def test_remove_isolated_pixels_input_copy(self): """Check whether the input image is altered during process.""" # Input image ################# input_img = np.array([[1, 0, 1], [1, 0, 0], [1, 0, 1]]) input_img_copy = np.copy(input_img) # Output image ################ output_img = filter_pixels_clusters(input_img) # Check whether the input image has changed np.testing.assert_array_equal(input_img_copy, input_img)
def test_remove_isolated_pixels_example1(self): """Check the output of the filter_pixels_clusters function.""" # Input image ################# input_img = np.array([[0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0], [1, 1, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0]]) # Output image ################ output_img = filter_pixels_clusters(input_img) # Expected output image ####### expected_output_img = np.array([[0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) np.testing.assert_array_equal(output_img, expected_output_img)
def test_whether_selection_is_based_on_pixels_value(self): """Check whether selection is based on pixels value or number of pixels in clusters.""" # Input image ################# input_img = np.array([[0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 10, 0], [1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) # Output image ################ output_img = filter_pixels_clusters(input_img) # Expected output image ####### expected_output_img = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 10, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) np.testing.assert_array_equal(output_img, expected_output_img)
def test_remove_isolated_pixels_example_negative_threshold(self): """Check the output of the filter_pixels_clusters function.""" # Input image ################# input_img = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) # Output image ################ output_img = filter_pixels_clusters(input_img, threshold=-2) # Expected output image ####### expected_output_img = np.array( [[0, 0, 0, 0, 0, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, -1, -1, -1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) # < Here there is only one big island! np.testing.assert_array_equal(output_img, expected_output_img)
def test_remove_isolated_pixels_example_threshold(self): """Check that every values below threshold is set to 0.""" # Input image ################# input_img = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.1, 1.0, 1.0, 1.0, 0.1, 1.0], [0.0, 0.5, 1.0, -1.0, 1.0, 0.5, 0.0], [1.0, 0.1, 1.0, 1.0, 1.0, 0.1, 1.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]) # Output image ################ output_img = filter_pixels_clusters(input_img, threshold=0.2) # Expected output image ####### expected_output_img = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0], [0.0, 0.5, 1.0, 0.0, 1.0, 0.5, 0.0], [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]) np.testing.assert_array_equal(output_img, expected_output_img)
def prepare_event(self, source, return_stub=True, save_images=False, debug=False): """ Calibrate, clean and reconstruct the direction of an event. Parameters ---------- source : ctapipe.io.EventSource A container of selected showers from a simtel file. geom_cam_tel: dict Dictionary of of MyCameraGeometry objects for each camera in the file return_stub : bool If True, yield also images from events that won't be reconstructed. This feature is not currently available. save_images : bool If True, save photoelectron images from reconstructed events. debug : bool If True, print some debugging information (to be expanded). Yields ------ PreparedEvent: dict Dictionary containing event-image information to be written. """ # ============================================================= # TRANSFORMED CAMERA GEOMETRIES # ============================================================= # These are the camera geometries were the Hillas parametrization will # be performed. # They are transformed to TelescopeFrame using the effective focal # lengths # These geometries could be used also to performe the image cleaning, # but for the moment we still do that in the CameraFrame geom_cam_tel = {} for camera in source.subarray.camera_types: # Original geometry of each camera geom = camera.geometry # Same but with focal length as an attribute # This is planned to disappear and be imported by ctapipe focal_length = effective_focal_lengths(camera.camera_name) geom_cam_tel[camera.camera_name] = MyCameraGeometry( camera_name=camera.camera_name, pix_type=geom.pix_type, pix_id=geom.pix_id, pix_x=geom.pix_x, pix_y=geom.pix_y, pix_area=geom.pix_area, cam_rotation=geom.cam_rotation, pix_rotation=geom.pix_rotation, frame=CameraFrame(focal_length=focal_length), ).transform_to(TelescopeFrame()) # ============================================================= ievt = 0 for event in source: # Display event counts if debug: print( bcolors.BOLD + f"EVENT #{event.count}, EVENT_ID #{event.index.event_id}" + bcolors.ENDC) print(bcolors.BOLD + f"has triggered telescopes {event.r1.tel.keys()}" + bcolors.ENDC) ievt += 1 # if (ievt < 10) or (ievt % 10 == 0): # print(ievt) self.event_cutflow.count("noCuts") # LST stereo condition # whenever there is only 1 LST in an event, we remove that telescope # if the remaining telescopes are less than min_tel we remove the event lst_tel_ids = set( source.subarray.get_tel_ids_for_type("LST_LST_LSTCam")) triggered_LSTs = set(event.r0.tel.keys()).intersection(lst_tel_ids) n_triggered_LSTs = len(triggered_LSTs) n_triggered_non_LSTs = len(event.r0.tel.keys()) - n_triggered_LSTs bad_LST_stereo = False if self.LST_stereo and self.event_cutflow.cut( "no-LST-stereo + <2 other types", n_triggered_LSTs, n_triggered_non_LSTs): bad_LST_stereo = True if return_stub: print( bcolors.WARNING + "WARNING: LST_stereo is set to 'True'\n" + f"This event has < {self.min_ntel_LST} triggered LSTs\n" + "and < 2 triggered telescopes from other telescope types.