Image analysis for the mTor project
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import functools
import numpy
import re
from collections import namedtuple
from .luts import FIRE_LUT # noqa: F401
IMG_MAX_INTENSITY = 65535 # 2^16 - 1
LABEL_SELECTED = "selected"
LABEL_DISCARDED = "discarded"
OUTPUT_FOLDER_NAMES = (
"colored",
"cuts",
f"cuts_{LABEL_DISCARDED}",
f"cuts_{LABEL_SELECTED}",
"data",
)
RE_DIGITS = re.compile(r"(\d+)")
ROI_STATISTIC_FUNCTIONS = {
"min": numpy.amin,
"max": numpy.amax,
"1quantile": functools.partial(numpy.percentile, q=25),
"2quantile": functools.partial(numpy.percentile, q=50),
"3quantile": functools.partial(numpy.percentile, q=75),
"average": numpy.average,
"median": numpy.median,
"std": numpy.std,
}
TifImage = namedtuple("TifImage", ["path", "frame"])
TifArray = namedtuple("TifArray", ["path", "frame", "data"])
WorkflowResult = namedtuple("WorkflowResult", ["data", "parameters"])