prepare for observation combinaison

package/Combine.py

@@ -0,0 +1,142 @@
+#!/usr/bin/python
+# -*- coding:utf-8 -*-
+# Project libraries
+
+import numpy as np
+
+
+def same_reduction(infiles):
+    """
+    Test if infiles are pipeline productions with same parameters.
+    """
+    from astropy.io.fits import open as fits_open
+
+    params = {"IQU": [], "TARGNAME": [], "BKG_SUB": [], "SAMPLING": [], "SMOOTHING": []}
+    for file in infiles:
+        with fits_open(file) as f:
+            # test for presence of I, Q, U images
+            datatype = []
+            for hdu in f:
+                try:
+                    datatype.append(hdu.header["datatype"])
+                except KeyError:
+                    pass
+            test_IQU = True
+            for look in ["I_stokes", "Q_stokes", "U_stokes", "IQU_cov_matrix"]:
+                test_IQU *= look in datatype
+            params["IQU"].append(test_IQU)
+            # look for information on reduction procedure
+            for key in ["TARGNAME", "BKG_SUB", "SAMPLING", "SMOOTHING"]:
+                try:
+                    params[key].append(f[0].header[key])
+                except KeyError:
+                    params[key].append("null")
+    result = np.all(params["IQU"])
+    for key in ["TARGNAME", "BKG_SUB", "SAMPLING", "SMOOTHING"]:
+        result *= np.unique(params[key]).size == 1
+
+    return result
+
+
+def same_obs(infiles, data_folder):
+    """
+    Group infiles into same observations.
+    """
+
+    import astropy.units as u
+    from astropy.io.fits import getheader
+    from astropy.table import Table
+    from astropy.time import Time, TimeDelta
+
+    headers = [getheader("/".join([data_folder,file])) for file in infiles]
+    files = {}
+    files["PROPOSID"] = np.array([str(head["PROPOSID"]) for head in headers],dtype=str)
+    files["ROOTNAME"] = np.array([head["ROOTNAME"].lower()+"_c0f.fits" for head in headers],dtype=str)
+    files["EXPSTART"] = np.array([Time(head["EXPSTART"],format='mjd') for head in headers])
+    products = Table(files)
+
+    new_infiles = []
+    for pid in np.unique(products["PROPOSID"]):
+        obs = products[products["PROPOSID"] == pid].copy()
+        close_date = np.unique(
+            [[np.abs(TimeDelta(obs["EXPSTART"][i].unix - date.unix, format="sec")) < 7.0 * u.d for i in range(len(obs))] for date in obs["EXPSTART"]], axis=0
+        )
+        if len(close_date) > 1:
+            for date in close_date:
+                new_infiles.append(list(products["ROOTNAME"][np.any([products["ROOTNAME"] == dataset for dataset in obs["ROOTNAME"][date]], axis=0)]))
+        else:
+            new_infiles.append(list(products["ROOTNAME"][products["PROPOSID"] == pid]))
+    return new_infiles
+
+
+def combine_Stokes(infiles):
+    """
+    Combine I, Q, U from different observations of a same object.
+    """
+    print("not implemented yet")
+
+    return infiles
+
+
+def main(infiles, target=None, output_dir="./data/"):
+    """ """
+    if target is None:
+        target = input("Target name:\n>")
+
+    if not same_reduction(infiles):
+        print("NOT SAME REDUC")
+        from FOC_reduction import main as FOC_reduction
+        prod = np.array([["/".join(filepath.split("/")[:-1]), filepath.split("/")[-1]] for filepath in infiles], dtype=str)
+        data_folder = prod[0][0]
+        infiles = [p[1] for p in prod]
+        # Reduction parameters
+        kwargs = {}
+        # Background estimation
+        kwargs["error_sub_type"] = "freedman-diaconis"  # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
+        kwargs["subtract_error"] = 0.7
+
+        # Data binning
+        kwargs["pxsize"] = 0.1
+        kwargs["pxscale"] = "arcsec"  # pixel, arcsec or full
+
+        # Smoothing
+        kwargs["smoothing_function"] = "combine"  # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
+        kwargs["smoothing_FWHM"] = 0.2  # If None, no smoothing is done
+        kwargs["smoothing_scale"] = "arcsec"  # pixel or arcsec
+
+        #  Polarization map output
+        kwargs["SNRp_cut"] = 3.0  # P measurments with SNR>3
+        kwargs["SNRi_cut"] = 1.0  # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
+        kwargs["flux_lim"] = 1e-19, 3e-17  # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
+        kwargs["scale_vec"] = 5
+        kwargs["step_vec"] = (
+            1  # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
+        )
+        grouped_infiles = same_obs(infiles, data_folder)
+        print(grouped_infiles)
+
+        new_infiles = []
+        for i,group in enumerate(grouped_infiles):
+            new_infiles.append(FOC_reduction(target=target+"-"+str(i+1), infiles=["/".join([data_folder,file]) for file in group], interactive=True, **kwargs))
+
+        combined_Stokes = combine_Stokes(new_infiles)
+
+    else:
+        print("SAME REDUC")
+        combined_Stokes = combine_Stokes(infiles)
+
+    return combined_Stokes
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Combine different observations of a single object")
+    parser.add_argument("-t", "--target", metavar="targetname", required=False, help="the name of the target", type=str, default=None)
+    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(
+        "-o", "--output_dir", metavar="directory_path", required=False, help="output directory path for the data products", type=str, default="./data"
+    )
+    args = parser.parse_args()
+    exitcode = main(target=args.target, infiles=args.files, output_dir=args.output_dir)
+    print("Written to: ", exitcode)

package/__init__.py

@@ -1,2 +1,3 @@
 from . import lib
 from . import src
+from . import FOC_reduction