From 1b7d88d92d90fa1213ec92727b20b64c287f6f69 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Thu, 14 Nov 2019 17:03:55 +0100
Subject: [PATCH] Fast ccd ai
---
FastCCD.py | 639 ++++++++++++++++++++++++++++++++++++++++++++++++++++
__init__.py | 1 +
2 files changed, 640 insertions(+)
create mode 100644 FastCCD.py
diff --git a/FastCCD.py b/FastCCD.py
new file mode 100644
index 0000000..1b8ca48
--- /dev/null
+++ b/FastCCD.py
@@ -0,0 +1,639 @@
+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.read_machinery import find_proposal
+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 glob import glob
+
+from imageio import imread
+
+class FastCCD:
+
+ def __init__(self, proposal, distance=1, raw=False):
+ """ Create a FastCCD object to process FaStCCD data.
+
+ inputs:
+ proposal: (int,str) proposal number string
+ distance: (float) distance sample to FastCCD detector in meter
+ raw: use processed data from the calibration pipeline or raw files
+
+ """
+ if isinstance(proposal,int):
+ proposal = 'p{:06d}'.format(proposal)
+
+ self.runFolder = find_proposal(proposal)
+ self.semester = self.runFolder.split('/')[-2]
+ self.proposal = proposal
+ self.topic = self.runFolder.split('/')[-3]
+ self.tempdir = None
+ self.save_folder = os.path.join(self.runFolder, 'usr/condensed_runs/')
+ self.distance = distance
+ self.px_pitch_h = 30 # pitch in microns
+ self.px_pitch_v = 30 # pitch in microns
+
+ self.aspect = 1 # aspect ratio of the FastCCD images
+ self.mask = None
+ self.max_fraction_memory = 0.8
+ self.filter_mask = None
+ self.raw = raw
+ self.gain = 1
+
+ print('FastCCD 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 FastCCD distance: {self.distance} m')
+ print(f'Using raw files: {self.raw}')
+
+ if not os.path.exists(self.save_folder):
+ warnings.warn(f'Default save folder does not exist: {self.save_folder}')
+
+ self.max_fraction_memory = 0.8
+ self.Nworker = 10
+ self.maxSaturatedPixel = 1
+
+
+ def __del__(self):
+ # deleting temporay folder
+ if self.tempdir:
+ shutil.rmtree(self.tempdir)
+
+ def open_run(self, run_nr, isDark=False, t0=0.0):
+ """ Open a run with karabo-data and prepare the virtual dataset for multiprocessing
+
+ inputs:
+ run_nr: the run number
+ isDark: True if the run is a dark run
+ t0: optional t0 in mm
+ """
+
+ print('Opening run data with karabo-data')
+ self.run_nr = run_nr
+ self.xgm = None
+
+ self.run = kd.open_run(self.proposal, self.run_nr)
+ self.plot_title = f'{self.proposal} run: {self.run_nr}'
+ self.isDark = isDark
+ self.fpt = 1
+ #self.nbunches = self.run.get_array('SCS_RR_UTC/MDL/BUNCH_DECODER', 'sase3.nPulses.value')
+ #self.nbunches = np.unique(self.nbunches)
+ self.nbunches = 1
+ #if len(self.nbunches) == 1:
+ # self.nbunches = self.nbunches[0]
+ #else:
+ # warnings.warn('not all trains have same length FastCCD data')
+ # print(f'nbunches: {self.nbunches}')
+ # self.nbunches = self.nbunches[-1]
+
+ print(f'FastCCD frames per train: {self.fpt}')
+ print(f'SA3 bunches per train: {self.nbunches}')
+
+ print('Collecting FastCCD module files')
+ self.collect_fastccd_file()
+
+ print(f'Loading XGM data')
+ try:
+ self.xgm = self.run.get_array(tb.mnemonics['SCS_SA3']['source'],
+ tb.mnemonics['SCS_SA3']['key'],
+ roi=kd.by_index[:self.nbunches])
+ self.xgm = self.xgm.squeeze() # remove the pulseId dimension since XGM should have only 1 value per train
+ except:
+ self.xgm = xr.DataArray(np.zeros_like(self.run.train_ids),dims = 'trainId', coords = {"trainId":self.run.train_ids})
+
+ print(f'Loading mono nrj data')
+ try:
+ self.nrj = self.run.get_array(tb.mnemonics['nrj']['source'],
+ tb.mnemonics['nrj']['key'])
+
+ except:
+ self.nrj = xr.DataArray(np.zeros_like(self.run.train_ids),dims = 'trainId', coords = {"trainId":self.run.train_ids})
+
+
+ print(f'Loading delay line data')
+ try:
+ self.delay_mm = self.run.get_array(tb.mnemonics['PP800_DelayLine']['source'],
+ tb.mnemonics['PP800_DelayLine']['key'])
+ except:
+ self.delay_mm = xr.DataArray(np.zeros_like(self.run.train_ids),dims = 'trainId', coords = {"trainId":self.run.train_ids})
+ self.t0 = t0
+ self.delay_ps = tb.positionToDelay(self.delay_mm, origin=self.t0, invert=True)
+
+ print(f'Loading Fast ADC5 data')
+ try:
+ self.FastADC5 = self.run.get_array(tb.mnemonics['FastADC5raw']['source'], tb.mnemonics['FastADC5raw']['key']).max('dim_0')
+ self.FastADC5[self.FastADC5<35000] = 0
+ self.FastADC5[self.FastADC5>=35000] = 1
+ except:
+ self.FastADC5 = xr.DataArray(np.zeros_like(self.run.train_ids),dims = 'trainId', coords = {"trainId":self.run.train_ids})
+
+ # create a dummy scan variable for dark run
+ # for other type or run, use FastCCD.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 = self.scan.to_dataset(name='scan_variable')
+ self.scan_vname = 'dummy'
+
+ def load_gain(self, fname):
+ """ Load a gain map by giving the filename
+ """
+ self.gain = np.load(fname)['arr_0'][:,:,0]
+
+ def collect_fastccd_file(self):
+ """ Collect the raw fastCCD h5 files.
+ """
+
+ if self.raw:
+ folder = 'raw'
+ else:
+ folder = 'proc'
+
+ pattern = self.runFolder + f'/{folder}/r{self.run_nr:04d}/RAW-R{self.run_nr:04d}-DA05-S*.h5'
+ self.h5list = glob(pattern)
+
+ def define_scan(self, vname, bins):
+ """
+ Prepare the binning of the FastCCD 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 vname == 'delay_ps':
+ self.scan = self.delay_ps
+ else:
+ 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.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 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.
+ """
+
+ 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.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
+
+ filter_mask = (self.xgm > self.xgm_low) * (self.xgm < self.xgm_high)
+
+ if self.filter_mask is None:
+ self.filter_mask = filter_mask
+ else:
+ self.filter_mask = self.filter_mask*filter_mask
+
+ valid = filter_mask.astype(bool)
+ self.xgm = self.xgm.where(valid).dropna('trainId')
+ self.scan = self.scan.sel({'trainId': self.xgm.trainId})
+ nrejected = len(self.run.train_ids) - len(self.xgm.trainId)
+ 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_mask(self, fname, plot=True):
+ """ Load a FastCCD mask file.
