diff --git a/src/toolbox_scs/detectors/__init__.py b/src/toolbox_scs/detectors/__init__.py
index 65809f4ac5edca64a17745c81a3b9396f020d5b8..c4fb3265fb777370801b66c333fcb79aad98bf37 100644
--- a/src/toolbox_scs/detectors/__init__.py
+++ b/src/toolbox_scs/detectors/__init__.py
@@ -3,6 +3,8 @@ from .xgm import (
autoFindFastAdcPeaks)
from .tim import (
load_TIM,)
+from .digitizers import(
+ get_peaks, get_tim_peaks, get_fast_adc_peaks, find_integ_params)
from .dssc_data import (
save_xarray, load_xarray, get_data_formatted, save_attributes_h5)
from .dssc_misc import (
@@ -20,6 +22,10 @@ __all__ = (
"cleanXGMdata",
"load_TIM",
"matchXgmTimPulseId",
+ "get_peaks",
+ "get_tim_peaks",
+ "get_fast_adc_peaks",
+ "find_integ_params",
"load_dssc_info",
"create_dssc_bins",
"calc_xgm_frame_indices",
@@ -60,6 +66,7 @@ clean_ns = [
'FastCCD',
'tim',
'xgm',
+ 'digitizers'
]
diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..519b4e3ec53b288896ee046a8f2b3beb3aa5a365
--- /dev/null
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -0,0 +1,509 @@
+""" Digitizers related sub-routines
+
+ Copyright (2021) SCS Team.
+
+ (contributions preferrably comply with pep8 code structure
+ guidelines.)
+"""
+
+import logging
+
+import numpy as np
+import xarray as xr
+import matplotlib.pyplot as plt
+from scipy.signal import find_peaks
+import extra_data as ed
+
+from ..constants import mnemonics as _mnemonics
+from ..misc.bunch_pattern_external import is_sase_1, is_sase_3, is_ppl
+
+
+log = logging.getLogger(__name__)
+
+def peaks_from_raw_trace(traces,
+ intstart,
+ intstop,
+ bkgstart,
+ bkgstop,
+ period,
+ npulses,
+ extra_dim='pulseId'
+ ):
+ """
+ Computes peaks from raw digitizer traces by trapezoidal integration.
+
+ Parameters
+ ----------
+ traces: xarray DataArray or numpy array containing raw traces. If
+ numpy array is provided, the second dimension is that of the samples.
+ intstart: int or list or 1D-numpy array
+ trace index of integration start. If 1d array, each value is the start
+ of one peak. The period and npulses parameters are ignored.
+ intstop: int
+ trace index of integration stop
+ bkgstart: int
+ trace index of background start
+ bkgstop: int
+ trace index of background stop
+ period: int
+ number of samples between two peaks
+ npulses: int
+ number of pulses
+ extra_dim: str
+ Name given to the dimension along the peaks
+
+ Returns
+ -------
+ xarray DataArray
+ """
+
+ assert len(traces.shape)==2
+
+ if type(traces) is xr.DataArray:
+ ntid = traces.sizes['trainId']
+ coords = traces.coords
+ traces = traces.values
+ if traces.shape[0] != ntid:
+ traces = traces.T
+ else:
+ coords = None
+
+ if hasattr(intstart, '__len__'):
+ intstart = np.array(intstart)
+ pulses = intstart - intstart[0]
+ intstart = intstart[0]
+ else:
+ pulses = range(0, npulses*period, period)
+
+ results = xr.DataArray(np.empty((traces.shape[0], len(pulses))),
+ coords=coords,
+ dims=['trainId', extra_dim])
+
+ for i,p in enumerate(pulses):
+ a = intstart + p
+ b = intstop + p
+ bkga = bkgstart + p
+ bkgb = bkgstop + p
+ if b > traces.shape[1]:
+ break
+ bg = np.outer(np.median(traces[:,bkga:bkgb], axis=1),
+ np.ones(b-a))
+ results[:,i] = np.trapz(traces[:,a:b] - bg, axis=1)
+ return results
+
+
+def get_peaks(proposal,
+ runNB,
+ data=None,
+ source=None,
+ key=None,
+ digitizer='ADQ412',
+ useRaw=True,
+ autoFind=True,
+ integParams=None,
+ bunchPattern='sase3',
+ extra_dim=None,
+ indices=None,
+ display=False
+ ):
+ """
+ Extract peaks from digitizer data.
+
+ Parameters
+ ----------
+ proposal: int or str
+ proposal number
+ runNB: int or str
+ run number
+ data: xarray DataArray or str
+ array containing the raw traces or peak-integrated values from the
+ digitizer. If str, must be one of the ToolBox mnemonics. If None,
+ the data is loaded via the source and key arguments.
