diff --git a/py/20200221_experiment_scheduling.py b/py/20200221_experiment_scheduling.py
index 640c2b8..fe9e60f 100644
--- a/py/20200221_experiment_scheduling.py
+++ b/py/20200221_experiment_scheduling.py
@@ -1,16 +1,23 @@
 #%% [markdown]
-# Complex cognitive experiments require participants to perform multiple tasks across a space of time and then capture observations with different modalities. This notebook demonstrates some possible solutions for the problem of scheduling experimental tasks, and mainly concerns reformulating experiments as a resource allocation problem.
+# Complex cognitive experiments require participants to perform multiple tasks across a span of limited lab time or online crowdsourcing time, hence demand flexible scheduling of tasks. 
+# This notebook demonstrates a possible solution for the problem of scheduling experimental tasks, and mainly concerns reformulating experiments as a resource allocation problem.
+#
+# Typical experiments are comprised of several session, in each session participants view a set of questionnaires, perform a test, and then respond to another set of questionnaires. This structure is repeated for each srssion throughout the experiment.
 
-# 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`
+# To orgnaize questionniare and tasks in a limited time, we first model a study as an ordered list of sessions (here, for example, 12 session). Sessions consist of several questionnaires before and after a set of experimental tasks. We then define constraints on sueverys and tasks (e.g., pre-task, post-task, end of study, and start of study surveys), and solve for a schedule for the given scenario. Note that his example scenario only plans the experiment for a single subject.
+#
+# Note: Use the following command to install dependencies: `pip install pyschedule`
 
 #%%
 from math import ceil
-from pyschedule import Scenario, solvers, plotters, alt
+
+from pyschedule import Scenario, plotters, alt
+from pyschedule.solvers.mip import solve
+from pyschedule.plotters.matplotlib import plot
+
 import seaborn as sns
 
-
+# 1. set theme and style
 sns.color_palette("colorblind")
 sns.set_style('white')
 # experiment duration in minutes
@@ -18,12 +25,16 @@
 game_duration = 45
 n_subjects = 2
 n_sessions = 12
-duration_per_question = .1666667 # minutes (~10sec)
+duration_per_question = .16 # minutes (~10sec)
 
 prestudy_tasks = [
   "xcit_demographics"
 ]
 
+pregame_tasks = [
+
+]
+
 postgame_tasks = [
   "nasa_tlx",
   "xcit_postgame_debrief"
@@ -52,6 +63,7 @@
   "ipaq_sf": 7,
   "maas": 15,
   "mfs": 12,
+  #"mmi_part1": 66,
   "mw": 8,
   "mwq": 5,
   "ncs_6": 6,
@@ -76,43 +88,44 @@
 sessions = s.Resources('Session', num = n_sessions)
 
 
-# 2. games
+def _duration(questions):
+  t = ceil(questions * duration_per_question)
+  return t
+
+# 3. games
 games = s.Tasks('Game',length=game_duration, num=n_sessions, is_group=False)
 games += alt(sessions)
 
-# questionnaires
+# 3. questionnaires
 for q in n_questions.keys():
   n = n_questions.get(q, 0)
-  duration = ceil(n * duration_per_question)
-  task = s.Task(q, length= duration, delay_cost=1)
-  if q in poststudy_tasks:
-    print(q, 1)
-    task += sessions[-1]
+  task = s.Task(q, length= _duration(n), delay_cost=1)
+
+  if q in prestudy_tasks:
+    task += sessions[0] # first session
+  elif q in poststudy_tasks:
+    task += sessions[-1] # last session
     s += games < task
-  elif q in prestudy_tasks:
-    print(q, 2)
-    task += sessions[0]
     s += task < games
   elif q in postgame_tasks:
-    print(q, 3)
     task += sessions
     s += games < task
+  elif q in pregame_tasks:
+    task += sessions
+    s += task < games
   else:
-    print(q, 4)
     task += alt(sessions)
 
 print(s.tasks)
 
-solvers.mip.solve(s)
+# 4. solve resource allocation
+solve(s)
+
+# 5. print last computed solution
 print(s.solution())
 
-plotters.matplotlib.plot(s, fig_size=(50,5))
-
-#%%
-#%% [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.
+# 6. pot gantt
+plot(s, fig_size=(50,5))
 
 #%%
 # MPILX Scheduling