From 1ebfa5837bb5d1cb5ec055107bd58afd3d841523 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Wed, 17 Jan 2018 15:35:35 +1100
Subject: [PATCH 01/28] Make sequencer constraint

*make the Sequencer class
*veto=True if current_obs is in obs_list
*might want a better string to be returned
---
 pocs/scheduler/constraint.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 3907baaa3..070bcb1a6 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -159,3 +159,21 @@ def get_score(self, time, observer, observation, **kwargs):
 
     def __str__(self):
         return "Moon Avoidance"
+
+class Seqencer(BaseConstraint):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def get_score(self, time, observer, observation, **kwargs):
+        target = observation.field
+
+        veto = False
+        score = self._score
+
+        if scheduler.current_observation in scheduler.observed_list:
+            veto = True
+    return None
+
+    def __str__(self):
+        return "Don't repeat observations"

From 497b4dd48e00d46f2ca81f780f55bfe53247a66b Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Wed, 17 Jan 2018 15:57:59 +1100
Subject: [PATCH 02/28] *can't use scheduler here (try observation)

*return veto and score (does not need to be changed)
---
 pocs/scheduler/constraint.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 070bcb1a6..480cda0f8 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -166,14 +166,15 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def get_score(self, time, observer, observation, **kwargs):
-        target = observation.field
-
         veto = False
         score = self._score
 
-        if scheduler.current_observation in scheduler.observed_list:
+        target = observation.field
+
+        if target in self.observation.observed_list:
             veto = True
-    return None
+
+    return veto, score * self.weight
 
     def __str__(self):
         return "Don't repeat observations"

From 7623a69498b35f1aa6f4d56079a95a706fa7d3c7 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Wed, 17 Jan 2018 16:34:09 +1100
Subject: [PATCH 03/28] *edit

---
 pocs/scheduler/constraint.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 480cda0f8..c100a8f1e 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -170,8 +170,9 @@ def get_score(self, time, observer, observation, **kwargs):
         score = self._score
 
         target = observation.field
+        observed = observation.observed_list
 
-        if target in self.observation.observed_list:
+        if target in observed:
             veto = True
 
     return veto, score * self.weight

From beac3739557588662575aa57c8c7b8e31f1e628a Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Wed, 17 Jan 2018 23:43:16 +1100
Subject: [PATCH 04/28] Change names

*change name of method
*change string returned by method
---
 pocs/scheduler/constraint.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index c100a8f1e..6949cc349 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -160,7 +160,7 @@ def get_score(self, time, observer, observation, **kwargs):
     def __str__(self):
         return "Moon Avoidance"
 
-class Seqencer(BaseConstraint):
+class AlreadyVisited(BaseConstraint):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -178,4 +178,4 @@ def get_score(self, time, observer, observation, **kwargs):
     return veto, score * self.weight
 
     def __str__(self):
-        return "Don't repeat observations"
+        return "Already Visited"

From 2260d04f5fdaef8135082da61c814ae99678f722 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 09:22:47 +1100
Subject: [PATCH 05/28] Start making test for constraint

*small change to constraint
*template of test
**makes observed list
**checks two different obs, one in list one not
---
 pocs/scheduler/constraint.py   |  6 +++---
 pocs/tests/test_constraints.py | 22 ++++++++++++++++++++++
 2 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 6949cc349..430acc865 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -170,10 +170,10 @@ def get_score(self, time, observer, observation, **kwargs):
         score = self._score
 
         target = observation.field
-        observed = observation.observed_list
+        observed_list = kwargs['observed_list']
 
-        if target in observed:
-            veto = True
+            if target in observed_list:
+                veto = True
 
     return veto, score * self.weight
 
diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index f620453bb..6dea192fe 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -14,6 +14,7 @@
 from pocs.scheduler.constraint import BaseConstraint
 from pocs.scheduler.constraint import Duration
 from pocs.scheduler.constraint import MoonAvoidance
+from pocs.scheduler.constraint import AlreadyVisited
 
