = 2 * pi * point * numpy.arange(0, len(coeffs))\n",
+ " terms = coeffs * cos(args)\n",
+ " return numpy.sum(terms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "several-kenya",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nperseg = 4000\n",
+ "window = [nuttall4(i) for i in numpy.arange(0, 1, 1 / nperseg)]\n",
+ "\n",
+ "def psd(x, fs):\n",
+ " f, psd = scipy.signal.welch(x, fs, nperseg=nperseg, detrend=None, window=window)\n",
+ " plt.loglog(f, psd)\n",
+ " plt.xlabel('Frequency [Hz]')\n",
+ " plt.ylabel('PSD [/Hz]')\n",
+ " plt.grid()\n",
+ " return f, psd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "neither-monitor",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lisanode_taps = [1.7370496192150388e-12, 5.3973042628503018e-12, 1.3284086389101661e-11, 2.8599277001513139e-11, 5.6220105475485999e-11, 1.0327860928816974e-10, 1.7983319933162832e-10, 2.9960345565464502e-10, 4.8071341268417346e-10, 7.4635367425942318e-10, 1.1252271766608453e-09, 1.6515858100418969e-09, 2.3645941079340109e-09, 3.3066708165016626e-09, 4.5203588075706634e-09, 6.0431585688373568e-09, 7.8996312586838086e-09, 1.0089935911259692e-08, 1.2573814864871470e-08, 1.5248886280543800e-08, 1.7921948678014295e-08, 2.0271857388632085e-08, 2.1802406140342715e-08, 2.1783549914143351e-08, 1.9179250820822210e-08, 1.2560231826452138e-08, 1.6708937905040257e-22, -2.1047330932470814e-08, -5.3888611276885739e-08, -1.0275340609624424e-07, -1.7297616079053463e-07, -2.7119830122514583e-07, -4.0558889497012730e-07, -5.8608141728450865e-07, -8.2462288508809134e-07, -1.1354301418717916e-06, -1.5352464002237026e-06, -2.0435892877570903e-06, -2.6829796165592943e-06, -3.4791379350585062e-06, 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+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "contemporary-canon",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def design_filter(attenuation, freqs, fs):\n",
+ " nyquist = fs / 2\n",
+ " normwidth = (freqs[1] - freqs[0]) / (0.5 * fs)\n",
+ " numtaps, beta = scipy.signal.kaiserord(attenuation, (freqs[1] - freqs[0]) / nyquist)\n",
+ " cutoff = (freqs[1] + freqs[0]) / (2 * nyquist)\n",
+ " return scipy.signal.firwin(numtaps, cutoff, window=('kaiser', beta))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "billion-revelation",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "253 463\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.lines.Line2D at 0x1349c5fa0>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fs = 30\n",
+ "attenuation = 240\n",
+ "freqs = (0.1 * fs/10, 0.45 * fs/10)\n",
+ "nyquist = fs / 2\n",
+ "\n",
+ "taps = design_filter(attenuation, freqs, fs)\n",
+ "print(len(lisanode_taps), len(taps))\n",
+ "\n",
+ "plt.plot(lisanode_taps, label='lisanode')\n",
+ "plt.plot(taps, label='python')\n",
+ "plt.legend()\n",
+ "\n",
+ "plt.figure()\n",
+ "white = lisainstrument.noises.white(fs, 1000000, 1)\n",
+ "lisanode = scipy.signal.lfilter(lisanode_taps, 1, white)\n",
+ "lisapy = scipy.signal.lfilter(taps, 1.0, white)\n",
+ "f, _ = psd(lisanode, fs)\n",
+ "f, _ = psd(lisapy, fs)\n",
+ "\n",
+ "plt.loglog(f, [1] * len(f), 'black')\n",
+ "plt.loglog(f, [10**(-attenuation/10)] * len(f), 'black')\n",
+ "plt.axvline(x=(freqs[0] + freqs[1]) / 2, ls='--', color='black')\n",
+ "plt.axvline(x=freqs[0], color='black')\n",
+ "plt.axvline(x=freqs[1], color='black')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ancient-subscriber",
+ "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": "3.9.0"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/instrument.ipynb b/tests/instrument.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8b62446d9c53ce5416210ec2d44e80561d1d7cb6
--- /dev/null
+++ b/tests/instrument.ipynb
@@ -0,0 +1,1809 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Testing instrument\n",
+ "\n",
+ "We try to apply laser noise cancellation to check instrumental noise propagation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import lisainstrument\n",
+ "import logging\n",
+ "import pytdi\n",
+ "import h5py\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from numpy import pi\n",
+ "import scipy.signal as sig\n",
+ "from scipy.constants import c"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logging.getLogger().setLevel(logging.WARNING)\n",
+ "orbits='/Users/jbayle/Documents/Developer/lisanode-next/simple-python/keplerian-orbits.h5'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def nuttall4(point):\n",
+ " coeffs = [0.3125, -0.46875, 0.1875, -0.03125]\n",
+ " args = 2 * np.pi * point * np.arange(0, len(coeffs))\n",
+ " terms = coeffs * np.cos(args)\n",
+ " return np.sum(terms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nperseg = 4000\n",
+ "window = [nuttall4(i) for i in np.arange(0, 1, 1 / nperseg)]\n",
+ "\n",
+ "def psd(x, fs):\n",
+ " f, psd = sig.welch(x, fs, nperseg=nperseg, detrend=None, window=window)\n",
+ " plt.loglog(f, psd)\n",
+ " plt.xlabel('Frequency [Hz]')\n",
+ " plt.ylabel('PSD [/Hz]')\n",
+ " plt.grid()\n",
+ " return f, psd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Writing results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " new.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "new = h5py.File('new.h5', 'w')\n",
+ "new['X'] = np.stack((np.arange(i.physics_size) * i.physics_dt, X / i.central_freq), axis=-1)\n",
+ "new['isc'] = np.stack((np.arange(i.size) * i.dt, file['isc_carrier_fluctuations']['12'] / i.central_freq), axis=-1)\n",
+ "new.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " new.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "new = h5py.File('new.h5', 'w')\n",
