diff --git a/CHANGELOG.md b/CHANGELOG.md
index ab3c52e05..416dce683 100755
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,24 @@
 # Change Log
 All notable changes to this project will be documented in this file.
 
+## [1.6.0] = 2020-03-23
+
+### Added
+- tutorial for optotagging for ecephys notebook
+- get\_receptive\_field() method in ecephys receptive field mapping
+
+### Changed
+- remove redundant sham\_change column in behavior sessions.trials table
+- versions for NWB output for ecephys and ophys behavior.
+- monitor delay is now calculated for BehaviorOphysLimsApi rather than defaulting to 0.0351
+
+### Bug Fixes
+- Fixed a bug where auto-rewarded trials were not properly attributed in the rewards property of a visual behavior
+- return None rather than raise exception if no container id was returned from lims id for given ophys id
+- Project caches no longer accept arbitrary keywords
+- matplotloib.pyplot.hist parameter normed no longer supported
+
+
 ## [1.5.0] = 2020-02-10
 
 ### Added
diff --git a/allensdk/__init__.py b/allensdk/__init__.py
index a36b0c4c4..ad41116d0 100644
--- a/allensdk/__init__.py
+++ b/allensdk/__init__.py
@@ -36,7 +36,7 @@
 import logging
 
 
-__version__ = '1.5.1'
+__version__ = '1.6.0'
 
 
 try:
diff --git a/allensdk/brain_observatory/behavior/behavior_project_cache.py b/allensdk/brain_observatory/behavior/behavior_project_cache.py
index c24a3ed71..15f305759 100644
--- a/allensdk/brain_observatory/behavior/behavior_project_cache.py
+++ b/allensdk/brain_observatory/behavior/behavior_project_cache.py
@@ -2,7 +2,8 @@
 import os.path
 import csv
 from functools import partial
-from typing import Type, Callable, Optional, List, Any, Dict
+from typing import Type, Optional, List, Any, Dict, Union
+from pathlib import Path
 import pandas as pd
 import time
 import logging
@@ -15,8 +16,7 @@
     import BehaviorProjectBase
 from allensdk.api.caching_utilities import one_file_call_caching, call_caching
 from allensdk.core.exceptions import MissingDataError
-from allensdk.core.auth_config import LIMS_DB_CREDENTIAL_MAP
-from allensdk.core.authentication import credential_injector, DbCredentials
+from allensdk.core.authentication import DbCredentials
 
 BehaviorProjectApi = Type[BehaviorProjectBase]
 
@@ -64,11 +64,17 @@ def __init__(
             self,
             fetch_api: Optional[BehaviorProjectApi] = None,
             fetch_tries: int = 2,
-            **kwargs):
+            manifest: Optional[Union[str, Path]] = None,
+            version: Optional[str] = None,
+            cache: bool = True):
         """ Entrypoint for accessing visual behavior data. Supports
         access to summaries of session data and provides tools for
         downloading detailed session data (such as dff traces).
 
+        Likely you will want to use a class constructor, such as `from_lims`,
+        to initialize a BehaviorProjectCache, rather than calling this
+        directly.
+
         --- NOTE ---
         Because NWB files are not currently supported for this project (as of
         11/2019), this cache will not actually save any files of session data
@@ -87,38 +93,88 @@ def __init__(
             Used to pull data from remote sources, after which it is locally
             cached. Any object inheriting from BehaviorProjectBase is
             suitable. Current options are:
-                EcephysProjectLimsApi :: Fetches bleeding-edge data from the
+                BehaviorProjectLimsApi :: Fetches bleeding-edge data from the
                     Allen Institute"s internal database. Only works if you are
                     on our internal network.
         fetch_tries :
             Maximum number of times to attempt a download before giving up and
-            raising an exception. Note that this is total tries, not retries
-        **kwargs :
-            manifest : str or Path
-                full path at which manifest json will be stored
-            version : str
-                version of manifest file. If this mismatches the version
-                recorded in the file at manifest, an error will be raised.
-            other kwargs are passed to allensdk.api.cache.Cache
+            raising an exception. Note that this is total tries, not retries.
+            Default=2.
+        manifest : str or Path
+            full path at which manifest json will be stored. Defaults
+            to "behavior_project_manifest.json" in the local directory.
+        version : str
+            version of manifest file. If this mismatches the version
+            recorded in the file at manifest, an error will be raised.
+            Defaults to the manifest version in the class.
+        cache : bool
+            Whether to write to the cache. Default=True.
         """
-        kwargs["manifest"] = kwargs.get("manifest",
-                                        "behavior_project_manifest.json")
-        kwargs["version"] = kwargs.get("version", self.MANIFEST_VERSION)
+        manifest_ = manifest or "behavior_project_manifest.json"
+        version_ = version or self.MANIFEST_VERSION
 
-        super().__init__(**kwargs)
-        self.fetch_api = fetch_api or BehaviorProjectLimsApi.default()
+        super().__init__(manifest=manifest_, version=version_, cache=cache)
+        self.fetch_api = fetch_api
         self.fetch_tries = fetch_tries
         self.logger = logging.getLogger(self.__class__.__name__)
 
     @classmethod
-    def from_lims(cls, lims_credentials: Optional[DbCredentials] = None,
+    def from_lims(cls, manifest: Optional[Union[str, Path]] = None,
+                  version: Optional[str] = None,
+                  cache: bool = True,
+                  fetch_tries: int = 2,
+                  lims_credentials: Optional[DbCredentials] = None,
                   mtrain_credentials: Optional[DbCredentials] = None,
-                  app_kwargs: Dict[str, Any] = None, **kwargs):
-        return cls(fetch_api=BehaviorProjectLimsApi.default(
+                  host: Optional[str] = None,
+                  scheme: Optional[str] = None,
+                  asynchronous: bool = True) -> "BehaviorProjectCache":
+        """
+        Construct a BehaviorProjectCache with a lims api. Use this method
+        to create a  BehaviorProjectCache instance rather than calling
+        BehaviorProjectCache directly.
+
+        Parameters
+        ==========
+        manifest : str or Path
+            full path at which manifest json will be stored
+        version : str
+            version of manifest file. If this mismatches the version
+            recorded in the file at manifest, an error will be raised.
+        cache : bool
+            Whether to write to the cache
+        fetch_tries : int
+            Maximum number of times to attempt a download before giving up and
+            raising an exception. Note that this is total tries, not retries
+        lims_credentials : DbCredentials
+            Optional credentials to access LIMS database.
+            If not set, will look for credentials in environment variables.
+        mtrain_credentials: DbCredentials
+            Optional credentials to access mtrain database.
+            If not set, will look for credentials in environment variables.
+        host : str
+            Web host for the app_engine. Currently unused. This argument is
+            included for consistency with EcephysProjectCache.from_lims.
+        scheme : str
+            URI scheme, such as "http". Currently unused. This argument is
+            included for consistency with EcephysProjectCache.from_lims.
+        asynchronous : bool
+            Whether to fetch from web asynchronously. Currently unused.
+        Returns
+        =======
+        BehaviorProjectCache
+            BehaviorProjectCache instance with a LIMS fetch API
+        """
+        if host and scheme:
+            app_kwargs = {"host": host, "scheme": scheme,
+                          "asynchronous": asynchronous}
+        else:
+            app_kwargs = None
+        fetch_api = BehaviorProjectLimsApi.default(
                         lims_credentials=lims_credentials,
                         mtrain_credentials=mtrain_credentials,
-                        app_kwargs=app_kwargs),
-                   **kwargs)
+                        app_kwargs=app_kwargs)
+        return cls(fetch_api=fetch_api, manifest=manifest, version=version,
+                   cache=cache, fetch_tries=fetch_tries)
 
     def get_session_table(
             self,
diff --git a/allensdk/brain_observatory/behavior/rewards_processing.py b/allensdk/brain_observatory/behavior/rewards_processing.py
index 46238c396..103ce2764 100644
--- a/allensdk/brain_observatory/behavior/rewards_processing.py
+++ b/allensdk/brain_observatory/behavior/rewards_processing.py
@@ -3,15 +3,15 @@
 
 
 def get_rewards(data, stimulus_rebase_function):
-    trial_df = pd.DataFrame(data["items"]["behavior"]['trial_log'])
-    rewards_dict = {'volume': [], 'timestamps': [], 'autorewarded': []}
+    trial_df = pd.DataFrame(data["items"]["behavior"]["trial_log"])
+    rewards_dict = {"volume": [], "timestamps": [], "autorewarded": []}
     for idx, trial in trial_df.iterrows():
         rewards = trial["rewards"]  # as i write this there can only ever be one reward per trial
         if rewards:
             rewards_dict["volume"].append(rewards[0][0])
             rewards_dict["timestamps"].append(stimulus_rebase_function(rewards[0][1]))
-            rewards_dict["autorewarded"].append('auto_rewarded' in trial['trial_params'])
+            rewards_dict["autorewarded"].append(trial["trial_params"]["auto_reward"])
 