 
 @pytest.fixture
@@ -197,3 +198,24 @@ def test_moon_avoidance(observer):
 
     assert veto1 is False and veto2 is False
     assert score2 > score1
+
+
+def test_already_visited(observer):
+    avc = AlreadyVisited()
+
+    time = Time('2016-08-13 10:00:00')
+
+    observation1 = Observation(Field('HD189733', '20h00m43.7135s +22d42m39.0645s'))  # HD189733
+    observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
+    observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
+
+    observed_list = OrderedDict()
+
+    observed_list[observation1.seq_time] = observation1
+    observed_list[observation2.seq_time] = observation2
+
+    veto1, score1 = avc.get_score(time, observer, observation1, observed_list=observed_list)
+    veto2, score2 = avc.get_score(time, observer, observation3, observed_list=observed_list)
+
+    assert veto1 is True
+    assert veto2 is False

From b9f78775346d3bbdcef007f54dfd803d2aab354a Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 10:41:11 +1100
Subject: [PATCH 06/28] Edits

*add docstring
*use observer.observed_list
*change test to use observer.observed_list
---
 pocs/scheduler/constraint.py   |  7 ++++++-
 pocs/tests/test_constraints.py | 10 +++++-----
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 430acc865..8d5242444 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -162,6 +162,11 @@ def __str__(self):
 
 class AlreadyVisited(BaseConstraint):
 
+    """ Simple Already Visited Constraint
+    
+    A simple already visited constraint that determines if the given `observation`
+    has already been visited before.
+    """
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -170,7 +175,7 @@ def get_score(self, time, observer, observation, **kwargs):
         score = self._score
 
         target = observation.field
-        observed_list = kwargs['observed_list']
+        observed_list = kwargs.get('observed_list', observer.observed_list)
 
             if target in observed_list:
                 veto = True
diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index 6dea192fe..b943657ee 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -209,13 +209,13 @@ def test_already_visited(observer):
     observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
     observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
 
-    observed_list = OrderedDict()
+    observer.observed_list = OrderedDict()
 
-    observed_list[observation1.seq_time] = observation1
-    observed_list[observation2.seq_time] = observation2
+    observer.observed_list[observation1.seq_time] = observation1
+    observer.observed_list[observation2.seq_time] = observation2
 
-    veto1, score1 = avc.get_score(time, observer, observation1, observed_list=observed_list)
-    veto2, score2 = avc.get_score(time, observer, observation3, observed_list=observed_list)
+    veto1, score1 = avc.get_score(time, observer, observation1)
+    veto2, score2 = avc.get_score(time, observer, observation3)
 
     assert veto1 is True
     assert veto2 is False

From 440a466356d9e82041226b72a21a4444cf4c25a0 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 10:55:12 +1100
Subject: [PATCH 07/28] *fix indent typo

---
 pocs/scheduler/constraint.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 8d5242444..de628b8ab 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -163,7 +163,7 @@ def __str__(self):
 class AlreadyVisited(BaseConstraint):
 
     """ Simple Already Visited Constraint
-    
+
     A simple already visited constraint that determines if the given `observation`
     has already been visited before.
     """
@@ -177,8 +177,8 @@ def get_score(self, time, observer, observation, **kwargs):
         target = observation.field
         observed_list = kwargs.get('observed_list', observer.observed_list)
 
-            if target in observed_list:
-                veto = True
+        if target in observed_list:
+            veto = True
 
     return veto, score * self.weight
 

From f4ab18ef3c3ce7305036f48d80509b8f792461f1 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 11:04:00 +1100
Subject: [PATCH 08/28] *put return into function

---
 pocs/scheduler/constraint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index de628b8ab..8b73321ce 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -180,7 +180,7 @@ def get_score(self, time, observer, observation, **kwargs):
         if target in observed_list:
             veto = True
 
-    return veto, score * self.weight
+        return veto, score * self.weight
 
     def __str__(self):
         return "Already Visited"

From cfce4224e226fc5015f4614e731cf3fadc064893 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 11:34:40 +1100
Subject: [PATCH 09/28] *import OrderedDict

---
 pocs/scheduler/constraint.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 8b73321ce..b7fcf855d 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -1,5 +1,7 @@
 from astropy import units as u
 
+from collections import OrderedDict
+
 from pocs import PanBase
 
 

From c8e118ed0cd306aa79bde48416e8438105180ed0 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 12:08:44 +1100
Subject: [PATCH 10/28] *initialize observed_list to be empty

---
 pocs/tests/test_constraints.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index b943657ee..42c76321e 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -209,6 +209,7 @@ def test_already_visited(observer):
     observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
     observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
 
