DSSC multiprocessing analysis

The current code is a condensed clean up of several notebook cells written by several different people. With this we can:

• multiprocess dark DSSC runs and save the processed data
• multiprocess DSSC runs, binning the data by nrj, delay, ... and save the processed data
• azimuthal integration and plots
• linescans on azimuthal integration

Attached a notebook (.ipynb and pdf) showing how to use the functionalities.

2019-10-15_DSSC_multiprocessing.ipynb

2019-10-15_DSSC_multiprocessing.pdf

DSSC.py

+import multiprocessing
+from time import strftime
+import tempfile
+import shutil
+from tqdm.auto import tqdm
+import os
+import warnings
+import psutil
+
+import karabo_data as kd
+from karabo_data.geometry2 import DSSC_1MGeometry
+import ToolBox as tb
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import ImageGrid
+import numpy as np
+import xarray as xr
+import h5py
+
+from imageio import imread
+
+class DSSC:
+    
+    def __init__(self, semester, proposal, topic='SCS', distance=1):
+        """ Create a DSSC object to process DSSC data.
+        
+            inputs:
+                semester: semester string
+                proposal: proposal number string
+                topic: topic, by default SCS
+                distance: distance sample to DSSC detector in meter
+            
+        """
+        self.semester = semester
+        self.proposal = proposal
+        self.topic = topic
+        self.tempdir = None
+        self.save_folder = f'/gpfs/exfel/exp/{self.topic}/{self.semester}/{self.proposal}/usr/condensed_runs/'
+        self.distance = distance
+        self.px_pitch_h = 236 # horizontal pitch in microns
+        self.px_pitch_v = 204 # vertical pitch in microns
+        self.aspect = self.px_pitch_v/self.px_pitch_h # aspect ratio of the DSSC images
+        self.geom = None
+        self.mask = None
+        
+        print('DSSC configuration')
+        print(f'Topic: {self.topic}')
+        print(f'Semester: {self.semester}')
+        print(f'Proposal: {self.proposal}')
+        print(f'Default save folder: {self.save_folder}')
+        print(f'Sample to DSSC distance: {self.distance} m')
+        
+        if not os.path.exists(self.save_folder):
+            warnings.warn(f'Default save folder does not exist: {self.save_folder}')
+        
+    def __del__(self):
+        # deleting temporay folder
+        if self.tempdir:
+            shutil.rmtree(self.tempdir)
+    
+    def open_run(self, run_nr, isDark=False):
+        """ Open a run with karabo-data and prepare the virtual dataset for multiprocessing
+        
+            inputs:
+                run_nr: the run number
+                isDark: a boolean to specify if the run is a dark run or not
+        
+        """
+        
+        print('Opening run data with karabo-data')
+        self.run_nr = run_nr
+        self.xgm = None
+        self.scan_vname = None
+        
+        self.run = kd.open_run(self.proposal, self.run_nr)
+        self.isDark = isDark
+        self.plot_title = f'{self.proposal} run: {self.run_nr}'
+        
+        self.fpt = self.run.detector_info('SCS_DET_DSSC1M-1/DET/0CH0:xtdf')['frames_per_train']
+        self.nbunches = self.run.get_array('SCS_RR_UTC/MDL/BUNCH_DECODER', 'sase3.nPulses.value')
+        self.nbunches = np.unique(self.nbunches)
+        if len(self.nbunches) == 1:
+            self.nbunches = self.nbunches[0]
+        else:
+            warnings.warn('not all trains have same length DSSC data')
+            print(f'nbunches: {self.nbunches}')
+            self.nbunches = self.nbunches[-1]
+            
+        print(f'DSSC frames per train: {self.fpt}')
+        print(f'SA3 bunches per train: {self.nbunches}')
+        
+        if self.tempdir is not None:
+            shutil.rmtree(self.tempdir)
+        
+        self.tempdir = tempfile.mkdtemp()
+        print(f'Temporary directory: {self.tempdir}')
+
+        print('Creating virtual dataset')
+        self.vdslist = self.create_virtual_dssc_datasets(self.run, path=self.tempdir)
+        
+        # create a dummy scan variable for dark run
+        # for other type or run, use DSSC.define_run function to overwrite it
+        self.scan = xr.DataArray(np.ones_like(self.run.train_ids), dims=['trainId'],
+                                 coords={'trainId': self.run.train_ids})
+        self.scan_vname = 'dummy'
+        self.vds_scan = None
+        
+    def define_scan(self, vname, bins):
+        """
+            Prepare the binning of the DSSC data.