-    df = pd.DataFrame(rewards_dict).set_index('timestamps', drop=True)
+    df = pd.DataFrame(rewards_dict).set_index("timestamps", drop=True)
 
     return df
diff --git a/allensdk/brain_observatory/behavior/trials_processing.py b/allensdk/brain_observatory/behavior/trials_processing.py
index 11769fd36..f4327f494 100644
--- a/allensdk/brain_observatory/behavior/trials_processing.py
+++ b/allensdk/brain_observatory/behavior/trials_processing.py
@@ -345,6 +345,7 @@ def get_trials(data, licks_df, rewards_df, stimulus_presentations_df, rebase):
 
     trials = pd.DataFrame(all_trial_data).set_index('trial')
     trials.index = trials.index.rename('trials_id')
+    del trials["sham_change"]
 
     return trials
 
diff --git a/allensdk/brain_observatory/ecephys/copy_utility/__main__.py b/allensdk/brain_observatory/ecephys/copy_utility/__main__.py
index 2ec28d362..6f07fbaf1 100644
--- a/allensdk/brain_observatory/ecephys/copy_utility/__main__.py
+++ b/allensdk/brain_observatory/ecephys/copy_utility/__main__.py
@@ -12,11 +12,17 @@
 from allensdk.brain_observatory.argschema_utilities import write_or_print_outputs
 
 
-def hash_file(path, hasher_cls):
-    with open(path, 'rb') as file_obj:
-        hasher = hasher_cls()
-        hasher.update(file_obj.read())
-        return hasher.digest()
+def hash_file(path, hasher_cls, blocks_per_chunk=128):
+    """
+
+    """
+    hasher = hasher_cls()
+    with open(path, 'rb') as f:
+        # TODO: Update to new assignment syntax if drop < python 3.8 support
+        for chunk in iter(
+                lambda: f.read(hasher.block_size*blocks_per_chunk), b""):
+            hasher.update(chunk)
+    return hasher.digest()
 
 
 def walk_fs_tree(root, fn):
diff --git a/allensdk/brain_observatory/ecephys/ecephys_project_cache.py b/allensdk/brain_observatory/ecephys/ecephys_project_cache.py
index e615ced9f..e8d7d23b5 100644
--- a/allensdk/brain_observatory/ecephys/ecephys_project_cache.py
+++ b/allensdk/brain_observatory/ecephys/ecephys_project_cache.py
@@ -1,6 +1,6 @@
 from functools import partial
 from pathlib import Path
-from typing import Any, List, Optional
+from typing import Any, List, Optional,  Union, Callable
 import ast
 
 import pandas as pd
@@ -9,14 +9,11 @@
 import pynwb
 
 from allensdk.api.cache import Cache
-
+from allensdk.core.authentication import DbCredentials
 from allensdk.brain_observatory.ecephys.ecephys_project_api import (
-    EcephysProjectApi, EcephysProjectLimsApi, EcephysProjectWarehouseApi, 
+    EcephysProjectApi, EcephysProjectLimsApi, EcephysProjectWarehouseApi,
     EcephysProjectFixedApi
 )
-from allensdk.brain_observatory.ecephys.ecephys_project_api.rma_engine import (
-    AsyncRmaEngine,
-)
 from allensdk.brain_observatory.ecephys.ecephys_project_api.http_engine import (
     write_bytes_from_coroutine, write_from_stream
 )
@@ -74,11 +71,13 @@ class EcephysProjectCache(Cache):
     )
 
     def __init__(
-        self, 
-        fetch_api: EcephysProjectApi = EcephysProjectWarehouseApi.default(), 
-        fetch_tries: int = 2, 
-        stream_writer = write_from_stream,
-        **kwargs):
+            self,
+            fetch_api: EcephysProjectApi = EcephysProjectWarehouseApi.default(), 
+            fetch_tries: int = 2,
+            stream_writer: Callable = write_from_stream,
+            manifest: Optional[Union[str, Path]] = None,
+            version: Optional[str] = None,
+            cache: bool = True):
         """ Entrypoint for accessing ecephys (neuropixels) data. Supports 
         access to cross-session data (like stimulus templates) and high-level 
         summaries of sessionwise data and provides tools for downloading detailed 
@@ -88,33 +87,34 @@ def __init__(
         ==========
         fetch_api :
             Used to pull data from remote sources, after which it is locally
-            cached. Any object exposing the EcephysProjectApi interface is 
+            cached. Any object exposing the EcephysProjectApi interface is
             suitable. Standard options are:
-                EcephysProjectWarehouseApi :: The default. Fetches publically 
+                EcephysProjectWarehouseApi :: The default. Fetches publically
                     available Allen Institute data
-                EcephysProjectFixedApi :: Refuses to fetch any data - only the 
-                    existing local cache is accessible. Useful if you want to 
-                    settle on a fixed dataset for analysis.
-                EcephysProjectLimsApi :: Fetches bleeding-edge data from the 
-                    Allen Institute's internal database. Only works if you are 
+                EcephysProjectFixedApi :: Refuses to fetch any data - only the
+                    existing local cache is accessible. Useful if you want to
+                    settle on a fixed dataset for analysis
+                EcephysProjectLimsApi :: Fetches bleeding-edge data from the
+                    Allen Institute's internal database. Only works if you are
                     on our internal network.
-        fetch_tries : 
-            Maximum number of times to attempt a download before giving up and 
+        fetch_tries : int
+            Maximum number of times to attempt a download before giving up and
             raising an exception. Note that this is total tries, not retries
-        **kwargs :
-            manifest : str or Path
-                full path at which manifest json will be stored
-            version : str
-                version of manifest file. If this mismatches the version 
-                recorded in the file at manifest, an error will be raised.
-            other kwargs are passed to allensdk.api.cache.Cache
-
+        manifest : str or Path
+            full path at which manifest json will be stored (default =
+            "ecephys_project_manifest.json" in the local directory.)
+        version : str
+            version of manifest file. If this mismatches the version
+            recorded in the file at manifest, an error will be raised.
+        cache: bool
+            Whether to write to the cache (default=True)
         """
+        manifest_ = manifest or "ecephys_project_manifest.json"
+        version_ = version or self.MANIFEST_VERSION
 
-        kwargs['manifest'] = kwargs.get('manifest', 'ecephys_project_manifest.json')
-        kwargs['version'] = kwargs.get('version', self.MANIFEST_VERSION)
-
-        super(EcephysProjectCache, self).__init__(**kwargs)
+        super(EcephysProjectCache, self).__init__(manifest=manifest_,
+                                                  version=version_,
+                                                  cache=cache)
         self.fetch_api = fetch_api
         self.fetch_tries = fetch_tries
         self.stream_writer = stream_writer
@@ -516,7 +516,7 @@ def _from_http_source_default(cls, fetch_api_cls, fetch_api_kwargs, **kwargs):
             "asynchronous": True
         } if fetch_api_kwargs is None else fetch_api_kwargs
 
-        if "stream_writer" not in kwargs:
+        if kwargs.get("stream_writer") is None:
             if fetch_api_kwargs.get("asynchronous", True):
                 kwargs["stream_writer"] = write_bytes_from_coroutine
             else:
@@ -528,21 +528,124 @@ def _from_http_source_default(cls, fetch_api_cls, fetch_api_kwargs, **kwargs):
         )
 