+ source: str
+ Name of digitizer source, e.g. 'SCS_UTC1_ADQ/ADC/1:network'. Only
+ required if data is a DataArray or None.
+ key: str
+ Key for digitizer data, e.g. 'digitizers.channel_1_A.raw.samples'.
+ Only required if data a DataArray or is None.
+ digitizer: string
+ name of digitizer, e.g. 'FastADC' or 'ADQ412'. Used to determine
+ the sampling rate when useRaw is True.
+ useRaw: bool
+ If True, extract peaks from raw traces. If False, uses the APD (or
+ peaks) data from the digitizer.
+ autoFind: bool
+ If True, finds integration parameters by inspecting the average raw
+ trace. Only valid if useRaw is True.
+ integParams: dict
+ dictionnary containing the integration parameters for raw trace
+ integration: 'intstart', 'intstop', 'bkgstart', 'bkgstop', 'period',
+ 'npulses'. Not used if autoFind is True. All keys are required when
+ bunch pattern is missing.
+ bunchPattern: string or dict
+ match the peaks to the bunch pattern: 'sase3', 'scs_ppl'.
+ Alternatively, a dict with source, key and pattern can be provided,
+ e.g. {'source':'SCS_RR_UTC/TSYS/TIMESERVER',
+ 'key':'bunchPatternTable.value', 'pattern':'sase3'}
+ extra_dim: str
+ Name given to the dimension along the peaks.
+ indices: array, slice
+ indices from the peak-integrated data to retrieve. Only required
+ when bunch pattern is missing and useRaw is False.
+ display: bool
+ displays info if True
+
+ Returns
+ -------
+ xarray.DataArray
+ """
+ if data is None and (source is None or key is None):
+ raise ValueError('At least data or source + key arguments ' +
+ 'are required.')
+ # load data
+ run = ed.open_run(proposal, runNB)
+ if data is None:
+ arr = run.get_array(source, key)
+ elif type(data) is str:
+ arr = run.get_array(*_mnemonics[data].values())
+ source = _mnemonics[data]['source']
+ key = _mnemonics[data]['key']
+ else:
+ arr = data
+ dim = [d for d in arr.dims if d is not 'trainId'][0]
+ #check if bunch pattern is provided
+ bpt = None
+ if type(bunchPattern) is dict:
+ bpt = run.get_array(bunchPattern['source'],
+ bunchPattern['key']).rename({'dim_0':'pulse_slot'})
+ pattern = bunchPattern['pattern']
+ elif _mnemonics['bunchPatternTable']['source'] in run.all_sources:
+ bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ pattern = bunchPattern
+ if bpt is not None:
+ #load mask and extract pulse Id:
+ if pattern is 'sase3':
+ mask = is_sase_3(bpt)
+ extra_dim = 'sa3_pId'
+ elif pattern is 'sase1':
+ mask = is_sase_1(bpt)
+ extra_dim = 'sa1_pId'
+ elif pattern is 'scs_ppl':
+ mask = is_ppl(bpt)
+ extra_dim = 'ol_pId'
+ else:
+ extra_dim = 'pulseId'
+ valid_tid = mask.where(mask.sum(dim='pulse_slot')>0,
+ drop=True).trainId
+ mask_on = mask.sel(trainId=valid_tid)
+ if (mask_on == mask_on[0]).all().values == False:
+ if display:
+ print('Pattern changed during the run!')
+ pid = np.unique(np.where(mask_on)[1])
+ npulses = len(pid)
+ extra_coords = pid
+ mask_final = mask_on.where(mask_on, drop=True).fillna(False)
+ mask_final = mask_final.astype(bool).rename({'pulse_slot':extra_dim})
+ if display:
+ print(f'Bunch pattern: {npulses} pulses for {pattern}.')
+ if extra_dim is None:
+ extra_dim = 'pulseId'
+
+ # 1. Use peak-integrated data from digitizer
+ if useRaw is False:
+ #1.1 No bunch pattern provided
+ if bpt is None:
+ if indices is None:
+ raise TypeError(f'indices argument must be provided '+
+ 'when bunch pattern info is missing.')
+ return arr.isel({dim:indices}).rename({dim:'pulseId'})
+
+ #1.2 Bunch pattern is provided
+ if npulses == 1:
+ return arr.sel({dim:0}).expand_dims(extra_dim).T.assign_coords(
+ {extra_dim:extra_coords})
+ # verify period used by APD and match it to pid from bunchPattern
+ if digitizer is 'FastADC':
+ adc_source = source.split(':')[0]
+ enable_key = (source.split(':')[1].split('.')[0]
+ + '.enablePeakComputation.value')
+ if run.get_array(adc_source, enable_key)[0] is False:
+ raise ValueError('The digitizer did not record '+
+ 'peak-integrated data.')
