From fa39c78993a7749c19183e52407464fb4466be6c Mon Sep 17 00:00:00 2001 From: elizabethg60 Date: Wed, 15 Jan 2025 16:02:37 -0500 Subject: [PATCH] LSF + Fine Time --- __pycache__/NEID_LSF.cpython-310.pyc | Bin 0 -> 4514 bytes data/NEID_October_15s_time.csv | 781 ++ data/NEID_October_fine_time.csv | 7281 +++++++++++++++++ ...FParameterCoefficients20230824HRSci_v1.txt | 867 ++ scripts/NEID_GRASS_all_lines.jl | 394 +- scripts/NEID_LSF.py | 164 + scripts/NEID_RV_ccf.jl | 1 + scripts/debug_gpu_eclipse.jl | 88 +- projected_RV.jl => scripts/projected_RV.jl | 2 +- scripts/slurm_gpu.slurm | 4 +- src/convenience_eclipse.jl | 5 +- src/disk_sim_eclipse.jl | 4 +- src/gpu/gpu_precomps_eclipse.jl | 7 +- src/gpu/gpu_sim_eclipse.jl | 11 +- src/gpu/gpu_synthesis.jl | 4 +- src/observing/convolutions.jl | 32 + src/velocities.jl | 9 +- 17 files changed, 9557 insertions(+), 97 deletions(-) create mode 100644 __pycache__/NEID_LSF.cpython-310.pyc create mode 100644 data/NEID_October_15s_time.csv create mode 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using EchelleCCFs: λ_vac_to_air +using PyCall +py""" +import sys +sys.path.append('.') +""" +neid_lsf = pyimport("NEID_LSF") + GRASS.get_kernels() function neid_all_lines(neid_time, granulation_status, filename, LD_type, ext_toggle) @@ -67,12 +74,26 @@ function neid_all_lines(neid_time, granulation_status, filename, LD_type, ext_to depth = GRASS.get_depth(lp) lfile = GRASS.get_file(lp) + #original rv = Vector{Vector{Float64}}(undef,size(name)...) rv_error = Vector{Vector{Float64}}(undef,size(name)...) - rvs_cpu = Vector{Float64}(undef,size(time_stamps)...) - sigs_cpu = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + rv_var = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + NEID LSF + rv_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + #NEID LSF + rv_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_lsf = Vector{Vector{Float64}}(undef,size(name)...) resolution = 7e5 # loop over lines + orders = [57, 57, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 64, 64, 74, 74, 75, 75, 75, 75, 77, 77] + pixels = [3021:3086, 3062:3128, 2227:2287, 2355:2416, 2464:2526, 2564:2626, 2702:2764, 2738:2801, 2881:2944, 3003:3067, 3057:3093, 3070:3134, 2303:2362, 2529:2589, 3563:3622, 3765:3824, 378:425, 414:461, 478:526, 674:722, 734:781, 802:849] for i in eachindex(lp.λrest) println("\t>>> Template: " * string(splitdir(lfile[i])[2])) # set up parameters for synthesis @@ -97,20 +118,39 @@ function neid_all_lines(neid_time, granulation_status, filename, LD_type, ext_to mu_grid, z_rot_sub, stored_μs, stored_ax_codes, stored_dA, "three", ext_toggle = ext_toggle, verbose=true, use_gpu=false) wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv[i], rv_error[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) + + for t in 1:size(flux_sim, 2) + itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) + wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) + flux_sim_itp = itp.(wavs_sim_itp) + # measure velocities + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var[t], sigs_cpu_var[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + end + + rv_var[i] = deepcopy(rvs_cpu_var) + rv_error_var[i] = deepcopy(sigs_cpu_var) + + kernel = pycall(neid_lsf.get_variable_lsf_kernel_values, PyObject, mean(pixels[i]), collect(pixels[i]), maximum(diff(lambdas_cpu)), orders[i]) + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, kernel, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv_lsf[i], rv_error_lsf[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) for t in 1:size(flux_sim, 2) itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) flux_sim_itp = itp.(wavs_sim_itp) # measure velocities - v_grid_cpu, ccf_cpu, ccf_var_out = deepcopy(GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) - rvs_cpu[t], sigs_cpu[t] = deepcopy(GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var_lsf[t], sigs_cpu_var_lsf[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) end - rv[i] = deepcopy(rvs_cpu) - rv_error[i] = deepcopy(sigs_cpu) + rv_var_lsf[i] = deepcopy(rvs_cpu_var_lsf) + rv_error_var_lsf[i] = deepcopy(sigs_cpu_var_lsf) end - return rv, rv_error, λrest + return rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest end function neid_all_lines_gpu(neid_time, granulation_status, filename, LD_type, ext_toggle) @@ -133,9 +173,9 @@ function neid_all_lines_gpu(neid_time, granulation_status, filename, LD_type, ex alt = 2.097938 # set up paramaters for disk - N = 197 + N = 50 Nt = length(time_stamps) - disk = GRASS.DiskParamsEclipse(N=N, Nt=Nt, Nsubgrid=40) + disk = GRASS.DiskParamsEclipse(N=N, Nt=Nt, Nsubgrid=10) # get lines to construct templates lp = GRASS.LineProperties() @@ -144,12 +184,26 @@ function neid_all_lines_gpu(neid_time, granulation_status, filename, LD_type, ex depth = GRASS.get_depth(lp) lfile = GRASS.get_file(lp) + #original rv = Vector{Vector{Float64}}(undef,size(name)...) rv_error = Vector{Vector{Float64}}(undef,size(name)...) - rvs_cpu = Vector{Float64}(undef,size(time_stamps)...) - sigs_cpu = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + rv_var = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + NEID LSF + rv_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + #NEID LSF + rv_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_lsf = Vector{Vector{Float64}}(undef,size(name)...) resolution = 7e5 # loop over lines + orders = [57, 57, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 64, 64, 74, 74, 75, 75, 75, 75, 77, 77] + pixels = [3021:3086, 3062:3128, 2227:2287, 2355:2416, 2464:2526, 2564:2626, 2702:2764, 2738:2801, 2881:2944, 3003:3067, 3057:3093, 3070:3134, 2303:2362, 2529:2589, 3563:3622, 3765:3824, 378:425, 414:461, 478:526, 674:722, 734:781, 802:849] for i in eachindex(lp.λrest) println("\t>>> Template: " * string(splitdir(lfile[i])[2])) # set up parameters for synthesis @@ -169,23 +223,170 @@ function neid_all_lines_gpu(neid_time, granulation_status, filename, LD_type, ex spec = GRASS.SpecParams(lines=lines, depths=depths, variability=variability, blueshifts=blueshifts, templates=templates, resolution=resolution) - lambdas_cpu, outspec_cpu = GRASS.synth_Eclipse_gpu(spec, disk, true, Float64, falses(disk.Nt), LD_type, + lambdas_cpu, unbinned_flux, brightness = GRASS.synth_Eclipse_gpu(spec, disk, true, Float64, falses(disk.Nt), LD_type, obs_long, obs_lat, alt, time_stamps, - lines, "three", ext_toggle) + lines, "three", ext_toggle) + + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv[i], rv_error[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) + + for t in 1:size(flux_sim, 2) + itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) + wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) + flux_sim_itp = itp.