def main(chan, doPlot=False):
chan = int(chan)
wf_idxs = np.arange(0, 2)
directory = "{}pulsers_test_chan{}".format(len(wf_idxs), chan)
wf_file = "pulser_data/chan{}_pulsers.npz".format(chan)
wf_conf = {
"wf_file_name": wf_file,
"wf_idxs": wf_idxs,
"align_idx": 5,
"num_samples": 500,
"align_percent": 5000 #ADC value rather than percentage for pulsers
}
model_conf = [
("AntialiasingFilterModel", {}),
# ("AntialiasingFilterModel", {"pmag_lims":[0.6, 0.7], "pphi_lims":[0.05, 0.15]}),
("OscillationFilterModel", {
"pphi_lims": [0.1, 50]
}), #preamp oscillation
# ("OscillationFilterModel", {"pphi_lims":[15,50]}), #preamp oscillation
# ("DigitalFilterModel", {"order":2, "include_zeros":True}),
# ("DigitalFilterModel", {"order":2, "include_zeros":False}),
("OvershootFilterModel", {
"zmag_lims": [1, 3]
})
# ("DigitalFilterModel", {"order":1, "include_zeros":True, "pmag_lims":[-15, 0], "zmag_lims":[-15, 0], "pphi_lims":[-15,0], "zphi_lims":[-15,0], "exp":True}),
]
conf = FitConfiguration("",
directory=directory,
wf_conf=wf_conf,
model_conf=model_conf,
joint_energy=True,
joint_risetime=True,
interpType="linear")
if os.path.isdir(directory):
pass
# if len(os.listdir(directory)) >0:
# raise OSError("Directory {} already exists: not gonna over-write it".format(directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=1, model_type="PulserTrainingModel")
conf.save_config()
fm.fit(numLevels=1000,
directory=directory,
new_level_interval=5000,
numParticles=3)
def __init__(self,
result_directory,
num_samples,
sample_dec=1,
model_type="Model"):
super().__init__(result_directory, num_samples, sample_dec)
configuration = FitConfiguration(directory=result_directory,
loadSavedConfig=True)
if model_type == "Model":
self.model = Model(configuration)
elif model_type == "PulserTrainingModel":
self.model = PulserTrainingModel(configuration)
self.num_wf_params = self.model.num_wf_params
def __init__(self, result_directory, num_samples, sample_dec=1):
self.parse_samples("sample.txt", result_directory, num_samples, sample_dec)
self.configuration = FitConfiguration(directory=result_directory, loadSavedConfig=True)
if model_type == "Model":
self.model = Model(self.configuration)
elif model_type == "PulserTrainingModel":
self.model = PulserTrainingModel(self.configuration)
self.num_wf_params = self.model.num_wf_params
self.wf_conf = self.model.conf.wf_conf
self.model_conf = self.model.conf.model_conf
# For plots:
self.width = 18
def __init__(self, result_directory, num_samples, sample_dec, wf_model):
# super().__init__(result_directory=result_directory, num_samples=num_samples, model_type="Waveform")
print("I'm pretty sure this class is broken")
raise NotImplementedError
self.wf_model = wf_model
self.dnest_postprocess(result_directory)
self.parse_samples("posterior_sample.txt", result_directory, num_samples, sample_dec)
# self.parse_samples("sample.txt", result_directory, num_samples, sample_dec)
self.configuration = FitConfiguration.from_file(result_directory)
# self.num_wf_params = self.model.num_wf_params
# self.wf_conf = self.model.conf.wf_conf
# self.model_conf = self.model.conf.model_conf
# For plots:
self.width = 18
def __init__(self,
result_directory,
num_samples=None,
sample_dec=1,
model_type="Model"): #, posterior=False, waveform=False):
"""The base class used for all the postprocessing checks.
result_directory: The directory containing the sample.txt etc
num_samples: The number of samples to look at from the selected chain (will use the most recent N)
sample_dec: The decimation factor, by which to only look at some fraction of the result
model_type: Probably just want to use "Model"
posterior: Sample the chain or the posterior.
waveform: Was it a waveform fit (true) or a training fit (false)?