+ period_key = (source.split(':')[1].split('.')[0] +
+ '.pulsePeriod.value')
+ period = run.get_array(adc_source, period_key)[0].values/24
+ if digitizer is 'ADQ412':
+ board_source = source.split(':')[0]
+ board = key[19]
+ channel = key[21]
+ channel_to_number = {'A':0, 'B':1, 'C':2, 'D':3}
+ channel = channel_to_number[channel]
+ in_del_key = (f'board{board}.apd.channel_{channel}'+
+ '.initialDelay.value')
+ initialDelay = run.get_array(board_source, in_del_key)[0].values
+ up_lim_key = (f'board{board}.apd.channel_{channel}'+
+ '.upperLimit.value')
+ upperLimit = run.get_array(board_source, up_lim_key)[0].values
+ period = (upperLimit - initialDelay)/440
+ stride = (pid[1] - pid[0]) / period
+ if period < 1:
+ print('Warning: the pulse period in digitizer was smaller '+
+ 'than the separation of two pulses at 4.5 MHz.')
+ stride = 1
+ if stride < 1:
+ raise ValueError('Pulse period in digitizer was too large '+
+ 'compared to actual pulse separation. Some pulses '+
+ 'were not recorded.')
+ stride = int(stride)
+ if display:
+ print('period', period, 'stride', stride)
+ if npulses*stride > arr.sizes[dim]:
+ raise ValueError('The number of pulses recorded by digitizer '+
+ f'that correspond to actual {pattern} pulses '+
+ 'is too small.')
+ peaks = arr.isel({dim:slice(0,npulses*stride,stride)}).rename(
+ {dim:extra_dim})
+ peaks = peaks.where(mask_final, drop=True)
+ return peaks.assign_coords({extra_dim:extra_coords})
+
+ # 2. Use raw data from digitizer
+ #minimum samples between two pulses, according to digitizer type
+ min_distance = 1
+ if digitizer is 'FastADC':
+ min_distance = 24
+ if digitizer is 'ADQ412':
+ min_distance = 440
+ if autoFind:
+ integParams = find_integ_params(arr.mean(dim='trainId'),
+ min_distance=min_distance)
+ if display:
+ print('auto find peaks result:', integParams)
+
+ # 2.1. No bunch pattern provided
+ if bpt is None:
+ required_keys=['intstart', 'intstop', 'bkgstart',
+ 'bkgstop', 'period', 'npulses']
+ if integParams is None or not all(name in integParams
+ for name in required_keys):
+ raise TypeError('All keys of integParams argument '+
+ f'{required_keys} are required when '+
+ 'bunch pattern info is missing.')
+ print(f'Retrieving {integParams["npulses"]} pulses.')
+ if extra_dim is None:
+ extra_dim = 'pulseId'
+ return peaks_from_raw_trace(arr, integParams['intstart'],
+ integParams['intstop'],
+ integParams['bkgstart'],
+ integParams['bkgstop'],
+ integParams['period'],
+ integParams['npulses'],
+ extra_dim=extra_dim)
+
+ # 2.2 Bunch pattern is provided
+ sample_id = (pid-pid[0])*min_distance
+ #override auto find parameters
+ if isinstance(integParams['intstart'], (int, np.integer)):
+ integParams['intstart'] = sample_id + integParams['intstart']
+ integParams['period'] = integParams['npulses'] = None
+ aligned_arr, _ = xr.align(arr, valid_tid)
+ peaks = peaks_from_raw_trace(aligned_arr, integParams['intstart'],
+ integParams['intstop'],
+ integParams['bkgstart'],
+ integParams['bkgstop'],
+ integParams['period'],
+ integParams['npulses'],
+ extra_dim=extra_dim)
+ peaks = peaks.where(mask_final, drop=True)
+ return peaks.assign_coords({extra_dim:extra_coords})
+
+
+def find_integ_params(trace, min_distance=1, height=None, width=1):
+ """
+ find integration parameters necessary for peak integration of a raw
+ digitizer trace.
+
+ Parameters
+ ----------
+ trace: numpy array or xarray DataArray
+ The digitier raw trace used to find peaks
+ min_distance: int
+ minimum number of samples between two peaks
+ height: int
+ minimum threshold for peak determination
+ width: int
+ minimum width of peak
+
+ Returns
+ -------
+ dict with keys 'intstart', 'intstop', 'bkgstart', 'bkgstop', 'period',
+ 'npulses' and values in number of samples.
