Python logutils.get_logger函数代码示例

 def getProcessedFringe(self, rc):
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     caltype = "processed_fringe"
     source = rc["source"]
     if source == None:
         rc.run("getCalibration(caltype=%s)" % caltype)
     else:
         rc.run("getCalibration(caltype=%s, source=%s)" % (caltype,source))
         
     # List calibrations found
     # Fringe correction is always optional, so don't raise errors if fringe
     # not found
     first = True
     for ad in rc.get_inputs_as_astrodata():
         calurl = rc.get_cal(ad, caltype) #get from cache
         if calurl:
             cal = AstroData(calurl)
             if cal.filename is not None:
                 if first:
                     log.stdinfo("getCalibration: Results")
                     first = False
                 log.stdinfo("   %s\n      for %s" % (cal.filename,
                                                  ad.filename))
     
     yield rc

 def showCals(self, rc):
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     if str(rc["showcals"]).lower() == "all":
         num = 0
         # print "pG256: showcals=all", repr (rc.calibrations)
         for calkey in rc.calibrations:
             num += 1
             log.stdinfo(rc.calibrations[calkey], category="calibrations")
         if (num == 0):
             log.stdinfo("There are no calibrations in the cache.")
     else:
         for adr in rc.inputs:
             sid = IDFactory.generate_astro_data_id(adr.ad)
             num = 0
             for calkey in rc.calibrations:
                 if sid in calkey :
                     num += 1
                     log.stdinfo(rc.calibrations[calkey], 
                                  category="calibrations")
         if (num == 0):
             log.stdinfo("There are no calibrations in the cache.")
     
     yield rc

    def separateLampOff(self, rc):
        """
        This primitive is intended to run on gcal imaging flats. 
        It goes through the input list and figures out which ones are lamp-on
        and which ones are lamp-off
        """
        # Instantiate the log
        log = logutils.get_logger(__name__)

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "separateLampOff", "starting"))

        # Initialize the list of output AstroData objects
        lampon_list = []
        lampoff_list = []

        # Loop over the input frames
        for ad in rc.get_inputs_as_astrodata():
            if('GCAL_IR_ON' in ad.types):
                log.stdinfo("%s is a lamp-on flat" % ad.data_label())
                #rc.run("addToList(purpose=lampOn)")
                lampon_list.append(ad)
            elif('GCAL_IR_OFF' in ad.types):
                log.stdinfo("%s is a lamp-off flat" % ad.data_label())
                #rc.run("addToList(purpose=lampOff)")
                lampoff_list.append(ad)
            else:
                log.warning("Not a GCAL flatfield? Cannot tell if it is lamp-on or lamp-off for %s" % ad.data_label())

        rc.report_output(lampon_list, stream="lampOn")
        rc.report_output(lampoff_list, stream="lampOff")

        yield rc

 def failCalibration(self,rc):
     # Mark a given calibration "fail" and upload it 
     # to the system. This is intended to be used to mark a 
     # calibration file that has already been uploaded, so that
     # it will not be returned as a valid match for future data.
     
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     # Initialize the list of output AstroData objects 
     adoutput_list = []
     
     # Loop over each input AstroData object in the input list
     for ad in rc.get_inputs_as_astrodata():
         
         # Change the two keywords -- BAD and NO = Fail
         ad.phu_set_key_value("RAWGEMQA","BAD",
                              comment=self.keyword_comments["RAWGEMQA"])
         ad.phu_set_key_value("RAWPIREQ","NO",
                              comment=self.keyword_comments["RAWPIREQ"])
         log.fullinfo("%s has been marked %s" % (ad.filename,ad.qa_state()))
         
         # Append the output AstroData object to the list
         # of output AstroData objects
         adoutput_list.append(ad)
     
     # Report the list of output AstroData objects to the 
     # reduction context
     rc.report_output(adoutput_list)
     
     # Run the storeCalibration primitive, so that the 
     # failed file gets re-uploaded
     rc.run("storeCalibration")
     
     yield rc

 def storeProcessedFlat(self, rc):
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     # Log the standard "starting primitive" debug message
     log.debug(gt.log_message("primitive", "storeProcessedFlat",
                              "starting"))
     
     # Loop over each input AstroData object in the input list
     for ad in rc.get_inputs_as_astrodata():
         
         # Updating the file name with the suffix for this primitive and
         # then report the new file to the reduction context
         ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"],
                                           strip=True)
         
