From b75a5a655193f2f08cf6ee3ab1b9a5132c358ae7 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:07:09 -0800
Subject: [PATCH 01/10] move workshops

---
 .../AMATH 342}/Brain+Observatory+SDK+Basics.ipynb   |   0
 ...omputing_a_Spatial_Receptive_Field_Example.ipynb |   0
 .../AMATH 342}/Computing_a_tuning_curve.ipynb       |   0
 .../Computing_a_tuning_curve_Solutions.ipynb        |   0
 .../BrainObservatory/BrainObservatory.ipynb         |   0
 .../BrainObservatory_solutions.ipynb                |   0
 .../CNS 2018 Tutorial}/README.md                    |   0
 .../docker/anaconda3_base/Dockerfile                |   0
 .../docker/anaconda3_base/README.md                 |   0
 .../anaconda3_base/build_scripts/build_DiPDE.sh     |   0
 .../anaconda3_base/build_scripts/build_NEST.sh      |   0
 .../anaconda3_base/build_scripts/build_NEURON.sh    |   0
 .../build_scripts/build_Tensorflow.sh               |   0
 .../anaconda3_base/build_scripts/pull_bmtk.sh       |   0
 .../CNS 2018 Tutorial}/docker/jupyterhub/Dockerfile |   0
 .../CNS 2018 Tutorial}/docker/jupyterhub/README.md  |   0
 .../jupyterhub/mechanisms/modfiles/CaDynamics.mod   |   0
 .../jupyterhub/mechanisms/modfiles/Ca_HVA.mod       |   0
 .../jupyterhub/mechanisms/modfiles/Ca_LVA.mod       |   0
 .../docker/jupyterhub/mechanisms/modfiles/Ih.mod    |   0
 .../docker/jupyterhub/mechanisms/modfiles/Im.mod    |   0
 .../docker/jupyterhub/mechanisms/modfiles/Im_v2.mod |   0
 .../docker/jupyterhub/mechanisms/modfiles/K_P.mod   |   0
 .../docker/jupyterhub/mechanisms/modfiles/K_T.mod   |   0
 .../docker/jupyterhub/mechanisms/modfiles/Kd.mod    |   0
 .../jupyterhub/mechanisms/modfiles/Kv2like.mod      |   0
 .../docker/jupyterhub/mechanisms/modfiles/Kv3_1.mod |   0
 .../docker/jupyterhub/mechanisms/modfiles/NaTa.mod  |   0
 .../docker/jupyterhub/mechanisms/modfiles/NaTs.mod  |   0
 .../docker/jupyterhub/mechanisms/modfiles/NaV.mod   |   0
 .../docker/jupyterhub/mechanisms/modfiles/Nap.mod   |   0
 .../docker/jupyterhub/mechanisms/modfiles/SK.mod    |   0
 .../jupyterhub/mechanisms/modfiles/vecevent.mod     |   0
 .../CNS 2018 Tutorial}/resources/ocns-logo.png      | Bin
 .../FENS Technical Workshop-Complete.ipynb          |   0
 ...sing the Allen SDK to find and access data.ipynb |   0
 {SFN 2018 => workshops/SFN 2018}/SFN_Logo_2018.png  | Bin
 .../SFN 2018}/SfN_population_decoding_intro.ipynb   |   0
 38 files changed, 0 insertions(+), 0 deletions(-)
 rename {AMATH 342 => workshops/AMATH 342}/Brain+Observatory+SDK+Basics.ipynb (100%)
 rename {AMATH 342 => workshops/AMATH 342}/Computing_a_Spatial_Receptive_Field_Example.ipynb (100%)
 rename {AMATH 342 => workshops/AMATH 342}/Computing_a_tuning_curve.ipynb (100%)
 rename {AMATH 342 => workshops/AMATH 342}/Computing_a_tuning_curve_Solutions.ipynb (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/BrainObservatory/BrainObservatory.ipynb (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/BrainObservatory/BrainObservatory_solutions.ipynb (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/README.md (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/Dockerfile (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/README.md (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/build_scripts/build_DiPDE.sh (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/build_scripts/build_NEST.sh (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/build_scripts/build_NEURON.sh (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/build_scripts/build_Tensorflow.sh (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/anaconda3_base/build_scripts/pull_bmtk.sh (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/Dockerfile (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/README.md (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/CaDynamics.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Ca_HVA.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Ca_LVA.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Ih.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Im.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Im_v2.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/K_P.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/K_T.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Kd.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Kv2like.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Kv3_1.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/NaTa.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/NaTs.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/NaV.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/Nap.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/SK.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/docker/jupyterhub/mechanisms/modfiles/vecevent.mod (100%)
 rename {CNS 2018 Tutorial => workshops/CNS 2018 Tutorial}/resources/ocns-logo.png (100%)
 rename {FENS 2018 Technical Workshop => workshops/FENS 2018 Technical Workshop}/FENS Technical Workshop-Complete.ipynb (100%)
 rename {SFN 2018 => workshops/SFN 2018}/2. Hands-on Tutorial - Using the Allen SDK to find and access data.ipynb (100%)
 rename {SFN 2018 => workshops/SFN 2018}/SFN_Logo_2018.png (100%)
 rename {SFN 2018 => workshops/SFN 2018}/SfN_population_decoding_intro.ipynb (100%)

diff --git a/AMATH 342/Brain+Observatory+SDK+Basics.ipynb b/workshops/AMATH 342/Brain+Observatory+SDK+Basics.ipynb
similarity index 100%
rename from AMATH 342/Brain+Observatory+SDK+Basics.ipynb
rename to workshops/AMATH 342/Brain+Observatory+SDK+Basics.ipynb
diff --git a/AMATH 342/Computing_a_Spatial_Receptive_Field_Example.ipynb b/workshops/AMATH 342/Computing_a_Spatial_Receptive_Field_Example.ipynb
similarity index 100%
rename from AMATH 342/Computing_a_Spatial_Receptive_Field_Example.ipynb
rename to workshops/AMATH 342/Computing_a_Spatial_Receptive_Field_Example.ipynb
diff --git a/AMATH 342/Computing_a_tuning_curve.ipynb b/workshops/AMATH 342/Computing_a_tuning_curve.ipynb
similarity index 100%
rename from AMATH 342/Computing_a_tuning_curve.ipynb
rename to workshops/AMATH 342/Computing_a_tuning_curve.ipynb
diff --git a/AMATH 342/Computing_a_tuning_curve_Solutions.ipynb b/workshops/AMATH 342/Computing_a_tuning_curve_Solutions.ipynb
similarity index 100%
rename from AMATH 342/Computing_a_tuning_curve_Solutions.ipynb
rename to workshops/AMATH 342/Computing_a_tuning_curve_Solutions.ipynb
diff --git a/CNS 2018 Tutorial/BrainObservatory/BrainObservatory.ipynb b/workshops/CNS 2018 Tutorial/BrainObservatory/BrainObservatory.ipynb
similarity index 100%
rename from CNS 2018 Tutorial/BrainObservatory/BrainObservatory.ipynb
rename to workshops/CNS 2018 Tutorial/BrainObservatory/BrainObservatory.ipynb
diff --git a/CNS 2018 Tutorial/BrainObservatory/BrainObservatory_solutions.ipynb b/workshops/CNS 2018 Tutorial/BrainObservatory/BrainObservatory_solutions.ipynb
similarity index 100%
rename from CNS 2018 Tutorial/BrainObservatory/BrainObservatory_solutions.ipynb
rename to workshops/CNS 2018 Tutorial/BrainObservatory/BrainObservatory_solutions.ipynb
diff --git a/CNS 2018 Tutorial/README.md b/workshops/CNS 2018 Tutorial/README.md
similarity index 100%
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rename to workshops/CNS 2018 Tutorial/README.md
diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/Dockerfile b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/Dockerfile
similarity index 100%
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diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/README.md b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/README.md
similarity index 100%
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diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_DiPDE.sh b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_DiPDE.sh
