exemplar.md

Exemplar - run ASHLAR on demo data with the batch script

1. Download FIJI and put it in C:\Users\Public\Downloads\

2. Save the imagej_basic_ashlar.py to C:\Users\Public\Downloads\Fiji.app\plugins\

3. Launch command prompt

4. In command prompt, change directory to the demo data folder

   d:

   cd d:\ashlar-batch-csv-demo\demo_data-20181001_Jerry

5. In command prompt, list all folderpaths in the current directory and write it to folderslist.txt

   dir /s /b /o:n /a:d > folderslist.txt

6. Create a csv file with three required headings - Directory, Correction, Pyramid

7. Enter the paths for the slide directories, each contains multiple cycles of rcpnl files - you can copy the list from the folderslist.txt you just created

8. Declare whether you want Correction and/or Pyramid for your images

   1, yes, y, true or t means yes (case insensitive), others are no

9. Save the csv file, it's 20181001-demo.csv for this demo

10. Make sure you have the run_ashlar_csv_batch.py file in the current directory.

11. In command prompt, call python to run the script and take in the csv file as config

    python run_ashlar_csv_batch.py 20181001-demo.csv

12. When the task is done, the ome-tif (if you choose to generate pyramid images) will be in the directories you specified in the csv file, the filenames are the folders' names. the ffps and dfps (if you turn on correction) will also be in the same folders.

run_ashlar_batch_csv_incell.py

from __future__ import print_function
import csv
from subprocess import call
try:
    import pathlib
except ImportError:
    import pathlib2 as pathlib
import argparse
import os
import datetime

def text_to_bool(text):
    return False \
        if not str(text) \
        else str(text).lower() in '1,yes,y,true,t'
def path_to_date(path):
    return os.path.getmtime(str(path))

parser = argparse.ArgumentParser(
    description='Read a csv config file and run ashlar'
)
parser.add_argument(
    'csv_filepath', metavar='CSVFILE',
    help='a csv file with header row: Directory, Correction, Pyramid'
)
parser.add_argument(
    '-f', '--from-dir', dest='from_dir', default=0, type=int, metavar='FROM',
    help=('starting directory; numbering starts at 0')
)
parser.add_argument(
    '-t', '--to-dir', dest='to_dir', default=None, type=int, metavar='TO',
    help=('ending directory; numbering starts at 0')
)
args = parser.parse_args()

csv_path = pathlib.Path(args.csv_filepath)
if not csv_path.is_file() or csv_path.suffix != '.csv':
    print('A csv file is required to proceed.')
    parser.print_usage()
    parser.exit()

with open(str(csv_path)) as exp_config:
    exp_config_reader = csv.DictReader(exp_config)
    exps = [dict(row) for row in exp_config_reader]


for exp in exps[args.from_dir : args.to_dir]:
    path_exp = pathlib.Path(exp['Directory'])
    files_exp = sorted(path_exp.glob('**/**/*xdce'))
    files_exp.sort(key=path_to_date)

    if len(files_exp) == 0:
        print('No xdce files found in', str(path_exp))
        continue

    print('Processing files in', str(path_exp))
    print(datetime.datetime.now())
    print()
    if text_to_bool(exp['Correction']):
        lambda_flat = '0.1'
        lambda_dark = '0.01'
        ffp_list = []
        dfp_list = []
        for j in files_exp:
            print('Run BaSiC')
            print('\r    ' + 'Generating ffp and dfp for ' + j.name)
            ffp_file_name = j.name.replace('.xdce', '-ffp.tif')
            dfp_file_name = j.name.replace('.xdce', '-dfp.tif')
            if (j.parent / ffp_file_name).exists() and (j.parent / dfp_file_name).exists():
                print('\r        ' + ffp_file_name + ' already exists')
                print('\r        ' + dfp_file_name + ' already exists')
                print('already exists')
            else:
                call("C:\Users\Public\Downloads\Fiji.app\ImageJ-win64.exe --ij2 --headless --run C:\Users\Public\Downloads\Fiji.app\plugins\imagej_basic_ashlar.py \"filename='%s', output_dir='%s', experiment_name='%s', lambda_flat=%s, lambda_dark=%s\"" %(str(j), str(j.parent), j.name.replace('.xdce', ''), lambda_flat, lambda_dark))
                print('\r        ' + ffp_file_name + ' generated')
                print('\r        ' + dfp_file_name + ' generated')
                pass
            ffp_list.append(str(j.parent / ffp_file_name))
            dfp_list.append(str(j.parent / dfp_file_name))

