def test_LISA_prop1(source_space_based):
T_obs = 4 * u.yr # Observing time in years
L = 2.5e9 * u.m # armlength in meters
A_acc = 3e-15 * u.m / u.s / u.s
f_acc_break_low = 0.4 * u.mHz.to("Hz") * u.Hz
f_acc_break_high = 8.0 * u.mHz.to("Hz") * u.Hz
f_IMS_break = 2.0 * u.mHz.to("Hz") * u.Hz
A_IMS = 10e-12 * u.m
Background = False
T_type = "N"
LISA_prop1 = detector.SpaceBased(
"LISA_ESA",
T_obs,
L,
A_acc,
f_acc_break_low,
f_acc_break_high,
A_IMS,
f_IMS_break,
Background=Background,
T_type=T_type,
)
[lisa_sample_x, lisa_sample_y, lisa_SNR] = snr.Get_SNR_Matrix(
source_space_based, LISA_prop1, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_LISA_params_Aaccvz(source_space_based, LISA_ESA):
# Variable on x-axis
var_x = "A_acc"
# Variable on y-axis
var_y = "z"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_space_based, LISA_ESA, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True,
dl_axis=True,
)
plt.close(fig)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True,
lb_axis=True,
smooth_contours=False,
)
plt.close(fig)
def test_aLIGO_params_MvIL(source_ground_based, aLIGO_gwinc):
# Variable on x-axis
var_x = "M"
# Variable on y-axis
var_y = "Infrastructure Temp"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_ground_based, aLIGO_gwinc, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display_cbar=True,
y_axis_label=False,
smooth_contours=False,
logLevels_min=-1.0,
logLevels_max=5.0,
y_axis_line=295,
yticklabels_kwargs={
"rotation": 70,
"y": 0.02
},
xlabels_kwargs={"labelpad": 0.45},
display=False,
return_plt=True,
)
plt.close(fig)
def test_aLIGO_params_SGvMST(source_ground_based, aLIGO_gwinc):
# Variable on x-axis
var_x = "Seismic Gamma"
# Variable on y-axis
var_y = "Materials Substrate Temp"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_ground_based, aLIGO_gwinc, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
cfill=False,
display=False,
return_plt=True,
)
plt.close(fig)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
smooth_contours=False,
cfill=True,
display=False,
return_plt=True,
x_axis_label=False,
y_axis_label=False,
)
plt.close(fig)
def test_NANOGrav_WN_params_Mvcadence(source_pta, NANOGrav_WN):
source_pta.q = 1.0
source_pta.chi1 = 0.0
source_pta.chi2 = 0.0
source_pta.z = 0.1
source_pta.f_min = 1e-9
T_obs = 15.0 * u.yr # Observing time in years
T_obs_min = 5.0 * u.yr
T_obs_max = 30.0 * u.yr
NANOGrav_WN.T_obs = [T_obs, T_obs_min, T_obs_max]
NANOGrav_WN.sigma = [sigma, sigma_min, sigma_max]
NANOGrav_WN.n_p = [N_p, N_p_min, N_p_max]
NANOGrav_WN.cadence = [cadence, cadence_min, cadence_max]
# Variable on x-axis
var_x = "M"
# Variable on y-axis
var_y = "cadence"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_pta, NANOGrav_WN, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True)
plt.close(fig)
def test_NANOGrav_WN_params_Mvchi1(source_pta, NANOGrav_WN):
# Variable on x-axis
var_x = "M"
# Variable on y-axis
var_y = "chi1"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_pta, NANOGrav_WN, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
cfill=False,
display=False,
return_plt=True,
)
plt.close(fig)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
smooth_contours=False,
cfill=True,
display=False,
return_plt=True,
)
plt.close(fig)
def test_aLIGO_params_LPvz(source_ground_based, aLIGO_gwinc):
# Variable on x-axis
var_x = "Laser Power"
# Variable on y-axis
var_y = "z"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_ground_based, aLIGO_gwinc, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
cfill=False,
display=False,
return_plt=True,
x_axis_line=125,
)
plt.close(fig)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
smooth_contours=False,
cfill=True,
display=False,
return_plt=True,
x_axis_label=False,
y_axis_label=False,
)
plt.close(fig)
def test_aLIGO_params_ILvIT(source_ground_based, aLIGO_gwinc):
# Variable on x-axis
var_x = "Infrastructure Length"
# Variable on y-axis
var_y = "Infrastructure Temp"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_ground_based, aLIGO_gwinc, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
cfill=False,
display=False,
return_plt=True,
x_axis_line=3995,
)
plt.close(fig)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
smooth_contours=False,
cfill=True,
display=False,
return_plt=True,
x_axis_label=False,
y_axis_label=False,
)
plt.close(fig)
def test_ET(source_ground_based):
ET_filedirectory = load_directory + "/EinsteinTelescope/"
ET_filename = "ET_D_data.txt"
ET_filelocation = ET_filedirectory + ET_filename
T_obs = 4 * u.yr # Observing time in years
ET = detector.GroundBased("ET", T_obs, load_location=ET_filelocation, I_type="A")