(wavs_sim_itp) + # measure velocities + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var[t], sigs_cpu_var[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + end + rv_var[i] = deepcopy(rvs_cpu_var) + rv_error_var[i] = deepcopy(sigs_cpu_var) + + kernel = pycall(neid_lsf.get_variable_lsf_kernel_values, PyObject, mean(pixels[i]), collect(pixels[i]), maximum(diff(lambdas_cpu)), orders[i]) + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, kernel, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv_lsf[i], rv_error_lsf[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) + + for t in 1:size(flux_sim, 2) + itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) + wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) + flux_sim_itp = itp.(wavs_sim_itp) + # measure velocities + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var_lsf[t], sigs_cpu_var_lsf[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + end + + rv_var_lsf[i] = deepcopy(rvs_cpu_var_lsf) + rv_error_var_lsf[i] = deepcopy(sigs_cpu_var_lsf) + end + return rv, rv_error, λrest, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf +end + +function neid_all_lines_gpu_fine_time(neid_time, granulation_status, filename, LD_type, ext_toggle) + # convert from utc to et as needed by SPICE + time_stamps = utc2et.(neid_time) + + variable_names = ["wavelength", "variance"] + + # Open the JLD2 file and read the variables into a dictionary + data = jldopen("data/NEID_October_eclipse_spectrum_info.jld2", "r") do file + Dict(var => read(file, var) for var in variable_names) + end + + NEID_wavelength = deepcopy(data["wavelength"]) + NEID_variance = deepcopy(data["variance"]) + + # NEID location + obs_lat = 31.9583 + obs_long = -111.5967 + alt = 2.097938 + + # set up paramaters for disk + N = 50 + Nt = length(time_stamps) + disk = GRASS.DiskParamsEclipse(N=N, Nt=Nt, Nsubgrid=10) + + # get lines to construct templates + lp = GRASS.LineProperties() + name = GRASS.get_name(lp) + λrest = GRASS.get_rest_wavelength(lp) + depth = GRASS.get_depth(lp) + lfile = GRASS.get_file(lp) + + #original + # brightness = Vector{Vector{Float64}}(undef,size([λrest[1]])...) + rv = Vector{Vector{Float64}}(undef,size(name)...) + rv_error = Vector{Vector{Float64}}(undef,size(name)...) + #variance + interpolation + rv_var = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + NEID LSF + rv_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_var_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rvs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + #NEID LSF + rv_lsf = Vector{Vector{Float64}}(undef,size(name)...) + rv_error_lsf = Vector{Vector{Float64}}(undef,size(name)...) + resolution = 7e5 + # loop over lines + orders = [57, 57, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 64, 64, 74, 74, 75, 75, 75, 75, 77, 77] + pixels = [3021:3086, 3062:3128, 2227:2287, 2355:2416, 2464:2526, 2564:2626, 2702:2764, 2738:2801, 2881:2944, 3003:3067, 3057:3093, 3070:3134, 2303:2362, 2529:2589, 3563:3622, 3765:3824, 378:425, 414:461, 478:526, 674:722, 734:781, 802:849] + for i in eachindex(lp.λrest) + println("\t>>> Template: " * string(splitdir(lfile[i])[2])) + # set up parameters for synthesis + lines = [λrest[i]] + templates = [lfile[i]] + depths = [depth[i]] + + if granulation_status == true + variability = trues(length(lines)) # whether or not the bisectors should "dance" + end + if granulation_status == false + variability = falses(length(lines)) + end + blueshifts = zeros(length(lines)) # set convective blueshift value + + # make the spec composite type instances + spec = GRASS.SpecParams(lines=lines, depths=depths, variability=variability, + blueshifts=blueshifts, templates=templates, resolution=resolution) + + lambdas_cpu, unbinned_flux, brightness = GRASS.synth_Eclipse_gpu(spec, disk, true, Float64, falses(disk.Nt), LD_type, + obs_long, obs_lat, alt, time_stamps, + lines, "three", ext_toggle) + block_size = 6#56 + # Number of blocks + num_blocks = size(unbinned_flux, 2) ÷ block_size + # Initialize a new matrix to store the means + outspec_cpu = zeros(size(unbinned_flux, 1), num_blocks) + # Loop through each block and compute the mean of the block + for i in 1:num_blocks + # Slice the block (5 columns at a time) + start_col = (i - 1) * block_size + 1 + end_col = i * block_size + # outspec_cpu[:, i] = mean(unbinned_flux[:, start_col:end_col], dims=2) + outspec_cpu[:, i] = mean(unbinned_flux[:, start_col:end_col][:, 1:4], dims=2) + end + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv[i], rv_error[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) + + # NEID_ind = 1 + for t in 1:size(flux_sim, 2) + itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) + wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) + flux_sim_itp = itp.(wavs_sim_itp) + # measure velocities + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var[t], sigs_cpu_var[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + # if t == (NEID_ind * 56) + # NEID_ind += 1 + # end + end + rv_var[i] = deepcopy(rvs_cpu_var) + rv_error_var[i] = deepcopy(sigs_cpu_var) + + kernel = pycall(neid_lsf.get_variable_lsf_kernel_values, PyObject, mean(pixels[i]), collect(pixels[i]), maximum(diff(lambdas_cpu)), orders[i]) + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, kernel, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv_lsf[i], rv_error_lsf[i] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) for t in 1:size(flux_sim, 2) itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) flux_sim_itp = itp.(wavs_sim_itp) # measure velocities - v_grid_cpu, ccf_cpu, ccf_var_out = deepcopy(GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) - rvs_cpu[t], sigs_cpu[t] = deepcopy(GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var_lsf[t], sigs_cpu_var_lsf[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) end - rv[i] = deepcopy(rvs_cpu) - rv_error[i] = deepcopy(sigs_cpu) + + rv_var_lsf[i] = deepcopy(rvs_cpu_var_lsf) + rv_error_var_lsf[i] = deepcopy(sigs_cpu_var_lsf) end - return rv, rv_error, λrest + return rv, rv_error, λrest, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf end function gpu_N_convergence(neid_time, granulation_status, filename, LD_type, ext_toggle) @@ -214,13 +415,27 @@ function gpu_N_convergence(neid_time, granulation_status, filename, LD_type, ext depth = GRASS.get_depth(lp) lfile = GRASS.get_file(lp) - N_range = 50:5:500#10:5:200 + N_range = 10:5:200 #50:5:500 count = 1 rv = Vector{Vector{Float64}}(undef,size(N_range)...) rv_error = Vector{Vector{Float64}}(undef,size(N_range)...) - rvs_cpu = Vector{Float64}(undef,size(time_stamps)...) - sigs_cpu = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + rv_var = Vector{Vector{Float64}}(undef,size(N_range)...) + rv_error_var = Vector{Vector{Float64}}(undef,size(N_range)...) + rvs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var = Vector{Float64}(undef,size(time_stamps)...) + #variance + interpolation + NEID LSF + rv_var_lsf = Vector{Vector{Float64}}(undef,size(N_range)...) + rv_error_var_lsf = Vector{Vector{Float64}}(undef,size(N_range)...) + rvs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + sigs_cpu_var_lsf = Vector{Float64}(undef,size(time_stamps)...) + #NEID LSF + rv_lsf = Vector{Vector{Float64}}(undef,size(N_range)...) + rv_error_lsf = Vector{Vector{Float64}}(undef,size(N_range)...) resolution = 7e5 + # loop over lines + orders = [57, 57, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 64, 64, 74, 74, 75, 75, 75, 75, 77, 77] + pixels = [3021:3086, 3062:3128, 2227:2287, 2355:2416, 2464:2526, 2564:2626, 2702:2764, 2738:2801, 2881:2944, 3003:3067, 3057:3093, 3070:3134, 2303:2362, 2529:2589, 3563:3622, 3765:3824, 378:425, 414:461, 478:526, 674:722, 734:781, 802:849] i = 1 println("\t>>> Template: " * string(splitdir(lfile[i])[2])) @@ -243,109 +458,196 @@ function gpu_N_convergence(neid_time, granulation_status, filename, LD_type, ext for j in N_range # set up paramaters for disk - N = j + N = 197 Nt = length(time_stamps) - disk = GRASS.DiskParamsEclipse(N=N, Nt=Nt, Nsubgrid=40) + disk = GRASS.DiskParamsEclipse(N=N, Nt=Nt, Nsubgrid=j) lambdas_cpu, outspec_cpu = GRASS.synth_Eclipse_gpu(spec, disk, true, Float64, falses(disk.Nt), LD_type, obs_long, obs_lat, alt, time_stamps, lines, "three", ext_toggle) wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv[count], rv_error[count] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) + + for t in 1:size(flux_sim, 2) + itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) + wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) + flux_sim_itp = itp.(wavs_sim_itp) + # measure velocities + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var[t], sigs_cpu_var[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + end + + rv_var[count] = deepcopy(rvs_cpu_var) + rv_error_var[count] = deepcopy(sigs_cpu_var) + + kernel = pycall(neid_lsf.get_variable_lsf_kernel_values, PyObject, mean(pixels[i]), collect(pixels[i]), maximum(diff(lambdas_cpu)), orders[i]) + wavs_sim, flux_sim = GRASS.convolve_gauss(lambdas_cpu, outspec_cpu, kernel, new_res=11e4) + v_grid_cpu, ccf_cpu = GRASS.calc_ccf(wavs_sim, flux_sim, lines, depths, 11e4) + rv_lsf[count], rv_error_lsf[count] = GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu) for t in 1:size(flux_sim, 2) itp = interpolate((wavs_sim,), flux_sim[:,t], Gridded(Linear())) wavs_sim_itp = λ_vac_to_air.(NEID_wavelength[i][t]) flux_sim_itp = itp.(wavs_sim_itp) # measure velocities - v_grid_cpu, ccf_cpu, ccf_var_out = deepcopy(GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) - rvs_cpu[t], sigs_cpu[t] = deepcopy(GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) + v_grid_cpu, ccf_cpu, ccf_var_out = (GRASS.calc_ccf(wavs_sim_itp, flux_sim_itp, filter(!=(0), NEID_variance[i][:,t]), lines, depths, 11e4)) + rvs_cpu_var_lsf[t], sigs_cpu_var_lsf[t] = (GRASS.calc_rvs_from_ccf(v_grid_cpu, ccf_cpu, ccf_var_out)) end - rv[count] = deepcopy(rvs_cpu) - rv_error[count] = deepcopy(sigs_cpu) + rv_var_lsf[count] = deepcopy(rvs_cpu_var_lsf) + rv_error_var_lsf[count] = deepcopy(sigs_cpu_var_lsf) + count = count + 1 end - return rv, rv_error, N_range + return rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, N_range end function neid_october_eclipse_var_off(LD_type, ext_toggle) neid_october = ["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"] - rv, rv_error, λrest = deepcopy(neid_all_lines(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest = deepcopy(neid_all_lines(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) @save "neid_all_lines_rv_off_$(LD_type).jld2" jldopen("neid_all_lines_rv_off_$(LD_type).jld2", "a+") do file file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end end function neid_october_eclipse_var_on(LD_type, ext_toggle) neid_october = ["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"] - rv, rv_error, λrest = deepcopy(neid_all_lines(neid_october, true, "neid_october_N_50", LD_type, ext_toggle)) + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest = deepcopy(neid_all_lines(neid_october, true, "neid_october_N_50", LD_type, ext_toggle)) @save "neid_all_lines_rv_regular_$(LD_type).jld2" jldopen("neid_all_lines_rv_regular_$(LD_type).jld2", "a+") do file file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end end function neid_october_eclipse_var_off_gpu(LD_type, ext_toggle) neid_october = ["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"] - rv, rv_error, N_range = deepcopy(gpu_N_convergence(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) - @save "N_convergence.jld2" - jldopen("N_convergence.jld2", "a+") do file - file["N_range"] = deepcopy(N_range) + rv, rv_error, λrest, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf = deepcopy(neid_all_lines_gpu(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) + @save "neid_all_lines_rv_off_$(LD_type)_gpu.jld2" + jldopen("neid_all_lines_rv_off_$(LD_type)_gpu.jld2", "a+") do file + file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end - - # rv, rv_error, λrest = deepcopy(neid_all_lines_gpu(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) - # @save "neid_all_lines_rv_off_$(LD_type)_gpu.jld2" - # jldopen("neid_all_lines_rv_off_$(LD_type)_gpu.jld2", "a+") do file - # file["name"] = deepcopy(λrest) - # file["rv"] = deepcopy(rv) - # file["rv_error"] = deepcopy(rv_error) - # end end function neid_october_eclipse_var_on_gpu(LD_type, ext_toggle) neid_october = ["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"] - rv, rv_error, λrest = deepcopy(neid_all_lines_gpu(neid_october, true, "neid_october_N_50", LD_type, ext_toggle)) + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest = deepcopy(neid_all_lines_gpu(neid_october, true, "neid_october_N_50", LD_type, ext_toggle)) @save "neid_all_lines_rv_regular_$(LD_type)_gpu.jld2" jldopen("neid_all_lines_rv_regular_$(LD_type)_gpu.jld2", "a+") do file file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end end function neid_october_nxt_day_var_off(LD_type, ext_toggle) neid_1015 = ["2023-10-15T16:33:02.500", "2023-10-15T16:34:25.500", "2023-10-15T16:35:48.500", "2023-10-15T16:37:10.500", "2023-10-15T16:38:33.500", "2023-10-15T16:39:56.500", "2023-10-15T16:41:18.500", "2023-10-15T16:42:41.500", "2023-10-15T16:44:04.500", "2023-10-15T16:45:26.500", "2023-10-15T16:46:49.500", "2023-10-15T16:48:12.500", "2023-10-15T16:49:34.500", "2023-10-15T16:50:57.500", "2023-10-15T16:52:20.500", "2023-10-15T16:53:42.500", "2023-10-15T16:55:05.500", "2023-10-15T16:56:28.500", "2023-10-15T16:57:50.500", "2023-10-15T16:59:13.500", "2023-10-15T17:00:36.500", "2023-10-15T17:01:59.500", "2023-10-15T17:03:21.500", "2023-10-15T17:04:44.500", "2023-10-15T17:06:07.500", "2023-10-15T17:07:29.500", "2023-10-15T17:08:52.500", "2023-10-15T17:10:15.500", "2023-10-15T17:11:37.500", "2023-10-15T17:13:00.500", "2023-10-15T17:14:23.500", "2023-10-15T17:15:45.500", "2023-10-15T17:17:08.500", "2023-10-15T17:18:31.500", "2023-10-15T17:19:53.500", "2023-10-15T17:21:16.500", "2023-10-15T17:22:39.500", "2023-10-15T17:24:01.500", "2023-10-15T17:25:24.500", "2023-10-15T17:26:47.500", "2023-10-15T17:28:09.500", "2023-10-15T17:29:32.500", "2023-10-15T17:30:55.500", "2023-10-15T17:32:18.500", "2023-10-15T17:33:40.500", "2023-10-15T17:35:03.500", "2023-10-15T17:36:26.500", "2023-10-15T17:37:48.500", "2023-10-15T17:39:11.500", "2023-10-15T17:40:34.500", "2023-10-15T17:41:56.500", "2023-10-15T17:43:19.500", "2023-10-15T17:44:42.500", "2023-10-15T17:46:04.500", "2023-10-15T17:47:27.500", "2023-10-15T17:48:50.500", "2023-10-15T17:50:12.500", "2023-10-15T17:51:35.500", "2023-10-15T17:52:58.500", "2023-10-15T17:54:20.500", "2023-10-15T17:57:10.500", "2023-10-15T17:58:33.500", "2023-10-15T17:59:55.500", "2023-10-15T18:01:18.500", "2023-10-15T18:02:41.500", "2023-10-15T18:04:03.500", "2023-10-15T18:05:26.500", "2023-10-15T18:06:49.500", "2023-10-15T18:08:11.500", "2023-10-15T18:09:34.500", "2023-10-15T18:10:57.500", "2023-10-15T18:12:19.500", "2023-10-15T18:13:42.500", "2023-10-15T18:15:05.500", "2023-10-15T18:16:27.500", "2023-10-15T18:17:50.500", "2023-10-15T18:19:13.500", "2023-10-15T18:20:35.500", "2023-10-15T18:21:58.500", "2023-10-15T18:23:21.500", "2023-10-15T18:24:44.500", "2023-10-15T18:26:06.500", "2023-10-15T18:27:29.500", "2023-10-15T18:28:52.500", "2023-10-15T18:30:14.500", "2023-10-15T18:31:37.500", "2023-10-15T18:33:00.500", "2023-10-15T18:34:22.500", "2023-10-15T18:35:45.500", "2023-10-15T18:37:08.500", "2023-10-15T18:38:30.500", "2023-10-15T18:39:53.500", "2023-10-15T18:41:16.500", "2023-10-15T18:42:38.500", "2023-10-15T18:44:01.500", "2023-10-15T18:45:24.500", "2023-10-15T18:46:46.500", "2023-10-15T18:48:09.500", "2023-10-15T18:49:32.500", "2023-10-15T18:50:54.500", "2023-10-15T18:52:17.500", "2023-10-15T18:53:40.500", "2023-10-15T18:55:03.500", "2023-10-15T18:56:25.500", "2023-10-15T18:57:48.500", "2023-10-15T18:59:11.500", "2023-10-15T19:00:33.500", "2023-10-15T19:01:56.500", "2023-10-15T19:03:19.500", "2023-10-15T19:04:41.500", "2023-10-15T19:06:04.500"] - rv, rv_error, λrest = deepcopy(neid_all_lines(neid_1015, false, "neid_october_N_50_nxt_day", LD_type, ext_toggle)) + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest = deepcopy(neid_all_lines(neid_1015, false, "neid_october_N_50_nxt_day", LD_type, ext_toggle)) @save "neid_all_lines_rv_off_$(LD_type)_nxt_day.jld2" jldopen("neid_all_lines_rv_off_$(LD_type)_nxt_day.jld2", "a+") do file file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end end function neid_october_nxt_day_var_on(LD_type, ext_toggle) neid_1015 = ["2023-10-15T16:33:02.500", "2023-10-15T16:34:25.500", "2023-10-15T16:35:48.500", "2023-10-15T16:37:10.500", "2023-10-15T16:38:33.500", "2023-10-15T16:39:56.500", "2023-10-15T16:41:18.500", "2023-10-15T16:42:41.500", "2023-10-15T16:44:04.500", "2023-10-15T16:45:26.500", "2023-10-15T16:46:49.500", "2023-10-15T16:48:12.500", "2023-10-15T16:49:34.500", "2023-10-15T16:50:57.500", "2023-10-15T16:52:20.500", "2023-10-15T16:53:42.500", "2023-10-15T16:55:05.500", "2023-10-15T16:56:28.500", "2023-10-15T16:57:50.500", "2023-10-15T16:59:13.500", "2023-10-15T17:00:36.500", "2023-10-15T17:01:59.500", "2023-10-15T17:03:21.500", "2023-10-15T17:04:44.500", "2023-10-15T17:06:07.500", "2023-10-15T17:07:29.500", "2023-10-15T17:08:52.500", "2023-10-15T17:10:15.500", "2023-10-15T17:11:37.500", "2023-10-15T17:13:00.500", "2023-10-15T17:14:23.500", "2023-10-15T17:15:45.500", "2023-10-15T17:17:08.500", "2023-10-15T17:18:31.500", "2023-10-15T17:19:53.500", "2023-10-15T17:21:16.500", "2023-10-15T17:22:39.500", "2023-10-15T17:24:01.500", "2023-10-15T17:25:24.500", "2023-10-15T17:26:47.500", "2023-10-15T17:28:09.500", "2023-10-15T17:29:32.500", "2023-10-15T17:30:55.500", "2023-10-15T17:32:18.500", "2023-10-15T17:33:40.500", "2023-10-15T17:35:03.500", "2023-10-15T17:36:26.500", "2023-10-15T17:37:48.500", "2023-10-15T17:39:11.500", "2023-10-15T17:40:34.500", "2023-10-15T17:41:56.500", "2023-10-15T17:43:19.500", "2023-10-15T17:44:42.500", "2023-10-15T17:46:04.500", "2023-10-15T17:47:27.500", "2023-10-15T17:48:50.500", "2023-10-15T17:50:12.500", "2023-10-15T17:51:35.500", "2023-10-15T17:52:58.500", "2023-10-15T17:54:20.500", "2023-10-15T17:57:10.500", "2023-10-15T17:58:33.500", "2023-10-15T17:59:55.500", "2023-10-15T18:01:18.500", "2023-10-15T18:02:41.500", "2023-10-15T18:04:03.500", "2023-10-15T18:05:26.500", "2023-10-15T18:06:49.500", "2023-10-15T18:08:11.500", "2023-10-15T18:09:34.500", "2023-10-15T18:10:57.500", "2023-10-15T18:12:19.500", "2023-10-15T18:13:42.500", "2023-10-15T18:15:05.500", "2023-10-15T18:16:27.500", "2023-10-15T18:17:50.500", "2023-10-15T18:19:13.500", "2023-10-15T18:20:35.500", "2023-10-15T18:21:58.500", "2023-10-15T18:23:21.500", "2023-10-15T18:24:44.500", "2023-10-15T18:26:06.500", "2023-10-15T18:27:29.500", "2023-10-15T18:28:52.500", "2023-10-15T18:30:14.500", "2023-10-15T18:31:37.500", "2023-10-15T18:33:00.500", "2023-10-15T18:34:22.500", "2023-10-15T18:35:45.500", "2023-10-15T18:37:08.500", "2023-10-15T18:38:30.500", "2023-10-15T18:39:53.500", "2023-10-15T18:41:16.500", "2023-10-15T18:42:38.500", "2023-10-15T18:44:01.500", "2023-10-15T18:45:24.500", "2023-10-15T18:46:46.500", "2023-10-15T18:48:09.500", "2023-10-15T18:49:32.500", "2023-10-15T18:50:54.500", "2023-10-15T18:52:17.500", "2023-10-15T18:53:40.500", "2023-10-15T18:55:03.500", "2023-10-15T18:56:25.500", "2023-10-15T18:57:48.500", "2023-10-15T18:59:11.500", "2023-10-15T19:00:33.500", "2023-10-15T19:01:56.500", "2023-10-15T19:03:19.500", "2023-10-15T19:04:41.500", "2023-10-15T19:06:04.500"] - rv, rv_error, λrest = deepcopy(neid_all_lines(neid_1015, true, "neid_october_N_50_nxt_day", LD_type, ext_toggle)) + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, λrest = deepcopy(neid_all_lines(neid_1015, true, "neid_october_N_50_nxt_day", LD_type, ext_toggle)) @save "neid_all_lines_rv_regular_$(LD_type)_nxt_day.jld2" jldopen("neid_all_lines_rv_regular_$(LD_type)_nxt_day.jld2", "a+") do file file["name"] = deepcopy(λrest) file["rv"] = deepcopy(rv) file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) + end +end + +function neid_october_eclipse_var_off_gpu_fine_time(LD_type, ext_toggle) + df = CSV.File("data/NEID_October_15s_time.csv") |> DataFrame + + # Display the DataFrame + neid_october = string.