print(40*"$")
print("Starting run with the following settings:")
print(cf['pa'])
print("Generated filename(s) for this session: {}".format(filename_raw))
print(filename)
print(40*"$")
bins = const.linear_equisized_bins_10 #const.SMICA_lowell_bins #
# Load smica spectrum, i.e. cmb output
freqdset = cf['pa']['freqdset']
specsmicanpipesim_sc_path_name = io.out_specsmica_path + freqdset + cf[mch][freqdset]['sim_id'] + io.specsmica_sc_filename
smica_spec_bnd = io.load_data(specsmicanpipesim_sc_path_name)[0,0,:]
# Load cmb input
cf['pa']['freqdset'] = freqdset+"_cmb"
total_filename = io.make_filenamestring(cf)
cmb_in_spec = io.load_data(cf[mch]['outdir_spectrum_ap'] + cf['pa']["freqdset"] + "/SPEC" + total_filename)
#two paths possible from here
# 1. compare smica_spec with hp.anafast(cmb_map_in) using eq 9 from diffuse comp sep paper
# note: any of the cmb_in_spec works, as signal is same in every detector?
tf = calc_transferfunction(smica_spec_bnd, bin_it_1D(cmb_in_spec['143-143']["EE"], bins=bins))
cf['pa']['freqdset'] = freqdset[:-4]
io.save_data(tf, cf[mch]['outdir_misc_ap']+"tf{}.npy".format(cf[mch][freqdset]['sim_id']))
# The following lines are bollocks as it tries to mv combine signal only channels. the way this is done here is not possible
###################################
# cov = pw.build_covmatrices(cmb_in_spec, lmax=lmax, freqfilter=freqfilter, specfilter=specfilter)
# icov_l = np.nan_to_num(pw.invert_covmatrices(cov, lmax=lmax, freqfilter=freqfilter, specfilter=specfilter))
# spec_data = hpf.reorder_spectrum_dict(cmb_in_spec)
# spec_data_wweighted = mv(icov_l, spec_data)
def plot_compare_optimalspectrum(fname):
spec_pick = "EE"
def _weightspec(icov_l, spectrum):
import copy
# retspec = copy.deepcopy(spectrum)
for specc, data in spectrum.items():
if specc == spec_pick:
icovsum = np.array(
[np.sum(icov_l[specc][l]) for l in range(lmax)])
# buff += spectrum[specc][freqc][:lmax] * spectrum[specc][freqc][:lmax] * weights[specc]["channel @{}GHz".format(freqs[0])].to_numpy()[:lmax] * weights[specc]["channel @{}GHz".format(freqs[0])].to_numpy()[:lmax]/(normaliser * normaliser)
retspec = {
specc: {
'optimal-optimal':
np.array([
1 / icovsum_l if icovsum_l is not None else 0
for icovsum_l in icovsum
])
}
}
return retspec
fname = io.make_filenamestring(cf)
lmax = cf['pa']['lmax']
freqcomb = [
"{}-{}".format(FREQ, FREQ2) for FREQ in PLANCKMAPFREQ
if FREQ not in freqfilter for FREQ2 in PLANCKMAPFREQ
if (FREQ2 not in freqfilter) and (int(FREQ2) >= int(FREQ))
]
dc = dcf["plot"]["spectrum"]
def _inpathname(freqc, spec):
return dc["indir_root"] + dc["indir_rel"] + spec + freqc + "-" + dc[
"in_desc"] + fname
speccs = [spec for spec in PLANCKSPECTRUM if spec not in specfilter]
spectrum = {
freqc: {
spec: np.array(io.load_cl(_inpathname(freqc, spec)))
for spec in speccs
}
for freqc in freqcomb
}
cov = pw.build_covmatrices(spectrum,
lmax=lmax,
freqfilter=freqfilter,
specfilter=specfilter)
icov_l = pw.invert_covmatrices(cov,
lmax=lmax,
freqfilter=freqfilter,
specfilter=specfilter)
spec_data = hpf.reorder_spectrum_dict(spectrum)
spec_data_wweighted = _weightspec(icov_l, spec_data)
spectrum_truth = io.load_truthspectrum(
abspath='/mnt/c/Users/sebas/OneDrive/Desktop/Uni/')
for specc, data in spec_data_wweighted.items():
title_string = "{} spectrum - {}".format(specc,
"optimal spectrum included")
if "Planck-" + specc in spectrum_truth.columns:
spectrum_trth = spectrum_truth["Planck-" + specc]
else:
spectrum_trth = None
mp = cplt.plot_powspec_binned(data,
lmax,
title_string=title_string,
truthfile=spectrum_trth,
truth_label="Planck-" + specc)
outpath_name = \
dc["outdir_root"] + \
dc["outdir_rel"] + \
specc+"_spectrum/" + \
specc+"_spectrum" + "-" + \
"withoptimal" + "-" + \
fname + ".jpg"
io.save_figure(mp=mp, path_name=outpath_name)
print("spectrum saved to {}".format(outpath_name))
def plot_tf(fname):
"""
quite similar to plot_spectrum_comparison()
"""
path1 = io.out_spec_path
name1 = "SPEC" + io.make_filenamestring(cf)
pathname1 = path1 + name1
dcf2 = copy.deepcopy(dcf)
dcf2['pa']['smoothing_window'] = 0
dcf2['pa']['max_polynom'] = 0
pathname1 = path1 + name1
spectrum1 = io.load_data(io.specsmica_sc_path_name)[0, 0, :]
spectrum2 = io.load_data(path_name=cf[mch]['outdir_misc_ap'] +
"cmb_spec_in.npy")
spectrum1_re = hpf.reorder_spectrum_dict(spectrum1)
spectrum2_re = hpf.reorder_spectrum_dict(spectrum2)
lmax = cf['pa']['lmax']
spectrum_truth = io.load_truthspectrum()
plotsubtitle = '{freqdset}"{split}" dataset - {mskset} masks'.format(
mskset=mskset,
freqdset=freqdset,
split="Full"
if cf['pa']["freqdatsplit"] == "" else cf['pa']["freqdatsplit"])
diff_spectrum = io.load_data(cf[mch]['outdir_misc_ap'] + "tf.npy")
for specc, diff_data in diff_spectrum.items():
color = ['red', 'green', 'blue', 'yellow', 'black', 'orange', 'purple']
title_string = specc + "-spectrum - " + plotsubtitle
if "Planck-" + specc in spectrum_truth.columns:
spectrum_trth = spectrum_truth["Planck-" + specc][:lmax + 1] / (
hpf.llp1e12(np.array([range(lmax + 1)])))[0] * 1e12
plt.figure(figsize=(8, 6))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax1 = plt.subplot(gs[0])
mp = cplt.plot_compare_powspec_binned(plt,
spectrum1_re[specc],
spectrum2_re[specc],
lmax,
title_string=title_string,
truthfile=spectrum_trth,
truth_label="Planck-" + specc)
ax2 = mp.subplot(gs[1])
mp = cplt.plot_powspec_diff_binned(mp,
diff_spectrum[specc],
lmax,
color=color)
outpath = "/global/cscratch1/sd/sebibel/vis/" + \
specc+"_spectrum/"
io.iff_make_dir(outpath)
outpath_name = outpath + \
specc+"_transferfunction" + "-" + ".jpg"
io.save_figure(mp=mp, path_name=outpath_name)
def plot_weights_comparison():
ylim = {
"TT": (1e2, 1e5),
"EE": (1e-6, 1e1),
"BB": (1e-5, 1e5),
"TE": (1e-2, 1e4),
"TB": (-1e-3, 1e3),
"EB": (-1e-3, 1e3),
"ET": (1e-2, 1e5),
"BT": (-1e-3, 1e3),
"BE": (-1e-3, 1e3)
}
dc = dcf["plot"]["weights_comparison"]
lmax = cf['pa']['lmax']
plotsubtitle = '{freqdset}"{split}" dataset - {mskset} masks'.format(
mskset=mskset, freqdset=freqdset, split=split)
cf['pa']['mskset'] = dc['mskset1']
cf['pa']['freqdset'] = dc['freqdset1']
cf['pa']['smoothing_window'] = dc['smoothing_window1']
cf['pa']['max_polynom'] = dc['max_polynom1']
filename1 = io.make_filenamestring(cf)
weight_path1 = cf[mch]['abs_path'] + io.component_separation_path + cf[
mch]['outdir_weight'] + dc["freqdset1"] + "/"
weight_path_name1 = weight_path1 + "-" + cf['pa'][
"Tscale"] + "-" + filename1
weight_data1 = io.load_data(weight_path_name1)
cf['pa']['mskset'] = dc['mskset2']
cf['pa']['freqdset'] = dc['freqdset2']
cf['pa']['smoothing_window'] = dc['smoothing_window2']
cf['pa']['max_polynom'] = dc['max_polynom2']
filename2 = io.make_filenamestring(cf)
weight_path2 = cf[mch]['abs_path'] + io.component_separation_path + cf[