+ "new['X'] = np.stack((np.arange(i.size) * i.dt, X / i.central_freq), axis=-1)\n",
+ "new['isc'] = np.stack((np.arange(i.size) * i.dt, file['isc_carrier_fluctuations']['12'] / i.central_freq), axis=-1)\n",
+ "new.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## One laser per spacecraft, only one spacecraft, fixed PPRs, no filter\n",
+ "\n",
+ "* Three lasers\n",
+ "* Only laser noise in one spacecraft\n",
+ "* Constants PPRs\n",
+ "* No filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "laser {'12': 28.2, '23': 0, '31': 0, '13': 0, '32': 0, '21': 0}\n"
+ ]
+ }
+ ],
+ "source": [
+ "i = lisainstrument.Instrument(size=10000, three_lasers=True, aafilter=None)\n",
+ "i.disable_all_noises()\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "i.laser_asds['12'] = 28.2\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 572 ms, sys: 319 ms, total: 892 ms\n",
+ "Wall time: 937 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We build a custom simple TDI combination\n",
+ "$$y_{21} - D_{21} y_{12} = D_{21} y_{12} - D_{21} y_{12} = 0$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.51106702e-58, 4.41363138e-58, 4.03795074e-58, ...,\n",
+ " 4.48072821e-58, 3.93689027e-58, 1.70365402e-58]))"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X = pytdi.LISATDICombination({\n",
+ " 'y_12': [(-1, ['D_21'])],\n",
+ " 'y_21': [(1, [])]\n",
+ "}).build({\n",
+ " 'y_12': file['local_carrier_fluctuations']['12'],\n",
+ " 'y_21': file['distant_carrier_fluctuations']['21'],\n",
+ "}, {\n",
+ " f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size))\n",
+ " for mosa in i.MOSAS\n",
+ "}, i.physics_fs)\n",
+ "\n",
+ "psd(file['local_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now apply the entire $X$ combination (without $\\eta$ and $\\xi$, as we only have three lasers). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([4.32768962e-57, 7.86906588e-57, 7.40858330e-57, ...,\n",
+ " 7.14180355e-29, 7.70883271e-29, 4.11285467e-29]))"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X = pytdi.michelson.X1_ETA.build(\n",
+ " {f'eta_{mosa}': file['tps_isc_carrier_fluctuations'][mosa] for mosa in i.MOSAS},\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X[1000:] / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([7.59293430e-40, 1.51858683e-39, 1.51858674e-39, ...,\n",
+ " 8.10743115e-29, 7.25172871e-29, 3.40740688e-29]))"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([7.59293430e-40, 1.51858683e-39, 1.51858674e-39, ...,\n",
+ " 8.10743115e-29, 7.25172871e-29, 3.40740688e-29]))"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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7FLgAyAUecvfnzawAuAfYDax397tCjCgi0uckdM/DzB42s1IzW92ifbqZvWNmG8zs1vbm4e5PufsNwI3AldHmycAT7n49cGJCwouISJsSfdjqEWB6bIOZpQP3AucBRcAMMysys8lm9kyLnxExk34nOh3Aa8BMM/sL8FyC10FERFpI6GErd19sZoUtmqcCG9x9I4CZzQUucfc7gQtbzsPMDLgLeNbdV0SbPwfcFp3/E8CvE7UOIiIp6fbBQfeWf8DQwm6ffRgnzMcAW2M+l0Tb2vJl4BzgcjO7Mdr2HHCzmd0PbGptIjObZWbLzWz5zp07Dz21iEiqeP67zf0/n5KQRST9CXN3v4fg5Hhs22rg8g6mmw3MBiguLvaEBRQRSSZ1lfD3ezoe7xCFseexDTgi5nN+tE1ERA7VD0b3yGLCKB7LgAlmNs7MsoCrgHkh5BAR6V0ijR9uyzs6IYtK9KW6c4AlwEQzKzGzme7eANwELATWAo+7+5pE5hAR6RPe/euH24ZPSMiiEn211Yw22hcACxK5bBGRPuf3n2ruv+kNmHs1kJhTvno8iYhIb/MvX4O88ZCRBZ6Y4pH0V1uJiEgnPPet5v5TPh90L/wZZPRLyOJUPEREeoPX/re5f9CooJtfnLDF6bCViEiqa3loKi3xX+0qHiIiqW73xub+G1q54ioBVDxERFLd7z7Z3D/mpB5ZpIqHiEiq27u5xxep4iEikspiz3ec98MeW6yKh4hIKvvjDc39U2f12GJVPEREUtlbf2juN+uxxap4iIhI3FQ8RERSVWN9c//pN/XoolU8RERS1Q9iXsI6+dM9umgVDxGRVNVY29w/+oQeXbSKh4iIxE3FQ0Qk1X37gx5fpIqHiEgqqqts7s/M7vHFq3iIiKSiBz4a6uJVPEREUtHOdaEuXsVDRCSV9eAjSWIl/ZsEzWwScAuQB7zk7veZ2QDgf4E6YJG7/z7MjCIioTnjy6EsNqF7Hmb2sJmVmtnqFu3TzewdM9tgZre2Nw93X+vuNwJXAGdGmy8DnnD3G4CLExJeRCRZ1dc09w8pCCVCog9bPQJMj20ws3TgXuA8oAiYYWZFZjbZzJ5p8TMiOs3FwHxgQXQ2+cDWaH9jgtdBRCS5bHo17ASJPWzl7ovNrLBF81Rgg7tvBDCzucAl7n4ncGEb85kHzDOz+cCjQAlBAVlFGwXQzGYBswAKCsKpzCIiCfHmY0H3+CtDixDGOY8xNO81QFAITm1rZDObRnCYqh/Nex5/BH5pZhcAT7c2nbvPBmYDFBcXe2vjiIikpLceD7q71ocWIelPmLv7ImBRi7ZK4HNh5BERSRpnfzO0RYdxqe424IiYz/nRNhER6Ujsa2cLTgstRhjFYxkwwczGmVkWcBUwL4QcIiKp58Xbm/v7DwstRqIv1Z0DLAEmmlmJmc109wbgJmAhsBZ43N3XJDKHiEiv8befhZ0ASPzVVjPaaF9A88lvERHprIxsaKjpeLwE0+NJRERSSVPh+G5ZqDFUPEREUlF6uBfLqniIiKSKSPI8UEPFQ0QkVfzzhbATHKDiISKSKuaE9ziSllQ8RERSzZRWL2TtUSoeIiKp5rQvhp1AxUNEJOWMOj7sBO3fJGhmJ3ViHvXu/lY35RERkfacckPYCYCO7zB/meBZVNbOOOOAwu4KJCIiragoDbrLHoAL7g43Cx0Xj2Xu/tH2RjCzv3RjHhERac09nTkQ1HPaPefRUeHo7DgiInKI6vYH3fPD3+uATp4wN7OXzOz8Fm2zExNJRETaNDU5znl09mqrccB/mNltMW3FCcgjIiItVe0OO8GHdLZ47AU+Bow0s6fNbHDiIomIyEG2rQg7wYd0tniYuze4+xeBJ4FXgRGJiyUiIgfsXBd2gg/p7DN972/qcfdHzOwt4EuJiSQiIgfpNzDoFp4Vbo4YHd0k2PSC3D/E9AO8B3w9YalERKTZzvVBd/qd4eaI0dGexxuAE9wkOAp4n+YbBh04MnHRREQEgNfuDbpZA8LNEaPd4uHu45r6zWylu5+Y+EgiItKqQaPDTnBAPO8x9ISl6ICZTQJuAfKAl9z9PjO7FLgAyAUecvfnw8onIpIw5e8392dmh5ejhYQ/VdfMHjazUjNb3aJ9upm9Y2YbzOzW9ubh7mvd/UbgCuDMaNtT7n4DcCOQPG9IERHpTi//T9AdcFi4OVro6IT512I+jmjxGXf/SSeW8QjwS+C3MfNNB+4FPg6UAMvMbB6QDrQ8I3S9u5ea2cXAF4DftRj+nei8RER6n3Xzg25aZrg5WujosNWgmP4HWnzuFHdfbGaFLZqnAhvcfSOAmc0FLnH3O4EL25jPPGCemc0HHjUzA+4CnnX3D91BY2azgFkABQUF8cYWEUkOlTuD7sTp4eZooaPi8U9gobuXdfNyxwBbYz6XAKe2NbKZTQMuA/oBC6LNXwbOAQab2Xh3vz92GnefDcwGKC4uDu18jYhIt5h0cdgJDtJR8TiC4B6PTOAl4Flgqbv36Jexuy8CFrVouwe4pydziIiE5shpYSc4SEePZP+f6CPXzwf+AVwPrDCzR83ss2Y2sovL3UZQmJrkR9tERKRJJNLcb+29k6/ndepSXXffD/wp+oOZFQHnEZwE/0QXlrsMmGBm4wiKxlXA1V2Yj4hI71VbHnQLTg83Ryva3fMws8Nba3f3t939x+7+ibbGiZnHHGAJMNHMSsxsprs3ADcBC4G1wOPuvqZrqyAi0ktVRU83n3hNuDla0dGexwKgo3cftjuOu89oo30BzSe/RUSkpXejb/neszncHK3oqHhMMbPydoYb0N5wERHpqn9GH5xRdEm4OVrR0bOt0nsqiIiItNBUPLJzw83RioQ/nkRERA5RelbYCT5ExUNEJBlFGpv7M3PCy9EGFQ8RkWRUtTvopmVC9uBws7SiU/d5mNlk4Jjox7Xuvrq98UVE5BCtfjLoTmv3oeOh6eipuoOBPxPcDf4mwdVVk81sC8GDDHWllYhIIjQVj9zkeQFUrI4OW30PWA5McPdPuvulwASCO8TvSHA2EZG+q2Rp0J3S6q1yoevosNU5wPHufuABK+4eMbP/BN5KaDIREUm6Z1o16WjPoy76KJGDRNtqExNJRESSXUd7HtlmdiLBuY5YRvBuDRERSZRBo8JO0KaOiscOoK1Xze7o5iwiIgJQXx10x38s3Bzt6OjxJNN6KIeIiDRZ8dugu/L/4JJ7w83Sho4eyX5K7CPXoy+A+rOZ3WNmwxIfT0SkD3r2m0E3LTPcHO3o6IT5r4A6ADM7G7iL4AVQ+4i+H1xERBLkwrbOGoSvo3Me6e4evUeeK4HZ7v4k8KSZrUpoMhGRvu6kz4adoE0d7Xmkm1lTgfkY8JeYYZ16tImIiPQ+HRWAOcDLZrYLqAZeATCz8QSHrkREpDtVloWdoFM6utrqDjN7CRgFPO/uHh2UBnw50eFERPqciuhdEHlHh5ujAx1dbZUNnEZwyOqapkNY7r7e3Vf0QD7MbJKZ3W9mT5jZF2LaB5jZcjO7sCdyiIj0iKronscFPw43Rwc6OufxG6CY4DlW5wFxrY2ZPWxmpWa2ukX7dDN7x8w2mFm7zxt297XufiNwBXBmzKD/AB6PJ4+ISNKr3BV0++eFm6MDHZ3zKHL3yQBm9hCwNM75PwL8kuDyXqLzSQfuBT4OlADLzGwekA7c2WL669291MwuBr4A/C46j48DbwPZceYREUluT3wu6A5I7eJR39Tj7g0W59Md3X2xmRW2aJ4KbHD3jQBmNpfg3SB3Aq0egnL3ecA8M5sPPApMAwYARUC1mS2IffKviEjKy0nu+7A7Kh5TzKzphU8G5EQ/G+DuntuFZY4BtsZ8LgFObWtkM5sGXEbwIMYFBAv+dnTYdcCu1gqHmc0CZgEUFBR0IaaISIjSk/tuiI6utkrvqSDtZFgELGpj2CPtTDeb6F3wxcXF3tZ4IiJJo+l8Rwro6IR5ImwjeK1tk/xom4hI37bmT2En6LQwiscyYIKZjTOzLOAqYF4IOUREkssLt4WdoNMSWjzMbA6wBJhoZiVmNjP6FsKbgIXAWuBxd1+TyBwiIinh/B8G3WufDjdHJyT0jIy7t/rmdndfQPTkt4iIRP35S0F3SPJf5BPGYSsREWlPkl+mCyoeIiLJJ7srd0H0LBUPERGJm4qHiEgyqKsMuhb67XWdouIhIpIMlv866J7+xXBzdJKKh4hIMnj+20H3778IN0cnqXiIiCSTzP5hJ+gUFQ8RkWRy0T1hJ+gUFQ8RkWRy3KfCTtApKh4iIslgwGGQlgFpqfG1nBopRUR6u8qdcEyr78NLSioeIiJhW/980H37qVBjxEPFQ0QkbHNbfYZsUlPxEBEJ2/hzgu7AkeHmiIOKh4hI2EqWBd3PvxRujjioeIiIhK2qLOhmDw43RxxUPEREwtb0MMQUeBR7ExUPEZGweWPYCeKm4iEiEqaKnWEn6BIVDxGRMD33H0E3b2K4OeKUEXaAjpjZJOAWIA94yd3vM7M04HtALrDc3X8TZkYRkS6rqwq6Q44IN0ecErrnYWYPm1mpma1u0T7dzN4xsw1mdmt783D3te5+I3AFcGa0+RIgH6gHShKRXUSkRwzOD7of+69wc8Qp0YetHgGmxzaYWTpwL3AeUATMMLMiM5tsZs+0+BkRneZiYD6wIDqbicDf3f1rwBcSvA4iIomz7IGgm3d0uDnilNDDVu6+2MwKWzRPBTa4+0YAM5sLXOLudwKtPhXM3ecB88xsPvAowd5GXXRw6l2mICLSUkZ22AniEsY5jzHA1pjPJcCpbY1sZtOAy4B+NO95/BH4hZmdBSxuY7pZwCyAgoKCQ80sIpIYeRPhsIlgFnaSuCT9CXN3XwQsatFWBczsYLrZwGyA4uJiT1A8EZFDU70b+g8LO0XcwrhUdxsQe1lBfrRNRKRvcYfqPZCj4tEZy4AJZjbOzLKAq4B5IeQQEQlX7X6INEDO0LCTxC3Rl+rOAZYAE82sxMxmunsDcBOwEFgLPO7uaxKZQ0QkKe3ZFHRT7GQ5JP5qq1bfcOLuC2g++S0i0jetmx90R58Qaoyu0ONJRETC8vJdQTd7SKgxukLFQ0QkbFn9w04QNxUPEZGw5Y4JO0HcVDxERMI0fELK3SAIKh4iIuGIRMDS4NhLw07SJSoeIiJhqKsAj6TUe8tjqXiIiIThrceDbvWecHN0kYqHiEgY5v970E3Bu8tBxUNEJFxjzwg7QZeoeIiIhOnw48NO0CUqHiIiYUrPDDtBlyT9+zxERHql4eNh1JSwU3SZ9jxERMJQtgFIvZsDm6h4iIj0tIrSoLvplXBzHAIVDxGRnrZ7Y9A9+xvh5jgEKh4iIj3td58MulkDws1xCFQ8RER6Wn1V0C08K9wch0DFQ0QkLEOOCDtBl6l4iIhI3FQ8RER6knvYCbpF0t8kaGaTgFuAPOAld7/PzAqAe4DdwHp3vyvMjCIinVa7P+wE3SKhex5m9rCZlZrZ6hbt083sHTPbYGa3tjcPd1/r7jcCVwBnRpsnA0+4+/XAiQkJLyKSCGUbgu75d4eb4xAl+rDVI8D02AYzSwfuBc4DioAZZlZkZpPN7JkWPyOi01wMzAcWRGfzGjDTzP4CPJfgdRAR6T4PfCTo7ngz3ByHKKHFw90XExxaijUV2ODuG929DpgLXOLub7n7hS1+SqPzmefu5wH/Fp3H54Db3P2jwAWtLdvMZpnZcjNbvnPnzoSsn4hIl02ZEXaCQxLGOY8xwNaYzyXAqW2NbGbTgMuAfjTveTwH3G5mVwObWpvO3WcDswGKi4t7xxkqEek9Ck4PO8EhSfoT5u6+CFjUom01cHkYeUREuoWl7kMRIZxLdbcBsXfG5EfbRER6t15ymS6EUzyWARPMbJyZZQFXAfNCyCEi0rPeWdDxOCki0ZfqzgGWABPNrMTMZrp7A3ATsBBYCzzu7msSmUNEJCk8cX3YCbpNQs95uHurlxO4+wKaT36LiPQNDTVB98xbws3RDfR4EhGRnvax28NOcMhUPEREesL6hc39aan/1Zv6ayAikgoevSLsBN1KxUNEJNG2vRF2gm6X9DcJdovGeih/v4cXGsINQD1+05HWMTGLDOPmsR5eZorfINcpkcbgIYhP3wy71je3f31DeJm6Ud8oHh+shp9MCjuFiAgMPCzsBN2ibxSPIQVw0W09t7xQ7iLt4WVqHXuPHl/PELZrT69jYx0suRcqdhzc/u0drY+fgvpG8eg/HE6+LuwUItKXnHlz2AkSSifMRUQkbioeIiISNxUPERGJm4qHiIjETcVDRETipuIhIiJxU/EQEZG4qXiIiEjcVDxERCRuKh4iIhI3FQ8REYmbioeIiMTNvA88OdTMdgKbgcHAvjZGa21YZ9rygF3dELOz2luH7p6+M+N2NE5bw7W9uz5+vL/HbbVre/fs9m6tPdm391h3b/0Z8u7eZ36A2fEM60wbsDxZ1qG7p+/MuB2N09Zwbe/EbHNt7+Td3m1s35Td3n3tsNXTcQ7rbFtPOtTlxzN9Z8btaJy2hmt7d338eH+P22rX9u7Z7d1ae8pu7z5x2CqRzGy5uxeHnaOv0PbuWdrePSuVtndf2/NIhNlhB+hjtL17lrZ3z0qZ7a09DxERiZv2PEREJG4qHiIiEjcVDxERiZuKRwKZ2aVm9oCZPWZm54adp7czsyPN7CEzeyLsLL2VmQ0ws99Ef6//Lew8vV0y/06reLTBzB42s1IzW92ifbqZvWNmG8zs1vbm4e5PufsNwI3AlYnMm+q6aXtvdPeZiU3a+8S57S8Dnoj+Xl/c42F7gXi2dzL/Tqt4tO0RYHpsg5mlA/cC5wFFwAwzKzKzyWb2TIufETGTfic6nbTtEbpve0t8HqGT2x7IB7ZGR2vswYy9ySN0fnsnrYywAyQrd19sZoUtmqcCG9x9I4CZzQUucfc7gQtbzsPMDLgLeNbdVyQ4ckrrju0tXRPPtgdKCArIKvTHZ5fEub3f7uF4naZ//PiMofmvLgj+I41pZ/wvA+cAl5vZjYkM1kvFtb3NbLiZ3Q+caGbfSnS4Xq6tbf9H4FNmdh/hP1qjN2l1eyfz77T2PBLI3e8B7gk7R1/h7mUE55ckQdy9Evhc2Dn6imT+ndaeR3y2AUfEfM6PtkliaHuHR9u+Z6Xc9lbxiM8yYIKZjTOzLOAqYF7ImXozbe/waNv3rJTb3ioebTCzOcASYKKZlZjZTHdvAG4CFgJrgcfdfU2YOXsLbe/waNv3rN6yvfVgRBERiZv2PEREJG4qHiIiEjcVDxERiZuKh4iIxE3FQ0RE4qbiISIicVPxkD7FzBrNbFXMT2HYmbqDmV1nZjvN7MHo52lm9kyLcR4xs8vbmcePzGyHmX090Xkl9enZVtLXVLv7Ca0NiD4F2dw90rORus1j7n5TVyd292+YWWV3BpLeS3se0qeZWWH0BTy/BVYDR5jZN8xsmZm9aWb/HTPut81svZm9amZzmv5CN7NFZlYc7c8zs03R/vToX/NN8/p/0fZp0WmeMLN1Zvb7aOHCzE4xs7+b2T/MbKmZDTKzxWZ2QkyOV81syiGsc3HMntdbZqY7hSVu2vOQvibHzFZF+98DvgpMAK5199cseF3wBIL3Kxgwz8zOBioJnjd0AsH/mxXAGx0sayawz91PMbN+wN/M7PnosBOBY4H3gb8BZ5rZUuAx4Ep3X2ZmuUA18BBwHfAVMzsayHb3f3RiXc+KWVeAAuAZd18eXQ/M7EfAc52Yl8hBVDykrznosFX0nMdmd38t2nRu9Gdl9PNAgmIyCPiTu1dFp+vMQ+vOBY6POc8wODqvOmCpu5dE57UKKAT2AdvdfRmAu5dHh/8B+K6ZfQO4nuBNdJ3xirsfeGmWmR00nZldCZwUzSkSFxUPkWCvookBd7r7r2JHMLOvtDN9A82HgLNbzOvL7r6wxbymAbUxTY2083/R3avM7AWCN8tdAZzcTpZOMbPjgNuBs91dr5OVuOmch8jBFgLXm9lAADMbY8H70RcDl5pZjpkNAi6KmWYTzV/ol7eY1xfMLDM6r6PNbEA7y34HGGVmp0THH2RmTUXlQYIXiy1z9z2HsoJmNgSYA3zW3Xceyryk79Keh0gMd3/ezCYBS6LnsCuAa9x9hZk9BvwDKCV4/0KTu4HHzWwWMD+m/UGCw1EroifEdwKXtrPsuuihpF+YWQ7B+Y5zgAp3f8PMyoFfd8NqXgKMBR6IriNtXYEm0hY9kl2kC8zsdoIv9bt7aHmjgUXAMa1dSmxm1wHFh3KpbnQ+t9OD6yWpS4etRJKcmX0WeB34djv3oFQD5zXdJNjF5fwIuIaDzwGJtEp7HiIiEjfteYiISNxUPEREJG4qHiIiEjcVDxERiZuKh4iIxE3FQ0RE4vb/AT7eDk1PG+xKAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered and downsampled beatnotes.\n",
+ "\n",
+ "We expect aliasing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-04, 1.66666667e-03, ...,\n",
