doc/Dask DSSC module binning.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 import numpy as np
 %matplotlib notebook
 import matplotlib.pyplot as plt
 plt.rcParams['figure.constrained_layout.use'] = True
 
 import dask
 print(f'dask: {dask.__version__}')
 import dask.array as da
 
 import xarray as xr
 ```
 
 %% Output
 
     dask: 2.11.0
 
 %% Cell type:code id: tags:
 
 ``` python
 from psutil import virtual_memory
 import gc
 # gc.collect() # run garbage collection to free possible memory
 
 mem = virtual_memory()
 print(f'Physical memory: {mem.total/1024/1024/1024:.0f} Gb')  # total physical memory available
 ```
 
 %% Output
 
     Physical memory: 504 Gb
 
 %% Cell type:code id: tags:
 
 ``` python
 import logging
 logging.basicConfig(filename='example.log', level=logging.DEBUG)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 %load_ext autoreload
 
 %autoreload 2
 
 import toolbox_scs as tb
 print(tb.__file__)
 from toolbox_scs.routines.boz import load_dssc_module
 
 from extra_data import open_run
 ```
 
 %% Output
 
     /home/lleguy/notebooks/ToolBox/src/toolbox_scs/__init__.py
 
 %% Cell type:markdown id: tags:
 
 # Parameters
 
 %% Cell type:code id: tags:parameters
 
 ``` python
 proposalNB = 2719
 dark_runNB = 180
 runNB = 179
 module_group = 0
 pulse_pattern = ['pumped', 'unpumped']
 xaxis = 'delay' # 'nrj'
 bin_width = 0.1 # [ps]
 path = f'/gpfs/exfel/exp/SCS/202002/p002719/scratch/tests/r{runNB}/'
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 proposalNB = int(proposalNB)
 dark_runNB = int(dark_runNB)
 runNB = int(runNB)
 module_group = int(module_group)
 bin_width = float(bin_width)
 
 moduleNB = list(range(module_group*4, (module_group+1)*4))
 ```
 
 %% Cell type:markdown id: tags:
 
 # Processing function
 
 %% Cell type:code id: tags:
 
 ``` python
 def process(module):
     # Load dark
     arr_dark, tid_dark = load_dssc_module(proposalNB, dark_runNB, module, drop_intra_darks=False)
     arr_dark = arr_dark.rechunk((100, -1, -1, -1))
     dark_img = arr_dark.mean(axis=0).compute()
 
     # Load module data
     arr, tid = load_dssc_module(proposalNB, runNB, module, drop_intra_darks=False)
     arr = arr.rechunk((100, -1, -1, -1))
 
     # dark and intra dark correction
     arr = arr - dark_img
     arr = arr[:, ::2, :, :] - arr[:, 1::2, :, :]
 
     # Load slow data against which to bin
     if xaxis == 'delay':
         run, v = tb.load(proposalNB, runNB, ['PP800_DelayLine', 'BAM1932M', 'SCS_XGM'])
     else:
         run, v = tb.load(proposalNB, runNB, [xaxis, 'SCS_XGM'])
 
     # select part of the run
     # v = v.isel({'trainId':slice(0,3000)})
 
     # combine slow and DSSC module data
     xr_data = xr.DataArray(arr,
                        coords={'trainId': tid,
                                'sa3_pId': v['sa3_pId'].values},
                        dims = ['trainId', 'sa3_pId', 'y', 'x'])
     xr_data = xr_data.expand_dims(module=[module], axis=2)
     r = xr.merge([xr_data.to_dataset(name='DSSC'), v], join='inner')
 
     # calculate bins
     if xaxis == 'delay':
         r['delay'] = tb.misc.positionToDelay(r['PP800_DelayLine'])
         bam = r['BAM1932M'] - r['BAM1932M'].mean()
         r['bin_delay'] = ((r['delay'] - bam)/bin_width).round()*bin_width
     else:
         r['bin_' + xaxis] = (r[xaxis]/bin_width).round()*bin_width
 
     # add the pulse pattern coordinates
     Nrepeats = int(len(v['sa3_pId'].values)/len(pulse_pattern))
     pp = pulse_pattern*Nrepeats
     pp = np.array(pp)
     r = r.assign_coords(pp=("sa3_pId", pp))
 
     # select pattern and bin data
     bin_data = None
     for p in np.unique(pp):
         # slice using non-index coordinates
         # https://github.com/pydata/xarray/issues/2028
         sub_r = r.sel(sa3_pId=(r.pp == p))
 
-        res = sub_r.groupby('bin_'+xaxis).mean()
+        # calculate mean on bin, then mean to remove the dimension
+        res = sub_r.groupby('bin_'+xaxis).mean().mean(['sa3_pId'])
 
         if bin_data is None:
             bin_data = res
             bin_data['DSSC'] = res['DSSC'].expand_dims(pp=[p])
             bin_data['SCS_SA3'] = res['SCS_SA3'].expand_dims(pp=[p])
         else:
             bin_data = xr.merge([bin_data,
                                  res['DSSC'].expand_dims(pp=[p]),
                                  res['SCS_SA3'].expand_dims(pp=[p])])
 
     # save the result
     fname = path + f'run{runNB}-darkrun{dark_runNB}-module{module}.h5'
     print(fname)
     bin_data.to_netcdf(fname, format='NETCDF4', engine='h5netcdf')
 ```
 
 %% Cell type:markdown id: tags:
 
 # Processing
 
 %% Cell type:code id: tags:
 
