add ref combine not finish yet

csst/msc/ref_combine.py

+# 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