627 lines
25 KiB
Python
Executable File
627 lines
25 KiB
Python
Executable File
#!/usr/bin/env python3
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import matplotlib.pyplot as plt
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import numpy as np
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from astropy.io.fits import getheader, getdata, hdu
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from os.path import join as join_path, exists as path_exists
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from os import system
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from copy import deepcopy
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# consecutive spectra are made up of the summ of all previous ACCUMs, so the S/N increases along sequence
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# _c0f.fits - calibrated vacuum wavelength
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# _c1f.fits - calibrated fluxes (ergs sec^-1 cm^-2 Angs^-1)
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# _c2f.fits - statistical errors (no sky, bkg subtraction, flatfield or sensitivity error)
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# _c3f.fits - for SPECTROPOLARIMETRY mode contains I, Q, U, V, linear and circular polarization and polarization position angle
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# _c4f.fits - object+sky count rate spectrum (corrected for overscanning, noise rejection, lost signal)
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# _c5f.fits - flat-fielded object count rate spectrum (corrected for paired pulses, detector background, flatfield structure, GIM effects)
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# _c6f.fits - flat-fielded sky count rate spectrum (corrected for paired pulses, detector background, flatfield structure, GIM effects)
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# _c7f.fits - background count rate spectrum (scaled background subtracted from c4 products)
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# _c8f.fits - flat-fielded and sky-subtracted object count rate spectrum
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def princ_angle(ang):
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"""
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Return the principal angle in the 0° to 180° quadrant as PA is always
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defined at p/m 180°.
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----------
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Inputs:
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ang : float, numpy.ndarray
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Angle in degrees. Can be an array of angles.
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----------
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Returns:
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princ_ang : float, numpy.ndarray
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Principal angle in the 0°-180° quadrant in the same shape as input.
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"""
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if not isinstance(ang, np.ndarray):
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A = np.array([ang])
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else:
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A = np.array(ang)
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while np.any(A < 0.0):
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A[A < 0.0] = A[A < 0.0] + 360.0
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while np.any(A >= 180.0):
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A[A >= 180.0] = A[A >= 180.0] - 180.0
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if type(ang) is type(A):
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return A
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else:
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return A[0]
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class specpol(object):
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"""
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Class object for studying spectropolarimetry.
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"""
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def __init__(self, other=None):
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if isinstance(other, __class__):
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# Copy constructor
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self.hd = deepcopy(other.hd)
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self.wav = deepcopy(other.wav)
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self.wav_err = deepcopy(other.wav_err)
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self.I = deepcopy(other.I)
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self.Q = deepcopy(other.Q)
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self.U = deepcopy(other.U)
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self.V = deepcopy(other.V)
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self.IQUV_cov = deepcopy(other.IQUV_cov)
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else:
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# Initialise to zero
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if isinstance(other, int):
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self.zero(other)
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else:
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self.zero()
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@classmethod
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def zero(self, n=1):
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"""
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Set all values to zero.
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"""
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self.hd = dict([])
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self.wav = np.zeros(n)
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self.wav_err = np.zeros((n, 2))
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self.I = np.zeros(n)
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self.Q = np.zeros(n)
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self.U = np.zeros(n)
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self.V = np.zeros(n)
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self.IQUV_cov = np.zeros((4, 4, n))
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@property
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def wav_rest(self, z=None):
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if z is None and "REDSHIFT" not in self.hd.keys():
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from astroquery.ipac.ned import Ned
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z = Ned.query_object(self.hd["TARGNAME"])["Redshift"][0]
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self.hd["REDSHIFT"] = z
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elif z is None:
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z = self.hd["REDSHIFT"]
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return self.wav / (z + 1)
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@property
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def wav_rest_err(self, z=None):
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if z is None and "REDSHIFT" not in self.hd.keys():
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from astroquery.ipac.ned import Ned
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z = Ned.query_object(self.hd["TARGNAME"])["Redshift"][0]
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self.hd["REDSHIFT"] = z
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elif z is None:
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z = self.hd["REDSHIFT"]
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return self.wav_err / (z + 1)
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@property
