From 660045ed0cc20fdcce528c56e36c6cdca4d7b8ab Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Tue, 29 Oct 2019 09:46:35 +0100
Subject: [PATCH] Adds documentation, simplifies selectSASEfromXGM()
---
xgm.py | 97 +++++++++++++++++++++++++---------------------------------
1 file changed, 41 insertions(+), 56 deletions(-)
diff --git a/xgm.py b/xgm.py
index 85e0058..61eba7e 100644
--- a/xgm.py
+++ b/xgm.py
@@ -199,11 +199,9 @@ def cleanXGMdata(data, npulses=None, sase3First=True):
def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=None):
''' Given an array containing both SASE1 and SASE3 data, extracts SASE1-
- or SASE3-only XGM data.
- There are various cases depending on i) the mode of operation (10 Hz
- with fresh bunch, dedicated trains to one SASE, pulse on demand),
- ii) the potential change of number of pulses per train in each SASE
- and iii) the order (SASE1 first, SASE3 first, interleaved mode).
+ or SASE3-only XGM data. The function tracks the changes of bunch patterns
+ in sase 1 and sase 3 and applies a mask to the XGM array to extract the
+ relevant pulses. This way, all complicated patterns are accounted for.
Inputs:
data: xarray Dataset containing xgm data
@@ -240,6 +238,7 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=
return xgmData[:,start:start+npulses]
#2. case where bunch pattern is provided
+ #2.1 Merge sase1 and sase3 bunch patterns to get indices of all pulses
xgm_arr = data[xgm].where(data[xgm] != 1., drop=True)
sa3 = data['sase3'].where(data['sase3'] > 1, drop=True)
sa3_val=np.unique(sa3)
@@ -252,51 +251,38 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=
dims=['trainId', 'bunchId'],
coords={'trainId':data.trainId},
name='sase_all')
- idxListAll, invAll = np.unique(sa_all.fillna(-1), axis=0, return_inverse=True)
- idxList3 = np.unique(sa3)
- idxList1 = np.unique(sa1)
-
if sase=='sase3':
- big_sa3 = []
- for i,idxXGM in enumerate(idxListAll):
- idxXGM = np.isin(idxXGM, idxList3)
- idxTid = invAll==i
- mask = xr.DataArray(np.zeros((data.dims['trainId'], sa_all['bunchId'].shape[0]), dtype=bool),
- dims=['trainId', 'XGMbunchId'],
- coords={'trainId':data.trainId,
- 'XGMbunchId':sa_all['bunchId'].values},
- name='mask')
- mask[idxTid, idxXGM] = True
- sa3 = xgm_arr.where(mask, drop=True)
- if sa3.trainId.size > 0:
- sa3 = sa3.assign_coords(XGMbunchId=np.arange(sa3.XGMbunchId.size))
- big_sa3.append(sa3)
- if len(big_sa3) > 0:
- da_sa3 = xr.concat(big_sa3, dim='trainId').rename('{}_SA3'.format(xgm.split('_')[0]))
- else:
- da_sa3 = xr.DataArray([], dims=['trainId'], name='{}_SA3'.format(xgm.split('_')[0]))
- return da_sa3
-
- if sase=='sase1':
- big_sa1 = []
- for i,idxXGM in enumerate(idxListAll):
- idxXGM = np.isin(idxXGM, idxList1)
- idxTid = invAll==i
- mask = xr.DataArray(np.zeros((data.dims['trainId'], sa_all['bunchId'].shape[0]), dtype=bool),
- dims=['trainId', 'XGMbunchId'],
- coords={'trainId':data.trainId,
- 'XGMbunchId':sa_all['bunchId'].values},
- name='mask')
- mask[idxTid, idxXGM] = True
- sa1 = xgm_arr.where(mask, drop=True)
- if sa1.trainId.size > 0:
- sa1 = sa1.assign_coords(XGMbunchId=np.arange(sa1.XGMbunchId.size))
- big_sa1.append(sa1)
- if len(big_sa1) > 0:
- da_sa1 = xr.concat(big_sa1, dim='trainId').rename('{}_SA1'.format(xgm.split('_')[0]))
- else:
- da_sa1 = xr.DataArray([], dims=['trainId'], name='{}_SA1'.format(xgm.split('_')[0]))
- return da_sa1
+ idxListSase = np.unique(sa3)
+ newName = xgm.split('_')[0] + '_SA3'
+ else:
+ idxListSase = np.unique(sa1)
+ newName = xgm.split('_')[0] + '_SA1'
+
+ #2.2 track the changes of pulse patterns and the indices at which they occured (invAll)
+ idxListAll, invAll = np.unique(sa_all.fillna(-1), axis=0, return_inverse=True)
+
+ #2.3 define a mask, loop over the different patterns and update the mask for each pattern
+ mask = xr.DataArray(np.zeros((data.dims['trainId'], sa_all['bunchId'].shape[0]), dtype=bool),
+ dims=['trainId', 'XGMbunchId'],
+ coords={'trainId':data.trainId,
+ 'XGMbunchId':sa_all['bunchId'].values},
+ name='mask')
+
+ big_sase = []
+ for i,idxXGM in enumerate(idxListAll):
+ mask.values = np.zeros(mask.shape, dtype=bool)
+ idxXGM = np.isin(idxXGM, idxListSase)
+ idxTid = invAll==i
+ mask[idxTid, idxXGM] = True
+ sa_arr = xgm_arr.where(mask, drop=True)
+ if sa_arr.trainId.size > 0:
+ sa_arr = sa_arr.assign_coords(XGMbunchId=np.arange(sa_arr.XGMbunchId.size))
+ big_sase.append(sa_arr)
+ if len(big_sase) > 0:
+ da_sase = xr.concat(big_sase, dim='trainId').rename(newName)
+ else:
+ da_sase = xr.DataArray([], dims=['trainId'], name=newName)
+ return da_sase
def saseContribution(data, sase='sase1', xgm='XTD10_XGM'):
''' Calculate the relative contribution of SASE 1 or SASE 3 pulses
@@ -533,9 +519,8 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
''' Extract peak-integrated data from TIM where pulses are from SASE3 only.
If use_apd is False it calculates integration from raw traces.
The missing values, in case of change of number of pulses, are filled
- with NaNs.
- If no bunch pattern info is available, the function assumes that
- SASE 3 comes first and that the number of pulses is fixed in both
+ with NaNs. If no bunch pattern info is available, the function assumes
+ that SASE 3 comes first and that the number of pulses is fixed in both
SASE 1 and 3.
Inputs:
@@ -544,7 +529,7 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
intstop: trace index of integration stop
bkgstart: trace index of background start
bkgstop: trace index of background stop
- t_offset: index separation between two pulses
+ t_offset: number of ADC samples between two pulses
mcp: MCP channel number
npulses: int, optional. Number of pulses to compute. Required if
no bunch pattern info is available.
@@ -602,6 +587,7 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
npulses=pulseIdDim)
sa3 = sa3/period
+ #2.3 track the changes of pulse patterns and the indices at which they occured (invAll)
idxList, inv = np.unique(sa3, axis=0, return_inverse=True)
mask = xr.DataArray(np.zeros((data.dims['trainId'], pulseIdDim), dtype=bool),
dims=['trainId', pulseId],
@@ -993,11 +979,10 @@ def mergeFastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop,
intstop: trace index of integration stop
bkgstart: trace index of background start
bkgstop: trace index of background stop
- period: Number of samples separation between two pulses. Needed
+ period: Number of ADC samples between two pulses. Needed
if bunch pattern info is not available. If None, checks the
pulse pattern and determine the period assuming a resolution
- of 9.23 ns per sample which leads to 24 samples between
- two bunches @ 4.5 MHz.
+ of 9.23 ns per sample = 24 samples between two pulses @ 4.5 MHz.
npulses: number of pulses. If None, takes the maximum number of
pulses according to the bunch patter (field 'npulses_sase3')
dim: name of the xr dataset dimension along the peaks
--
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