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diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEST.sh b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEST.sh
similarity index 100%
rename from CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEST.sh
rename to workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEST.sh
diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEURON.sh b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEURON.sh
similarity index 100%
rename from CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEURON.sh
rename to workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_NEURON.sh
diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_Tensorflow.sh b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_Tensorflow.sh
similarity index 100%
rename from CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_Tensorflow.sh
rename to workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/build_Tensorflow.sh
diff --git a/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/pull_bmtk.sh b/workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/pull_bmtk.sh
similarity index 100%
rename from CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/pull_bmtk.sh
rename to workshops/CNS 2018 Tutorial/docker/anaconda3_base/build_scripts/pull_bmtk.sh
diff --git a/CNS 2018 Tutorial/docker/jupyterhub/Dockerfile b/workshops/CNS 2018 Tutorial/docker/jupyterhub/Dockerfile
similarity index 100%
rename from CNS 2018 Tutorial/docker/jupyterhub/Dockerfile
rename to workshops/CNS 2018 Tutorial/docker/jupyterhub/Dockerfile
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similarity index 100%
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/CaDynamics.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/CaDynamics.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ca_HVA.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ca_HVA.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ca_LVA.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ca_LVA.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ih.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Ih.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Im.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Im.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/K_P.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/K_P.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/K_T.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/K_T.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kd.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kd.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kv2like.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kv2like.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kv3_1.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Kv3_1.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/NaTa.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/NaTa.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Nap.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/Nap.mod
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diff --git a/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/SK.mod b/workshops/CNS 2018 Tutorial/docker/jupyterhub/mechanisms/modfiles/SK.mod
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diff --git a/FENS 2018 Technical Workshop/FENS Technical Workshop-Complete.ipynb b/workshops/FENS 2018 Technical Workshop/FENS Technical Workshop-Complete.ipynb
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diff --git a/SFN 2018/2. Hands-on Tutorial - Using the Allen SDK to find and access data.ipynb b/workshops/SFN 2018/2. Hands-on Tutorial - Using the Allen SDK to find and access data.ipynb
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From fa5d1b1d7c83beee4f724ebd04f86d45e52e23a0 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:09:02 -0800
Subject: [PATCH 02/10] more notebooks

---
 README.md                                  |   47 +-
 gallery/decoding_from_sweep_response.ipynb |  224 ++
 gallery/joy_plots.ipynb                    |  338 +++
 gallery/requirements.txt                   |    8 +
 gallery/runtime.txt                        |    1 +
 hyptertools.ipynb                          |  185 ++
 mutual_info.ipynb                          | 2628 ++++++++++++++++++++
 sweep_response_to_csv.ipynb                |  195 ++
 tutorial/brain_observatory.ipynb           | 1083 ++++++++
 tutorial/brain_observatory_analysis.ipynb  |  881 +++++++
 tutorial/brain_observatory_monitor.ipynb   |  406 +++
 tutorial/brain_observatory_stimuli.ipynb   |  378 +++
 tutorial/cell_specimen_mapping.ipynb       |  263 ++
 tutorial/receptive_fields.ipynb            |  446 ++++
 tutorial/requirements.txt                  |    3 +
 tutorial/runtime.txt                       |    1 +
 umap.ipynb                                 |  186 ++
 17 files changed, 7272 insertions(+), 1 deletion(-)
 create mode 100644 gallery/decoding_from_sweep_response.ipynb
 create mode 100644 gallery/joy_plots.ipynb
 create mode 100644 gallery/requirements.txt
 create mode 100644 gallery/runtime.txt
 create mode 100644 hyptertools.ipynb
 create mode 100644 mutual_info.ipynb
 create mode 100644 sweep_response_to_csv.ipynb
 create mode 100644 tutorial/brain_observatory.ipynb
 create mode 100755 tutorial/brain_observatory_analysis.ipynb
 create mode 100755 tutorial/brain_observatory_monitor.ipynb
 create mode 100644 tutorial/brain_observatory_stimuli.ipynb
 create mode 100644 tutorial/cell_specimen_mapping.ipynb
 create mode 100644 tutorial/receptive_fields.ipynb
 create mode 100644 tutorial/requirements.txt
 create mode 100644 tutorial/runtime.txt
 create mode 100644 umap.ipynb

diff --git a/README.md b/README.md
index 7b3e4d7..43becd5 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,46 @@
-Example tutorials that we have developed for trainings and courses.
+# Brain Observatory Examples
+Example Jupyter notebooks for the Allen Brain Observatory
+
+This repository contains 3 sets of notebooks:
+
+- `tutorial` contains a set of notebooks that provide a self-guided tour of
+  accessing the Brain Observatory through the AllenSDK. It is expected that
+  users will explore these notebooks in order.
+- `workshops` contains notebooks used for any technical workshops that are run
+  by the Allen Institute. These are a snapshot and record of
+- `gallery` contains example notebooks that demonstrate some analysis or
+  visualization using the Brain Observatory and AllenSDK. These notebooks are
+  standalone entities. If it doesn't belong in the main tutorial and it is not
+  from a technical workshop, it probably goes here.
+
+```
+brain_observatory_gallery
+├─ tutorial/                  # Official tutorial for the Allen Brain Observatory
+│  ├─ requirements.txt        # packages required to run the tutorial    
+│  ├─ runtime.txt             # Python version required to run the tutorial
+│  ├─ Index.ipynb    
+│  ├─ 1. Intro to Brain Observatory.ipynb                              
+│  └─ ...      
+├─ gallery/                   # analysis files per experiment session
+│  ├─ requirements.txt        # packages required to run the tutorial    
+│  ├─ runtime.txt              
+│  ├─ joy_plots.ipynb           
+│  └─ ...                 
+└─ workshops/                 # technical workshops
+   ├─ 2018 Workshop Example/  # Example workshop
+   │  ├─ requirements.txt         
+   │  ├─ runtime.txt           
+   │  ├─ 1. Intro to Brain Observatory.ipynb                              
+   │  └─ ...      
+   ├─ 2018 Other Example/
+   └─ ...              
+```              
+
+
+## Running the examples
+
+Each folder contains the necessary assets to build a Docker container for the notebooks contained therein using repo2docker...