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def I_err(self):
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return np.sqrt(self.IQUV_cov[0][0])
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@property
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def Q_err(self):
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return np.sqrt(self.IQUV_cov[1][1])
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@property
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def U_err(self):
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return np.sqrt(self.IQUV_cov[2][2])
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@property
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def V_err(self):
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return np.sqrt(self.IQUV_cov[3][3])
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@property
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def QN(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.Q / np.where(self.I > 0, self.I, np.nan)
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@property
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def QN_err(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.Q_err / np.where(self.I > 0, self.I, np.nan)
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@property
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def UN(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.U / np.where(self.I > 0, self.I, np.nan)
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@property
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def UN_err(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.U_err / np.where(self.I > 0, self.I, np.nan)
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@property
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def VN(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.V / np.where(self.I > 0, self.I, np.nan)
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@property
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def VN_err(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.V_err / np.where(self.I > 0, self.I, np.nan)
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@property
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def PF(self):
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np.seterr(divide="ignore", invalid="ignore")
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return np.sqrt(self.Q**2 + self.U**2 + self.V**2)
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@property
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def PF_err(self):
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np.seterr(divide="ignore", invalid="ignore")
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return np.sqrt(self.Q**2 * self.Q_err**2 + self.U**2 * self.U_err**2 + self.V**2 * self.V_err**2) / np.where(self.PF > 0, self.PF, np.nan)
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@property
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def P(self):
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np.seterr(divide="ignore", invalid="ignore")
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return self.PF / np.where(self.I > 0, self.I, np.nan)
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@property
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def P_err(self):
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np.seterr(divide="ignore", invalid="ignore")
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return np.where(self.I > 0, np.sqrt(self.PF_err**2 + (self.PF / self.I) ** 2 * self.I_err**2) / self.I, np.nan)
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@property
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def PA(self):
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return princ_angle((90.0 / np.pi) * np.arctan2(self.U, self.Q))
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@property
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def PA_err(self):
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return princ_angle((90.0 / np.pi) * np.sqrt(self.U**2 * self.Q_err**2 + self.Q**2 * self.U_err**2) / np.where(self.PF > 0, self.PF**2, np.nan))
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def rotate(self, angle):
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alpha = np.pi / 180.0 * angle
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mrot = np.array(
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[
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[1.0, 0.0, 0.0, 0.0],
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[0.0, np.cos(2.0 * alpha), np.sin(2.0 * alpha), 0.0],
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[0.0, -np.sin(2.0 * alpha), np.cos(2.0 * alpha), 0.0],
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[0.0, 0.0, 0.0, 1.0],
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]
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)
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self.I, self.Q, self.U, self.V = np.dot(mrot, np.array([self.I, self.Q, self.U, self.V]))
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self.IQUV_cov = np.dot(mrot, np.dot(self.IQUV_cov.T, mrot.T).T)
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def bin(self, bin_edges):
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"""
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Rebin spectra to given list of bin edges.
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"""
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in_bin = np.digitize(self.wav, bin_edges) - 1
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spec_b = specpol(bin_edges.shape[0] - 1)
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for i in range(bin_edges.shape[0] - 1):
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spec_b.wav[i] = np.mean(self.wav[in_bin == i])
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spec_b.wav_err[i] = (spec_b.wav[i] - bin_edges[i], bin_edges[i + 1] - spec_b.wav[i])
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spec_b.I[i] = np.sum(self.I[in_bin == i])
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spec_b.Q[i] = np.sum(self.Q[in_bin == i])
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spec_b.U[i] = np.sum(self.U[in_bin == i])
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spec_b.V[i] = np.sum(self.V[in_bin == i])
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for m in range(4):
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spec_b.IQUV_cov[m][m][i] = np.sum(self.IQUV_cov[m][m][in_bin == i])
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for n in [k for k in range(4) if k != m]:
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spec_b.IQUV_cov[m][n][i] = np.sqrt(np.sum(self.IQUV_cov[m][n][in_bin == i] ** 2))
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spec_b.hd = deepcopy(self.hd)
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spec_b.hd["NAXIS1"] = bin_edges.shape[0] - 1
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spec_b.hd["DATAMIN"], spec_b.hd["DATAMAX"] = spec_b.I.min(), spec_b.I.max()
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spec_b.hd["MINWAV"], spec_b.hd["MAXWAV"] = spec_b.wav.min(), spec_b.wav.max()
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return spec_b
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def bin_size(self, size):
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"""
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Rebin spectra to selected bin size in Angstrom.
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"""
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bin_edges = np.arange(np.floor(self.wav.min()), np.ceil(self.wav.max()), size)
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return self.bin(bin_edges)
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def dump_txt(self, filename, output_dir=""):
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"""
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Dump current spectra to a text file.
