add ref combine not finish yet (1138c47c) · Commits · Bo Zhang / csst

csst/msc/ref_combine.py

0 → 100644

+104 −0

Original line number	Diff line number	Diff line
		# not finish yet
		import numpy as np
		from astropy.io import fits

		from ..core.processor import CsstProcessor, CsstProcStatus


		class CsstMscRefProc(CsstProcessor):
		_status = CsstProcStatus.empty

		def __init__(self):
		super(CsstMscRefProc).__init__()
		self.bias = None
		self.dark = None
		self.flat = None

		def array_combine(self, ndarray, mode="mean") -> np.ndarray:
		""" Function to combine 3-D data array

		Parameters
		----------
		ndarray: array, input data cube (3D)
		model: mean, median, sum, mean_clip, median_clip, default is mean
		"""
		if mode == "median":
		array = np.median(ndarray, axis=0)
		elif mode == "median_clip":
		ndarray = np.sort(ndarray, axis=0)[1:-1]
		array = np.median(ndarray, axis=0)
		elif mode == "sum":
		array = np.sum(ndarray, axis=0)
		elif mode == "mean":
		array = np.mean(ndarray, axis=0)
		elif mode == "mean_clip":
		ndarray = np.sort(ndarray, axis=0)[1:-1]
		array = np.mean(ndarray, axis=0)
		return array

		def load_bias(self, path: str) -> np.ndarray:
		with fits.open(path) as hdul:
		du = hdul[1].data
		du = du.astype(int)
		return du

		def load_dark(self, path: str) -> np.ndarray:
		with fits.open(path) as hdul:
		du = hdul[1].data
		hu = hdul[0].header
		du = du.astype(int)
		du = du - self.bias
		du = du / hu["EXPTIME"]
		return du

		def load_flat(self, path: str) -> np.ndarray:
		with fits.open(path) as hdul:
		du = hdul[1].data
		hu = hdul[0].header
		du = du.astype(int)
		du = du - self.bias - self.dark * hu["EXPTIME"]
		du = du / hu["EXPTIME"]
		du = du / np.median(du)
		return du

		def combine(self, func, mode: str, path_list, *args) -> np.ndarray:
		du_list = [func(path, *args) for path in path_list]
		du = self.array_combine(du_list, mode)
		return du

		def prepare(self, b_p_lst, d_p_lst, f_p_lst, save_path, mode_list=["median", "median", "median", ]):
		"""

		Parameters
		----------
		b_p_lst:
		List of currently ccd number bias file path
		d_p_lst:
		List of currently ccd number dark file path
		f_p_lst:
		List of currently ccd number flat file path
		save_path:
		as u c
		mode_list:
		[0] bias combine mode
		[1] dark combine mode
		[2] flat combine mode
		mean, median, sum, mean_clip, median_clip
		"""
		self.b_p_lst = b_p_lst
		self.d_p_lst = d_p_lst
		self.f_p_lst = f_p_lst
		self.save_path = save_path
		self.mode_list = mode_list

		def run(self):
		self.bias = self.combine(self.load_bias, self.mode_list[0], self.b_p_lst)
		self.dark = self.combine(self.load_dark, self.mode_list[1], self.d_p_lst)
		self.flat = self.combine(self.load_flat, self.mode_list[2], self.f_p_lst)
		return self.bias, self.dark, self.flat

		def cleanup(self):
		self.bias = None
		self.dark = None
		self.flat = None
		pass