diff --git a/py/20200221_experiment_scheduling.py b/py/20200221_experiment_scheduling.py
index e782ec5..9ef163d 100644
--- a/py/20200221_experiment_scheduling.py
+++ b/py/20200221_experiment_scheduling.py
@@ -3,31 +3,91 @@
 
 # First, we model a study with 12 sessions. Sessions consist of several surveys before and after a set of experimental tasks. We then define constraints and solve for a schedule for the given scenario. This scenario only plans the experiment for a single subject.
 
+# Use the following command to install dependencies: `pip install pyschedule`
+
 #%%
-#! pip install pyschedule
 
 from pyschedule import Scenario, solvers, plotters, alt
 
+# experiment duration in minutes
+duration = 60
+questionnaires = ["xcit-demographics", "aiss", "arces", "asrs", "avg2019", "bfi-2", "bisbas", "bis11", "cfq", "cfs", "dyslexia", "ehi-sf", "grit12", "i-panas-sf", "ipaq-sf", "maas", "mfs", "mw", "mwq", "ncs-6", "nfc", "psqi", "rei", "sci", "sqs", "upps-sf", "webexec", "who5", "whoqol","nasa-tlx", "xcit-postgame-debrief"]
+
+
 # the planning horizon has 
-s = Scenario('Prolific500', horizon=60)
+s = Scenario('Prolific500', horizon=duration)
 
-session = s.Resource('Session', num=1)
+session = s.Resource('session', num=12)
 
-mmi = s.Task('MMI', length=3, delay_cost=1)
-games = s.Task('games',length=45, delay_cost=1, is_group=True)
-nasa_tlx = s.Task('NSA_TLX', length=1, delay_cost=1)
-
-# define the tasks that each subject must perform in first session
+mmi = s.Task('mmi', length=3, delay_cost=1)
 mmi += alt(session)
+
+games = s.Task('games',length=45, delay_cost=1, is_group=True)
 games += alt(session)
+
+nasa_tlx = s.Task('nasa_tlx', length=1, delay_cost=1)
 nasa_tlx += alt(session)
 
-# add precedences
+# add constraints on precedences
 s += mmi < games 
 s += games < nasa_tlx
 
+
 # compute and print session schedules
-solvers.mip.solve(s,msg=1)
+solvers.mip.solve(s)
+print(s.solution())
+
+plotters.matplotlib.plot(s)
+
+#%%
+#%% [markdown]
+# Complex experiments in cognitive science require participants to perform multiple tasks and capture observations with different modalities. This notebook demonstrates some possible solutions for the problem of scheduling experimental tasks.
+
+# First, we model a study with 12 sessions. Sessions consist of several surveys before and after a set of experimental tasks. We then define constraints and solve for a schedule for the given scenario. This scenario only plans the experiment for a single subject.
+
+#%%
+# MPILX Scheduling
+from pyschedule import Scenario, solvers, plotters, alt
+
+# experiment duration for each subject in minutes
+study_duration = 12 * 24 * 60 # days * hours * minutes
+n_subjects = 2
+task_durations = {
+  'pretest_presurvey': 30,
+  'pretest_DWI': 15,
+  'pretest_rsfMRI': 15,
+  'pretest_anatomical': 15,
+  'pretest_taskfMRI': 45,
+  'pretest_postsurvey': 30,
+  'pretest_presurvey': 30,
+  'training_presurvey': 30,
+  'training_': 10 * 24 * 60, #TODO expand to daily
+  'training_postsurvey': 30,
+  'posttest_DWI': 15,
+  'posttest_rsfMRI': 15,
+  'posttest_anatomical': 15,
+  'posttest_taskfMRI': 45,
+  'posttest_postsurvey': 30
+}
+
+# the planning horizon has 
+s = Scenario('MPILX50', horizon=study_duration)
+
+subject = s.Resource('subject', num=n_subjects)
+
+tasks = list()
+
+for t in task_durations.keys():
+  duration = task_durations[t]
+  task = s.Task(t, length=duration, delay_cost=1)
+  task += alt(subject)
+  tasks.append(task)
+
+for i in range(len(tasks)-1):
+  s += tasks[i] < tasks[i+1]
+
+# compute and print session schedules
+solvers.mip.solve(s)
 print(s.solution())
 
 plotters.matplotlib.plot(s)