+            
+            inputs:
+                vname: variable name for the scan, can be a mnemonic string from ToolBox
+                    or a dictionnary with ['source', 'key'] fields
+                bins: step size (or bins_edge but not yet implemented)
+        """
+
+        if type(vname) is dict:
+            self.scan = self.run.get_array(vname['source'], vname['key'])
+        elif type(vname) is str:
+            if vname not in tb.mnemonics:
+                raise ValueError(f'{vname} not found in the ToolBox mnemonics table')
+            self.scan = self.run.get_array(tb.mnemonics[vname]['source'], tb.mnemonics[vname]['key'])
+        else:
+            raise ValueError(f'vname should be a string or a dict. We got {type(vname)}')
+            
+        if (type(bins) is int) or (type(bins) is float):
+            self.scan = bins * np.round(self.scan / bins)
+        else:
+            # TODO: digitize the data
+            raise ValueError(f'To be implemented')
+        self.scan_vname = vname
+        
+        self.vds_scan = os.path.join(self.tempdir, 'scan_variable.h5')
+        if os.path.isfile(self.vds_scan):
+            os.remove(self.vds_scan)
+            
+        self.scan = self.scan.to_dataset(name='scan_variable')
+        self.scan['xgm_pumped'] = self.xgm[:, :self.nbunches:2].mean('dim_0')
+        self.scan['xgm_unpumped'] = self.xgm[:, 1:self.nbunches:2].mean('dim_0')
+        self.scan.to_netcdf(self.vds_scan, group='data')
+
+        self.scan_counts = xr.DataArray(np.ones(len(self.scan['scan_variable'])),
+                                        dims=['scan_variable'],
+                                        coords={'scan_variable': self.scan['scan_variable'].values},
+                                        name='counts')
+        self.scan_points = self.scan.groupby('scan_variable').mean('trainId').coords['scan_variable'].values
+        self.scan_points_counts = self.scan_counts.groupby('scan_variable').sum()
+        self.plot_scan()
+        
+    def plot_scan(self):
+        """ Plot a previously defined scan to see the scan range and the statistics.
+        """
+        if self.scan:
+            fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=[5, 5])
+        else:
+            fig, ax1 = plt.subplots(nrows=1, figsize=[5, 2.5])
+            
+        ax1.plot(self.scan_points, self.scan_points_counts, 'o-', ms=2)
+        ax1.set_xlabel(f'{self.scan_vname}')
+        ax1.set_ylabel('# trains')
+        ax1.set_title(self.plot_title)
+        
+        if self.scan:
+            ax2.plot(self.scan['scan_variable'])
+            ax2.set_xlabel('train #')
+            ax2.set_ylabel(f'{self.scan_vname}')
+            
+    def load_xgm(self):
+        """ Loads pulse resolved dedicated SAS3 data from the SCS XGM.
+        
+        """
+        if self.xgm is None:
+            self.xgm = self.run.get_array(tb.mnemonics['SCS_SA3']['source'],
+                                          tb.mnemonics['SCS_SA3']['key'], roi=kd.by_index[:self.nbunches])
+            
+    def plot_xgm_hist(self, nbins=100):
+        """ Plots an histogram of the SCS XGM dedicated SAS3 data.
+        
+            inputs:
+                nbins: number of the bins for the histogram.
