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main.py
executable file
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/
main.py
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#!/lab/solexa_young/scratch/jon_henninger/tools/venv/bin/python
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams['text.usetex'] = False
matplotlib.rcParams['font.sans-serif'] = 'Arial'
matplotlib.rcParams['font.family'] = 'sans-serif'
#import in_vitro_droplet_assay.methods as methods
#import in_vitro_droplet_assay.grapher as grapher
import methods
import grapher
import pandas as pd
import numpy as np
import os
import sys
import math
#import matplotlib
# matplotlib.use('Agg')
# matplotlib.use('Qt5Agg')
# from matplotlib import pyplot as plt
import argparse
import json
from datetime import datetime
from types import SimpleNamespace
from skimage import io, filters, measure, color, exposure, morphology, feature, img_as_float, img_as_uint
#TODO Implement watershed algorithm on the droplets
#TODO correct for uneven illumination
#TODO Better background subtraction that isn't just a straight value
#TODO Better [Cout] by averaging randomized images
# This is written so that all replicates for a given experiment are in a folder together (both .TIF and .nd files)
# parse input
parser = argparse.ArgumentParser()
input_params = methods.parse_arguments(parser)
input_params.parent_path = input_params.parent_path.replace("Volumes","lab")
input_params.output_path = input_params.output_path.replace("Volumes","lab")
input_params = methods.make_output_directories(input_params)
# get number of experiments/sub-directories to analyze
dir_list = os.listdir(input_params.parent_path)
dir_list.sort(reverse=False)
file_ext = ".nd"
# this loops over EXPERIMENT FOLDERS
sample_writer = pd.ExcelWriter(os.path.join(input_params.output_dirs['output_summary'], 'summary_droplet_output.xlsx'),
engine='xlsxwriter')
replicate_writer = pd.ExcelWriter(
os.path.join(input_params.output_dirs['output_individual'], 'individual_droplet_output.xlsx'),
engine='xlsxwriter')
graph_input = []
sample_list = []
sample_output = pd.DataFrame()
for folder in dir_list:
if not folder.startswith('.') and os.path.isdir(os.path.join(input_params.parent_path, folder)):
print()
# print(f'Sample: {folder}')
print('Sample: ', folder)
sample_list.append(folder)
file_list = os.listdir(os.path.join(input_params.parent_path, folder))
base_name_files = [f for f in file_list if file_ext in f
and os.path.isfile(os.path.join(input_params.parent_path, folder, f))]
base_name_files.sort(reverse=False)
# this loops over REPLICATES
bulk_sig = {} # dictionary where key = channel name and value = list of bulk (non-droplet) intensities per replicate
total_sig = {} # dictionary where key = channel name and value = list of total intensities per replicate
replicate_output = pd.DataFrame()
input_params.replicate_count = 1
for idx, file in enumerate(base_name_files):
data = SimpleNamespace() # this is the session data object that will be passed to functions. Corresponds to one replicate
sample_name = file.replace(file_ext, '')
replicate_files = [os.path.join(input_params.parent_path, folder, r) for r in file_list if sample_name in r
and os.path.isfile(os.path.join(input_params.parent_path, folder, r))]
replicate_files = np.sort(replicate_files)
data = methods.load_images(replicate_files, data, input_params, folder)
data = methods.find_scaffold(data, input_params)
data, rep_bulk, rep_total = methods.find_droplets(data, input_params)
data = methods.measure_droplets(data, input_params, rep_bulk)
replicate_output = replicate_output.append(data.replicate_output, ignore_index=True)
if len(bulk_sig) == 0:
for c in data.channel_names:
bulk_sig[c] = [rep_bulk[c]]
total_sig[c] = [rep_total[c]]
else:
for c in data.channel_names:
bulk_sig[c].append(rep_bulk[c])
total_sig[c].append(rep_total[c])
input_params.replicate_count += 1
sheet_name = folder
if len(sheet_name) > 30:
total_length = len(sheet_name)
start_idx = math.floor((total_length - 30)/2)
stop_idx = total_length - start_idx
sheet_name = sheet_name[start_idx:stop_idx]
replicate_output.to_excel(replicate_writer, sheet_name=sheet_name, index=False)
if len(replicate_output) > 0:
graph_input.append(replicate_output)
grapher.make_droplet_size_histogram(folder, replicate_output, input_params.output_dirs, input_params)
if len(data.channel_names) > 1:
grapher.make_droplet_intensity_scatter(folder, data, input_params.output_dirs, input_params)
temp_sample_output = methods.calc_summary_stats(folder, data.channel_names, replicate_output, input_params, bulk_sig, total_sig)
sample_output = sample_output.append(temp_sample_output, ignore_index=True)
print('Finished sample ', folder, ' at ', datetime.now())
sample_output = sample_output.reindex(sorted(sample_output.columns, reverse=True), axis=1)
sample_output.to_excel(sample_writer, sheet_name='summary', index=False)
# adjust width of Excel columns in output to make for easier reading before writing the file
for key, sheet in replicate_writer.sheets.items():
for idx, name in enumerate(replicate_output.columns):
col_width = len(name) + 2
sheet.set_column(idx, idx, col_width)
for key, sheet in sample_writer.sheets.items():
for idx, name in enumerate(sample_output.columns):
col_width = len(name) + 2
sheet.set_column(idx, idx, col_width)
replicate_writer.save()
sample_writer.save()
# make boxplot with all droplets
if len(graph_input) > 0:
grapher.make_droplet_boxplot(graph_input, sample_list, input_params.output_dirs, input_params)
# if len(sample_output) > 0:
# grapher.make_average_sample_graph(sample_output, output_dirs, input_args)
print()
print('Finished making plots at: ', datetime.now())
with open(os.path.join(input_params.output_dirs['output_parent'], 'output_analysis_parameters.txt'), 'w') as file:
file.write(json.dumps(input_params, default=str))
print('---------------------------------')
print('Completed at ', datetime.now())