175 lines
4.7 KiB
Python
175 lines
4.7 KiB
Python
import re
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from collections import defaultdict
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import numpy as np
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from sfdata import SFDataFiles, sfdatafile, SFScanInfo
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import joblib
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from joblib import Parallel, delayed, Memory
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from . import utils
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from .utils import ROI
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memory = None
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def setup_cachedirs(pgroup=None, cachedir=None):
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"""
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Sets the path to a persistent cache directory either from the given p-group (e.g. "p20841")
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or an explicitly given directory.
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If heuristics fail we use "/tmp" as a non-persistent alternative.
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"""
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global memory
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if cachedir is not None:
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# explicit directory given, use this choice
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memory = Memory(cachedir, verbose=0, compress=2)
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return
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try:
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if pgroup is None:
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pgroup_no = utils.heuristic_extract_pgroup()
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else:
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parts = re.split(r"(\d.*)", pgroup) # ['p', '2343', '']
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pgroup_no = parts[-2]
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cachedir = f"/das/work/units/cristallina/p{pgroup_no}/cachedir"
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except KeyError as e:
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print(e)
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cachedir = "/das/work/units/cristallina/p19739/cachedir"
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try:
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memory = Memory(cachedir, verbose=0, compress=2)
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except PermissionError as e:
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cachedir = "/tmp"
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memory = Memory(cachedir, verbose=0, compress=2)
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setup_cachedirs()
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@memory.cache(ignore=["batch_size"]) # we ignore batch_size for caching purposes
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def perform_image_calculations(
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fileset,
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channel="JF16T03V01",
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alignment_channels=None,
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batch_size=10,
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roi: ROI = None,
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preview=False,
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operations=["sum"],
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):
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"""
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Performs one or more calculations ("sum", "mean" or "std") for a given region of interest (roi)
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for an image channel from a fileset (e.g. "run0352/data/acq0001.*.h5" or step.fnames from a SFScanInfo object).
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Allows alignment, i.e. reducing only to a common subset with other channels.
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Calculations are performed in batches to reduce maximum memory requirements.
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Preview only applies calculation to first batch and returns.
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Returns a dictionary ({"JF16T03V01_intensity":[11, 18, 21, 55, ...]})
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with the given channel values for each pulse and corresponding pulse id.
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"""
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possible_operations = {
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"sum": ["intensity", np.sum],
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"mean": ["mean", np.mean],
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"std": ["mean", np.std],
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}
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with SFDataFiles(*fileset) as data:
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if alignment_channels is not None:
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channels = [channel] + [ch for ch in alignment_channels]
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else:
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channels = [channel]
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subset = data[channels]
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subset.drop_missing()
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Images = subset[channel]
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res = defaultdict(list)
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res["roi"] = repr(roi)
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for image_slice in Images.in_batches(batch_size):
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index_slice, im = image_slice
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if roi is None:
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im_ROI = im[:]
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else:
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im_ROI = im[:, roi.rows, roi.cols]
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# iterate over all operations
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for op in operations:
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label, func = possible_operations[op]
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res[f"{channel}_{label}"].extend(func(im_ROI, axis=(1, 2)))
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res["pids"].extend(Images.pids[index_slice])
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# only return first batch
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if preview:
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break
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return res
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@memory.cache(ignore=["batch_size"]) # we ignore batch_size for caching purposes
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def sum_images(
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fileset,
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channel="JF16T03V01",
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alignment_channels=None,
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batch_size=10,
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roi: ROI = None,
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preview=False,
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):
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"""
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Sums a given region of interest (roi) for an image channel from a
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given fileset (e.g. "run0352/data/acq0001.*.h5" or step.fnames from a SFScanInfo object).
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Allows alignment, i.e. reducing only to a common subset with other channels.
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Summation is performed in batches to reduce maximum memory requirements.
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Preview only sums and returns the first batch.
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Returns a dictionary ({"JF16T03V01_intensity":[11, 18, 21, 55, ...]})
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with the given channel intensity for each pulse and corresponding pulse id.
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"""
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return perform_image_calculations(
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fileset,
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channel=channel,
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alignment_channels=alignment_channels,
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batch_size=batch_size,
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roi=roi,
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preview=preview,
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operations=["sum"],
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)
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def get_contrast_images(
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fileset,
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channel="JF16T03V01",
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alignment_channels=None,
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batch_size=10,
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roi: ROI = None,
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preview=False,
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):
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"""
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See perform_image_calculations. Here calculates mean and standard deviation for a given set of images.
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"""
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return perform_image_calculations(
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fileset,
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channel=channel,
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alignment_channels=alignment_channels,
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batch_size=batch_size,
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roi=roi,
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preview=preview,
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operations=["mean", "std"],
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)
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