def tc_mapping(self): """ Obtain the TargetCalib mapping class """ from target_calib import CameraConfiguration version = self.mapping.metadata.version camera_config = CameraConfiguration(version) return camera_config.GetMapping(self.n_modules == 1)
def __init__(self, path, max_events=None): """ Reads simtelarray files utilising the SimTelEventSource from ctapipe Parameters ---------- path : str Path to the simtel file max_events : int Maximum number of events to read from the file """ super().__init__(path, max_events) try: from ctapipe.io import SimTelEventSource, EventSeeker except ModuleNotFoundError: msg = "Cannot find ctapipe installation" raise ModuleNotFoundError(msg) try: from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) self.path = path reader = SimTelEventSource(input_url=path, max_events=max_events, back_seekable=True) self.seeker = EventSeeker(reader) first_event = self.seeker[0] tels = list(first_event.r0.tels_with_data) self.tel = tels[0] shape = first_event.r0.tel[self.tel].waveform.shape _, self.n_pixels, self.n_samples = shape self.n_modules = self.n_pixels // 64 self.index = 0 n_modules = 32 camera_version = "1.1.0" self._camera_config = CameraConfiguration(camera_version) tc_mapping = self._camera_config.GetMapping(n_modules == 1) self.mapping = get_clp_mapping_from_tc_mapping(tc_mapping) pix_x = first_event.inst.subarray.tel[tels[0]].camera.pix_x.value pix_y = first_event.inst.subarray.tel[tels[0]].camera.pix_y.value self.mapping['xpix'] = pix_x self.mapping['ypix'] = pix_y self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.camera_version = self._camera_config.GetVersion() self.gps_time = None self.mc_true = None self.mc = None self.pointing = None self.mcheader = None
def __init__(self, config=None, parent=None, **kwargs): super().__init__(config=config, parent=parent, **kwargs) self._data = None self._event_index = None self._event_id = 0 self._time_tack = None self._time_sec = None self._time_ns = None self._reader = WaveformArrayReader(self.input_url, 2, 1) self._n_events = self._reader.fNEvents self._first_event_id = self._reader.fFirstEventID self._last_event_id = self._reader.fLastEventID self._obs_id = self._reader.fRunID n_modules = self._reader.fNModules n_pix = self._reader.fNPixels n_samples = self._reader.fNSamples self.camera_config = CameraConfiguration(self._reader.fCameraVersion) self._n_cells = self.camera_config.GetNCells() m = self.camera_config.GetMapping(n_modules == 1) self._optical_foclen = u.Quantity(2.15, u.m) self._mirror_area = u.Quantity(14.126, u.m ** 2) self._n_pixels = m.GetNPixels() self._xpix = np.array(m.GetXPixVector()) * u.m self._ypix = np.array(m.GetYPixVector()) * u.m self._refshape = np.zeros(10) # TODO: Get correct values for CHEC-S self._refstep = 0 # TODO: Get correct values for CHEC-S self._time_slice = 0 # TODO: Get correct values for CHEC-S self._chec_tel = 0 # Init fields self._r0_samples = None self._r1_samples = None self._first_cell_ids = np.zeros(n_pix, dtype=np.uint16) # Check if file is already r1 (Information obtained from a flag # in the file's header) is_r1 = self._reader.fR1 if is_r1: self._r1_samples = np.zeros( (1, n_pix, n_samples), dtype=np.float32 ) self._get_tio_event = self._reader.GetR1Event self._samples = self._r1_samples[0] else: self._r0_samples = np.zeros( (1, n_pix, n_samples), dtype=np.uint16 ) self._get_tio_event = self._reader.GetR0Event self._samples = self._r0_samples[0] self._init_container()
def plot_from_tc_mapping(): """ Plot using the TargetCalib Mapping class """ from target_calib import CameraConfiguration c = CameraConfiguration("1.1.0") m = c.GetMapping() camera = CameraImage.from_tc_mapping(m) image = np.zeros(m.GetNPixels()) image[::2] = 1 camera.image = image plt.show()
def configure(self, config={}): # should be read from the file in the future from target_calib import CameraConfiguration self.cam_config = CameraConfiguration("1.1.0") self._mapping = self.cam_config.GetMapping() config[self.cout_camconfig] = self.cam_config self.pixsize = self._mapping.GetSize() self.pix_posx = np.array(self._mapping.GetXPixVector()) self.pix_posy = np.array(self._mapping.GetYPixVector()) self.pix_pos = np.array(list(zip(self.pix_posx, self.pix_posy))) # for the time being we load all data at once config["n_frames"] = 1
def plot_tm(): """ Make a camera image plot for values that are per superpixel """ from target_calib import CameraConfiguration c = CameraConfiguration("1.1.0") m = c.GetMapping() df = get_tm_mapping(get_clp_mapping_from_tc_mapping(m)) camera = CameraImage.from_mapping(df) image = np.zeros(m.GetNModules()) image[::2] = 1 camera.image = image plt.show()
def __init__(self, path, max_events=None): try: from target_io import WaveformArrayReader from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) if not os.path.exists(path): raise FileNotFoundError("File does not exist: {}".format(path)) self.path = path self._reader = WaveformArrayReader(self.path, 2, 1) self.is_r1 = self._reader.fR1 self.n_events = self._reader.fNEvents self.run_id = self._reader.fRunID self.n_pixels = self._reader.fNPixels self.n_modules = self._reader.fNModules self.n_tmpix = self.n_pixels // self.n_modules self.n_samples = self._reader.fNSamples self._camera_config = CameraConfiguration(self._reader.fCameraVersion) self.tc_mapping = self._camera_config.GetMapping(self.n_modules == 1) self._pixel = self._PixelWaveforms(self) self.n_cells = self._camera_config.GetNCells() self.camera_version = self._camera_config.GetVersion() self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.current_tack = None self.current_cpu_ns = None self.current_cpu_s = None self.first_cell_ids = np.zeros(self.n_pixels, dtype=np.uint16) if self.is_r1: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.float32) self.get_tio_event = self._reader.GetR1Event else: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.uint16) self.get_tio_event = self._reader.GetR0Event if max_events and max_events < self.n_events: self.n_events = max_events
def plot_from_coordinates(): """ Plot directly with coordinates """ from target_calib import CameraConfiguration c = CameraConfiguration("1.1.0") m = c.GetMapping() xpix = np.array(m.GetXPixVector()) ypix = np.array(m.GetYPixVector()) size = m.GetSize() camera = CameraImage(xpix, ypix, size) image = np.zeros(xpix.size) image[::2] = 1 camera.image = image plt.show()
def obtain_pixel_list(superpixels): mapping = CameraConfiguration("1.1.0").GetMapping() mappingsp = MappingSP(mapping) pix_dict = dict() for sp in superpixels.keys(): pix_dict[sp] = list(mappingsp.GetContainedPixels(sp)) return pix_dict
def configure(self, config): # should be read from the file in the future from target_calib import CameraConfiguration config[self.cout_camconfig] = CameraConfiguration("1.1.0") # for the time being we load all data at once config["n_frames"] = 1
