Merge branch 'pyFAI' into 'master'

Example on using pyFAI and pixel splitting for azimuthal integration of DSSC Closes #46 and #8 See merge request !174

Merge branch 'pyFAI' into 'master'
Example on using pyFAI and pixel splitting for azimuthal integration of DSSC Closes #46 and #8 See merge request !174
42769e28 · Loïc Le Guyader · ab937207 · cc6e5156 · 42769e28 · 42769e28
Commit 42769e28 authored 3 years ago by Loïc Le Guyader
--- a/doc/Azimuthal integration of DSSC with pyFAI.ipynb
+++ b/doc/Azimuthal integration of DSSC with pyFAI.ipynb
--- a/doc/Dask DSSC module binning.ipynb
+++ b/doc/Dask DSSC module binning.ipynb
@@ -190,7 +190,8 @@
    "        # https://github.com/pydata/xarray/issues/2028\n",
    "        sub_r = r.sel(sa3_pId=(r.pp == p))\n",
    "        \n",
-    "        res = sub_r.groupby('bin_'+xaxis).mean()\n",
+    "        # calculate mean on bin, then mean to remove the dimension\n",
+    "        res = sub_r.groupby('bin_'+xaxis).mean().mean(['sa3_pId'])\n",
    "\n",
    "        if bin_data is None:\n",
    "            bin_data = res\n",

 %% 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)
 ```

--- a/doc/changelog.rst
+++ b/doc/changelog.rst
@@ -7,7 +7,9 @@ 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`

--- a/doc/howtos.rst
+++ b/doc/howtos.rst
@@ -73,7 +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
 #############################
@@ -143,3 +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