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"""
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data_dump = np.array([self.wav, self.I, self.Q, self.U, self.V, self.P, self.PA]).T
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np.savetxt(join_path(output_dir, filename + ".txt"), data_dump)
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return join_path(output_dir, filename)
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def plot(self, fig=None, ax=None, rest=False, savename=None, plots_folder=""):
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"""
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Display current spectra.
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"""
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if fig is None:
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if ax is None:
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self.fig, self.ax = plt.subplots(1, 2, sharex=True, figsize=(20, 5), layout="constrained")
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self.fig.suptitle("_".join([self.hd["TARGNAME"], str(self.hd["PROPOSID"]), self.hd["ROOTNAME"], self.hd["APER_ID"]]))
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else:
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self.ax = ax
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else:
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if ax is None:
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self.fig = fig
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self.ax = self.fig.add_subplot(111)
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else:
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self.fig = fig
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self.ax = ax
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if isinstance(self.ax, np.ndarray):
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ax1, ax2 = self.ax[:2]
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else:
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ax1 = self.ax
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if rest:
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wav, wav_err = self.wav_rest, self.wav_rest_err
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rest_str = "Rest "
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else:
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wav, wav_err = self.wav, self.wav_err
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rest_str = ""
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# Display flux and polarized flux on first ax
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ax1.set_xlabel(rest_str + r"Wavelength [$\AA$]")
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ax1.errorbar(wav, self.I, xerr=wav_err.T, yerr=self.I_err, color="k", fmt=".", label="I")
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ax1.errorbar(wav, self.PF, xerr=wav_err.T, yerr=self.PF_err, color="b", fmt=".", label="PF")
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ax1.set_ylabel(r"F$_\lambda$ [erg s$^{-1}$ cm$^{-2} \AA^{-1}$]")
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ax1.set_ylim(ymin=0.0)
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ax1.legend(ncols=2, loc=1)
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if isinstance(self.ax, np.ndarray):
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# When given 2 axes, display P and PA on second
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ax2.set_xlabel(rest_str + r"Wavelength [$\AA$]")
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ax2.errorbar(wav, self.P, xerr=wav_err.T, yerr=self.P_err, color="b", fmt=".", label="P")
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ax2.set_ylim([0.0, 1.0])
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ax2.set_ylabel(r"P", color="b")
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ax2.tick_params(axis="y", color="b", labelcolor="b")
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ax22 = ax2.twinx()
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ax22.errorbar(wav, self.PA, xerr=wav_err.T, yerr=self.PA_err, color="r", fmt=".", label="PA [°]")
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ax22.set_ylim([0.0, 180.0])
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ax22.set_ylabel(r"PA", color="r")
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ax22.tick_params(axis="y", color="r", labelcolor="r")
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h2, l2 = ax2.get_legend_handles_labels()
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h22, l22 = ax22.get_legend_handles_labels()
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ax2.legend(h2 + h22, l2 + l22, ncols=2, loc=1)
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if hasattr(self, "fig") and savename is not None:
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self.fig.savefig(join_path(plots_folder, savename + ".pdf"), dpi=300, bbox_inches="tight")
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return self.fig, self.ax, join_path(plots_folder, savename + ".pdf")
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elif hasattr(self, "fig"):
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return self.fig, self.ax
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else:
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return self.ax
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def __add__(self, other):
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"""
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Spectra addition, if not same binning default to self bins.
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"""
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spec_a = specpol(self)
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if np.all(self.wav == other.wav):
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spec_b = other
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else:
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bin_edges = np.zeros(spec_a.wav.shape[0] + 1)
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bin_edges[:-1], bin_edges[-1] = spec_a.wav - spec_a.wav_err[:, 0], spec_a.wav[-1] + spec_a.wav_err[-1:1]
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spec_b = other.bin(bin_edges=bin_edges)
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spec_a.I += deepcopy(spec_b.I)
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spec_a.Q += deepcopy(spec_b.Q)
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spec_a.U += deepcopy(spec_b.U)
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spec_a.V += deepcopy(spec_b.V)
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spec_a.IQUV_cov += deepcopy(spec_b.IQUV_cov)
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spec_a.hd["DATAMIN"], spec_a.hd["DATAMAX"] = spec_a.I.min(), spec_a.I.max()
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spec_a.hd["EXPTIME"] += spec_b.hd["EXPTIME"]
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rootnames = [spec_a.hd["ROOTNAME"], spec_b.hd["ROOTNAME"]]
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spec_a.hd["ROOTNAME"] = "".join(p for p, *r in zip(*rootnames) if all(p == c for c in r)) + "_SUM"
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return spec_a
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def __deepcopy__(self, memo={}):
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spec = specpol(self.wav.shape[0])
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spec.__dict__.update(self.__dict__)
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spec.hd = deepcopy(self.hd, memo)
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spec.wav = deepcopy(self.wav, memo)
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spec.wav_err = deepcopy(self.wav_err, memo)
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spec.I = deepcopy(self.I, memo)
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spec.Q = deepcopy(self.Q, memo)
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spec.U = deepcopy(self.U, memo)
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spec.V = deepcopy(self.V, memo)
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spec.IQUV_cov = deepcopy(self.IQUV_cov, memo)
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return spec
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class FOSspecpol(specpol):
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"""
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Class object for studying FOS SPECTROPOLARYMETRY mode spectra.