+        """
+        if self.xgm is None:
+            self.load_xgm()
+            
+        hist, bins_edges = np.histogram(self.xgm, nbins, density=True)
+        width = 1.0 * (bins_edges[1] - bins_edges[0])
+        bins_center = 0.5*(bins_edges[:-1] + bins_edges[1:])
+        
+        plt.figure(figsize=(5,3))
+        plt.bar(bins_center, hist, align='center', width=width)
+        plt.xlabel(f"{tb.mnemonics['SCS_SA3']['source']}{tb.mnemonics['SCS_SA3']['key']}")
+        plt.ylabel('density')
+        plt.title(self.plot_title)
+        
+    def xgm_filter(self, xgm_low=-np.inf, xgm_high=np.inf):
+        """ Filters the data by train. If one pulse within a train has an SASE3 SCS XGM value below
+            xgm_low or above xgm_high, that train will be dropped from the dataset.
+            
+            inputs:
+                xgm_low: low threshold value
+                xgm_high: high threshold value
+        """
+                   
+        if self.xgm is None:
+            self.load_xgm()
+        
+        if self.isDark:
+            warnings.warn(f'This run was loaded as dark. Filtering on xgm makes no sense. Aborting')
+            return
+        
+        self.xgm_low = xgm_low
+        self.xgm_high = xgm_high
+        
+        valid = ((self.xgm > self.xgm_low) * (self.xgm < self.xgm_high)).prod('dim_0').astype(bool)
+        xgm_valid = self.xgm.where(valid)
+        xgm_valid = xgm_valid.dropna('trainId')
+        self.scan = self.scan.sel({'trainId': xgm_valid.trainId})
+        nrejected = len(self.run.train_ids) - len(self.scan)
+        print((f'Rejecting {nrejected} out of {len(self.run.train_ids)} trains due to xgm '
+               f'thresholds: [{self.xgm_low}, {self.xgm_high}]'))
+
+    def load_geom(self):
+        """ Loads and return the DSSC geometry.
+        """
+        quad_pos = [
+            (-124.100,    3.112),  # TR
+            (-133.068, -110.604),  # BR
+            (   0.988, -125.236),  # BL
+            (   4.528,   -4.912)   # TL
+            ]
+        path = '/gpfs/exfel/sw/software/exfel_environments/misc/git/karabo_data/docs/dssc_geo_june19.h5'
+        self.geom = DSSC_1MGeometry.from_h5_file_and_quad_positions(path, quad_pos)
+        return self.geom
+               
+    def load_mask(self, fname, plot=True):
+        """ Load a DSSC mask file.
+            
+            input:
+                fname: string of the filename of the mask file
+                plot: if True, the loaded mask is plotted
+        """
+                   
+
+        dssc_mask = imread(fname)
+        dssc_mask = dssc_mask.astype(float)[..., 0] // 255
+        dssc_mask[dssc_mask==0] = np.nan
+        self.mask = dssc_mask
+        
+        if plot:
+            plt.figure()
+            plt.imshow(self.mask)
+
+    def create_virtual_dssc_datasets(self, run, path=''):
+        """ Create virtual datasets for each 16 DSSC modules used for the multiprocessing.