+ " 1.66500000e+00, 1.66583333e+00, 1.66666667e+00]),\n",
+ " array([1.12690209e-36, 1.06667543e-35, 4.36044349e-35, ...,\n",
+ " 6.75665572e-28, 7.48343652e-28, 4.11163876e-28]))"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## One laser per spacecraft, fixed PPRs, no filter\n",
+ "\n",
+ "* Three lasers\n",
+ "* Constants PPRs\n",
+ "* No filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "laser {'12': 28.2, '23': 28.2, '31': 28.2, '13': 28.2, '32': 28.2, '21': 28.2}\n"
+ ]
+ }
+ ],
+ "source": [
+ "i = lisainstrument.Instrument(size=10000, three_lasers=True, aafilter=None)\n",
+ "i.disable_all_noises(but='laser')\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 600 ms, sys: 258 ms, total: 858 ms\n",
+ "Wall time: 907 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([6.72840457e-57, 1.36873419e-56, 1.63145514e-56, ...,\n",
+ " 1.05617562e-26, 1.28420342e-26, 6.95526332e-27]))"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X = pytdi.michelson.X1_ETA.build(\n",
+ " {f'eta_{mosa}': file['tps_isc_carrier_fluctuations'][mosa] for mosa in i.MOSAS},\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X[1000:] / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.27702272e-36, 4.54574989e-36, 4.52242058e-36, ...,\n",
+ " 1.00616045e-26, 1.04291165e-26, 5.54737449e-27]))"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.27702272e-36, 4.54574989e-36, 4.52242058e-36, ...,\n",
+ " 1.00616045e-26, 1.04291165e-26, 5.54737449e-27]))"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.27702272e-36, 4.54574989e-36, 4.52242058e-36, ...,\n",
+ " 1.00616045e-26, 1.04291165e-26, 5.54737449e-27]))"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered and downsampled beatnotes.\n",
+ "\n",
+ "We expect aliasing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-04, 1.66666667e-03, ...,\n",
+ " 1.66500000e+00, 1.66583333e+00, 1.66666667e+00]),\n",
+ " array([3.01490431e-28, 1.94809864e-27, 7.65357717e-27, ...,\n",
+ " 1.61333925e-25, 1.43921461e-25, 7.00859844e-26]))"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fixed PPRs, no filter\n",
+ "\n",
+ "* Constants PPRs\n",
+ "* No filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "laser {'12': 28.2, '23': 28.2, '31': 28.2, '13': 28.2, '32': 28.2, '21': 28.2}\n"
+ ]
+ }
+ ],
+ "source": [
+ "i = lisainstrument.Instrument(size=10000, aafilter=None)\n",
+ "i.disable_all_noises(but='laser')\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 635 ms, sys: 292 ms, total: 927 ms\n",
+ "Wall time: 987 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.90554787e-35, 5.65953690e-35, 5.24384555e-35, ...,\n",
+ " 2.50206338e-26, 1.56456179e-26, 5.35621226e-27]))"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.90554787e-35, 5.65953690e-35, 5.24384555e-35, ...,\n",
+ " 2.50206338e-26, 1.56456179e-26, 5.35621226e-27]))"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-03, 1.66666667e-02, ...,\n",
+ " 1.66500000e+01, 1.66583333e+01, 1.66666667e+01]),\n",
+ " array([2.90554787e-35, 5.65953690e-35, 5.24384555e-35, ...,\n",
+ " 2.50206338e-26, 1.56456179e-26, 5.35621226e-27]))"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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lj1am5CEi7Vl5ZYin3vmEKz6etv8wyyDj1y07TtNY8tAV5iIiSS4jLYUrvlL/GX6bO4+pt/5AKXmIiLQHWxfW/nzCTyklg92ZQ+KyOCUPEZH24Pn/rP15wpWQ2ZWhvTvXP/4BUvIQEUl2Jfv2r+s5lE5pqRzUbf9b4bQGJQ8RkWR396jan8df4t/jeOaZkoeISLKrrHO7l8PPipTjdEatkoeISHszMnzKbvfB0LlX4+O2kJ7nISLS3lR3V137TuPjHQC1PERE2pOfrWt6nFag5CEi0p5k9WiTxSh5iIgksw0f1v6cktomi1XyEBFJZjNOD2SxSh4iIu3FT1a02aKUPERE2os26rICJQ8RkfYjs1ubLUrJQ0QkWVWU1P6c3qnNFq3kISKSrJ68LLBFK3mIiCSr7Z8FtmglDxGRZFUU9XjZSde06aIT/t5WZnYOcAbQDXjYOfemmZ0AXIaPf7Rz7tgAQxQRCV7n3m26uLi2PMzsETPbaWZf1KmfamYrzWyNmd3U2Dyccy84564FrgcuCte975y7HpgFPBav+EVEkkbOCW26uHi3PGYA9wGPV1eYWSpwP/B1YAsw38xeAlKBO+pMf5Vzbme4/MvwdNEuBa5u/bBFRJJM/zFturi4Jg/n3Bwzy6lTPRlY45xbB2BmTwJnO+fuAM6sOw8zM+BO4DXn3KKo+iFAnnOuoL5lm9l1wHUAQ4bE5wHwIiKBqfuQpzg9t6MhQRwwHwhsjvq8JVzXkO8DU4Dzzez6qPqrgUcbmsg596BzbqJzbmLfvn0PJF4RkcSzbXGgi0/4A+bOuXuBe+up/3UA4YiIJIbKskAXH0TLYyswOOrzoHCdiIg0V1lUj/1Z97X54oNIHvOBEWY2zMwygIuBlwKIQ0QkeT1xQaR8ZNtfaR7vU3VnAh8DI81si5ld7ZyrBG4E3gCWA08755bGMw4RkXYtpe3bAfE+2+qSBupfBV6N57JFRCR+dHsSEZFkEwpFym18ZXk1JQ8RkWSzYlakfH6DVyzElZKHiEiyKS8KOgIlDxGRpPPBPVEfXIOjxZOSh4hIstm9MlJ2oYbHiyMlDxGRZFb3HldtRMlDRCSZDTgqkMUqeYiIJLM2vptuNSUPEZFkUpoXdASAkoeISHLZEXU3p7SswMJQ8hARSSbv3RUpn/KLwMJQ8hARSSZlhZHyxKsCC0PJQ0QkmWxdEClnZAcWhpKHiEiyCOiajvooeYiIJItlLwYdQQ0lDxGRZLF1YdAR1FDyEBFJFqHKSHn8pcHFgZKHiEjyiL4JYpd+wcWBkoeISPKYOz1SPvnm4OJAyUNEJDmlZQa6eCUPERGJmZKHiIjETMlDRCQZhIJ5YmBDlDxERJKBqwo6glqUPEREkkEoKnmM+WZwcYQpeYiIJIOlz0XK5z0SXBxhSh4iIsmgeE+kbBZcHGFKHiIiyeDTpyJlJQ8REWmWHZ8HHUEtSh4iIhIzJQ8REYmZkoeIiMRMyUNERGKW8MnDzM4xs7+Z2VNmdlq4brSZPW1mfzWz84OOUUQkrqKfIHjyL4KLI0pck4eZPWJmO83sizr1U81spZmtMbObGpuHc+4F59y1wPXAReHqacCfnXM3AFfGJXgRkUSxPepMq24DgosjSlqc5z8DuA94vLrCzFKB+4GvA1uA+Wb2EpAK3FFn+qucczvD5V+GpwP4O/BrMzsL6B236EVEEoFzQUewn7gmD+fcHDPLqVM9GVjjnFsHYGZPAmc75+4Azqw7DzMz4E7gNefcovB8dwLfCyei5+pOIyLSruRvjZRHnh5cHFEaTR5mNqEZ86hwzsVy9cpAYHPU5y3A0Y2M/31gCtDdzA51zk0PJ6SbgWzg9/VNZGbXAdcBDBkyJIbwREQSzJyozVznXsHFEaWplsd7wHygsWvhhwE5rRVQXc65e4F769RtIJwYGpnuQeBBgIkTJyZem09EJIk1lTzmO+dOaWwEM5sd4zK3AoOjPg8K14mISJJo9GyrphJHc8epYz4wwsyGmVkGcDHwUozzEBHpGBLwYDk081RdM3vHzE6vU/dgM6abCXwMjDSzLWZ2tXOuErgReANYDjztnFsae+giIh1AVUWkPPa84OKoo7lnWw0D/sfMJjnnfhOum9jURM65SxqofxV4tZnLFhHpwKJaHhOvDi6MOpp7kWAucCrQ38xeNrPu8QtJRERq5G2JlAdNCi6OOpqbPMw5V+mc+y7wLPAB0C9+YYmICAAf3BMpp2UEF0cdze22ml5dcM7NMLPPge/FJyQREamx+O9BR1Cvpi4SrL4a5V9RZYD1wE/jFpWIiCS0ploeC/FHaww4GNhG5IJBBxwSv9BERCRRNZo8nHPDqstmttg5d1T8QxIRkUQXyy3ZE/NKFRERaXMJ/zAoERFJPE0dMP9J1Md+dT7jnPtjXKISEZHa+o4KOoJamjpg3jWq/Lc6n0VEJJ6i72t13A+Di6MeTSWP1cAbzrk9bRGMiIhEWfLPSNlSg4ujHk0lj8H4azzSgXeA14B5ziXobR5FRNqTF6OuxU6QZ5dXa+qW7HeFb7l+OvApcBWwyMyeMLMrzax/WwQpItLhDTsh6AhqadbtSZxzBcDz4RdmNhqYBjwO/EfcohMRkYTUaMvDzA6qr945t8w5d7dz7j8aGkdERNqvpq7zaM4zN/RcDhGRDqapbqvxZpbfyHADGhsuIiLtUFP3tkqsc8NERDqiBDtNF3R7EhGRxJS3NVK+YEZgYTREyUNEJBFVFEfKo88KLo4GKHmIiCSiBL8Wu1nXeZjZOKD6rlzLnXNfxC8kERFJdE3dVbc78CL+NiWf4c+uGmdmm4CznXM600pEJB5CFUFH0Kimuq1uAxYAI5xz33TOnQOMAOYDt8c5NhGRjuuvxwYdQaOa6raaAhzhnAtVVzjnQmZ2M/B5XCMTEZGE1VTLo9w5V1m3MlxXFp+QRESkxsFHBh1BvZpqeXQys6PwxzqiGZAZn5BERKRGVs+gI6hXU8ljO9DQo2a3t3IsIiJSV1pi7qc3dXuSk9ooDhERqc9JNwUdQb2auiX7pOhbrocfAPWimd1rZr3iH56ISAeXnh10BPVq6oD5A0A5gJmdCNyJfwBUHvBgfEMTEemgoq8uj5zsmlCaOuaR6pzbGy5fBDzonHsWeNbMlsQ1MhGRjip3Y6ScoAfMm2p5pJpZdYI5FZgdNaxZtzYREZEYrXojUu7aP7g4GtFUApgJvGdmu4ES4H0AMzsU33UlIiKt7bX/DjqCJjV1ttXtZvYOcDDwpnM1HXEpwPfjHRyAmR0O/BDoA7zjnPurmR0C/ALo7pw7vy3iEBGRiKbOtuoEHIPvsrq8ugvLObfKObeoqZmb2SNmttPMvqhTP9XMVprZGjNr9Dw059xy59z1wIXAceG6dc65q5tavoiIxEdTxzweAybi72M1Dbg7xvnPAKZGV5hZKnB/eH6jgUvMbLSZjTOzWXVe/cLTnAW8Arwa4/JFRJLXtN8HHUGDmjrmMdo5Nw7AzB4G5sUyc+fcHDPLqVM9GVjjnFsXnu+T+Nu73wGc2cB8XgJeMrNXgCdiiUFEJGmlZQQdQYOaannU3FC+vhskttBAYHPU5y3hunqZ2UnhixIfINzyMLPeZjYdOMrMft7AdNeZ2QIzW7Br165WCl1EpA117h10BA1qquUx3syqH/hkQFb4swHOOdctrtH5hbwLvFunbg9wfRPTPUj4QsaJEycm9vMcRUTqM6rezpiE0NTZVqlxWOZW/JMJqw0K14mISPTV5Vb3huaJo6luq3iYD4wws2FmlgFcDLwUQBwiIolnz9qgI2iWuCYPM5sJfAyMNLMtZnZ1+NjJjcAbwHLgaefc0njGISKSNBL0XlZ1xfUWI865SxqofxWddisisr+8TUFH0CxBdFuJiEhD/nFe0BE0i5KHiIjETMlDRERipuQhIiIxU/IQEUkUVRVNj5MglDxERBJFWUGkPGBCcHE0g5KHiEiiiL7Go0tiPkGwmpKHiEii+OjPkfJ/3B5cHM2g5CEikijK8iPl3sODi6MZlDxERBKFxeNetPGh5CEikig2zw06gmZT8hARSRTbPws6gmZT8hARkZgpeYiISMyUPEREEs3RNwQdQZOUPEREEs2IKUFH0CQlDxGRhJO4zy6vpuQhIpIInIuULfE3zYkfoYhIRxCqipSHnRhcHM2k5CEikghClZFySuJfaa7kISKSCFbMCjqCmCh5iIgkgi3zg44gJkoeIiKJYO70oCOIiZKHiEgiOeZ7QUfQLEoeIiKJ5KBxQUfQLEoeIiKJJAnOtAIlDxGRxHJo4t+aBJQ8REQSS+deQUfQLEoeIiISMyUPEZGgffyXoCOImZKHiEjQ3vh50BHETMlDRERipuQhIiIxU/IQEZGYJXzyMLPDzWy6mT1jZjc0VCcikvQmXxd0BM0W1+RhZo+Y2U4z+6JO/VQzW2lma8zspsbm4Zxb7py7HrgQOK6hOhGRpNd7RNARNFu8Wx4zgKnRFWaWCtwPTANGA5eY2WgzG2dms+q8+oWnOQt4BXg1aj771YmIJLUkeIJgtbR4ztw5N8fMcupUTwbWOOfWAZjZk8DZzrk7gDMbmM9LwEtm9grwREN10czsOuA6gCFDhrTOFxIRiad+o4KOoNnimjwaMBDYHPV5C3B0QyOb2UnAuUAm4VZGfXV1OeceBB4EmDhxoqtvHBGRwOVuCjqCFgkiecTEOfcu8G5TdSIiSenLT4OOoEWCONtqKzA46vOgcJ2ISMdjyXEL9rqCSB7zgRFmNszMMoCLgZcCiENEJHhmQUfQIvE+VXcm8DEw0sy2mNnVzrlK4EbgDWA58LRzbmk84xARSVzJmTzifbbVJQ3Uv4pOsRURgZXJuSlM+CvMRUTatUWPBR1Biyh5iIhIzJQ8REQSwaDJQUcQEyUPEZFEcHi9N9hIWEoeIiKJIMmu91DyEBEJSllhpJyi5CEiIs2x7t1IeeixgYXREkoeIiJBCVVEygePDy6OFlDyEBEJymuNPgsvoSl5iIgEpXC7fz/ysmDjaAElDxGRoA38StARxEzJQ0QkaEl4Z10lDxGRIJTmR8rZ/YKLo4WUPEREgjD9+Eh51BnBxdFCSh4iIkHI3Rgpq9tKRESaVLgz6AgOmJKHiEhbm/e3oCM4YHF9kqCISIdTVQFFu6GiGPauh7I82LsOdiyD3athx+e1xx99djBxHiAlDxHpuEIhCFVCZSngoKLEnwVVVQbFe/zn4r1QuAOqymHnMigrgH0boLw4cpHfgRhf79O6E56ShyQ35/w/dtEuSEmH1HToehBkZMdvmZXlkLfZb0z6HNayu6EW7/Ux9xoOqU38GzoHe9ZC517+1ZA9axv+7oW7wFX54dFK86EsH7oPitSFQrD9MzhoXOS7VZRCwTbodUh4nCrYthgGTICUFL9B3bseDj7CD9/+OXQb6OPdvcZ/x545fu87LRN6D4d17/n7ObkQbFsEh06B1W/5urwtgIOMrrB7JRx0BGz8yM9/xzLoMdj/3Qt2+JjWvwv9x/p1ULzHLyt3oz+2kNEFSsIJoHivH14edTfboKWmBx1Bi3SM5PHlp3Bb35ZPn5oReaVlQGpmnXK6/4eoVc6IvNeU0/04+5Wj5p3eGTr1gKwe/j29UyuthHairADWvw9r34Et8/3Gou6GwFKg7+EwaCKMOx9yTmids1lK8+CDe2DhDCjZ5+uyesIx34Xjfuj/lk2pqoS3boF5D/g93t6HwgUz/Ia63vEr4JnvwPKX/Ubw/EfhsNP2H+/V//bz7DoArn0Hug2IDPvyU3hkqt/gf/sVGDzJ15cVwgMn+r3oS56EkVN9/Xt3wnt3waRr4Iy7fd0/zoONH8AVL8Dwk/3w9+6C034Hx34fXvguLH8JvjXLb9inH+/X++XPwl+OBgz+Zz1MP86vsytfhMfPgsPP8uvt83/BeQ/Ds1fDYVNh1et+uX0Ph13Lod9ov9efkl77ZoLtQUpyboaTM+pYZfeFr17Xsmmd8//AVeW+KVtZHi6XQ2VZpFxcFB6vLFxfUWf8spYtPy3LJ5KsnuGk0rPO5zrDOvf0G5D2lHRK82HFLL+BWf++33ikd4bBk/09gfqMgC79/Ma4qsL3L29dCEufh0WP+Q30Md+FCVe2fC9v/Rx4/ga/9z3qDDhsmk9Sy1+Gf98OK1+Fy56B7D4Nz8M5eOlG+HQmHHWFj//fd8Dj5/gNfs+c/aeZfZtfxrE/gLX/hmevge99Ujs5bPjQJ47Dpvo437oFznsoMvzDe/1vML2zH3bVa77+i2dg33pffu8unzwqy+GT6b5u0eMw5Td+r33jB77us6d88vh0pv+85Am/bqs39iteidwddsP7sOlj/3cB+Oxp38oo3gMrXg2PPws69/blxX/379XzAp84wCcOaH+JA5Q8Elq3ATDl1mBjqJWE6iSe6HJFMZTk+j3b0vB7yb5wXa5vin+5xJcrihpeXuc+vitiv9dg353Qpb/vbkhUleWw5i2/wVn1uu+T7pkDx9zguzeGHNP0nn5FCSx9AeY/BK/8BOZOh6//1m9km9sScQ7mPgBv/Nx3MV3zdu37EB15id+4P3uN37v/zmvQpYFW7twH/Eb3azfByT/3dUOOhb+dAs9eC1e9XrsLbMcy+Og+n/ROu80nxfuPhtm3wzn3R8b78P/83/uCGTD7d5Hv2W2A765a9qJvRXQb4JPHnrW+2+jzZ6D3CJhwha/ftxFyN/kDvJOvg3kP+gRQtNsvp/9YWPM25G3143Xp7zfqG973v13wrcHoDfynT0bKnz0dKX/xTHj9hnz3HdR+tkVHUt9OQxLoGMkjEZj5bqm0jNabZ2V5VIIJvxfvgfxtvk8+fyvsWeP/Ket27aSk+41JjyF+Q9JruN9D7z3c/5ib0wXT2qoq/Z7qF8/4jX5prt8oTrgSxl0AgybF1v2UnuU37uMvhpWv+Q3kzIthxH/AtDsj/fcNxlMBr/7Ud1ONPB3OfRAyu+4/3uHfgCueh7+fC/8413cNdepWe5wNH8AbN8PIM+Br/xOp73MonPEHeO5a+Pg+3/0FPmm9/j9+eVN+4+t6HQJH/6dPKMdc77u6diyF1W/Cyb/033fytfDJX3zCPPUWWPIPvzGfeJWf11u/9kljwpU+ppNu8vG/dYtPgkW7/J7wST+Hxf/0xyBK83yiOPp633Ja9JiP58Sf+fXzwT3+86gzfSwVJf63tGeNT5YZ4XW2+ZPI9969qll/wnbvkidrH29KIkoeySwtw3fXdGnivjjO+Q1A3hb/yt8SKe/bAMte8l0T1SzF/6B7DffJpPehkXKPIa17gK94r9+bXfW6fy/Ng/RsOPxMnzAOOenAl2cGo06HEV/3e//v3gH3HwPH/xiO/5Hf6Na1byO8cANs/BCO/wmc8qvGW2pDj4WL/u6T08xL4PJnIvPN2wJPf8tv/L85ff/5jLvAtw5m3+4TW79R/vP6OXD6H2ofJD/hv2DxP+DNX/rjDx/+ya+vSVf74T1zfKJb8IiPe8Gj/thD35F+eM7xvvuvU3fAwZhzfVz9x/nkUV4EQ77qlznsBH9sqbwIDjnZ14NPFunZvuvtnd/6nZOsnjDmm74baudSH+eCR/3v6qCx/je18UO/nJJ9/jdYfRwDfDyleTH9WduFQZODjqDFlDw6ArPwsZEe/h+5PsV7fbfInrWwd23k/bOn/dk41VLSfAKpbqn0HOpbB517+b7r6vf0zv4AbUWx3xOtKPYbob1r/d5y9au6zz27H4z6hj8YfOiU+JwtlZoOx94IY8/zG9/37oRPn/B75WO+6b9H4Q7/nT/6s19v5/4NjriwefMf8XU4Zzo8dw08dhac81fftfj0t3zX5MVP7N8iAb+cM+/xXVLP/yecfZ9vpfQf52OLltXTt1xev8knwc//5Y85RCeYr37Pb8SfuMh3c075dWTY2PNg1o98q6bfGOh7mK8ffZY/dgORls7wUyPHHw45ye88dO4Dxbt9skzvBEOPg1WvwcCJMOCoyHIGTfJdWOvn+DOlqpNHnxG+RZy/xR87qk4eo8/xLZohx8Kmj5q3vpPdrcmdLJU8xKs+DXTQxNr1zvk+7+iEsif82vhRw8ddLNWfGlrvsBSffA4eD0ddBsNPgYOPartjMN0OhvMfhq98yx8jePtW/4p22FS/199jcGzzPuICn6ReuAHuCx8byeoFVzwX2VDXp0s/OOten2imH++7ei76R/2nAU+82h+PeO8u35104k9rDx/yVRh8jD/I3XeUP6Op2phv+i6qsnzfdVVt9DmR5DHufP8+ciq89jNfrj5OdNhU3xV26BRfnxNOHoecBD2H+TMIq8r8MakNH/jkMXhy5KD5gKN8Atw8F4Z9Deb83tcPP8Unj1Fn+OTRY4hvRa2f41tjq9/wLagN7/v36gPxnXr47s1Y9MzxLW7wOzqW6nds+o7088zs5ltC5YV+uAtBZhf/d0xJC5+cUeX/X1Iz/OnPzvm/haVEegIysgFL7GOLB8Ccc0HHEHcTJ050CxYsCDqM9sc532IpCZ87H/0qzffdNjWvzv7Vc6jfoNXXVRSUPWth7WzfOkrvDCOnHXg/dN5Wf+ZRSgocfnbDB9Hr2rbYb3RHnBbpaqpP7mZ/htfIaX5DW1fRHn+8Yey5tc/MAti5HLYsgCMvrZ2cVr/tWxM5UXd7XTvbb0yrdypK82D5LJ9g0jL99R8rZvmrpFPTfWuyNB+GftVvkNfO9t1ozvluyUOn+JMfti6EYeHThF3Ib9A3feITze7VfuOdEt6odxsABdv9mWwF2/0JH+WF+A1zqp9f14P9bzEt02/AU6uPLbrIeEl488GgmdlC59zEeocpeYiISH0aSx7tsz0lIiJxpeQhIiIxU/IQEZGYKXmIiEjMlDxERCRmSh4iIhIzJQ8REYmZkoeIiMSsQ1wkaGa7gI1Ad6ChG8rUN6w5dX2A3a0QZnM19h1ae/rmjNvUOA0N1/pu2fha3607/YH+xtv7+h7qnKv/9gjOuQ7zAh6MZVhz6oAFifIdWnv65ozb1DgNDdf6bt31qfUdn/Xd1DgdeX13tG6rl2Mc1ty6tnSgy49l+uaM29Q4DQ3X+m7Z+FrfrTv9gf7GO+z67hDdVvFkZgtcA/d+kdan9d22tL7bVjKt747W8oiHB4MOoIPR+m5bWt9tK2nWt1oeIiISM7U8REQkZkoeIiISMyUPERGJmZJHHJnZOWb2NzN7ysxOCzqe9s7MDjGzh83smaBjaa/MLNvMHgv/ri8LOp72LpF/00oeDTCzR8xsp5l9Uad+qpmtNLM1ZnZTY/Nwzr3gnLsWuB64KJ7xJrtWWt/rnHNXxzfS9ifGdX8u8Ez4d31WmwfbDsSyvhP5N63k0bAZwNToCjNLBe4HpgGjgUvMbLSZjTOzWXVe/aIm/WV4OmnYDFpvfUtsZtDMdQ8MAjaHR6tqwxjbkxk0f30nrLSgA0hUzrk5ZpZTp3oysMY5tw7AzJ4EznbO3QGcWXceZmbAncBrzrlFcQ45qbXG+paWiWXdA1vwCWQJ2vlskRjX97I2Dq/Z9MePzUAie13g/5EGNjL+94EpwPlmdn08A2unYlrfZtbbzKYDR5nZz+MdXDvX0Lp/DjjPzP5K8LfWaE/qXd+J/JtWyyOOnHP3AvcGHUdH4Zzbgz++JHHinCsCvhN0HB1FIv+m1fKIzVZgcNTnQeE6iQ+t7+Bo3betpFvfSh6xmQ+MMLNhZpYBXAy8FHBM7ZnWd3C07ttW0q1vJY8GmNlM4GNgpJltMbOrnXOVwI3AG8By4Gnn3NIg42wvtL6Do3XfttrL+taNEUVEJGZqeYiISMyUPEREJGZKHiIiEjMlDxERiZmSh4iIxEzJQ0REYqbkIR2KmVWZ2ZKoV07QMbUGM/u2me0ys4fCn08ys1l1xplhZuc3Mo/fm9l2M/tpvOOV5Kd7W0lHU+KcO7K+AeG7IJtzLtS2IbWap5xzN7Z0Yufcz8ysqDUDkvZLLQ/p0MwsJ/wAnseBL4DBZvYzM5tvZp+Z2W+ixv2Fma0ysw/MbGb1HrqZvWtmE8PlPma2IVxODe/NV8/rP8P1J4WnecbMVpjZP8OJCzObZGYfmdmnZjbPzLqa2RwzOzIqjg/MbPwBfOeJUS2vz81MVwpLzNTykI4my8yWhMvrgR8DI4BvOec+Mf+44BH45ysY8JKZnQgU4e83dCT+/2YRsLCJZV0N5DnnJplZJvChmb0ZHnYUMAbYBnwIHGdm84CngIucc/PNrBtQAjwMfBv4kZkdBnRyzn3ajO96QtR3BRgCzHLOLQh/D8zs98DrzZiXSC1KHtLR1Oq2Ch/z2Oic+yRcdVr4tTj8uQs+mXQFnnfOFYena85N604Djog6ztA9PK9yYJ5zbkt4XkuAHCAP+NI5Nx/AOZcfHv4v4Fdm9jPgKvyT6JrjfedczUOzzKzWdGZ2ETAhHKdITJQ8RHyropoBdzjnHogewcx+1Mj0lUS6gDvVmdf3nXNv1JnXSUBZVFUVjfwvOueKzewt/JPlLgS+0kgszWJmY4FbgROdc3qcrMRMxzxEansDuMrMugCY2UDzz0efA5xjZllm1hX4RtQ0G4hs0M+vM68bzCw9PK/DzCy7kWWvBA42s0nh8buaWXVSeQj/YLH5zrl9B/IFzawHMBO40jm360DmJR2XWh4iUZxzb5rZ4cDH4WPYhcDlzrlFZvYU8CmwE//8hWp/AJ42s+uAV6LqH8J3Ry0KHxDfBZzTyLLLw11JfzazLPzxjilAoXNuoZnlA4+2wtc8GxgK/C38HWnoDDSRhuiW7CItYGa34jfqf2ij5Q0A3gVG1XcqsZl9G5h4IKfqhudzK234vSR5qdtKJMGZ2ZXAXOAXjVyDUgJMq75IsIXL+T1wObWPAYnUSy0PERGJmVoeIiISMyUPERGJmZKHiIjETMlDRERipuQhIiIxU/IQEZGY/X9aA3XfNXwGkAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered and downsampled beatnotes.\n",
+ "\n",
+ "We expect aliasing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000000e+00, 8.33333333e-04, 1.66666667e-03, ...,\n",
+ " 1.66500000e+00, 1.66583333e+00, 1.66666667e+00]),\n",
+ " array([3.83929818e-28, 3.36799235e-27, 1.48552714e-26, ...,\n",
+ " 2.45171136e-25, 1.47230832e-25, 5.79430154e-26]))"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fixed PPRs, LISANode filter\n",
+ "\n",
+ "* Constants PPRs\n",
+ "* Used coefficients from LISANode"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lisanode_taps = [1.7370496192150388e-12, 5.3973042628503018e-12, 1.3284086389101661e-11, 2.8599277001513139e-11, 5.6220105475485999e-11, 1.0327860928816974e-10, 1.7983319933162832e-10, 2.9960345565464502e-10, 4.8071341268417346e-10, 7.4635367425942318e-10, 1.1252271766608453e-09, 1.6515858100418969e-09, 2.3645941079340109e-09, 3.3066708165016626e-09, 4.5203588075706634e-09, 6.0431585688373568e-09, 7.8996312586838086e-09, 1.0089935911259692e-08, 1.2573814864871470e-08, 1.5248886280543800e-08, 1.7921948678014295e-08, 2.0271857388632085e-08, 2.1802406140342715e-08, 2.1783549914143351e-08, 1.9179250820822210e-08, 1.2560231826452138e-08, 1.6708937905040257e-22, -2.1047330932470814e-08, -5.3888611276885739e-08, -1.0275340609624424e-07, -1.7297616079053463e-07, -2.7119830122514583e-07, -4.0558889497012730e-07, -5.8608141728450865e-07, -8.2462288508809134e-07, -1.1354301418717916e-06, -1.5352464002237026e-06, -2.0435892877570903e-06, -2.6829796165592943e-06, -3.4791379350585062e-06, -4.4611336637652911e-06, -5.6614693126462315e-06, -7.1160799887361828e-06, -8.8642261995439218e-06, -1.0948255922658790e-05, -1.3413210135885712e-05, -1.6306244584367295e-05, -1.9675839606815941e-05, -2.3570769461349983e-05, -2.8038802892767972e-05, -3.3125107775560495e-05, -3.8870334652970705e-05, -4.5308356964771802e-05, -5.2463649794121040e-05, -6.0348294127755177e-05, -6.8958599952408865e-05, -7.8271349015533498e-05, -8.8239666741538844e-05, -9.8788542563251546e-05, -1.0981002871253909e-04, -1.2115815918547679e-04, -1.3264364298719492e-04, -1.4402839866011117e-04, -1.5502001025774459e-04, -1.6526619805822142e-04, -1.7434941009675646e-04, -1.8178165268560600e-04, -1.8699968911122490e-04, -1.8936074526447968e-04, -1.8813886867710637e-04, -1.8252209291560103e-04, -1.7161056214610882e-04, -1.5441577057917803e-04, -1.2986106811720632e-04, -9.6783576594226871e-05, -5.3937650311655365e-05, -3.0683550915236435e-19, 6.6423419178730407e-05, 1.4678867739476402e-04, 2.4260324568387126e-04, 3.5541345499290962e-04, 4.8679004942656329e-04, 6.3831188009849475e-04, 8.1154782393292159e-04, 1.0080370512994719e-03, 1.2292678067744619e-03, 