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"""
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def __init__(self, stokes, data_folder=""):
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"""
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Initialise object from fits filename, fits hdulist or copy.
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"""
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if isinstance(stokes, __class__):
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# Copy constructor
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self.rootname = deepcopy(stokes.rootname)
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self.hd = deepcopy(stokes.hd)
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self.wav = deepcopy(stokes.wav)
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self.wav_err = deepcopy(stokes.wav_err)
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self.I = deepcopy(stokes.I)
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self.Q = deepcopy(stokes.Q)
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self.U = deepcopy(stokes.U)
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self.V = deepcopy(stokes.V)
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self.IQUV_cov = deepcopy(stokes.IQUV_cov)
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self.P_fos = deepcopy(stokes.P_fos)
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self.P_fos_err = deepcopy(stokes.P_fos_err)
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self.PC_fos = deepcopy(stokes.PC_fos)
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self.PC_fos_err = deepcopy(stokes.PC_fos_err)
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self.PA_fos = deepcopy(stokes.PA_fos)
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self.PA_fos_err = deepcopy(stokes.PA_fos_err)
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self.subspec = {}
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for name in ["PASS1", "PASS2", "PASS12", "PASS12corr"]:
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spec = deepcopy(stokes.subspec[name])
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self.subspec[name] = spec
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elif stokes is None or isinstance(stokes, int):
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self.zero(n=stokes)
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else:
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self.from_file(stokes, data_folder=data_folder)
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@classmethod
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def zero(self, n=1):
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"""
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Set all values to zero.
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"""
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self.rootname = ""
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self.hd = dict([])
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self.wav = np.zeros((4, n))
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self.wav_err = np.zeros((4, n, 2))
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self.I = np.zeros((4, n))
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self.Q = np.zeros((4, n))
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self.U = np.zeros((4, n))
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self.V = np.zeros((4, n))
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self.IQUV_cov = np.zeros((4, 4, 4, n))
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self.subspec = {}
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for i, name in enumerate(["PASS1", "PASS2", "PASS12", "PASS12corr"]):
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spec = specpol(n)
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spec.hd, spec.wav, spec.wav_err, spec.I, spec.Q, spec.U, spec.V = self.hd, self.wav[i], self.wav_err[i], self.I[i], self.Q[i], self.U[i], self.V[i]
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spec.IQUV_cov = self.IQUV_cov[:, :, i, :]
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self.subspec[name] = spec
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self.P_fos = np.zeros(self.I.shape)
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self.P_fos_err = np.zeros(self.I.shape)
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self.PC_fos = np.zeros(self.I.shape)
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self.PC_fos_err = np.zeros(self.I.shape)
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self.PA_fos = np.zeros(self.I.shape)
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self.PA_fos_err = np.zeros(self.I.shape)
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def from_file(self, stokes, data_folder=""):
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"""
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Initialise object from fits file or hdulist.