"""
self.parse_samples("sample.txt", result_directory, num_samples,
sample_dec)
self.configuration = FitConfiguration(directory=result_directory,
loadSavedConfig=True)
if model_type == "Model":
self.model = Model(self.configuration)
elif model_type == "PulserTrainingModel":
self.model = PulserTrainingModel(self.configuration)
# elif model_type == "Waveform":
# self.model =
self.num_samples = num_samples
self.num_wf_params = self.model.num_wf_params
self.wf_conf = self.model.conf.wf_conf
self.model_conf = self.model.conf.model_conf
self.id = result_directory.replace("/", "")
# For plots:
self.width = 18
def main(chan, doPlot=False):
# directory = "4wf_648_zero"
# wf_file = "training_data/chan648_8wfs.npz"
# conf_name = "P42664A.conf"
chan = int(chan)
directory = "../../Data/Detector/8wf_zero_{}".format(chan)
wf_file = "../../Data/Detector/chan{}_8wfs_DS1-1.npz".format(chan)
conf_name = "{}.conf".format(chan_dict[chan])
wf_idxs = np.arange(0, 8)
datadir = os.environ['DATADIR']
conf_file = datadir + "/siggen/config_files/" + conf_name
#rc_ms = 72
rc_us = 70
rc_ms = 1E3 * 70
wf_conf = {
"wf_file_name": wf_file,
"wf_idxs": wf_idxs,
"align_idx": 125,
"align_percent": 0.95,
"num_samples": 1000,
"do_smooth": True,
"smoothing_type": "gauss"
}
model_conf = [
("VelocityModel", {
"include_beta": False
}),
#Preamp effects
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.8 * rc_us, 1.2 * rc_us]
}), #rc decay filter (~70 us), second stage
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.98 * rc_ms, 1.02 * rc_ms]
}),
("FirstStageFilterModel", {}), # This is a LPF
("AntialiasingFilterModel", {}), # Digitizer front end
("OvershootFilterModel", {}), # ADC response
("OscillationFilterModel", {}), # match the observed oscillation
("ImpurityModelEnds", {}),
("TrappingModel", {})
]
conf = FitConfiguration(conf_file,
directory=directory,
wf_conf=wf_conf,
model_conf=model_conf)
if doPlot:
import matplotlib.pyplot as plt
# conf.plot_training_set()
fm = LocalFitManager(conf, num_threads=1)
for wf in fm.model.wfs:
plt.plot(wf.windowed_wf)
print(wf.window_length)
plt.show()
exit()
if os.path.isdir(directory):
if len(os.listdir(directory)) > 0:
raise OSError(
"Directory {} already exists: not gonna over-write it".format(
directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=4)
conf.save_config()
fm.fit(numLevels=1000,
directory=directory,
new_level_interval=5000,
numParticles=3)
def main(chan, doPlot=False):
# directory = "4wf_648_zero"
# wf_file = "training_data/chan648_8wfs.npz"
# conf_name = "P42664A.conf"
chan = int(chan)
directory = "8wf_zero_{}".format(chan)
wf_file = "training_data/chan{}_8wfs.npz".format(chan)
conf_name = "{}.conf".format(chan_dict[chan])
wf_idxs = np.arange(0, 8)
# wf_idxs = [1,3]
# wf_file = "16wf_set_chan{}.npz".format(chan)
# wf_idxs = np.arange(0,16,4)
# wf_idxs = [1,4,8,12]
datadir = os.environ['DATADIR']
conf_file = datadir + "siggen/config_files/" + conf_name
wf_conf = {
"wf_file_name": wf_file,
"wf_idxs": wf_idxs,
"align_idx": 125,
"num_samples": 1000,
"do_smooth": True,
"smoothing_type": "gaussian"
}
model_conf = [("VelocityModel", {
"include_beta": False
}), ("LowPassFilterModel", {
"order": 2
}), ("LowPassFilterModel", {
"order": 2,
"include_zeros": True
}), ("HiPassFilterModel", {
"order": 2
}), ("OvershootFilterModel", {}), ("ImpurityModelEnds", {}),
("TrappingModel", {})]
conf = FitConfiguration(conf_file,
directory=directory,
wf_conf=wf_conf,
model_conf=model_conf)
if doPlot:
import matplotlib.pyplot as plt
# conf.plot_training_set()
fm = LocalFitManager(conf, num_threads=1)
for wf in fm.model.wfs:
plt.plot(wf.windowed_wf)
print(wf.window_length)
plt.show()
exit()
