From 9fbeb8cb3aafa86f83787c660ae1354c997cb490 Mon Sep 17 00:00:00 2001 From: Aaron LI Date: Thu, 7 Sep 2017 16:04:16 +0800 Subject: calc_radial_psd.py: Default to show verbose progress --- python/calc_radial_psd.py | 112 ++++++++++++++++++---------------------------- 1 file changed, 43 insertions(+), 69 deletions(-) (limited to 'python') diff --git a/python/calc_radial_psd.py b/python/calc_radial_psd.py index dcc772a..b7d1743 100755 --- a/python/calc_radial_psd.py +++ b/python/calc_radial_psd.py @@ -82,7 +82,7 @@ class PSD: self.pixel = pixel self.normalize = normalize - def calc_psd2d(self, verbose=False): + def calc_psd2d(self): """ Computes the 2D power spectral density of the given image. Note that the low frequency components are shifted to the center @@ -97,22 +97,19 @@ class PSD: 2D power spectral density, which is dimensionless if normalized, otherwise has unit ${pixel_unit}^2. """ - if verbose: - print("Calculating 2D power spectral density ... ", - end="", flush=True) + print("Calculating 2D power spectral density ... ", end="", flush=True) rows, cols = self.img.shape - ## Compute the power spectral density (i.e., power spectrum) + # Compute the power spectral density (i.e., power spectrum) imgf = np.fft.fftshift(np.fft.fft2(self.img)) if self.normalize: norm = rows * cols * self.pixel[0]**2 else: norm = 1.0 # Do not normalize self.psd2d = (np.abs(imgf) / norm) ** 2 - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) return self.psd2d - def calc_radial_psd1d(self, verbose=False): + def calc_radial_psd1d(self): """ Computes the radially averaged power spectral density from the provided 2D power spectral density. @@ -124,11 +121,9 @@ class PSD: frequency radial_psd_err: standard deviations of each radial_psd """ - if verbose: - print("Calculating radial (1D) power spectral density ... ", - end="", flush=True) - if verbose: - print("padding ... ", end="", flush=True) + print("Calculating radial (1D) power spectral density ... ", + end="", flush=True) + print("padding ... ", end="", flush=True) psd2d = self.pad_square(self.psd2d, value=np.nan) dim = psd2d.shape[0] dim_half = (dim+1) // 2 @@ -141,9 +136,7 @@ class PSD: rho = np.around(rho).astype(np.int) radial_psd = np.zeros(dim_half) radial_psd_err = np.zeros(dim_half) - if verbose: - print("radially averaging ... ", - end="", flush=True) + print("radially averaging ... ", end="", flush=True) for r in range(dim_half): # Get the indices of the elements satisfying rho[i,j]==r ii, jj = (rho == r).nonzero() @@ -158,8 +151,7 @@ class PSD: self.freqs = freqs self.psd1d = radial_psd self.psd1d_err = radial_psd_err - if verbose: - print("DONE", end="", flush=True) + print("DONE", flush=True) return (freqs, radial_psd, radial_psd_err) @staticmethod @@ -267,10 +259,10 @@ class AstroImage: # exposure time of the background map exposure_bkg = None - def __init__(self, image, expmap=None, bkgmap=None, verbose=False): - self.load_image(image, verbose=verbose) - self.load_expmap(expmap, verbose=verbose) - self.load_bkgmap(bkgmap, verbose=verbose) + def __init__(self, image, expmap=None, bkgmap=None): + self.load_image(image) + self.load_expmap(expmap) + self.load_bkgmap(bkgmap) @staticmethod def open_image(infile): @@ -312,32 +304,26 @@ class AstroImage: infile, data.shape)) return (header, image) - def load_image(self, image, verbose=False): - if verbose: - print("Loading image ... ", end="", flush=True) + def load_image(self, image): + print("Loading image ... ", end="", flush=True) self.header, self.image = self.open_image(image) self.exposure = self.header.get("EXPOSURE") - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) - def load_expmap(self, expmap, verbose=False): + def load_expmap(self, expmap): if expmap: - if verbose: - print("Loading exposure map ... ", end="", flush=True) + print("Loading exposure map ... ", end="", flush=True) __, self.expmap = self.open_image(expmap) - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) - def load_bkgmap(self, bkgmap, verbose=False): + def load_bkgmap(self, bkgmap): if bkgmap: - if verbose: - print("Loading