+
+1. install Docker
+2. install repo2docker: `pip install repo2docker` (you may want to install from source)
+3. build a docker container & launch a notebook server: `repo2docker ./`
diff --git a/gallery/decoding_from_sweep_response.ipynb b/gallery/decoding_from_sweep_response.ipynb
new file mode 100644
index 0000000..54c4f05
--- /dev/null
+++ b/gallery/decoding_from_sweep_response.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## requirements\n",
+    "# allensdk\n",
+    "# scikit-learn > 0.19\n",
+    "# xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/local1/miniconda2/envs/jk/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boc = BrainObservatoryCache(manifest_file='/local1/data/boc/manifest.json',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 541206592\n",
+    "\n",
+    "# Initializations:\n",
+    "nwb_dataset = boc.get_ophys_experiment_data(oeid)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
+    "\n",
+    "def get_ns_msr(nwb_dataset):\n",
+    "    ns = NaturalScenes(nwb_dataset)\n",
+    "    mean_sweep_response = ns.mean_sweep_response.copy()\n",
+    "    \n",
+    "    # I don't know what dx is. goodbye!\n",
+    "    mean_sweep_response.drop('dx',axis=1,inplace=True)\n",
+    "    \n",
+    "    # annotate the dataframe with useful indices and columns\n",
+    "    time = pd.Series(\n",
+    "        ns.timestamps[ns.stim_table['start']],\n",
+    "        name='time',\n",
+    "    )\n",
+    "    neurons = pd.Series(\n",
+    "        ns.cell_id,\n",
+    "        name='neuron',\n",
+    "    )\n",
+    "    mean_sweep_response.set_index(time,inplace=True)\n",
+    "    mean_sweep_response.columns = neurons\n",
+    "    \n",
+    "    images = ns.stim_table\n",
+    "    \n",
+    "    return images, mean_sweep_response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   frame  start    end\n",
+      "0     92  16125  16132\n",
+      "1     27  16133  16140\n",
+      "2     52  16140  16147\n",
+      "3     37  16148  16155\n",
+      "4    103  16155  16162\n",
+      "neuron     541510267  541510270  541510307  541510405  588381938  541510410  \\\n",
+      "time                                                                          \n",
+      "545.19658   2.750398   3.113332   3.283231   1.035660   2.312769   1.005320   \n",
+      "545.46195   5.472741   4.520462   1.848134   1.509070   3.900594   2.375818   \n",
+      "545.69416   4.938696   1.872071   0.822514  -0.366550   0.590227   0.577107   \n",
+      "545.95953   0.686303  -1.502568  -2.128904  -0.779033  -3.098761   0.632175   \n",
+      "546.19174  -2.763241  -2.317277  -1.518564  -1.131271  -3.716857   0.189942   \n",
+      "\n",
+      "neuron     541511183  541510394  588381886  541511196    ...      588381999  \\\n",
+      "time                                                     ...                  \n",
+      "545.19658   1.795959   1.302906   1.109441   2.644591    ...       3.245388   \n",
+      "545.46195   0.627758   0.452645  -1.946570  -0.088816    ...       5.051270   \n",
+      "545.69416  -0.431362   3.239566  -2.953792   0.519713    ...       0.117338   \n",
+      "545.95953   2.272473   4.147108  -4.235024  -0.116484    ...      -3.436971   \n",
+      "546.19174   1.049816   0.004382  -3.942320  -2.070535    ...      -3.320092   \n",
+      "\n",
+      "neuron     541510679  541509977  541510142  541509981  541509952  541510950  \\\n",
+      "time                                                                          \n",
+      "545.19658   1.204343   3.223873  -0.614828   1.594057   2.538169   9.322724   \n",
+      "545.46195   3.157544   2.147983   3.462305   1.155841   3.599893  10.790494   \n",
+      "545.69416   2.204452   0.433436   1.692538   4.946897  -2.181524   1.229501   \n",
+      "545.95953   1.661621  -1.256703   0.234285   5.248888  -1.481765  -3.012385   \n",
+      "546.19174   0.768980   0.713630   4.319497   1.107409   0.044746  -4.763652   \n",
+      "\n",
+      "neuron     541511172  541509957  541511118  \n",
+      "time                                        \n",
+      "545.19658   3.246232  17.838305  44.883263  \n",
+      "545.46195   3.700325  41.864319  55.052734  \n",
+      "545.69416   1.446858  19.081219  -0.731454  \n",
+      "545.95953   0.283391  -6.771274 -18.988461  \n",
+      "546.19174  -0.807627 -15.687251 -24.453199  \n",
+      "\n",
+      "[5 rows x 154 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y, X = get_ns_msr(nwb_dataset)\n",
+    "print y.head()\n",
+    "print X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def decode(msrx):\n",
+    "    \n",
+    "    # get features and output\n",
+    "    X = msrx.data\n",
+    "    y = msrx['natural_image']\n",
+    "    \n",
+    "    # split training & testing\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,stratify=y)\n",
+    "    \n",
+    "    # do the classification\n",
+    "    lm = LogisticRegression(\n",
+    "        solver='saga',\n",
+    "        multi_class='ovr',\n",
+    "        penalty='l1',\n",
+    "        n_jobs=-1,\n",
+    "    )\n",
+    "    lm.fit(X_train,y_train)\n",
+    "    return lm.score(X_test,y_test) * len(np.unique(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19.9\n",
+      "1 loop, best of 1: 50.1 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print decode(msrx)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:allensdk]",
+   "language": "python",
+   "name": "conda-env-allensdk-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gallery/joy_plots.ipynb b/gallery/joy_plots.ipynb
new file mode 100644
index 0000000..662c61e
--- /dev/null
+++ b/gallery/joy_plots.ipynb
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "boc = BrainObservatoryCache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 501794235"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_set = boc.get_ophys_experiment_data(ophys_experiment_id=oeid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
+    "ns = NaturalScenes(data_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep_response = ns.sweep_response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stim_table = data_set.get_stimulus_table('natural_scenes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = 40"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweeps = sweep_response[stim_table['frame']==image]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 153)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sweeps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "sweep_tensor = np.array(sweeps.values.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 153, 63)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sweep_tensor.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_sweeps = sweep_tensor.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-13.952503509521485"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_sweeps.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56.698078384399416"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_sweeps.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_sweeps /= mean_sweeps.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.24608423966200135"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_sweeps.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "for ii, cell_response in enumerate(mean_sweeps):\n",
+    "    peak_loc = np.argmax(cell_response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14, 40, 46, 56, 41, 25, 32, 12, 18, 31, 43, 19, 62, 25, 43, 43, 27,\n",
+       "       42, 57, 58, 55, 49, 46, 56, 29, 58, 58, 58, 49, 39, 45, 28, 46,  9,\n",
+       "        8, 46, 43, 62,  1, 62, 47, 18,  2, 46,  3, 42,  0, 45, 13, 46, 41,\n",
+       "       40, 32, 48, 37, 28,  2,  1, 62, 50, 45,  4, 57, 35, 40, 58, 54, 46,\n",
+       "       38, 54, 57, 60, 40, 40, 46, 62, 29, 50, 11, 40, 51, 53, 48, 27, 57,\n",
+       "       40, 60, 47, 19, 27, 12, 36, 37, 26, 12, 49, 43, 56, 40, 36, 52, 42,\n",
+       "       32, 30, 62, 10, 28,  5, 58, 55, 43, 20, 18, 51, 22, 42, 45, 46, 45,\n",
+       "        6,  6, 38, 38, 60, 12, 58, 33, 20, 30, 62, 15, 56, 43, 54, 55, 57,\n",
+       "       41, 18, 35, 24, 21, 49, 34, 12, 31, 53, 31, 55, 16, 61, 18, 44, 43],\n",
+       "      dtype=int64)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peak_loc = np.apply_along_axis(\n",
+    "    func1d=np.argmax,\n",
+    "    axis=1,\n",
+    "    arr=mean_sweeps,\n",
+    ")\n",
+    "peak_loc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order = np.argsort(peak_loc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_sweeps = mean_sweeps[order,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "scale_factor = 5\n",
+    "    \n",
+    "f, ax = plt.subplots(figsize=(5,10))\n",
+    "\n",
+    "background = '0.1'\n",
+    "linecolor = 'white'\n",
+    "\n",
+    "ax.set_facecolor(background)\n",
+    "ax.get_xaxis().set_visible(False)\n",
+    "ax.get_yaxis().set_visible(False)\n",
+    "for ii, cell_response in enumerate(mean_sweeps):\n",
+    "    x = np.arange(cell_response.shape[0])\n",
+    "    y = -ii + cell_response * scale_factor\n",
+    "    ax.plot(x,y,color=linecolor,zorder=ii,)\n",
+    "    ax.fill_between(x,-ii,y,zorder=ii,facecolor=background)\n",
+    "    \n",
+    "    \n",