     @classmethod
-    def from_lims(cls, lims_kwargs=None, **kwargs):
+    def from_lims(cls, lims_credentials: Optional[DbCredentials] = None,
+                  scheme: Optional[str] = None,
+                  host: Optional[str] = None, 
+                  asynchronous: bool = True,
+                  manifest: Optional[str] = None, 
+                  version: Optional[str] = None, 
+                  cache: bool = True,
+                  fetch_tries: int = 2):
+        """
+        Create an instance of EcephysProjectCache with an 
+        EcephysProjectLimsApi. Retrieves bleeding-edge data stored
+        locally on Allen Institute servers. Only available for use
+        on-site at the Allen Institute or through a vpn. Requires Allen
+        Institute database credentials.
+
+        Parameters
+        ==========
+        lims_credentials : DbCredentials
+            Credentials to access LIMS database. If not provided will
+            attempt to find credentials in environment variables.
+        scheme : str
+            URI scheme, such as "http". Defaults to 
+            EcephysProjectLimsApi.default value if unspecified.
+            Will not be used unless `host` is also specified.
+        host : str
+            Web host. Defaults to EcephysProjectLimsApi.default 
+            value if unspecified. Will not be used unless `scheme` is
+            also specified.
+        asynchronous : bool
+            Whether to fetch file asynchronously. Defaults to True.
+        manifest : str or Path
+            full path at which manifest json will be stored
+        version : str
+            version of manifest file. If this mismatches the version 
+            recorded in the file at manifest, an error will be raised.
+        cache: bool
+            Whether to write to the cache (default=True)
+        fetch_tries : int
+            Maximum number of times to attempt a download before giving up and 
+            raising an exception. Note that this is total tries, not retries
+        """
+        if scheme and host:
+            app_kwargs = {"scheme": scheme, "host": host}
+        else:
+            app_kwargs = None
         return cls._from_http_source_default(
-            EcephysProjectLimsApi, lims_kwargs, **kwargs
-        )
+            EcephysProjectLimsApi, 
+            {"lims_credentials": lims_credentials,
+             "app_kwargs": app_kwargs,
+             "asynchronous": asynchronous,
+            },    # expects dictionary of kwargs
+            manifest=manifest, version=version, cache=cache, 
+            fetch_tries=fetch_tries)
 
     @classmethod
-    def from_warehouse(cls, warehouse_kwargs=None, **kwargs):
+    def from_warehouse(cls, 
+                       scheme: Optional[str] = None, 
+                       host: Optional[str] = None,
+                       asynchronous: bool = True,
+                       manifest: Optional[Union[str, Path]] = None, 
+                       version: Optional[str] = None,
+                       cache: bool = True, 
+                       fetch_tries: int = 2):
+        """
+        Create an instance of EcephysProjectCache with an 
+        EcephysProjectWarehouseApi. Retrieves released data stored in
+        the warehouse. 
+        
+        Parameters
+        ==========
+        scheme : str
+            URI scheme, such as "http". Defaults to 
+            EcephysProjectWarehouseAPI.default value if unspecified.
+            Will not be used unless `host` is also specified.
+        host : str
+            Web host. Defaults to EcephysProjectWarehouseApi.default 
+            value if unspecified. Will not be used unless `scheme` is also
+            specified.
+        asynchronous : bool
+            Whether to fetch file asynchronously. Defaults to True.
+        manifest : str or Path
+            full path at which manifest json will be stored
+        version : str
+            version of manifest file. If this mismatches the version 
+            recorded in the file at manifest, an error will be raised.
+        cache: bool
+            Whether to write to the cache (default=True)
+        fetch_tries : int
+            Maximum number of times to attempt a download before giving up and 
+            raising an exception. Note that this is total tries, not retries
+        """
+        if scheme and host:
+            app_kwargs = {"scheme": scheme, "host": host, 
+                          "asynchronous": asynchronous}
+        else:
+            app_kwargs = None
         return cls._from_http_source_default(
-            EcephysProjectWarehouseApi, warehouse_kwargs, **kwargs
+            EcephysProjectWarehouseApi, app_kwargs, manifest=manifest,
+             version=version, cache=cache, fetch_tries=fetch_tries
         )
 
-
     @classmethod
-    def fixed(cls, **kwargs):
-        return cls(fetch_api=EcephysProjectFixedApi(), **kwargs)
+    def fixed(cls, manifest=None, version=None):
+        """
+        Creates a EcephysProjectCache that refuses to fetch any data 
+        - only the existing local cache is accessible. Useful if you
+        want to settle on a fixed dataset for analysis.
+        
+        Parameters
+        ==========
+        manifest : str or Path
+            full path to existing manifest json
+        version : str
+            version of manifest file. If this mismatches the version 
+            recorded in the file at manifest, an error will be raised.
+        """
+        return cls(fetch_api=EcephysProjectFixedApi(), manifest=manifest,
+                   version=version)
 
 
 def count_owned(this, other, foreign_key, count_key, inplace=False):
diff --git a/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_extension.yaml b/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_extension.yaml
index 3f0c12718..ae7c47caa 100644
--- a/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_extension.yaml
+++ b/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_extension.yaml
@@ -3,10 +3,6 @@ groups:
     neurodata_type_inc: ElectrodeGroup
     doc: A group consisting of the channels on a single neuropixels probe.
     attributes:
-    - name: help
-      dtype: text
-      value: A physical grouping of channels
-      doc: Value is 'Metadata about a physical grouping of channels'
     - name: description
       dtype: text
       doc: description of this electrode group
diff --git a/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_namespace.yaml b/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_namespace.yaml
index 658ea895b..142bad888 100644
--- a/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_namespace.yaml
+++ b/allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_namespace.yaml
@@ -1,5 +1,6 @@
 namespaces:
 - doc: ""
+  version: 0.2.0
   name: AIBS_ecephys
   schema:
   - namespace: core
diff --git a/allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py b/allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py
index c7cd4ae3b..3d849c1ad 100644
--- a/allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py
+++ b/allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py
@@ -184,6 +184,12 @@ def _get_stim_table_stats(self):
         self._pos_x = np.sort(self.stimulus_conditions.loc[self.stimulus_conditions[self._col_pos_x]
                                                            != 'null'][self._col_pos_x].unique())
 
+    def get_receptive_field(self, unit_id):
+        """ Alias for _get_rf
+        """
+        
+        return self._get_rf(unit_id)
+
     def _get_rf(self, unit_id):
         """ Extract the receptive field for one unit
 
diff --git a/allensdk/brain_observatory/ecephys/write_nwb/__main__.py b/allensdk/brain_observatory/ecephys/write_nwb/__main__.py
index ba9b21b47..3473fa504 100644
--- a/allensdk/brain_observatory/ecephys/write_nwb/__main__.py
+++ b/allensdk/brain_observatory/ecephys/write_nwb/__main__.py
@@ -625,7 +625,7 @@ def write_probe_lfp_file(session_start_time, log_level, probe):
 
     with pynwb.NWBHDF5IO(probe['lfp']['output_path'], 'w') as lfp_writer:
         logging.info(f"writing probe lfp file to {probe['lfp']['output_path']}")
-        lfp_writer.write(nwbfile)
+        lfp_writer.write(nwbfile, cache_spec=True)
     return {"id": probe["id"], "nwb_path": probe["lfp"]["output_path"]}
 
 
@@ -851,10 +851,9 @@ def write_ecephys_nwb(
                                                  eye_gaze_data=eye_gaze_data)
 
     Manifest.safe_make_parent_dirs(output_path)
-    io = pynwb.NWBHDF5IO(output_path, mode='w')
-    logging.info(f"writing session nwb file to {output_path}")
-    io.write(nwbfile)
-    io.close()
+    with pynwb.NWBHDF5IO(output_path, mode='w') as io:
+        logging.info(f"writing session nwb file to {output_path}")
+        io.write(nwbfile, cache_spec=True)
 
     probes_with_lfp = [p for p in probes if p["lfp"] is not None]
     probe_outputs = write_probewise_lfp_files(probes_with_lfp, session_start_time, pool_size=pool_size)
diff --git a/allensdk/brain_observatory/nwb/AIBS_ophys_behavior_namespace.yaml b/allensdk/brain_observatory/nwb/AIBS_ophys_behavior_namespace.yaml
index b9f3d1822..b6c5c45cd 100644
--- a/allensdk/brain_observatory/nwb/AIBS_ophys_behavior_namespace.yaml
+++ b/allensdk/brain_observatory/nwb/AIBS_ophys_behavior_namespace.yaml
@@ -2,6 +2,7 @@ namespaces:
 - doc: "LabMetaData extensions: ['OphysBehaviorMetaData', 'OphysBehaviorTaskParameters']\
     \ (AIBS_ophys_behavior)"
   name: AIBS_ophys_behavior
+  version: 0.1.0
   schema:
   - namespace: core
   - source: AIBS_ophys_behavior_extension.yaml
diff --git a/allensdk/brain_observatory/observatory_plots.py b/allensdk/brain_observatory/observatory_plots.py
index cefeb7f83..25ef1a834 100644
--- a/allensdk/brain_observatory/observatory_plots.py
+++ b/allensdk/brain_observatory/observatory_plots.py
@@ -260,7 +260,7 @@ def plot_condition_histogram(vals, bins, color=STIM_COLOR):
         n, hbins, patches = plt.hist(vals,
                                      bins=np.arange(len(bins)+1)+1,
                                      align='left',
-                                     normed=False,
+                                     density=False,
                                      rwidth=.8,
                                      color=color,
                                      zorder=3)
@@ -287,7 +287,7 @@ def plot_selectivity_cumulative_histogram(sis,
 