         # Adding a PROCFLAT time stamp to the PHU
         gt.mark_history(adinput=ad, keyword="PROCFLAT")
         
         # Refresh the AD types to reflect new processed status
         ad.refresh_types()
     
     # Upload to cal system
     rc.run("storeCalibration")
     
     yield rc

    def subtractLampOnLampOff(self, rc):
        """
        This primitive subtracts the lamp off stack from the lampon stack. It expects there to be only
        one file (the stack) on each stream - call stackLampOnLampOff to do the stacking before calling this
        """

        # Instantiate the log
        log = logutils.get_logger(__name__)

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "subtractLampOnLampOff", "starting"))

        # Initialize the list of output AstroData objects
        adoutput_list = []

        lampon = rc.get_stream(stream="lampOn", style="AD")[0]
        lampoff = rc.get_stream(stream="lampOff", style="AD")[0]

        log.stdinfo("Lamp ON is: %s %s" % (lampon.data_label(), lampon.filename))
        log.stdinfo("Lamp OFF is: %s %s" % (lampoff.data_label(), lampoff.filename))
        lampon.sub(lampoff)
        lampon.filanme = gt.filename_updater(adinput=lampon, suffix="lampOnOff")

        adoutput_list.append(lampon)
        rc.report_output(adoutput_list)

        yield rc

 def getProcessedArc(self, rc):
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     caltype = "processed_arc"
     source = rc["source"]
     if source == None:
         rc.run("getCalibration(caltype=%s)" % caltype)
     else:
         rc.run("getCalibration(caltype=%s, source=%s)" % (caltype,source))
     
     # List calibrations found
     first = True
     for ad in rc.get_inputs_as_astrodata():
         calurl = rc.get_cal(ad, caltype) #get from cache
         if calurl:
             cal = AstroData(calurl)
             if cal.filename is None:
                 if "qa" not in rc.context:
                     raise Errors.InputError("Calibration not found for " \
                                             "%s" % ad.filename)
             else:
                 if first:
                     log.stdinfo("getCalibration: Results")
                     first = False
                 log.stdinfo("   %s\n      for %s" % (cal.filename,
                                                      ad.filename))
         else: 
             if "qa" not in rc.context:
                 raise Errors.InputError("Calibration not found for %s" % 
                                         ad.filename)
     
     yield rc

 def storeCalibration(self, rc):
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     # Log the standard "starting primitive" debug message
     log.debug(gt.log_message("primitive", "storeCalibration", "starting"))
     
     # Determine the path where the calibration will be stored
     storedcals = rc["cachedict"]["storedcals"]
     
     # Loop over each input AstroData object in the input list
     for ad in rc.get_inputs_as_astrodata():
         
         # Construct the filename of the calibration, including the path
         fname = os.path.join(storedcals, os.path.basename(ad.filename))
         
         # Write the calibration to disk. Use rename=False so that
         # ad.filename does not change (i.e., does not include the
         # calibration path)
         ad.write(filename=fname, rename=False, clobber=True)
         log.stdinfo("Calibration stored as %s" % fname)
         
         if "upload" in rc.context:
             try:
                 upload_calibration(fname)
             except:
                 log.warning("Unable to upload file to calibration system")
             else:
                 log.stdinfo("File %s uploaded to fitsstore." % 
                             os.path.basename(ad.filename))
         yield rc
     
     yield rc

    def normalizeFlat(self, rc):
        """
        This primitive normalizes each science extension of the input
        AstroData object by its mean
        """
        # Instantiate the log
        log = logutils.get_logger(__name__)
        
        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "normalizeFlat", "starting"))
        
        # Define the keyword to be used for the time stamp for this primitive
        timestamp_key = self.timestamp_keys["normalizeFlat"]

        # Initialize the list of output AstroData objects
        adoutput_list = []
        
        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():
            
            # Check whether the normalizeFlat primitive has been run previously
            if ad.phu_get_key_value(timestamp_key):
                log.warning("No changes will be made to %s, since it has " \
                            "already been processed by normalizeFlat" \
                            % (ad.filename))
                # Append the input AstroData object to the list of output
                # AstroData objects without further processing
                adoutput_list.append(ad)
                continue
            
            # Loop over each science extension in each input AstroData object
            for ext in ad[SCI]:
                
                # Normalise the input AstroData object. Calculate the mean
                # value of the science extension
                mean = np.mean(ext.data, dtype=np.float64)
                # Divide the science extension by the mean value of the science
                # extension
                log.fullinfo("Normalizing %s[%s,%d] by dividing by the mean " \
                             "= %f" % (ad.filename, ext.extname(),
                                       ext.extver(), mean))
                ext = ext.div(mean)

            # Add the appropriate time stamps to the PHU
            gt.mark_history(adinput=ad, keyword=timestamp_key)

            # Change the filename
            ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"], 
                                              strip=True)

            # Append the output AstroData object to the list
            # of output AstroData objects
            adoutput_list.append(ad)
        
        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)
        
        yield rc

    def cutFootprints(self, rc):
 
        """
        This primitive will create and append multiple HDU to the output
        AD object. Each HDU correspond to a rectangular cut containing a
        slit from a MOS Flat exposure or a XD flat exposure as in the
        Gnirs case.

