update example_interface.py

doc/source/ch08_csst_common.rst

@@ -161,6 +161,9 @@ raw                        0     ``dm.l0_log(detector=detector)``
 
 ``Slit-Less Spectra 1D``
 ^^^^^^^^^^^^^^^^^^^^^^^^
+
+Note that for 1D pipeline, ``dm.target_detectors`` is a list containing only one element.
+
 =========================== ===== =============================================================== ==================
 Data source                 Level File name / access code                                         Notes
 =========================== ===== =============================================================== ==================
@@ -283,7 +286,7 @@ Source code
 
 .. literalinclude:: csst_common/example_interface.py
     :caption: ``example_interface.py``
-    :emphasize-lines: 7-11,36-41,85,87-88,91-92,95-96,99-101,111-116,148,155-165,167-173,178-179,182-183,186-187,190-191
+    :emphasize-lines: 7-11,25-29,75-86,101-112,169-171
     :linenos:
     :language: python
 
@@ -298,7 +301,14 @@ Rendered ``docstring``
 ``csst_common.params``
 ----------------------
 
-to be updated
+See https://csst-tb.bao.ac.cn/code/csst-l1/csst_common/-/blob/main/csst_common/data/csst_params.yml.
+To use the parameters, use
+
+.. code-block:: python
+
+    from csst_common.params import CSST_PARAMS as CP
+
+
 
 Module Identifier
 -----------------

doc/source/csst_common/example_interface.py

@@ -21,18 +21,6 @@ def process_data(data: np.ndarray) -> np.ndarray:
     return np.fliplr(np.flipud(data))
 
 
-def check_results(dm: CsstMsDataManager, logger: logging.Logger) -> bool:
-    """ Check whether processed data are generated. """
-    existence = [
-        os.path.exists(dm.l1_detector(detector=detector, post="L1_processed.fits"))
-        for detector in dm.target_detectors
-    ]
-    if all(existence):
-        return True
-    else:
-        return False
-
-
 # process a single image (NOT RECOMMENDED!)
 def process_single_image(
         filepath_input: str,
@@ -74,26 +62,28 @@ def process_single_image(
     fr = FileRecorder()
 
     # process data
-    try:
-        # this will NOT be written into the log file
-        logger.debug("Reading the image {}".format(filepath_input))
-        # start processing
-        data = read_image(filepath_input)
-        data_processed = process_data(data)
-        np.save(filepath_output, data_processed)
-        # record file!
-        fr.add_record(filepath=filepath_output, db=True, comment="the processed image")
-        # this will be written into the log file
-        logger.info("Processed image saved to {}".format(filepath_output))
-        return CsstStatus.PERFECT, fr
-    except DeprecationWarning:
-        # this will be written into the log file
-        logger.warning("Suffered DeprecationWarning!")
+    logger.info("Start processing image {}".format(filepath_input))
+    # start processing
+    data = read_image(filepath_input)
+    data_processed = process_data(data)
+    np.save(filepath_output, data_processed)
+    # record file!
+    fr.add_record(filepath=filepath_output, db=True, comment="the processed image")
+    # this will be written into the log file
+    logger.info("Finish processing, result saved to {}".format(filepath_output))
+
+    # check result existence
+    if os.path.exists(filepath_output):
+        # file exists, check precison
+        if fits.getheader(filepath_output)["TOL"] < 1e-5:
+            return CsstStatus.PERFECT, fr
+        else:
+            return CsstStatus.WARNING, fr
+    else:
+        # file doesn't exist, do your fallback solution
+        fits.HDUList(fits.PrimaryHDU()).writeto(filepath_output)
+        assert os.path.exists(filepath_output)
         return CsstStatus.WARNING, fr
-    except IOError:
-        # this will be written into the log file
-        logger.error("Suffered IOError!")
-        return CsstStatus.ERROR, fr
 
 
 # process multiple images in an exposure (RECOMMENDED, at least for MBI or SLS)
@@ -102,17 +92,29 @@ def one_job(dm: CsstMsDataManager, detector: int):
     """ Process a single image, defined for parallel processing. """
     filepath_input = dm.l0_detector(detector=detector)
     filepath_output = dm.l1_detector(detector=detector, post="L1_processed.fits")
+
+    # data processing
     data = read_image(filepath_input)
     data_processed = process_data(data)
     np.save(filepath_output, data_processed)
-    return
+
+    # check result existence
+    if os.path.exists(filepath_output):
+        # file exists, check precison
+        if fits.getheader(filepath_output)["TOL"] < 1e-5:
+            return CsstStatus.PERFECT
+        else:
+            return CsstStatus.WARNING
+    else:
+        # file doesn't exist, do your fallback solution
+        fits.HDUList(fits.PrimaryHDU()).writeto(filepath_output)
+        assert os.path.exists(filepath_output)
+        return CsstStatus.WARNING
 