class Reader(ProcessingModule): def __init__( self, filename, focal_length=2.15, mirror_area=6.5, location=EarthLocation.from_geodetic(lon=14.974609, lat=37.693267, height=1730), ): super().__init__("DataReader") self.reader = DataReader(filename) print(self.reader) print("Simulation config:", self.reader.sim_attr_dict) self.cout_camconfig = "CameraConfiguration" self.out_raw_resp = "raw_resp" self._loaded_data = False self.location = location self.focal_length = focal_length self.mirror_area = mirror_area def configure(self, config={}): # should be read from the file in the future from target_calib import CameraConfiguration self.cam_config = CameraConfiguration("1.1.0") self._mapping = self.cam_config.GetMapping() config[self.cout_camconfig] = self.cam_config self.pixsize = self._mapping.GetSize() self.pix_posx = np.array(self._mapping.GetXPixVector()) self.pix_posy = np.array(self._mapping.GetYPixVector()) self.pix_pos = np.array(list(zip(self.pix_posx, self.pix_posy))) # for the time being we load all data at once config["n_frames"] = 1 def run(self, frame={}): # Only read the data once if not self._loaded_data: self.res = [] self.times = [] for r in self.reader.read(): self.res.append(r.flatten()) self.times.append(self.reader.cpu_t) self._loaded_data = True self.res = np.array(self.res) self.times = np.array(self.times) frame[self.out_raw_resp] = SlowSignalData( copy.copy(self.res), copy.copy(self.times), {"xpix": self.pix_posx, "ypix": self.pix_posy, "size": self.pixsize}, focal_length=self.focal_length, mirror_area=self.mirror_area, location=self.location, ) return frame
def main(): input_path = "/Volumes/gct-jason/data_checs/dynamicrange_180514/tf_pchip/spe_three.h5" file_dir = os.path.dirname(os.path.abspath(__file__)) output_dir = os.path.join(file_dir, "outputs") dead = [677, 293, 27, 1925] store = pd.HDFStore(input_path) df = store['coeff_pixel'] df_array = store['array_camera'] df = df.loc[~df['pixel'].isin(dead)] df_mean = df.mean().to_dict() norm = (df_mean['norm0'] + df_mean['norm1'] + df_mean['norm2']) / 3 spe = df_mean['spe'] config = CameraConfiguration("1.1.0") ref_path = config.GetReferencePulsePath() cc = CrossCorrelation(1, 96, reference_pulse_path=ref_path) d = dict(norm=1, eped=cc.get_pulse_height(df_mean['eped']), eped_sigma=cc.get_pulse_height(df_mean['eped_sigma']), spe=cc.get_pulse_height(df_mean['spe']), spe_sigma=cc.get_pulse_height(df_mean['spe_sigma']), lambda_=1, opct=df_mean['opct'], pap=df_mean['pap'], dap=df_mean['dap']) hist = df_array.loc[0, 'hist'] / (norm * 800) edges = cc.get_pulse_height(df_array.loc[0, 'edges']) between = cc.get_pulse_height(df_array.loc[0, 'between']) x = np.linspace(-5, 15, 1000) y = pe_signal(1, x, **d) p_hist = SPEHist() label = "fadc_amplitude = spe = {:.3f}".format(d['spe']) p_hist.plot(hist, edges, between, x, y, label) output_path = os.path.join(output_dir, "checs_fadc_amplitude.pdf") p_hist.save(output_path)
def from_camera_version(cls, camera_version, single=False, **kwargs): """ Generate the class using the camera version (required TargetCalib) Parameters ---------- camera_version : str Version of the camera (e.g. "1.0.1" corresponds to CHEC-S) single : bool Designate if it is just a single module you wish to plot kwargs Arguments passed to `CHECLabPy.plottong.setup.Plotter` Returns ------- `CameraImage` """ from target_calib import CameraConfiguration config = CameraConfiguration(camera_version) tc_mapping = config.GetMapping(single) return cls.from_tc_mapping(tc_mapping, **kwargs)
def main(): input_path = "/Volumes/gct-jason/thesis_data/checs/lab/dynrange/tf/tf_poly/spe.h5" file_dir = os.path.dirname(os.path.abspath(__file__)) output_dir = os.path.join(file_dir, "outputs") dead = [677, 293, 27, 1925] store = pd.HDFStore(input_path) df = store['coeff_pixel'] df_array = store['array_camera'] df = df.loc[~df['pixel'].isin(dead)] df_mean = df.mean().to_dict() norm = (df_mean['norm0'] + df_mean['norm1'] + df_mean['norm2']) / 3 config = CameraConfiguration("1.1.0") ref_path = config.GetReferencePulsePath() cc = CrossCorrelation(1, 96, reference_pulse_path=ref_path) d = dict(norm=1, eped=cc.get_pulse_height(df_mean['eped']), eped_sigma=cc.get_pulse_height(df_mean['eped_sigma']), lambda_=1) hist = df_array.loc[0, 'hist'] / (norm * 1000) edges = cc.get_pulse_height(df_array.loc[0, 'edges']) between = cc.get_pulse_height(df_array.loc[0, 'between']) x = np.linspace(-5, 15, 1000) y = pedestal_signal(x, **d) p_hist = SPEHist() label = "fadc_noise = eped_sigma = {:.3f}".format(d['eped_sigma']) p_hist.plot(hist, edges, between, x, y, label) output_path = os.path.join(output_dir, "checs_fadc_noise.pdf") p_hist.save(output_path)
def main(): pm = PixelMasks() dead = np.where(np.logical_or(pm.dead, np.repeat(pm.bad_hv, 4)))[0] ref_path = CameraConfiguration("1.1.0").GetReferencePulsePath() cc = CrossCorrelation(1, 96, reference_pulse_path=ref_path) spe_path = get_astri_2019('d2019-04-23_nudges/spe_+0.h5') with pd.HDFStore(spe_path) as store: coeff = store['coeff_pixel'] coeff = coeff.loc[~coeff['pixel'].isin(dead)] spe = cc.get_pulse_height(np.median(coeff['spe'])) spe_sigma = cc.get_pulse_height(np.median(coeff['spe_sigma'])) opct = np.median(coeff['opct']) output_dir = get_plot("d190716_simtel_cfg") generate_spectrum.call(output_dir, spe, spe_sigma, opct, 0, 0)
class SimtelReader(WaveformReader): def __init__(self, path, max_events=None): """ Reads simtelarray files utilising the SimTelEventSource from ctapipe Parameters ---------- path : str Path to the simtel file max_events : int Maximum number of events to read from the file """ super().__init__(path, max_events) try: from ctapipe.io import SimTelEventSource, EventSeeker except ModuleNotFoundError: msg = "Cannot find ctapipe installation" raise ModuleNotFoundError(msg) try: from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) self.path = path reader = SimTelEventSource(input_url=path, max_events=max_events, back_seekable=True) self.seeker = EventSeeker(reader) first_event = self.seeker[0] tels = list(first_event.r0.tels_with_data) self.tel = tels[0] shape = first_event.r0.tel[self.tel].waveform.shape _, self.n_pixels, self.n_samples = shape self.n_modules = self.n_pixels // 64 self.index = 0 n_modules = 32 camera_version = "1.1.0" self._camera_config = CameraConfiguration(camera_version) tc_mapping = self._camera_config.GetMapping(n_modules == 1) self.mapping = get_clp_mapping_from_tc_mapping(tc_mapping) pix_x = first_event.inst.subarray.tel[tels[0]].camera.pix_x.value pix_y = first_event.inst.subarray.tel[tels[0]].camera.pix_y.value self.mapping['xpix'] = pix_x self.mapping['ypix'] = pix_y self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.camera_version = self._camera_config.GetVersion() self.gps_time = None self.mc_true = None self.mc = None self.pointing = None self.mcheader = None def _get_event(self, iev): event = self.seeker[iev] self._fill_event_containers(event) return event.r1.tel[self.tel].waveform[0] @staticmethod def is_compatible(path): # read the first 4 bytes with open(path, 'rb') as f: marker_bytes = f.read(4) # if file is gzip, read the first 4 bytes with gzip again if marker_bytes[0] == 0x1f and marker_bytes[1] == 0x8b: with gzip.open(path, 'rb') as f: marker_bytes = f.read(4) # check for the simtel magic marker int_marker, = struct.unpack('I', marker_bytes) return int_marker == 3558836791 or int_marker == 931798996 def __iter__(self): for event in self.seeker: self._fill_event_containers(event) yield event.r1.tel[self.tel].waveform[0] def _fill_event_containers(self, event): self.index = event.count self.run_id = event.r0.obs_id self.gps_time = event.trig.gps_time self.mc_true = event.mc.tel[self.tel].photo_electron_image self.mc = dict(iev=self.index, t_cpu=self.t_cpu, energy=event.mc.energy.value, alt=event.mc.alt.value, az=event.mc.az.value, core_x=event.mc.core_x.value, core_y=event.mc.core_y.value, h_first_int=event.mc.h_first_int.value, x_max=event.mc.x_max.value, shower_primary_id=event.mc.shower_primary_id) self.pointing = dict( iev=self.index, t_cpu=self.t_cpu, azimuth_raw=event.mc.tel[self.tel].azimuth_raw, altitude_raw=event.mc.tel[self.tel].altitude_raw, azimuth_cor=event.mc.tel[self.tel].azimuth_cor, altitude_cor=event.mc.tel[self.tel].altitude_cor, ) if self.mcheader is None: mch = event.mcheader