        :param logLevel: Verbosity setting for log messages to the screen.
        :type logLevel: integer from 0-6, 0=nothing to screen, 6=everything to
                        screen. OR the message level as a string (i.e.,
                        'critical', 'status', 'fullinfo'...)
        """

        # Instantiate the log
        log = logutils.get_logger(__name__)

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "cutFootprints", "starting"))

        # Initialize the list of output AstroData objects
        adoutput_list = []

        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():
            # Call the  user level function
      
            # Check that the input ad has the TRACEFP extension,
            # otherwise, create it.
            if ad['TRACEFP'] == None:
                ad = trace_footprints(ad)

            log.stdinfo("Cutting_footprints for: %s"%ad.filename)
            try:
                adout = cut_footprints(ad)
            except:
                log.error("Error in cut_slits with file: %s"%ad.filename)
                # DO NOT add this input ad to the adoutput_lis
                continue
               
               
            # Change the filename
            adout.filename = gt.filename_updater(adinput=ad, 
                                                 suffix=rc["suffix"],
                                                 strip=True)

            # Append the output AstroData object to the list of output 
            # AstroData objects.
            adoutput_list.append(adout)

        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)

        yield rc

    def traceFootprints(self, rc):
 
        """
        This primitive will create and append a 'TRACEFP' Bintable HDU to the
        AD object. The content of this HDU is the footprints information 
        from the espectroscopic flat in the SCI array.

        :param logLevel: Verbosity setting for log messages to the screen.
        :type logLevel: integer from 0-6, 0=nothing to screen, 6=everything to
                        screen. OR the message level as a string (i.e.,
                        'critical', 'status', 'fullinfo'...)
        """
        # Instantiate the log
        log = logutils.get_logger(__name__)
        
        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "", "starting"))

        # Initialize the list of output AstroData objects
        adoutput_list = []

        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():
            # Check whether this primitive has been run previously
            if ad.phu_get_key_value("TRACEFP"):
                log.warning("%s has already been processed by traceSlits" \
                            % (ad.filename))
                # Append the input AstroData object to the list of output
                # AstroData objects without further processing
                adoutput_list.append(ad)
                continue
            # Call the  user level function
            try:
               adout = trace_footprints(ad,function=rc["function"],
                                  order=rc["order"],
                                  trace_threshold=rc["trace_threshold"])
            except:
               log.warning("Error in traceFootprints with file: %s"%ad.filename)
               
            # Change the filename
            adout.filename = gt.filename_updater(adinput=ad, 
                                                 suffix=rc["suffix"],
                                                 strip=True)

            # Append the output AstroData object to the list of output 
            # AstroData objects.
            adoutput_list.append(adout)

        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)

        yield rc

    def wcalResampleToLinearCoords(self,rc):

        """ Uses the Wavecal fit_image solution
        """
        # Instantiate the log
        log = logutils.get_logger(__name__)
        
        # Define the keyword to be used for the time stamp
        timestamp_key = self.timestamp_keys["wcalResampleToLinearCoords"]

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "wcalResampleToLinearCoords", 
                                 "starting"))
                
        # Initialize the list of output AstroData objects
        adoutput_list = []

        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():

            # Check for a wavelength solution
            if ad["WAVECAL"] is None:
                if "qa" in rc.context:
                    log.warning("No wavelength solution found for %s" %
                                ad.filename)

                    adout=ad   # Don't do anything 
                else:
                    raise Errors.InputError("No wavelength solution found "\
                                            "for %s" % ad.filename)
            else:
                # Wavelength solution found. 
                wc = Wavecal(ad)
                wc.read_wavecal_table()
                adout = wc.resample_image_asAstrodata()

            # Add the appropriate time stamps to the PHU
            gt.mark_history(adinput=adout, keyword=timestamp_key)

            # Change the filename
            adout.filename = gt.filename_updater(
                adinput=adout, suffix=rc["suffix"], strip=True)
            
            # Append the output AstroData object to the list
            # of output AstroData objects
            adoutput_list.append(adout)
        
        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)
        
        yield rc

 def validateData(self, rc):
     """
     This primitive is used to validate NIRI data, specifically.
     