+
+ input:
+ fname: string of the filename of the mask file
+ plot: if True, the loaded mask is plotted
+ """
+
+
+ fccd_mask = imread(fname)
+ fccd_mask = fccd_mask.astype(float)[..., 0] // 255
+ fccd_mask[fccd_mask==0] = np.nan
+ self.mask = fccd_mask
+
+ if plot:
+ plt.figure()
+ plt.imshow(self.mask)
+
+ def binning(self):
+ """ Bin the FastCCD data by the predifined scan type (FastCCD.define()) using multiprocessing
+
+ """
+
+ # 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(self.max_fraction_memory*max_GB * 1024**3 // (self.Nworker * 16 * 1934 * 960 * self.fpt))
+
+ print('processing', self.chunksize, 'trains per chunk')
+ assert self.chunksize > 500, "cannot load FastCCD h5 files in memory"
+
+ jobs = []
+
+ for m,h5fname in enumerate(self.h5list):
+ jobs.append(dict(
+ fpt=self.fpt,
+ h5fname=h5fname,
+ chunksize=self.chunksize,
+ nbunches=self.nbunches,
+ workerId=m,
+ Nworker=self.Nworker,
+ scan = self.scan,
+ xgm = self.xgm,
+ FastADC5 = self.FastADC5
+ #maxSaturatedPixel=self.maxSaturatedPixel
+ ))
+
+ timestamp = strftime('%X')
+ print(f'start time: {timestamp}')
+
+ with multiprocessing.Pool(self.Nworker) as pool:
+ res = list(tqdm(pool.imap(process_one_module, jobs), total=len(self.h5list)))
+
+ print('finished:', strftime('%X'))
+
+ # rearange the multiprocessed data
+ # this is to get rid of the worker dimension, there is no sum over worker really involved
+ self.module_data = xr.concat(res, dim='workerId').sum(dim='workerId')
+
+ self.module_data['pumped'] = self.module_data['pumped'] / self.module_data['sum_count_pumped']
+ self.module_data['unpumped'] = self.module_data['unpumped'] / self.module_data['sum_count_unpumped']
+ self.module_data['xgm_pumped'] = self.module_data['xgm_pumped'] / self.module_data['sum_count_pumped']
+ self.module_data['xgm_unpumped'] = self.module_data['xgm_unpumped'] / self.module_data['sum_count_unpumped']
+
+ self.module_data['run'] = self.run_nr
+ self.module_data['t0'] = self.t0
+
+ self.plot_title = f"{self.proposal} run: {self.module_data['run'].values}"
+ self.module_data.attrs['plot_title'] = self.plot_title
+ 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 = self.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=None, xgm_norm = True, save_folder=None):
+ """ load previously binned (crunched) FastCCD data by FastCCD.crunch() and FastCCD.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}'
+
+ binned = xr.open_dataset(os.path.join(save_folder, f'run{runNB}.h5'), group='data', cache=False)
+
+ if dark_runNB is not None:
+ dark = xr.open_dataset(os.path.join(save_folder, f'run{dark_runNB}_dark.h5'), group='data', cache=False)
+ binned['pumped'] = self.gain*(binned['pumped'] - dark['pumped'].squeeze(drop=True))
+ binned['unpumped'] = self.gain*(binned['unpumped'] - dark['unpumped'].squeeze(drop=True))
+
+ 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_pumped'] + binned['sum_count_unpumped']
+ self.scan_vname = binned.attrs['scan_variable']
+ self.scan = None
+
+ self.binned = binned
+
+ def plot_FastCCD(self, use_mask = True, p_low = 1, p_high = 98, vmin = None, vmax = None):
+ """ Plot pumped and unpumped FastCCD images.
+
+ inputs:
+ use_mask: if True, a mask is applied on the FastCCD.
+ 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 = ''
+
+
+ im_pump_mean = self.binned['pumped'].mean('scan_variable')
+ im_unpump_mean = self.binned['unpumped'].mean('scan_variable')
+
+ self.im_pump_mean = mask*im_pump_mean
+ self.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,
+ )
+
+ tmp = self.im_pump_mean.values.flatten()
+ try:
+ _vmin, _vmax = np.percentile(tmp[~np.isnan(tmp)], [p_low, p_high])
+ except:
+ _vmin, _vmax = (None, None)
+
+ if vmin is None:
+ vmin = _vmin
+ if vmax is None:
+ vmax = _vmax
+
+ im = grid[0].imshow(self.im_pump_mean, vmin=vmin, vmax=vmax, aspect=self.aspect)
+ grid[0].set_title('pumped' + mask_txt)
+
+ im = grid[1].imshow(self.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, 180-1e-6], dr=1, use_mask=True):
+ """ Perform azimuthal integration of 1D binned FastCCD 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 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 = self.binned['pumped'].values
+ im_unpumped_arranged = 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)
+
+ 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, kind='difference', lim=None):
+ """ Plot a computed azimuthal integration.
+
+ inputs:
+ kind: (str) either 'difference' or 'relative' to change the type of plot.