if os.path.isdir(directory):
if len(os.listdir(directory)) > 0:
raise OSError(
"Directory {} already exists: not gonna over-write it".format(
directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=2)
conf.save_config()
fm.fit(numLevels=1000,
directory=directory,
new_level_interval=5000,
numParticles=3)
def main(chan, doPlot=False):
# directory = "4wf_648_zero"
# wf_file = "training_data/chan648_8wfs.npz"
# conf_name = "P42664A.conf"
chan = int(chan)
wf_idxs = np.arange(0, 8)
# wf_idxs = [0,3]
directory = "8wf_free_{}".format(chan)
wf_file = "training_data/chan{}_8wfs.npz".format(chan)
conf_name = "{}.conf".format(chan_dict[chan])
# wf_file = "16wf_set_chan{}.npz".format(chan)
# wf_idxs = np.arange(0,16,4)
# wf_idxs = [1,4,8,12]
datadir = os.environ['DATADIR']
conf_file = datadir + "siggen/config_files/" + conf_name
wf_conf = {
"wf_file_name": wf_file,
"wf_idxs": wf_idxs,
"align_idx": 125,
"num_samples": 1000,
"do_smooth": False
}
#Load the PZ params from the fit
proc = DataProcessor(None)
df_pz = pd.read_hdf(proc.channel_info_file_name, key="pz")
pz_chan = df_pz.loc[df_pz.channel == chan]
rc_us = pz_chan.rc_us.values
rc_ms = 1E3 * pz_chan.rc_ms.values
#this is ch 594 from the pulser
overshoot_pole_mag = -5.246669688365981
overshoot_zero_mag = 2.6703616866699833
model_conf = [
#Preamp effects
("LowPassFilterModel", {
"order": 2,
"pmag_lims": [0.95, 1],
"pphi_lims": [0, 0.01]
}), #preamp?
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.9 * rc_us, 1.1 * rc_us]
}), #rc decay filter (~70 us), second stage
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.9 * rc_ms, 1.1 * rc_ms]
}),
# ("HiPassFilterModel", {"order":1, "pmag_lims": [0.99*rc_us,1.01*rc_us]}), #rc decay filter (~70 us), second stage
# ("HiPassFilterModel", {"order":1, "pmag_lims": [0.99*rc_ms,1.01*rc_ms]}),
#Gretina card effects
("AntialiasingFilterModel", {}), #antialiasing
("OvershootFilterModel", {}), #gretina overshoot
# ("OvershootFilterModel",{"zmag_lims": [0.99*overshoot_zero_mag,1.01*overshoot_zero_mag], "pmag_lims": [1.01*overshoot_pole_mag, 0.99*overshoot_pole_mag]}), #gretina overshoot
#Detector effects
("ImpurityModelEnds", {}),
("TrappingModel", {}),
("VelocityModel", {
"include_beta": True
}),
]
conf = FitConfiguration(conf_file,
directory=directory,
wf_conf=wf_conf,
model_conf=model_conf)
if doPlot:
import matplotlib.pyplot as plt
# conf.plot_training_set()
fm = LocalFitManager(conf, num_threads=1)
for wf in fm.model.wfs:
plt.plot(wf.windowed_wf)
print(wf.window_length)
plt.show()
exit()
if os.path.isdir(directory):
if len(os.listdir(directory)) > 0:
raise OSError(
"Directory {} already exists: not gonna over-write it".format(
directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=len(wf_idxs))
conf.save_config()
fm.fit(numLevels=1000,
directory=directory,
new_level_interval=5000,
numParticles=3)
def main(chan, doPlot=False):
chan = int(chan)
wf_idxs = np.arange(0,2)
directory = "{}pulsers_test_chan{}".format(len(wf_idxs), chan)
wf_file = "pulser_data/chan{}_pulsers.npz".format(chan)
wf_conf = {
"wf_file_name":wf_file,
"wf_idxs":wf_idxs,
"align_idx":50,
"num_samples":1000,
"align_percent":50 #ADC value rather than percentage for pulsers
}
rc_antialias = 2 * 49.9 * 33E-12
rc_antialias_hi = np.exp(-1./1E9/(2*rc_antialias))
rc_antialias_lo = np.exp(-1./1E9/(0.5*rc_antialias))
#Load the PZ params from the fit
proc = DataProcessor(None)
df_pz = pd.read_hdf(proc.channel_info_file_name, key="pz")
pz_chan = df_pz.loc[df_pz.channel==chan]
rc_us = pz_chan.rc_us.values
rc_ms = 1E3*pz_chan.rc_ms.values
model_conf = [
#Preamp effects
("LowPassFilterModel", {"order":2,"pmag_lims":[0.95,1], "pphi_lims":[0,0.01]}), #preamp?