     :param repair: Set to True to repair the data, if necessary. Note: this
                    feature does not work yet. 
     :type repair: Python boolean
     """
     # Instantiate the log
     log = logutils.get_logger(__name__)
     
     # Log the standard "starting primitive" debug message
     log.debug(gt.log_message("primitive", "validateData", "starting"))
     
     # Define the keyword to be used for the time stamp for this primitive
     timestamp_key = self.timestamp_keys["validateData"]
     
     # Initialize the list of output AstroData objects
     adoutput_list = []
     
     # Loop over each input AstroData object in the input list
     for ad in rc.get_inputs_as_astrodata():
         
         # Check whether the validateData primitive has been run previously
         if ad.phu_get_key_value(timestamp_key):
             log.warning("No changes will be made to %s, since it has "
                         "already been processed by validateData"
                         % ad.filename)
             
             # Append the input AstroData object to the list of output
             # AstroData objects without further processing
             adoutput_list.append(ad)
             continue
         
         # Validate the input AstroData object.
         log.status("No validation required for %s" % ad.filename)
         
         # Add the appropriate time stamps to the PHU
         gt.mark_history(adinput=ad, keyword=timestamp_key)
         
         # Change the filename
         ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"],
                                           strip=True)
         
         # Append the output AstroData object to the list of output
         # AstroData objects
         adoutput_list.append(ad)
     
     # Report the list of output AstroData objects to the reduction context
     rc.report_output(adoutput_list)
     
     yield rc

    def skyCorrectFromSlit(self,rc):

        # Instantiate the log
        log = logutils.get_logger(__name__)
        
        # Define the keyword to be used for the time stamp
        timestamp_key = self.timestamp_keys["skyCorrectFromSlit"]

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "skyCorrectFromSlit", "starting"))
                
        # Initialize the list of output AstroData objects
        adoutput_list = []

        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():

            try:
                xbin = ad.detector_x_bin().as_pytype()
                ybin = ad.detector_y_bin().as_pytype()
                bin_factor = xbin*ybin
                roi = ad.detector_roi_setting().as_pytype()
            except:
                bin_factor = 1
                roi = "unknown"

            if bin_factor<=2 and roi=="Full Frame" and "qa" in rc.context:
                log.warning("Frame is too large to subtract sky efficiently; not "\
                            "subtracting sky for %s" % ad.filename)
                adoutput_list.append(ad)
                continue

            # Instantiate ETI and then run the task 
            gsskysub_task = eti.gsskysubeti.GsskysubETI(rc,ad)
            adout = gsskysub_task.run()

            # Add the appropriate time stamps to the PHU
            gt.mark_history(adinput=adout, keyword=timestamp_key)

            # Change the filename
            adout.filename = gt.filename_updater(
                adinput=adout, suffix=rc["suffix"], strip=True)
            
            # Append the output AstroData object to the list
            # of output AstroData objects
            adoutput_list.append(adout)
        
        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)
        
        yield rc

    def determineWavelengthSolution(self,rc):

        # Instantiate the log
        log = logutils.get_logger(__name__)
        
        # Define the keyword to be used for the time stamp
        timestamp_key = self.timestamp_keys["determineWavelengthSolution"]

        # Log the standard "starting primitive" debug message
        log.debug(gt.log_message("primitive", "determineWavelengthSolution",
                                 "starting"))
                
        # Initialize the list of output AstroData objects
        adoutput_list = []

        # Loop over each input AstroData object in the input list
        for ad in rc.get_inputs_as_astrodata():

            # Instantiate ETI and then run the task 
            # Run in a try/except because gswavelength sometimes fails
            # badly, and we want to be able to continue without
            # wavelength calibration in the QA case
            gswavelength_task = eti.gswavelengtheti.GswavelengthETI(rc,ad)
            try:
                adout = gswavelength_task.run()
            except Errors.OutputError:
                gswavelength_task.clean()
                if "qa" in rc.context:
                    log.warning("gswavelength failed for input " + ad.filename)
                    adoutput_list.append(ad)
                    continue
                else:
                    raise Errors.ScienceError("gswavelength failed for input "+
                                              ad.filename + ". Try interactive"+
                                              "=True")

            # Add the appropriate time stamps to the PHU
            gt.mark_history(adinput=adout, keyword=timestamp_key)

            # Change the filename
            adout.filename = gt.filename_updater(
                adinput=adout, suffix=rc["suffix"], strip=True)
            
            # Append the output AstroData object to the list
            # of output AstroData objects
            adoutput_list.append(adout)
        
        # Report the list of output AstroData objects to the reduction
        # context
        rc.report_output(adoutput_list)

        yield rc