+ """
+ if type(trace) is xr.DataArray:
+ trace = trace.values
+ bl = np.median(trace)
+ trace_no_bl = trace - bl
+ if np.max(trace_no_bl) < np.abs(np.min(trace_no_bl)):
+ posNeg = 'negative'
+ trace_no_bl *= -1
+ trace = bl + trace_no_bl
+ noise = trace[:100]
+ noise_ptp = np.max(noise) - np.min(noise)
+ if height is None:
+ height = trace_no_bl.max()/20
+ centers, peaks = find_peaks(trace_no_bl, distance=min_distance,
+ height=height, width=width)
+ npulses = len(centers)
+ if npulses==0:
+ raise ValueError('Could not automatically find peaks.')
+ elif npulses==1:
+ period = 0
+ else:
+ period = np.median(np.diff(centers)).astype(int)
+ intstart = np.round(peaks['left_ips'][0]
+ - 0.5*np.mean(peaks['widths'])).astype(int)
+ intstop = np.round(peaks['right_ips'][0]
+ + 0.5*np.mean(peaks['widths'])).astype(int)
+ bkgstop = intstart - int(0.5*np.mean(peaks['widths']))
+ bkgstart = bkgstop - 100
+ result = {'intstart':intstart, 'intstop':intstop,
+ 'bkgstart':bkgstart, 'bkgstop':bkgstop,
+ 'period':period, 'npulses':npulses}
+ return result
+
+def get_tim_peaks(data, bunchPattern='sase3',
+ integParams=None, keepAllSase=False,
+ display=False):
+ """
+ Automatically computes TIM peaks from sources in data. Sources
+ can be raw traces (e.g. "MCP2raw") or peak-integrated data
+ (e.g. "MCP2apd"). The bunch pattern table is used to assign
+ the pulse id coordinates.
+
+ Parameters
+ ----------
+ data: xarray Dataset containing TIM data
+ bunchPattern: str
+ 'sase1' or 'sase3' or 'scs_ppl', bunch pattern
+ used to extract peaks.
+ integParams: dict
+ dictionnary for raw trace integration, e.g.
+ {'intstart':100, 'intstop':200, 'bkgstart':50,
+ 'bkgstop':99, 'period':24, 'npulses':500}.
+ If None, integration parameters are computed
+ automatically.
+ keepAllSase: bool
+ Only relevant in case of sase-dedicated trains. If
+ True, the trains for SASE 1 are kept, else they are
+ dropped.
+ display: bool
+ If True, displays information
+
+ Returns
+ -------
+ xarray Dataset with all TIM variables substituted by
+ the peak caclulated values (e.g. "MCP2raw" becomes
+ "MCP2peaks").
+ """
+
+ proposal = data.attrs['runFolder'].split('/')[-3]
+ runNB = data.attrs['runFolder'].split('/')[-1]
+ peakDict = {}
+ toRemove = []
+ autoFind=True
+ if integParams is not None:
+ autoFind = False
+ for c in range(1,5):
+ key = f'MCP{c}raw'
+ useRaw = True
+ if key not in data:
+ key = f'MCP{c}apd'
+ useRaw = False
+ if key not in data:
+ continue
+ if display:
+ print(f'Retrieving TIM peaks from {key}...')
+ mnemonic = _mnemonics[key]
+ peaks = get_peaks(proposal, runNB, data[key],
+ source=mnemonic['source'],
+ key=mnemonic['key'],
+ digitizer='ADQ412',
+ useRaw=useRaw,
+ autoFind=autoFind,
+ integParams=integParams,
+ bunchPattern=bunchPattern,
+ display=display)
+ peakDict[f'MCP{c}peaks'] = peaks
+ toRemove.append(key)
+ ds = data.drop(toRemove)
+ join = 'outer' if keepAllSase else 'inner'
+ ds = ds.merge(peakDict, join=join)
+ return ds
+
+def get_fast_adc_peaks(data, bunchPattern='scs_ppl',
+ integParams=None, keepAllSase=False,
+ display=False):
+ """
+ Automatically computes Fast ADC peaks from sources in data.
+ Sources can be raw traces (e.g. "FastADC2raw") or peak-
+ integrated data (e.g. "FastADC2peaks"). The bunch pattern
+ table is used to assign the pulse id coordinates.
+
+ Parameters
+ ----------
+ data: xarray Dataset containing TIM data
+ bunchPattern: str
+ 'sase1' or 'sase3' or 'scs_ppl', bunch pattern
+ used to extract peaks.
+ integParams: dict
+ dictionnary for raw trace integration, e.g.