1.4766549078097271e-03, 1.7515162058000628e-03, 2.0550482920709901e-03, 2.3883017667359618e-03, 2.7521564202655753e-03, 3.1472967050599284e-03, 3.5741878964484436e-03, 4.0330533585532400e-03, 4.5238533396239466e-03, 5.0462657231758259e-03, 5.5996691550601945e-03, 6.1831289521388248e-03, 6.7953861753551892e-03, 7.4348502187100074e-03, 8.0995952261511198e-03, 8.7873606010667096e-03, 9.4955558185002235e-03, 1.0221269689147229e-02, 1.0961284157577049e-02, 1.1712092646047166e-02, 1.2469922880970031e-02, 1.3230764062909135e-02, 1.3990398164362339e-02, 1.4744435064037569e-02, 1.5488351153371398e-02, 1.6217530982206348e-02, 1.6927311447314033e-02, 1.7613027971232917e-02, 1.8270062070978917e-02, 1.8893889677738401e-02, 1.9480129540649458e-02, 2.0024591030997338e-02, 2.0523320658165561e-02, 2.0972646615811087e-02, 2.1369220696056831e-02, 2.1710056940836466e-02, 2.1992566442450168e-02, 2.2214587759225593e-02, 2.2374412476000954e-02, 2.2470805511828482e-02, 2.2503019857509909e-02, 2.2470805511828482e-02, 2.2374412476000954e-02, 2.2214587759225593e-02, 2.1992566442450168e-02, 2.1710056940836466e-02, 2.1369220696056831e-02, 2.0972646615811087e-02, 2.0523320658165561e-02, 2.0024591030997338e-02, 1.9480129540649458e-02, 1.8893889677738401e-02, 1.8270062070978917e-02, 1.7613027971232917e-02, 1.6927311447314033e-02, 1.6217530982206348e-02, 1.5488351153371398e-02, 1.4744435064037569e-02, 1.3990398164362339e-02, 1.3230764062909135e-02, 1.2469922880970031e-02, 1.1712092646047166e-02, 1.0961284157577049e-02, 1.0221269689147229e-02, 9.4955558185002235e-03, 8.7873606010667096e-03, 8.0995952261511198e-03, 7.4348502187100074e-03, 6.7953861753551892e-03, 6.1831289521388248e-03, 5.5996691550601945e-03, 5.0462657231758259e-03, 4.5238533396239466e-03, 4.0330533585532400e-03, 3.5741878964484436e-03, 3.1472967050599284e-03, 2.7521564202655753e-03, 2.3883017667359618e-03, 2.0550482920709901e-03, 1.7515162058000628e-03, 1.4766549078097271e-03, 1.2292678067744619e-03, 1.0080370512994719e-03, 8.1154782393292159e-04, 6.3831188009849475e-04, 4.8679004942656329e-04, 3.5541345499290962e-04, 2.4260324568387126e-04, 1.4678867739476402e-04, 6.6423419178730407e-05, -3.0683550915236435e-19, -5.3937650311655365e-05, -9.6783576594226871e-05, -1.2986106811720632e-04, -1.5441577057917803e-04, -1.7161056214610882e-04, -1.8252209291560103e-04, -1.8813886867710637e-04, -1.8936074526447968e-04, -1.8699968911122490e-04, -1.8178165268560600e-04, -1.7434941009675646e-04, -1.6526619805822142e-04, -1.5502001025774459e-04, -1.4402839866011117e-04, -1.3264364298719492e-04, -1.2115815918547679e-04, -1.0981002871253909e-04, -9.8788542563251546e-05, -8.8239666741538844e-05, -7.8271349015533498e-05, -6.8958599952408865e-05, -6.0348294127755177e-05, -5.2463649794121040e-05, -4.5308356964771802e-05, -3.8870334652970705e-05, -3.3125107775560495e-05, -2.8038802892767972e-05, -2.3570769461349983e-05, -1.9675839606815941e-05, -1.6306244584367295e-05, -1.3413210135885712e-05, -1.0948255922658790e-05, -8.8642261995439218e-06, -7.1160799887361828e-06, -5.6614693126462315e-06, -4.4611336637652911e-06, -3.4791379350585062e-06, -2.6829796165592943e-06, -2.0435892877570903e-06, -1.5352464002237026e-06, -1.1354301418717916e-06, -8.2462288508809134e-07, -5.8608141728450865e-07, -4.0558889497012730e-07, -2.7119830122514583e-07, -1.7297616079053463e-07, -1.0275340609624424e-07, -5.3888611276885739e-08, -2.1047330932470814e-08, 1.6708937905040257e-22, 1.2560231826452138e-08, 1.9179250820822210e-08, 2.1783549914143351e-08, 2.1802406140342715e-08, 2.0271857388632085e-08, 1.7921948678014295e-08, 1.5248886280543800e-08, 1.2573814864871470e-08, 1.0089935911259692e-08, 7.8996312586838086e-09, 6.0431585688373568e-09, 4.5203588075706634e-09, 3.3066708165016626e-09, 2.3645941079340109e-09, 1.6515858100418969e-09, 1.1252271766608453e-09, 7.4635367425942318e-10, 4.8071341268417346e-10, 2.9960345565464502e-10, 1.7983319933162832e-10, 1.0327860928816974e-10, 5.6220105475485999e-11, 2.8599277001513139e-11, 1.3284086389101661e-11, 5.3973042628503018e-12, 1.7370496192150388e-12, ]\n",
+ "lisanode_filter = lambda x: sig.lfilter(lisanode_taps, 1, x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "laser {'12': 28.2, '23': 28.2, '31': 28.2, '13': 28.2, '32': 28.2, '21': 28.2}\n"
+ ]
+ }
+ ],
+ "source": [
+ "i = lisainstrument.Instrument(dt=1/3, size=10000, aafilter=lisanode_filter)\n",
+ "i.disable_all_noises(but='laser')\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 741 ms, sys: 214 ms, total: 955 ms\n",
+ "Wall time: 1 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x13e5e68b0>]"
+ ]
+ },
+ "execution_count": 130,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "f, _ = psd(X / i.central_freq, i.physics_fs)\n",
+ "\n",
+ "plt.loglog(f, [2E-26] * len(f))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000e+00, 7.50000e-03, 1.50000e-02, ..., 1.49850e+01,\n",
+ " 1.49925e+01, 1.50000e+01]),\n",
+ " array([1.73472280e-37, 1.72689692e-36, 5.62515750e-36, ...,\n",
+ " 5.32627651e-29, 4.56411569e-29, 1.96588150e-29]))"
+ ]
+ },
+ "execution_count": 131,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000e+00, 7.50000e-03, 1.50000e-02, ..., 1.49850e+01,\n",
+ " 1.49925e+01, 1.50000e+01]),\n",
+ " array([1.73472280e-37, 1.72689692e-36, 5.62515750e-36, ...,\n",
+ " 5.32627651e-29, 4.56411569e-29, 1.96588150e-29]))"
+ ]
+ },
+ "execution_count": 132,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the filter transfer function…"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.lines.Line2D at 0x13e748e80>"
+ ]
+ },