\n" + "The event will be processed up to DL1b." + bcolors.ENDC) # we show this, but we proceed to analyze the event up to # DL1a/b for the associated benchmarks # this checks for < 2 triggered telescopes of ANY type if self.event_cutflow.cut("min2Tels trig", len(event.r1.tel.keys())): if return_stub: print( bcolors.WARNING + f"WARNING : < {self.min_ntel} triggered telescopes!" + bcolors.ENDC) # we show this, but we proceed to analyze it # ============================================================= # CALIBRATION # ============================================================= if debug: print(bcolors.OKBLUE + "Extracting all calibrated images..." + bcolors.ENDC) self.calib(event) # Calibrate the event # ============================================================= # BEGINNING OF LOOP OVER TELESCOPES # ============================================================= dl1_phe_image = {} dl1_phe_image_mask_reco = {} dl1_phe_image_mask_clusters = {} mc_phe_image = {} max_signals = {} n_pixel_dict = {} hillas_dict_reco = {} # for direction reconstruction hillas_dict = {} # for discrimination leakage_dict = {} concentration_dict = {} n_tels = { "Triggered": len(event.r1.tel.keys()), "LST_LST_LSTCam": 0, "MST_MST_NectarCam": 0, "MST_MST_FlashCam": 0, "MST_SCT_SCTCam": 0, "SST_1M_DigiCam": 0, "SST_ASTRI_ASTRICam": 0, "SST_GCT_CHEC": 0, } n_cluster_dict = {} impact_dict_reco = {} # impact distance measured in tilt system point_azimuth_dict = {} point_altitude_dict = {} good_for_reco = {} # 1 = success, 0 = fail # Array pointing in AltAz frame az = event.pointing.array_azimuth alt = event.pointing.array_altitude array_pointing = SkyCoord(az, alt, frame=AltAz()) ground_frame = GroundFrame() tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing) for tel_id in event.r1.tel.keys(): point_azimuth_dict[tel_id] = event.pointing.tel[tel_id].azimuth point_altitude_dict[tel_id] = event.pointing.tel[ tel_id].altitude if debug: print(bcolors.OKBLUE + f"Working on telescope #{tel_id}..." + bcolors.ENDC) self.image_cutflow.count("noCuts") camera = source.subarray.tel[tel_id].camera.geometry # count the current telescope according to its type tel_type = str(source.subarray.tel[tel_id]) n_tels[tel_type] += 1 # use ctapipe's functionality to get the calibrated image # and scale the reconstructed values if required pmt_signal = event.dl1.tel[tel_id].image / self.calibscale # If required... if save_images is True: # Save the simulated and reconstructed image of the event dl1_phe_image[tel_id] = pmt_signal mc_phe_image[tel_id] = event.simulation.tel[ tel_id].true_image # We now ASSUME that the event will be good good_for_reco[tel_id] = 1 # later we change to 0 if any condition is NOT satisfied if self.cleaner_reco.mode == "tail": # tail uses only ctapipe # Cleaning used for direction reconstruction image_biggest, mask_reco = self.cleaner_reco.clean_image( pmt_signal, camera) # calculate the leakage (before filtering) leakages = {} # this is needed by both cleanings # The check on SIZE shouldn't be here, but for the moment # I prefer to sacrifice elegancy... if np.sum(image_biggest[mask_reco]) != 0.0: leakage_biggest = leakage_parameters( camera, image_biggest, mask_reco) leakages["leak1_reco"] = leakage_biggest[ "intensity_width_1"] leakages["leak2_reco"] = leakage_biggest[ "intensity_width_2"] else: leakages["leak1_reco"] = 0.0 leakages["leak2_reco"] = 0.0 # find all islands using this cleaning num_islands, labels = number_of_islands(camera, mask_reco) if num_islands == 1: # if only ONE islands is left ... # ...use directly the old mask and reduce dimensions # to make Hillas parametrization faster camera_biggest = camera[mask_reco] image_biggest = image_biggest[mask_reco] if save_images is True: dl1_phe_image_mask_reco[tel_id] = mask_reco elif num_islands > 1: # if more islands survived.. # ...find the biggest one mask_biggest = largest_island(labels) # and also reduce dimensions camera_biggest = camera[mask_biggest] image_biggest = image_biggest[mask_biggest] if save_images is True: dl1_phe_image_mask_reco[tel_id] = mask_biggest else: # if no islands survived use old camera and image camera_biggest = camera dl1_phe_image_mask_reco[tel_id] = mask_reco # Cleaning used for score/energy estimation image_extended, mask_extended = self.cleaner_extended.clean_image( pmt_signal, camera) dl1_phe_image_mask_clusters[tel_id] = mask_extended # calculate the leakage (before filtering) # this part is not well coded, but for the moment it works if np.sum(image_extended[mask_extended]) != 0.0: leakage_extended = leakage_parameters( camera, image_extended, mask_extended) leakages["leak1"] = leakage_extended[ "intensity_width_1"] leakages["leak2"] = leakage_extended[ "intensity_width_2"] else: leakages["leak1"] = 0.0 leakages["leak2"] = 0.0 # find all islands with this cleaning # we will also register how many have been found n_cluster_dict[tel_id], labels = number_of_islands( camera, mask_extended) # NOTE: the next check shouldn't be necessary if we keep # all the isolated pixel clusters, but for now the # two cleanings are set the same in analysis.yml because # the performance of the extended one has never been really # studied in model estimation. # if