background map ... ", end="", flush=True) + print("Loading background map ... ", end="", flush=True) header, self.bkgmap = self.open_image(bkgmap) self.exposure_bkg = header.get("EXPOSURE") - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) - def fix_shapes(self, tolerance=2, verbose=False): + def fix_shapes(self, tolerance=2): """ Fix the shapes of self.expmap and self.bkgmap to make them have the same shape as the self.image. @@ -352,14 +338,12 @@ class AstroImage: Arguments: * tolerance: allow absolute difference between images """ - def _fix_shape(img, ref, tol=tolerance, verbose=verbose): + def _fix_shape(img, ref, tol=tolerance): if img.shape == ref.shape: - if verbose: - print("SKIPPED", flush=True) + print("SKIPPED", flush=True) return img elif np.allclose(img.shape, ref.shape, atol=tol): - if verbose: - print(img.shape, "->", ref.shape, flush=True) + print(img.shape, "->", ref.shape, flush=True) rows, cols = img.shape rows_ref, cols_ref = ref.shape # rows @@ -380,22 +364,18 @@ class AstroImage: "(%d, %d) vs. (%d, %d)" % (img.shape + ref.shape)) # if self.bkgmap is not None: - if verbose: - print("Fixing shape for bkgmap ... ", end="", flush=True) + print("Fixing shape for bkgmap ... ", end="", flush=True) self.bkgmap = _fix_shape(self.bkgmap, self.image) if self.expmap is not None: - if verbose: - print("Fixing shape for expmap ... ", end="", flush=True) + print("Fixing shape for expmap ... ", end="", flush=True) self.expmap = _fix_shape(self.expmap, self.image) - def subtract_bkg(self, verbose=False): - if verbose: - print("Subtracting background ... ", end="", flush=True) + def subtract_bkg(self): + print("Subtracting background ... ", end="", flush=True) self.image -= (self.bkgmap / self.exposure_bkg * self.exposure) - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) - def correct_exposure(self, cut=0.015, verbose=False): + def correct_exposure(self, cut=0.015): """ Correct the image for exposure by dividing by the expmap to create the exposure-corrected image. @@ -406,22 +386,18 @@ class AstroImage: than this threshold will be excluded/clipped (set to ZERO) if set to None, then skip clipping image """ - if verbose: - print("Correcting image for exposure ... ", end="", flush=True) + print("Correcting image for exposure ... ", end="", flush=True) with np.errstate(divide="ignore", invalid="ignore"): self.image /= self.expmap # set invalid values to ZERO self.image[ ~ np.isfinite(self.image) ] = 0.0 - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) if cut is not None: # clip image according the exposure threshold - if verbose: - print("Clipping image (%s) ... " % cut, end="", flush=True) + print("Clipping image (%s) ... " % cut, end="", flush=True) threshold = cut * np.max(self.expmap) self.image[ self.expmap < threshold ] = 0.0 - if verbose: - print("DONE", flush=True) + print("DONE", flush=True) def main(): @@ -430,8 +406,6 @@ def main(): epilog="Version: %s (%s)" % (__version__, __date__)) parser.add_argument("-V", "--version", action="version", version="%(prog)s " + "%s (%s)" % (__version__, __date__)) - parser.add_argument("-v", "--verbose", dest="verbose", - action="store_true", help="show verbose information") parser.add_argument("-C", "--clobber", dest="clobber", action="store_true", help="overwrite the output files if already exist") @@ -458,17 +432,17 @@ def main(): # Load image data image = AstroImage(image=args.infile, expmap=args.expmap, - bkgmap=args.bkgmap, verbose=args.verbose) - image.fix_shapes(verbose=args.verbose) + bkgmap=args.bkgmap) + image.fix_shapes() if args.bkgmap: - image.subtract_bkg(verbose=args.verbose) + image.subtract_bkg() if args.expmap: - image.correct_exposure(verbose=args.verbose) + image.correct_exposure() # Calculate the power spectral density psd = PSD(img=image.image, normalize=True) - psd.calc_psd2d(verbose=args.verbose) - freqs, psd1d, psd1d_err = psd.calc_radial_psd1d(verbose=args.verbose) + psd.calc_psd2d() + freqs, psd1d, psd1d_err = psd.calc_radial_psd1d() # Write out PSD results psd_data = np.column_stack((freqs, psd1d, psd1d_err)) -- cgit v1.2.2