+    observer.observed_list = None
     observer.observed_list = OrderedDict()
 
     observer.observed_list[observation1.seq_time] = observation1

From 0c72b443376bca26cd5cbc13dea4f105cd926b56 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 12:28:31 +1100
Subject: [PATCH 11/28] Couple fixups

*consistent formatting
*add import OrderedDict to test_constraints.py
---
 pocs/scheduler/constraint.py   | 1 +
 pocs/tests/test_constraints.py | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index b7fcf855d..da40411b7 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -162,6 +162,7 @@ def get_score(self, time, observer, observation, **kwargs):
     def __str__(self):
         return "Moon Avoidance"
 
+
 class AlreadyVisited(BaseConstraint):
 
     """ Simple Already Visited Constraint
diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index 42c76321e..f563a7fe6 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -7,6 +7,8 @@
 from astropy.coordinates import get_moon
 from astropy.time import Time
 
+from collections import OrderedDict
+
 from pocs.scheduler.field import Field
 from pocs.scheduler.observation import Observation
 

From 86c6f55611fff5829373d6773917e28c40bd96fc Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 12:52:44 +1100
Subject: [PATCH 12/28] *not checking observation.field

---
 pocs/scheduler/constraint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index da40411b7..eeea8d97c 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -177,7 +177,7 @@ def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 
-        target = observation.field
+        target = observation
         observed_list = kwargs.get('observed_list', observer.observed_list)
 
         if target in observed_list:

From 71735abaa418c082f391f8a3408ddff8b2b7195a Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 13:25:16 +1100
Subject: [PATCH 13/28] *try for loop

---
 pocs/scheduler/constraint.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index eeea8d97c..41858b898 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -177,11 +177,12 @@ def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 
-        target = observation
+        target = observation.field
         observed_list = kwargs.get('observed_list', observer.observed_list)
 
-        if target in observed_list:
-            veto = True
+        for seq_time, Observation in observed_list:
+            if target = observation:
+                veto = True
 
         return veto, score * self.weight
 

From 4548b9b40b96fd48943e515ee1371459331cadbc Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 13:37:16 +1100
Subject: [PATCH 14/28] *fix typos

---
 pocs/scheduler/constraint.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 41858b898..23559d9fe 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -177,11 +177,11 @@ def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 
-        target = observation.field
+        target = observation
         observed_list = kwargs.get('observed_list', observer.observed_list)
 
-        for seq_time, Observation in observed_list:
-            if target = observation:
+        for seq_time, previous_obs in observed_list:
+            if target == previous_obs:
                 veto = True
 
         return veto, score * self.weight

From 8106c11b62a54865e6ce1c7cc80a6cc296014e41 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 14:06:38 +1100
Subject: [PATCH 15/28] *put print to see what observed_list looks like

---
 pocs/scheduler/constraint.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 23559d9fe..6c79a8780 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -179,6 +179,7 @@ def get_score(self, time, observer, observation, **kwargs):
 
         target = observation
         observed_list = kwargs.get('observed_list', observer.observed_list)
+        print(observed_list)
 
         for seq_time, previous_obs in observed_list:
             if target == previous_obs:

From dcfba36637cab0d68fad2378b96b44e1003d30ed Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 14:52:58 +1100
Subject: [PATCH 16/28] Hopefully final fix!

*add .items() at the end of observed_list
---
 pocs/scheduler/constraint.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 6c79a8780..12185ba9d 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -177,12 +177,10 @@ def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 
-        target = observation
         observed_list = kwargs.get('observed_list', observer.observed_list)
-        print(observed_list)
 
-        for seq_time, previous_obs in observed_list:
-            if target == previous_obs:
+        for seq_time, previous_obs in observed_list.items():
+            if observation == previous_obs:
                 veto = True
 
         return veto, score * self.weight

From aa6abe08809365a6646775274d3ad7ecc8db96cf Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 15:37:51 +1100
Subject: [PATCH 17/28] *try dict instead of OrderedDict

---
 pocs/tests/test_constraints.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index f563a7fe6..1975d6d94 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -211,8 +211,7 @@ def test_already_visited(observer):
     observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
     observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
 