some islands survived if n_cluster_dict[tel_id] > 0: # keep all of them and reduce dimensions camera_extended = camera[mask_extended] image_extended = image_extended[mask_extended] else: # otherwise continue with the old camera and image camera_extended = camera # could this go into `hillas_parameters` ...? # this is basically the charge of ALL islands # not calculated later by the Hillas parametrization! max_signals[tel_id] = np.max(image_extended) else: # for wavelets we stick to old pywi-cta code try: # "try except FileNotFoundError" not clear to me, but for now it stays... with warnings.catch_warnings(): # Image with biggest cluster (reco cleaning) image_biggest, mask_reco = self.cleaner_reco.clean_image( pmt_signal, camera) image_biggest2d = geometry_converter.image_1d_to_2d( image_biggest, camera.camera_name) image_biggest2d = filter_pixels_clusters( image_biggest2d) image_biggest = geometry_converter.image_2d_to_1d( image_biggest2d, camera.camera_name) # Image for score/energy estimation (with clusters) ( image_extended, mask_extended, ) = self.cleaner_extended.clean_image( pmt_signal, camera) # This last part was outside the pywi-cta block # before, but is indeed part of it because it uses # pywi-cta functions in the "extended" case # For cluster counts image_2d = geometry_converter.image_1d_to_2d( image_extended, camera.camera_name) n_cluster_dict[ tel_id] = pixel_clusters.number_of_pixels_clusters( array=image_2d, threshold=0) # could this go into `hillas_parameters` ...? max_signals[tel_id] = np.max(image_extended) except FileNotFoundError as e: print(e) continue # ============================================================= # PRELIMINARY IMAGE SELECTION # ============================================================= cleaned_image_is_good = True # we assume this if self.image_selection_source == "extended": cleaned_image_to_use = image_extended elif self.image_selection_source == "biggest": cleaned_image_to_use = image_biggest else: raise ValueError( "Only supported cleanings are 'biggest' or 'extended'." ) # Apply some selection if self.image_cutflow.cut("min pixel", cleaned_image_to_use): if debug: print(bcolors.WARNING + "WARNING : not enough pixels!" + bcolors.ENDC) good_for_reco[tel_id] = 0 # we record it as BAD cleaned_image_is_good = False if self.image_cutflow.cut("min charge", np.sum(cleaned_image_to_use)): if debug: print(bcolors.WARNING + "WARNING : not enough charge!" + bcolors.ENDC) good_for_reco[tel_id] = 0 # we record it as BAD cleaned_image_is_good = False if debug and (not cleaned_image_is_good): # BAD image quality print(bcolors.WARNING + "WARNING : The cleaned image didn't pass" + " preliminary cuts.\n" + "An attempt to parametrize it will be made," + " but the image will NOT be used for" + " direction reconstruction." + bcolors.ENDC) # ============================================================= # IMAGE PARAMETRIZATION # ============================================================= with np.errstate(invalid="raise", divide="raise"): try: # Filter the cameras in TelescopeFrame with the same # cleaning masks camera_biggest_tel = geom_cam_tel[camera.camera_name][ camera_biggest.pix_id] camera_extended_tel = geom_cam_tel[camera.camera_name][ camera_extended.pix_id] # Parametrize the image in the TelescopeFrame moments_reco = hillas_parameters( camera_biggest_tel, image_biggest) # for geometry (eg direction) moments = hillas_parameters( camera_extended_tel, image_extended ) # for discrimination and energy reconstruction if debug: print( "Image parameters from main cluster cleaning:") print(moments_reco) print( "Image parameters from all-clusters cleaning:") print(moments) # Add concentration parameters concentrations = {} concentrations_extended = concentration_parameters( camera_extended_tel, image_extended, moments) concentrations[ "concentration_cog"] = concentrations_extended[ "cog"] concentrations[ "concentration_core"] = concentrations_extended[ "core"] concentrations[ "concentration_pixel"] = concentrations_extended[ "pixel"] # =================================================== # PARAMETRIZED IMAGE SELECTION # =================================================== if self.image_selection_source == "extended": moments_to_use = moments else: moments_to_use = moments_reco # if width and/or length are zero (e.g. when there is # only only one pixel or when all pixel are exactly # in one row), the parametrisation # won't be very useful: skip if self.image_cutflow.cut("poor moments", moments_to_use): if debug: print(bcolors.WARNING + "WARNING : poor moments!" + bcolors.ENDC) good_for_reco[tel_id] = 0 # we record it as BAD if self.image_cutflow.cut("close to the edge", moments_to_use, camera.camera_name): if debug: print( bcolors.WARNING + "WARNING : out of containment radius!