(df[!, "FineTime"]) + + rv, rv_error, λrest, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf = deepcopy(neid_all_lines_gpu_fine_time(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) + @save "neid_all_lines_rv_off_$(LD_type)_gpu_15s_time.jld2" + jldopen("neid_all_lines_rv_off_$(LD_type)_gpu_15s_time.jld2", "a+") do file + file["name"] = deepcopy(λrest) + file["rv"] = deepcopy(rv) + file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) + end +end + +function neid_october_eclipse_var_off_gpu_convergence(LD_type, ext_toggle) + neid_october = ["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"] + + rv, rv_error, rv_var, rv_error_var, rv_var_lsf, rv_error_var_lsf, rv_lsf, rv_error_lsf, N_range = deepcopy(gpu_N_convergence(neid_october, false, "neid_october_N_50", LD_type, ext_toggle)) + @save "subgrid_convergence.jld2" + jldopen("subgrid_convergence.jld2", "a+") do file + file["N_range"] = deepcopy(N_range) + file["rv"] = deepcopy(rv) + file["rv_error"] = deepcopy(rv_error) + file["rv_var"] = deepcopy(rv_var) + file["rv_error_var"] = deepcopy(rv_error_var) + file["rv_lsf"] = deepcopy(rv_lsf) + file["rv_error_lsf"] = deepcopy(rv_error_lsf) + file["rv_var_lsf"] = deepcopy(rv_var_lsf) + file["rv_error_var_lsf"] = deepcopy(rv_error_var_lsf) end end -neid_october_eclipse_var_off_gpu("SSD", true) \ No newline at end of file +neid_october_eclipse_var_off_gpu_fine_time("SSD", true) \ No newline at end of file diff --git a/scripts/NEID_LSF.py b/scripts/NEID_LSF.py new file mode 100644 index 00000000..c78516f2 --- /dev/null +++ b/scripts/NEID_LSF.py @@ -0,0 +1,164 @@ +from astropy.io import fits +import matplotlib.pyplot as plt +import numpy as np +from scipy.special import erf + +import sys +import os + +# Add the directory containing the script to sys.path +sys.path.append(os.path.abspath(os.path.dirname(__file__))) + +def tophat_gauss_kernel( x, mean, fwhm, box_width ): + """A function to return a top hat convolved Gaussian kernel provided the two width parameters. + Note that the kernel values are not normalized (needed for convolution). + + Parameters + ---------- + x : array + An array of pixel locations to compute the kernel at. + + mean : float + The mean of the kernel at the point in the observed spectrum to convolve at. + + fwhm : float + The Gaussian FWHM of the kernel at the point in the observed spectrum to convolve at. + + box_width : float + The top hat box width of the kernel at the point in the observed spectrum to convolve at. + + Returns + ------- + kernel_values : array + The values of the LSF convolution kernel. + """ + + # First convert the FWHM to a sigma + sigma = fwhm / ( 2 * np.sqrt( 2 * np.log( 2 ) ) ) + + # Generate the arguments for the two erf components. + erf_arg_1 = ( ( 2 * mean + box_width ) - 2 * x ) / ( 2 * np.sqrt( 2 ) * sigma ) + erf_arg_2 = ( (-2 * mean + box_width ) + 2 * x ) / ( 2 * np.sqrt( 2 ) * sigma ) + + # Generate the kernel values + kernel_values = erf( erf_arg_1 ) + erf( erf_arg_2 ) + + return kernel_values + +def read_lsf_masterfile(file_name): + """Reads in a text file (in form of IRAF beam trace file) as a dictionary. + + Taken from the PRVextract.libs.beamtrace library file in the NEID DRP. + + Parameters + ---------- + file_name : str + The path to the text file to read in. + + Returns + ------- + file_dict : dict of dicts + The dictionary corresponding to the input file. + """ + + # Initialize output dictionary + file_dict = {} + + # Go through each of the lines in the file to append to output dictionary + current_aperture = None + for line in open(file_name): + line = line.split() + + # Skip empty lines + if not line: + continue + + # Read the line + key = line[0] + value = [parseValue(value) for value in line[1:]] + + # If only one value for line, turn from list to single value + if len(value) == 1: + value = value[0] + + # Insert the information into the dictionary + if key == 'APERTURE': + if current_aperture is not None: + file_dict[current_aperture['APERTURE']] = current_aperture + current_aperture = dict({'APERTURE': value}) + else: + current_aperture[key] = value + + if current_aperture is not None: + file_dict[current_aperture['APERTURE']] = current_aperture + + return file_dict + +def parseValue(value): + """Utility function for parsing a value when reading in beam trace file. + """ + + for parse_function in [int, float, str]: + try: + return parse_function(value) + except: + continue + + return value + +def get_variable_lsf_kernel_values(obs_pixel, kernel_pixel_arr, wavelength_spacing, order): + """Function to generate a 1D LSF kernel array given parameterization defined in the input masterfile. + The LSF profile has parameters that are fit with polynomials in pixel space across each order. + + Parameters + ---------- + obs_pixel : float + The pixel location within spectral order to generate LSF at. + + kernel_pixel_arr : array. + The pixel locations at which the kernel will be evaluated. + + kernel_profile_information : dict + Dictionary with GLOBAL information about the LSF profile parameterization. + + kernel_parameters : dict + The order-specific dictionary from the input LSF masterfile containing the kernel parameter fit information. + + wavelength_spacing : float + The equal spacing of the wavelength grid being convolved, for normalizing the kernel. + + Returns + ------- + lsf_kernel_values : array + The values of the LSF convolution kernel for the input kernel_pixel_arr. + """ + lsf_info = read_lsf_masterfile("data/neidMaster_LSFParameterCoefficients20230824HRSci_v1.txt") + kernel_profile_information = lsf_info['GLOBAL'] + kernel_parameters = lsf_info[order] + # Scale the input observation pixel value to be in the domain over which the LSF parameters are fit (turning [0,9215] to [-1,1]) + obs_pixel_m1top1 = scale_interval_m1top1(obs_pixel, *kernel_profile_information['DOMAIN']) + + ### Pick out the right line profile parameterization from the input master file. Currently just top hat gaussian + if kernel_profile_information['PROFILE'] == 'TOP_HAT_GAUSSIAN': + + # Take the polynomial coefficients describing the kernel parameters vs. pixel for this order + kernel_fwhm_poly = kernel_parameters['COEFF_1'] # Coefficient 1 is the Gaussian FWHM + kernel_boxwidth_poly = kernel_parameters['COEFF_2'] # Coefficient 2 is the top hat box width + + # Now apply the fits to the input observed pixel value + kernel_fwhm = np.polyval( kernel_fwhm_poly, obs_pixel_m1top1 ) + kernel_boxwidth = np.polyval( kernel_boxwidth_poly, obs_pixel_m1top1 ) + + # Generate the actual kernel values + lsf_kernel_values = tophat_gauss_kernel(kernel_pixel_arr, obs_pixel, kernel_fwhm, kernel_boxwidth) + + # Normalize the kernel values so that their integral is 1 (for flux conservation) + lsf_kernel_values /= np.trapz(lsf_kernel_values, dx = wavelength_spacing) + + return lsf_kernel_values + +def scale_interval_m1top1(x,a,b): + """Scales input x values over interval a to b onto the range of -1 to 1. + """ + + return (2.0 * x - (b + a)) / (b - a) diff --git a/scripts/NEID_RV_ccf.jl b/scripts/NEID_RV_ccf.jl index 6cd94a54..197a6c11 100644 --- a/scripts/NEID_RV_ccf.jl +++ b/scripts/NEID_RV_ccf.jl @@ -25,6 +25,7 @@ vacwav = λ_air_to_vac.