diff options
Diffstat (limited to 'astro')
| -rwxr-xr-x | astro/calc_psd.py | 109 |
1 files changed, 89 insertions, 20 deletions
diff --git a/astro/calc_psd.py b/astro/calc_psd.py index da5465f..1b9fea8 100755 --- a/astro/calc_psd.py +++ b/astro/calc_psd.py @@ -36,8 +36,24 @@ class PSD: """ Computes the 2D power spectral density and the azimuthally averaged power spectral density (i.e., 1D radial power spectrum). + + Parameters + ---------- + image : 2D `~numpy.ndarray` + Input image array + pixel : (float, str), optional + Specify the pixel size and its unit of the image. + e.g., (0.33, "arcmin") + step : float, optional + If specified, then a log-even grid with the given step ratio will + be used to do the azimuthal averages. Otherwise, a evenly + pixel-by-pixel (along radial direction) is adopted. + meanstd : bool, optional + By default, the median and 16% and 84% percentiles (i.e., 68% IQR) + will be calculated for each averaged annulus. If this option is + ``True`` then calculate the mean and standard deviation instead. """ - def __init__(self, image, pixel=(1.0, "pixel"), step=None): + def __init__(self, image, pixel=(1.0, "pixel"), step=None, meanstd=False): self.image = np.array(image, dtype=float) self.shape = self.image.shape if self.shape[0] != self.shape[1]: @@ -48,6 +64,8 @@ class PSD: if step is not None and step <= 1: raise ValueError("step must be greater than 1") + self.meanstd = meanstd + @property @lru_cache() def radii(self): @@ -115,11 +133,21 @@ class PSD: Returns ------- - frequencies: 1D float `~numpy.ndarray` + frequencies : 1D float `~numpy.ndarray` Spatial frequencies, [{pixel_unit}^(-1)] - psd1d, psd1d_err: 1D float `~numpy.ndarray` - Azimuthally averaged powers and their standard deviations at + psd1d : 1D float `~numpy.ndarray` + The median or mean (``self.meanstd=True``) of the powers within each (radial) spatial frequency bin. + psd1d_errl, psd1d_erru : 1D float `~numpy.ndarray` + The lower and upper errors of the powers. By default, they are + determined from the 16% and 84% percentiles w.r.t. the median. + If ``self.meanstd=True`` then they are the standard deviation. + + Attributes + ---------- + psd1d + psd1d_errl + psd1d_erru """ if not hasattr(self, "ps2d") or self.psd2d is None: self.calc_psd2d() @@ -143,7 +171,8 @@ class PSD: else: print(" %d data points ... " % nr, end="", flush=True) psd1d = np.zeros(nr) - psd1d_err = np.zeros(nr) + psd1d_errl = np.zeros(nr) # lower error + psd1d_erru = np.zeros(nr) # upper error for i, r in enumerate(radii): if (i+1) % 100 == 0: percent = 100 * (i+1) / nr @@ -151,13 +180,22 @@ class PSD: ii, jj = (rho <= r).nonzero() rho[ii, jj] = np.inf data = self.psd2d[ii, jj] - psd1d[i] = np.mean(data) - psd1d_err[i] = np.std(data) + if self.meanstd: + psd1d[i] = np.mean(data) + std = np.std(data) + psd1d_errl[i] = std + psd1d_erru[i] = std + else: + median, q16, q84 = np.percentile(data, q=(50, 16, 84)) + psd1d[i] = median + psd1d_errl[i] = median - q16 + psd1d_erru[i] = q84 - median print("DONE", flush=True) self.psd1d = psd1d - self.psd1d_err = psd1d_err - return (self.frequencies, psd1d, psd1d_err) + self.psd1d_errl = psd1d_errl + self.psd1d_erru = psd1d_erru + return (self.frequencies, psd1d, psd1d_errl, psd1d_erru) @staticmethod def cart2pol(x, y): @@ -177,6 +215,27 @@ class PSD: y = rho * np.sin(phi) return (x, y) + def save(self, outfile): + data = np.column_stack((self.frequencies, self.psd1d, + self.psd1d_errl, self.psd1d_erru)) + header = [ + "pixel: %s [%s]" % self.pixel, + "frequency: [%s^-1]" % self.pixel[1], + ] + if self.meanstd: + header += [ + "psd1d: *mean* powers of radial spectral annuli", + "psd1d_errl, psd1d_erru: *standard deviation* (lower, upper)", + ] + else: + header += [ + "psd1d: *median* powers of radial spectral annuli", + "psd1d_errl, psd1d_erru: 16% and 84% *percentiles*", + ] + header += ["", "frequency psd1d psd1d_errl psd1d_erru"] + np.savetxt(outfile, data, header="\n".join(header)) + print("Saved PSD data to: %s" % outfile) + def plot(self, ax): """ Make a plot of the 1D radial power spectrum. @@ -185,15 +244,24 @@ class PSD: xmin = freqs[1] / 1.2 # ignore the first 0 xmax = freqs[-1] * 1.1 ymin = np.min(self.psd1d) / 10.0 - ymax = np.max(self.psd1d[1:] + self.psd1d_err[1:]) * 2 + ymax = np.max(self.psd1d[1:] + self.psd1d_erru[1:]) * 1.5 - ax.errorbar(freqs, self.psd1d, yerr=self.psd1d_err, fmt="none") - ax.plot(freqs, self.psd1d, marker="o") + if self.meanstd: + label = "mean" + labelerr = "standard deviation" + else: + label = "median" + labelerr = "68% IQR" + yerr = np.row_stack((self.psd1d_errl, self.psd1d_erru)) + ax.errorbar(freqs, self.psd1d, yerr=yerr, + fmt="none", label=labelerr) + ax.plot(freqs, self.psd1d, marker="o", label=label) ax.set(xscale="log", yscale="log", xlim=(xmin, xmax), ylim=(ymin, ymax), title="Radial (Azimuthally Averaged) Power Spectral Density", xlabel=r"k [%s$^{-1}$]" % self.pixel[1], ylabel="Power") + ax.legend() if self.pixel[1] != "pixel": # Add an additional X axis for pixel-based frequencies @@ -271,6 +339,11 @@ def main(): parser.add_argument("-p", "--pixelsize", dest="pixelsize", type=float, help="image spatial pixel size [arcsec] " + "(will try to obtain from FITS header)") + parser.add_argument("-m", "--mean-std", dest="meanstd", + action="store_true", + help="calculate the mean and standard deviation " + + "for each averaged annulus instead of the median " + + "16%% and 84%% percentiles (i.e., 68%% IQR)") parser.add_argument("-P", "--plot", dest="plot", action="store_true", help="plot the PSD and save as a PNG image") parser.add_argument("-i", "--infile", dest="infile", nargs="+", @@ -317,20 +390,16 @@ def main(): else: raise ValueError("image has different unit: %s" % bunit2) - psdobj = PSD(image=image, pixel=pixel, step=args.step) - freqs, psd, psd_err = psdobj.calc_psd() - - # Write out PSD results - psd_data = np.column_stack((freqs, psd, psd_err)) - np.savetxt(args.outfile, psd_data, header="freqs psd psd_err") - print("Saved PSD data to: %s" % args.outfile) + psd = PSD(image=image, pixel=pixel, step=args.step, meanstd=args.meanstd) + psd.calc_psd() + psd.save(args.outfile) if args.plot: # Make and save a plot fig = Figure(figsize=(8, 8)) FigureCanvas(fig) ax = fig.add_subplot(1, 1, 1) - psdobj.plot(ax=ax) + psd.plot(ax=ax) fig.tight_layout() fig.savefig(plotfile, format="png", dpi=150) print("Plotted PSD and saved to image: %s" % plotfile) |