\n" + f"Camera radius = {self.camera_radius[camera.camera_name]}\n" + f"COG radius = {moments_to_use.r}" + bcolors.ENDC) good_for_reco[tel_id] = 0 if self.image_cutflow.cut("bad ellipticity", moments_to_use): if debug: print(bcolors.WARNING + "WARNING : bad ellipticity" + bcolors.ENDC) good_for_reco[tel_id] = 0 if debug and good_for_reco[tel_id] == 1: print( bcolors.OKGREEN + "Image survived and correctly parametrized." # + "\nIt will be used for direction reconstruction!" + bcolors.ENDC) elif debug and good_for_reco[tel_id] == 0: print( bcolors.WARNING + "Image not survived or " + "not good enough for parametrization." # + "\nIt will be NOT used for direction reconstruction, " # + "BUT it's information will be recorded." + bcolors.ENDC) hillas_dict[tel_id] = moments hillas_dict_reco[tel_id] = moments_reco n_pixel_dict[tel_id] = len( np.where(image_extended > 0)[0]) leakage_dict[tel_id] = leakages concentration_dict[tel_id] = concentrations except ( FloatingPointError, HillasParameterizationError, ValueError, ) as e: if debug: print(bcolors.FAIL + "Parametrization error: " + f"{e}\n" + "Dummy parameters recorded." + bcolors.ENDC) good_for_reco[tel_id] = 0 hillas_dict[ tel_id] = HillasParametersTelescopeFrameContainer( ) hillas_dict_reco[ tel_id] = HillasParametersTelescopeFrameContainer( ) n_pixel_dict[tel_id] = len( np.where(image_extended > 0)[0]) leakage_dict[tel_id] = leakages concentration_dict[tel_id] = concentrations # END OF THE CYCLE OVER THE TELESCOPES # ============================================================= # DIRECTION RECONSTRUCTION # ============================================================= if bad_LST_stereo: if debug: print( bcolors.WARNING + "WARNING: This event was triggered with 1 LST image and <2 images from other telescope types." + "\nWARNING : direction reconstruction will not be performed." + bcolors.ENDC) # Set all the involved images as NOT good for recosntruction # even though they might have been # but this is because of the LST stereo trigger.... for tel_id in event.r0.tel.keys(): good_for_reco[tel_id] = 0 # and set the number of good and bad images accordingly n_tels["GOOD images"] = 0 n_tels[ "BAD images"] = n_tels["Triggered"] - n_tels["GOOD images"] # create a dummy container for direction reconstruction reco_result = ReconstructedShowerContainer() if return_stub: # if saving all events (default) if debug: print(bcolors.OKBLUE + "Recording event..." + bcolors.ENDC) print( bcolors.WARNING + "WARNING: This is event shall NOT be used further along the pipeline." + bcolors.ENDC) yield stub( # record event with dummy info event, mc_phe_image, dl1_phe_image, dl1_phe_image_mask_reco, dl1_phe_image_mask_clusters, good_for_reco, hillas_dict, hillas_dict_reco, n_tels, leakage_dict, concentration_dict) continue else: continue # Now in case the only triggered telescopes were # - < self.min_ntel_LST LST, # - >=2 any other telescope type, # we remove the single-LST image and continue reconstruction with # the images from the other telescope types if self.LST_stereo and (n_triggered_LSTs < self.min_ntel_LST) and ( n_triggered_LSTs != 0) and (n_triggered_non_LSTs >= 2): if debug: print( bcolors.WARNING + f"WARNING: LST stereo trigger condition is active.\n" + f"This event triggered < {self.min_ntel_LST} LSTs " + f"and {n_triggered_non_LSTs} images from other telescope types.\n" + bcolors.ENDC) for tel_id in triggered_LSTs: # in case we test for min_ntel_LST>2 if good_for_reco[tel_id]: # we don't use it for reconstruction good_for_reco[tel_id] = 0 print( bcolors.WARNING + f"WARNING: LST image #{tel_id} removed, even though it passed quality cuts." + bcolors.ENDC) # TODO: book-keeping of this kind of events doesn't seem easy # convert dictionary in numpy array to get a "mask" images_status = np.asarray(list(good_for_reco.values())) # record how many images will be used for reconstruction n_tels["GOOD images"] = len( np.extract(images_status == 1, images_status)) n_tels["BAD images"] = n_tels["Triggered"] - n_tels["GOOD images"] if self.event_cutflow.cut("min2Tels reco", n_tels["GOOD images"]): if debug: print( bcolors.FAIL + f"WARNING: < {self.min_ntel} ({n_tels['GOOD images']}) images remaining!" + "\nWARNING : direction reconstruction is not possible!" + bcolors.ENDC) # create a dummy container for direction reconstruction reco_result = ReconstructedShowerContainer() if return_stub: # if saving all events (default) if debug: print(bcolors.OKBLUE + "Recording event..." + bcolors.ENDC) print( bcolors.WARNING + "WARNING: This is event shall NOT be used further along the pipeline." + bcolors.ENDC) yield stub( # record event with dummy info event, mc_phe_image, dl1_phe_image, dl1_phe_image_mask_reco, dl1_phe_image_mask_clusters, good_for_reco, hillas_dict, hillas_dict_reco, n_tels, leakage_dict, concentration_dict) continue else: continue if debug: print(bcolors.OKBLUE + "Starting direction reconstruction..." + bcolors.ENDC) try: with warnings.catch_warnings(): warnings.simplefilter("ignore") if self.image_selection_source == "extended": hillas_dict_to_use = hillas_dict else: hillas_dict_to_use = hillas_dict_reco # use only the successfully parametrized images # to reconstruct the direction of this event successfull_hillas = np.where(images_status == 1)[0] all_images = np.asarray(list(good_for_reco.keys())) good_images = set(all_images[successfull_hillas]) good_hillas_dict = { k: v for k, v in hillas_dict_to_use.items() if k in good_images } if debug: print( bcolors.PURPLE + f"{len(good_hillas_dict)} images " + f"(from telescopes #{list(good_hillas_dict.keys())}) will be " + "used to recover the shower's direction..." + bcolors.ENDC) # Reconstruction results reco_result = self.shower_reco.predict( good_hillas_dict, source.subarray, SkyCoord(alt=alt, az=az, frame="altaz"), None, # use the array direction ) # Impact parameter for telescope-wise energy estimation subarray = source.subarray for tel_id in hillas_dict_to_use.keys(): pos = subarray.positions[tel_id] tel_ground = SkyCoord(pos[0], pos[1], pos[2], frame=ground_frame) core_ground = SkyCoord( reco_result.core_x, reco_result.core_y, 0 * u.m, frame=ground_frame, ) # Go back to the tilted frame # this should be the same... tel_tilted = tel_ground.transform_to(tilted_frame) # but this not core_tilted = SkyCoord(x=core_ground.x, y=core_ground.y, frame=tilted_frame) impact_dict_reco[tel_id] = np.sqrt( (core_tilted.x - tel_tilted.x)**2 + (core_tilted.y - tel_tilted.y)**2) except (Exception, TooFewTelescopesException, InvalidWidthException) as e: if debug: print("exception in reconstruction:", e) raise if return_stub: if debug: print( bcolors.FAIL + "Shower could NOT be correctly reconstructed! " + "Recording event..." + "WARNING: This is event shall NOT be used further along the pipeline." + bcolors.ENDC) yield stub( # record event with dummy info event, mc_phe_image, dl1_phe_image, dl1_phe_image_mask_reco, dl1_phe_image_mask_clusters, good_for_reco, hillas_dict, hillas_dict_reco, n_tels, leakage_dict, concentration_dict) else: continue if self.event_cutflow.cut("direction nan", reco_result): if debug: print(bcolors.WARNING + "WARNING: undefined direction!" + bcolors.ENDC) if return_stub: if debug: print( bcolors.FAIL + "Shower could NOT be correctly reconstructed! " + "Recording event..." + "WARNING: This is event shall NOT be used further along the pipeline." + bcolors.ENDC) yield stub( # record event with dummy info event, mc_phe_image, dl1_phe_image, dl1_phe_image_mask_reco, dl1_phe_image_mask_clusters, good_for_reco, hillas_dict, hillas_dict_reco, n_tels, leakage_dict, concentration_dict) else: continue if debug: print(bcolors.BOLDGREEN + "Shower correctly reconstructed! " + "Recording event..." + bcolors.ENDC) yield PreparedEvent( event=event, dl1_phe_image=dl1_phe_image, dl1_phe_image_mask_reco=dl1_phe_image_mask_reco, dl1_phe_image_mask_clusters=dl1_phe_image_mask_clusters, mc_phe_image=mc_phe_image, n_pixel_dict=n_pixel_dict, hillas_dict=hillas_dict, hillas_dict_reco=hillas_dict_reco, leakage_dict=leakage_dict, concentration_dict=concentration_dict, n_tels=n_tels, max_signals=max_signals, n_cluster_dict=n_cluster_dict, reco_result=reco_result, impact_dict=impact_dict_reco, good_event=True, good_for_reco=good_for_reco, )
def clean_image( input_image, type_of_filtering=hard_filter.DEFAULT_TYPE_OF_FILTERING, filter_thresholds=hard_filter.DEFAULT_FILTER_THRESHOLDS, last_scale_treatment=mrtransform_wrapper.DEFAULT_LAST_SCALE_TREATMENT, detect_only_positive_structures=False, kill_isolated_pixels=False, noise_distribution=None, tmp_files_directory=".", output_data_dict=None, **kwargs): """Clean the `input_image` image. Apply the wavelet transform, filter planes and return the reverse transformed image. Parameters ---------- input_image : array_like The image to clean. type_of_filtering : str Type of filtering: 'hard_filtering' or 'ksigma_hard_filtering'. filter_thresholds : list of float Thresholds used for the plane filtering. last_scale_treatment : str Last plane treatment: 'keep', 'drop' or 'mask'. detect_only_positive_structures : bool Detect only positive structures. kill_isolated_pixels : bool Suppress isolated pixels in the support. noise_distribution : bool The JSON file containing the Cumulated Distribution Function of the noise model used to inject artificial noise in blank pixels (those with a NaN value). tmp_files_directory : str The path of the directory where temporary files are written. output_data_dict : dict A dictionary used to return results and intermediate results. Returns ------- Return the cleaned image. """ if DEBUG: print("Filter thresholds:", filter_thresholds) number_of_scales = len(filter_thresholds) + 1 if DEBUG: print("Number of scales:", number_of_scales) # COMPUTE THE WAVELET TRANSFORM ####################################### wavelet_planes = wavelet_transform(input_image, number_of_scales=number_of_scales, tmp_files_directory=tmp_files_directory, noise_distribution=noise_distribution) if DEBUG: for index, plane in enumerate(wavelet_planes): images.plot(plane, "Plane " + str(index)) # FILTER WAVELET PLANES ############################################### filtered_wavelet_planes = filter_planes( wavelet_planes, method=type_of_filtering, thresholds=filter_thresholds, detect_only_positive_structures=detect_only_positive_structures) #if DEBUG: # for index, plane in enumerate(filtered_wavelet_planes): # images.plot(plane, "Filtered plane " + str(index)) # COMPUTE THE INVERSE TRANSFORM ####################################### cleaned_image = inverse_wavelet_transform(filtered_wavelet_planes, last_plane=last_scale_treatment) if DEBUG: images.plot(cleaned_image, "Cleaned image") # REMOVE ISOLATED PIXELS ############################################## if output_data_dict is not None: img_cleaned_clusters_delta_intensity, img_cleaned_clusters_delta_abs_intensity, img_cleaned_clusters_delta_num_pixels = filter_pixels_clusters_stats( cleaned_image) img_cleaned_num_islands = number_of_pixels_clusters(cleaned_image) output_data_dict[ "img_cleaned_clusters_delta_intensity"] = img_cleaned_clusters_delta_intensity output_data_dict[ "img_cleaned_clusters_delta_abs_intensity"] = img_cleaned_clusters_delta_abs_intensity output_data_dict[ "img_cleaned_clusters_delta_num_pixels"] = img_cleaned_clusters_delta_num_pixels output_data_dict["img_cleaned_num_islands"] = img_cleaned_num_islands if kill_isolated_pixels: cleaned_image = filter_pixels_clusters(cleaned_image) if DEBUG: images.plot(cleaned_image, "Cleaned image after cluster filtering") return cleaned_image
