Added license, examples

.gitignore

@@ -1,6 +1,9 @@
 # Compiled python modules.
 *.pyc
 
+# Simulation results folders
+/examples/results/
+
 # Setuptools distribution folder.
 /dist/
 

LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2017 Jinyu Xie
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

examples/apply_customized_controller.py

@@ -0,0 +1,32 @@
+from simglucose.simulation.user_interface import simulate
+from simglucose.controller.base import Controller, Action
+
+
+class MyController(Controller):
+    def __init__(self, init_state):
+        self.state = init_state
+
+    def policy(self, observation, reward, done, **info):
+        '''
+        Every controller must have this implementation!
+        ----
+        Inputs:
+        observation - a namedtuple defined in simglucose.simulation.env. It has
+                      CHO and CGM two entries.
+        reward      - current reward returned by environment
+        done        - True, game over. False, game continues
+        info        - additional information as key word arguments,
+                      simglucose.simulation.env.T1DSimEnv returns patient_name
+                      and sample_time
+        ----
+        Output:
+        action - a namedtuple defined at the beginning of this file. The
+                 controller action contains two entries: basal, bolus
+        '''
+        self.state = observation
+        action = Action(basal=0, bolus=0)
+        return action
+
+
+ctrller = MyController(0)
+simulate(controller=ctrller)

examples/run_user_interface.py

@@ -0,0 +1,2 @@
+from simglucose.simulation.user_interface import simulate
+simulate()

setup.py

@@ -3,7 +3,7 @@
 
 setup(name='simglucose',
       version='0.1',
-      description='A Type-1 Diabetes simulator as an OpenAI gym environment',
+      description='A Type-1 Diabetes Simulator as a Reinforcement Learning Environment',
       url='',
       author='Jinyu Xie',
       author_email='[email protected]',
@@ -14,6 +14,7 @@
           'numpy',
           'scipy',
           'matplotlib',
+          'pathos'
       ],
       include_package_data=True,
       zip_safe=False)

simglucose/controller/basal_bolus_ctrller.py

@@ -19,14 +19,15 @@ def __init__(self, target=140):
             PATIENT_PARA_FILE)
         self.target = target
 
-    def policy(self, observation, env):
-        # sample_time = kwargs.get('sample_time', 1)
-        # pname = kwargs.get('patient_name')
+    def policy(self, observation, reward, done, **kwargs):
+        sample_time = kwargs.get('sample_time', 1)
+        pname = kwargs.get('patient_name')
+
         action = self._bb_policy(
-            env.patient.name,
+            pname,
             observation.CHO,
             observation.CGM,
-            env.sample_time)
+            sample_time)
         return action
 
     def _bb_policy(self, name, meal, glucose, env_sample_time):

simglucose/controller/base.py

@@ -7,7 +7,23 @@ class Controller(object):
     def __init__(self, init_state):
         self.state = init_state
 
-    def policy(self, observation, env):
+    def policy(self, observation, reward, done, **info):
+        '''
+        Every controller must have this implementation!
+        ----
+        Inputs:
+        observation - a namedtuple defined in simglucose.simulation.env. It has
+                      CHO and CGM two entries.
+        reward      - current reward returned by environment
+        done        - True, game over. False, game continues
+        info        - additional information as key word arguments,
+                      simglucose.simulation.env.T1DSimEnv returns patient_name
+                      and sample_time
+        ----
+        Output:
+        action - a namedtuple defined at the beginning of this file. The
+                 controller action contains two entries: basal, bolus
+        '''
         self.state = observation
-        action = self.state[0]
+        action = Action(basal=0, bolus=0)
         return action

simglucose/simulation/env.py

@@ -2,7 +2,6 @@
 from simglucose.patient.t1dpatient import Action
 from simglucose.analysis.risk import risk_index
 import pandas as pd
-from datetime import datetime
 from datetime import timedelta
 import logging
 from collections import namedtuple
@@ -14,8 +13,20 @@
     from rllab.spaces import Box
 except ImportError:
     print('You could use rllab features, if you have rllab module.')
+    _Step = namedtuple("Step",
+                       ["observation", "reward", "done", "info"])
+
+    def Step(observation, reward, done, **kwargs):
+        """
+        Convenience method creating a namedtuple with the results of the
+        environment.step method.
+        Put extra diagnostic info in the kwargs
+        """
+        return _Step(observation, reward, done, kwargs)
+
     rllab = False
-    Step = namedtuple("Step", ["observation", "reward", "done", "info"])
+    Step = namedtuple("Step",
+                      ["observation", "reward", "done", "info"])
 
 Observation = namedtuple('Observation', ['CHO', 'CGM'])
 logger = logging.getLogger(__name__)
@@ -106,7 +117,11 @@ def step(self, action):
         reward = - np.log(risk)
         done = BG < 70 or BG > 350
         obs = Observation(CHO=CHO, CGM=CGM)
-        return Step(observation=obs, reward=reward, done=done)
+        return Step(observation=obs,
+                    reward=reward,
+                    done=done,
+                    sample_time=self.sample_time,
+                    patient_name=self.patient.name)
 
     def reset(self):
         self.patient.reset()