-    observer.observed_list = None
-    observer.observed_list = OrderedDict()
+    observer.observed_list = {}
 
     observer.observed_list[observation1.seq_time] = observation1
     observer.observed_list[observation2.seq_time] = observation2

From b50a07d7686f2949eb7cfe209d42fa5886afab51 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 15:48:40 +1100
Subject: [PATCH 18/28] *change back to OrderedDict

*set seq_time for all observations
---
 pocs/tests/test_constraints.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index 1975d6d94..b8b0081dc 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -211,7 +211,11 @@ def test_already_visited(observer):
     observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
     observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
 
-    observer.observed_list = {}
+    observer.observed_list = OrderedDict()
+
+    observation1.seq_time = '01:00'
+    observation2.seq_time = '02:00'
+    observation3.seq_time = '03:00'
 
     observer.observed_list[observation1.seq_time] = observation1
     observer.observed_list[observation2.seq_time] = observation2

From e33509edb7916051af969c13c5b1f66c6f249136 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 16:07:37 +1100
Subject: [PATCH 19/28] *add sentence in class description

---
 pocs/scheduler/constraint.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 12185ba9d..529dc19bd 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -168,7 +168,8 @@ class AlreadyVisited(BaseConstraint):
     """ Simple Already Visited Constraint
 
     A simple already visited constraint that determines if the given `observation`
-    has already been visited before.
+    has already been visited before. If given `observation` has already been
+    visited then it will not be considered for a call to become the `current observation`.
     """
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)

From 773d2b36c897d53aed0906ed61b418c5c0b28a68 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 16:14:11 +1100
Subject: [PATCH 20/28] *don't need import OrderedDict in constraint.py

---
 pocs/scheduler/constraint.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 529dc19bd..3b4e318fd 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -1,7 +1,5 @@
 from astropy import units as u
 
-from collections import OrderedDict
-
 from pocs import PanBase
 
 

From 25b1a3ee4bd825d6d9c7ed273d91c627546d9d5a Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Thu, 18 Jan 2018 16:43:49 +1100
Subject: [PATCH 21/28] *avoid loop

---
 pocs/scheduler/constraint.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 3b4e318fd..53ac3eada 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -178,9 +178,8 @@ def get_score(self, time, observer, observation, **kwargs):
 
         observed_list = kwargs.get('observed_list', observer.observed_list)
 
-        for seq_time, previous_obs in observed_list.items():
-            if observation == previous_obs:
-                veto = True
+        if observation.seq_time in observed_list:
+            veto = True
 
         return veto, score * self.weight
 

From 5b7b6fec1d24076e0d46cc30e49a7ac2ded43947 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Fri, 19 Jan 2018 08:39:57 +1100
Subject: [PATCH 22/28] Edit the scheduler playground

*include `AlreadyVisited` constraint
*import from constraint
*add to `constrains` array
---
 examples/notebooks/Scheduler Playground.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/notebooks/Scheduler Playground.ipynb b/examples/notebooks/Scheduler Playground.ipynb
index 17b0ba455..65eb8ef13 100644
--- a/examples/notebooks/Scheduler Playground.ipynb	
+++ b/examples/notebooks/Scheduler Playground.ipynb	
@@ -31,7 +31,7 @@
     "from astroplan import Observer\n",
     "\n",
     "from pocs.scheduler.dispatch import Scheduler\n",
-    "from pocs.scheduler.constraint import Duration, MoonAvoidance\n",
+    "from pocs.scheduler.constraint import AlreadyVisited, Duration, MoonAvoidance\n",
     "from pocs.utils.config import load_config\n",
     "from pocs.utils import listify\n",
     "\n",
@@ -72,7 +72,7 @@
     "simple_fields_file = config['directories']['targets'] + '/simple.yaml'\n",
     "\n",
     "# Simple constraint to maximize duration above a certain altitude\n",
-    "constraints = [MoonAvoidance(), Duration(30 * u.deg)]\n",
+    "constraints = [AlreadyVisited(), MoonAvoidance(), Duration(30 * u.deg)]\n",
     "\n",
     "# Create a scheduler\n",
     "scheduler = Scheduler(observer, fields_file=simple_fields_file, constraints=constraints)"

From 5c0b274b6e46e28450243e28c0a1df036a145c6a Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Fri, 19 Jan 2018 21:43:04 +1100
Subject: [PATCH 23/28] Fixing issue with observed_list