+            
+            input:
+                run: karabo-data run
+                path: string where the virtual files are created
+        """
+        
+        vds_list = []
+        for m in tqdm(range(16)):
+            vds_filename = os.path.join(path, f'dssc{m}_vds.h5')
+            if os.path.isfile(vds_filename):
+                os.remove(vds_filename)
+            module_vds = run.get_virtual_dataset(f'SCS_DET_DSSC1M-1/DET/{m}CH0:xtdf',
+                                                 'image.data', filename=vds_filename)
+            vds_list.append([vds_filename, module_vds])
+        return vds_list
+    
+    def binning(self):
+        """ Bin the DSSC data by the predifined scan type (DSSC.define()) using multiprocessing
+        
+        """
+        if self.vds_scan is None:
+            # probably a dark run with a dummy scan variable
+            self.vds_scan = os.path.join(self.tempdir, 'scan_variable.h5')
+            if os.path.isfile(self.vds_scan):
+                os.remove(self.vds_scan)
+            
+            self.scan = self.scan.to_dataset(name='scan_variable')
+            self.scan.to_netcdf(self.vds_scan, group='data')
+
+        # get available memory in GB, we will try to use 80 % of it
+        max_GB = psutil.virtual_memory().available/1024**3
+        print(f'max available memory: {max_GB} GB')
+        
+        # max_GB / (8byte * 16modules * 128px * 512px * N_pulses)
+        self.chunksize = int(0.8*max_GB * 1024**3 // (8 * 16 * 128 * 512 * self.fpt))
+        
+        print('processing', self.chunksize, 'trains per chunk')
+                   
+        jobs = []
+        for m in range(16):
+            jobs.append(dict(
+            module=m,
+            fpt=self.fpt,
+            vdf_module=os.path.join(self.tempdir, f'dssc{m}_vds.h5'),
+            chunksize=self.chunksize,
+            vdf_scan=self.vds_scan,
+            nbunches=self.nbunches,
+            run_nr=self.run_nr,
+            ))
+            
+        timestamp = strftime('%X')
+        print(f'start time: {timestamp}')
+
+        with multiprocessing.Pool(16) as pool:
+            module_data = pool.map(process_one_module, jobs)
+    
+        print('finished:', strftime('%X'))
+    
+        # rearange the multiprocessed data
+        self.module_data = xr.concat(module_data, dim='module')
+        self.module_data['run'] = self.run_nr
+        self.module_data = self.module_data.transpose('scan_variable', 'module', 'x', 'y')
+                   
+        self.module_data = xr.merge([self.module_data, self.scan.groupby('scan_variable').mean('trainId')])
+        self.module_data = self.module_data.squeeze()
+        
+        self.module_data.attrs['scan_variable'] = self.scan_vname
+
+    def save(self, save_folder=None, overwrite=False):
+        """ Save the crunched data.
+        
+            inputs:
+                save_folder: string of the fodler where to save the data.
+                overwrite: boolean whether or not to overwrite existing files.
+        """
+        if save_folder is None:
+            save_folder = this.save_folder
+
+        if self.isDark:
+            fname = f'run{self.run_nr}_dark.h5'  # no scan
+        else:
+            fname = f'run{self.run_nr}.h5'  # run with delay scan (change for other scan types!)
+
+
+        save_path = os.path.join(save_folder, fname)
+        file_exists = os.path.isfile(save_path)
+
+        if not file_exists or (file_exists and overwrite):
+            if file_exists:
+                warnings.warn(f'Overwriting file: {save_path}')
+                os.remove(save_path)
+            self.module_data.to_netcdf(save_path, group='data')
+            os.chmod(save_path, 0o664)
+            print('saving: ', save_path)
+        else:
+            print('file', save_path, 'exists and overwrite is False')
+                   
+    def load_binned(self, runNB, dark_runNB, xgm_norm = True, save_folder=None):
+        """ load previously binned (crunched) DSSC data by DSSC.crunch() and DSSC.save()
+        
+            inputs:
+                runNB: run number to load
+                dark_runNB: run number of the corresponding dark
+                xgm_norm: normlize by XGM data if True
+                save_folder: path string  where the crunched data are saved
+        """
+
+        if save_folder is None:
+            save_folder = self.save_folder
+
+        self.plot_title = f'{self.proposal} run: {runNB} dark: {dark_runNB}'
+                   
+        dark = xr.open_dataset(os.path.join(save_folder, f'run{dark_runNB}_dark.h5'), group='data')
+        binned = xr.open_dataset(os.path.join(save_folder, f'run{runNB}.h5'), group='data')
+
+        binned['pumped'] = (binned['pumped'] - dark['pumped'].values)
+        binned['unpumped'] = (binned['unpumped'] - dark['unpumped'].values)
+
+        if xgm_norm:
+            binned['pumped'] = binned['pumped'] / binned['xgm_pumped']
+            binned['unpumped'] = binned['unpumped'] / binned['xgm_unpumped']
+        
+        self.scan_points = binned['scan_variable']
+        self.scan_points_counts = binned['sum_count'][:, 0]
+        self.scan_vname = binned.attrs['scan_variable']
+        self.scan = None
+
+        self.binned = binned
+                   
+    def plot_DSSC(self, use_mask = True, p_low = 1, p_high = 98, vmin = None, vmax = None):
+        """ Plot pumped and unpumped DSSC images.
