class PointingFit:
def __init__(self, expectation_key, data_key):
self.fit_chain = ProcessingChain(silent=True)
# self.fit_chain.add(self)
self.configured = False
self.params = []
self.expectation_key = expectation_key
self.data_key = data_key
def addFitModule(self, module):
self.params += module.parameters.values()
self.fit_chain.add(module)
def _configure(self):
if not self.configured:
self.aggregator = Aggregate([self.expectation_key, self.data_key])
self.fit_chain.add(self.aggregator)
self.configured = True
def compute_exp(self, x=None):
if x is not None:
for i, xp in enumerate(x):
self.params[i].val = xp
if not self.configured:
self._configure()
self.fit_chain.run()
self.fit_chain.config_run = True
exp = self.aggregator.aggr[self.expectation_key][0]
self.data = self.aggregator.aggr[self.data_key][0]
self.aggregator.clear()
return exp
def __call__(self, x):
print(x)
exp = self.compute_exp(x)
# m = self.data>0
chi2 = 0
for expv, datav in zip(exp.items(), self.data.items()):
chi2 += np.sum((expv[1][1] - datav[1][1]) ** 2 / datav[1][1])
# chi2 = np.sum((exp - self.data) ** 2/self.data)
print(chi2)
return chi2
def get_your_data(runfile):
data_proc = pchain.ProcessingChain()
path = '/data/CHECS-data/astri_onsky/slowsignal/' + str(runfile) + '.hdf5'
reader = Reader(path)
data_proc.add(reader)
frame_cleaner = PFCleaner()
data_proc.add(frame_cleaner)
aggr = Aggregate(["raw_resp"])
data_proc.add(aggr)
data_proc.run()
data = aggr.aggr["raw_resp"][0]
dt = data.time - data.time[0]
return (data, dt)
# ============================================================================= # # Read in raw data # ============================================================================= # Pick runfile runfile = "Run13637_ss" # initializing our process chain data_proc = pchain.ProcessingChain() path = '/data/CHECS-data/astri_onsky/slowsignal/'+str(runfile)+'.hdf5' reader = Reader(path) data_proc.add(reader) #This module removes incomplete frames and marks bad and unstable pixels frame_cleaner = PFCleaner() data_proc.add(frame_cleaner) #The Aggregate module collects the computed object from the frame aggr = Aggregate(["raw_resp"]) data_proc.add(aggr) # Simple visualization of the chain print(data_proc) #Execute the chain data_proc.run() data = aggr.aggr["raw_resp"][0] dt = data.time - data.time[0] ############################################################################# # Remove eventally new broken pixels new_good_pix = np.ones(data.data.shape,dtype = "bool") new_good_pix[np.nan_to_num(data.data) == 0] = False new_good_pix[:,1404] = False new_good_pix[:,1426] = False
Reader,
ClusterCleaning,
ClusterReduction,
SmoothSlowSignal,
)
data_proc = ProcessingChain()
reader = Reader("VegaTrack15minFlatNSB10Hz.hdf")
reader.out_raw_resp = "raw_resp"
reader.out_time = "time"
c_cleaner = ClusterCleaning(270.0, 140.0)
c_cleaner.in_data = reader.out_raw_resp
c_cleaner.out_cleaned = "cluster_cleaned"
data_smoother = SmoothSlowSignal(n_readouts=30)
clust_red = ClusterReduction()
aggr = Aggregate([reader.out_raw_resp, c_cleaner.out_cleaned, reader.out_time])
data_proc.add(reader)
data_proc.add(data_smoother)
data_proc.add(c_cleaner)
data_proc.add(clust_red)
data_proc.add(aggr)
print(data_proc)
data_proc.run()
from ssm.star_cat.hipparcos import load_hipparcos_cat
from ssm.models import pixel_model
from ssm.models.calibration import RateCalibration
from ssm.fit import fit, fitmodules
from astropy.coordinates import SkyCoord
]
#dumm = ['Run13769_ss']
dumm = ["Run13312"]
for runfile in dumm:
print(runfile)
data_proc = pchain.ProcessingChain()
path = '/data/CHECS-data/astri_onsky/slowsignal/' + str(runfile) + '.hdf5'
reader = Reader(path)
data_proc.add(reader)
#This module removes incomplete frames and marks bad and unstable pixels
frame_cleaner = PFCleaner()
data_proc.add(frame_cleaner)
#The Aggregate module collects the computed object from the frame
aggr = Aggregate(["raw_resp"])
data_proc.add(aggr)
# Simple visualization of the chain
print(data_proc)
#Execute the chain
data_proc.run()
data = aggr.aggr["raw_resp"][0]
dt = data.time - data.time[0]
property_path = os.path.join(current_path + '/' + runfile,
"CAL_with_Run13638")
with open(property_path, 'rb') as handle:
readout = pickle.load(handle)
def _configure(self):
if not self.configured:
self.aggregator = Aggregate([self.expectation_key, self.data_key])
self.fit_chain.add(self.aggregator)
self.configured = True