def prepare_event(self, source, return_stub=False, save_images=False): for event in source: self.event_cutflow.count("noCuts") if self.event_cutflow.cut("min2Tels trig", len(event.dl0.tels_with_data)): if return_stub: yield stub(event) else: continue self.calib(event) # telescope loop tot_signal = 0 dl1_phe_image = None mc_phe_image = None max_signals = {} n_pixel_dict = {} hillas_dict_reco = {} # for direction reconstruction hillas_dict = {} # for discrimination n_tels = { "tot": len(event.dl0.tels_with_data), "LST_LST_LSTCam": 0, "MST_MST_NectarCam": 0, "SST": 0, # add later correct names when testing on Paranal } n_cluster_dict = {} impact_dict_reco = {} # impact distance measured in tilt system point_azimuth_dict = {} point_altitude_dict = {} # Compute impact parameter in tilt system run_array_direction = event.mcheader.run_array_direction az, alt = run_array_direction[0], run_array_direction[1] ground_frame = GroundFrame() for tel_id in event.dl0.tels_with_data: self.image_cutflow.count("noCuts") camera = event.inst.subarray.tel[tel_id].camera # count the current telescope according to its size tel_type = str(event.inst.subarray.tel[tel_id]) # use ctapipe's functionality to get the calibrated image pmt_signal = event.dl1.tel[tel_id].image # Save the calibrated image after the gain has been chosen # automatically by ctapipe, together with the simulated one if save_images is True: dl1_phe_image = pmt_signal mc_phe_image = event.mc.tel[tel_id].photo_electron_image if self.cleaner_reco.mode == "tail": # tail uses only ctapipe # Cleaning used for direction reconstruction image_biggest, mask_reco = self.cleaner_reco.clean_image( pmt_signal, camera) # find all islands using this cleaning num_islands, labels = number_of_islands(camera, mask_reco) if num_islands == 1: # if only ONE islands is left ... # ...use directly the old mask and reduce dimensions # to make Hillas parametrization faster camera_biggest = camera[mask_reco] image_biggest = image_biggest[mask_reco] elif num_islands > 1: # if more islands survived.. # ...find the biggest one mask_biggest = largest_island(labels) # and also reduce dimensions camera_biggest = camera[mask_biggest] image_biggest = image_biggest[mask_biggest] else: # if no islands survived use old camera and image camera_biggest = camera # Cleaning used for score/energy estimation image_extended, mask_extended = self.cleaner_extended.clean_image( pmt_signal, camera) # find all islands with this cleaning # we will also register how many have been found n_cluster_dict[tel_id], labels = number_of_islands( camera, mask_extended) # NOTE: the next check shouldn't be necessary if we keep # all the isolated pixel clusters, but for now the # two cleanings are set the same in analysis.yml because # the performance of the extended one has never been really # studied in model estimation. # (This is a nice way to ask for volunteers :P) # if some islands survived if num_islands > 0: # keep all of them and reduce dimensions camera_extended = camera[mask_extended] image_extended = image_extended[mask_extended] else: # otherwise continue with the old camera and image camera_extended = camera # could this go into `hillas_parameters` ...? # this is basically the charge of ALL islands # not calculated later by the Hillas parametrization! max_signals[tel_id] = np.max(image_extended) else: # for wavelets we stick to old pywi-cta code try: # "try except FileNotFoundError" not clear to me, but for now it stays... with warnings.catch_warnings(): # Image with biggest cluster (reco cleaning) image_biggest, mask_reco = self.cleaner_reco.clean_image( pmt_signal, camera) image_biggest2d = geometry_converter.image_1d_to_2d( image_biggest, camera.cam_id) image_biggest2d = filter_pixels_clusters( image_biggest2d) image_biggest = geometry_converter.image_2d_to_1d( image_biggest2d, camera.cam_id) # Image for score/energy estimation (with clusters) image_extended, mask_extended = self.cleaner_extended.clean_image( pmt_signal, camera) # This last part was outside the pywi-cta block # before, but is indeed part of it because it uses # pywi-cta functions in the "extended" case # For cluster counts image_2d = geometry_converter.image_1d_to_2d( image_extended, camera.cam_id) n_cluster_dict[ tel_id] = pixel_clusters.number_of_pixels_clusters( array=image_2d, threshold=0) # could this go into `hillas_parameters` ...? max_signals[tel_id] = np.max(image_extended) except FileNotFoundError as e: # JLK, WHAT? print(e) continue # ============================================================== # Apply some selection if self.image_cutflow.cut("min pixel", image_biggest): continue if self.image_cutflow.cut("min charge", np.sum(image_biggest)): continue # do the hillas reconstruction of the images # QUESTION should this change in numpy behaviour be done here # or within `hillas_parameters` itself? # JLK: make selection on biggest cluster with np.errstate(invalid="raise", divide="raise"): try: moments_reco = hillas_parameters( camera_biggest, image_biggest) # for geometry (eg direction) moments = hillas_parameters( camera_extended, image_extended ) # for discrimination and energy reconstruction # if width and/or length are zero (e.g. when there is # only only one pixel or when all pixel are exactly # in one row), the parametrisation # won't be very useful: skip if self.image_cutflow.cut("poor moments", moments_reco): continue if self.image_cutflow.cut("close to the edge", moments_reco, camera.cam_id): continue if self.image_cutflow.cut("bad ellipticity", moments_reco): continue except (FloatingPointError, hillas.HillasParameterizationError): continue point_azimuth_dict[ tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad point_altitude_dict[ tel_id] = event.mc.tel[tel_id].altitude_raw * u.rad n_tels[tel_type] += 1 hillas_dict[tel_id] = moments hillas_dict_reco[tel_id] = moments_reco n_pixel_dict[tel_id] = len(np.where(image_extended > 0)[0]) tot_signal += moments.intensity n_tels["reco"] = len(hillas_dict_reco) n_tels["discri"] = len(hillas_dict) if self.event_cutflow.cut("min2Tels reco", n_tels["reco"]): if return_stub: yield stub(event) else: continue try: with warnings.catch_warnings(): warnings.simplefilter("ignore") # Reconstruction results reco_result = self.shower_reco.predict( hillas_dict_reco, event.inst, SkyCoord(alt=alt, az=az, frame="altaz"), { tel_id: SkyCoord( alt=point_altitude_dict[tel_id], az=point_azimuth_dict[tel_id], frame="altaz", ) # cycle only on tels which still have an image for tel_id in point_altitude_dict.keys() }, ) # Impact parameter for energy estimation (/ tel) subarray = event.inst.subarray for tel_id in hillas_dict.keys(): pos = subarray.positions[tel_id] tel_ground = SkyCoord(pos[0], pos[1], pos[2], frame=ground_frame) core_ground = SkyCoord( reco_result.core_x, reco_result.core_y, 0 * u.m, frame=ground_frame, ) # Should be better handled (tilted frame) impact_dict_reco[tel_id] = np.sqrt( (core_ground.x - tel_ground.x)**2 + (core_ground.y - tel_ground.y)**2) except Exception as e: print("exception in reconstruction:", e) raise if return_stub: yield stub(event) else: continue if self.event_cutflow.cut("direction nan", reco_result): if return_stub: yield stub(event) else: continue yield PreparedEvent( event=event, dl1_phe_image=dl1_phe_image, mc_phe_image=mc_phe_image, n_pixel_dict=n_pixel_dict, hillas_dict=hillas_dict, hillas_dict_reco=hillas_dict_reco, n_tels=n_tels, tot_signal=tot_signal, max_signals=max_signals, n_cluster_dict=n_cluster_dict, reco_result=reco_result, impact_dict=impact_dict_reco, )
def filter_planes(wavelet_planes, method=DEFAULT_TYPE_OF_FILTERING, thresholds=DEFAULT_FILTER_THRESHOLDS, detect_only_positive_structures=False): """Filter the wavelet planes. The last plane (called residuals) is kept unmodified. Parameters ---------- wavelet_planes : list of array_like The wavelet planes to filter, including the last *residual* plane. method : str, optional The filtering method to use. So far, only the 'hard_filtering' and 'ksigma_hard_filtering' methods are implemented. thresholds : list of float Thresholds used for the plane filtering. detect_only_positive_structures : bool Detect only positive structures. Returns ------- list Return a list containing the filtered wavelet planes. """ filtered_wavelet_planes = copy.deepcopy(wavelet_planes) # The last plane is kept unmodified for plane_index, plane in enumerate(wavelet_planes[0:-1]): if method in ('hard_filtering', 'common_hard_filtering'): with np.errstate( invalid='ignore' ): # TODO: to disable warnings on images containing "NaN" values (temporary solution) if detect_only_positive_structures: plane_mask = plane > thresholds[plane_index] else: plane_mask = abs(plane) > thresholds[plane_index] filtered_plane = plane * plane_mask elif method == 'ksigma_hard_filtering': # Compute the standard deviation of the plane ## plane_noise_std = np.std( plane ) # TODO: this is wrong... it should be the estimated std of the **noise** # Apply a threshold on the plane ############### # Remark: "abs(plane) > (plane_noise_std * 3.)" should be the correct way to # make the image mask, but sometimes results looks better when all # negative coefficients are dropped ("plane > (plane_noise_std * 3.)") if detect_only_positive_structures: plane_mask = plane > (plane_noise_std * thresholds[plane_index]) else: plane_mask = abs(plane) > (plane_noise_std * thresholds[plane_index]) filtered_plane = plane * plane_mask elif method == 'cluster_filtering': if plane_index == 0: plane_mask = plane > thresholds[plane_index] filtered_plane = plane * plane_mask else: filtered_plane = filter_pixels_clusters( plane, threshold=thresholds[plane_index]) else: raise ValueError( 'Unknown method "{}". Should be "hard_filtering" or "ksigma_hard_filtering".' .format(method)) filtered_wavelet_planes[plane_index] = filtered_plane if DEBUG: images.plot(plane, title="Plane {}".format(plane_index)) images.plot(plane_mask, title="Binary mask for plane {}".format(plane_index)) images.plot(filtered_plane, title="Filtered plane {}".format(plane_index)) if method == 'common_hard_filtering': # Use the same significant pixels on each plane # Init the common pixel mask to "all pixels rejected" common_significant_pixels_mask = np.zeros(wavelet_planes[0].shape) for filtered_plane in filtered_wavelet_planes[0:-1]: current_significant_pixels_mask = (np.isfinite(filtered_plane) * (filtered_plane != 0)) common_significant_pixels_mask = np.logical_or( common_significant_pixels_mask, current_significant_pixels_mask) for plane_index, plane in enumerate(wavelet_planes[0:-1]): filtered_plane = plane * common_significant_pixels_mask filtered_wavelet_planes[plane_index] = filtered_plane # The next commented part is actually quite useless as a post processing island filtering does more or less the same job... #elif method == 'cluster_filtering': # # Only keep first plane's pixels that are *significant* in the others planes # significant_pixels_mask = np.zeros(filtered_wavelet_planes[0].shape) # for filtered_plane in filtered_wavelet_planes[1:-1]: # significant_pixels_mask[filtered_plane != 0] = 1 # filtered_wavelet_planes[0] += filtered_wavelet_planes[0] * significant_pixels_mask return filtered_wavelet_planes