 
 # process in serial / parallel
 def process_multiple_images(
         dm: CsstMsDataManager,
-        logger: Union[None, logging.Logger] = None,
-        n_jobs: int = -1,
 ) -> tuple[CsstStatus, FileRecorder]:
     """
     Flip all images.
@@ -123,10 +125,6 @@ def process_multiple_images(
     ----------
     dm : CsstMsDataManager
         The data manager of the specified exposure.
-    logger : {None, logging.Logger}
-        The logger. If None, use the default logger.
-    n_jobs : int
-        The number of prcesses.
 
     Returns
     -------
@@ -137,55 +135,37 @@ def process_multiple_images(
     --------
     >>> dm = CsstMsDataManager.quickstart(
     >>>     ver_sim="C5.2", dir_l1="", datatype="sls", exposure_id=100)
-    >>> logger = get_logger()
-    >>> process_multiple_images(dm, logger)
+    >>> process_multiple_images(dm)
     """
 
-    # set default logger
-    if logger is None:
-        logger = get_logger()
-
     # get an empty file recorder
     fr = FileRecorder()
 
     # process data
-    try:
-        # start processing (dm.target_detectors is a list of detector number that should be processed)
-
-        # [1/2] single-thread mode
-        for detector in dm.target_detectors:
-            # this will NOT be written into the log file
-            logger.debug("Processing for detector {}".format(detector))
-            filepath_input = dm.l0_detector(detector=detector)
-            filepath_output = dm.l1_detector(detector=detector, post="L1_processed.fits")
-            data = read_image(filepath_input)
-            data_processed = process_data(data)
-            np.save(filepath_output, data_processed)
-            # record file!
-            fr.add_record(filepath=filepath_output, db=True, comment="processed file for Detector {}".format(detector))
-
-        # [2/2] multi-processing mode
-        joblib.Parallel(n_jobs=n_jobs, backend="multiprocessing")(
-            joblib.delayed(one_job)(dm, detector) for detector in dm.target_detectors
-        )
-        for detector in dm.target_detectors:
-            filepath_output = dm.l1_detector(detector=detector, post="L1_processed.fits")
-            fr.add_record(filepath=filepath_output, db=True, comment="processed file for Detector {}".format(detector))
-
-        # check results
-        if check_results(dm=dm, logger=logger):
-            # this will be written into the log file
-            logger.info("All processed files are generated!")
-            return CsstStatus.PERFECT, fr
-        else:
-            # not all images are properly processed
-            logger.warning("Not all processed files are generated!")
-            return CsstStatus.ERROR, fr
-    except DeprecationWarning:
-        # this will be written into the log file
-        logger.warning("Suffered DeprecationWarning!")
-        return CsstStatus.WARNING, fr
-    except IOError:
-        # this will be written into the log file
-        logger.error("Suffered IOError!")
-        return CsstStatus.ERROR, fr
+    # start processing (dm.target_detectors is a list of detector number that should be processed)
+
+    # [1/2] single-thread mode
+    for detector in dm.target_detectors:
+        # this will NOT be written into the log file
+        dm.logger_mod.info("Start data processing for detector {}".format(detector))
+        filepath_input = dm.l0_detector(detector=detector)
+        filepath_output = dm.l1_detector(detector=detector, post="L1_processed.fits")
+        data = read_image(filepath_input)
+        data_processed = process_data(data)
+        np.save(filepath_output, data_processed)
+        # record file!
+        fr.add_record(filepath=filepath_output, db=True, comment="processed file for Detector {}".format(detector))
+
+    # [2/2] multi-processing mode
+    dm.logger_mod.info("Starting data processing with multiprocessing ...")
+    status_list = joblib.Parallel(n_jobs=dm.n_jobs, backend=dm.backend)(
+        joblib.delayed(one_job)(dm, detector) for detector in dm.target_detectors
+    )
+    dm.logger_mod.info("Finished processing ...")
+    for detector in dm.target_detectors:
+        filepath_output = dm.l1_detector(detector=detector, post="L1_processed.fits")
+        fr.add_record(filepath=filepath_output, db=True, comment="processed file for Detector {}".format(detector))
+
+    # check results
+    assert fr.is_good()
+    return CsstStatus.PERFECT if all([_ == CsstStatus.PERFECT for _ in status_list]) else CsstStatus.WARNING, fr