self.mcheader = dict( corsika_version=mch.corsika_version, simtel_version=mch.simtel_version, energy_range_min=mch.energy_range_min.value, energy_range_max=mch.energy_range_max.value, prod_site_B_total=mch.prod_site_B_total.value, prod_site_B_declination=mch.prod_site_B_declination.value, prod_site_B_inclination=mch.prod_site_B_inclination.value, prod_site_alt=mch.prod_site_alt.value, spectral_index=mch.spectral_index, shower_prog_start=mch.shower_prog_start, shower_prog_id=mch.shower_prog_id, detector_prog_start=mch.detector_prog_start, detector_prog_id=mch.detector_prog_id, num_showers=mch.num_showers, shower_reuse=mch.shower_reuse, max_alt=mch.max_alt.value, min_alt=mch.min_alt.value, max_az=mch.max_az.value, min_az=mch.min_az.value, diffuse=mch.diffuse, max_viewcone_radius=mch.max_viewcone_radius.value, min_viewcone_radius=mch.min_viewcone_radius.value, max_scatter_range=mch.max_scatter_range.value, min_scatter_range=mch.min_scatter_range.value, core_pos_mode=mch.core_pos_mode, injection_height=mch.injection_height.value, atmosphere=mch.atmosphere, corsika_iact_options=mch.corsika_iact_options, corsika_low_E_model=mch.corsika_low_E_model, corsika_high_E_model=mch.corsika_high_E_model, corsika_bunchsize=mch.corsika_bunchsize, corsika_wlen_min=mch.corsika_wlen_min.value, corsika_wlen_max=mch.corsika_wlen_max.value, corsika_low_E_detail=mch.corsika_low_E_detail, corsika_high_E_detail=mch.corsika_high_E_detail, ) @property def n_events(self): return len(self.seeker) @property def t_cpu(self): return pd.to_datetime(self.gps_time.value, unit='s')
def get_clp_mapping_from_version(version, single_module=False): from target_calib import CameraConfiguration tc_mapping = CameraConfiguration(version).GetMapping( singleModule=single_module ) return get_clp_mapping_from_tc_mapping(tc_mapping)
class DynamicPlotter: def __init__(self, ip, port, sampleinterval=0.1, timewindow=10.0, size=(600, 350)): # PyQtGraph stuff self._interval = int(sampleinterval * 1000) self.app = QtGui.QApplication([]) self.win = pg.GraphicsWindow() self.win.setWindowTitle("Slow signal viewer") # data self.data = defaultdict( list) # {'hv_current':list(),'hv_voltage':list()} self.time = list() self.plts = {} self.curve_names = list() self.plot_time_window = 10 self.lastframeindicator = pg.LabelItem(text="") self.lastframe = datetime.now() self.win.addItem(self.lastframeindicator) self.gotdataindicator = pg.LabelItem( text='<font color="{}">{}</font>'.format("green", "connected")) self.win.addItem(self.gotdataindicator) self.counter = 0 self.win.nextRow() # self.win.addItem(pg.TextItem(text='HEJHEJHEJ', color=(200, 200, 200), html=None, anchor=(0, 0), border=None, fill=None, angle=0, rotateAxis=None),row=1 ) self._add_plot( "TMs per readout", ("Number of TMs", ""), ("Time", "min"), ["current nTMs", "av nTMs"], ) self._add_plot( "Slow signal amplitude", ("Amplitude", "mV"), ("Time", "min"), ["total amplitude", "max amplitude X 10"], ) self.win.nextRow() # self._add_plot('DAC HV Voltage (Super pixels 0-8)',('Voltage','V'),('Time','min'),['dac_suppix_%d'%i for i in range(9)]) # self._add_plot('DAC HV Voltage (Super pixels 9-15)',('Voltage','V'),('Time','min'),['dac_suppix_%d'%i for i in range(9,16)]) # self._add_plot('Temperature',('Temperature',u"\u00B0"+'C'),('Time','min'),['temp_powb','temp_auxb','temp_primb'])#,'temp_sipm' self.badpixs = np.array([ 25, 58, 101, 304, 449, 570, 653, 1049, 1094, 1158, 1177, 1381, 1427, 1434, 1439, 1765, 1829, 1869, 1945, 1957, 2009, 2043, ]) self.img = pg.ImageItem(levels=(0, 400)) self.p = self.win.addPlot() self.p.addItem(self.img) self.c = CameraConfiguration("1.1.0") self.m = self.c.GetMapping() self.xpix = np.array(self.m.GetXPixVector()) self.ypix = np.array(self.m.GetYPixVector()) self.xmap = np.array(self.m.GetRowVector()) self.ymap = np.array(self.m.GetColumnVector()) self.map = np.zeros((48, 48), dtype=np.uint64) for i in range(2048): self.map[self.xmap[i], self.ymap[i]] = i self.map = self.map.flatten() # label = pg.LabelItem() # label2 = pg.TextItem("BLAH") # # label2.setText() # label.setText("test",color='CCFF00') # self.win.addItem(label) # self.win.addItem(label2) # Add a color scale # self.gl = pg.GradientLegend((20, 150), (-10, -10)) # self.gl.setParentItem(self.img) # self.gl.scale(1,-1) # self.gl # self.colorScale.setLabels('Label') # self.p.scene().addItem(self.colorScale) # QTimer self.timer = QtCore.QTimer() self.timer.timeout.connect(self.updateplots) self.timer.start(self._interval) self.ss_listener = subscribers.SSReadoutSubscriber(port=port, ip=ip) self.ss_listener.start() # if(isinstance(ss_calib, np.array)): # else: # self.ss_calib = np.zeros(64) def _add_plot(self, title, labely, labelx, curves): self.curve_names += curves self.plts[title] = (self.win.addPlot(title=title), curves, list()) self.plts[title][0].setLabel("left", labely[0], labely[1]) self.plts[title][0].setLabel("bottom", labelx[0], labelx[1]) self.plts[title][0].addLegend() self.plts[title][0].showGrid(x=True, y=True) for i, curve in enumerate(curves): t = sum((color_set1[i], (20, )), ()) self.plts[title][2].append(self.plts[title][0].plot( np.linspace(0, 10, 10), np.linspace(0, 10, 10), pen=color_set1[i], fillLevel=0, fillBrush=t, name=curve, )) self.plts[title][0].setXRange(-self.plot_time_window, 0) # self.plts[title][2][i].setClickable() def get_data(self): evs = [] ntries = 0 self.counter += 1 while ntries < 10: try: ev = self.ss_listener.get_data(timeout=0.1) evs.append(ev._get_asic_mapped()) except: ntries += 1 break if len(evs) == 0: return else: data = evs[-1] self.gotdata = True self.lastframe = datetime.now() imgdata = np.zeros((48, 48)) m = ~np.isnan(data) parttms = set(np.where(m)[0]) nparttms = len(parttms) data = data.flatten() data[data <= 0] = np.nan # data = np.log10(data) data[self.badpixs] = np.nan # print(data[data>0]) imgdata[self.xmap, self.ymap] = data imgdata[np.isnan(imgdata)] = 0 self.img.setImage(imgdata.T, levels=(0, 400)) self.data["total amplitude"].append(np.nansum(data)) self.data["max amplitude X 10"].append(np.nanmax(data) * 10) self.data["current nTMs"].append(nparttms) n = np.min([10, len(self.data["current nTMs"])]) self.data["av nTMs"].append(np.mean(self.data["current nTMs"][-n:])) self.time.append(datetime.now()) def run(self): self.app.exec_() def _update_plot(self, time, plot): for i in range(len(plot[2])): plot[2][i].setData(time, self.data[plot[1][i]][::-1], fillLevel=0.5) def updateplots(self): self.gotdata = False self.get_data() if self.gotdata: stat = ("green", "connected") else: stat = ("red", "disconnected") self.gotdataindicator.setText( '<font color="{}">{}</font>'.format(*stat)) self.lastframeindicator.setText( "time since last received frame: {} ".format(datetime.now() - self.lastframe)) time = list() now = datetime.now() for t in self.time: time.append((t - now).total_seconds() / 60) t = np.array(time) if len(t) > 0: trange = t[t > t[-1] - self.plot_time_window] for k, v in self.plts.items(): # v[0].setXRange(trange[0], trange[-1]) self._update_plot(time, v) self.app.processEvents()