+    "# plt.axis('off')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:allensdk]",
+   "language": "python",
+   "name": "conda-env-allensdk-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gallery/requirements.txt b/gallery/requirements.txt
new file mode 100644
index 0000000..c265781
--- /dev/null
+++ b/gallery/requirements.txt
@@ -0,0 +1,8 @@
+allensdk>=0.14.5
+hypertools
+umap-learn
+scikit-learn>=0.19.2
+xarray
+seaborn
+cython
+neuroglia
diff --git a/gallery/runtime.txt b/gallery/runtime.txt
new file mode 100644
index 0000000..16e8214
--- /dev/null
+++ b/gallery/runtime.txt
@@ -0,0 +1 @@
+python-2.7
diff --git a/hyptertools.ipynb b/hyptertools.ipynb
new file mode 100644
index 0000000..24ae7ee
--- /dev/null
+++ b/hyptertools.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boc = BrainObservatoryCache(manifest_file='/local1/data/boc/manifest.json',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 541206592\n",
+    "\n",
+    "# Initializations:\n",
+    "nwb_dataset = boc.get_ophys_experiment_data(oeid)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
+    "\n",
+    "def get_ns_msrx(nwb_dataset):\n",
+    "    ns = NaturalScenes(nwb_dataset)\n",
+    "    mean_sweep_response = ns.mean_sweep_response.copy()\n",
+    "    \n",
+    "    # I don't know what dx is. goodbye!\n",
+    "    mean_sweep_response.drop('dx',axis=1,inplace=True)\n",
+    "    \n",
+    "    # annotate the dataframe with useful indices and columns\n",
+    "    time = pd.Series(\n",
+    "        ns.timestamps[ns.stim_table['start']],\n",
+    "        name='time',\n",
+    "    )\n",
+    "    neurons = pd.Series(\n",
+    "        ns.cell_id,\n",
+    "        name='neuron',\n",
+    "    )\n",
+    "    mean_sweep_response.set_index(time,inplace=True)\n",
+    "    mean_sweep_response.columns = neurons\n",
+    "    \n",
+    "    # convert this to xarray & annotate images\n",
+    "    srx = xr.DataArray(mean_sweep_response)\n",
+    "    srx.coords['natural_image'] = ('time',ns.stim_table['frame'])\n",
+    "    \n",
+    "    return srx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (time: 5950, neuron: 154)>\n",
+      "array([[ 2.750398,  3.113332,  3.283231, ...,  3.246232, 17.838305, 44.883263],\n",
+      "       [ 5.472741,  4.520462,  1.848134, ...,  3.700325, 41.864319, 55.052734],\n",
+      "       [ 4.938696,  1.872071,  0.822514, ...,  1.446858, 19.081219, -0.731454],\n",
+      "       ...,\n",
+      "       [ 0.984122, -1.035176,  2.519683, ..., -0.894795, 14.030047,  1.291967],\n",
+      "       [ 1.705522,  1.379635,  1.116845, ...,  0.169055,  6.26528 ,  1.80034 ],\n",
+      "       [ 0.420941,  2.543081,  2.991403, ...,  0.980473, -0.720777,  4.779262]])\n",
+      "Coordinates:\n",
+      "  * time           (time) float64 545.2 545.5 545.7 546.0 546.2 546.5 546.7 ...\n",
+      "  * neuron         (neuron) int64 541510267 541510270 541510307 541510405 ...\n",
+      "    natural_image  (time) int64 92 27 52 37 103 1 54 19 54 -1 74 115 44 88 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "msrx = get_ns_msrx(nwb_dataset)\n",
+    "print msrx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def decode(msrx):\n",
+    "    \n",
+    "    # get features and output\n",
+    "    X = msrx.data\n",
+    "    y = msrx['natural_image']\n",
+    "    \n",
+    "    # split training & testing\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,stratify=y)\n",
+    "    \n",
+    "    # do the classification\n",
+    "    lm = LogisticRegression(\n",
+    "        solver='saga',\n",
+    "        multi_class='ovr',\n",
+    "        penalty='l1',\n",
+    "        n_jobs=-1,\n",
+    "    )\n",
+    "    lm.fit(X_train,y_train)\n",
+    "    return lm.score(X_test,y_test) * len(np.unique(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/local1/miniconda2/envs/jk/lib/python2.7/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19.400000000000002\n",
+      "1 loop, best of 1: 49.1 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print decode(msrx)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:jk]",
+   "language": "python",
+   "name": "conda-env-jk-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mutual_info.ipynb b/mutual_info.ipynb
new file mode 100644
index 0000000..0c66b9b
--- /dev/null
+++ b/mutual_info.ipynb
@@ -0,0 +1,2628 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boc = BrainObservatoryCache(manifest_file='/local1/data/boc/manifest.json',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 541206592\n",
+    "\n",
+    "# Initializations:\n",
+    "nwb_dataset = boc.get_ophys_experiment_data(oeid)\n",
+    "\n",
+    "# Get Data:\n",
+    "timestamps, dff = nwb_dataset.get_dff_traces()\n",
+    "neuron_ids = nwb_dataset.get_cell_specimen_ids()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            541510267  541510270  541510307  541510405  588381938  541510410  \\\n",
+      "3795.81159  -0.008279   0.003694   0.050467   0.006070  -0.042818   0.012401   \n",
+      "3795.84476  -0.047829   0.058493  -0.021686   0.018117  -0.038725   0.050487   \n",
+      "3795.87794   0.010168  -0.024952   0.047753   0.072545  -0.047447   0.004467   \n",
+      "3795.91111  -0.066454  -0.020247   0.053140   0.028778  -0.014109  -0.022748   \n",
+      "3795.94429   0.011435   0.096762  -0.006261  -0.018426   0.026748   0.065441   \n",
+      "\n",
+      "            541511183  541510394  588381886  541511196    ...      588381999  \\\n",
+      "3795.81159   0.009308  -0.030401  -0.022308   0.030732    ...       0.055724   \n",
+      "3795.84476   0.009110   0.042929  -0.023901   0.007453    ...      -0.056759   \n",
+      "3795.87794  -0.037506   0.020595   0.019189   0.077029    ...       0.018929   \n",
+      "3795.91111   0.014085   0.000569   0.040021   0.040235    ...      -0.131435   \n",
+      "3795.94429   0.042813  -0.025608  -0.007862   0.076943    ...      -0.033498   \n",
+      "\n",
+      "            541510679  541509977  541510142  541509981  541509952  541510950  \\\n",
+      "3795.81159   0.040850   0.044201  -0.023874   0.074496  -0.019515  -0.022506   \n",
+      "3795.84476  -0.032394   0.000214   0.033986   0.074511   0.025373   0.049433   \n",
+      "3795.87794   0.095493  -0.054615   0.032305   0.098815   0.009293   0.040606   \n",
+      "3795.91111   0.005144  -0.004524  -0.024333   0.032721  -0.035023   0.012460   \n",
+      "3795.94429   0.065112   0.002721   0.040421   0.060181  -0.002181   0.004858   \n",
+      "\n",
+      "            541511172  541509957  541511118  \n",
+      "3795.81159   0.069492   0.236333   0.019619  \n",
+      "3795.84476  -0.060595  -0.035044  -0.051129  \n",
+      "3795.87794   0.013742   0.128578   0.012092  \n",
+      "3795.91111   0.106299   0.092218  -0.034070  \n",
+      "3795.94429   0.052745  -0.051219   0.010408  \n",
+      "\n",
+      "[5 rows x 154 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "traces = pd.DataFrame(\n",
+    "    dff.T,\n",
+    "    columns=neuron_ids,\n",
+    "    index=timestamps,\n",
+    ")\n",
+    "\n",
+    "print(traces.tail())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   frame  start    end\n",
+      "0     92  16125  16132\n",
+      "1     27  16133  16140\n",
+      "2     52  16140  16147\n",
+      "3     37  16148  16155\n",
+      "4    103  16155  16162\n",
+      "   image_id  start    end       time  duration\n",
+      "0        92  16125  16132  545.29658       0.5\n",
+      "1        27  16133  16140  545.56195       0.5\n",
+      "2        52  16140  16147  545.79416       0.5\n",
+      "3        37  16148  16155  546.05953       0.5\n",
+      "4       103  16155  16162  546.29174       0.5\n",
+      "           541510267  541510270  541510307  541510405  588381938  541510410  \\\n",
+      "time                                                                          \n",
+      "545.29658   0.058292   0.068429   0.051853   0.008764   0.106076   0.036484   \n",
+      "545.56195   0.073360   0.071688   0.045226  -0.003462   0.089385   0.035545   \n",
+      "545.79416   0.051901   0.033619   0.022081  -0.005889   0.050934   0.026966   \n",
+      "546.05953   0.037321   0.019387   0.017736  -0.003050   0.038106   0.025693   \n",
+      "546.29174   0.025841   0.018571   0.042617  -0.021718   0.049283   0.026685   \n",
+      "\n",
+      "           541511183  541510394  588381886  541511196    ...      588381999  \\\n",
+      "time                                                     ...                  \n",
+      "545.29658  -0.008327   0.007280   0.115299   0.031072    ...       0.097836   \n",
+      "545.56195  -0.015378   0.027857   0.095451   0.032568    ...       0.079392   \n",
+      "545.79416  -0.014779   0.035851   0.078672   0.019473    ...       0.038399   \n",
+      "546.05953  -0.011675   0.023899   0.066588   0.010468    ...       0.035736   \n",
+      "546.29174  -0.011433   0.020377   0.042410   0.023014    ...       0.036705   \n",
+      "\n",
+      "           541510679  541509977  541510142  541509981  541509952  541510950  \\\n",
+      "time                                                                          \n",
+      "545.29658   0.024154   0.056202   0.022427   0.009614   0.046140   0.114141   \n",