*take observer.observed_list out constraint
*add observed_list to common_properties
*just define observed_list in test (?)
---
 pocs/scheduler/constraint.py   | 2 +-
 pocs/scheduler/dispatch.py     | 3 ++-
 pocs/tests/test_constraints.py | 6 +++---
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 53ac3eada..1d438af8f 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -176,7 +176,7 @@ def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 
-        observed_list = kwargs.get('observed_list', observer.observed_list)
+        observed_list = kwargs.get('observed_list')
 
         if observation.seq_time in observed_list:
             veto = True
diff --git a/pocs/scheduler/dispatch.py b/pocs/scheduler/dispatch.py
index e965a0fcc..e1ec903b1 100644
--- a/pocs/scheduler/dispatch.py
+++ b/pocs/scheduler/dispatch.py
@@ -49,7 +49,8 @@ def get_observation(self, time=None, show_all=False, reread_fields_file=False):
 
         common_properties = {
             'end_of_night': self.observer.tonight(time=time, horizon=-18 * u.degree)[-1],
-            'moon': get_moon(time, self.observer.location)
+            'moon': get_moon(time, self.observer.location),
+            'observed_list': self.observed_list
         }
 
         for constraint in listify(self.constraints):
diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index b8b0081dc..fdb02f495 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -211,14 +211,14 @@ def test_already_visited(observer):
     observation2 = Observation(Field('Hat-P-16', '00h38m17.59s +42d27m47.2s'))  # Hat-P-16
     observation3 = Observation(Field('Sabik', '17h10m23s -15d43m30s'))  # Sabik
 
-    observer.observed_list = OrderedDict()
+    observed_list = OrderedDict()
 
     observation1.seq_time = '01:00'
     observation2.seq_time = '02:00'
     observation3.seq_time = '03:00'
 
-    observer.observed_list[observation1.seq_time] = observation1
-    observer.observed_list[observation2.seq_time] = observation2
+    observed_list[observation1.seq_time] = observation1
+    observed_list[observation2.seq_time] = observation2
 
     veto1, score1 = avc.get_score(time, observer, observation1)
     veto2, score2 = avc.get_score(time, observer, observation3)

From fd783070a9e5c9ee65241d162e2e5b85593f894d Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Sat, 20 Jan 2018 11:06:54 +1100
Subject: [PATCH 24/28] *pass observed_list into get_score for already visited
 constraint

---
 pocs/scheduler/constraint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index d20b98677..613c82c70 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -167,7 +167,7 @@ class AlreadyVisited(BaseConstraint):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-    def get_score(self, time, observer, observation, **kwargs):
+    def get_score(self, time, observer, observation, observed_list=observed_list, **kwargs):
         veto = False
         score = self._score
 

From 058bc654ea4638cc55485ee8bb4c772eb409bdbe Mon Sep 17 00:00:00 2001
From: jermainegug <32515601+jermainegug@users.noreply.github.com>
Date: Sat, 20 Jan 2018 21:33:55 +1100
Subject: [PATCH 25/28] *put observed_list into get_score

---
 pocs/tests/test_constraints.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pocs/tests/test_constraints.py b/pocs/tests/test_constraints.py
index 93ccbae7d..564ac9c0e 100644
--- a/pocs/tests/test_constraints.py
+++ b/pocs/tests/test_constraints.py
@@ -253,8 +253,8 @@ def test_already_visited(observer):
     observed_list[observation1.seq_time] = observation1
     observed_list[observation2.seq_time] = observation2
 
-    veto1, score1 = avc.get_score(time, observer, observation1)
-    veto2, score2 = avc.get_score(time, observer, observation3)
+    veto1, score1 = avc.get_score(time, observer, observation1, observed_list=observed_list)
+    veto2, score2 = avc.get_score(time, observer, observation3, observed_list=observed_list)
 
     assert veto1 is True
     assert veto2 is False

From f2cba6c61cc1488e8d77a10e00a95e5314b4fbf4 Mon Sep 17 00:00:00 2001
From: jermainegug <32515601+jermainegug@users.noreply.github.com>
Date: Sat, 20 Jan 2018 21:38:29 +1100
Subject: [PATCH 26/28] Undo commit