def prepare_event(self, source, return_stub=False): # configuration for the camera calibrator # modifies the integration window to be more like in MARS # JLK, only for LST!!!! # Option for integration correction is done above cfg = Config() cfg["ChargeExtractorFactory"]["window_width"] = 5 cfg["ChargeExtractorFactory"]["window_shift"] = 2 self.calib = CameraCalibrator( config=cfg, extractor_product="LocalPeakIntegrator", eventsource=source, tool=None, ) for event in source: self.event_cutflow.count("noCuts") if self.event_cutflow.cut("min2Tels trig", len(event.dl0.tels_with_data)): if return_stub: yield stub(event) else: continue # calibrate the event self.calib.calibrate(event) # telescope loop tot_signal = 0 max_signals = {} n_pixel_dict = {} hillas_dict_reco = {} # for geometry hillas_dict = {} # for discrimination n_tels = { "tot": len(event.dl0.tels_with_data), "LST": 0, "MST": 0, "SST": 0, } n_cluster_dict = {} impact_dict_reco = {} # impact distance measured in tilt system point_azimuth_dict = {} point_altitude_dict = {} # To compute impact parameter in tilt system run_array_direction = event.mcheader.run_array_direction az, alt = run_array_direction[0], run_array_direction[1] ground_frame = GroundFrame() for tel_id in event.dl0.tels_with_data: self.image_cutflow.count("noCuts") camera = event.inst.subarray.tel[tel_id].camera # count the current telescope according to its size tel_type = event.inst.subarray.tel[tel_id].optics.tel_type # JLK, N telescopes before cut selection are not really interesting for # discrimination, too much fluctuations # n_tels[tel_type] += 1 # the camera image as a 1D array and stuff needed for calibration # Choose gain according to pywicta's procedure image_1d = simtel_event_to_images(event=event, tel_id=tel_id, ctapipe_format=True) pmt_signal = image_1d.input_image # calibrated image # clean the image try: with warnings.catch_warnings(): # Image with biggest cluster (reco cleaning) image_biggest = self.cleaner_reco.clean_image( pmt_signal, camera) image_biggest2d = geometry_converter.image_1d_to_2d( image_biggest, camera.cam_id) image_biggest2d = filter_pixels_clusters( image_biggest2d) image_biggest = geometry_converter.image_2d_to_1d( image_biggest2d, camera.cam_id) # Image for score/energy estimation (with clusters) image_extended = self.cleaner_extended.clean_image( pmt_signal, camera) except FileNotFoundError as e: # JLK, WHAT? print(e) continue # Apply some selection if self.image_cutflow.cut("min pixel", image_biggest): continue if self.image_cutflow.cut("min charge", np.sum(image_biggest)): continue # For cluster counts image_2d = geometry_converter.image_1d_to_2d( image_extended, camera.cam_id) n_cluster_dict[ tel_id] = pixel_clusters.number_of_pixels_clusters( array=image_2d, threshold=0) # could this go into `hillas_parameters` ...? max_signals[tel_id] = np.max(image_extended) # do the hillas reconstruction of the images # QUESTION should this change in numpy behaviour be done here # or within `hillas_parameters` itself? # JLK: make selection on biggest cluster with np.errstate(invalid="raise", divide="raise"): try: moments_reco = hillas_parameters( camera, image_biggest) # for geometry (eg direction) moments = hillas_parameters( camera, image_extended ) # for discrimination and energy reconstruction # if width and/or length are zero (e.g. when there is only only one # pixel or when all pixel are exactly in one row), the # parametrisation won't be very useful: skip if self.image_cutflow.cut("poor moments", moments_reco): # print('poor moments') continue if self.image_cutflow.cut("close to the edge", moments_reco, camera.cam_id): # print('close to the edge') continue if self.image_cutflow.cut("bad ellipticity", moments_reco): # print('bad ellipticity: w={}, l={}'.format(moments_reco.width, moments_reco.length)) continue except (FloatingPointError, hillas.HillasParameterizationError): continue point_azimuth_dict[ tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad point_altitude_dict[ tel_id] = event.mc.tel[tel_id].altitude_raw * u.rad n_tels[tel_type] += 1 hillas_dict[tel_id] = moments hillas_dict_reco[tel_id] = moments_reco n_pixel_dict[tel_id] = len(np.where(image_extended > 0)[0]) tot_signal += moments.intensity n_tels["reco"] = len(hillas_dict_reco) n_tels["discri"] = len(hillas_dict) if self.event_cutflow.cut("min2Tels reco", n_tels["reco"]): if return_stub: yield stub(event) else: continue try: with warnings.catch_warnings(): warnings.simplefilter("ignore") # Reconstruction results reco_result = self.shower_reco.predict( hillas_dict_reco, event.inst, point_altitude_dict, point_azimuth_dict, ) # shower_sys = TiltedGroundFrame(pointing_direction=HorizonFrame( # az=reco_result.az, # alt=reco_result.alt # )) # Impact parameter for energy estimation (/ tel) subarray = event.inst.subarray for tel_id in hillas_dict.keys(): pos = subarray.positions[tel_id] tel_ground = SkyCoord(pos[0], pos[1], pos[2], frame=ground_frame) # tel_tilt = tel_ground.transform_to(shower_sys) core_ground = SkyCoord( reco_result.core_x, reco_result.core_y, 0 * u.m, frame=ground_frame, ) # core_tilt = core_ground.transform_to(shower_sys) # Should be better handled (tilted frame) impact_dict_reco[tel_id] = np.sqrt( (core_ground.x - tel_ground.x)**2 + (core_ground.y - tel_ground.y)**2) except Exception as e: print("exception in reconstruction:", e) raise if return_stub: yield stub(event) else: continue if self.event_cutflow.cut("direction nan", reco_result): if return_stub: yield stub(event) else: continue yield PreparedEvent( event=event, n_pixel_dict=n_pixel_dict, hillas_dict=hillas_dict, hillas_dict_reco=hillas_dict_reco, n_tels=n_tels, tot_signal=tot_signal, max_signals=max_signals, n_cluster_dict=n_cluster_dict, reco_result=reco_result, impact_dict=impact_dict_reco, )