(airwav) # line information for identification orders = [57, 57, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 64, 64, 74, 74, 75, 75, 75, 75, 77, 77] +pixels = [3021:3086, 3062:3128, 2227:2287, 2355:2416, 2464:2526, 2564:2626, 2702:2764, 2738:2801, 2881:2944, 3003:3067, 3057:3093, 3070:3134, 2303:2362, 2529:2589, 3563:3622, 3765:3824, 378:425, 414:461, 478:526, 674:722, 734:781, 802:849] datadir = string(abspath("data")) df = CSV.read(joinpath(datadir, "optimized_depth.csv"), DataFrame) diff --git a/scripts/debug_gpu_eclipse.jl b/scripts/debug_gpu_eclipse.jl index 90b33395..e8182b4f 100644 --- a/scripts/debug_gpu_eclipse.jl +++ b/scripts/debug_gpu_eclipse.jl @@ -4,6 +4,7 @@ using SPICE using CSV using DataFrames import PyPlot +using Statistics plt = PyPlot #NEID location @@ -12,7 +13,7 @@ obs_long = -111.5967 alt = 2.097938 #convert from utc to et as needed by SPICE -time_stamps = utc2et.(["2023-10-14T14:29:23.500000"]) +time_stamps = utc2et.(["2023-10-14T15:26:45.500000", "2023-10-14T15:28:07.500000", "2023-10-14T15:29:30.500000", "2023-10-14T15:30:53.500000", "2023-10-14T15:32:15.500000", "2023-10-14T15:33:38.500000", "2023-10-14T15:35:01.500000", "2023-10-14T15:36:23.500000", "2023-10-14T15:37:46.500000", "2023-10-14T15:39:09.500000", "2023-10-14T15:40:31.500000", "2023-10-14T15:41:54.500000", "2023-10-14T15:43:17.500000", "2023-10-14T15:44:39.500000", "2023-10-14T15:46:02.500000", "2023-10-14T15:47:25.500000", "2023-10-14T15:48:47.500000", "2023-10-14T15:50:10.500000", "2023-10-14T15:51:33.500000", "2023-10-14T15:52:56.500000", "2023-10-14T15:54:18.500000", "2023-10-14T15:55:41.500000", "2023-10-14T15:57:04.500000", "2023-10-14T15:58:26.500000", "2023-10-14T15:59:49.500000", "2023-10-14T16:01:12.500000", "2023-10-14T16:02:34.500000", "2023-10-14T16:03:57.500000", "2023-10-14T16:05:20.500000", "2023-10-14T16:06:42.500000", "2023-10-14T16:08:05.500000", "2023-10-14T16:09:28.500000", "2023-10-14T16:10:50.500000", "2023-10-14T16:12:13.500000", "2023-10-14T16:13:36.500000", "2023-10-14T16:14:58.500000", "2023-10-14T16:16:21.500000", "2023-10-14T16:17:44.500000", "2023-10-14T16:19:06.500000", "2023-10-14T16:20:29.500000", "2023-10-14T16:21:52.500000", "2023-10-14T16:23:15.500000", "2023-10-14T16:24:37.500000", "2023-10-14T16:26:00.500000", "2023-10-14T16:27:23.500000", "2023-10-14T16:28:45.500000", "2023-10-14T16:30:08.500000", "2023-10-14T16:31:31.500000", "2023-10-14T16:32:53.500000", "2023-10-14T16:34:16.500000", "2023-10-14T16:35:39.500000", "2023-10-14T16:37:01.500000", "2023-10-14T16:38:24.500000", "2023-10-14T16:39:47.500000", "2023-10-14T16:41:09.500000", "2023-10-14T16:42:32.500000", "2023-10-14T16:43:55.500000", "2023-10-14T16:45:17.500000", "2023-10-14T16:46:40.500000", "2023-10-14T16:48:03.500000", "2023-10-14T16:49:25.500000", "2023-10-14T16:50:48.500000", "2023-10-14T16:52:11.500000", "2023-10-14T16:53:33.500000", "2023-10-14T16:54:56.500000", "2023-10-14T16:56:19.500000", "2023-10-14T16:57:42.500000", "2023-10-14T16:59:04.500000", "2023-10-14T17:00:27.500000", "2023-10-14T17:01:50.500000", "2023-10-14T17:03:12.500000", "2023-10-14T17:04:35.500000", "2023-10-14T17:05:58.500000", "2023-10-14T17:07:20.500000", "2023-10-14T17:08:43.500000", "2023-10-14T17:10:06.500000", "2023-10-14T17:11:28.500000", "2023-10-14T17:12:51.500000", "2023-10-14T17:14:14.500000", "2023-10-14T17:15:36.500000", "2023-10-14T17:16:59.500000", "2023-10-14T17:18:22.500000", "2023-10-14T17:19:44.500000", "2023-10-14T17:21:07.500000", "2023-10-14T17:22:30.500000", "2023-10-14T17:23:52.500000", "2023-10-14T17:25:15.500000", "2023-10-14T17:26:38.500000", "2023-10-14T17:28:01.500000", "2023-10-14T17:29:23.500000", "2023-10-14T17:30:46.500000", "2023-10-14T17:32:09.500000", "2023-10-14T17:33:31.500000", "2023-10-14T17:34:54.500000", "2023-10-14T17:36:17.500000", "2023-10-14T17:37:39.500000", "2023-10-14T17:39:02.500000", "2023-10-14T17:40:25.500000", "2023-10-14T17:41:47.500000", "2023-10-14T17:43:10.500000", "2023-10-14T17:44:33.500000", "2023-10-14T17:45:55.500000", "2023-10-14T17:47:18.500000", "2023-10-14T17:48:41.500000", "2023-10-14T17:50:03.500000", "2023-10-14T17:51:26.500000", "2023-10-14T17:52:49.500000", "2023-10-14T17:54:11.500000", "2023-10-14T17:55:34.500000", "2023-10-14T17:56:57.500000", "2023-10-14T17:58:20.500000", "2023-10-14T17:59:42.500000", "2023-10-14T18:01:05.500000", "2023-10-14T18:02:28.500000", "2023-10-14T18:03:50.500000", "2023-10-14T18:05:13.500000", "2023-10-14T18:06:36.500000", "2023-10-14T18:07:58.500000", "2023-10-14T18:09:21.500000", "2023-10-14T18:10:44.500000", "2023-10-14T18:12:06.500000", "2023-10-14T18:13:29.500000", "2023-10-14T18:14:52.500000", "2023-10-14T18:16:14.500000", "2023-10-14T18:17:37.500000", "2023-10-14T18:19:00.500000", "2023-10-14T18:20:22.500000", "2023-10-14T18:21:45.500000", "2023-10-14T18:23:08.500000", "2023-10-14T18:24:30.500000", "2023-10-14T18:25:53.500000", "2023-10-14T18:27:16.500000", "2023-10-14T18:28:38.500000", "2023-10-14T18:30:01.500000", "2023-10-14T18:31:24.500000", "2023-10-14T18:32:47.500000", "2023-10-14T18:34:09.500000", "2023-10-14T18:35:32.500000", "2023-10-14T18:36:55.500000", "2023-10-14T18:38:17.500000", "2023-10-14T18:39:40.500000", "2023-10-14T18:41:03.500000", "2023-10-14T18:42:25.500000", "2023-10-14T18:43:48.500000", "2023-10-14T18:45:11.500000", "2023-10-14T18:46:33.500000", "2023-10-14T18:47:56.500000", "2023-10-14T18:49:19.500000", "2023-10-14T18:50:41.500000", "2023-10-14T18:52:04.500000", "2023-10-14T18:53:27.500000", "2023-10-14T18:54:49.500000", "2023-10-14T18:56:12.500000", "2023-10-14T18:57:35.500000", "2023-10-14T18:58:57.500000", "2023-10-14T19:00:20.500000", "2023-10-14T19:01:43.500000", "2023-10-14T19:03:06.500000"]) N = 50 Nt = length(time_stamps) @@ -46,34 +47,59 @@ z_rot_matrix = Matrix{Matrix{Float64}}(undef, length(disk.ϕc), maximum(disk.Nθ zenith_matrix = Matrix{Matrix{Float64}}(undef, length(disk.ϕc), maximum(disk.Nθ)) # compute geometry for timestamp -t = 1 +for t in 1:length(time_stamps) -GRASS.eclipse_compute_quantities!(disk, time_stamps[t], t, obs_long, obs_lat, alt, wsp.ϕc, wsp.θc, - wsp.