mch]['outdir_weight'] + dc["freqdset1"] + "/"
weight_path_name2 = weight_path2 + "-" + cf['pa'][
"Tscale"] + "-" + filename2
weight_data2 = io.load_data(weight_path_name2)
print(weight_data2)
diff_weights = np.nan_to_num(
np.array([(weight_data1[idx] - weight_data2[idx]) / weight_data1[idx]
for idx, specc in enumerate(weight_data1)]))
for idx, spec in enumerate(PLANCKSPECTRUM):
if spec not in specfilter:
plt.figure(figsize=(8, 6))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax1 = plt.subplot(gs[0])
base = 2
plt.xlabel("Multipole l")
plt.xscale("log", base=base)
title_string = "{} weigthts - {}".format(spec, plotsubtitle)
mp = cplt.plot_compare_weights_binned(plt,
weight_data2[idx],
weight_data1[idx],
lmax,
title_string=title_string)
ax2 = mp.subplot(gs[1])
mp = cplt.plot_weights_diff_binned(mp, diff_weights[idx], lmax)
outpath_name = \
dc["outdir_root"] + \
dc["outdir_rel"] + \
spec+"_weights/" + \
spec+"_weights_comparison" + "-" + \
dc["out_desc"] +"-" + \
(filename1+filename2)[::3] + ".jpg"
print(spec + "processed " + outpath_name)
io.save_figure(mp=mp, path_name=outpath_name)
def plot_spectrum_comparison(fname):
"""
Creates a plot with two diagrams, comparing two sets of spectra with one another.
This can be used to validate_spectra and validate_smica, and just compare two spectra, i.e. compare,
1. synspectra to spectra
2. smica spectra to input spectra
3. two different spectra (e.g. varying in masks)
"""
name1 = "SPEC" + io.make_filenamestring(cf)
path1 = io.out_spec_path
pathname1 = path1 + name1
cf2 = copy.deepcopy(cf)
cf2['pa']['smoothing_window'] = 0
cf2['pa']['max_polynom'] = 0
name2 = "SPEC" + io.make_filenamestring(cf2)
path2 = io.out_spec_path
pathname2 = path2 + name2
spectrum1 = io.load_data(path_name=pathname1)
spectrum2 = io.load_data(path_name=pathname2)
spectrum1_re = hpf.reorder_spectrum_dict(spectrum1)
spectrum2_re = hpf.reorder_spectrum_dict(spectrum2)
lmax = cf['pa']['lmax']
plotsubtitle = '{freqdset}"{split}" dataset - {mskset} masks'.format(
mskset=mskset,
freqdset=freqdset,
split="Full"
if cf['pa']["freqdatsplit"] == "" else cf['pa']["freqdatsplit"])
diff_spectrum = dict()
for specc, va in spectrum1_re.items():
for freqc, val in spectrum1_re[specc].items():
if specc not in diff_spectrum.keys():
diff_spectrum.update({specc: {}})
diff_spectrum[specc].update({
freqc:
(spectrum1_re[specc][freqc] - spectrum2_re[specc][freqc]) /
spectrum1_re[specc][freqc]
})
spectrum_truth = io.load_truthspectrum()
for specc, diff_data in diff_spectrum.items():
color = ['red', 'green', 'blue', 'yellow', 'black', 'orange', 'purple']
title_string = specc + "-spectrum - " + plotsubtitle
if "Planck-" + specc in spectrum_truth.columns:
spectrum_trth = spectrum_truth["Planck-" + specc][:lmax + 1] / (
hpf.llp1e12(np.array([range(lmax + 1)])))[0] * 1e12
plt.figure(figsize=(8, 6))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax1 = plt.subplot(gs[0])
mp = cplt.plot_compare_powspec_binned(plt,
spectrum1_re[specc],
spectrum2_re[specc],
lmax,
title_string=title_string,
truthfile=spectrum_trth,
truth_label="Planck-" + specc)
ax2 = mp.subplot(gs[1])
mp = cplt.plot_powspec_diff_binned(mp,
diff_spectrum[specc],
lmax,
color=color)
outpath = "/global/cscratch1/sd/sebibel/vis/" + \
specc+"_spectrum/"
io.iff_make_dir(outpath)
outpath_name = outpath + \
specc+"_spectrumbias" + "-" + ".jpg"
io.save_figure(mp=mp, path_name=outpath_name)