from subprocess import check_output
from batchlib.util.logger import get_logger
logger = get_logger('Workflow.GitUtil')
def get_commit_id():
try:
commit_id = check_output(['git', 'rev-parse', 'HEAD']).decode('utf-8').rstrip('\n')
except Exception as e:
logger.warning(f'Cannot get git commit SHA: {e}')
commit_id = None
return commit_id
import json
from concurrent import futures
import numpy as np
from tqdm import tqdm
import scipy.stats
from batchlib.util.logger import get_logger
from ..base import BatchJobWithSubfolder
from ..util.io import open_file
logger = get_logger('Workflow.BatchJob.PixelAnalysis')
def compute_weighted_serum(mask, intensity, q):
if np.sum(mask) == 0:
return 0.
if q == "mean":
return float(np.mean(intensity[mask]))
else:
return float(np.quantile(intensity[mask], q))
def ratio(a, b):
if b != 0:
return a / b
else:
return 0.
import configargparse
from batchlib import run_workflow
from batchlib.analysis.cell_level_analysis import InstanceFeatureExtraction, CellLevelAnalysis, DenoiseByGrayscaleOpening
from batchlib.analysis.pixel_level_analysis import all_plots
from batchlib.analysis.summary import CellLevelSummary
from batchlib.outliers.outlier import get_outlier_predicate
from batchlib.preprocessing import Preprocess
from batchlib.segmentation import SeededWatershed
from batchlib.segmentation.stardist_prediction import StardistPrediction
from batchlib.segmentation.torch_prediction import TorchPrediction
from batchlib.segmentation.voronoi_ring_segmentation import VoronoiRingSegmentation
from batchlib.segmentation.unet import UNet2D
from batchlib.util.logger import get_logger
logger = get_logger('Workflow.InstanceAnalysis2')
def get_input_keys(config):
nuc_in_key = 'nuclei'
serum_in_key = 'serum'
marker_in_key = 'marker'
if config.segmentation_on_corrected:
nuc_seg_in_key = nuc_in_key + '_corrected'
serum_seg_in_key = serum_in_key + '_corrected'
else:
nuc_seg_in_key = nuc_in_key
serum_seg_in_key = serum_in_key
#! /home/covid19/software/miniconda3/envs/antibodies-gpu/bin/python
import os
import time
import configargparse
from glob import glob
from batchlib import run_workflow
from batchlib.analysis import PixellevelAnalysis, all_plots
from batchlib.outliers.outlier import get_outlier_predicate
from batchlib.preprocessing import Preprocess
from batchlib.segmentation import IlastikPrediction
from batchlib.util.logger import get_logger
logger = get_logger('Workflow.PixelAnalysis')
def get_input_keys(config):
nuc_in_key = 'nuclei'
serum_in_key = 'serum'
marker_in_key = 'marker'
if config.segmentation_on_corrected:
nuc_seg_in_key = nuc_in_key + '_corrected'
marker_seg_in_key = marker_in_key + '_corrected'
serum_seg_in_key = serum_in_key + '_corrected'
else:
nuc_seg_in_key = nuc_in_key
marker_seg_in_key = marker_in_key
import numpy as np
import skimage.morphology
from scipy.stats import ttest_ind
from tqdm.auto import tqdm
from glob import glob
from batchlib.util.logger import get_logger
from ..base import BatchJobOnContainer
from ..util.image import seg_to_edges
from ..util.io import (open_file, image_name_to_site_name,
image_name_to_well_name, get_column_dict, has_table,
read_table, write_image_information,
in_file_to_image_name, to_image_table)
logger = get_logger('Workflow.BatchJob.CellLevelAnalysis')
def index_cell_properties(cell_properties, ind):
return {
key: {
inner_key: inner_value[ind.astype(np.bool)]
for inner_key, inner_value in value.items()
} if isinstance(value, dict) else None
for key, value in cell_properties.items()
}
def join_cell_properties(*cell_property_list):
# copy to avoid changing inputs
cell_property_list = list(map(copy, cell_property_list))