     # orientation selectivity cumulative histogram
     if len(sis) > 0:
-        n, bins, patches = plt.hist(sis, normed=True, bins=bins,
+        n, bins, patches = plt.hist(sis, density=True, bins=bins,
                                     cumulative=True, histtype='stepfilled',
                                     color=color)
     plt.xlim(si_range)
diff --git a/allensdk/core/brain_observatory_nwb_data_set.py b/allensdk/core/brain_observatory_nwb_data_set.py
index bf8a1227f..69d437c4a 100755
--- a/allensdk/core/brain_observatory_nwb_data_set.py
+++ b/allensdk/core/brain_observatory_nwb_data_set.py
@@ -218,7 +218,7 @@ def get_stimulus_epoch_table(self):
                                     'duration':duration_signature_list,
                                     'interval':interval_signature_list})
 
-        # Gaps are ininformative; remove them:
+        # Gaps are uninformative; remove them:
         interval_df = interval_df[interval_df.stimulus != 'gap']
         interval_df['start'] = [x[0] for x in interval_df['interval'].values]
         interval_df['end'] = [x[1] for x in interval_df['interval'].values]
diff --git a/allensdk/internal/api/behavior_data_lims_api.py b/allensdk/internal/api/behavior_data_lims_api.py
index 3c544632f..a2d7087b3 100644
--- a/allensdk/internal/api/behavior_data_lims_api.py
+++ b/allensdk/internal/api/behavior_data_lims_api.py
@@ -93,6 +93,8 @@ def _get_ids(self) -> Dict[str, Optional[Union[int, List[int]]]]:
                 WHERE ophys_session_id = {ids_dict["ophys_session_id"]};
                 """
             oed = self.lims_db.fetchall(oed_query)
+            if len(oed) == 0:
+                oed = None
 
             container_query = f"""
             SELECT DISTINCT
@@ -102,7 +104,10 @@ def _get_ids(self) -> Dict[str, Optional[Union[int, List[int]]]]:
             WHERE
                 ophys_experiment_id IN ({",".join(set(map(str, oed)))});
             """
-            container_id = self.lims_db.fetchone(container_query, strict=True)
+            try:
+                container_id = self.lims_db.fetchone(container_query, strict=True)
+            except OneResultExpectedError:
+                container_id = None
 
             ids_dict.update({"ophys_experiment_ids": oed,
                              "ophys_container_id": container_id})
diff --git a/allensdk/internal/api/behavior_ophys_api.py b/allensdk/internal/api/behavior_ophys_api.py
index 53bbd5fbd..268073bc9 100644
--- a/allensdk/internal/api/behavior_ophys_api.py
+++ b/allensdk/internal/api/behavior_ophys_api.py
@@ -10,6 +10,7 @@
 from allensdk.internal.api.ophys_lims_api import OphysLimsApi
 from allensdk.brain_observatory.behavior.sync import (
     get_sync_data, get_stimulus_rebase_function, frame_time_offset)
+from allensdk.internal.brain_observatory.time_sync import OphysTimeAligner
 from allensdk.brain_observatory.behavior.stimulus_processing import get_stimulus_presentations, get_stimulus_templates, get_stimulus_metadata
 from allensdk.brain_observatory.behavior.metadata_processing import get_task_parameters
 from allensdk.brain_observatory.behavior.running_processing import get_running_df
@@ -38,8 +39,10 @@ def get_sync_data(self):
 
     @memoize
     def get_stimulus_timestamps(self):
-        monitor_delay = .0351
-        return self.get_sync_data()['stimulus_times_no_delay'] + monitor_delay
+        sync_path = self.get_sync_file()
+        timestamps, _, _ = (OphysTimeAligner(sync_file=sync_path)
+                            .corrected_stim_timestamps)
+        return timestamps
 
     @memoize
     def get_ophys_timestamps(self):
diff --git a/allensdk/internal/brain_observatory/time_sync.py b/allensdk/internal/brain_observatory/time_sync.py
index d7aa449b6..3133fb3e8 100644
--- a/allensdk/internal/brain_observatory/time_sync.py
+++ b/allensdk/internal/brain_observatory/time_sync.py
@@ -15,7 +15,7 @@
 REG_PHOTODIODE_MAX = 2.1 # seconds
 PHOTODIODE_ANOMALY_THRESHOLD = 0.5 # seconds
 LONG_STIM_THRESHOLD = 0.2 # seconds
-ASSUMED_DELAY = 0.0351 # seconds
+ASSUMED_DELAY = 0.0215 # seconds
 MAX_MONITOR_DELAY = 0.07 # seconds
 
 VERSION_1_KEYS = {
diff --git a/allensdk/test/brain_observatory/behavior/test_rewards_processing.py b/allensdk/test/brain_observatory/behavior/test_rewards_processing.py
index fcb7cbd68..1d7bac4ee 100644
--- a/allensdk/test/brain_observatory/behavior/test_rewards_processing.py
+++ b/allensdk/test/brain_observatory/behavior/test_rewards_processing.py
@@ -9,21 +9,26 @@ def test_get_rewards():
             "behavior": {
                 "trial_log": [
                     {
-                        'rewards': [(0.007, 1085.965144219165, 64775)],
+                        'rewards': [(0.007, 1085.96, 64775)],
                         'trial_params': {
                             'catch': False, 'auto_reward': False,
                             'change_time': 5}},
+                    {
+                        'rewards': [(0.007, 1090.01, 64780)],
+                        'trial_params': {
+                            'catch': False, 'auto_reward': True,
+                            'change_time': 6}},
                     {
                         'rewards': [],
                         'trial_params': {
                             'catch': False, 'auto_reward': False,
-                            'change_time': 4}
-                    }
+                            'change_time': 4},
+                    },
                     ]
                 }}}
     expected = pd.DataFrame(
-        {"volume": [0.007],
-         "timestamps": [1086.965144219165],
-         "autorewarded": False}).set_index("timestamps", drop=True)
+        {"volume": [0.007, 0.007],
+         "timestamps": [1086.96, 1091.01],
+         "autorewarded": [False, True]}).set_index("timestamps", drop=True)
 
     pd.testing.assert_frame_equal(expected, get_rewards(data, lambda x: x+1.0))
diff --git a/allensdk/test/brain_observatory/ecephys/test_ecephys_project_cache.py b/allensdk/test/brain_observatory/ecephys/test_ecephys_project_cache.py
index 85c56992d..6ff10a082 100644
--- a/allensdk/test/brain_observatory/ecephys/test_ecephys_project_cache.py
+++ b/allensdk/test/brain_observatory/ecephys/test_ecephys_project_cache.py
@@ -373,7 +373,7 @@ def test_from_lims_default(tmpdir_factory):
     tmpdir = str(tmpdir_factory.mktemp("test_from_lims_default"))
 