+ {'intstart':100, 'intstop':200, 'bkgstart':50,
+ 'bkgstop':99, 'period':24, 'npulses':500}.
+ If None, integration parameters are computed
+ automatically.
+ keepAllSase: bool
+ Only relevant in case of sase-dedicated trains. If
+ True, the trains for SASE 1 are kept, else they are
+ dropped.
+ display: bool
+ If True, displays information
+
+ Returns
+ -------
+ xarray Dataset with all Fast ADC variables substituted by
+ the peak caclulated values (e.g. "FastADC2raw" becomes
+ "FastADC2peaks").
+ """
+
+ proposal = data.attrs['runFolder'].split('/')[-3]
+ runNB = data.attrs['runFolder'].split('/')[-1]
+ peakDict = {}
+ toRemove = []
+ autoFind=True
+ if integParams is not None:
+ autoFind = False
+ for c in range(1,10):
+ key = f'FastADC{c}raw'
+ useRaw = True
+ if key not in data:
+ key = f'FastADC{c}peaks'
+ useRaw = False
+ if key not in data:
+ continue
+ if display:
+ print(f'Retrieving Fast ADC peaks from {key}...')
+ mnemonic = _mnemonics[key]
+ peaks = get_peaks(proposal, runNB, data[key],
+ source=mnemonic['source'],
+ key=mnemonic['key'],
+ digitizer='FastADC',
+ useRaw=useRaw,
+ autoFind=autoFind,
+ integParams=integParams,
+ bunchPattern=bunchPattern,
+ display=display)
+ peakDict[f'FastADC{c}peaks'] = peaks
+ toRemove.append(key)
+ ds = data.drop(toRemove)
+ join = 'outer' if keepAllSase else 'inner'
+ ds = ds.merge(peakDict, join=join)
+ return ds
diff --git a/src/toolbox_scs/detectors/xgm.py b/src/toolbox_scs/detectors/xgm.py
index cb77bded175c5295d970414477c320c17d080336..6d21fbba8ace4c183645909c303f34982b7e0cb5 100644
--- a/src/toolbox_scs/detectors/xgm.py
+++ b/src/toolbox_scs/detectors/xgm.py
@@ -15,6 +15,7 @@ from scipy.signal import find_peaks
import extra_data as ed
from ..constants import mnemonics as _mnemonics
+from ..misc.bunch_pattern_external import is_sase_1, is_sase_3, is_ppl
log = logging.getLogger(__name__)
@@ -54,7 +55,7 @@ def load_xgm(run, xgm_mnemonic='SCS_SA3'):
return xgm
-def cleanXGMdata(data, npulses=None, sase3First=True):
+def cleanXGMdata(data, npulses=None, sase3First=True, keepAllSase=False):
''' Cleans the XGM data arrays obtained from load() function.
The XGM "TD" data arrays have arbitrary size of 1000 and default value 1.0
when there is no pulse. This function sorts the SASE 1 and SASE 3 pulses.
@@ -62,93 +63,140 @@ def cleanXGMdata(data, npulses=None, sase3First=True):
the function selectSASEinXGM is used to extract sase-resolved pulses.
Inputs:
data: xarray Dataset containing XGM TD arrays.
- npulses: number of pulses, needed if pulse pattern not available.
+ npulses: number of SASE 3 pulses, needed if pulse pattern not available.
sase3First: bool, needed if pulse pattern not available.
Output:
xarray Dataset containing sase- and pulse-resolved XGM data, with
dimension names 'sa1_pId' and 'sa3_pId'
'''
- dropList = []
- mergeList = []
+ proposal = data.attrs['runFolder'].split('/')[-3]
+ runNB = data.attrs['runFolder'].split('/')[-1]
+ run = ed.open_run(proposal, runNB)
load_sa1 = True
- if 'sase3' in data:
- if np.all(data['npulses_sase1'].where(data['npulses_sase3'] !=0,
- drop=True) == 0):
- print('Dedicated trains, skip loading SASE 1 data.')
+ #check bunch pattern table
+ if _mnemonics['bunchPatternTable']['source'] in run.all_sources:
+ bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ mask_sa1 = is_sase_1(bpt).sel(trainId=data.trainId)
+ mask_sa3 = is_sase_3(bpt).sel(trainId=data.trainId)
+ valid_tid_sa1 = mask_sa1.where(mask_sa1.sum(dim='pulse_slot')>0,
+ drop=True).trainId
+ valid_tid_sa3 = mask_sa3.where(mask_sa3.sum(dim='pulse_slot')>0,
+ drop=True).trainId
+ if valid_tid_sa1.size==0:
load_sa1 = False
- npulses_sa1 = None
+ if np.intersect1d(valid_tid_sa1, valid_tid_sa3,
+ assume_unique=True).size==0:
+ load_sa1 = keepAllSase
+ if keepAllSase:
+ print('Dedicated trains, loading both '+
+ 'SASE1 and SASE 3.')