    print('Run ashlar')
    print(datetime.datetime.now())
    print()
    out_dir = path_exp / 'registration'
    if not out_dir.exists():
        out_dir.mkdir()

    xdce_files = ' '.join([str(f) for f in files_exp])
    command = 'ashlar ' + xdce_files + ' -m 30 -o ' + str(out_dir)

    if text_to_bool(exp['Pyramid']):
        command += ' --pyramid -f ' + path_exp.name + '.ome.tif'
    
    if text_to_bool(exp['Correction']):
        ffps = ' '.join(ffp_list)
        dfps = ' '.join(dfp_list)
        command += ' --ffp ' + ffps + ' --dfp ' + dfps
    
    print(command)
    call(command)

run_ashlar_csv_batch.py

from __future__ import print_function
import csv
from subprocess import call
try:
    import pathlib
except ImportError:
    import pathlib2 as pathlib
import argparse
import os
import datetime

def text_to_bool(text):
    return False \
        if not str(text) \
        else str(text).lower() in '1,yes,y,true,t'
def path_to_date(path):
    return os.path.getmtime(str(path))

parser = argparse.ArgumentParser(
    description='Read a csv config file and run ashlar'
)
parser.add_argument(
    'csv_filepath', metavar='CSVFILE',
    help='a csv file with header row: Directory, Correction, Pyramid'
)
parser.add_argument(
    '-f', '--from-dir', dest='from_dir', default=0, type=int, metavar='FROM',
    help=('starting directory; numbering starts at 0')
)
parser.add_argument(
    '-t', '--to-dir', dest='to_dir', default=None, type=int, metavar='TO',
    help=('ending directory; numbering starts at 0')
)
args = parser.parse_args()

csv_path = pathlib.Path(args.csv_filepath)
if not csv_path.is_file() or csv_path.suffix != '.csv':
    print('A csv file is required to proceed.')
    parser.print_usage()
    parser.exit()

with open(str(csv_path)) as exp_config:
    exp_config_reader = csv.DictReader(exp_config)
    exps = [dict(row) for row in exp_config_reader]


for exp in exps[args.from_dir : args.to_dir]:
    path_exp = pathlib.Path(exp['Directory'])
    files_exp = sorted(path_exp.glob('*rcpnl'))
    files_exp.sort(key=path_to_date)

    if len(files_exp) == 0:
        print('No rcpnl files found in', str(path_exp))
        continue
        
    print('Processing files in', str(path_exp))
    print(datetime.datetime.now())
    print()
    if text_to_bool(exp['Correction']):
        lambda_flat = '0.1'
        lambda_dark = '0.01'
        ffp_list = []
        dfp_list = []
        for j in files_exp:
            print('\r    ' + 'Generating ffp and dfp for ' + j.name)
            ffp_file_name = j.name.replace('.rcpnl', '-ffp.tif')
            dfp_file_name = j.name.replace('.rcpnl', '-dfp.tif')
            if (path_exp / ffp_file_name).exists() and (path_exp / dfp_file_name).exists():
                print('\r        ' + ffp_file_name + ' already exists')
                print('\r        ' + dfp_file_name + ' already exists')
            else:
                call("C:\Users\Public\Downloads\Fiji.app\ImageJ-win64.exe --ij2 --headless --run C:\Users\Public\Downloads\Fiji.app\plugins\imagej_basic_ashlar.py \"filename='%s', output_dir='%s', experiment_name='%s', lambda_flat=%s, lambda_dark=%s\"" %(str(j), str(path_exp), j.name.replace('.rcpnl', ''), lambda_flat, lambda_dark))
                print('\r        ' + ffp_file_name + ' generated')
                print('\r        ' + dfp_file_name + ' generated')
            ffp_list.append(str(path_exp / ffp_file_name))
            dfp_list.append(str(path_exp / dfp_file_name))

    print('Run ashlar')
    print(datetime.datetime.now())
    print()
    out_dir = path_exp / 'registration'
    if not out_dir.exists():
        out_dir.mkdir()
        
    rcpnl_files = ' '.join([str(f) for f in files_exp])
    command = 'ashlar ' + rcpnl_files + ' -m 30 -o ' + str(out_dir)

    if text_to_bool(exp['Pyramid']):
        command += ' --pyramid -f ' + path_exp.name + '.ome.tif'
    
    if text_to_bool(exp['Correction']):
        ffps = ' '.join(ffp_list)
        dfps = ' '.join(dfp_list)
        command += ' --ffp ' + ffps + ' --dfp ' + dfps
    
    # print(command)
    call(command)