def process(input_path, output_path, hv_path, event_slice):
reader = TIOReader(input_path)
n_events = reader.n_events
n_pixels = reader.n_pixels
n_samples = reader.n_samples
mapping = reader.mapping
mappingtc = reader.tc_mapping
mappingsp = MappingSP(mappingtc)
waveforms = reader[event_slice]
sp_arr = np.vstack(mapping.groupby("superpixel").pixel.apply(np.array))
avg_wfs = waveforms[:, sp_arr].mean((0, 2))
amplitudes = avg_wfs.max(1)
hvs = hv_file_reader(hv_path)
hv255 = np.where(hvs == 255)
df = get_superpixel_mapping(mapping)
camera = CameraImage.from_mapping(df)
image = amplitudes
camera.image = image
camera.add_colorbar("Superpixel-Waveform Max (mV)")
camera.highlight_pixels(hv255, 'red')
camera.save(output_path)
lt75 = np.where(amplitudes < 75)
print(lt75)
def __init__(self, mapping):
sp_mapping = get_superpixel_mapping(mapping)
self.fig = plt.figure(figsize=(10, 5))
self.ax_trigger = self.fig.add_subplot(1, 1, 1)
self.ci_trigger = self.create_image(sp_mapping,
ax=self.ax_trigger,
clabel="Trigger")
self.n_superpixels = sp_mapping.index.size
sp_zero = np.zeros(self.n_superpixels, dtype=bool)
self.ci_trigger.image = sp_zero
def plot_superpixel():
"""
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_superpixel_mapping(get_clp_mapping_from_tc_mapping(m))
camera = CameraImage.from_mapping(df)
image = np.zeros(m.GetNSuperPixels())
image[::2] = 1
camera.image = image
plt.show()
def main():
# path = get_astri_2019("d2019-06-10_ledflashers/flasher_comparison_May-June/unit0pattern-low_r1.tio")
# path = get_astri_2019("d2019-06-10_ledflashers/flasher_comparison_May-June/Run13270_r1.tio")
path = get_astri_2019(
"d2019-06-10_ledflashers/flasher_comparison_May-June/Run13401_r1.tio")
output_dir = path.replace("_r1.tio", "")
reader = TIOReader(path, max_events=100)
waveforms = reader[:].mean(0)
mapping = get_superpixel_mapping(reader.mapping)
n_pixels, n_samples = waveforms.shape
n_sp = n_pixels // 4
correction = pd.read_csv(join(DIR, "sp_illumination_profile.dat"),
sep='\t')['correction'].values
sp_waveforms = waveforms.reshape((n_sp, 4, n_samples)).sum(1)
sp_waveforms_corrected = sp_waveforms * correction[:, None]
amplitude = sp_waveforms_corrected.max(1)
median = np.median(amplitude)
threshold = median * 1.10
trigger_off = amplitude > threshold
# trigger_off[[ # TODO: Remove?
# 355, 356, 357, 358, 359, 382, 375, 460, 459, 457, 458, 465, 289, 489,
# 502, 254, 247, 154, 144, 56, 46, 39, 24, 25, 76
# ]] = True
hv_off = np.zeros(n_sp, dtype=bool)
hv_off[[24, 453]] = True
print(f"SP Trigger OFF: {np.where(trigger_off)[0].tolist()}")
print(f"SP HV OFF: {np.where(hv_off)[0].tolist()}")
plot = CameraPlotter(mapping)
plot.highlight_pixels(trigger_off, 'yellow')
plot.highlight_pixels(hv_off, 'red')
plot.plot_waveforms(sp_waveforms)
plot.save(join(output_dir, "led.pdf"))
plot.plot_waveforms(sp_waveforms_corrected)
plot.save(join(output_dir, "uniform.pdf"))
df = pd.DataFrame(
dict(
superpixel=np.arange(n_sp),
hv_on=(~hv_off).astype(int),
trigger_off=trigger_off.astype(int),
))
df.to_csv(join(output_dir, "sp_settings.txt"), sep='\t', index=False)
def __init__(self, mapping):
super().__init__()
sp_mapping = get_superpixel_mapping(mapping)
self.fig = plt.figure(figsize=(10, 5))
self.ax_spmax = self.fig.add_subplot(1, 2, 1)
self.ax_trigger = self.fig.add_subplot(1, 2, 2)
self.ci_spmax = self.create_image(
sp_mapping, ax=self.ax_spmax, clabel="Superpixel-WF Max"
)
self.ci_trigger = self.create_image(
sp_mapping, ax=self.ax_trigger, clabel="Trigger"
)
self.n_superpixels = sp_mapping.index.size
sp_zero = np.zeros(self.n_superpixels, dtype=bool)
self.ci_spmax.image = sp_zero
self.ci_trigger.image = sp_zero
self.highlight = self.ci_spmax.highlight_pixels(sp_zero, color='red')
def __init__(self, mapping):
super().__init__()
sp_mapping = get_superpixel_mapping(mapping)
self.fig = plt.figure(figsize=(10, 10))
self.ax_spmax = self.fig.add_subplot(2, 2, 1)
self.ax_spargmax = self.fig.add_subplot(2, 2, 2)
self.ax_c = self.fig.add_subplot(2, 2, 3)
self.ax_t = self.fig.add_subplot(2, 2, 4)
self.ci_spmax = self.create_image(