+ else:
+ print('Dedicated trains, only loading SASE 3.')
+ isBunchPattern = True
else:
print('Missing bunch pattern info!')
- if npulses is None:
- raise TypeError('npulses argument is required when bunch pattern ' +
- 'info is missing.')
- #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",
- "SCS_XGM_sigma", "SCS_SA3_sigma", "SCS_SA1_sigma"]
+ bpt = None
+ isBunchPattern = False
+ dropList = []
+ mergeList = []
+ keys_all_sase = ["XTD10_XGM", "XTD10_XGM_sigma",
+ "SCS_XGM", "SCS_XGM_sigma"]
+ keys_all_sase = list(set(keys_all_sase) & set(data.keys()))
+ keys_dedicated = ["XTD10_SA3", "XTD10_SA1",
+ "XTD10_SA3_sigma", "XTD10_SA1_sigma",
+ "SCS_SA3", "SCS_SA1",
+ "SCS_SA3_sigma", "SCS_SA1_sigma"]
+ keys_dedicated = list(set(keys_dedicated) & set(data.keys()))
- for whichXgm in ['SCS', 'XTD10']:
- load_sa1 = True
- if (f"{whichXgm}_SA3" not in data and f"{whichXgm}_XGM" in data):
- #no SASE-resolved arrays available
- if not 'sase3' in data:
- npulses_xgm = data[f'{whichXgm}_XGM'].where(data[f'{whichXgm}_XGM'], drop=True).shape[1]
- npulses_sa1 = npulses_xgm - npulses
- if npulses_sa1==0:
- load_sa1 = False
- if npulses_sa1 < 0:
- raise ValueError(f'npulses = {npulses} is larger than the total number'
- +f' of pulses per train = {npulses_xgm}')
- sa3 = selectSASEinXGM(data, xgm=f'{whichXgm}_XGM', sase='sase3', npulses=npulses,
- sase3First=sase3First).rename({'XGMbunchId':'sa3_pId'}).rename(f"{whichXgm}_SA3")
- mergeList.append(sa3)
- if f"{whichXgm}_XGM_sigma" in data:
- sa3_sigma = selectSASEinXGM(data, xgm=f'{whichXgm}_XGM_sigma', sase='sase3', npulses=npulses,
- sase3First=sase3First).rename({'XGMbunchId':'sa3_pId'}).rename(f"{whichXgm}_SA3_sigma")
- mergeList.append(sa3_sigma)
- dropList.append(f'{whichXgm}_XGM_sigma')
- if load_sa1:
- sa1 = selectSASEinXGM(data, xgm=f'{whichXgm}_XGM', sase='sase1',
- npulses=npulses_sa1, sase3First=sase3First).rename(
- {'XGMbunchId':'sa1_pId'}).rename(f"{whichXgm}_SA1")
- mergeList.append(sa1)
- if f"{whichXgm}_XGM_sigma" in data:
- sa1_sigma = selectSASEinXGM(data, xgm=f'{whichXgm}_XGM_sigma', sase='sase1', npulses=npulses_sa1,
- sase3First=sase3First).rename({'XGMbunchId':'sa1_pId'}).rename(f"{whichXgm}_SA1_sigma")
- mergeList.append(sa1_sigma)
- dropList.append(f'{whichXgm}_XGM')
- keys.remove(f'{whichXgm}_XGM')
-
- for key in keys:
- if key not in data:
- continue
+ #split non-dedicated sase data in SASE 1 and SASE 3 arrays
+ npulses_sa1 = None
+ for key in keys_all_sase:
+ if isBunchPattern is False:
+ if npulses is None:
+ raise TypeError('npulses argument is required '
+ 'when retrieving non-dedicated XGM arrays '+
+ 'without bunch pattern info. Consider using '+
+ 'SASE 1 or SASE 3 dedicated data arrays.')
+ npulses_xgm = data[key].where(data[key]!=1.,
+ drop=True).sizes['XGMbunchId']
+ npulses_sa1 = npulses_xgm - npulses
+ if npulses_sa1 < 0:
+ raise ValueError('Required number of pulses is too large. '+
+ 'The XGM array contains only {npulses_xgm} '+
+ 'pulses but npulses={npulses}.')
