From b8e480218e5c4c0dd7406d1a8f62216abb2fe670 Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Mon, 28 Oct 2019 17:56:32 +0100
Subject: [PATCH] Adds warning if apd parameters were wrong during acquisition
---
xgm.py | 66 ++++++++++++++++++++++++++++++++++++----------------------
1 file changed, 41 insertions(+), 25 deletions(-)
diff --git a/xgm.py b/xgm.py
index fecea7d..85e0058 100644
--- a/xgm.py
+++ b/xgm.py
@@ -9,7 +9,6 @@ import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
-
# Machine
def pulsePatternInfo(data, plot=False):
''' display general information on the pulse patterns operated by SASE1 and SASE3.
@@ -139,6 +138,7 @@ def cleanXGMdata(data, npulses=None, sase3First=True):
drop=True) == 0):
dedicated = True
print('Dedicated trains, skip loading SASE 1 data.')
+ #pulse-resolved signals from XGMs
keys = ["XTD10_XGM", "XTD10_SA3", "XTD10_SA1",
"XTD10_XGM_sigma", "XTD10_SA3_sigma", "XTD10_SA1_sigma",
"SCS_XGM", "SCS_SA3", "SCS_SA1",
@@ -185,6 +185,9 @@ def cleanXGMdata(data, npulses=None, sase3First=True):
continue
res = data[key].where(data[key] != 1.0, drop=True).rename(
{'XGMbunchId':'{}pId'.format(sase)}).rename(key)
+ res = res.assign_coords(
+ {f'{sase}pId':np.arange(res[f'{sase}pId'].shape[0])})
+
dropList.append(key)
mergeList.append(res)
mergeList.append(data.drop(dropList))
@@ -567,39 +570,52 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
return tim
#2. If bunch pattern available, define a mask that corresponds to the SASE 3 pulses
- initialDelay = data.attrs['run'].get_array(
- 'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.initialDelay.value')[0].values
- upperLimit = data.attrs['run'].get_array(
- 'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.upperLimit.value')[0].values
- period = upperLimit - initialDelay #period of the apd in number of digitizer samples
sa3 = data['sase3'].where(data['sase3']>1, drop=True)
sa3 -= sa3[0,0]
- sa3 = sa3/int(period/440) #440 = samples between two pulses @4.5 MHz with ADQ412 digitizer
+ #2.1 case where apd is used:
+ if use_apd:
+ pulseId = 'apdId'
+ pulseIdDim = data.dims['apdId']
+ initialDelay = data.attrs['run'].get_array(
+ 'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.initialDelay.value')[0].values
+ upperLimit = data.attrs['run'].get_array(
+ 'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.upperLimit.value')[0].values
+ #440 = samples between two pulses @4.5 MHz with ADQ412 digitizer:
+ period = int((upperLimit - initialDelay)/440)
+ #display some warnings if apd parameters do not match pulse pattern:
+ period_from_bunch_pattern = int(np.nanmin(np.diff(sa3)))
+ if period > period_from_bunch_pattern:
+ print(f'Warning: apd parameter was set to record 1 pulse out of {period} @ 4.5 MHz ' +
+ f'but XFEL delivered 1 pulse out of {period_from_bunch_pattern}.')
+ maxPulses = data['npulses_sase3'].max().values
+ if period*pulseIdDim < period_from_bunch_pattern*maxPulses:
+ print(f'Warning: Number of pulses and/or rep. rate in apd parameters were set ' +
+ f'too low ({pulseIdDim})to record the {maxPulses} SASE 3 pulses')
+ peaks = data[f'MCP{mcp}apd']
+
+ #2.2 case where integration is performed on raw trace:
+ else:
+ pulseId = f'MCP{mcp}fromRaw'
+ pulseIdDim = int(np.max(sa3).values) + 1
+ period = int(np.nanmin(np.diff(sa3)))
+ peaks = mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=mcp, t_offset=period*440,
+ npulses=pulseIdDim)
+
+ sa3 = sa3/period
idxList, inv = np.unique(sa3, axis=0, return_inverse=True)
- mask = xr.DataArray(np.zeros((data.dims['trainId'], data.dims['apdId']), dtype=bool),
- dims=['trainId', 'apdId'],
+ mask = xr.DataArray(np.zeros((data.dims['trainId'], pulseIdDim), dtype=bool),
+ dims=['trainId', pulseId],
coords={'trainId':data.trainId,
- 'apdId':np.arange(data.dims['apdId'])},
- name='mask')
+ pulseId:np.arange(pulseIdDim)})
mask = mask.sel(trainId=sa3.trainId)
for i,idxApd in enumerate(idxList):
idxApd = idxApd[idxApd>=0].astype(int)
idxTid = inv==i
mask[idxTid, idxApd] = True
-
- #2.1 case where apd is used:
- if use_apd:
- res = data[f'MCP{mcp}apd'].where(mask, drop=True)
- res = res.assign_coords(apdId=np.arange(res['apdId'].shape[0]))
- return res
- #2.2 case where integration is performed on raw trace:
- else:
- peaks = mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=mcp, t_offset=period,
- npulses=data.dims['apdId'])
- mask = mask.rename({'apdId':f'MCP{mcp}fromRaw'})
- res = peaks.where(mask, drop=True)
- res = res.assign_coords({f'MCP{mcp}fromRaw':np.arange(res[f'MCP{mcp}fromRaw'].shape[0])})
- return res
+
+ peaks = peaks.where(mask, drop=True)
+ peaks = peaks.assign_coords({pulseId:np.arange(peaks[pulseId].shape[0])})
+ return peaks
def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intstart=None,
--
GitLab