     cache = epc.EcephysProjectCache.from_lims(
-        manifest_path=os.path.join(tmpdir, "manifest.json")
+        manifest=os.path.join(tmpdir, "manifest.json")
     )
     assert isinstance(cache.fetch_api.app_engine, AsyncHttpEngine)
     assert cache.stream_writer is epc.write_bytes_from_coroutine
\ No newline at end of file
diff --git a/doc_template/examples_root/examples/internal/Lims Behavior Project Cache.ipynb b/doc_template/examples_root/examples/internal/Lims Behavior Project Cache.ipynb
index b6228fd06..ef9348104 100644
--- a/doc_template/examples_root/examples/internal/Lims Behavior Project Cache.ipynb	
+++ b/doc_template/examples_root/examples/internal/Lims Behavior Project Cache.ipynb	
@@ -892,7 +892,7 @@
    ],
    "source": [
     "# But it will work if we use one that already exists\n",
-    "cache.get_session_data(978244684, fixed=True)"
+    "cache.get_session_data(latest.name, fixed=True)"
    ]
   },
   {
diff --git a/doc_template/examples_root/examples/nb/ecephys_optotagging.ipynb b/doc_template/examples_root/examples/nb/ecephys_optotagging.ipynb
new file mode 100644
index 000000000..8455d12cc
--- /dev/null
+++ b/doc_template/examples_root/examples/nb/ecephys_optotagging.ipynb
@@ -0,0 +1,1312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optotagging Analysis\n",
+    "\n",
+    "## Tutorial overview\n",
+    "\n",
+    "This Jupyter notebook will demonstrate how to analyze responses to optotagging stimuli in Neuropixels Brain Observatory datasets. Optotagging makes it possible to link _in vivo_ spike trains to genetically defined cell classes. By expressing a light-gated ion channel (in this case, ChR2) in a Cre-dependent manner, we can activate Cre+ neurons with light pulses delivered to the cortical surface. Units that fire action potentials in response to these light pulses are likely to express the gene of interest.\n",
+    "\n",
+    "Of course, there are some shortcomings to this approach, most notably that the presence of light artifacts can create the appearance of false positives, and that false negatives (cells that are Cre+ but do not respond to light) are nearly impossible to avoid. We will explain how to deal with these caveats in order to incorporate the available cell type information into your analyses.\n",
+    "\n",
+    "This tutorial will cover the following topics:\n",
+    "\n",
+    "* <a href='#Finding-datasets-of-interest'>Finding datasets of interest</a>\n",
+    "* <a href='#Types-of-optotagging-stimuli'>Types of optotagging stimuli</a>\n",
+    "* <a href='#Aligning-spikes-to-light-pulses'>Aligning spikes to light pulses</a>\n",
+    "* <a href='#Identifying-Cre+-units'>Identifying Cre+ units</a>\n",
+    "* <a href='#Differences-across-genotypes'>Differences across genotypes</a>\n",
+    "\n",
+    "This tutorial assumes you've already created a data cache, or are working with NWB files on AWS. If you haven't reached that step yet, we recommend going through the [data access tutorial](./ecephys_data_access.ipynb) first.\n",
+    "\n",
+    "Functions related to analyzing responses to visual stimuli will be covered in other tutorials. For a full list of available tutorials, see the [SDK documentation](https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, let's deal with the necessary imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we'll create an `EcephysProjectCache` object that points to a new or existing manifest file.\n",
+    "\n",
+    "If you're not sure what a manifest file is or where to put it, please check out [this tutorial](./ecephys_data_access.ipynb) before going further."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_directory = '/mnt/nvme0/ecephys_cache_dir'\n",
+    "\n",
+    "manifest_path = os.path.join(data_directory, \"manifest.json\")\n",
+    "\n",
+    "cache = EcephysProjectCache.from_warehouse(manifest=manifest_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding datasets of interest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `sessions` table contains information about all the experiments available in the `EcephysProjectCache`. The `full_genotype` column contains information about the genotype of the mouse used in each experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "wt/wt                                              30\n",
+       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt      12\n",
+       "Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt     8\n",
+       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt       8\n",
+       "Name: full_genotype, dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sessions = cache.get_session_table()\n",
+    "\n",
+    "sessions.full_genotype.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "About half the mice are wild type (`wt/wt`), while the other half are a cross between a Cre line and the Ai32 reporter line. The Cre mice express ChR2 in one of three interneuron subtypes: Parvalbumin-positive neurons (`Pvalb`), Somatostatin-positive neurons (`Sst`), and Vasoactive Intestinal Polypeptide neurons (`Vip`). We know that these genes are expressed in largely non-overlapping populations of inhibitory cells, and that, taken together, they [cover nearly the entire range of cortical GABAergic neurons](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556905/#!po=8.92857), with the caveat that VIP+ cells are a subset of a larger group of 5HT3aR-expressing cells.\n",
+    "\n",
+    "To find experiments performed on a specific genotype, we can filter the sessions table on the `full_genotype` column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>published_at</th>\n",
+       "      <th>specimen_id</th>\n",
+       "      <th>session_type</th>\n",
+       "      <th>age_in_days</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>full_genotype</th>\n",
+       "      <th>unit_count</th>\n",
+       "      <th>channel_count</th>\n",
+       "      <th>probe_count</th>\n",
+       "      <th>ecephys_structure_acronyms</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>721123822</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>707296982</td>\n",
+       "      <td>brain_observatory_1.1</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>444</td>\n",
+       "      <td>2229</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746083955</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>726170935</td>\n",
+       "      <td>brain_observatory_1.1</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>582</td>\n",
+       "      <td>2216</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[VPM, TH, LGd, CA3, CA2, CA1, VISal, nan, grey...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760345702</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>739783171</td>\n",
+       "      <td>brain_observatory_1.1</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>501</td>\n",
+       "      <td>1862</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[MB, TH, PP, PIL, DG, CA3, CA1, VISal, nan, gr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773418906</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>757329624</td>\n",
+       "      <td>brain_observatory_1.1</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>546</td>\n",
+       "      <td>2232</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[PPT, NOT, SUB, ProS, CA1, VISam, nan, APN, DG...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797828357</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>776061251</td>\n",
+       "      <td>brain_observatory_1.1</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>611</td>\n",
+       "      <td>2232</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[PPT, MB, APN, NOT, HPF, ProS, CA1, VISam, nan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>829720705</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>811322619</td>\n",
+       "      <td>functional_connectivity</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>529</td>\n",
+       "      <td>1841</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[SCig, SCop, SCsg, SCzo, POST, VISp, nan, CA1,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>839557629</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>821469666</td>\n",
+       "      <td>functional_connectivity</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>450</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[APN, NOT, MB, DG, CA1, VISam, nan, VISpm, LGd...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>840012044</th>\n",
+       "      <td>2019-10-03T00:00:00Z</td>\n",
+       "      <td>820866121</td>\n",
+       "      <td>functional_connectivity</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>758</td>\n",
+       "      <td>2298</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[APN, DG, CA1, VISam, nan, LP, VISpm, VISp, LG...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   published_at  specimen_id             session_type  \\\n",
+       "id                                                                      \n",
+       "721123822  2019-10-03T00:00:00Z    707296982    brain_observatory_1.1   \n",
+       "746083955  2019-10-03T00:00:00Z    726170935    brain_observatory_1.1   \n",
+       "760345702  2019-10-03T00:00:00Z    739783171    brain_observatory_1.1   \n",
+       "773418906  2019-10-03T00:00:00Z    757329624    brain_observatory_1.1   \n",
+       "797828357  2019-10-03T00:00:00Z    776061251    brain_observatory_1.1   \n",
+       "829720705  2019-10-03T00:00:00Z    811322619  functional_connectivity   \n",
+       "839557629  2019-10-03T00:00:00Z    821469666  functional_connectivity   \n",
+       "840012044  2019-10-03T00:00:00Z    820866121  functional_connectivity   \n",
+       "\n",
+       "           age_in_days sex                                    full_genotype  \\\n",
+       "id                                                                            \n",
+       "721123822        125.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "746083955         98.0   F  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "760345702        103.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "773418906        124.0   F  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "797828357        107.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "829720705        112.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "839557629        115.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "840012044        116.0   M  Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   \n",
+       "\n",
+       "           unit_count  channel_count  probe_count  \\\n",
+       "id                                                  \n",
+       "721123822         444           2229            6   \n",
+       "746083955         582           2216            6   \n",
+       "760345702         501           1862            5   \n",
+       "773418906         546           2232            6   \n",
+       "797828357         611           2232            6   \n",
+       "829720705         529           1841            5   \n",
+       "839557629         450           1853            5   \n",
+       "840012044         758           2298            6   \n",
+       "\n",
+       "                                  ecephys_structure_acronyms  \n",
+       "id                                                            \n",
+       "721123822  [MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ...  \n",
+       "746083955  [VPM, TH, LGd, CA3, CA2, CA1, VISal, nan, grey...  \n",
+       "760345702  [MB, TH, PP, PIL, DG, CA3, CA1, VISal, nan, gr...  \n",
+       "773418906  [PPT, NOT, SUB, ProS, CA1, VISam, nan, APN, DG...  \n",
+       "797828357  [PPT, MB, APN, NOT, HPF, ProS, CA1, VISam, nan...  \n",
+       "829720705  [SCig, SCop, SCsg, SCzo, POST, VISp, nan, CA1,...  \n",
+       "839557629  [APN, NOT, MB, DG, CA1, VISam, nan, VISpm, LGd...  \n",