+ if npulses_sa1 == 0:
+ load_sa1 = False
+ if load_sa1:
+ sa1 = selectSASEinXGM(data, xgm=key, sase='sase1',
+ bpt=bpt,npulses=npulses_sa1,
+ sase3First=sase3First).rename(
+ {'XGMbunchId':'sa1_pId'})
+ sa1 = sa1.rename(key.replace('XGM', 'SA1'))
+ mergeList.append(sa1)
+ sa3 = selectSASEinXGM(data, xgm=key, sase='sase3',
+ bpt=bpt, npulses=npulses,
+ sase3First=sase3First).rename(
+ {'XGMbunchId':'sa3_pId'})
+ sa3 = sa3.rename(key.replace('XGM', 'SA3'))
+ mergeList.append(sa3)
+ dropList.append(key)
+
+ #dedicated sase data
+ for key in keys_dedicated:
if "sa3" in key.lower():
sase = 'sa3_'
- elif "sa1" in key.lower():
+ else:
sase = 'sa1_'
- if not load_sa1:
+ if load_sa1 is False:
dropList.append(key)
continue
- else:
- dropList.append(key)
- 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])})
-
+ {'XGMbunchId':'{}pId'.format(sase)}).rename(key)
dropList.append(key)
mergeList.append(res)
+
mergeList.append(data.drop(dropList))
- subset = xr.merge(mergeList, join='inner')
+ ds = xr.merge(mergeList, join='outer')
+
+ #assign pulse Id coordinates if bunch pattern is provided
+ if isBunchPattern:
+ for sase in ['sa1_pId', 'sa3_pId']:
+ if sase is 'sa1_pId':
+ mask = mask_sa1
+ sase_keys = [key for key in ds if "SA1" in key]
+ else:
+ mask = mask_sa3
+ sase_keys = [key for key in ds if "SA3" in key]
+ mask_flat = mask.values.flatten()
+ mask_flat_arg = np.argwhere(mask_flat)
+ for key in sase_keys:
+ xgm_flat = np.hstack((ds[key].fillna(1.),
+ np.ones((ds[key].sizes['trainId'],
+ 2700-ds[key].sizes[sase])))).flatten()
+ xgm_flat_arg = np.argwhere(xgm_flat!=1.0)
+ if(xgm_flat_arg.shape != mask_flat_arg.shape):
+ print('The XGM data does not match the bunch pattern! '+
+ 'Cannot assign pulse id coordinates.')
+ break
+ new_xgm_flat = np.ones(xgm_flat.shape)
+ new_xgm_flat[mask_flat_arg] = xgm_flat[xgm_flat_arg]
+ new_xgm = new_xgm_flat.reshape((ds[key].sizes['trainId'], 2700))
+ new_xgm = xr.DataArray(new_xgm, dims=['trainId', sase],
+ coords={'trainId':ds[key].trainId,
+ sase:np.arange(2700)},
+ name=ds[key])
+ new_xgm = new_xgm.where(new_xgm!=1, drop=True)
+ ds[key] = new_xgm
+
+ #copy original dataset attributes
for k in data.attrs.keys():
- subset.attrs[k] = data.attrs[k]
- return subset
+ ds.attrs[k] = data.attrs[k]
+ return ds
-def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=None):
+def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM',
+ bpt = None, sase3First=True, npulses=None):
''' Given an array containing both SASE1 and SASE3 data, extracts SASE1-
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
@@ -158,6 +206,7 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=
data: xarray Dataset containing xgm data
sase: key of sase to select: {'sase1', 'sase3'}
xgm: key of xgm to select: {'XTD10_XGM[_sigma]', 'SCS_XGM[_sigma]'}
+ bpt: xarray DataArray or numpy array: bunch pattern table
sase3First: bool, optional. Used in case no bunch pattern was recorded
npulses: int, optional. Required in case no bunch pattern was recorded.
@@ -168,7 +217,7 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=
are filled with NaNs.
'''
#1. case where bunch pattern is missing:
- if sase not in data:
+ if bpt is None:
print('Retrieving {} SASE {} pulses assuming that '.format(npulses, sase[4])
+'SASE {} pulses come first.'.format('3' if sase3First else '1'))
#in older version of DAQ, non-data numbers were filled with 0.0.