+      "545.56195   0.024571   0.037259   0.021269   0.035784   0.014315   0.073816   \n",
+      "545.79416   0.018292   0.016657  -0.000266   0.039731   0.004575   0.039130   \n",
+      "546.05953   0.033971   0.043381   0.048330   0.047458   0.022751   0.027335   \n",
+      "546.29174   0.025637   0.048814   0.060430   0.055936   0.014187   0.022914   \n",
+      "\n",
+      "           541511172  541509957  541511118  \n",
+      "time                                        \n",
+      "545.29658   0.040217   0.354254   0.666060  \n",
+      "545.56195   0.039812   0.371455   0.428499  \n",
+      "545.79416   0.021901   0.218425   0.177625  \n",
+      "546.05953   0.013848   0.114489   0.100423  \n",
+      "546.29174   0.010461   0.087542   0.062152  \n",
+      "\n",
+      "[5 rows x 154 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#############################################\n",
+    "# Next, we'll load epochs\n",
+    "\n",
+    "epochs = nwb_dataset.get_stimulus_table('natural_scenes')\n",
+    "print(epochs.head())\n",
+    "\n",
+    "#############################################\n",
+    "# The epochs lists stimulus times in terms of the start and end frames of\n",
+    "# the calcium traces, but we need start times and durations for neuroglia, so\n",
+    "# we'll need to reshape\n",
+    "\n",
+    "epochs['time'] = timestamps[epochs['start']] + 0.1\n",
+    "epochs['duration'] = 0.5\n",
+    "\n",
+    "epochs.rename(columns={'frame':'image_id'},inplace=True)\n",
+    "\n",
+    "epochs = epochs[epochs['image_id']>=0]\n",
+    "\n",
+    "print(epochs.head())\n",
+    "\n",
+    "####################################\n",
+    "# We can get the average elicited spike count with the `ResponseReducer`\n",
+    "\n",
+    "from neuroglia.epoch import EpochTraceReducer\n",
+    "import numpy as np\n",
+    "reducer = EpochTraceReducer(traces=traces,func=np.mean)\n",
+    "responses = reducer.fit_transform(epochs)\n",
+    "print(responses.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyentropy import quantise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.05829171  0.07336037  0.05190103 ..., -0.00184361  0.00682828\n",
+      " -0.00736815]\n"
+     ]
+    }
+   ],
+   "source": [
+    "vector = responses[541510267].values\n",
+    "print vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyentropy import utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bin_responses(vector):\n",
+    "    bins, _ = utils.quantise(vector.values,2,uniform='sampling',centers=False)\n",
+    "    return bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantised_responses = responses.apply(bin_responses,axis=0)\n",
+    "# X.apply along_axis(lambda v: len(v),axis=0)axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546.29174</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 154 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           541510267  541510270  541510307  541510405  588381938  541510410  \\\n",
+       "time                                                                          \n",
+       "545.29658          1          1          1          1          1          1   \n",
+       "545.56195          1          1          1          0          1          1   \n",
+       "545.79416          1          1          1          0          1          1   \n",
+       "546.05953          1          1          1          0          1          1   \n",
+       "546.29174          1          1          1          0          1          1   \n",
+       "\n",
+       "           541511183  541510394  588381886  541511196    ...      588381999  \\\n",
+       "time                                                     ...                  \n",
+       "545.29658          0          1          1          1    ...              1   \n",
+       "545.56195          0          1          1          1    ...              1   \n",
+       "545.79416          0          1          1          1    ...              1   \n",
+       "546.05953          0          1          1          1    ...              1   \n",
+       "546.29174          0          1          1          1    ...              1   \n",
+       "\n",
+       "           541510679  541509977  541510142  541509981  541509952  541510950  \\\n",
+       "time                                                                          \n",
+       "545.29658          1          1          1          0          1          1   \n",
+       "545.56195          1          1          1          1          1          1   \n",
+       "545.79416          1          1          0          1          1          1   \n",
+       "546.05953          1          1          1          1          1          1   \n",
+       "546.29174          1          1          1          1          1          1   \n",
+       "\n",
+       "           541511172  541509957  541511118  \n",
+       "time                                        \n",
+       "545.29658          1          1          1  \n",
+       "545.56195          1          1          1  \n",
+       "545.79416          1          1          1  \n",
+       "546.05953          1          1          1  \n",
+       "546.29174          1          1          1  \n",
+       "\n",
+       "[5 rows x 154 columns]"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quantised_responses.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyentropy import DiscreteSystem\n",
+    "\n",
+    "def mutual_info(response,stimulus,shuffle=False):\n",
+    "    \n",
+    "    X = response.values.T.astype(np.int)\n",
+    "    X = np.atleast_2d(X)\n",
+    "    X_dims = (\n",
+    "        X.shape[0], # number of neurons\n",
+    "        X.max()+1, # alphabet size\n",
+    "    )\n",
+    "    \n",
+    "    Y = stimulus.values.astype(np.int)\n",
+    "    \n",
+    "    if shuffle:\n",
+    "        Y = np.random.permutation(Y)\n",
+    "    Y = np.atleast_2d(Y)\n",
+    "    Y_dims = (\n",
+    "        1, # number of stimulus conditions\n",
+    "        Y.max()+1, # alphabet size \n",
+    "    )\n",
+    "    \n",
+    "    system = DiscreteSystem(X, X_dims, Y, Y_dims)\n",
+    "    system.calculate_entropies(method='pt', calc=['HX', 'HXY'])\n",
+    "    return system.I()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01400362286111978"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mutual_info(quantised_responses[541510267],epochs['image_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi = quantised_responses.apply(\n",
+    "    lambda r: mutual_info(r,epochs['image_id']),\n",
+    "    axis=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "sns.set_style('white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff964419f90>"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff95f3e4490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(mi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi_null = quantised_responses.apply(\n",
+    "    lambda r: mutual_info(r,epochs['image_id'],shuffle=True),\n",
+    "    axis=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff963ecd3d0>"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff9643d1290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(mi_null)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neuron_stimulus_info = pd.DataFrame({'mutual_info':mi,'mutual_info_null':mi_null})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff95f330090>]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff95f314f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jp = sns.jointplot(x='mutual_info_null',y='mutual_info',data=neuron_stimulus_info)\n",
+    "jp.ax_joint.plot([0,0.01],[0,0.01],color='0.5',zorder=-100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "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>acquisition_age_days</th>\n",
+       "      <th>cre_line</th>\n",
+       "      <th>donor_name</th>\n",
+       "      <th>experiment_container_id</th>\n",
+       "      <th>fail_eye_tracking</th>\n",
+       "      <th>imaging_depth</th>\n",
+       "      <th>reporter_line</th>\n",
+       "      <th>session_type</th>\n",
+       "      <th>specimen_name</th>\n",
+       "      <th>targeted_structure</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>511458874</th>\n",
+       "      <td>72</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>234584</td>\n",
+       "      <td>511511089</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-234584</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562296530</th>\n",
+       "      <td>129</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>276949</td>\n",
+       "      <td>561463418</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571099190</th>\n",
+       "      <td>144</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>283278</td>\n",
+       "      <td>570994450</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-283278</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503772253</th>\n",
+       "      <td>103</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>225037</td>\n",
+       "      <td>511510822</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-225037</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580124131</th>\n",
+       "      <td>93</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>300663</td>\n",
