diff options
Diffstat (limited to 'astro/ps1d_eorwindow.py')
| -rwxr-xr-x | astro/ps1d_eorwindow.py | 216 |
1 files changed, 216 insertions, 0 deletions
diff --git a/astro/ps1d_eorwindow.py b/astro/ps1d_eorwindow.py new file mode 100755 index 0000000..bbb0ded --- /dev/null +++ b/astro/ps1d_eorwindow.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2017 Weitna LI <weitian@aaronly.me> +# MIT License +# + +""" +Average the 2D power spectrum within the EoR window (i.e., excluding the +foreground contaminated wedge) to derive the 1D spherically averaged +power spectrum. +""" + +import os +import argparse + +import numpy as np + +import matplotlib +import matplotlib.style +from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas +from matplotlib.figure import Figure + +from eor_window import PS2D + + +# Matplotlib settings +matplotlib.style.use("ggplot") +for k, v in [("font.family", "monospace"), + ("image.cmap", "jet"), + ("xtick.major.size", 7.0), + ("xtick.major.width", 2.0), + ("xtick.minor.size", 4.0), + ("xtick.minor.width", 1.5), + ("ytick.major.size", 7.0), + ("ytick.major.width", 2.0), + ("ytick.minor.size", 4.0), + ("ytick.minor.width", 1.5)]: + matplotlib.rcParams[k] = v + + +class PS1D: + """ + Calculate the 1D spherically averaged power spectrum from 2D PS. + """ + def __init__(self, ps2d): + self.ps2d = ps2d + self.data = ps2d.ps2d # shape: [n_k_los, n_k_perp] + self.data_err = ps2d.ps2d_err + + @property + def k_perp(self): + return self.ps2d.k_perp + + @property + def k_los(self): + return self.ps2d.k_los + + @property + def eor_window(self): + return self.ps2d.eor_window() + + def calc_ps1d(self, normalize=True): + """ + Calculate the 1D spherically averaged power spectrum by averaging + the 2D cylindrical power spectrum. + + Parameters + ---------- + normalize : bool + Whether to normalize the 1D power spectrum to obtain the + dimensionless power spectrum, i.e., + Δ^2(k) = (k^3 / (2*π^2)) P(k) + """ + eor_window = self.eor_window + data = self.data.copy() + data_err = self.data_err.copy() + data[~eor_window] = np.nan + data_err[~eor_window] = np.nan + + k_perp = self.k_perp + k_los = self.k_los + dk_perp = k_perp[1] - k_perp[0] + dk_los = k_los[1] - k_los[0] + dk = np.sqrt(dk_perp * dk_los) + print("dk = %.6f [Mpc^-1]" % dk) + k_max = np.sqrt(k_perp[-1]**2 + k_los[-1]**2) + nk = int(k_max / dk) + 1 + print("number of k points: %d" % nk) + ps1d_k = np.arange(nk) * dk + + # PS1D's 3 columns: [k, ps1d, ps1d_err] + ps1d = np.zeros(shape=(nk, 3)) + ps1d[:, 0] = ps1d_k + + print("averaging 2D power spectrum ...") + mx, my = np.meshgrid(k_perp, k_los) + mk = np.sqrt(mx**2 + my**2) + for i, k in enumerate(ps1d_k): + ii, jj = (mk <= k).nonzero() + mk[ii, jj] = np.inf + cells = data[ii, jj] + cells = cells[np.isfinite(cells)] + if len(cells) > 0: + ps1d[i, 1] = np.mean(cells) + cells = data_err[ii, jj] + cells = cells[np.isfinite(cells)] + ps1d[i, 2] = np.sqrt(np.sum((cells/len(cells))**2)) + + if normalize: + coef = ps1d_k**3 / (2*np.pi**2) + ps1d[:, 1] *= coef + ps1d[:, 2] *= coef + self.ps1d_normalized = True + else: + self.ps1d_normalized = False + + self.ps1d = ps1d + return ps1d + + def save(self, outfile): + ps1d = self.ps1d + header = [ + "EoR window:", + " FoV: %f [deg]" % self.ps2d.fov, + " e_ConvWidth: %f" % self.ps2d.e, + " k_perp_min: %f [Mpc^-1]" % self.ps2d.k_perp_min, + " k_perp_max: %f [Mpc^-1]" % self.ps2d.k_perp_max, + " k_los_min: %f [Mpc^-1]" % self.ps2d.k_los_min, + " k_los_max: %f [Mpc^-1]" % self.ps2d.k_los_max, + "", + "k: wavenumber [Mpc^-1]", + ] + if self.ps1d_normalized: + header += ["ps1d: normalized power [K^2]"] + else: + header += ["ps1d: power [K^2 Mpc^3]"] + header += [ + "ps1d_err: power errors", + "", + "k ps1d ps1d_err" + ] + np.savetxt(outfile, ps1d, header="\n".join(header)) + print("saved 1D power spectrum to file: %s" % outfile) + + def plot(self, ax): + ps1d = self.ps1d + if self.ps1d_normalized: + ylabel = r"$\Delta^2(k)$ [K$^2$]" + else: + ylabel = r"$P(k)$ [K$^2$ Mpc$^3$]" + + x = ps1d[:, 0] + y = ps1d[:, 1] + yerr = ps1d[:, 2] + ax.errorbar(x[1:], y[1:], yerr=yerr[1:], fmt="none") + ax.plot(x[1:], y[1:], marker="o") + ax.set(xscale="log", yscale="log", + xlabel=r"[Mpc$^{-1}$]", ylabel=ylabel, + title="1D Spherically Average Power Spectrum") + return ax + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate 1D power spectrum within the EoR window") + parser.add_argument("-C", "--clobber", dest="clobber", action="store_true", + help="overwrite the output files if already exist") + parser.add_argument("-F", "--fov", dest="fov", + type=float, required=True, + help="instrumental FoV to determine the EoR window; " + + "SKA1-Low has FoV ~ 3.12 / (nu/200MHz) [deg], i.e., " + + "~5.03 @ 124, ~3.95 @ 158, ~3.18 @ 196") + parser.add_argument("-e", "--conv-width", dest="conv_width", + type=float, default=3.0, + help="characteristic convolution width (default: 3.0)") + parser.add_argument("-p", "--k-perp-min", dest="k_perp_min", type=float, + help="minimum k wavenumber perpendicular to LoS; " + + "unit: [Mpc^-1]") + parser.add_argument("-P", "--k-perp-max", dest="k_perp_max", type=float, + help="maximum k wavenumber perpendicular to LoS") + parser.add_argument("-l", "--k-los-min", dest="k_los_min", type=float, + help="minimum k wavenumber along LoS") + parser.add_argument("-L", "--k-los-max", dest="k_los_max", type=float, + help="maximum k wavenumber along LoS") + parser.add_argument("--no-plot", dest="noplot", action="store_true", + help="do not plot and save the calculated 1D power " + + "power within the EoR window") + parser.add_argument("-i", "--infile", dest="infile", required=True, + help="2D power spectrum FITS file") + parser.add_argument("-o", "--outfile", dest="outfile", required=True, + help="output TXT file to save the PSD data") + args = parser.parse_args() + + if (not args.clobber) and os.path.exists(args.outfile): + raise OSError("outfile '%s' already exists" % args.outfile) + + ps2d = PS2D(args.infile, fov=args.fov, e=args.conv_width, + k_perp_min=args.k_perp_min, k_perp_max=args.k_perp_max, + k_los_min=args.k_los_min, k_los_max=args.k_los_max) + ps1d = PS1D(ps2d) + ps1d.calc_ps1d() + ps1d.save(args.outfile) + + if not args.noplot: + fig = Figure(figsize=(8, 8), dpi=150) + FigureCanvas(fig) + ax = fig.add_subplot(1, 1, 1) + ps1d.plot(ax=ax) + fig.tight_layout() + plotfile = os.path.splitext(args.outfile)[0] + ".png" + fig.savefig(plotfile) + print("Plotted 1D power spectrum within EoR window: %s" % plotfile) + + +if __name__ == "__main__": + main() |