# Copyright 2018-2020 Nick Anthony, Backman Biophotonics Lab, Northwestern University
#
# This file is part of PWSpy.
#
# PWSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PWSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PWSpy. If not, see <https://www.gnu.org/licenses/>.
from __future__ import annotations
from dataclasses import dataclass
from typing import Tuple, List
import numpy as np
from ._abstract import AbstractCompilerSettings, AbstractRoiCompilationResults, AbstractRoiCompiler
from .. import warnings
from ..dynamics import DynamicsAnalysisResults
from ...dataTypes import Roi
from ...dataTypes._other import RoiFile
[docs]@dataclass
class DynamicsCompilerSettings(AbstractCompilerSettings):
"""These settings determine how a Dynamics acquisition should be compiled."""
meanReflectance: bool = False
rms_t_squared: bool = False
diffusion: bool = False
[docs]@dataclass
class DynamicsRoiCompilationResults(AbstractRoiCompilationResults):
cellIdTag: str
analysisName: str
reflectance: float
rms_t_squared: float
diffusion: float
[docs]class DynamicsRoiCompiler(AbstractRoiCompiler):
def __init__(self, settings: DynamicsCompilerSettings):
super().__init__(settings)
[docs] def run(self, results: DynamicsAnalysisResults, roi: Roi) -> Tuple[DynamicsRoiCompilationResults, List[warnings.AnalysisWarning]]:
reflectance = self._avgOverRoi(roi, results.meanReflectance) if self.settings.meanReflectance else None
rms_t_squared = self._avgOverRoi(roi, results.rms_t_squared) if self.settings.rms_t_squared else None # Unlike with diffusion we should not have any nan values for rms_t. If we get nan then something is wrong with the analysis.
diffusion = self._avgOverRoi(roi, results.diffusion, np.logical_not(np.isnan(results.diffusion))) if self.settings.diffusion else None # Don't include nan values in the average. Diffusion is expected to have many Nans due to low SNR.
results = DynamicsRoiCompilationResults(
cellIdTag=results.imCubeIdTag,
analysisName=results.analysisName,
reflectance=reflectance,
rms_t_squared=rms_t_squared,
diffusion=diffusion)
warns = [] # Strip None from warns list
return results, warns
@staticmethod
def _avgOverRoi(roi: Roi, arr: np.ndarray, condition: np.ndarray = None) -> float:
"""Returns the average of arr over the ROI.
if condition is provided then only value of arr where the condition is satisfied are included."""
assert len(arr.shape) == 2
if condition is not None:
return arr[np.logical_and(roi.mask, condition)].mean()
else:
return arr[roi.mask].mean()