+ """
+ fig, [ax1, ax2, ax3] = plt.subplots(nrows=3, sharex=True, sharey=True)
+
+ xr.plot.imshow(self.azimuthal.pumped, ax=ax1, vmin=0, robust=True)
+ ax1.set_title('pumped')
+
+ xr.plot.imshow(self.azimuthal.unpumped, ax=ax2, vmin=0, robust=True)
+ ax2.set_title('unpumped')
+
+ if kind == 'difference':
+ val = self.azimuthal.pumped - self.azimuthal.unpumped
+ ax3.set_title('pumped - unpumped')
+ elif kind == 'relative':
+ val = (self.azimuthal.pumped - self.azimuthal.unpumped)/self.azimuthal.unpumped
+ ax3.set_title('(pumped - unpumped)/unpumped')
+ else:
+ raise ValueError('kind should be either difference or relative')
+
+ if lim is None:
+ xr.plot.imshow(val, ax=ax3, robust=True)
+ else:
+ xr.plot.imshow(val, ax=ax3, vmin=lim[0], vmax=lim[1])
+
+ ax3.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 FastCCD class so that it can be used
+# by the multiprocessing pool.map function
+def process_one_module(job):
+ fpt = job['fpt']
+ Nworker = job['Nworker']
+ workerId = job['workerId']
+ scan = job['scan']
+ chunksize = job['chunksize']
+ nbunches = job['nbunches']
+ h5fname = job['h5fname']
+ xgm = job['xgm']
+ FastADC5 = job['FastADC5']
+ #maxSaturatedPixel = job['maxSaturatedPixel']
+
+ image_path = f'/INSTRUMENT/SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput/data/image/pixels'
+
+ # crunching
+ with h5py.File(h5fname, 'r') as m:
+ fastccd_trains = m['/INSTRUMENT/SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput/data/trainId'][()]
+ data = m[image_path][()].squeeze().astype(np.float64)
+
+ unique_trainIds, unique_list = np.unique(fastccd_trains, return_index = True)
+ unique_nz_list = np.nonzero(unique_trainIds)[0]
+
+ fastccd_trains = unique_trainIds[unique_nz_list]
+ coords = {'trainId': fastccd_trains}
+
+ fastccd = xr.DataArray(data[unique_nz_list, :, :], dims=['trainId', 'x', 'y'], coords=coords)
+ fastccd = fastccd.where(fastccd.sum(('x','y'), skipna=True) > 0)
+
+ aligned_vals = xr.align(*[fastccd, xgm, FastADC5], join='inner')
+ ds = xr.Dataset(dict(zip(['fastccd', 'xgm', 'FastADC5'], aligned_vals)))
+ ds['sum_count'] = xr.full_like(ds['fastccd'][..., 0, 0], fill_value=1)
+
+ # grouping and summing
+ ds['scan_variable'] = scan['scan_variable'] # this only adds scan data for matching trainIds
+ ds = ds.dropna('trainId')
+ #print(ds)
+
+ data_pumped = ds.where(ds['FastADC5'] > 0, drop=True).groupby('scan_variable').sum('trainId')
+ data_unpumped = ds.where(ds['FastADC5'] < 1, drop=True).groupby('scan_variable').sum('trainId')
+
+ module_data = data_pumped['fastccd'].to_dataset('pumped')
+ module_data['unpumped'] = data_unpumped['fastccd']
+ module_data['sum_count_pumped'] = data_pumped['sum_count']
+ module_data['sum_count_unpumped'] = data_unpumped['sum_count']
+ module_data['xgm_pumped'] = data_pumped['xgm']
+ module_data['xgm_unpumped'] = data_unpumped['xgm']
+ module_data['workerId'] = workerId
+
+ return module_data
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
index 1ee52fb..4af2bd2 100644
--- a/__init__.py
+++ b/__init__.py
@@ -6,3 +6,4 @@ from ToolBox.Laser_utils import *
from ToolBox.DSSC import DSSC
from ToolBox.azimuthal_integrator import *
from ToolBox.DSSC1module import *
+from ToolBox.FastCCD import *
--
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