+        
+            inputs:
+                use_mask: if True, a mask is applied on the DSSC.
+                p_low: low percentile value to adjust the contrast scale on the unpumped and pumped image
+                p_high: high percentile value to adjust the contrast scale on the unpumped and pumped image
+                vmin: low value of the image scale
+                vmax: high value of the image scale
+        """
+                   
+        if use_mask:
+            if self.mask is None:
+                raise ValueError('No mask was loaded !')
+                   
+            mask = self.mask
+            mask_txt = ' masked'
+        else:
+            mask = 1
+            mask_txt = ''
+        
+        if self.geom is None:
+            self.load_geom()
+                   
+        im_pump_mean, _ = self.geom.position_modules_fast(self.binned['pumped'].mean('scan_variable'))
+        im_unpump_mean, _ = self.geom.position_modules_fast(self.binned['unpumped'].mean('scan_variable'))
+        
+        im_pump_mean = mask*im_pump_mean
+        im_unpump_mean = mask*im_unpump_mean
+                           
+        fig = plt.figure(figsize=(9, 4))
+        grid = ImageGrid(fig, 111,
+                 nrows_ncols=(1,2),
+                 axes_pad=0.15,
+                 share_all=True,
+                 cbar_location="right",
+                 cbar_mode="single",
+                 cbar_size="7%",
+                 cbar_pad=0.15,
+                 )
+
+        _vmin, _vmax = np.percentile(im_pump_mean[~np.isnan(im_pump_mean)], [p_low, p_high])
+        if vmin is None:
+            vmin = _vmin
+        if vmax is None:
+            vmax = _vmax
+                         
+        im = grid[0].imshow(im_pump_mean, vmin=vmin, vmax=vmax, aspect=self.aspect)
+        grid[0].set_title('pumped' + mask_txt)
+
+        im = grid[1].imshow(im_unpump_mean, vmin=vmin, vmax=vmax, aspect=self.aspect)
+        grid[1].set_title('unpumped' + mask_txt)
+                   
+        grid[-1].cax.colorbar(im)
+        grid[-1].cax.toggle_label(True)
+        
+        fig.suptitle(self.plot_title)
+                   
+                   
+    def azimuthal_int(self, wl, center=None, angle_range=[0, 360], dr=1, use_mask=True):
+        """ Perform azimuthal integration of 1D binned DSSC run.
+        
+            inputs:
+                wl: photon wavelength
+                center: center of integration
+                angle_range: angles of integration
+                dr: dr
+                use_mask: if True, use the loaded mask
+        """
+
+        if self.geom is None:
+            self.load_geom()
+
+        if use_mask:
+            if self.mask is None:
+                raise ValueError('No mask was loaded !')