 ``` python
 for m in moduleNB:
     process(m)
 ```

doc/changelog.rst

@@ -5,9 +5,15 @@ unreleased
 ----------
 
 - **Bug fixes**
-  
+
+    - fix :issue:`45` SLURM scripts embedded in and download link available from documentation :mr:`171`
+    - fix :issue:`8` regarding azimuthal integration with pyFAI and hexagonal DSSC pixel splitting by providing an example notebook :mr:`174`
+    - fix :issue:`46` with a change in dask groupby mean behavior :mr:`174`
+
 - **Improvements**
 
+    - update version of BAM mnemonics :mr:`175`
+    - update version GATT-related mnemonics, add `transmission_col2` :mr:`172`
     - reorganize the Howto section :mr:`169`
   
 - **New Features**

doc/howtos.rst

@@ -41,7 +41,14 @@ which will be repeated. XGM data will also be binned similarly to the DSSC
 data.
 
 Since this data reduction step can be quite time consuming for large datasets,
-it is recommended to launch the notebook via a SLURM script:
+it is recommended to launch the notebook via a SLURM script. The script can be
+downloaded from :download:`scripts/bin_dssc_module_job.sh` and reads as:
+
+.. literalinclude:: scripts/bin_dssc_module_job.sh
+   :language: bash
+   :linenos:
+
+It is launched with the following:
 
 .. code:: bash
     
@@ -66,7 +73,11 @@ toolbox source. This files can then be loaded and combined with:
 DSSC azimuthal integration
 ##########################
 
-*To be documented*.
+Azimuthal integration can be performed with pyFAI_ which can utilize the
+hexagonal pixel shape information from the DSSC geometry to split
+the intensity in a pixel in the bins covered by it. An example notebook
+:doc:`Azimuthal integration of DSSC with pyFAI <Azimuthal integration of DSSC with pyFAI>` is available.
+
 
 Legacy DSSC binning procedure
 #############################
@@ -101,8 +112,14 @@ non-linearity calculation by taking the JSON file as soon as it appears.
 The determination of the non-linearity correction is a lot longer and can take
 some 2 to 8 hours depending on the number of pulses in the
 train. For this reason it is possible to use a script
-``scripts/boz_parameters_job.sh`` in the toolbox to launch the first notebook
-via slurm:
+that can be downloaded from :download:`scripts/boz_parameters_job.sh` and
+reads as:
+
+.. literalinclude:: scripts/boz_parameters_job.sh
+   :language: bash
+   :linenos:
+
+It uses the first notebook and is launched via slurm:
 
 ``sbatch ./boz_parameters_job.sh 615 614 3``
 
@@ -130,3 +147,5 @@ Detectors that produce one point per pulse, or 0D detectors, are all handled in
 
 * :doc:`extract data from point detectors <point_detectors/point_detectors>`.
 
+
+.. _pyFAI: https://pyfai.readthedocs.io

src/toolbox_scs/constants.py

@@ -40,12 +40,21 @@ mnemonics = {
 
     # Bunch Arrival Monitors
     "BAM414": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
+                'key': 'data.absoluteTD',
+                'dim': ['BAMbunchId']},
+               {'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
                 'key': 'data.lowChargeArrivalTime',
                 'dim': ['BAMbunchId']},),
     "BAM1932M": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
+                  'key': 'data.absoluteTD',
+                  'dim': ['BAMbunchId']},
+                 {'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
                   'key': 'data.lowChargeArrivalTime',
                   'dim': ['BAMbunchId']},),
     "BAM1932S": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
+                  'key': 'data.absoluteTD',
+                  'dim': ['BAMbunchId']},
+                 {'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
                   'key': 'data.lowChargeArrivalTime',
                   'dim': ['BAMbunchId']},),
 
@@ -66,10 +75,20 @@ mnemonics = {
     "HSLIT": ({'source': 'SCS_XTD10_HSLIT/MDL/BLADE',
                'key': 'actualGap.value',
                'dim': None},),
-    "transmission": ({'source': 'SA3_XTD10_GATT/MDL/GATT_TRANSMISSION_MONITOR',
+    "transmission": ({'source': 'SA3_XTD10_VAC/MDL/GATT_TRANSMISSION_MONITOR',
+                      'key': 'Estimated_Tr.value',
+                      'dim': None},
+                     {'source': 'SA3_XTD10_GATT/MDL/GATT_TRANSMISSION_MONITOR',
                       'key': 'Estimated_Tr.value',
                       'dim': None},),
-    "GATT_pressure": ({'source': 'P_GATT',
+    "transmission_col2": (
+        {'source': 'SA3_XTD10_VAC/MDL/GATT_TRANSMISSION_MONITOR',
+         'key': 'second_color_Estimated_Tr.value',
+         'dim': None},),
+    "GATT_pressure": ({'source': 'SA3_XTD10_VAC/MDL/GATT_P_CELL',
+                       'key': 'value.value',
+                       'dim': None},
+                      {'source': 'P_GATT',
                        'key': 'value.value',
                        'dim': None},),
     "navitar": ({'source': 'SCS_XTD10_IMGES/CAM/BEAMVIEW_NAVITAR:daqOutput',
@@ -78,6 +97,12 @@ mnemonics = {
     "UND": ({'source': 'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY',
              'key': 'actualPosition.value',
              'dim': None},),
+    "UND2": ({'source': 'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY_COLOR2',
+              'key': 'actualPosition.value',
+              'dim': None},),
+    "UND3": ({'source': 'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY_COLOR3',
+              'key': 'actualPosition.value',
+              'dim': None},),
 
     # PES
     "PES_N_raw": ({'source': 'SA3_XTD10_PES/ADC/1:network',