Source code for pwspy.analysis.compilation._dynamics

# 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 pwspy.dataTypes import Roi
from ._abstract import AbstractCompilerSettings, AbstractRoiCompilationResults, AbstractRoiCompiler
from .. import warnings
from ..dynamics import DynamicsAnalysisResults


[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()