+       "      <td>580051757</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-300663</td>\n",
+       "      <td>VISrl</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           acquisition_age_days        cre_line donor_name  \\\n",
+       "id                                                           \n",
+       "511458874                    72  Rbp4-Cre_KL100     234584   \n",
+       "562296530                   129   Emx1-IRES-Cre     276949   \n",
+       "571099190                   144    Cux2-CreERT2     283278   \n",
+       "503772253                   103    Cux2-CreERT2     225037   \n",
+       "580124131                    93  Rbp4-Cre_KL100     300663   \n",
+       "\n",
+       "           experiment_container_id  fail_eye_tracking  imaging_depth  \\\n",
+       "id                                                                     \n",
+       "511458874                511511089               True            375   \n",
+       "562296530                561463418              False            175   \n",
+       "571099190                570994450              False            175   \n",
+       "503772253                511510822               True            175   \n",
+       "580124131                580051757              False            375   \n",
+       "\n",
+       "                reporter_line     session_type  \\\n",
+       "id                                               \n",
+       "511458874  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "562296530  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "571099190  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "503772253  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "580124131  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "\n",
+       "                                   specimen_name targeted_structure  \n",
+       "id                                                                   \n",
+       "511458874        Rbp4-Cre;Camk2a-tTA;Ai93-234584               VISl  \n",
+       "562296530   Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949              VISpm  \n",
+       "571099190    Cux2-CreERT2;Camk2a-tTA;Ai93-283278              VISam  \n",
+       "503772253    Cux2-CreERT2;Camk2a-tTA;Ai93-225037              VISpm  \n",
+       "580124131  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-300663              VISrl  "
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "experiments = pd.DataFrame(boc.get_ophys_experiments(stimuli=['natural_scenes',])).set_index('id')\n",
+    "experiments.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_mutual_info(oeid):\n",
+    "    nwb_dataset = boc.get_ophys_experiment_data(oeid)\n",
+    "\n",
+    "    # Get Data:\n",
+    "    timestamps, dff = nwb_dataset.get_dff_traces()\n",
+    "    neuron_ids = nwb_dataset.get_cell_specimen_ids()\n",
+    "    \n",
+    "    traces = pd.DataFrame(\n",
+    "        dff.T,\n",
+    "        columns=neuron_ids,\n",
+    "        index=timestamps,\n",
+    "    )\n",
+    "\n",
+    "    \n",
+    "    epochs = nwb_dataset.get_stimulus_table('natural_scenes')\n",
+    "\n",
+    "    epochs['time'] = timestamps[epochs['start']] + 0.1\n",
+    "    epochs['duration'] = 0.5\n",
+    "\n",
+    "    epochs.rename(columns={'frame':'image_id'},inplace=True)\n",
+    "\n",
+    "    epochs = epochs[epochs['image_id']>=0]\n",
+    "    \n",
+    "    reducer = EpochTraceReducer(traces=traces,func=np.mean)\n",
+    "    responses = reducer.fit_transform(epochs)\n",
+    "    \n",
+    "    quantised_responses = responses.apply(bin_responses,axis=0)\n",
+    "    \n",
+    "    mi = quantised_responses.apply(\n",
+    "        lambda r: mutual_info(r,epochs['image_id']),\n",
+    "        axis=0,\n",
+    "    )\n",
+    "\n",
+    "    mi_null = quantised_responses.apply(\n",
+    "        lambda r: mutual_info(r,epochs['image_id'],shuffle=True),\n",
+    "        axis=0,\n",
+    "    )\n",
+    "    \n",
+    "    neuron_stimulus_info = pd.DataFrame({'mutual_info':mi,'mutual_info_null':mi_null})\n",
+    "    neuron_stimulus_info['oeid'] = oeid\n",
+    "    \n",
+    "    return neuron_stimulus_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>mutual_info</th>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>154 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           mutual_info  mutual_info_null       oeid\n",
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+       "588381797     0.010405         -0.001856  541206592\n",
+       "541509790     0.004893          0.000567  541206592\n",
+       "541510557     0.013460         -0.000835  541206592\n",
+       "588382013     0.006189         -0.002601  541206592\n",
+       "541510881     0.013660          0.002563  541206592\n",
+       "588381926     0.011036          0.000534  541206592\n",
+       "541511180     0.023603          0.001298  541206592\n",
+       "541510562     0.028260          0.000722  541206592\n",
+       "541510868     0.014070          0.000590  541206592\n",
+       "541510415     0.008354         -0.001841  541206592\n",
+       "541510431     0.012637          0.000174  541206592\n",
+       "588382090     0.015803          0.001929  541206592\n",
+       "...                ...               ...        ...\n",
+       "541510805     0.008394         -0.001255  541206592\n",
+       "541509824     0.012372         -0.001821  541206592\n",
+       "541510495     0.011955          0.000789  541206592\n",
+       "541509989     0.006812         -0.001541  541206592\n",
+       "541510593     0.018928          0.000220  541206592\n",
+       "541511105     0.007237          0.003370  541206592\n",
+       "541510817     0.009931          0.000261  541206592\n",
+       "541510820     0.002657         -0.001100  541206592\n",
+       "541511146     0.016910         -0.001118  541206592\n",
+       "588381910     0.014114         -0.001044  541206592\n",
+       "588382034     0.018248         -0.001997  541206592\n",
+       "541509654     0.019100         -0.001091  541206592\n",
+       "541509657     0.022848          0.001158  541206592\n",
+       "541510551     0.016744         -0.000621  541206592\n",
+       "541509769     0.018003          0.000743  541206592\n",
+       "541511040     0.015979         -0.002794  541206592\n",
+       "541510646     0.007659          0.000987  541206592\n",
+       "588381898     0.014850          0.001397  541206592\n",
+       "588381904     0.008182          0.000906  541206592\n",
+       "541511092     0.016372          0.000265  541206592\n",
+       "588381999     0.012317          0.003498  541206592\n",
+       "541510679     0.017626         -0.000057  541206592\n",
+       "541509977     0.011702         -0.002005  541206592\n",
+       "541510142     0.011228         -0.001136  541206592\n",
+       "541509981     0.008026          0.000579  541206592\n",
+       "541509952     0.010150          0.003023  541206592\n",
+       "541510950     0.026272          0.000516  541206592\n",
+       "541511172     0.017314          0.001662  541206592\n",
+       "541509957     0.005616          0.002834  541206592\n",
+       "541511118     0.010208         -0.003117  541206592\n",
+       "\n",
+       "[154 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_mutual_info(oeid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi_data = []\n",
+    "for oeid in experiments.index:\n",
+    "    neuron_stimulus_info = compute_mutual_info(oeid)\n",
+    "    mi_data.append(neuron_stimulus_info)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi_data = pd.concat(mi_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>acquisition_age_days</th>\n",
+       "      <th>cre_line</th>\n",
+       "      <th>donor_name</th>\n",
+       "      <th>experiment_container_id</th>\n",
+       "      <th>fail_eye_tracking</th>\n",
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+       "      <th>reporter_line</th>\n",
+       "      <th>session_type</th>\n",
+       "      <th>specimen_name</th>\n",
+       "      <th>targeted_structure</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>511458874</th>\n",
+       "      <td>72</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>234584</td>\n",
+       "      <td>511511089</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
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+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-234584</td>\n",
+       "      <td>VISl</td>\n",
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+       "    <tr>\n",
+       "      <th>562296530</th>\n",
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+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>276949</td>\n",
+       "      <td>561463418</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571099190</th>\n",
+       "      <td>144</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>283278</td>\n",
+       "      <td>570994450</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-283278</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503772253</th>\n",
+       "      <td>103</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>225037</td>\n",
+       "      <td>511510822</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-225037</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580124131</th>\n",