sp_mapping, ax=self.ax_spmax, clabel="Above Threshold"
)
self.ci_spargmax = self.create_image(
sp_mapping, ax=self.ax_spargmax, clabel="SP Argmax"
)
self.ci_c = self.create_image(
mapping, ax=self.ax_c, clabel="Extracted Charge (Photons)"
)
self.ci_t = self.create_image(
mapping, ax=self.ax_t, clabel="Pulse Time (ns)"
)
self.n_pixels = mapping.index.size
self.n_superpixels = sp_mapping.index.size
pix_zero = np.zeros(self.n_pixels, dtype=bool)
sp_zero = np.zeros(self.n_superpixels, dtype=bool)
self.ci_spmax.image = sp_zero
self.ci_spargmax.image = sp_zero
self.ci_c.image = pix_zero
self.ci_t.image = pix_zero
self.highlights = dict(
sp_max=self.ci_spmax.highlight_pixels(sp_zero, color='red'),
sp_argmax=self.ci_spargmax.highlight_pixels(sp_zero, color='red'),
c=self.ci_c.highlight_pixels(pix_zero, color='black', linewidth=0.05),
t=self.ci_t.highlight_pixels(pix_zero, color='black', linewidth=0.05),
)
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 main():
paths = dict(
d190502=
"/Volumes/ICYBOX/astri_onsky_archive/d2019-06-10_ledflashers/flasher_comparison_May-June/unit0pattern-low_r1.tio",
d190508=
"/Volumes/ICYBOX/astri_onsky_archive/d2019-06-10_ledflashers/flasher_comparison_May-June/Run13270_r1.tio",
d190610=
"/Volumes/ICYBOX/astri_onsky_archive/d2019-06-10_ledflashers/flasher_comparison_May-June/Run13401_r1.tio",
)
readers = {
key: TIOReader(path, max_events=100)
for key, path in paths.items()
}
waveforms = {key: reader[:].mean(0) for key, reader in readers.items()}
mapping = get_superpixel_mapping(readers['d190502'].mapping)
pixelmasks = dict(d190428T2149=PixelMasks(
get_calib_data("pixel_masks/d190428T2149.dat")), )
# Calculate coeff
waveforms_coeff = waveforms['d190502']
n_pixels, n_samples = waveforms_coeff.shape
n_sp = n_pixels // 4
waveforms_sp = waveforms_coeff.reshape((n_sp, 4, n_samples)).sum(1)
coeff = (waveforms_sp.max(1).mean() / waveforms_sp.max(1))[:, None]
sp_waveforms = {
key: wfs.reshape((n_sp, 4, n_samples)).sum(1)
for key, wfs in waveforms.items()
}
sp_waveforms_cal = {key: wfs * coeff for key, wfs in sp_waveforms.items()}
trigger_off = dict()
for key, wfs in sp_waveforms_cal.items():
amplitude = wfs.max(1)
median = np.median(amplitude)
threshold = median * 1.10
trigger_off[key] = amplitude > threshold
trigger_off['d190610'][[
355, 356, 357, 358, 359, 382, 375, 460, 459, 457, 458, 465, 289, 489,
502, 254, 247, 154, 144, 56, 46, 39, 24, 25, 76
]] = True
for key, sp in trigger_off.items():
print(f"{key}: f{np.where(sp)}")
plots = GetPlot(mapping)
plots['d190428T2149'].highlight_pixels(trigger_off['d190502'])
plots['d190428T2149'].plot_waveforms(sp_waveforms['d190502'])
plots['d190428T2149'].save(get_plot("d190611_led_flasher/led/d190502.pdf"))
plots['d190428T2149'].plot_waveforms(sp_waveforms_cal['d190502'])
plots['d190428T2149'].save(
get_plot("d190611_led_flasher/coeff/d190502.pdf"))
plots['d190428T2149'].reset_pixel_highlight()
plots['d190428T2149'].highlight_pixels(trigger_off['d190508'])
plots['d190428T2149'].plot_waveforms(sp_waveforms['d190508'])
plots['d190428T2149'].save(get_plot("d190611_led_flasher/led/d190508.pdf"))
plots['d190428T2149'].plot_waveforms(sp_waveforms_cal['d190508'])
plots['d190428T2149'].save(
get_plot("d190611_led_flasher/coeff/d190508.pdf"))
plots['d190428T2149'].reset_pixel_highlight()
plots['d190428T2149'].highlight_pixels(trigger_off['d190610'])
plots['d190428T2149'].plot_waveforms(sp_waveforms['d190610'])
plots['d190428T2149'].save(get_plot("d190611_led_flasher/led/d190610.pdf"))
plots['d190428T2149'].plot_waveforms(sp_waveforms_cal['d190610'])
plots['d190428T2149'].save(
get_plot("d190611_led_flasher/coeff/d190610.pdf"))
plots['d190428T2149'].reset_pixel_highlight()
hv_off = [24, 453]
hv_off_array = np.zeros(n_sp, dtype=bool)
hv_off_array[hv_off] = True
trigger_off_array = np.zeros(n_sp, dtype=bool)
trigger_off_array[trigger_off['d190610']] = True
df = pd.DataFrame(
dict(
superpixel=np.arange(n_sp),
hv_on=(~hv_off_array).astype(int),
trigger_off=trigger_off_array.astype(int),
))
df.to_csv(get_plot("d190611_led_flasher/hv_list.txt"),
sep='\t',
index=False)