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