diff options
Diffstat (limited to 'astro/calc_psd.py')
| -rwxr-xr-x | astro/calc_psd.py | 34 |
1 files changed, 18 insertions, 16 deletions
diff --git a/astro/calc_psd.py b/astro/calc_psd.py index 6f0c2f6..2d4f4fe 100755 --- a/astro/calc_psd.py +++ b/astro/calc_psd.py @@ -25,17 +25,29 @@ from functools import lru_cache import numpy as np from astropy.io import fits -import matplotlib.pyplot as plt +import matplotlib +import matplotlib.style from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure -plt.style.use("ggplot") +# Matplotlib settings +matplotlib.style.use("ggplot") +for k, v in [("font.family", "monospace"), + ("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 PSD: """ - Computes the 2D power spectral density and the azimuthally averaged power - spectral density (i.e., 1D radial power spectrum). + Calculate the 2D power spectral density and then apply azimuthal + averaging to obtain the 1D radial power spectrum. Parameters ---------- @@ -124,9 +136,8 @@ class PSD: """ print("Calculating 2D power spectral density ... ", end="", flush=True) rows, cols = self.shape - # Compute the power spectral density (i.e., power spectrum) imgf = np.fft.fftshift(np.fft.fft2(self.image)) - # Normalization w.r.t. image size + # NOTE: normalize w.r.t. image size norm = rows * cols * self.pixel[0]**2 self.psd2d = (np.abs(imgf) ** 2) / norm print("DONE", flush=True) @@ -134,7 +145,7 @@ class PSD: def calc_psd(self): """ - Azimuthally average the above 2D power spectral density to generate + Azimuthally average the above 2D power spectral density to obtain the 1D radial power spectral density. Returns @@ -212,15 +223,6 @@ class PSD: phi = np.arctan2(y, x) return (rho, phi) - @staticmethod - def pol2cart(rho, phi): - """ - Convert polar coordinates to Cartesian coordinates. - """ - x = rho * np.cos(phi) - 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)) |