def __init__(self, ip, port, sampleinterval=0.1, timewindow=10.0, size=(600, 350)): # PyQtGraph stuff self._interval = int(sampleinterval * 1000) self.app = QtGui.QApplication([]) self.win = pg.GraphicsWindow() self.win.setWindowTitle("Slow signal viewer") # data self.data = defaultdict( list) # {'hv_current':list(),'hv_voltage':list()} self.time = list() self.plts = {} self.curve_names = list() self.plot_time_window = 10 self.lastframeindicator = pg.LabelItem(text="") self.lastframe = datetime.now() self.win.addItem(self.lastframeindicator) self.gotdataindicator = pg.LabelItem( text='<font color="{}">{}</font>'.format("green", "connected")) self.win.addItem(self.gotdataindicator) self.counter = 0 self.win.nextRow() # self.win.addItem(pg.TextItem(text='HEJHEJHEJ', color=(200, 200, 200), html=None, anchor=(0, 0), border=None, fill=None, angle=0, rotateAxis=None),row=1 ) self._add_plot( "TMs per readout", ("Number of TMs", ""), ("Time", "min"), ["current nTMs", "av nTMs"], ) self._add_plot( "Slow signal amplitude", ("Amplitude", "mV"), ("Time", "min"), ["total amplitude", "max amplitude X 10"], ) self.win.nextRow() # self._add_plot('DAC HV Voltage (Super pixels 0-8)',('Voltage','V'),('Time','min'),['dac_suppix_%d'%i for i in range(9)]) # self._add_plot('DAC HV Voltage (Super pixels 9-15)',('Voltage','V'),('Time','min'),['dac_suppix_%d'%i for i in range(9,16)]) # self._add_plot('Temperature',('Temperature',u"\u00B0"+'C'),('Time','min'),['temp_powb','temp_auxb','temp_primb'])#,'temp_sipm' self.badpixs = np.array([ 25, 58, 101, 304, 449, 570, 653, 1049, 1094, 1158, 1177, 1381, 1427, 1434, 1439, 1765, 1829, 1869, 1945, 1957, 2009, 2043, ]) self.img = pg.ImageItem(levels=(0, 400)) self.p = self.win.addPlot() self.p.addItem(self.img) self.c = CameraConfiguration("1.1.0") self.m = self.c.GetMapping() self.xpix = np.array(self.m.GetXPixVector()) self.ypix = np.array(self.m.GetYPixVector()) self.xmap = np.array(self.m.GetRowVector()) self.ymap = np.array(self.m.GetColumnVector()) self.map = np.zeros((48, 48), dtype=np.uint64) for i in range(2048): self.map[self.xmap[i], self.ymap[i]] = i self.map = self.map.flatten() # label = pg.LabelItem() # label2 = pg.TextItem("BLAH") # # label2.setText() # label.setText("test",color='CCFF00') # self.win.addItem(label) # self.win.addItem(label2) # Add a color scale # self.gl = pg.GradientLegend((20, 150), (-10, -10)) # self.gl.setParentItem(self.img) # self.gl.scale(1,-1) # self.gl # self.colorScale.setLabels('Label') # self.p.scene().addItem(self.colorScale) # QTimer self.timer = QtCore.QTimer() self.timer.timeout.connect(self.updateplots) self.timer.start(self._interval) self.ss_listener = subscribers.SSReadoutSubscriber(port=port, ip=ip) self.ss_listener.start()
class DynamicTRPlotter: def __init__(self, ip, port, sampleinterval=0.1, timewindow=10.0, size=(600, 350)): # PyQtGraph stuff self._interval = int(sampleinterval * 1000) self.app = QtGui.QApplication([]) self.win = pg.GraphicsWindow() self.win.setWindowTitle("Triggerpattern viewer") # data self.data = defaultdict( list) # {'hv_current':list(),'hv_voltage':list()} self.time = list() self.plts = {} self.curve_names = list() self.plot_time_window = 10 # self.win.addItem(pg.TextItem(text='HEJHEJHEJ', color=(200, 200, 200), html=None, anchor=(0, 0), border=None, fill=None, angle=0, rotateAxis=None),row=1 ) self.lastframeindicator = pg.LabelItem(text="") self.lastframe = datetime.now() self.win.addItem(self.lastframeindicator) self.gotdataindicator = pg.LabelItem( text='<font color="{}">{}</font>'.format("green", "connected")) self.win.addItem(self.gotdataindicator) self.win.nextRow() self.countersindicator = pg.LabelItem(text="") self.win.addItem(self.countersindicator) self.win.nextRow() self.last_uc_ev = 0 self.missed_counter = 0 self.readout_counter = 0 self._add_plot( "Trigger rate", ("Rate Hz", ""), ("Time", "min"), ["Nominal triggers"], # , "Busy triggers"], ) # self._add_plot( # "Slow signal amplitude", # ("Amplitude", "mV"), # ("Time", "min"), # ["total amplitude", "max amplitude X 10"], # ) self.win.nextRow() self.img = pg.ImageItem(levels=(0, 400)) self.p = self.win.addPlot() self.p.addItem(self.img) self.c = CameraConfiguration("1.1.0") self.m = self.c.GetMapping() from CHECLabPy.plotting.camera import CameraImage, CameraImageImshow from CHECLabPy.utils.mapping import ( get_clp_mapping_from_tc_mapping, get_superpixel_mapping, get_tm_mapping, ) self.sp_mapping = get_superpixel_mapping( get_clp_mapping_from_tc_mapping(self.m)) self.xpix = np.array(self.sp_mapping.xpix) self.ypix = np.array(self.sp_mapping.ypix) self.xmap = np.array(self.sp_mapping.row) self.ymap = np.array(self.sp_mapping.col) print(self.xmap.shape, self.ymap.shape) self.map = np.zeros((24, 24), dtype=np.uint64) for i in range(512): self.map[self.xmap[i], self.ymap[i]] = i self.map = self.map.flatten() from ssdaq.data._dataimpl.trigger_format import ( get_SP2bptrigg_mapping, get_bptrigg2SP_mapping, ) self.bptmap = get_bptrigg2SP_mapping() self.timer = QtCore.QTimer() self.timer.timeout.connect(self.updateplots) self.timer.start(self._interval) self.tr_listener = subscribers.BasicTriggerSubscriber(port=port, ip=ip) self.tr_listener.start() # if(isinstance(ss_calib, np.array)): # else: # self.ss_calib = np.zeros(64) def _add_plot(self, title, labely, labelx, curves): self.curve_names += curves self.plts[title] = (self.win.addPlot(title=title), curves, list()) self.plts[title][0].setLabel("left", labely[0], labely[1]) self.plts[title][0].setLabel("bottom", labelx[0], labelx[1]) self.plts[title][0].addLegend() self.plts[title][0].showGrid(x=True, y=True) for i, curve in enumerate(curves): t = sum((color_set1[i], (20, )), ()) self.plts[title][2].append(self.plts[title][0].plot( np.linspace(0, 10, 10), np.linspace(0, 10, 10), pen=color_set1[i], fillLevel=0, fillBrush=t, name=curve, )) self.plts[title][0].setXRange(-self.plot_time_window, 0) # self.plts[title][2][i].setClickable() def get_data(self): trs = [] ntries = 0 while ntries < 10: try: t = self.tr_listener.get_data(timeout=0.1) trs.append(t) if t.uc_ev == 0 or t.uc_ev < self.last_uc_ev: self.missed_counter = 0 self.readout_counter = 0 self.last_uc_ev = 0 if self.last_uc_ev != 0 and self.last_uc_ev + 1 != t.uc_ev: self.missed_counter += t.uc_ev - self.last_uc_ev - 1 self.readout_counter += 1 self.last_uc_ev = t.uc_ev except: ntries += 1 break if len(trs) == 0: return else: trigg = trs[-1] self.lastframe = datetime.now() imgdata = np.zeros((24, 24)) imgdata[self.xmap, self.ymap] = trigg.trigg_union[self.bptmap] imgdata[np.isnan(imgdata)] = 0 self.img.setImage(imgdata.T, levels=(0, 1)) trig0 = evs[0] self.data["Nominal triggers"].append( len(evs) / ((trigg.TACK - trig0.TACK) * 1e-9)) # self.data["max amplitude X 10"].append(np.nanmax(data) * 10) # self.data["current nTMs"].append(nparttms) # n = np.min([10, len(self.data["current nTMs"])]) # self.data["av nTMs"].append(np.mean(self.data["current nTMs"][-n:])) self.time.append(datetime.now()) def run(self): self.app.exec_() def _update_plot(self, time, plot): for i in range(len(plot[2])): plot[2][i].setData(time, self.data[plot[1][i]][::-1], fillLevel=0.5) def updateplots(self): self.gotdata = False self.get_data() self.countersindicator.setText( "Readout Triggers: {}, Lost packets: {}".format( self.readout_counter, self.missed_counter)) if self.gotdata: stat = ("green", "connected") else: stat = ("red", "disconnected") self.gotdataindicator.setText( '<font color="{}">{}</font>'.format(*stat)) self.lastframeindicator.setText( "time since last received frame: {} ".format(datetime.now() - self.lastframe)) time = list() now = datetime.now() for t in self.time: time.append((t - now).total_seconds() / 60) t = np.array(time) if len(t) > 0: trange = t[t > t[-1] - self.plot_time_window] for k, v in self.plts.items(): # v[0].setXRange(trange[0], trange[-1]) self._update_plot(time, v) self.app.processEvents()