+
+            mask = self.mask
+            mask_txt = ' masked'
+        else:
+            mask = 1
+            mask_txt = ''
+
+        im_pumped_arranged, c_geom = self.geom.position_modules_fast(self.binned['pumped'].values)
+        im_unpumped_arranged, c_geom = self.geom.position_modules_fast(self.binned['unpumped'].values)
+
+        im_pumped_arranged *= mask
+        im_unpumped_arranged *= mask
+
+        im_pumped_mean = im_pumped_arranged.mean(axis=0)
+        im_unpumped_mean = im_unpumped_arranged.mean(axis=0)
+
+        if center is None:
+            center = c_geom
+
+        ai = tb.azimuthal_integrator(im_pumped_mean.shape, center, angle_range, dr=dr)
+        norm = ai(~np.isnan(im_pumped_mean))
+
+        az_pump = []
+        az_unpump = []
+
+        for i in tqdm(range(len(self.binned['scan_variable']))):
+            az_pump.append(ai(im_pumped_arranged[i]) / norm)
+            az_unpump.append(ai(im_unpumped_arranged[i]) / norm)
+
+        az_pump = np.stack(az_pump)
+        az_unpump = np.stack(az_unpump)
+
+        coords = {'scan_variable': self.binned['scan_variable'], 'distance': ai.distance}
+        azimuthal = xr.DataArray(az_pump, dims=['scan_variable', 'distance'], coords=coords)
+        azimuthal = azimuthal.to_dataset(name='pumped')
+        azimuthal['unpumped'] = xr.DataArray(az_unpump, dims=['scan_variable', 'distance'], coords=coords)
+        azimuthal = azimuthal.transpose('distance', 'scan_variable')
+
+        #t0 = 225.5
+        #azimuthal['delay'] = (t0 - azimuthal.delay)*6.6
+        #azimuthal['delay'] = azimuthal.delay
+
+        azimuthal['delta_q (1/nm)'] = 2e-9 * np.pi * np.sin(
+            np.arctan(azimuthal.distance *  self.px_pitch_v*1e-6 / self.distance)) / wl
+        
+        azimuthal.attrs = self.binned.attrs
+
+        self.azimuthal = azimuthal.swap_dims({'distance': 'delta_q (1/nm)'})
+                   
+    def plot_azimuthal_int(self):
+        """ Plot a computed azimuthal integration.
+        """
+        fig, [ax1, ax2] = plt.subplots(nrows=2, sharex=True, sharey=True)
+
+        xr.plot.imshow(self.azimuthal.pumped, ax=ax1, robust=True)
+        ax1.set_title('unpumped')
+        ax1.set_xlabel(self.scan_vname)
+        xr.plot.imshow(self.azimuthal.pumped - self.azimuthal.unpumped, ax=ax2, robust=True)
+        ax2.set_title('pumped - unpumped')
+
+        ax2.set_xlabel(self.scan_vname)
+        fig.suptitle(f'{self.plot_title}')
+
+    def plot_azimuthal_line_cut(self, data, qranges, qwidths):
+        """ Plot line scans on top of the data.
+        
+            inputs:
+                data: an azimuthal integrated xarray DataArray with 'delta_q (1/nm)' as one of its dimension.
+                qranges: a list of q-range
+                qwidth: a list of q-width, same length as qranges
+        """
+                   
+        fig, [ax1, ax2] = plt.subplots(nrows=2, sharex=True, figsize=[8, 7])
+
+        xr.plot.imshow(data, ax=ax1, robust=True)
+
+        # attributes are not propagated during xarray mathematical operation https://github.com/pydata/xarray/issues/988
+        # so we might not have in data the scan vaiable name anymore
+        ax1.set_xlabel(self.scan_vname) 
+        fig.suptitle(f'{self.plot_title}')
+    
+        for i, (qr, qw) in enumerate(zip(qranges, qwidths)):
+            sel = (data['delta_q (1/nm)'] > (qr - qw/2)) * (data['delta_q (1/nm)'] < (qr + qw/2))
+            val = data.where(sel).mean('delta_q (1/nm)')
+            ax2.plot(data.scan_variable, val, c=f'C{i}', label=f'q = {qr:.2f}')
+        
+            ax1.axhline(qr - qw/2, c=f'C{i}', lw=1)
+            ax1.axhline(qr + qw/2, c=f'C{i}', lw=1)
+        ax2.legend()
+        ax2.set_xlabel(self.scan_vname)
+        
+                   