@@ -182,54 +231,63 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=
'This is not supported yet.')
else:
start=xgmData.shape[1]-npulses
- return xgmData[:,start:start+npulses].assign_coords(XGMbunchId=np.arange(npulses))
+ ds = xgmData[:,start:start+npulses]
+ return ds.assign_coords(XGMbunchId=np.arange(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)
- sa3_val = sa3_val[~np.isnan(sa3_val)]
- sa1 = data['sase1'].where(data['sase1'] > 1, drop=True)
- sa1_val=np.unique(sa1)
- sa1_val = sa1_val[~np.isnan(sa1_val)]
- sa_all = xr.concat([sa1, sa3], dim='bunchId').rename('sa_all')
- sa_all = xr.DataArray(np.sort(sa_all)[:,:xgm_arr['XGMbunchId'].shape[0]],
- dims=['trainId', 'bunchId'],
- coords={'trainId':data.trainId},
- name='sase_all')
- if sase=='sase3':
- idxListSase = np.unique(sa3)
- newName = xgm.split('_')[0] + '_SA3'
+ mask_sa1 = is_sase_1(bpt)
+ mask_sa3 = is_sase_3(bpt)
+ mask_all = xr.ufuncs.logical_or(mask_sa1, mask_sa3)
+ valid_tid = mask_all.where(mask_all.sum(dim='pulse_slot')>0,
+ drop=True).trainId
+ valid_tid = np.intersect1d(valid_tid, data.trainId)
+ npulses_max = mask_all.sum(dim='pulse_slot').max().values
+ mask_all = mask_all.sel(trainId=valid_tid)
+ xgm_arr = data[xgm].sel(trainId=valid_tid).isel(
+ XGMbunchId=slice(0,npulses_max))
+ pid_arr = xr.DataArray(np.outer(np.ones(valid_tid.shape),
+ np.arange(2700)).astype(int),
+ dims=['trainId', 'pulse_slot'],
+ coords={'trainId':valid_tid}
+ )
+ if sase is 'sase1':
+ mask = mask_sa1
+ mask_alt = mask_sa3
+ name='SA1'
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
+ mask = mask_sa3
+ mask_alt = mask_sa1
+ name='SA3'
+ pid = pid_arr.where(mask, drop=True).fillna(2700)
+ pid_alt = pid_arr.where(mask_alt, drop=True).fillna(2700)
+ pid_all = pid_arr.where(mask_all, drop=True).fillna(2700)
+ #list of various pulse patterns accross the run
+ patterns = np.unique(pid_all, axis=0)
+ subsets = []
+ #for each pattern, select trains of sase, check if
+ #alt-sase is present, select pulses of sase and
+ #append the sub-array into list
+ for pattern in patterns:
+ tid = pid.where((pid_all==pattern).all(axis=1),
+ drop=True).trainId
+ if tid.size == 0:
+ continue
+ p = pid.sel(trainId=tid[0]).values
+ tid_alt = pid_alt.where((pid_all==pattern).all(axis=1),
+ drop=True).trainId
+ if tid_alt.size > 0:
+ p_alt = pid_alt.sel(trainId=tid[0]).values
+ p_all = np.sort(np.concatenate((p,p_alt)))
+ pulses = np.atleast_1d(np.argwhere(
+ np.in1d(p_all, p)).squeeze())
+ else:
+ pulses = slice(np.sum(p<2700))
+ arr = xgm_arr.sel(trainId=tid).isel(XGMbunchId=pulses)
+ subsets.append(arr)
+ #finally, concatenate the sub-arrays with join='outer'
+ da = xr.concat(subsets, dim='trainId', join='outer')
+ da = da.rename(da.name.replace('XGM', name))
+ return da
def saseContribution(data, sase='sase1', xgm='XTD10_XGM'):
''' Calculate the relative contribution of SASE 1 or SASE 3 pulses
@@ -292,7 +350,7 @@ def calibrateXGMs(data, allPulses=False, plot=False, display=False):
print(f'Using fast data averages (slowTrain) for {whichXgm}')
slowTrainData.append(data[f'{whichXgm}_slowTrain'])
else:
- mnemo = _mnemonics[f'{whichXgm}_slowTrain']
+ mnemo = tb.mnemonics[f'{whichXgm}_slowTrain']
if mnemo['key'] in data.attrs['run'].keys_for_source(mnemo['source']):
if display:
print(f'Using fast data averages (slowTrain) for {whichXgm}')
@@ -451,7 +509,8 @@ def calibrateXGMsFromAllPulses(data, plot=False):
# TIM
-def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=1, t_offset=None, npulses=None):
+def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=1, t_offset=None,
+ npulses=None):
''' Computes peak integration from raw MCP traces.
Inputs:
@@ -487,7 +546,8 @@ def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=1, t_offset=None, n
t_offset = 440 * step
else:
t_offset = 1
- results = xr.DataArray(np.zeros((data.trainId.shape[0], npulses)), coords=data[keyraw].coords,
+ results = xr.DataArray(np.zeros((data.trainId.shape[0], npulses)),
+ coords=data[keyraw].coords,
dims=['trainId', 'MCP{}fromRaw'.format(mcp)])
for i in range(npulses):
a = intstart + t_offset*i