("HiPassFilterModel", {"order":1, "pmag_lims": [0.8*rc_us,1.2*rc_us]}), #rc decay filter (~70 us), second stage
("HiPassFilterModel", {"order":1, "pmag_lims": [0.8*rc_ms,1.2*rc_ms]}),
#Gretina card effects
("AntialiasingFilterModel", {}), #antialiasing
("OvershootFilterModel",{"zmag_lims":[1, 3]}), #gretina overshoot
#Shitty ringing
# ("OscillationFilterModel", {"include_zeros":True}), #preamp oscillation at 5 MHz
("OscillationFilterModel", {"include_zeros":True, "pphi_lims":[8, 15] }), #preamp oscillation at 10 Mhz
("OscillationFilterModel", {"include_zeros":True, "pphi_lims":[9, 11.0] }), #preamp oscillation at 10 Mhz
("OscillationFilterModel", {"include_zeros":True, "pphi_lims":[11, 15.0] }), #preamp oscillation at 10 Mhz
]
conf = FitConfiguration(
"",
directory = directory,
wf_conf=wf_conf,
model_conf=model_conf,
joint_energy=True,
joint_risetime=True,
interpType="linear"
)
if os.path.isdir(directory):
pass
# if len(os.listdir(directory)) >0:
# raise OSError("Directory {} already exists: not gonna over-write it".format(directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=1, model_type="PulserTrainingModel")
conf.save_config()
fm.fit(numLevels=1000, directory = directory,new_level_interval=5000, numParticles=3)
def main(chan, doPlot=False):
# directory = "4wf_648_zero"
# wf_file = "training_data/chan648_8wfs.npz"
# conf_name = "P42664A.conf"
chan = int(chan)
wf_idxs = np.arange(0,8)
# wf_idxs = [0,3]
directory = "8wf_free_{}".format(chan)
wf_file = "training_data/chan{}_8wfs.npz".format(chan)
conf_name = "{}.conf".format( chan_dict[chan] )
# wf_file = "16wf_set_chan{}.npz".format(chan)
# wf_idxs = np.arange(0,16,4)
# wf_idxs = [1,4,8,12]
datadir= os.environ['DATADIR']
conf_file = datadir +"siggen/config_files/" + conf_name
wf_conf = {
"wf_file_name":wf_file,
"wf_idxs":wf_idxs,
"align_idx":125,
"num_samples":1000,
"do_smooth":False
}
#Load the PZ params from the fit
proc = DataProcessor(None)
df_pz = pd.read_hdf(proc.channel_info_file_name, key="pz")
pz_chan = df_pz.loc[df_pz.channel==chan]
rc_us = pz_chan.rc_us.values
rc_ms = 1E3*pz_chan.rc_ms.values
#this is ch 594 from the pulser
overshoot_pole_mag = -5.246669688365981
overshoot_zero_mag = 2.6703616866699833
model_conf = [
#Preamp effects
("LowPassFilterModel", {"order":2,"pmag_lims":[0.95,1], "pphi_lims":[0,0.01]}), #preamp?