+# since 'self' is not pickable, this function has to be outside the DSSC class so that it can be used
+# by the multiprocessing pool.map function
+def process_one_module(job):
+    module = job['module']
+    fpt = job['fpt']
+    data_vdf = job['vdf_module']
+    scan_vdf = job['vdf_scan']
+    chunksize = job['chunksize']
+    nbunches = job['nbunches']
+
+    image_path = f'INSTRUMENT/SCS_DET_DSSC1M-1/DET/{module}CH0:xtdf/image/data'
+    npulse_path = f'INDEX/SCS_DET_DSSC1M-1/DET/{module}CH0:xtdf/image/count'
+    with h5py.File(data_vdf, 'r') as m:
+        all_trainIds = m['INDEX/trainId'][()]
+    n_trains = len(all_trainIds)
+    chunk_start = np.arange(n_trains, step=chunksize, dtype=int)
+
+    # load scan variable
+    scan = xr.open_dataset(scan_vdf, group='data')['scan_variable']
+    scan.name = 'scan'
+    len_scan = len(scan.groupby(scan))
+
+    # create empty dataset to add actual data to
+    module_data = xr.DataArray(np.empty([len_scan, 128, 512], dtype=np.float64),
+                               dims=['scan_variable', 'x', 'y'],
+                               coords={'scan_variable': np.unique(scan)})
+    module_data = module_data.to_dataset(name='pumped')
+    module_data['unpumped'] = xr.full_like(module_data['pumped'], 0)
+    module_data['sum_count'] = xr.DataArray(np.zeros_like(np.unique(scan)), dims=['scan_variable'])
+    module_data['module'] = module
+
+    # crunching
+    with h5py.File(data_vdf, 'r') as m:
+        #fpt_calc = int(len(m[image_path]) / n_trains)
+        #assert fpt_calc == fpt, f'data length does not match expected value (module {module})'
+        all_trainIds = m['INDEX/trainId'][()]
+        frames_per_train = m[npulse_path][()]
+        trains_with_data = all_trainIds[frames_per_train == fpt]
+        #print(np.unique(pulses_per_train), '/', fpt)
+        #print(len(trains_with_data))
+        chunk_start = np.arange(len(all_trainIds), step=chunksize, dtype=int)
+        trains_start = 0
+                   
+        # This line is the strange hack from https://github.com/tqdm/tqdm/issues/485
+        print(' ', end='', flush=True)
+        for c0 in tqdm(chunk_start, desc=f'pool.map#{module:02d}', position=module):
+            chunk_dssc = np.s_[int(c0 * fpt):int((c0 + chunksize) * fpt)]  # for dssc data
+            data = m[image_path][chunk_dssc].squeeze()
+            data = data.astype(np.float64)
+            n_trains = int(data.shape[0] // fpt)
+            trainIds_chunk = np.unique(trains_with_data[trains_start:trains_start + n_trains])
+            trains_start += n_trains
+            n_trains_actual = len(trainIds_chunk)
+            coords = {'trainId': trainIds_chunk}
+            data = np.reshape(data, [n_trains_actual, fpt, 128, 512])[:, :int(2 * nbunches)]
+            data = xr.DataArray(data, dims=['trainId', 'pulse', 'x', 'y'], coords=coords)
+            data_pumped = (data[:, ::4]).mean('pulse')
+            data_unpumped = (data[:, 2::4]).mean('pulse')
+            data = data_pumped.to_dataset(name='pumped')
+            data['unpumped'] = data_unpumped
+            data['sum_count'] = xr.DataArray(np.ones(n_trains_actual), dims=['trainId'], coords=coords)
+            # grouping and summing
+            data['scan_variable'] = scan  # this only adds scan data for matching trainIds
+            data = data.dropna('trainId')
+            data = data.groupby('scan_variable').sum('trainId')
+            where = {'scan_variable': data.scan_variable}
+            for var in ['pumped', 'unpumped', 'sum_count']:
+                module_data[var].loc[where] = module_data[var].loc[where] + data[var]
+    for var in ['pumped', 'unpumped']:
+        module_data[var] = module_data[var] / module_data.sum_count
+    #module_data = module_data.drop('sum_count')
+    return module_data