def __init__(self, ip, port, sampleinterval=0.1, timewindow=10.0, size=(600, 350)): # PyQtGraph stuff self._interval = int(sampleinterval * 1000) self.app = QtGui.QApplication([]) self.win = pg.GraphicsWindow() self.win.setWindowTitle("Triggerpattern viewer") # data self.data = defaultdict( list) # {'hv_current':list(),'hv_voltage':list()} self.time = list() self.plts = {} self.curve_names = list() self.plot_time_window = 10 # self.win.addItem(pg.TextItem(text='HEJHEJHEJ', color=(200, 200, 200), html=None, anchor=(0, 0), border=None, fill=None, angle=0, rotateAxis=None),row=1 ) self.lastframeindicator = pg.LabelItem(text="") self.lastframe = datetime.now() self.win.addItem(self.lastframeindicator) self.gotdataindicator = pg.LabelItem( text='<font color="{}">{}</font>'.format("green", "connected")) self.win.addItem(self.gotdataindicator) self.win.nextRow() self.countersindicator = pg.LabelItem(text="") self.win.addItem(self.countersindicator) self.win.nextRow() self.last_uc_ev = 0 self.missed_counter = 0 self.readout_counter = 0 self._add_plot( "Trigger rate", ("Rate Hz", ""), ("Time", "min"), ["Nominal triggers"], # , "Busy triggers"], ) # self._add_plot( # "Slow signal amplitude", # ("Amplitude", "mV"), # ("Time", "min"), # ["total amplitude", "max amplitude X 10"], # ) self.win.nextRow() self.img = pg.ImageItem(levels=(0, 400)) self.p = self.win.addPlot() self.p.addItem(self.img) self.c = CameraConfiguration("1.1.0") self.m = self.c.GetMapping() from CHECLabPy.plotting.camera import CameraImage, CameraImageImshow from CHECLabPy.utils.mapping import ( get_clp_mapping_from_tc_mapping, get_superpixel_mapping, get_tm_mapping, ) self.sp_mapping = get_superpixel_mapping( get_clp_mapping_from_tc_mapping(self.m)) self.xpix = np.array(self.sp_mapping.xpix) self.ypix = np.array(self.sp_mapping.ypix) self.xmap = np.array(self.sp_mapping.row) self.ymap = np.array(self.sp_mapping.col) print(self.xmap.shape, self.ymap.shape) self.map = np.zeros((24, 24), dtype=np.uint64) for i in range(512): self.map[self.xmap[i], self.ymap[i]] = i self.map = self.map.flatten() from ssdaq.data._dataimpl.trigger_format import ( get_SP2bptrigg_mapping, get_bptrigg2SP_mapping, ) self.bptmap = get_bptrigg2SP_mapping() self.timer = QtCore.QTimer() self.timer.timeout.connect(self.updateplots) self.timer.start(self._interval) self.tr_listener = subscribers.BasicTriggerSubscriber(port=port, ip=ip) self.tr_listener.start()
def cal_report(cal): hist_a = dashi.histogram.hist1d( np.linspace(np.nanmin(cal.a), np.nanmax(cal.a), 100)) hist_a.fill(cal.a) hist_b = dashi.histogram.hist1d( np.linspace(np.nanmin(cal.b), np.nanmax(cal.b), 100)) hist_b.fill(cal.b) hist_c = dashi.histogram.hist1d( np.linspace(np.nanmin(cal.c), np.nanmax(cal.c), 100)) hist_c.fill(cal.c) thresh = (-cal.b + np.sqrt(cal.b**2 - 4 * cal.a * cal.c)) / (2 * cal.a) hist_thresh = dashi.histogram.hist1d( np.linspace(np.nanmin(thresh), np.nanmax(thresh), 100)) hist_thresh.fill(thresh) plt.figure(figsize=(10, 8)) hist_a.line() hist_a.statbox() plt.title("a parameter") plt.savefig("plots/cal_report_a_par_hist.png") plt.figure(figsize=(10, 8)) hist_b.line() hist_b.statbox() plt.title("b parameter") plt.savefig("plots/cal_report_b_par_hist.png") plt.figure(figsize=(10, 8)) hist_c.line() hist_c.statbox() plt.title("c parameter") plt.savefig("plots/cal_report_c_par_hist.png") plt.figure(figsize=(10, 8)) hist_thresh.line() hist_thresh.statbox() plt.title("Threshold") plt.savefig("plots/cal_report_threshold_hist.png") from target_calib import CameraConfiguration cam_config = CameraConfiguration("1.1.0") mapping = cam_config.GetMapping() pixsize = mapping.GetSize() pix_posx = np.array(mapping.GetXPixVector()) pix_posy = np.array(mapping.GetYPixVector()) from CHECLabPy.plotting.camera import CameraImage f, a = plt.subplots(figsize=(13, 8)) camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a) camera.image = cal.a camera.add_colorbar("") camera.ax.set_title("a parameter") camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]), color="r", linewidth=2) # camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4) camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k") camera.highlight_pixels(list(cal.badpixs["bad_fit"]), color="b", linewidth=1.4) camera.set_limits_minmax(np.nanmin(cal.a), 0) plt.savefig("plots/cal_report_a_par.png") f, a = plt.subplots(figsize=(13, 8)) camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a) camera.image = cal.b camera.add_colorbar("") camera.ax.set_title("b parameter") camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]), color="r", linewidth=2) # camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4) camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k") camera.highlight_pixels(list(cal.badpixs["bad_fit"]), color="b", linewidth=1.4) camera.set_limits_minmax(0, np.nanmax(cal.b)) plt.savefig("plots/cal_report_b_par.png") f, a = plt.subplots(figsize=(13, 8)) camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a) camera.image = cal.c camera.add_colorbar("") camera.ax.set_title("c parameter") camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]), color="r", linewidth=2) # camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4) camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k") camera.highlight_pixels(list(cal.badpixs["bad_fit"]), color="b", linewidth=1.4) camera.set_limits_minmax(np.nanmin(cal.c), 0) plt.savefig("plots/cal_report_c_par.png") f, a = plt.subplots(figsize=(13, 8)) camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a) camera.image = thresh camera.add_colorbar("photon rate (MHz)") camera.ax.set_title("NSB threshold") camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]), color="r", linewidth=2) # camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4) camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k") camera.highlight_pixels(list(cal.badpixs["bad_fit"]), color="b", linewidth=1.4) # camera.set_limits_minmax(np.nanmin(cal.c),0) plt.savefig("plots/cal_report_nsb_thres.png")
def main(): description = ('Reduce a *_r1.tio file into a *_dl1.hdf5 file containing ' 'various parameters extracted from the waveforms') parser = argparse.ArgumentParser(description=description, formatter_class=Formatter) parser.add_argument('-f', '--files', dest='input_paths', nargs='+', help='path to the TIO r1 run files') parser.add_argument('-m', '--monitor', dest='monitor', action='store', help='path to the monitor file (OPTIONAL)') parser.add_argument('-o', '--output', dest='output_path', action='store', help='path to store the output HDF5 dl1 file ' '(OPTIONAL, will be automatically set if ' 'not specified)') parser.add_argument('-n', '--maxevents', dest='max_events', action='store', help='Number of events to process', type=int) parser.add_argument('-r', '--reducer', dest='reducer', action='store', default='AverageWF', choices=WaveformReducerFactory.subclass_names, help='WaveformReducer to use') parser.add_argument('-c', '--config', dest='configuration', help="""Configuration to pass to the waveform reducer (Usage: '{"window_shift":6, "window_size":6}') """) parser.add_argument('-p', '--plot', dest='plot', action='store_true', help="Plot stages for waveform reducers") args = parser.parse_args() if args.configuration: config = json.loads(args.configuration) config_string = args.configuration else: config = {} config_string = "" input_paths = args.input_paths n_files = len(input_paths) for i_path, input_path in enumerate(input_paths): print("PROGRESS: Reducing file {}/{}".format(i_path + 1, n_files)) kwargs = dict(input_url=input_path, max_events=args.max_events) reader = HESSIOEventSource(**kwargs) seeker = EventSeeker(reader) n_events = len(seeker) first_event = seeker[0] tels = list(first_event.r0.tels_with_data) _, n_pixels, n_samples = first_event.r0.tel[tels[0]].waveform.shape