μs, wsp.z_rot, wsp.dA, wsp.xyz, wsp.ax_codes, - mem.dA_total_proj_mean, mem.mean_weight_v_no_cb, - mem.mean_weight_v_earth_orb, mem.pole_vector_grid, - mem.SP_sun_pos, mem.SP_sun_vel, mem.SP_bary, mem.SP_bary_pos, - mem.SP_bary_vel, mem.OP_bary, mem.mu_grid, mem.projected_velocities_no_cb, - mem.distance, mem.v_scalar_grid, mem.v_earth_orb_proj, mu_grid_matrix, - dA_total_proj_matrix, idx1_matrix, idx3_matrix, z_rot_matrix, zenith_matrix) - -lambdas_cpu, outspec_cpu = GRASS.eclipse_compute_intensity(disk, lines, 1.0, LD_type, idx1_matrix, idx3_matrix, - mu_grid_matrix, z_rot_matrix, dA_total_proj_matrix, wsp.ld, wsp.z_rot, zenith_matrix, - wsp.μs, wsp.ax_codes, wsp.dA, wsp.μs, wsp.ax_codes, wsp.dA, true, t, wsp.ext) - -""" -GPU GEOMETRY CODE CALLS BELOW -""" -gpu_allocs = GRASS.GPUAllocsEclipse(spec, disk, 1) - -GRASS.calc_eclipse_quantities_gpu!(time_stamps[t], obs_long, obs_lat, alt, lines, LD_type, 1.0, 1.0, disk, gpu_allocs) - -# make a plot -plt.imshow(wsp.ext .- Array(gpu_allocs.ext)) -# plt.imshow(wsp.z_rot .* GRASS.c_ms, vmin=-2000, vmax=2000, cmap="seismic") -# plt.imshow(Array(gpu_allocs.z_rot) .* GRASS.c_ms .- wsp.z_rot .* GRASS.c_ms) -plt.title("ext") -plt.colorbar() -plt.savefig("gpu_test.png") -plt.show() -plt.clf() + GRASS.eclipse_compute_quantities!(disk, time_stamps[t], t, obs_long, obs_lat, alt, wsp.ϕc, wsp.θc, + wsp.μs, wsp.z_rot, wsp.dA, wsp.xyz, wsp.ax_codes, + mem.dA_total_proj_mean, mem.mean_weight_v_no_cb, + mem.mean_weight_v_earth_orb, mem.pole_vector_grid, + mem.SP_sun_pos, mem.SP_sun_vel, mem.SP_bary, mem.SP_bary_pos, + mem.SP_bary_vel, mem.OP_bary, mem.mu_grid, mem.projected_velocities_no_cb, + mem.distance, mem.v_scalar_grid, mem.v_earth_orb_proj, mu_grid_matrix, + dA_total_proj_matrix, idx1_matrix, idx3_matrix, z_rot_matrix, zenith_matrix) + +# lambdas_cpu, outspec_cpu = GRASS.eclipse_compute_intensity(disk, lines, 1.0, LD_type, idx1_matrix, idx3_matrix, +# mu_grid_matrix, z_rot_matrix, dA_total_proj_matrix, wsp.ld, wsp.z_rot, zenith_matrix, +# wsp.μs, wsp.ax_codes, wsp.dA, wsp.μs, wsp.ax_codes, wsp.dA, true, t, wsp.ext) + + mean_intensity = GRASS.eclipse_compute_intensity(disk, lines, 1.0, LD_type, idx1_matrix, idx3_matrix, + mu_grid_matrix, mem.mean_weight_v_no_cb[:, :, t], mem.mean_weight_v_earth_orb[:, :, t], + z_rot_matrix, dA_total_proj_matrix, wsp.ld, wsp.z_rot, zenith_matrix, true, wsp.ext) + idx_grid = mean_intensity[:, :, 1] .> 0.0 + brightness = mean_intensity[:, :, 1] .* mem.dA_total_proj_mean[:, :, t] .* wsp.ext[:, :, 1] + cheapflux = sum(view(brightness, idx_grid)) + + final_weight_v_no_cb = sum(view(mem.mean_weight_v_no_cb[:, :, t] .* brightness, idx_grid)) / cheapflux + final_weight_v_no_cb += mean(view(mem.mean_weight_v_earth_orb[:, :, t], idx_grid)) + + """ + GPU GEOMETRY CODE CALLS BELOW + """ + gpu_allocs = GRASS.GPUAllocsEclipse(spec, disk, 1) + + GRASS.calc_eclipse_quantities_gpu!(time_stamps[t], obs_long, obs_lat, alt, lines, LD_type, 1.0, 1.0, disk, gpu_allocs) + + idx_grid2 = Array(gpu_allocs.ld[:, :, 1]) .> 0.0 + + brightness2 = Array(gpu_allocs.ld[:, :, 1]) .* Array(gpu_allocs.dA) .* Array(gpu_allocs.ext[:, :, 1]) + + cheapflux2 = sum(view(brightness2, idx_grid2)) + + final_weight_v_no_cb2 = sum(view(Array(gpu_allocs.projected_v) .* brightness2, idx_grid2)) / cheapflux2 + final_weight_v_no_cb2 += mean(view(Array(gpu_allocs.earth_v), idx_grid2)) + + # plt.scatter(t, final_weight_v_no_cb - final_weight_v_no_cb2, color = "r") + println(final_weight_v_no_cb - final_weight_v_no_cb2) +end +# plt.savefig("test") +# plt.clf() + +# # make a plot +# plt.imshow(mem.mean_weight_v_earth_orb[:, :, t] .- Array(gpu_allocs.earth_v)) +# # plt.imshow(wsp.z_rot .* GRASS.c_ms, vmin=-2000, vmax=2000, cmap="seismic") +# # plt.imshow(Array(gpu_allocs.z_rot) .* GRASS.c_ms .- wsp.z_rot .* GRASS.c_ms) +# plt.title("earth_v") +# plt.colorbar() +# plt.savefig("gpu_test.png") +# plt.show() +# plt.clf() diff --git a/projected_RV.jl b/scripts/projected_RV.jl similarity index 99% rename from projected_RV.jl rename to scripts/projected_RV.jl index 7358e9b8..80ffb67a 100644 --- a/projected_RV.jl +++ b/scripts/projected_RV.jl @@ -153,12 +153,12 @@ function projected_RV_gpu(time, LD_type, ext_toggle) # make the spec composite type instances spec = GRASS.SpecParams(lines=lines, depths=depths, variability=variability, blueshifts=blueshifts, templates=templates, resolution=resolution) - gpu_allocs = GRASS.GPUAllocsEclipse(spec, disk, Int(length(lines)), precision=Float64, verbose=true) RV_list_no_cb = Vector{Float64}(undef,size(time_stamps)...) intensity_list = Vector{Float64}(undef,size(time_stamps)...) mean_intensity_list = Vector{Matrix{Float64}}(undef,size(time_stamps)...) for t in 1:disk.Nt + gpu_allocs = GRASS.GPUAllocsEclipse(spec, disk, Int(length(lines)), precision=Float64, verbose=true) if t < 25 neid_ext_coeff = extinction_coeff[extinction_coeff[!, "Wavelength"] .== λrest[i], "Ext1"][1] elseif t >= 25 && t < 46 diff --git a/scripts/slurm_gpu.slurm b/scripts/slurm_gpu.slurm index 70cd6d99..e499c545 100644 --- a/scripts/slurm_gpu.slurm +++ b/scripts/slurm_gpu.slurm @@ -6,9 +6,9 @@ #SBATCH --gpus-per-task=1 #SBATCH --mem-per-cpu=64GB #SBATCH --time=24:00:00 -#SBATCH --job-name=N_convergence +#SBATCH --job-name=FineTime #SBATCH --chdir=/storage/home/efg5335/work/GRASS -#SBATCH --output=/storage/home/efg5335/work/GRASS/N_convergence.out +#SBATCH --output=/storage/home/efg5335/work/GRASS/FineTime.out echo "About to start: $SLURM_JOB_NAME" date diff --git a/src/convenience_eclipse.jl b/src/convenience_eclipse.jl index e5257c25..4e64898d 100644 --- a/src/convenience_eclipse.jl +++ b/src/convenience_eclipse.jl @@ -104,6 +104,7 @@ function synth_Eclipse_gpu(spec::SpecParams{T}, disk::DiskParamsEclipse{T}, # allocate memory needed for rossiter computations gpu_allocs = GPUAllocsEclipse(spec, disk, Int(length(wavelength)), precision=precision, verbose=verbose) + brightness_arr = Vector{Vector{Float64}}(undef,size(templates)...) # run the simulation and return for (idx, file) in enumerate(templates) # get temporary specparams with lines for this run @@ -117,9 +118,9 @@ function synth_Eclipse_gpu(spec::SpecParams{T}, disk::DiskParamsEclipse{T}, soldata = GPUSolarData(soldata_cpu, precision=precision) # run the simulation and multiply flux by this spectrum - disk_sim_eclipse_gpu(spec_temp, disk, soldata, gpu_allocs, flux, + brightness_arr[idx] = disk_sim_eclipse_gpu(spec_temp, disk, soldata, gpu_allocs, flux, obs_long, obs_lat, alt, time_stamps, wavelength, ext_coeff, ext_toggle, LD_type, skip_times=skip_times) end - return spec.lambdas, flux + return spec.lambdas, flux, brightness_arr end \ No newline at end of file diff --git a/src/disk_sim_eclipse.jl b/src/disk_sim_eclipse.jl index 59162c67..5b6cf6e8 100644 --- a/src/disk_sim_eclipse.jl +++ b/src/disk_sim_eclipse.jl @@ -101,13 +101,13 @@ function disk_sim_eclipse(spec::SpecParams{T}, disk::DiskParamsEclipse{T}, solda wsp.widt .