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"""
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if isinstance(stokes, str):
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self.rootname = stokes.split("_")[0]
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self.hd = dict(getheader(join_path(data_folder, self.rootname + "_c0f.fits")))
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wav = getdata(join_path(data_folder, self.rootname + "_c0f.fits"))
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stokes = getdata(join_path(data_folder, self.rootname + "_c3f.fits"))
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elif isinstance(stokes, hdu.hdulist.HDUList):
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self.hd = dict(stokes.header)
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self.rootname = self.hd["FILENAME"].split("_")[0]
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wav = getdata(join_path(data_folder, self.rootname + "_c0f"))
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stokes = stokes.data
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else:
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raise ValueError("Input must be a path to a fits file or an HDUlist")
|
|
self.wav = np.concat((wav[0:2, :], wav[0].reshape(1, wav.shape[1]), wav[0].reshape(1, wav.shape[1])), axis=0)
|
|
self.wav_err = np.zeros((self.wav.shape[0], self.wav.shape[1], 2))
|
|
|
|
self.IQUV_cov = np.zeros((4, 4, self.wav.shape[0], self.wav.shape[1]))
|
|
|
|
self.I = stokes[0::14]
|
|
self.IQUV_cov[0, 0] = stokes[4::14] ** 2
|
|
self.Q = stokes[1::14]
|
|
self.IQUV_cov[1, 1] = stokes[5::14] ** 2
|
|
self.U = stokes[2::14]
|
|
self.IQUV_cov[2, 2] = stokes[6::14] ** 2
|
|
self.V = stokes[3::14]
|
|
self.IQUV_cov[3, 3] = stokes[7::14] ** 2
|
|
|
|
self.subspec = {}
|
|
for i, name in enumerate(["PASS1", "PASS2", "PASS12", "PASS12corr"]):
|
|
spec = specpol(self.wav[i].shape[0])
|
|
spec.hd, spec.wav, spec.wav_err, spec.I, spec.Q, spec.U, spec.V = self.hd, self.wav[i], self.wav_err[i], self.I[i], self.Q[i], self.U[i], self.V[i]
|
|
spec.IQUV_cov = self.IQUV_cov[:, :, i, :]
|
|
spec.rotate(-(name[-4:] != "corr") * spec.hd["PA_APER"])
|
|
self.subspec[name] = spec
|
|
|
|
self.P_fos = stokes[8::14]
|
|
self.P_fos_err = stokes[11::14]
|
|
self.PC_fos = stokes[9::14]
|
|
self.PC_fos_err = stokes[12::14]
|
|
self.PA_fos = princ_angle(
|
|
np.degrees(stokes[10::14]) + np.concat((np.ones((3, stokes.shape[1])), np.zeros((1, stokes.shape[1]))), axis=0) * self.hd["PA_APER"]
|
|
)
|
|
self.PA_fos_err = princ_angle(np.degrees(stokes[13::14]))
|
|
|
|
def dump_txt(self, filename, spec_list=None, output_dir=""):
|
|
"""
|
|
Dump current spectra to a text file.
|
|
"""
|
|
outfiles = []
|
|
if spec_list is None:
|
|
spec_list = self.subspec
|
|
for i, name in enumerate(["PASS1", "PASS2", "PASS12", "PASS12corr"]):
|
|
outfiles.append(spec_list[name].dump_txt(filename="_".join([filename, name]), output_dir=output_dir))
|
|
return outfiles
|
|
|
|
def plot(self, spec_list=None, rest=True, savename=None, plots_folder="", fos=False):
|
|
"""
|
|
Display current spectra in single figure.
|
|
"""
|
|
outfiles = []
|
|
if hasattr(self, "ax"):
|
|
del self.ax
|
|
if hasattr(self, "fig"):
|
|
del self.fig
|
|
if spec_list is None:
|
|
spec_list = self.subspec
|
|
self.fig, self.ax = plt.subplots(4, 2, sharex=True, sharey="col", figsize=(20, 10), layout="constrained")
|
|
for i, (name, title) in enumerate(
|
|
[("PASS1", "Pass Direction 1"), ("PASS2", "Pass Direction 2"), ("PASS12", "Pass Direction 1&2"), ("PASS12corr", "Pass Direction 1&2 corrected")]
|
|
):
|
|
self.ax[i][0].set_title(title)
|
|
if fos:
|
|
if rest:
|
|
wav, wav_err = self.wav_rest, self.wav_rest_err
|
|