n_modules = 32 n_cells = 1 pixel_array = np.arange(n_pixels) camera_version = "1.1.0" camera_config = CameraConfiguration(camera_version) tc_mapping = camera_config.GetMapping(n_modules == 1) mapping = get_clp_mapping_from_tc_mapping(tc_mapping) if 'reference_pulse_path' not in config: reference_pulse_path = camera_config.GetReferencePulsePath() config['reference_pulse_path'] = reference_pulse_path kwargs = dict(n_pixels=n_pixels, n_samples=n_samples, plot=args.plot, mapping=mapping, **config) reducer = WaveformReducerFactory.produce(args.reducer, **kwargs) baseline_subtractor = BaselineSubtractor(seeker) input_path = reader.input_url output_path = args.output_path if not output_path: output_path = input_path.replace(".simtel.gz", "_dl1.h5") output_path = output_path.replace("run", "Run") r1 = HESSIOR1Calibrator() with DL1Writer(output_path, n_events * n_pixels, args.monitor) as writer: t_cpu = 0 start_time = 0 desc = "Processing events" for event in tqdm(seeker, total=n_events, desc=desc): iev = event.count r1.calibrate(event) waveforms = event.r1.tel[tels[0]].waveform[0] mc_true = event.mc.tel[tels[0]].photo_electron_image t_cpu = pd.to_datetime(event.trig.gps_time.value, unit='s') if not start_time: start_time = t_cpu waveforms_bs = baseline_subtractor.subtract(waveforms) bs = baseline_subtractor.baseline params = reducer.process(waveforms_bs) df_ev = pd.DataFrame( dict(iev=iev, pixel=pixel_array, first_cell_id=0, t_cpu=t_cpu, t_tack=0, baseline_subtracted=bs, **params, mc_true=mc_true)) writer.append_event(df_ev) sn_dict = {} for tm in range(n_modules): sn_dict['TM{:02d}_SN'.format(tm)] = "NaN" metadata = dict(source="CHECLabPy", date_generated=pd.datetime.now(), input_path=input_path, n_events=n_events, n_modules=n_modules, n_pixels=n_pixels, n_samples=n_samples, n_cells=n_cells, start_time=start_time, end_time=t_cpu, camera_version=camera_version, reducer=reducer.__class__.__name__, configuration=config_string, **sn_dict) writer.add_metadata(**metadata) writer.add_mapping(mapping)
def main(): file_dir = os.path.dirname(os.path.abspath(__file__)) output_dir = os.path.join(file_dir, "outputs") output_path = os.path.join(output_dir, "checs_pixel_mapping.txt") config = CameraConfiguration("1.1.0") mapping = config.GetMapping() mappingsp = mapping.GetMappingSP() with open(output_path, 'w') as f: pixtype = """# PixType format: # Par. 1: pixel type (here always 1) # 2: PMT type (must be 0) # 3: cathode shape type # 4: visible cathode diameter [cm] # 5: funnel shape type (see above) # 6: funnel diameter (flat-to-flat for hex.) [cm] # 7: depth of funnel [cm] # 8: a) funnel efficiency "filename", b) funnel plate transparency # 9: a) optional wavelength "filename", b) funnel wall reflectivity # In case a) in column 8, columns 3+7 are not used. If in case a) the # optional file name for the wavelength dependence is provided, the # overall scale in the file provided as parameter 8 is ignored because # it is rescaled such that the average over all mirrors is equal to # the given wavelength dependent value. # # Shape types: 0: circ., 1: hex(flat x), 2: sq., 3: hex(flat y) # #Angular Dep currently all set to one for checks # Note that pixel size is scale from the actual 6.125 mm at the focal length # planned with GATE telescopes (228.3 cm) to the focal length of ASTRI (215 cm). # Similarly scaled are the pixel positions. PixType 1 0 2 0.623 2 0.623 0.0 "transmission_pmma_vs_theta_20150422.dat" "transmission_pmma_vs_lambda_meas0deg_coat_82raws.dat" # Pixel format: # Par. 1: pixel number (starting at 0) # 2: pixel type (must be 1) # 3: x position [cm] # 4: y position [cm] # 5: drawer/module number # 6: board number in module # 7: channel number n board # 8: board Id number ('0x....') # 9: pixel on (is on if parameter is missing) """ f.write(pixtype) for i in range(mapping.GetNPixels()): ipix = mapping.GetPixel(i) xpix = mapping.GetXPix(i) * 10**2 ypix = mapping.GetYPix(i) * 10**2 imod = mapping.GetSlot(i) ichan = mapping.GetTMPixel(i) l = "Pixel\t{}\t1\t{:.2f}\t{:.2f}\t{}\t0\t{}\t0x00\t1\n" lf = l.format(ipix, xpix, ypix, imod, ichan) f.write(lf) f.write('\n') for i in range(mappingsp.GetNSuperPixels()): nei = mappingsp.GetNeighbours(i, True) f.write("MajorityTrigger * of ") for isp in [i, *nei]: con = list(mappingsp.GetContainedPixels(isp)) f.write(str(isp)) f.write("[{}] ".format(','.join(str(x) for x in con))) f.write('\n')
def __init__(self, path, max_events=None): """ Reads simtelarray files utilising the SimTelEventSource from ctapipe Parameters ---------- path : str Path to the simtel file max_events : int Maximum number of events to read from the file """ super().__init__(path, max_events) try: from ctapipe.io import SimTelEventSource, EventSeeker from ctapipe.coordinates import EngineeringCameraFrame except ModuleNotFoundError: msg = "Cannot find ctapipe installation" raise ModuleNotFoundError(msg) try: from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) self.path = path reader = SimTelEventSource(input_url=path, max_events=max_events, back_seekable=True) self.seeker = EventSeeker(reader) first_event = self.seeker[0] tels = list(first_event.r0.tels_with_data) self.tel = tels[0] shape = first_event.r0.tel[self.tel].waveform.shape _, self.n_pixels, self.n_samples = shape self.n_modules = self.n_pixels // 64 n_modules = 32 camera_version = "1.1.0" self._camera_config = CameraConfiguration(camera_version) tc_mapping = self._camera_config.GetMapping(n_modules == 1) self.mapping = get_clp_mapping_from_tc_mapping(tc_mapping) n_rows = self.mapping.metadata['n_rows'] n_columns = self.mapping.metadata['n_columns'] camera_geom = first_event.inst.subarray.tel[tels[0]].camera engineering_frame = EngineeringCameraFrame(n_mirrors=2) engineering_geom = camera_geom.transform_to(engineering_frame) pix_x = engineering_geom.pix_x.value pix_y = engineering_geom.pix_y.value row, col = get_row_column(pix_x, pix_y) camera_2d = np.zeros((n_rows, n_columns), dtype=np.int) camera_2d[row, col] = np.arange(self.n_pixels, dtype=np.int) self.pixel_order = camera_2d[self.mapping['row'], self.mapping['col']] self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.camera_version = self._camera_config.GetVersion() self._iev = None self._t_cpu = None self.mc = None self.pointing = None self.mcheader = None
class TIOReader: """ Reader for the R0 and R1 tio files """ def __init__(self, path, max_events=None): try: from target_io import WaveformArrayReader from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) if not os.path.exists(path): raise FileNotFoundError("File does not exist: {}".format(path)) self.path = path self._reader = WaveformArrayReader(self.path, 2, 1) self.is_r1 = self._reader.fR1 self.n_events = self._reader.fNEvents self.run_id = self._reader.fRunID self.n_pixels = self._reader.fNPixels self.n_modules = self._reader.fNModules self.n_tmpix = self.n_pixels // self.n_modules self.n_samples = self._reader.fNSamples self._camera_config = CameraConfiguration(self._reader.fCameraVersion) self.tc_mapping = self._camera_config.GetMapping(self.n_modules == 1) self._pixel = self._PixelWaveforms(self) self.n_cells = self._camera_config.GetNCells() self.camera_version = self._camera_config.GetVersion() self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.current_tack = None self.current_cpu_ns = None self.current_cpu_s = None self.first_cell_ids = np.zeros(self.n_pixels, dtype=np.uint16) if self.is_r1: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.float32) self.get_tio_event = self._reader.GetR1Event else: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.uint16) self.get_tio_event = self._reader.GetR0Event if max_events and max_events < self.n_events: self.n_events = max_events def _get_event(self, iev): self.index = iev self.get_tio_event(iev, self.samples, self.first_cell_ids) self.current_tack = self._reader.fCurrentTimeTack self.current_cpu_ns = self._reader.fCurrentTimeNs self.current_cpu_s = self._reader.fCurrentTimeSec return self.samples def __iter__(self): for iev in range(self.n_events): yield self._get_event(iev) def __getitem__(self, iev): return np.copy(self._get_event(iev)) class _PixelWaveforms: def __init__(self, tio_reader): self.reader = tio_reader def __getitem__(self, p): if not isinstance(p, list) and not isinstance(p, np.ndarray): p = [p] n_events = self.reader.n_events n_pixels = len(p) n_samples = self.reader.n_samples waveforms = np.zeros((n_events, n_pixels, n_samples)) for iev, wf in enumerate(self.reader): waveforms[iev] = wf[p] return waveforms @property def pixel(self): return self._pixel @property def mapping(self): return get_clp_mapping_from_tc_mapping(self.tc_mapping) def get_sn(self, tm): if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) return self._reader.GetSN(tm) @staticmethod def is_compatible(filepath): try: h = fits.getheader(filepath, 0) if 'EVENT_HEADER_VERSION' not in h: return False except IOError: return False return True
class TargetIOEventSource(EventSource): """ EventSource for the targetio unofficial data format, the data format used by cameras containing TARGET modules, such as CHEC for the GCT SST. Extract waveform information from `target_io` to store them into `ctapipe.io.containers`. This Extractor can fill either the R0 or R1 event container, depending on the file being read (The header of the file is checked for a flag indicating that it has had R1 calibration applied to it). This reader requires the TARGET libraries. The instructions to install these libraries can be found here: https://forge.in2p3.fr/projects/gct/wiki/Installing_CHEC_Software Attributes ---------- _tio_reader : target_io.TargetIOEventReader() C++ event reader for TargetIO files. Handles the event building into an array of (n_pixels, n_samples) in C++, avoiding loops in Python _n_events : int number of events in the fits file _n_samples : int number of samples in the waveform _r0_samples : ndarray three dimensional array to store the R0 level waveform for each pixel (n_channels, n_pixels, n_samples) _r1_samples : ndarray three dimensional array to store the R1 level waveform for each pixel (n_channels, n_pixels, n_samples) _samples : ndarray pointer to the first index of either r0_samples or r1_samples (depending if the file has been R1 calibrated) for passing to TargetIO to be filled """ def __init__(self, config=None, parent=None, **kwargs): super().__init__(config=config, parent=parent, **kwargs) self._data = None self._event_index = None self._event_id = 0 self._time_tack = None self._time_sec = None self._time_ns = None self._reader = WaveformArrayReader(self.input_url, 2, 1) self._n_events = self._reader.fNEvents self._first_event_id = self._reader.fFirstEventID self._last_event_id = self._reader.fLastEventID self._obs_id = self._reader.fRunID n_modules = self._reader.fNModules n_pix = self._reader.fNPixels n_samples = self._reader.fNSamples self.camera_config = CameraConfiguration(self._reader.fCameraVersion) self._n_cells = self.camera_config.GetNCells() m = self.camera_config.GetMapping(n_modules == 1) self._optical_foclen = u.Quantity(2.15, u.m) self._mirror_area = u.Quantity(14.126, u.m ** 2) self._n_pixels = m.GetNPixels() self._xpix = np.array(m.GetXPixVector()) * u.m self._ypix = np.array(m.GetYPixVector()) * u.m self._refshape = np.zeros(10) # TODO: Get correct values for CHEC-S self._refstep = 0 # TODO: Get correct values for CHEC-S self._time_slice = 0 # TODO: Get correct values for CHEC-S self._chec_tel = 0 # Init fields self._r0_samples = None self._r1_samples = None self._first_cell_ids = np.zeros(n_pix, dtype=np.uint16) # Check if file is already r1 (Information obtained from a flag # in the file's header) is_r1 = self._reader.fR1 if is_r1: self._r1_samples = np.zeros( (1, n_pix, n_samples), dtype=np.float32 ) self._get_tio_event = self._reader.GetR1Event self._samples = self._r1_samples[0] else: self._r0_samples = np.zeros( (1, n_pix, n_samples), dtype=np.uint16 ) self._get_tio_event = self._reader.GetR0Event self._samples = self._r0_samples[0] self._init_container() @staticmethod def is_compatible(file_path): return file_path.endswith('.tio') def _init_container(self): """ Prepare the ctapipe event container, and fill it with the information that does not change with event, including the instrument information. """ chec_tel = 0 data = TargetIODataContainer() data.meta['origin'] = "targetio" data.meta['input'] = self.input_url data.meta['max_events'] = self.max_events # Instrument information camera = CameraGeometry( "CHEC", pix_id=np.arange(self._n_pixels), pix_x=self._xpix, pix_y=self._ypix, pix_area=None, pix_type='rectangular', ) optics = OpticsDescription( name="ASTRI", num_mirrors=2, equivalent_focal_length=self._optical_foclen, mirror_area=self._mirror_area, num_mirror_tiles=2, ) tel_descriptions = { chec_tel: TelescopeDescription( name="ASTRI", type="SST", camera=camera, optics=optics, ) } tel_positions = { chec_tel: u.Quantity(0, u.m) } data.inst.subarray =SubarrayDescription( "CHECMonoArray", tel_positions=tel_positions, tel_descriptions=tel_descriptions, ) self._data = data def _update_container(self): """ Update the ctapipe event containers with the information from the current event being pointed to in TargetIO. """ data = self._data chec_tel = 0 obs_id = self._obs_id event_id = self._event_id tels = {self._chec_tel} data.r0.obs_id = obs_id data.r0.event_id = event_id data.r0.tels_with_data = tels data.r1.obs_id = obs_id data.r1.event_id = event_id data.r1.tels_with_data = tels data.dl0.obs_id = obs_id data.dl0.event_id = event_id data.dl0.tels_with_data = tels data.trig.tels_with_trigger = [chec_tel] data.meta['tack'] = self._time_tack data.meta['sec'] = self._time_sec data.meta['ns'] = self._time_ns data.trig.gps_time = Time(self._time_sec * u.s, self._time_ns * u.ns, format='unix', scale='utc', precision=9) data.count = self._event_index data.r0.tel.clear() data.r1.tel.clear() data.dl0.tel.clear() data.dl1.tel.clear() data.mc.tel.clear() data.targetio.tel.clear() # load the data per telescope/chan data.r0.tel[chec_tel].waveform = self._r0_samples data.r1.tel[chec_tel].waveform = self._r1_samples # Load the TargetIO specific data per telescope/chan data.targetio.tel[chec_tel].first_cell_ids = self._first_cell_ids data.r0.tel[chec_tel].num_samples = self._samples.shape[-1] # Some information that currently exists in the mc container, but is # useful for real data (essentially the reference pulse shape, # which may be used in charge extraction methods) data.mc.tel[chec_tel].reference_pulse_shape = self._refshape data.mc.tel[chec_tel].meta['refstep'] = self._refstep data.mc.tel[chec_tel].time_slice = self._time_slice @property def _current_event_index(self): return self._event_index @_current_event_index.setter def _current_event_index(self, val): """ Setting the event index will cause the event to be saught from TargetIO, and the Containers to point to the correct event. The ctapipe event containers are then updated with this new event's information. """ self._event_index = val self._get_tio_event(val, self._samples, self._first_cell_ids) self._event_id = self._reader.fCurrentEventID self._time_tack = self._reader.fCurrentTimeTack self._time_sec = self._reader.fCurrentTimeSec self._time_ns = self._reader.fCurrentTimeNs self._update_container() def _generator(self): for self._current_event_index in range(self._n_events): yield self._data return def __len__(self): num = self._n_events if self.max_events and self.max_events < num: num = self.max_events return num def _get_event_by_index(self, index): self._current_event_index = index return self._data def _get_event_by_id(self, event_id): if ((event_id < self._first_event_id) | (event_id > self._last_event_id)): raise IndexError(f"Event id {event_id} not found in file") index = self._reader.GetEventIndex(event_id) return self._get_event_by_index(index)