*take out observed_list=observed_list from get_score
*was put into the wrong get_score
---
 pocs/scheduler/constraint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index 613c82c70..d20b98677 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -167,7 +167,7 @@ class AlreadyVisited(BaseConstraint):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-    def get_score(self, time, observer, observation, observed_list=observed_list, **kwargs):
+    def get_score(self, time, observer, observation, **kwargs):
         veto = False
         score = self._score
 

From 37c9616e5c244a4924b811fd3c185e946c3efebd Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Sun, 21 Jan 2018 11:57:40 +1100
Subject: [PATCH 27/28] *update shceduler notebook

---
 examples/notebooks/Scheduler Playground.ipynb | 119 ++++++++++--------
 1 file changed, 64 insertions(+), 55 deletions(-)

diff --git a/examples/notebooks/Scheduler Playground.ipynb b/examples/notebooks/Scheduler Playground.ipynb
index 8ba58618c..bd1ad4105 100644
--- a/examples/notebooks/Scheduler Playground.ipynb	
+++ b/examples/notebooks/Scheduler Playground.ipynb	
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -12,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,8 +43,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Get a location\n",
@@ -69,8 +73,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "simple_fields_file = config['directories']['targets'] + '/simple.yaml'\n",
@@ -85,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -94,6 +100,7 @@
      "text": [
       "Kepler 1100: 120.0 s exposures in blocks of 10, minimum 60, priority 125\n",
       "KIC 8462852: 120.0 s exposures in blocks of 10, minimum 60, priority 200\n",
+      "HD 23630: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "HD 189733: 100.0 s exposures in blocks of 10, minimum 60, priority 105\n",
       "HD 209458: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "Wasp 140: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
@@ -110,16 +117,18 @@
       "HAT-P-20: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "Qatar-1: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "Qatar-2: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
-      "Tres 3: 120.0 s exposures in blocks of 10, minimum 60, priority 200\n",
+      "Tres 3: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "EPIC-211089792: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "HAT-P-1: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "HAT-P-12: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
+      "HAT-P-30: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "HAT-P-36: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "HAT-P-37: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
       "M42: 120.0 s exposures in blocks of 5, minimum 10, priority 75\n",
       "M44: 120.0 s exposures in blocks of 10, minimum 60, priority 50\n",
       "M45: 120.0 s exposures in blocks of 10, minimum 60, priority 50\n",
-      "M5: 120.0 s exposures in blocks of 10, minimum 60, priority 50\n"
+      "M5: 120.0 s exposures in blocks of 10, minimum 60, priority 50\n",
+      "CoRoT-18: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n"
      ]
     }
    ],
@@ -130,15 +139,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Field: Wasp 140     Merit: 202.05040998300152\n",
-      "Field: M42          Merit: 177.33362524754662\n"
+      "Field: CoRoT-18     Merit: 202.3220647772819\n",
+      "Field: Wasp 35      Merit: 202.03480563499681\n",
+      "Field: Wasp 33      Merit: 201.72962664908442\n",
+      "Field: Wasp 77      Merit: 201.5670638382877\n",
+      "Field: M42          Merit: 177.13043661663085\n"
      ]
     }
    ],
@@ -153,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +174,7 @@
        "<Time object: scale='utc' format='jyear_str' value=J2000.000>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,14 +185,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ccQXTpk0jPz+/ux/Fnj17ePDBB9FoNOh0Ov761792n8PpdHLhhRfi8/l4pZeb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\n0QAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd1150c0c88>"
+       "<matplotlib.figure.Figure at 0x7f09a982aac8>"
       ]
      },
      "metadata": {},
@@ -199,22 +211,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/wtgee/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: The frac parameter was deprecated in version 2.1. Use tick padding via Axes.tick_params instead.\n",