rest_str = "Rest "
|
|
else:
|
|
wav, wav_err = self.wav, self.wav_rest
|
|
rest_str = ""
|
|
self.ax[i][0] = spec_list[name].plot(ax=self.ax[i][0], rest=rest)
|
|
self.ax[i][1].set_xlabel(rest_str + r"Wavelength [$\AA$]")
|
|
self.ax[i][1].errorbar(wav[i], self.P_fos[i], xerr=wav_err[i].T, yerr=self.P_fos_err[i], color="b", fmt=".", label="P_fos")
|
|
self.ax[i][1].set_ylim([0.0, 1.0])
|
|
self.ax[i][1].set_ylabel(r"P", color="b")
|
|
self.ax[i][1].tick_params(axis="y", color="b", labelcolor="b")
|
|
ax22 = self.ax[i][1].twinx()
|
|
ax22.errorbar(wav[i], self.PA_fos[i], xerr=wav_err[i].T, yerr=self.PA_fos_err[i], color="r", fmt=".", label="PA_fos [°]")
|
|
ax22.set_ylim([0.0, 180.0])
|
|
ax22.set_ylabel(r"PA", color="r")
|
|
ax22.tick_params(axis="y", color="r", labelcolor="r")
|
|
h2, l2 = self.ax[i][1].get_legend_handles_labels()
|
|
h22, l22 = ax22.get_legend_handles_labels()
|
|
self.ax[i][1].legend(h2 + h22, l2 + l22, ncols=2, loc=1)
|
|
else:
|
|
self.ax[i] = spec_list[name].plot(ax=self.ax[i])
|
|
self.ax[0][0].set_ylim(ymin=0.0)
|
|
|
|
self.fig.suptitle("_".join([self.hd["TARGNAME"], str(self.hd["PROPOSID"]), self.hd["ROOTNAME"], self.hd["APER_ID"]]))
|
|
if savename is not None:
|
|
self.fig.savefig(join_path(plots_folder, savename + ".pdf"), dpi=300, bbox_inches="tight")
|
|
outfiles.append(join_path(plots_folder, savename + ".pdf"))
|
|
return outfiles
|
|
|
|
def bin_size(self, size):
|
|
"""
|
|
Rebin spectra to selected bin size in Angstrom.
|
|
"""
|
|
key = "{0:.2f}bin".format(size)
|
|
if key not in self.subspec.keys():
|
|
self.subspec[key] = dict([])
|
|
for name in ["PASS1", "PASS2", "PASS12", "PASS12corr"]:
|
|
self.subspec[key][name] = self.subspec[name].bin_size(size)
|
|
return self.subspec[key]
|
|
|
|
def __add__(self, other):
|
|
"""
|
|
Spectra addition, if not same binning default to self bins.
|
|
"""
|
|
spec_a = FOSspecpol(self)
|
|
if np.all(self.wav == other.wav):
|
|
spec_b = other
|
|
else:
|
|
bin_edges = np.zeros(spec_a.wav.shape[0] + 1)
|
|
bin_edges[:-1], bin_edges[-1] = spec_a.wav - spec_a.wav_err[:, 0], spec_a.wav[-1] + spec_a.wav_err[-1:1]
|
|
spec_b = other.bin(bin_edges=bin_edges)
|
|
|
|
spec_a.I += deepcopy(spec_b.I)
|
|
spec_a.Q += deepcopy(spec_b.Q)
|
|
spec_a.U += deepcopy(spec_b.U)
|
|
spec_a.V += deepcopy(spec_b.V)
|
|
spec_a.IQUV_cov += deepcopy(spec_b.IQUV_cov)
|
|
for name in ["PASS1", "PASS2", "PASS12", "PASS12corr"]:
|
|
spec_a.subspec[name] += deepcopy(spec_b.subspec[name])
|
|
spec_a.subspec[name].hd["DATAMIN"], spec_a.subspec[name].hd["DATAMAX"] = spec_a.subspec[name].I.min(), spec_a.subspec[name].I.max()
|
|
spec_a.subspec[name].hd["EXPTIME"] += spec_b.subspec[name].hd["EXPTIME"]
|
|
spec_a.subspec[name].hd["ROOTNAME"] += "+" + spec_b.subspec[name].hd["ROOTNAME"]
|
|
|
|
spec_a.hd["DATAMIN"], spec_a.hd["DATAMAX"] = spec_a.I.min(), spec_a.I.max()
|
|
spec_a.hd["EXPTIME"] += spec_b.hd["EXPTIME"]
|
|
spec_a.hd["ROOTNAME"] += "+" + spec_b.hd["ROOTNAME"]
|
|
return spec_a
|
|
|
|
def __deepcopy__(self, memo):
|
|
spec = FOSspecpol(self.wav.shape[0])
|
|
spec.__dict__.update(self.__dict__)
|
|
|
|
for key in self.subspec.keys():
|
|
spec.subspec[key] = deepcopy(self.subspec[key])
|
|
|
|
return spec
|
|
|
|
def __del__(self):
|
|
if hasattr(self, "ax"):
|
|
del self.ax
|
|
if hasattr(self, "fig"):
|
|
del self.fig
|
|
|
|
|
|
def main(infiles, bin_size=None, output_dir=None):
|
|
outfiles = []
|
|
if infiles is not None:
|
|
prod = np.array([["/".join(filepath.split("/")[:-1]), filepath.split("/")[-1]] for filepath in infiles], dtype=str)
|
|
obs_dir = "/".join(infiles[0].split("/")[:-1])
|
|
if not path_exists(obs_dir):
|
|
system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
|
|
else:
|
|
print("Must input files to process.")
