new_contents: # Copyright (c) 2016. Mount Sinai School of Medicine
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import print_function
  
  import pandas as pd
  from numpy.random import choice, randint, seed
  
  from . import Cohort, Patient
  
  def random_cohort(size, cache_dir, seed_val=1234):
      seed(seed_val)
      d = {}
      d["id"] = [str(id) for id in range(size)]
      d["age"] = choice([10, 15, 28, 32, 59, 62, 64, 66, 68], size)
      d["OS"] = [os + randint(10) for os in choice([10, 100, 500, 1000], size)]
      # Note: these values are not currently consistent with each other.
      d["PFS"] = [int(os * 0.6) for os in d["OS"]]
      d["benefit"] = choice([False, True], size)
      d["random"] = [randint(100) for i in range(size)]
      d["random_boolean"] = choice([False, True], size)
      d["benefit_correlate"] = [randint(50) if benefit else randint(20) for benefit in d["benefit"]]
      d["benefit_correlate_boolean"] = [True if corr > 10 else False for corr in d["benefit_correlate"]]
      d["deceased"] = choice([False, True], size)
      d["progressed_or_deceased"] = choice([False, True], size)
      df = pd.DataFrame(d)
      patients = []
      for i, row in df.iterrows():
           patient = Patient(
               id=row["id"],
               os=row["OS"],
               pfs=row["PFS"],
               benefit=row["benefit"],
               deceased=row["deceased"],
               progressed_or_deceased=row["progressed_or_deceased"],
               additional_data=row)
           patients.append(patient)
      return Cohort(
          patients=patients,
          cache_dir=cache_dir)
  
subject: Add random cohort generation