""" Created by: Matthew Todd Script to project IMC WFS and MC2 Trans during various injections to the ISS to estimate the ambient noise witnessed in the ISS OUTER RIN witness. Portion of creating an IMC noise budget """ # %% from gwpy.timeseries import TimeSeriesDict, TimeSeries import numpy as np import matplotlib.pyplot as plt from dttxml import dtt_read import os, ipdb import scipy.constants as scc plt.rcParams.update( {'axes.labelsize': 24, 'axes.titlesize': 24, 'xtick.labelsize': 24, 'ytick.labelsize': 24, 'legend.fontsize': 14, 'legend.markerscale': 1.0, 'lines.linewidth': 2, 'lines.markersize': 3} ) # %% ### Create dictionary with all the asds ### data_dict = {} # load f, asd for RIN in the ISS during quiet time # gpstime for quiet_data 1446823543 quiet_gpstime = 1446823543 f, asd = np.loadtxt( fname=os.path.abspath("dtt_injections/quiet_data_iss_outer_rin.txt"), comments="#", delimiter=" ", unpack=True ) data_dict["iss_outer_rin"] = { "freq": f, "asd": asd, "label": "ISS_OUTER_RIN", "color": "C0", "linestyle": "-", } f, asd = np.loadtxt( fname=os.path.abspath("dtt_injections/quiet_data_iss_inner_rin.txt"), comments="#", delimiter=" ", unpack=True ) data_dict["iss_inner_rin"] = { "freq": f, "asd": asd, "label": "ISS_INNER_RIN", "color": "C1", "linestyle": "-", } # load f, calibrated asd from WFS/MC2 dirname = os.path.abspath("./dtt_injections") file_dict = { 0: { "filename": "pzt_pit_injection_1_15_Hz.xml", "calibrated_channel": "H1:IMC-WFS_A_DC_PIT_OUT_DQ", "label": "IMC_PZT_PIT", "color": "C2", "linestyle": "None", "marker": "o", "Xref": 3, "Yref": 4 }, 1: { "filename": "pzt_pit_injection_15_1000_Hz.xml", "calibrated_channel": "H1:IMC-WFS_A_DC_PIT_OUT_DQ", "label": "IMC_PZT_PIT", "color": "C3", "linestyle": "None", "marker": "o", "Xref": 3, "Yref": 4 }, 2: { "filename": "pzt_yaw_injection_1_15_Hz.xml", "calibrated_channel": "H1:IMC-WFS_A_DC_PIT_OUT_DQ", "label": "IMC_PZT_YAW", "color": "C4", "linestyle": "None", "marker": "s", "Xref": 3, "Yref": 4 }, 3: { "filename": "pzt_yaw_injection_15_1000_Hz.xml", "calibrated_channel": "H1:IMC-WFS_A_DC_PIT_OUT_DQ", "label": "IMC_PZT_YAW", "color": "C5", "linestyle": "None", "marker": "s", "Xref": 3, "Yref": 4 }, 4: { "filename": "imc_dof_1_pit_injection_1_4_Hz.xml", "calibrated_channel": "H1:IMC-WFS_B_DC_PIT_OUT_DQ", "label": "IMC_DOF_1_P", "color": "C6", "linestyle": "None", "marker": "o", "Xref": 6, "Yref": 7, "bandlimits": (0.2, 5) }, 5: { "filename": "imc_dof_1_yaw_injection_1_4_Hz.xml", "calibrated_channel": "H1:IMC-WFS_B_DC_YAW_OUT_DQ", "label": "IMC_DOF_1_Y", "color": "C7", "linestyle": "None", "marker": "s", "Xref": 5, "Yref": 6, "bandlimits": (0.2, 4) }, 6: { "filename": "imc_dof_2_pit_injection_1_4_Hz.xml", "calibrated_channel": "H1:IMC-MC2_TRANS_PIT_OUT_DQ", "label": "IMC_DOF_2_P", "color": "C8", "linestyle": "None", "marker": "o", "Xref": 4, "Yref": 7, "bandlimits": (0.2, 4) }, 7: { "filename": "imc_dof_2_yaw_injection_1_4_Hz.xml", "calibrated_channel": "H1:IMC-WFS_A_DC_YAW_OUT_DQ", "label": "IMC_DOF_2_Y", "color": "C9", "linestyle": "None", "marker": "s", "Xref": 4, "Yref": 7, "bandlimits": (0.2, 4) }, } for key, info in file_dict.items(): filepath = os.path.join(dirname, info["filename"]) data = dtt_read(filepath) if "bandlimits" in info.keys(): bandlimits = info["bandlimits"] else: name, ext = info["filename"].split(".") bandlimits = name.split("_")[-3:-1] bandlimits = [float(bandlimit) for bandlimit in bandlimits] FHz =data.references[info["Xref"]].FHz freq_indices = np.searchsorted(FHz, bandlimits) freq_indices[0] -= 1 freq_indices[1] += 1 X_PSD = data.references[info["Xref"]].PSD[0] Y_PSD = data.references[info["Yref"]].PSD[0] CSDxy_index = data.results.CSD[info["calibrated_channel"]].channelB_inv['H1:PSL-ISS_SECONDLOOP_RIN_OUTER_OUT_DQ'] CSDxx = data.results.PSD[info["calibrated_channel"]].PSD[0]**2 CSDxy = data.results.CSD[info["calibrated_channel"]].CSD[CSDxy_index] TF = np.abs(CSDxy/CSDxx) calibrated_x_asd = TF*X_PSD if info["label"] in data_dict.keys(): data_dict[info["label"]]["freq"] = np.concatenate(( data_dict[info["label"]]["freq"], FHz[slice(*freq_indices)] )) data_dict[info["label"]]["asd"] = np.concatenate(( data_dict[info["label"]]["asd"], calibrated_x_asd[slice(*freq_indices)] )) else: data_dict[info["label"]] = { "freq": FHz[slice(*freq_indices)], "asd": calibrated_x_asd[slice(*freq_indices)], "label": info["label"], "color": info.get("color", f"C{key+2}"), "linestyle": info.get("linestyle", f"-"), "marker": info.get("marker", None) } # %% ### Plot all the curves ### fig, axs = plt.subplots(figsize=(8, 8), tight_layout=True) # Plot projections and RIN for idx, (key, value) in enumerate(data_dict.items()): axs.loglog( value["freq"], value["asd"], label=value["label"], color=value["color"], ls=value["linestyle"], marker=value.get("marker", None), ) # Plot shot noise limit of outer dc_power = TimeSeries.fetch( channel="H1:PSL-ISS_SECONDLOOP_PDSUMOUTER_OUT_DQ", start=quiet_gpstime, end=quiet_gpstime+360, ).median().value*1e-3 lambda0 = 1064e-9 shot_noise_limit = np.sqrt(2*scc.h*(scc.c/lambda0) / dc_power) shot_noise_limit = np.ones_like(data_dict["iss_outer_rin"]['freq'])*shot_noise_limit axs.loglog( data_dict["iss_outer_rin"]['freq'], shot_noise_limit, color="black", ls="--" ) axs.text( x=0.3, y=shot_noise_limit[0]*0.99, s='Shot Noise Limit', color='k', verticalalignment='top', # Places the text just above the line horizontalalignment='center', # Centers the text at the x position fontsize=12, ) axs.set_xlabel("Frequency [Hz]") axs.set_ylabel("ASD [V/rtHz]") axs.grid(True, 'major', 'both', alpha=0.5) axs.grid(True, 'minor', 'both', alpha=0.2) axs.set_xlim(1e-1, 5000) axs.legend(loc="upper right") axs.set_title("ISS Noise Budget for LHO O4") fig.savefig("iss_noise_budget_LHO_O4.pdf") fig.savefig("iss_noise_budget_LHO_O4.png") # %%