= copy(view(soldata.wid[key], :, tloop[i,j])) # get amount of convective blueshift needed - # extra_z = spec.conv_blueshifts[l] - z_cbs_avg + extra_z = spec.conv_blueshifts[l] - z_cbs_avg # get shifted line center λΔD = spec.lines[l] λΔD *= (1.0 + z_rot) λΔD *= (1.0 + z_cbs) - # λΔD *= (1.0 + extra_z) + λΔD *= (1.0 + extra_z) # fix bisector and width if variability is turned off if !spec.variability[l] diff --git a/src/gpu/gpu_precomps_eclipse.jl b/src/gpu/gpu_precomps_eclipse.jl index da56921c..b8a50e0c 100644 --- a/src/gpu/gpu_precomps_eclipse.jl +++ b/src/gpu/gpu_precomps_eclipse.jl @@ -243,14 +243,12 @@ function calc_eclipse_quantities_gpu!(wavelength, μs, z_rot, ax_codes, angle = (OP_bary_x * vx + OP_bary_y * vy + OP_bary_z * vz) / (n1 * n2) v_rot_sub = (n2 * angle) v_rot_sub *= 1000.0 - projected_v_sum += v_rot_sub z_rot_sub = v_rot_sub / c_ms n2 = CUDA.sqrt(OS_bary[4]^2.0 + OS_bary[5]^2.0 + OS_bary[6]^2.0) angle = (OP_bary_x * OS_bary[4] + OP_bary_y * OS_bary[5] + OP_bary_z * OS_bary[6]) / (n1 * n2) v_orbit_sub = (n2 * angle) v_orbit_sub *= 1000.0 - earth_v_sum += v_orbit_sub z_rot_sub += (v_orbit_sub / c_ms) # get projected area element @@ -265,6 +263,9 @@ function calc_eclipse_quantities_gpu!(wavelength, μs, z_rot, ax_codes, continue end + projected_v_sum += v_rot_sub + earth_v_sum += v_orbit_sub + # iterate counter count += 1 @@ -322,8 +323,6 @@ function calc_eclipse_quantities_gpu!(wavelength, μs, z_rot, ax_codes, @inbounds ax_codes[m, n] = find_nearest_ax_gpu(yy / sun_radius, zz / sun_radius) else - # @inbounds μs[m,n] = 0.0 - # @inbounds dA[m,n] = 0.0 @inbounds μs[m,n] = μ_sum / μ_count @inbounds dA[m,n] = dA_sum for wl in eachindex(wavelength) diff --git a/src/gpu/gpu_sim_eclipse.jl b/src/gpu/gpu_sim_eclipse.jl index efc41ca9..ac01e142 100644 --- a/src/gpu/gpu_sim_eclipse.jl +++ b/src/gpu/gpu_sim_eclipse.jl @@ -79,6 +79,7 @@ function disk_sim_eclipse_gpu(spec::SpecParams{T1}, disk::DiskParamsEclipse{T1}, ext_toggle_gpu = 0.0 end + brightness = Vector{Float64}(undef,size(time_stamps)...) # loop over time for t in 1:Nt # sort out the system geometry @@ -125,14 +126,16 @@ function disk_sim_eclipse_gpu(spec::SpecParams{T1}, disk::DiskParamsEclipse{T1}, # get weighted disk average cbs @cusync sum_wts = CUDA.sum(dA .* ld[:,:,l] .* ext[:,:,l]) @cusync z_cbs_avg = CUDA.sum(z_cbs .* dA .* ld[:,:,l] .* ext[:,:,l]) / sum_wts + brightness[t] = sum_wts else # get weighted disk average cbs @cusync sum_wts = CUDA.sum(dA .* ld[:,:,l]) @cusync z_cbs_avg = CUDA.sum(z_cbs .* dA .* ld[:,:,l]) / sum_wts + brightness[t] = sum_wts end - # # calculate how much extra shift is needed - # extra_z = spec.conv_blueshifts .- z_cbs_avg + # calculate how much extra shift is needed + extra_z = spec.conv_blueshifts .- z_cbs_avg # trim all the bisector data @cusync @cuda threads=threads2 blocks=blocks2 trim_bisector_gpu!(spec.depths[l], spec.variability[l], @@ -144,7 +147,7 @@ function disk_sim_eclipse_gpu(spec::SpecParams{T1}, disk::DiskParamsEclipse{T1}, widall_mean) # assemble line shape on even int grid - @cusync @cuda threads=threads3 blocks=blocks3 fill_workspaces_2D_eclipse!(spec.lines[l], spec.variability[l], + @cusync @cuda threads=threads3 blocks=blocks3 fill_workspaces_2D_eclipse!(spec.lines[l], spec.variability[l], extra_z[l], tloop, dat_idx, z_rot, z_cbs, lenall_gpu, bisall_gpu_loop, intall_gpu_loop, @@ -166,5 +169,5 @@ function disk_sim_eclipse_gpu(spec::SpecParams{T1}, disk::DiskParamsEclipse{T1}, # make sure nothing is still running on GPU CUDA.synchronize() - return nothing + return brightness end diff --git a/src/gpu/gpu_synthesis.jl b/src/gpu/gpu_synthesis.jl index e1084a8a..ce76808e 100644 --- a/src/gpu/gpu_synthesis.jl +++ b/src/gpu/gpu_synthesis.jl @@ -211,7 +211,7 @@ function apply_line!(t, prof, flux, sum_wts) return nothing end -function fill_workspaces_2D_eclipse!(line, variability, tloop, dat_idx, z_rot, +function fill_workspaces_2D_eclipse!(line, variability, extra_z, tloop, dat_idx, z_rot, z_cbs, lenall, bisall, intall, widall, allwavs, allints) # get indices from GPU blocks + threads idx = threadIdx().x + blockDim().x * (blockIdx().x-1) @@ -242,7 +242,7 @@ function fill_workspaces_2D_eclipse!(line, variability, tloop, dat_idx, z_rot, t = tloop[m,n] # calculate shifted line center - λΔD = line * (1.0 + z_rot[m,n]) * (1.0 + z_cbs[m,n]) #* (1.0 + extra_z) + λΔD = line * (1.0 + z_rot[m,n]) * (1.0 + z_cbs[m,n]) * (1.0 + extra_z) # get length of input data arrays to loop over lent = 100 diff --git a/src/observing/convolutions.jl b/src/observing/convolutions.jl index 4ec3c619..8acbe31b 100644 --- a/src/observing/convolutions.jl +++ b/src/observing/convolutions.jl @@ -185,3 +185,35 @@ function convolve_gauss(xs::AA{T,1}, ys::AA{T,2}; new_res::T=1.17e5, return xs_out, ys_out end + +function convolve_gauss(xs::AA{T,1}, ys::AA{T,2}, kernel; new_res::T=1.17e5, + oversampling::T=1.0) where T<:AbstractFloat + # create vector to hold padded signal + signal = zeros(200 + size(ys, 1)) + + # get wavelength grid at lower resolution + Δlnλ = 1.0 / new_res + lnλs = range(log(first(xs)), log(last(xs)), step=Δlnλ/oversampling) + xs_out = exp.(lnλs) + + # allocate array for output spectrum + ys_out = zeros(length(xs_out), size(ys, 2)) + + # loop over times + for t in 1:size(ys,2) + # fill signal vector + signal[1:100] .= first(ys[:,t]) + signal[101:end-100] .= ys[:,t] + signal[end-100:end] .= last(ys[:,t]) + + # perform the convolution + # TODO: fft plan (outside loop?) + signal .= imfilter(signal, reflect(centered(kernel./maximum(kernel))), Fill(0)) + signal ./= maximum(signal[101:end-100]) + + # rebin the convolved spectrum + ys_out[:,t] .= rebin_spectrum(xs, view(signal, 101:length(signal)-100), xs_out) + end + + return xs_out, ys_out +end \ No newline at end of file diff --git a/src/velocities.jl b/src/velocities.jl index 32d23d05..266ff94b 100644 --- a/src/velocities.jl +++ b/src/velocities.jl @@ -271,12 +271,16 @@ function MeasureRvFromCCFGaussian_New(vels::A1, ccf::A2, ccf_var::A3, mrv) where local rvfit # fit and return the mean of the distribution + ccf_var[ccf_var .== 0.0] .= eps() result = curve_fit(gaussian_line_helper, view(vels,inds), view(ccf,inds), (1.0 ./ view(ccf_var,inds)), p0) - if result.converged rv = coef(result)[1] sigma_rv = stderror_new(result, view(ccf_var ./ maximum(ccf_var),inds))[1] rvfit = (rv=rv, σ_rv=sigma_rv) + else + @warn "Fit of Gaussian to CCF did not converge. Reverting to fit quadratic to CCF." + quad_fit_to_ccf = QuadraticFit(frac_of_width_to_fit=mrv.frac_of_width_to_fit,measure_width_at_frac_depth=mrv.measure_width_at_frac_depth) + rvfit = quad_fit_to_ccf(vels,ccf,ccf_var) end return rvfit end @@ -284,8 +288,7 @@ end function vcov_new(fit, ccf_var) # computes covariance matrix of fit parameters J = fit.jacobian - - covar = inv(J' * J) * mean(ccf_var) + covar = pinv(J' * J) * mean(ccf_var) return covar end