+       "      <td>93</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>300663</td>\n",
+       "      <td>580051757</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
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+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-300663</td>\n",
+       "      <td>VISrl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570909395</th>\n",
+       "      <td>126</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>286366</td>\n",
+       "      <td>570428250</td>\n",
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+       "      <td>350</td>\n",
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+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-286366</td>\n",
+       "      <td>VISrl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570006683</th>\n",
+       "      <td>139</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>282820</td>\n",
+       "      <td>569287964</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-282820</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568775666</th>\n",
+       "      <td>126</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>282817</td>\n",
+       "      <td>565216521</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-282817</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548227481</th>\n",
+       "      <td>113</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>260936</td>\n",
+       "      <td>547315012</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-260936</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569493514</th>\n",
+       "      <td>152</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>279430</td>\n",
+       "      <td>567919555</td>\n",
+       "      <td>False</td>\n",
+       "      <td>300</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-279430</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505696248</th>\n",
+       "      <td>84</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>229109</td>\n",
+       "      <td>511510836</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-229109</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571912779</th>\n",
+       "      <td>123</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>288600</td>\n",
+       "      <td>571684731</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-288600</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562172003</th>\n",
+       "      <td>96</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>280643</td>\n",
+       "      <td>560876149</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560689712</th>\n",
+       "      <td>95</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>277933</td>\n",
+       "      <td>560578597</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-277933</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553012563</th>\n",
+       "      <td>97</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>268133</td>\n",
+       "      <td>552760668</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-268133</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506520696</th>\n",
+       "      <td>84</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>229470</td>\n",
+       "      <td>511510893</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-229470</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565293865</th>\n",
+       "      <td>145</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>276949</td>\n",
+       "      <td>565039910</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573990411</th>\n",
+       "      <td>116</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>291865</td>\n",
+       "      <td>573378109</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291865</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501794235</th>\n",
+       "      <td>107</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>222424</td>\n",
+       "      <td>511507650</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-222424</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541206592</th>\n",
+       "      <td>129</td>\n",
+       "      <td>Scnn1a-Tg3-Cre</td>\n",
+       "      <td>250789</td>\n",
+       "      <td>540993888</td>\n",
+       "      <td>True</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-250789</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575708990</th>\n",
+       "      <td>125</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>291465</td>\n",
+       "      <td>575302106</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291465</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512124564</th>\n",
+       "      <td>80</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>234831</td>\n",
+       "      <td>512124562</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-234831</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504593468</th>\n",
+       "      <td>128</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>222431</td>\n",
+       "      <td>511510723</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-222431</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531124922</th>\n",
+       "      <td>109</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>249122</td>\n",
+       "      <td>530739574</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-249122</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528972913</th>\n",
+       "      <td>96</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>248894</td>\n",
+       "      <td>528792730</td>\n",
+       "      <td>True</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-248894</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575232864</th>\n",
+       "      <td>94</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>296702</td>\n",
+       "      <td>573261513</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-296702</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563500510</th>\n",
+       "      <td>117</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>280643</td>\n",
+       "      <td>564791561</td>\n",
+       "      <td>True</td>\n",
+       "      <td>365</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559082739</th>\n",
+       "      <td>118</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>271750</td>\n",
+       "      <td>555040113</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-271750</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507304910</th>\n",
+       "      <td>89</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>229106</td>\n",
+       "      <td>511510940</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-229106</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567763333</th>\n",
+       "      <td>124</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>282820</td>\n",
+       "      <td>566674370</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-282820</td>\n",
+       "      <td>VISl</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505687912</th>\n",
+       "      <td>133</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>222424</td>\n",
+       "      <td>511510776</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-222424</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502368172</th>\n",
+       "      <td>95</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>225037</td>\n",
+       "      <td>511510670</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-225037</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505801925</th>\n",
+       "      <td>89</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>228379</td>\n",
+       "      <td>511510994</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-228379</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555018432</th>\n",
+       "      <td>98</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>270341</td>\n",
+       "      <td>554037268</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-270341</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506156402</th>\n",
+       "      <td>85</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>229105</td>\n",
+       "      <td>511510998</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-229105</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539643002</th>\n",
+       "      <td>121</td>\n",
+       "      <td>Scnn1a-Tg3-Cre</td>\n",
+       "      <td>250789</td>\n",
+       "      <td>535575493</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-250789</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511458599</th>\n",
+       "      <td>86</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>232623</td>\n",
+       "      <td>511500480</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-232623</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572499364</th>\n",
+       "      <td>108</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>291865</td>\n",
+       "      <td>572376866</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291865</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576261945</th>\n",
+       "      <td>123</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>292490</td>\n",