+       "840012044  [APN, DG, CA1, VISam, nan, LP, VISpm, VISp, LG...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pvalb_sessions = sessions[sessions.full_genotype.str.match('Pvalb')]\n",
+    "\n",
+    "pvalb_sessions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The table above contains 8 sessions, 5 of which used the `brain_observatory_1.1` visual stimulus, and 3 of which used the `functional_connectivity` stimulus. Any experiments with the same stimulus set are identical across genotypes. Importantly, the optotagging stimulus does not occur until the end of the experiment, so any changes induced by activating a specific set of interneurons will not affect the visual responses that we measure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Types of optotagging stimuli"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's load one of the above sessions to see how to extract information about the optotagging stimuli that were delivered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session = cache.get_session_data(pvalb_sessions.index.values[-3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optotagging stimulus table is stored separately from the visual stimulus table. So instead of calling `session.stimulus_presentations`, we will use `session.optogenetic_stimulation_epochs` to load a DataFrame that contains the information about the optotagging stimuli:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>level</th>\n",
+       "      <th>name</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9208.46044</td>\n",
+       "      <td>9208.46544</td>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9210.64062</td>\n",
+       "      <td>9210.65062</td>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9212.37064</td>\n",
+       "      <td>9213.37064</td>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9214.40076</td>\n",
+       "      <td>9215.40076</td>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9216.55091</td>\n",
+       "      <td>9217.55091</td>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>9778.77516</td>\n",
+       "      <td>9779.77516</td>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>9780.72530</td>\n",
+       "      <td>9781.72530</td>\n",
+       "      <td>half-period of a cosine wave</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>raised_cosine</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>9782.66528</td>\n",
+       "      <td>9782.67028</td>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>9784.81538</td>\n",
+       "      <td>9784.82038</td>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>9786.60547</td>\n",
+       "      <td>9786.61547</td>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>300 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     start_time   stop_time                     condition  level  \\\n",
+       "id                                                                 \n",
+       "0    9208.46044  9208.46544         a single square pulse    2.0   \n",
+       "1    9210.64062  9210.65062         a single square pulse    1.7   \n",
+       "2    9212.37064  9213.37064        2.5 ms pulses at 10 Hz    1.7   \n",
+       "3    9214.40076  9215.40076        2.5 ms pulses at 10 Hz    1.3   \n",
+       "4    9216.55091  9217.55091        2.5 ms pulses at 10 Hz    2.0   \n",
+       "..          ...         ...                           ...    ...   \n",
+       "295  9778.77516  9779.77516        2.5 ms pulses at 10 Hz    2.0   \n",
+       "296  9780.72530  9781.72530  half-period of a cosine wave    2.0   \n",
+       "297  9782.66528  9782.67028         a single square pulse    1.3   \n",
+       "298  9784.81538  9784.82038         a single square pulse    1.3   \n",
+       "299  9786.60547  9786.61547         a single square pulse    1.3   \n",
+       "\n",
+       "              name  duration  \n",
+       "id                            \n",
+       "0            pulse     0.005  \n",
+       "1            pulse     0.010  \n",
+       "2      fast_pulses     1.000  \n",
+       "3      fast_pulses     1.000  \n",
+       "4      fast_pulses     1.000  \n",
+       "..             ...       ...  \n",
+       "295    fast_pulses     1.000  \n",
+       "296  raised_cosine     1.000  \n",
+       "297          pulse     0.005  \n",
+       "298          pulse     0.005  \n",
+       "299          pulse     0.010  \n",
+       "\n",
+       "[300 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "session.optogenetic_stimulation_epochs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This returns a table with information about each optotagging trial. To find the unique conditions across all trials, we can use the following Pandas syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>condition</th>\n",
+       "      <th>level</th>\n",
+       "      <th>name</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.5 ms pulses at 10 Hz</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>fast_pulses</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>a single square pulse</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>pulse</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>half-period of a cosine wave</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>raised_cosine</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>half-period of a cosine wave</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>raised_cosine</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>half-period of a cosine wave</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>raised_cosine</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       condition  level           name  duration\n",
+       "id                                                              \n",
+       "3         2.5 ms pulses at 10 Hz    1.3    fast_pulses     1.000\n",
+       "2         2.5 ms pulses at 10 Hz    1.7    fast_pulses     1.000\n",
+       "4         2.5 ms pulses at 10 Hz    2.0    fast_pulses     1.000\n",
+       "17         a single square pulse    1.3          pulse     0.005\n",
+       "7          a single square pulse    1.7          pulse     0.005\n",
+       "0          a single square pulse    2.0          pulse     0.005\n",
+       "13         a single square pulse    1.3          pulse     0.010\n",
+       "1          a single square pulse    1.7          pulse     0.010\n",
+       "8          a single square pulse    2.0          pulse     0.010\n",
+       "5   half-period of a cosine wave    1.3  raised_cosine     1.000\n",
+       "14  half-period of a cosine wave    1.7  raised_cosine     1.000\n",
+       "6   half-period of a cosine wave    2.0  raised_cosine     1.000"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns = ['name', 'duration','level']\n",
+    "\n",
+    "session.optogenetic_stimulation_epochs.drop_duplicates(columns).sort_values(by=columns).drop(columns=['start_time','stop_time'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optotagging portion of the experiment includes four categories of blue light stimuli: 2.5 ms pulses delivered at 10 Hz for one second, a single 5 ms pulse, a single 10 ms pulse, and a raised cosine pulse lasting 1 second. All of these stimuli are delivered through a 400 micron-diameter fiber optic cable positioned to illuminate the surface of visual cortex. Each stimulus is delivered at one of three power levels, defined by the peak voltage of the control signal delivered to the light source, not the actual light power at the tip of the fiber.\n",
+    "\n",
+    "Unfortunately, light power has not been perfectly matched across experiments. A little more than halfway through the data collection process, we switched from delivering light through an LED (maximum power at fiber tip = 4 mW) to a laser (maximum power at fiber tip = 35 mW), in order to evoke more robust optotagging responses. To check whether or not a particular experiment used a laser, you can use the following filter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sessions.index.values >= 789848216"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We realize that this makes it more difficult to compare results across experiments, but we decided it was better to improve the optotagging yield for later sessions than continue to use light levels that were not reliably driving spiking responses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aligning spikes to light pulses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aligning spikes to light pulses is a bit more involved than aligning spikes to visual stimuli. This is because we haven't yet created convenience functions for performing this alignment automatically, such as `session.presentationwise_spike_times` or `sesssion.presentationwise_spike_counts`. We are planning to incorporate such functions into the AllenSDK in the future, but for now, you'll have to write your own code for extracting spikes around light pulses (or copy the code below).\n",
+    "\n",
+    "Let's choose a stimulus condition (10 ms pulses) and a set of units (visual cortex only), then create a DataArray containing binned spikes aligned to the start of each stimulus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trials = session.optogenetic_stimulation_epochs[(session.optogenetic_stimulation_epochs.duration > 0.009) & \\\n",
+    "                                                (session.optogenetic_stimulation_epochs.duration < 0.02)]\n",
+    "\n",
+    "units = session.units[session.units.ecephys_structure_acronym.str.match('VIS')]\n",
+    "\n",
+    "time_resolution = 0.0005 # 0.5 ms bins\n",
+    "\n",
+    "bin_edges = np.arange(-0.01, 0.025, time_resolution)\n",
+    "\n",
+    "def optotagging_spike_counts(bin_edges, trials, units):\n",
+    "    \n",
+    "    time_resolution = np.mean(np.diff(bin_edges))\n",
+    "\n",
+    "    spike_matrix = np.zeros( (len(trials), len(bin_edges), len(units)) )\n",
+    "\n",
+    "    for unit_idx, unit_id in enumerate(units.index.values):\n",
+    "\n",
+    "        spike_times = session.spike_times[unit_id]\n",
+    "\n",
+    "        for trial_idx, trial_start in enumerate(trials.start_time.values):\n",
+    "\n",
+    "            in_range = (spike_times > (trial_start + bin_edges[0])) * \\\n",
+    "                       (spike_times < (trial_start + bin_edges[-1]))\n",
+    "\n",
+    "            binned_times = ((spike_times[in_range] - (trial_start + bin_edges[0])) / time_resolution).astype('int')\n",
+    "            spike_matrix[trial_idx, binned_times, unit_idx] = 1\n",
+    "\n",
+    "    return xr.DataArray(\n",
+    "        name='spike_counts',\n",
+    "        data=spike_matrix,\n",
+    "        coords={\n",
+    "            'trial_id': trials.index.values,\n",
+    "            'time_relative_to_stimulus_onset': bin_edges,\n",
+    "            'unit_id': units.index.values\n",
+    "        },\n",
+    "        dims=['trial_id', 'time_relative_to_stimulus_onset', 'unit_id']\n",
+    "    )\n",
+    "\n",
+    "da = optotagging_spike_counts(bin_edges, trials, units)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use this DataArray to plot the average firing rate for each unit as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_optotagging_response(da):\n",
+    "\n",
+    "    plt.figure(figsize=(5,10))\n",
+    "\n",
+    "    plt.imshow(da.mean(dim='trial_id').T / time_resolution, \n",
+    "               extent=[np.min(bin_edges), np.max(bin_edges),\n",
+    "                       0, len(units)],\n",
+    "               aspect='auto', vmin=0, vmax=200)    \n",
+    "\n",
+    "    for bound in [0.0005, 0.0095]:\n",
+    "        plt.plot([bound, bound],[0, len(units)], ':', color='white', linewidth=1.0)\n",
+    "\n",
+    "    plt.xlabel('Time (s)')\n",
+    "    plt.ylabel('Unit #')\n",
+    "\n",
+    "    cb = plt.colorbar(fraction=0.046, pad=0.04)\n",
+    "    cb.set_label('Mean firing rate (Hz)')\n",
+    "    \n",