[et_sample_x, et_sample_y, et_SNR] = snr.Get_SNR_Matrix(
source_ground_based, ET, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_NANOGrav_11yr(source_pta):
load_name = "NANOGrav_11yr_S_eff.txt"
load_location = load_directory + "/NANOGrav/StrainFiles/" + load_name
T_obs = 11.42 * u.yr # Observing time in years
nanograv = detector.PTA(
"NANOGrav 11yr", T_obs=T_obs, load_location=load_location, I_type="E"
)
[nanograv_sample_x, nanograv_sample_y, nanograv_SNR] = snr.Get_SNR_Matrix(
source_pta, nanograv, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_pta_NANOGrav_WN(source_pta):
NANOGrav_WN = detector.PTA(
"NANOGrav, WN Only",
N_p_nano,
T_obs=T_nano,
sigma=sigma_nano,
cadence=cadence_nano,
)
[NANOGrav_WN_sample_x, NANOGrav_WN_sample_y, NANOGrav_WN_SNR] = snr.Get_SNR_Matrix(
source_pta, NANOGrav_WN, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_aLIGO(source_ground_based):
aLIGO_filedirectory = load_directory + "/aLIGO/"
aLIGO_filename = "aLIGODesign.txt"
aLIGO_filelocation = aLIGO_filedirectory + aLIGO_filename
T_obs = 4 * u.yr # Observing time in years
aLIGO = detector.GroundBased(
"aLIGO", T_obs, load_location=aLIGO_filelocation, I_type="A"
)
[aLIGO_sample_x, aLIGO_sample_y, aLIGO_SNR] = snr.Get_SNR_Matrix(
source_ground_based, aLIGO, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_SKA(source_pta):
T_obs = 15 * u.yr # Observing time (years)
sigma = 10 * u.ns.to("s") * u.s # rms timing residuals in nanoseconds
N_p = 20 # Number of pulsars
cadence = 1 / (
u.wk.to("yr") * u.yr
) # Avg observation cadence of 1 every week in num/year
SKA = detector.PTA("SKA", N_p, T_obs=T_obs, sigma=sigma, cadence=cadence)
[SKA_sample_x, SKA_sample_y, SKA_SNR] = snr.Get_SNR_Matrix(
source_pta, SKA, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_NANOGrav_WN_params_Mvq(source_pta, NANOGrav_WN):
# Variable on x-axis
var_x = "M"
# Variable on y-axis
var_y = "q"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_pta, NANOGrav_WN, var_x,
sampleRate_x, var_y, sampleRate_y)
[_, _, _] = snr.Get_SNR_Matrix(source_pta,
NANOGrav_WN,
var_x,
sampleRate_x,
var_y,
sampleRate_y,
method="PN")
fig, ax = snrplot.Plot_SNR(var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True)
plt.close(fig)
def test_LISA_params_MvAIFO(source_space_based, LISA_ESA):
# Variable on x-axis
var_x = "chi1"
# Variable on y-axis
var_y = "A_IFO"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_space_based, LISA_ESA, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True)
plt.close(fig)
def test_LISA_params_Tobsvfaccbreakhigh(source_space_based, LISA_ESA):
# Variable on x-axis
var_x = "T_obs"
# Variable on y-axis
var_y = "f_acc_break_high"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_space_based, LISA_ESA, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True)
plt.close(fig)
def test_pta_NANOGrav_WN_GWB(source_pta):
NANOGrav_WN_GWB = detector.PTA(
"NANOGrav, WN and GWB",
N_p_nano,
T_obs=T_nano,
sigma=sigma_nano,
cadence=cadence_nano,
sb_amp=4e-16,
)
[
NANOGrav_WN_GWB_sample_x,
NANOGrav_WN_GWB_sample_y,
NANOGrav_WN_GWB_SNR,
] = snr.Get_SNR_Matrix(
source_pta, NANOGrav_WN_GWB, var_x, sampleRate_x, var_y, sampleRate_y
)
def test_NANOGrav_WN_params_MvTobs(source_pta, NANOGrav_WN):
# Variable on x-axis
var_x = "M"
# Variable on y-axis
var_y = "T_obs"
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source_pta, NANOGrav_WN, var_x,
sampleRate_x, var_y, sampleRate_y)
fig, ax = snrplot.Plot_SNR(
var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True,
xticklabels_kwargs={
"rotation": 70,
"y": 0.02
},
ylabels_kwargs={"labelpad": -5},
)
plt.close(fig)
def test_LISA_params_MvAacc(source_space_based, LISA_ESA):
# Variable on x-axis
var_x = "z"
# Variable on y-axis
var_y = "A_acc"
[sample_x, sample_y, SNRMatrix] = snr.Get_SNR_Matrix(
source_space_based,
LISA_ESA,
var_x,
sampleRate_x,
var_y,
sampleRate_y,
inc=0.0,
integral_consts=4.0,
)
fig, ax = snrplot.Plot_SNR(var_x,
sample_x,
var_y,
sample_y,
SNRMatrix,
display=False,
return_plt=True)
plt.close(fig)
f_IMS_break = 2. * u.mHz.to('Hz') * u.Hz
A_acc = 3e-15 * u.m / u.s / u.s
A_IMS = 10e-12 * u.m
Background = False
instrument = detector.SpaceBased('LISA_ESA',
T_obs,
L,
A_acc,
f_acc_break_low,
f_acc_break_high,
A_IMS,
f_IMS_break,
Background=Background,
T_type='N')
instrument.T_obs = [T_obs, T_obs_min, T_obs_max]
instrument.L = [L, L_min, L_max]
return instrument
#Whole Hog Creation of SNR Matrices and Samples
models = [0, 1, 2, 3, 4, 5]
for model in models:
instrument = Get_Instrument(model)
source = Get_Source(model)
[sample_x, sample_y,
SNRMatrix] = snr.Get_SNR_Matrix(source, instrument, var_x, sampleRate_x,
var_y, sampleRate_y)