|
|
return 1
|
|
data_folder = prod[0][0]
|
|
if output_dir is None:
|
|
output_dir = data_folder
|
|
try:
|
|
plots_folder = data_folder.replace("data", "plots")
|
|
except ValueError:
|
|
plots_folder = "."
|
|
if not path_exists(plots_folder):
|
|
system("mkdir -p {0:s} ".format(plots_folder))
|
|
infiles = [p[1] for p in prod]
|
|
|
|
roots = np.unique([file.split("_")[0] for file in infiles])
|
|
aper = dict([])
|
|
for rootname in roots:
|
|
print(rootname)
|
|
spec = FOSspecpol(rootname, data_folder)
|
|
filename = "_".join([spec.hd["TARGNAME"], "FOS", str(spec.hd["PROPOSID"]), spec.rootname, spec.hd["APER_ID"]])
|
|
if bin_size is not None:
|
|
key = "{0:.2f}bin".format(bin_size)
|
|
spec.bin_size(bin_size)
|
|
outfiles += spec.dump_txt("_".join([filename, key]), spec_list=spec.subspec[key], output_dir=output_dir)
|
|
outfiles += spec.plot(savename="_".join([filename, key]), spec_list=spec.subspec[key], plots_folder=plots_folder)
|
|
if spec.hd["APER_ID"] in aper.keys():
|
|
aper[str(spec.hd["APER_ID"])].append(specpol(spec.subspec[key]["PASS12corr"]))
|
|
else:
|
|
aper[str(spec.hd["APER_ID"])] = [specpol(spec.subspec[key]["PASS12corr"])]
|
|
else:
|
|
outfiles += spec.dump_txt(filename, output_dir=output_dir)
|
|
outfiles += spec.plot(savename=filename, plots_folder=plots_folder)
|
|
if spec.hd["APER_ID"] in aper.keys():
|
|
aper[str(spec.hd["APER_ID"])].append(specpol(spec.subspec["PASS12corr"]))
|
|
else:
|
|
aper[str(spec.hd["APER_ID"])] = [specpol(spec.subspec["PASS12corr"])]
|
|
plt.close("all")
|
|
for key in aper.keys():
|
|
spec = np.sum(aper[key])
|
|
rootnames = [s.hd["ROOTNAME"] for s in aper[key]]
|
|
spec.hd["ROOTNAME"] = "".join(p for p, *r in zip(*rootnames) if all(p == c for c in r)) + "_SUM"
|
|
filename = "_".join([spec.hd["TARGNAME"], "FOS", str(spec.hd["PROPOSID"]), spec.hd["ROOTNAME"]])
|
|
if bin_size is not None:
|
|
filename += "_{0:.2f}bin".format(bin_size)
|
|
outfiles.append(spec.dump_txt("_".join([filename, key]), output_dir=output_dir))
|
|
outfiles.append(spec.plot(savename="_".join([filename, key]), plots_folder=plots_folder)[2])
|
|
plt.show()
|
|
|
|
return outfiles
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(description="Display and dump FOS Spectropolarimetry")
|
|
parser.add_argument("-f", "--files", metavar="path", required=False, nargs="*", help="the full or relative path to the data products", default=None)
|
|
parser.add_argument("-b", "--bin", metavar="bin_size", required=False, help="The bin size to resample spectra", type=float, default=None)
|
|
parser.add_argument(
|
|
"-o", "--output_dir", metavar="directory_path", required=False, help="output directory path for the data products", type=str, default=None
|
|
)
|
|
args = parser.parse_args()
|
|
exitcode = main(infiles=args.files, bin_size=args.bin, output_dir=args.output_dir)
|
|
print("Written to: ", exitcode)
|