def __init__(self, path, max_events=None, skip_events=2, skip_end_events=1): """ Utilies TargetIO to read R0 and R1 tio files. Enables easy access to the waveforms for anaylsis. Waveforms can be read from the file by either indexing this reader or iterating over it: >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> wf = reader[3] # Obtain the waveforms for the third event >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> wfs = reader[:10] # Obtain the waveforms for the first 10 events >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> for wf in reader: # Iterate over all events in the file >>> print(wf) Parameters ---------- path : str Path to the _r0.tio or _r1.tio file max_events : int Maximum number of events to read from the file """ super().__init__(path, max_events) try: from target_io import WaveformArrayReader from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) self._reader = WaveformArrayReader( self.path, skip_events, skip_end_events ) self.is_r1 = self._reader.fR1 self._n_events = self._reader.fNEvents self.run_id = self._reader.fRunID self.n_pixels = self._reader.fNPixels self.n_superpixels_per_module = self._reader.fNSuperpixelsPerModule self.n_modules = self._reader.fNModules self.n_tmpix = self.n_pixels // self.n_modules self.n_samples = self._reader.fNSamples self._camera_config = CameraConfiguration(self._reader.fCameraVersion) self.tc_mapping = self._camera_config.GetMapping(self.n_modules == 1) self.n_cells = self._camera_config.GetNCells() self.camera_version = self._camera_config.GetVersion() self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.current_tack = None self.current_cpu_ns = None self.current_cpu_s = None self.first_cell_ids = np.zeros(self.n_pixels, dtype=np.uint16) self.stale = np.zeros(self.n_pixels, dtype=np.uint8) if self.is_r1: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.float32) self.get_tio_event = self._reader.GetR1Event else: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.uint16) self.get_tio_event = self._reader.GetR0Event if max_events and max_events < self._n_events: self._n_events = max_events
def get_ref_path_from_version(version): from target_calib import CameraConfiguration ref_path = CameraConfiguration(version).GetReferencePulsePath() return ref_path
class TIOReader(WaveformReader): def __init__(self, path, max_events=None, skip_events=2, skip_end_events=1): """ Utilies TargetIO to read R0 and R1 tio files. Enables easy access to the waveforms for anaylsis. Waveforms can be read from the file by either indexing this reader or iterating over it: >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> wf = reader[3] # Obtain the waveforms for the third event >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> wfs = reader[:10] # Obtain the waveforms for the first 10 events >>> path = "/path/to/file_r0.tio" >>> reader = TIOReader(path) >>> for wf in reader: # Iterate over all events in the file >>> print(wf) Parameters ---------- path : str Path to the _r0.tio or _r1.tio file max_events : int Maximum number of events to read from the file """ super().__init__(path, max_events) try: from target_io import WaveformArrayReader from target_calib import CameraConfiguration except ModuleNotFoundError: msg = ("Cannot find TARGET libraries, please follow installation " "instructions from https://forge.in2p3.fr/projects/gct/" "wiki/Installing_CHEC_Software") raise ModuleNotFoundError(msg) self._reader = WaveformArrayReader( self.path, skip_events, skip_end_events ) self.is_r1 = self._reader.fR1 self._n_events = self._reader.fNEvents self.run_id = self._reader.fRunID self.n_pixels = self._reader.fNPixels self.n_superpixels_per_module = self._reader.fNSuperpixelsPerModule self.n_modules = self._reader.fNModules self.n_tmpix = self.n_pixels // self.n_modules self.n_samples = self._reader.fNSamples self._camera_config = CameraConfiguration(self._reader.fCameraVersion) self.tc_mapping = self._camera_config.GetMapping(self.n_modules == 1) self.n_cells = self._camera_config.GetNCells() self.camera_version = self._camera_config.GetVersion() self.reference_pulse_path = self._camera_config.GetReferencePulsePath() self.current_tack = None self.current_cpu_ns = None self.current_cpu_s = None self.first_cell_ids = np.zeros(self.n_pixels, dtype=np.uint16) self.stale = np.zeros(self.n_pixels, dtype=np.uint8) if self.is_r1: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.float32) self.get_tio_event = self._reader.GetR1Event else: self.samples = np.zeros((self.n_pixels, self.n_samples), dtype=np.uint16) self.get_tio_event = self._reader.GetR0Event if max_events and max_events < self._n_events: self._n_events = max_events def _get_event(self, iev): self.index = iev try: # TODO: Remove try in future version self.get_tio_event(iev, self.samples, self.first_cell_ids, self.stale) except TypeError: warnings.warn( "This call to WaveformArrayReader has been deprecated. " "Please update TargetIO", SyntaxWarning ) self.get_tio_event(iev, self.samples, self.first_cell_ids) self.current_tack = self._reader.fCurrentTimeTack self.current_cpu_ns = self._reader.fCurrentTimeNs self.current_cpu_s = self._reader.fCurrentTimeSec return self.samples @staticmethod def is_compatible(path): with open(path, 'rb') as f: marker_bytes = f.read(1024) # if file is gzip, read the first 4 bytes with gzip again if marker_bytes[0] == 0x1f and marker_bytes[1] == 0x8b: with gzip.open(path, 'rb') as f: marker_bytes = f.read(1024) if b'FITS' not in marker_bytes: return False try: h = fits.getheader(path, 0) if 'EVENT_HEADER_VERSION' not in h: return False except IOError: return False return True @property def n_events(self): return self._n_events @property def t_cpu(self): return pd.to_datetime( np.int64(self.current_cpu_s * 1E9) + np.int64(self.current_cpu_ns), unit='ns' ) @property def mapping(self): return get_clp_mapping_from_tc_mapping(self.tc_mapping) def get_sn(self, tm): """ Get the SN of the TARGET module in a slot Parameters ---------- tm : int Slot number for the TARGET module Returns ------- int Serial number of the TM """ if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) return self._reader.GetSN(tm) def get_sipm_temp(self, tm): if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) return self._reader.GetSiPMTemp(tm) def get_primary_temp(self, tm): if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) return self._reader.GetPrimaryTemp(tm) def get_sp_dac(self, tm, sp): if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) if sp >= self.n_superpixels_per_module: raise IndexError("Requested SP out of range: {}".format(sp)) return self._reader.GetSPDAC(tm, sp) def get_sp_hvon(self, tm, sp): if tm >= self.n_modules: raise IndexError("Requested TM out of range: {}".format(tm)) if sp >= self.n_superpixels_per_module: raise IndexError("Requested SP out of range: {}".format(sp)) return self._reader.GetSPHVON(tm, sp)