From 83ae436764d761dfc2107270ac832c4b914c3c2b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Thu, 24 Oct 2019 18:16:29 +0200
Subject: [PATCH] Biining by intra-train pulse id
---
DSSC.py | 78 +++++++++++++++++++++++++++++++++++++++++++++------------
1 file changed, 62 insertions(+), 16 deletions(-)
diff --git a/DSSC.py b/DSSC.py
index 8f05bb7..7108427 100644
--- a/DSSC.py
+++ b/DSSC.py
@@ -44,6 +44,7 @@ class DSSC:
self.geom = None
self.mask = None
self.max_fraction_memory = 0.8
+ self.filter_mask = None
print('DSSC configuration')
print(f'Topic: {self.topic}')
@@ -72,6 +73,7 @@ class DSSC:
print('Opening run data with karabo-data')
self.run_nr = run_nr
self.xgm = None
+ self.filter_mask = None
self.scan_vname = None
self.run = kd.open_run(self.proposal, self.run_nr)
@@ -214,7 +216,14 @@ class DSSC:
self.xgm_low = xgm_low
self.xgm_high = xgm_high
- valid = ((self.xgm > self.xgm_low) * (self.xgm < self.xgm_high)).prod('dim_0').astype(bool)
+ filter_mask = (self.xgm > self.xgm_low) * (self.xgm < self.xgm_high)
+
+ if self.filter_mask:
+ self.filter_mask = self.filter_mask*filter_mask
+ else:
+ self.filter_mask = filter_mask
+
+ valid = filter_mask.prod('dim_0').astype(bool)
xgm_valid = self.xgm.where(valid)
xgm_valid = xgm_valid.dropna('trainId')
self.scan = self.scan.sel({'trainId': xgm_valid.trainId})
@@ -271,7 +280,7 @@ class DSSC:
vds_list.append([vds_filename, module_vds])
return vds_list
- def binning(self):
+ def binning(self, do_pulse_mean=True):
""" Bin the DSSC data by the predifined scan type (DSSC.define()) using multiprocessing
"""
@@ -303,6 +312,7 @@ class DSSC:
vdf_scan=self.vds_scan,
nbunches=self.nbunches,
run_nr=self.run_nr,
+ do_pulse_mean=do_pulse_mean
))
timestamp = strftime('%X')
@@ -318,11 +328,15 @@ class DSSC:
self.module_data['run'] = self.run_nr
self.module_data = self.module_data.transpose('scan_variable', 'module', 'x', 'y')
- self.module_data = xr.merge([self.module_data, self.scan.groupby('scan_variable').mean('trainId')])
+ if do_pulse_mean:
+ self.module_data = xr.merge([self.module_data, self.scan.groupby('scan_variable').mean('trainId')])
self.module_data = self.module_data.squeeze()
- self.module_data.attrs['scan_variable'] = self.scan_vname
-
+ if do_pulse_mean:
+ self.module_data.attrs['scan_variable'] = self.scan_vname
+ else:
+ self.module_data.attrs['scan_variable'] = 'pulse id'
+
def save(self, save_folder=None, overwrite=False):
""" Save the crunched data.
@@ -561,6 +575,7 @@ def process_one_module(job):
scan_vdf = job['vdf_scan']
chunksize = job['chunksize']
nbunches = job['nbunches']
+ do_pulse_mean = job['do_pulse_mean']
image_path = f'INSTRUMENT/SCS_DET_DSSC1M-1/DET/{module}CH0:xtdf/image/data'
npulse_path = f'INDEX/SCS_DET_DSSC1M-1/DET/{module}CH0:xtdf/image/count'
@@ -574,14 +589,25 @@ def process_one_module(job):
scan.name = 'scan'
len_scan = len(scan.groupby(scan))
- # create empty dataset to add actual data to
- module_data = xr.DataArray(np.empty([len_scan, 128, 512], dtype=np.float64),
- dims=['scan_variable', 'x', 'y'],
- coords={'scan_variable': np.unique(scan)})
- module_data = module_data.to_dataset(name='pumped')
- module_data['unpumped'] = xr.full_like(module_data['pumped'], 0)
- module_data['sum_count'] = xr.DataArray(np.zeros_like(np.unique(scan)), dims=['scan_variable'])
- module_data['module'] = module
+ if do_pulse_mean:
+ # create empty dataset to add actual data to
+ module_data = xr.DataArray(np.empty([len_scan, 128, 512], dtype=np.float64),
+ dims=['scan_variable', 'x', 'y'],
+ coords={'scan_variable': np.unique(scan)})
+ module_data = module_data.to_dataset(name='pumped')
+ module_data['unpumped'] = xr.full_like(module_data['pumped'], 0)
+ module_data['sum_count'] = xr.DataArray(np.zeros_like(np.unique(scan)), dims=['scan_variable'])
+ module_data['module'] = module
+ else:
+ scan = xr.full_like(scan, 1)
+ len_scan = len(scan.groupby(scan))
+ module_data = xr.DataArray(np.empty([len_scan, int(nbunches/2), 128, 512], dtype=np.float64),
+ dims=['scan_variable', 'pulse', 'x', 'y'],
+ coords={'scan_variable': np.unique(scan)})
+ module_data = module_data.to_dataset(name='pumped')
+ module_data['unpumped'] = xr.full_like(module_data['pumped'], 0)
+ module_data['sum_count'] = xr.full_like(module_data['pumped'][..., 0, 0], 0)
+ module_data['module'] = module
# crunching
with h5py.File(data_vdf, 'r') as m:
@@ -608,19 +634,39 @@ def process_one_module(job):
coords = {'trainId': trainIds_chunk}
data = np.reshape(data, [n_trains_actual, fpt, 128, 512])[:, :int(2 * nbunches)]
data = xr.DataArray(data, dims=['trainId', 'pulse', 'x', 'y'], coords=coords)
- data_pumped = (data[:, ::4]).mean('pulse')
- data_unpumped = (data[:, 2::4]).mean('pulse')
+
+ if do_pulse_mean:
+ data_pumped = (data[:, ::4]).mean('pulse')
+ data_unpumped = (data[:, 2::4]).mean('pulse')
+ else:
+ data_pumped = (data[:, ::4])
+ data_unpumped = (data[:, 2::4])
+
data = data_pumped.to_dataset(name='pumped')
data['unpumped'] = data_unpumped
- data['sum_count'] = xr.DataArray(np.ones(n_trains_actual), dims=['trainId'], coords=coords)
+ data['sum_count'] = xr.full_like(data['unpumped'][..., 0, 0], fill_value=1)
+
# grouping and summing
data['scan_variable'] = scan # this only adds scan data for matching trainIds
data = data.dropna('trainId')
data = data.groupby('scan_variable').sum('trainId')
where = {'scan_variable': data.scan_variable}
+
for var in ['pumped', 'unpumped', 'sum_count']:
module_data[var].loc[where] = module_data[var].loc[where] + data[var]
+
+ if not do_pulse_mean:
+ break
+
for var in ['pumped', 'unpumped']:
module_data[var] = module_data[var] / module_data.sum_count
#module_data = module_data.drop('sum_count')
+
+ if not do_pulse_mean:
+ #print(f'#{module}: {module_data}')
+ module_data = module_data.sum('scan_variable')
+ #print(f'#{module}: {module_data}')
+ module_data = module_data.rename({'pulse':'scan_variable'})
+ #print(f'#{module}: {module_data}')
+
return module_data
--
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