("HiPassFilterModel", {"order":1, "pmag_lims": [0.9*rc_us,1.1*rc_us]}), #rc decay filter (~70 us), second stage
("HiPassFilterModel", {"order":1, "pmag_lims": [0.9*rc_ms,1.1*rc_ms]}),
# ("HiPassFilterModel", {"order":1, "pmag_lims": [0.99*rc_us,1.01*rc_us]}), #rc decay filter (~70 us), second stage
# ("HiPassFilterModel", {"order":1, "pmag_lims": [0.99*rc_ms,1.01*rc_ms]}),
#Gretina card effects
("AntialiasingFilterModel", {}), #antialiasing
("OvershootFilterModel",{}), #gretina overshoot
# ("OvershootFilterModel",{"zmag_lims": [0.99*overshoot_zero_mag,1.01*overshoot_zero_mag], "pmag_lims": [1.01*overshoot_pole_mag, 0.99*overshoot_pole_mag]}), #gretina overshoot
#Detector effects
("ImpurityModelEnds", {}),
("TrappingModel", {}),
("VelocityModel", {"include_beta":True}),
]
conf = FitConfiguration(
conf_file,
directory = directory,
wf_conf=wf_conf,
model_conf=model_conf
)
if doPlot:
import matplotlib.pyplot as plt
# conf.plot_training_set()
fm = LocalFitManager(conf, num_threads=1)
for wf in fm.model.wfs:
plt.plot(wf.windowed_wf)
print (wf.window_length)
plt.show()
exit()
if os.path.isdir(directory):
if len(os.listdir(directory)) >0:
raise OSError("Directory {} already exists: not gonna over-write it".format(directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=len(wf_idxs))
conf.save_config()
fm.fit(numLevels=1000, directory = directory,new_level_interval=5000, numParticles=3)
def main(chan, doPlot=False):
chan = int(chan)
wf_idxs = np.arange(0, 2)
directory = "{}pulsers_test_chan{}".format(len(wf_idxs), chan)
wf_file = "pulser_data/chan{}_pulsers.npz".format(chan)
wf_conf = {
"wf_file_name": wf_file,
"wf_idxs": wf_idxs,
"align_idx": 50,
"num_samples": 1000,
"align_percent": 50 #ADC value rather than percentage for pulsers
}
rc_antialias = 2 * 49.9 * 33E-12
rc_antialias_hi = np.exp(-1. / 1E9 / (2 * rc_antialias))
rc_antialias_lo = np.exp(-1. / 1E9 / (0.5 * rc_antialias))
#Load the PZ params from the fit
proc = DataProcessor(None)
df_pz = pd.read_hdf(proc.channel_info_file_name, key="pz")
pz_chan = df_pz.loc[df_pz.channel == chan]
rc_us = pz_chan.rc_us.values
rc_ms = 1E3 * pz_chan.rc_ms.values
model_conf = [
#Preamp effects
("LowPassFilterModel", {
"order": 2,
"pmag_lims": [0.95, 1],
"pphi_lims": [0, 0.01]
}), #preamp?
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.8 * rc_us, 1.2 * rc_us]
}), #rc decay filter (~70 us), second stage
("HiPassFilterModel", {
"order": 1,
"pmag_lims": [0.8 * rc_ms, 1.2 * rc_ms]
}),
#Gretina card effects
("AntialiasingFilterModel", {}), #antialiasing
("OvershootFilterModel", {
"zmag_lims": [1, 3]
}), #gretina overshoot
#Shitty ringing
# ("OscillationFilterModel", {"include_zeros":True}), #preamp oscillation at 5 MHz
("OscillationFilterModel", {
"include_zeros": True,
"pphi_lims": [8, 15]
}), #preamp oscillation at 10 Mhz
("OscillationFilterModel", {
"include_zeros": True,
"pphi_lims": [9, 11.0]
}), #preamp oscillation at 10 Mhz
("OscillationFilterModel", {
"include_zeros": True,
"pphi_lims": [11, 15.0]
}), #preamp oscillation at 10 Mhz
]
conf = FitConfiguration("",
directory=directory,
wf_conf=wf_conf,
model_conf=model_conf,
joint_energy=True,
joint_risetime=True,
interpType="linear")
if os.path.isdir(directory):
pass
# if len(os.listdir(directory)) >0:
# raise OSError("Directory {} already exists: not gonna over-write it".format(directory))
else:
os.makedirs(directory)
fm = LocalFitManager(conf, num_threads=1, model_type="PulserTrainingModel")
conf.save_config()
fm.fit(numLevels=1000,
directory=directory,
new_level_interval=5000,
numParticles=3)