+    "plot_optotagging_response(da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this plot, we can see that a number of units increase their firing rate during the stimulus window, firing a burst of around three spikes. This is typical for Parvalbumin-positive neurons, which fire at high rates under natural conditions.\n",
+    "\n",
+    "However, there are also some units that seem to fire at the very beginning and/or very end of the light pulse. These spikes are almost certainly artifactual, as it takes at least 1 ms to generate a true light-evoked action potential. Therefore, we need to disregard these low-latency \"spikes\" in our analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identifying Cre+ units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we know how to align spikes, we can start assessing which units are reliably driven by the optotagging stimulus and are likely to be Cre+.\n",
+    "\n",
+    "There are a variety of ways to do this, but these are the most important things to keep in mind:\n",
+    "* Spikes that occur precisely at the start or end of a light pulse are likely artifactual, and need to be ignored.\n",
+    "* The bright blue light required for optotagging _can_ be seen by the mouse, so any spikes that occur more than 40 ms after the stimulus onset may result from retinal input, as opposed to direct optogenetic drive.\n",
+    "* The rate of false negatives (Cre+ cells that are not light-driven) will vary across areas, across depths, and across sessions. We've tried our best to evenly illuminate the entire visual cortex, and to use light powers that can drive spikes throughout all cortical layers, but some variation is inevitable.\n",
+    "\n",
+    "For these reasons, we've found that the 10 ms pulses are the most useful stimulus for finding true light-evoked activity. These pulses provide a long enough artifact-free window to observe light-evoked spikes, but do not last long enough to be contaminated by visually driven activity.\n",
+    "\n",
+    "Using the DataArray we created previously, we can search for units that increase their firing rate during the 10 ms pulse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseline = da.sel(time_relative_to_stimulus_onset=slice(-0.01,-0.002))\n",
+    "\n",
+    "baseline_rate = baseline.sum(dim='time_relative_to_stimulus_onset').mean(dim='trial_id') / 0.008\n",
+    "\n",
+    "evoked = da.sel(time_relative_to_stimulus_onset=slice(0.001,0.009))\n",
+    "\n",
+    "evoked_rate = evoked.sum(dim='time_relative_to_stimulus_onset').mean(dim='trial_id') / 0.008"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing the baseline and evoked rates, we can see a clear subset of units with a light-evoked increase in firing rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "\n",
+    "plt.scatter(baseline_rate, evoked_rate, s=3)\n",
+    "\n",
+    "axis_limit = 250\n",
+    "plt.plot([0,axis_limit],[0,axis_limit], ':k')\n",
+    "plt.plot([0,axis_limit],[0,axis_limit*2], ':r')\n",
+    "plt.xlim([0,axis_limit])\n",
+    "plt.ylim([0,axis_limit])\n",
+    "\n",
+    "plt.xlabel('Baseline rate (Hz)')\n",
+    "_ = plt.ylabel('Evoked rate (Hz)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select a threshold, such as 2x increase in firing rate (red line) to find the IDs for units that are robustly driven by the light:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([951131472, 951131470, 951131486, 951131478, 951131522, 951131506,\n",
+       "       951131782, 951131534, 951131558, 951131556, 951131564, 951131560,\n",
+       "       951131581, 951131589, 951131583, 951131593, 951131612, 951131643,\n",
+       "       951131689, 951132054, 951132138, 951132140, 951132159, 951132184,\n",
+       "       951132212, 951132205, 951132224, 951132236, 951133681, 951133822,\n",
+       "       951133909, 951134030, 951134026, 951134066, 951134100, 951134199,\n",
+       "       951136071, 951136175, 951136247, 951136394, 951136657, 951136717,\n",
+       "       951136829, 951137028, 951137073, 951137204, 951140485, 951140617,\n",
+       "       951141942, 951140861, 951140832, 951140821, 951141065, 951141978,\n",
+       "       951141097, 951141154, 951141292, 951141373, 951141485, 951141536])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cre_pos_units = da.unit_id[(evoked_rate / (baseline_rate + 1)) > 2].values # add 1 to prevent divide-by-zero errors\n",
+    "\n",
+    "cre_pos_units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because this is a Parvalbumin-Cre mouse, we expect the majority of light-driven units to be fast-spiking interneurons. We can check this by plotting the mean waveforms for the units we've identified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "\n",
+    "for unit_id in cre_pos_units:\n",
+    "    \n",
+    "    peak_channel = session.units.loc[unit_id].peak_channel_id\n",
+    "    wv = session.mean_waveforms[unit_id].sel(channel_id = peak_channel)\n",
+    "    \n",
+    "    plt.plot(wv.time * 1000, wv, 'k', alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('Time (ms)')\n",
+    "plt.ylabel('Amplitude (microvolts)')\n",
+    "_ =plt.plot([1.0, 1.0],[-160, 100],':c')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indeed, most of these units have stereotypical \"fast-spiking\" waveforms (with a peak earlier than 1 ms). The outliers are likely parvalbumin-positive pyramidal cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Differences across genotypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The example above is a \"best-case\" scenario. As you look across experiments, you will find that there is substantial variability in the fraction of light-driven neurons. Some of this can be accounted for by differences in light power. But much of the variability can be attributed to genotype: parvalbumin+ cells are the most abundant type of inhibitory cells in the cortex, with somastatin+ cells coming next, and VIP+ cells a distant third. There are also likely differences in our ability to record from different interneuron subtypes. For example, parvalbumin+ cells generally fire at the highest rates, which makes them easier to detect in extracellular electrophysiology experiments. The size of the cell's soma also plays a role in its recordability, and this likely varies across interneuron sub-classes.\n",
+    "\n",
+    "Overall, it is clear that VIP+ cells have proven the most difficult to identify through optotagging methods. The VIP-Cre mice we've recorded contain _very_ few light-driven units: the number is on the order of one per probe, and is sometimes zero across the whole experiment. We're not yet sure whether this is due to the difficultly of recording VIP+ cells with Neuropixels probes, or the difficulty of driving them with ChR2. To confounding things even further, VIP+ cells tend to have a _disinhibitory_ effect on the local circuit, so units that significantly increase their firing during the 1 s raised cosine light stimulus are not guaranteed to be Cre+.\n",
+    "\n",
+    "In any case, it will be helpful to look at some characteristic examples of light-evoked responses in Sst-Cre and Vip-Cre mice, so you know what to expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst_sessions = sessions[sessions.full_genotype.str.match('Sst')]\n",
+    "\n",
+    "session = cache.get_session_data(sst_sessions.index.values[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trials = session.optogenetic_stimulation_epochs[(session.optogenetic_stimulation_epochs.duration > 0.009) & \\\n",
+    "                                                (session.optogenetic_stimulation_epochs.duration < 0.02)]\n",
+    "\n",
+    "units = session.units[session.units.ecephys_structure_acronym.str.match('VIS')]\n",
+    "\n",
+    "bin_edges = np.arange(-0.01, 0.025, 0.0005)\n",
+    "\n",
+    "da = optotagging_spike_counts(bin_edges, trials, units)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_optotagging_response(da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this Sst-Cre mouse, we see a smaller fraction of light-driven units than in the Pvalb-Cre mouse. The light-driven units tend to spike at a range of latencies following light onset, rather than displaying the rhythmic firing pattern of Parvalbumin+ cells. Again, note that the spikes that are precisely aligned to the light onset or offset are likely artifactual.\n",
+    "\n",
+    "Now that we've computed the average responses, we can use the same method as above to find the units that are activated by the light."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseline = da.sel(time_relative_to_stimulus_onset=slice(-0.01,-0.002))\n",
+    "\n",
+    "baseline_rate = baseline.sum(dim='time_relative_to_stimulus_onset').mean(dim='trial_id') / 0.008\n",
+    "\n",
+    "evoked = da.sel(time_relative_to_stimulus_onset=slice(0.001,0.009))\n",
+    "\n",
+    "evoked_rate = evoked.sum(dim='time_relative_to_stimulus_onset').mean(dim='trial_id') / 0.008"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "\n",
+    "plt.scatter(baseline_rate, evoked_rate, s=3)\n",
+    "\n",
+    "axis_limit = 175\n",
+    "plt.plot([0,axis_limit],[0,axis_limit], ':k')\n",
+    "plt.plot([0,axis_limit],[0,axis_limit*2], ':r')\n",
+    "plt.xlim([0,axis_limit])\n",
+    "plt.ylim([0,axis_limit])\n",
+    "\n",
+    "plt.xlabel('Baseline rate (Hz)')\n",
+    "_ = plt.ylabel('Evoked rate (Hz)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a smaller fraction of light-driven units in this Sst-Cre mouse, but the effect of optogenetic stimulation is still obvious. Let's look at the waveforms for the units that increase their firing rate at least 2x above baseline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cre_pos_units = da.unit_id[(evoked_rate / (baseline_rate + 1)) > 2].values\n",
+    "\n",
+    "plt.figure(figsize=(5,5))\n",
+    "\n",
+    "for unit_id in cre_pos_units:\n",
+    "    \n",
+    "    peak_channel = session.units.loc[unit_id].peak_channel_id\n",
+    "    wv = session.mean_waveforms[unit_id].sel(channel_id = peak_channel)\n",
+    "    \n",
+    "    plt.plot(wv.time * 1000, wv, 'k', alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('Time (ms)')\n",
+    "plt.ylabel('Amplitude (microvolts)')\n",
+    "_ =plt.plot([1.0, 1.0],[-160, 100],':c')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, we see a mix of fast-spiking and regular-spiking waveforms, in contrast to the primarily fast-spiking waveforms of the Parvalbumin+ units."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's take a look at light-evoked activity in a VIP-Cre mouse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vip_sessions = sessions[sessions.full_genotype.str.match('Vip')]\n",
+    "\n",
+    "session = cache.get_session_data(vip_sessions.index.values[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trials = session.optogenetic_stimulation_epochs[(session.optogenetic_stimulation_epochs.duration > 0.009) & \\\n",
+    "                                                (session.optogenetic_stimulation_epochs.duration < 0.02)]\n",
+    "\n",
+    "units = session.units[session.units.ecephys_structure_acronym.str.match('VIS')]\n",
+    "\n",
+    "bin_edges = np.arange(-0.01, 0.025, 0.0005)\n",
+    "\n",
+    "da = optotagging_spike_counts(bin_edges, trials, units)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_optotagging_response(da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This response looks much different than the examples above. There is only one unit (out of more than 350 in cortex) that is obviously responding to the 10 ms light pulse. Even though the yield for VIP-Cre mice is extremely low, these units will be extremely valuable to analyze. If we can characterize the stereotypical firing patterns displayed by labeled VIP+ interneurons, we may be able to identify them in unlabeled recordings."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc_template/index.rst b/doc_template/index.rst
index 45ea05c74..42ec9c28a 100644
--- a/doc_template/index.rst
+++ b/doc_template/index.rst
@@ -90,6 +90,18 @@ The Allen SDK provides Python code for accessing experimental metadata along wit
 
