From 649fa4e717c3526dbec3daa5daa70729bf8cc3bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Wed, 16 Oct 2019 17:37:50 +0200
Subject: [PATCH] adds multiprocessing binning of DSSC data
---
DSSC.py | 331 ++++++++++++++++++++++++++++++++++++++++
__init__.py | 2 +
azimuthal_integrator.py | 63 ++++++++
3 files changed, 396 insertions(+)
create mode 100644 DSSC.py
create mode 100644 azimuthal_integrator.py
diff --git a/DSSC.py b/DSSC.py
new file mode 100644
index 0000000..4ea6eb6
--- /dev/null
+++ b/DSSC.py
@@ -0,0 +1,331 @@
+
+import multiprocessing
+from time import strftime
+import tempfile
+import shutil
+from tqdm.auto import tqdm
+import os
+import warnings
+
+import karabo_data as kd
+from karabo_data.geometry2 import DSSC_1MGeometry
+import ToolBox as tb
+import numpy as np
+import xarray as xr
+import matplotlib.pyplot as plt
+import h5py
+
+
+class DSSC:
+
+ def __init__(self, semester, proposal, topic='SCS'):
+ 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/'
+
+ 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}')
+
+ 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):
+ 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.semester} 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):
+ """
+ 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
+ """
+
+ 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')
+
+ fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=[5, 5])
+ ax1.plot(self.scan.groupby('scan_variable').mean('trainId').coords['scan_variable'].values,
+ self.scan_counts.groupby('scan_variable').sum(),
+ 'o-', ms=2)
+ ax1.set_xlabel('scan variable')
+ ax1.set_ylabel('# trains')
+ ax1.set_title(self.plot_title)
+
+ ax2.plot(self.scan['scan_variable'])
+ ax2.set_xlabel('train #')
+ ax2.set_ylabel(f'{vname}')
+
+ plt.tight_layout()
+
+ def load_xgm(self):
+ 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):
+ 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()
+ 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)
+ plt.tight_layout()
+
+ def xgm_filter(self, xgm_low=-np.inf, xgm_high=np.inf):
+ 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):
+ 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'
+ geom = DSSC_1MGeometry.from_h5_file_and_quad_positions(path, quad_pos)
+ return geom
+
+ def create_virtual_dssc_datasets(self, run, path=''):
+ 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 crunch(self):
+
+ 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')
+
+ max_GB = 400
+ # max_GB / (8byte * 16modules * 128px * 512px * N_pulses)
+ self.chunksize = int(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()
+
+ def save(self, save_folder=None, overwrite=False):
+ if save_folder is None:
+ save_folder = this.save_folder
+
+ if self.isDark:
+ fname = f'run{self.run_nr}_el.h5' # no scan
+ else:
+ fname = f'run{self.run_nr}_by-delay_el.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')
+
+# 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
diff --git a/__init__.py b/__init__.py
index 9266141..86c3187 100644
--- a/__init__.py
+++ b/__init__.py
@@ -3,3 +3,5 @@ from ToolBox.xgm import *
from ToolBox.XAS import *
from ToolBox.knife_edge import *
from ToolBox.Laser_utils import *
+from ToolBox.DSSC import DSSC
+from ToolBox.azimuthal_integrator import azimuthal_integrator
diff --git a/azimuthal_integrator.py b/azimuthal_integrator.py
new file mode 100644
index 0000000..da9a6dd
--- /dev/null
+++ b/azimuthal_integrator.py
@@ -0,0 +1,63 @@
+
+class azimuthal_integrator(object):
+ def __init__(self, imageshape, center, polar_range, dr=2):
+ '''
+ Create a reusable integrator for repeated azimuthal integration of similar
+ images. Calculates array indices for a given parameter set that allows
+ fast recalculation.
+
+ Parameters
+ ==========
+ imageshape : tuple of ints
+ The shape of the images to be integrated over.
+
+ center : tuple of ints
+ center coordinates in pixels
+
+ polar_range : tuple of ints
+ start and stop polar angle (in degrees) to restrict integration to wedges
+
+ dr : int, default 2
+ radial width of the integration slices. Takes non-square DSSC pixels into account.
+
+ Returns
+ =======
+ ai : azimuthal_integrator instance
+ Instance can directly be called with image data:
+ > az_intensity = ai(image)
+ radial distances and the polar mask are accessible as attributes:
+ > ai.distance
+ > ai.polar_mask
+ '''
+ self.shape = imageshape
+ cx, cy = center
+ sx, sy = imageshape
+ xcoord, ycoord = np.ogrid[:sx, :sy]
+ xcoord -= cx
+ ycoord -= cy
+
+ # distance from center, hexagonal pixel shape taken into account
+ dist_array = np.hypot(xcoord * 204 / 236, ycoord)
+
+ # array of polar angles
+ tmin, tmax = np.deg2rad(np.sort(polar_range)) % np.pi
+ polar_array = np.arctan2(xcoord, ycoord)
+ polar_array = np.mod(polar_array, np.pi)
+ self.polar_mask = (polar_array > tmin) * (polar_array < tmax)
+
+ self.maxdist = min(sx - cx, sy - cy)
+
+ ix, iy = np.indices(dimensions=(sx, sy))
+ self.index_array = np.ravel_multi_index((ix, iy), (sx, sy))
+
+ self.distance = np.array([])
+ self.flat_indices = []
+ for dist in range(dr, self.maxdist, dr):
+ ring_mask = self.polar_mask * (dist_array >= (dist - dr)) * (dist_array < dist)
+ self.flat_indices.append(self.index_array[ring_mask])
+ self.distance = np.append(self.distance, dist)
+
+ def __call__(self, image):
+ assert self.shape == image.shape, 'image shape does not match'
+ image_flat = image.flatten()
+ return np.array([np.nansum(image_flat[indices]) for indices in self.flat_indices])
--
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