+ "execution_count": 133,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "white = lisainstrument.noises.white(i.physics_fs, i.physics_size, 1)\n",
+ "filtered = i.aafilter(white)\n",
+ "\n",
+ "psd(white, i.physics_fs)\n",
+ "psd(filtered, i.physics_fs)\n",
+ "\n",
+ "plt.loglog(f, [1] * len(f), 'black')\n",
+ "plt.loglog(f, [10**(-240/10)] * len(f), 'black')\n",
+ "plt.axvline(x=0.1 * i.fs, color='black')\n",
+ "plt.axvline(x=0.45 * i.fs, color='black')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered beatnotes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000e+00, 7.50000e-03, 1.50000e-02, ..., 1.49850e+01,\n",
+ " 1.49925e+01, 1.50000e+01]),\n",
+ " array([3.35341032e-29, 2.29865630e-28, 1.03659687e-27, ...,\n",
+ " 9.58817436e-52, 4.23765962e-52, 1.04408418e-52]))"
+ ]
+ },
+ "execution_count": 134,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['filtered_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['filtered_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['filtered_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered and downsampled beatnotes.\n",
+ "\n",
+ "We don't expect aliasing as we filter the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000e+00, 7.50000e-04, 1.50000e-03, ..., 1.49850e+00,\n",
+ " 1.49925e+00, 1.50000e+00]),\n",
+ " array([5.04135780e-49, 1.00663099e-48, 1.00416790e-48, ...,\n",
+ " 5.55086533e-49, 5.51387236e-49, 2.77225399e-49]))"
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "Xpprs = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "Xmprs = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['mprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.fs)\n",
+ "psd(Xpprs / i.central_freq, i.fs)\n",
+ "psd(Xmprs / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can do the same for TDI 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0.00000e+00, 7.50000e-04, 1.50000e-03, ..., 1.49850e+00,\n",
+ " 1.49925e+00, 1.50000e+00]),\n",
+ " array([2.77676957e-44, 5.54956714e-44, 5.53760177e-44, ...,\n",
+ " 2.28316202e-45, 2.25704129e-45, 1.12421624e-45]))"
+ ]
+ },
+ "execution_count": 139,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "Xpprs = pytdi.michelson.X2.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "Xmprs = pytdi.michelson.X2.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['mprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.fs)\n",
+ "psd(Xpprs / i.central_freq, i.fs)\n",
+ "psd(Xmprs / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fixed PPRs\n",
+ "\n",
+ "* Constants PPRs\n",
+ "* Build filter in lisainstrument"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "i = lisainstrument.Instrument(dt=1/3, size=10000)\n",
+ "i.disable_all_noises(but='laser')\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the filter transfer function…"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at filtered and downsampled beatnotes.\n",
+ "\n",
+ "We don't expect aliasing as we filter the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs,\n",
+ " order=35\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.fs)\n",
+ "psd(X / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can do the same for TDI 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X2.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.size)) for mosa in i.MOSAS},\n",
+ " i.fs\n",
+ ")\n",
+ "\n",
+ "psd(file['isc_carrier_fluctuations']['12'] / i.central_freq, i.fs)\n",
+ "psd(X / i.central_freq, i.fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Keplerian orbits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "i = lisainstrument.Instrument(dt=1/3, size=100000, filter_taps=lisanode_filter)\n",
+ "i.disable_all_noises(but='laser')\n",
+ "i.disable_dopplers()\n",
+ "\n",
+ "print('laser', i.laser_asds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " file.close()\n",
+ "except:\n",
+ " pass\n",
+ "\n",
+ "%time i.write(mode='w', write_all=True)\n",
+ "file = h5py.File('measurements.h5', 'r')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['tps_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['tps_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['tps_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['tps_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now looking at beatnotes on THE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measurements = {}\n",
+ "for mosa in i.MOSAS:\n",
+ " measurements[f'sci_{mosa}'] = file['the_isc_carrier_fluctuations'][mosa]\n",
+ " measurements[f'ref_{mosa}'] = file['the_ref_carrier_fluctuations'][mosa]\n",
+ " measurements[f'tm_{mosa}'] = file['the_tm_carrier_fluctuations'][mosa]\n",
+ "\n",
+ "X = pytdi.michelson.X1.build(\n",
+ " measurements,\n",
+ " {f'd_{mosa}': np.broadcast_to(file['pprs'][mosa], (i.physics_size)) for mosa in i.MOSAS},\n",
+ " i.physics_fs,\n",
+ ")\n",
+ "\n",
+ "psd(file['the_isc_carrier_fluctuations']['12'] / i.central_freq, i.physics_fs)\n",
+ "psd(X / i.central_freq, i.physics_fs)"
+ ]
+ },
+ {
+ "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": "3.9.0"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "215.50271606445312px"
+ },
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tests/notebook_noises.ipynb b/tests/noises.ipynb
similarity index 99%
rename from tests/notebook_noises.ipynb
rename to tests/noises.ipynb
index 54e15b7501d74d8d2c4997e99165fc324d201660..c8e9bcf2a465573fcb4abe68bdb809ff8a087630 100644
--- a/tests/notebook_noises.ipynb
+++ b/tests/noises.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Test noise"
+ "# Noises"
]
},
{
@@ -963,7 +963,20 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.0"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
}
},
"nbformat": 4,