 See the `mouse connectivity section <connectivity.html>`_ for more details.
 
+What's New - 1.6.0 (March 23, 2020)
+-----------------------------------------------------------------------
+
+As of the 1.6.0 release:
+
+- added get_receptive_field alias() for _get_rf() in allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py
+- Added required version to namespace and caches spec in ecephy nwb outputs in allensdk/brain_observatory/ecephys/nwb/AIBS_ecephys_namespace.yaml
+- Added version for ophys behavior nwb output to allensdk/brain_observatory/nwb/AIBS_ophys_behavior_namespace.yaml
+- Behavior and ECEphys project caches no longer accept arbitrary keywords to prevent confusion when user supplies incorrect kwargs to constructor.
+- New ecephys notebook for optotagging tutorial.
+
+
 What's New - 1.5.0 (February 10, 2020)
 -----------------------------------------------------------------------
 
@@ -101,17 +113,9 @@ As of the 1.5.0 release:
 - morphology.apply_affine correctly rescales radii
 
 
-What's New - 1.4.0 (January 23, 2020)
------------------------------------------------------------------------
-
-As of the 1.4.0 release:
-
-- users of the ephys extractor can supply their own cutoff frequency for low-pass bessel filter.
-- (internal feature) the ophys time sync module writes an output json describing its results.
-
-
 Previous Release Notes
 ----------------------
+    * `1.4.0 <https://github.com/AllenInstitute/AllenSDK/releases/tag/v1.4.0>`_
     * `1.3.0 <https://github.com/AllenInstitute/AllenSDK/releases/tag/v1.3.0>`_
     * `1.2.0 <https://github.com/AllenInstitute/AllenSDK/releases/tag/v1.2.0>`_
     * `1.1.1 <https://github.com/AllenInstitute/AllenSDK/releases/tag/v1.1.1>`_
diff --git a/doc_template/visual_coding_neuropixels.rst b/doc_template/visual_coding_neuropixels.rst
index c5d15af71..86684eeb5 100644
--- a/doc_template/visual_coding_neuropixels.rst
+++ b/doc_template/visual_coding_neuropixels.rst
@@ -18,6 +18,7 @@ Additional tutorials are available on the following topics:
   2. `Unit quality metrics <_static/examples/nb/ecephys_quality_metrics.html>`_ `(download .ipynb) <_static/examples/nb/ecephys_quality_metrics.ipynb>`_
   3. `LFP data analysis <_static/examples/nb/ecephys_lfp_analysis.html>`_ `(download .ipynb) <_static/examples/nb/ecephys_lfp_analysis.ipynb>`_
   4. `Receptive field mapping <_static/examples/nb/ecephys_receptive_fields.html>`_ `(download .ipynb) <_static/examples/nb/ecephys_receptive_fields.ipynb>`_
+  5. `Optotagging <_static/examples/nb/ecephys_optotagging.html>`_ `(download .ipynb) <_static/examples/nb/ecephys_optotagging.ipynb>`_
 
 For detailed information about the experimental design, data acquisition, and informatics methods, please refer to our `technical whitepaper <https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/80/75/8075a100-ca64-429a-b39a-569121b612b2/neuropixels_visual_coding_-_white_paper_v10.pdf>`_. AllenSDK 
 API documentation `is available here <allensdk.brain_observatory.ecephys.html>`_.