-      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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0RP6xr5GG7nh347cOtVDfdfqWvVGvZVFJBrYUE+8ebqHFEf1rZTQa+Ncff8be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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd114fb77b8>"
+       "<matplotlib.figure.Figure at 0x7f09b0eb7588>"
       ]
      },
      "metadata": {},
@@ -238,15 +242,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Field: Wasp 140     Merit: 202.48191971386808\n",
-      "Field: M42          Merit: 176.95085564550402\n"
+      "Field: Wasp 43      Merit: 202.86697472045543\n",
+      "Field: Qatar-2      Merit: 202.78933258368687\n",
+      "Field: HAT-P-36     Merit: 202.77467695226636\n",
+      "Field: M44          Merit: 152.61639598540523\n"
      ]
     }
    ],
@@ -264,14 +270,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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CzhooSSlZumcpX5q+xGQ3ceeAO7ky5UqEEJ02ULKVbCVq1aNQsQcueAwmPeS3my1/zggh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd114fb7828>"
+       "<matplotlib.figure.Figure at 0x7f09a96a7a20>"
       ]
      },
      "metadata": {},
@@ -298,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "scrolled": false
    },
@@ -307,21 +313,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Start of night at 2018-01-19T10:46:31.214\n",
-      "Getting new observation at 2018-01-19T10:46:31.214\n",
-      "\tWill observe Wasp 140 with merit of 202.05041 for 7200.0 s\n",
+      "Start of night at 2018-01-21T05:24:25.064\n",
+      "Getting new observation at 2018-01-21T05:24:25.064\n",
+      "\tWill observe CoRoT-18 with merit of 202.32206 for 7200.0 s\n",
+      "\t60 of 60\n",
+      "Getting new observation at 2018-01-21T07:24:25.064\n",
+      "\tWill observe HAT-P-30 with merit of 202.61827 for 7200.0 s\n",
+      "\t60 of 60\n",
+      "Getting new observation at 2018-01-21T09:24:25.064\n",
+      "\tWill observe Wasp 104 with merit of 202.94821 for 7200.0 s\n",
       "\t60 of 60\n",
-      "Getting new observation at 2018-01-19T12:46:31.214\n",
-      "\tWill observe Wasp 43 with merit of 202.86619 for 7200.0 s\n",
+      "Getting new observation at 2018-01-21T11:24:25.064\n",
+      "\tWill observe HAT-P-36 with merit of 202.77436 for 7200.0 s\n",
       "\t60 of 60\n",
-      "Getting new observation at 2018-01-19T14:46:31.214\n",
-      "\tWill observe Wasp 36 with merit of 202.84805 for 7200.0 s\n",
+      "Getting new observation at 2018-01-21T13:24:25.064\n",
+      "\tWill observe Wasp 104 with merit of 202.93329 for 7200.0 s\n",
       "\t60 of 60\n",
-      "Getting new observation at 2018-01-19T16:46:31.214\n",
-      "\tWill observe Wasp 36 with merit of 202.84805 for 1200.0 s\n",
-      "\t70 of 60\n",
-      "Getting new observation at 2018-01-19T17:06:31.214\n",
-      "End of night at 2018-01-19T17:26:20.509\n"
+      "Getting new observation at 2018-01-21T15:24:25.064\n",
+      "End of night at 2018-01-21T15:42:40.727\n"
      ]
     }
    ],
@@ -368,16 +377,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wasp 24: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
-      "Wasp 44: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n",
-      "Wasp 44: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n"
+      "KIC 8462852: 120.0 s exposures in blocks of 10, minimum 60, priority 200\n",
+      "KIC 8462852: 120.0 s exposures in blocks of 10, minimum 60, priority 200\n",
+      "None\n"
      ]
     }
    ],
@@ -397,14 +406,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wasp 44: 120.0 s exposures in blocks of 10, minimum 60, priority 100\n"
+      "None\n"
      ]
     }
    ],
@@ -416,9 +425,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

From 8072579ff0a610f8fa6cb0ab3c77257410df6ef9 Mon Sep 17 00:00:00 2001
From: Unknown <tigergoogs@hotmail.com>
Date: Sun, 21 Jan 2018 13:08:10 +1100
Subject: [PATCH 28/28] *use field instead of seq_time

---
 pocs/scheduler/constraint.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pocs/scheduler/constraint.py b/pocs/scheduler/constraint.py
index d20b98677..cc74d3744 100644
--- a/pocs/scheduler/constraint.py
+++ b/pocs/scheduler/constraint.py
@@ -173,7 +173,9 @@ def get_score(self, time, observer, observation, **kwargs):
 
         observed_list = kwargs.get('observed_list')
 
-        if observation.seq_time in observed_list:
+        observed_field_list = [obs.field for obs in observed_list.values()]
+
+        if observation.field in observed_field_list:
             veto = True
 
         return veto, score * self.weight