text: import os
  import json
  from shutil import rmtree
  from shutil import copyfile
  
  import numpy as np
  import luigi
  
  from cluster_tools import LiftedMulticutSegmentationWorkflow
  from resolving import ResolvingWorkflow, LiftedEdgesLocal, LiftedEdgesSlurm
  from evaluate import evaluate_fib, CopyAndCropLocal, CopyAndCropSlurm
  
  # ROI from Michals mail
  ROI_START = [5074, 1600, 1800]
  ROI_SIZE = [2876, 2401, 2901]
  
  
  def copy_and_crop_seg(seg_path, seg_key, tmp_path, tmp_key, config_dir,
                        tmp_folder, target, max_threads):
      copy_task = CopyAndCropSlurm if target == 'slurm' else CopyAndCropLocal
  
      # copy and crop
      copy_conf = copy_task.default_task_config()
      copy_conf.update({'threads_per_job': max_threads,
                        'mem_limit': 256})
      with open(os.path.join(config_dir, 'copy_and_crop.config'), 'w') as f:
          json.dump(copy_conf, f)
  
      t = copy_task(tmp_folder=tmp_folder, config_dir=config_dir,
                    max_jobs=1,
                    input_path=seg_path, input_key=seg_key,
                    output_path=tmp_path, output_key=tmp_key,
                    roi_start=ROI_START, roi_size=ROI_SIZE)
      ret = luigi.build([t], local_scheduler=True)
      assert ret
  
  
  def lifted_problem(tmp_folder, config_dir,
                     target, max_threads,
                     path, exp_path, recall, precision):
      task = LiftedEdgesSlurm if target == 'slurm' else LiftedEdgesLocal
  
      config = task.default_task_config()
      config.update({'threads_per_job': max_threads})
      with open(os.path.join(config_dir, 'lifted_edges.config'), 'w') as f:
          json.dump(config, f)
  
      t = task(tmp_folder=tmp_folder, config_dir=config_dir, max_jobs=1,
               path=path, exp_path=exp_path, precision=precision, recall=recall)
      ret = luigi.build([t], local_scheduler=True)
      assert ret
  
  
  def solve_separately(path, exp_path, tmp_folder, target, max_jobs, n_threads):
      task = ResolvingWorkflow
  
      ws_key = 'volumes/segmentation/watershed'
      seg_key = 'volumes/segmentation/separately'
      objects_group = 'resolving/oracle'
  
      assignment_in_key = 'node_labels/multitcut_filtered'
      assignment_out_key = 'node_labels/separately'
  
      config_dir = './configs'
      conf = task.get_config()['resolve_inidividual_objects']
      conf.update({'threads_per_job': n_threads, 'mem_limit': 128})
      with open(os.path.join(config_dir, 'resolve_inidividual_objects.config'), 'w') as f:
          json.dump(conf, f)
  
      t = task(tmp_folder=tmp_folder, config_dir=config_dir,
               max_jobs=max_jobs, target=target,
               problem_path=exp_path, path=path,
               output_path=exp_path,
               objects_group=objects_group,
               assignment_in_key=assignment_in_key,
               assignment_out_key=assignment_out_key,
               ws_key=ws_key, out_key=seg_key)
      ret = luigi.build([t], local_scheduler=True)
      assert ret, "Resolving failed"
      return seg_key
  
  
  def solve_jointly(path, exp_path, tmp_folder, target, max_jobs, n_threads):
      task = LiftedMulticutSegmentationWorkflow
      seg_key = 'volumes/segmentation/jointly'
  
      assignment_out_key = 'node_labels/jointly'
  
      input_key = 'volumes/affinities'
      ws_key = 'volumes/segmentation/watershed'
      mask_key = 'volumes/masks/minfilter/s5'
  
      os.makedirs(tmp_folder, exist_ok=True)
      # make log for costs in order to skip all the problem computation
      copyfile('./tmp_mc/probs_to_costs.log',
               os.path.join(tmp_folder, 'probs_to_costs.log'))
  
      config_dir = './configs'
      configs = task.get_config()
      conf_names = ['solve_lifted_subproblems',
                    'solve_lifted_global',
                    'reduce_lifted_problem']
      for name in conf_names:
          conf = configs[name]
          conf.update({'threads_per_job': n_threads, 'mem_limit': 128,
                       'time_limit': 150, 'time_limit_solver': 90 * 60 * 60,
                       'agglomerator': 'greedy-additive'})
          with open(os.path.join(config_dir, '%s.config' % name), 'w') as f:
              json.dump(conf, f)
  
      n_scales = 1
      t = task(tmp_folder=tmp_folder, config_dir=config_dir,
               max_jobs=max_jobs, target=target, max_jobs_multicut=8,
               input_path=path, input_key=input_key,
               mask_path=path, mask_key=mask_key,
               ws_path=path, ws_key=ws_key,
               problem_path=exp_path,
               node_labels_key=assignment_out_key,
               output_path=exp_path, output_key=seg_key,
               lifted_labels_path='', lifted_labels_key='',
               lifted_prefix='resolving',
               n_scales=n_scales, skip_ws=True)
      ret = luigi.build([t], local_scheduler=True)
      assert ret, "Resolving failed"
      return seg_key
  
  
  def parse_runtimes(tmp_folder):
      return None, None
  
  
  def roc_point(recall, precision, target, max_jobs, n):
      path = '/g/kreshuk/data/FIB25/data.n5'
      gt_key1 = 'volumes/segmentation/groundtruth/cropped_google'
      gt_key2 = 'volumes/segmentation/groundtruth/cropped_full'
  
      tmp_folder = './tmp_roc/tmp_%s_%s' % (str(recall), str(precision))
      exp_path = os.path.join(tmp_folder, 'data.n5')
      config_dir = './configs'
  
      tmp_eval_sep = os.path.join(tmp_folder, 'tmp_eval_sep')
      tmp_eval_joint = os.path.join(tmp_folder, 'tmp_eval_joint')
  
      n_threads = 32
      result1 = []
      result2 = []
      for ii in range(n):
          # 1.) make the tmp and data folder
          os.makedirs(exp_path, exist_ok=True)
  
          # 2.) make the lifted problem
          lifted_problem(tmp_folder, config_dir, target, n_threads,
                         path, exp_path, recall, precision)
  
          # 3.) solve separate lifted problem
          key_sep = solve_separately(path, exp_path,
                                     tmp_folder, target, max_jobs, n_threads)
  
          # 4.) solve joint lifted problem
          key_joint = solve_jointly(path, exp_path,
                                    tmp_folder, target, max_jobs, n_threads)
          # rt_sep, rt_joint = parse_runtimes(tmp_folder)
  
          # 5.) copy and crop the two segmentations
          key_joint_c = 'volumes/segmentation/jointly_cropped'
          copy_and_crop_seg(exp_path, key_joint,
                            exp_path, key_joint_c,
                            config_dir,
                            os.path.join(tmp_folder, 'tmp_crop_joint'),
                            target='local', max_threads=n_threads)
          key_sep_c = 'volumes/segmentation/separately_cropped'
          copy_and_crop_seg(exp_path, key_sep,
                            exp_path, key_sep_c,
                            config_dir,
                            os.path.join(tmp_folder, 'tmp_crop_sep'),
                            target='local', max_threads=n_threads)
  
          # 6.) a) evaluate google groundtruth
          res_sep = evaluate_fib(exp_path, key_sep_c,
                                 path, gt_key1,
                                 tmp_eval_sep, config_dir,
                                 target, max_jobs, n_threads)
          res_joint = evaluate_fib(exp_path, key_joint_c,
                                   path, gt_key1,
                                   tmp_eval_joint, config_dir,
                                   target, max_jobs, n_threads)
          result1.append({'jointly': res_joint, 'separately': res_sep})
  
          # 6.) b) evaluate google groundtruth
          res_sep = evaluate_fib(exp_path, key_sep_c,
                                 path, gt_key2,
                                 tmp_eval_sep, config_dir,
                                 target, max_jobs, n_threads)
          res_joint = evaluate_fib(exp_path, key_joint_c,
                                   path, gt_key2,
                                   tmp_eval_joint, config_dir,
                                   target, max_jobs, n_threads)
          result2.append({'jointly': res_joint, 'separately': res_sep})
  
          # clean up
          print("Removing tmp")
          rmtree(tmp_folder)
      return result1, result2
  
  
  def roc():
      out_path1 = 'roc_results_googlegt.json'
      out_path2 = 'roc_results_fullgt.json'
  
      if os.path.exists(out_path1):
          with open(out_path1, 'r') as f:
              results1 = json.load(f)
      else:
          results1 = {}
  
      if os.path.exists(out_path2):
          with open(out_path2, 'r') as f:
              results2 = json.load(f)
      else:
          results2 = {}
  
      # TODO full evaluation range
      rec_values = np.linspace(0.5, 1., 11)
      prec_values = np.linspace(0.5, 1., 11)
  
      target = 'slurm'
      max_jobs = 250
      # target = 'local'
      # max_jobs = 48
  
      for rec in rec_values:
          for prec in prec_values:
  
              exp_key = str(rec) + '_' + str(prec)
  
              # Don't need to evaluate for perfect recall / precision
              if rec == prec == 1.:
                  continue
  
              # if exp_key in results1:
              #     continue
  
              print("Evaluationg rec", rec, "prec", prec)
              res1 = results1.get(exp_key, None)
              res2 = results2.get(exp_key, None)
  
              # TODO ideally, we want more statistics, n=5-10
              n = 4
              new_res1, new_res2 = roc_point(rec, prec, target, max_jobs, n=n)
  
              if res1 is None:
                  results1[exp_key] = new_res1
              else:
                  res1.extend(new_res1)
                  results1[exp_key] = res1
  
              if res2 is None:
                  results2[exp_key] = new_res2
              else:
                  res2.extend(new_res2)
                  results2[exp_key] = res2
  
              with open(out_path1, 'w') as f:
                  json.dump(results1, f)
              with open(out_path2, 'w') as f:
                  json.dump(results2, f)
  
  
  if __name__ == '__main__':
      roc()
  