+       "      <td>575771818</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre_KL100;Camk2a-tTA;Ai93-292490</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538803517</th>\n",
+       "      <td>78</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>257786</td>\n",
+       "      <td>538803515</td>\n",
+       "      <td>True</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-257786</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501929146</th>\n",
+       "      <td>110</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>222426</td>\n",
+       "      <td>511510715</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-222426</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550197614</th>\n",
+       "      <td>114</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>261969</td>\n",
+       "      <td>549855418</td>\n",
+       "      <td>True</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-261969</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509962140</th>\n",
+       "      <td>103</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>228786</td>\n",
+       "      <td>511510675</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-228786</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509517577</th>\n",
+       "      <td>98</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>229105</td>\n",
+       "      <td>511510681</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-229105</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552324309</th>\n",
+       "      <td>91</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>268133</td>\n",
+       "      <td>551888517</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-268133</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502810282</th>\n",
+       "      <td>97</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>225037</td>\n",
+       "      <td>511510699</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-225037</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563226901</th>\n",
+       "      <td>162</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>271732</td>\n",
+       "      <td>560722730</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-271732</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554219904</th>\n",
+       "      <td>86</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>271729</td>\n",
+       "      <td>554219902</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-271729</td>\n",
+       "      <td>VISl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528574532</th>\n",
+       "      <td>155</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>237706</td>\n",
+       "      <td>527676429</td>\n",
+       "      <td>True</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-237706</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557589954</th>\n",
+       "      <td>94</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>273576</td>\n",
+       "      <td>556936291</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-273576</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562660121</th>\n",
+       "      <td>111</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>280643</td>\n",
+       "      <td>564791547</td>\n",
+       "      <td>False</td>\n",
+       "      <td>265</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580878455</th>\n",
+       "      <td>119</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>296704</td>\n",
+       "      <td>575710989</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-296704</td>\n",
+       "      <td>VISrl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502741583</th>\n",
+       "      <td>87</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>226219</td>\n",
+       "      <td>511510896</td>\n",
+       "      <td>False</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-226219</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557420967</th>\n",
+       "      <td>91</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>273904</td>\n",
+       "      <td>560821491</td>\n",
+       "      <td>False</td>\n",
+       "      <td>325</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-273904</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577820172</th>\n",
+       "      <td>110</td>\n",
+       "      <td>Rorb-IRES2-Cre</td>\n",
+       "      <td>295995</td>\n",
+       "      <td>576411244</td>\n",
+       "      <td>False</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rorb-IRES2-Cre;Camk2a-tTA;Ai93-295995</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559869893</th>\n",
+       "      <td>80</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>279437</td>\n",
+       "      <td>559645337</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-279437</td>\n",
+       "      <td>VISam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504808661</th>\n",
+       "      <td>84</td>\n",
+       "      <td>Cux2-CreERT2</td>\n",
+       "      <td>228378</td>\n",
+       "      <td>511510884</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Cux2-CreERT2;Camk2a-tTA;Ai93-228378</td>\n",
+       "      <td>VISp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510517609</th>\n",
+       "      <td>75</td>\n",
+       "      <td>Rbp4-Cre_KL100</td>\n",
+       "      <td>233442</td>\n",
+       "      <td>511511006</td>\n",
+       "      <td>True</td>\n",
+       "      <td>375</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Rbp4-Cre;Camk2a-tTA;Ai93-233442</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560802931</th>\n",
+       "      <td>81</td>\n",
+       "      <td>Emx1-IRES-Cre</td>\n",
+       "      <td>280639</td>\n",
+       "      <td>560753319</td>\n",
+       "      <td>False</td>\n",
+       "      <td>175</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Emx1-IRES-Cre;Camk2a-tTA;Ai93-280639</td>\n",
+       "      <td>VISal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560916904</th>\n",
+       "      <td>97</td>\n",
+       "      <td>Nr5a1-Cre</td>\n",
+       "      <td>277933</td>\n",
+       "      <td>560809200</td>\n",
+       "      <td>False</td>\n",
+       "      <td>350</td>\n",
+       "      <td>Ai93(TITL-GCaMP6f)</td>\n",
+       "      <td>three_session_B</td>\n",
+       "      <td>Nr5a1-Cre;Camk2a-tTA;Ai93-277933</td>\n",
+       "      <td>VISpm</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>181 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           acquisition_age_days        cre_line donor_name  \\\n",
+       "id                                                           \n",
+       "511458874                    72  Rbp4-Cre_KL100     234584   \n",
+       "562296530                   129   Emx1-IRES-Cre     276949   \n",
+       "571099190                   144    Cux2-CreERT2     283278   \n",
+       "503772253                   103    Cux2-CreERT2     225037   \n",
+       "580124131                    93  Rbp4-Cre_KL100     300663   \n",
+       "570909395                   126       Nr5a1-Cre     286366   \n",
+       "570006683                   139    Cux2-CreERT2     282820   \n",
+       "568775666                   126    Cux2-CreERT2     282817   \n",
+       "548227481                   113   Emx1-IRES-Cre     260936   \n",
+       "569493514                   152       Nr5a1-Cre     279430   \n",
+       "505696248                    84    Cux2-CreERT2     229109   \n",
+       "571912779                   123  Rbp4-Cre_KL100     288600   \n",
+       "562172003                    96   Emx1-IRES-Cre     280643   \n",
+       "560689712                    95       Nr5a1-Cre     277933   \n",
+       "553012563                    97  Rorb-IRES2-Cre     268133   \n",
+       "506520696                    84    Cux2-CreERT2     229470   \n",
+       "565293865                   145   Emx1-IRES-Cre     276949   \n",
+       "573990411                   116  Rbp4-Cre_KL100     291865   \n",
+       "501794235                   107    Cux2-CreERT2     222424   \n",
+       "541206592                   129  Scnn1a-Tg3-Cre     250789   \n",
+       "575708990                   125  Rbp4-Cre_KL100     291465   \n",
+       "512124564                    80  Rorb-IRES2-Cre     234831   \n",
+       "504593468                   128  Rorb-IRES2-Cre     222431   \n",
+       "531124922                   109  Rorb-IRES2-Cre     249122   \n",
+       "528972913                    96       Nr5a1-Cre     248894   \n",
+       "575232864                    94   Emx1-IRES-Cre     296702   \n",
+       "563500510                   117   Emx1-IRES-Cre     280643   \n",
+       "559082739                   118  Rbp4-Cre_KL100     271750   \n",
+       "507304910                    89    Cux2-CreERT2     229106   \n",
+       "567763333                   124    Cux2-CreERT2     282820   \n",
+       "...                         ...             ...        ...   \n",
+       "505687912                   133    Cux2-CreERT2     222424   \n",
+       "502368172                    95    Cux2-CreERT2     225037   \n",
+       "505801925                    89    Cux2-CreERT2     228379   \n",
+       "555018432                    98       Nr5a1-Cre     270341   \n",
+       "506156402                    85    Cux2-CreERT2     229105   \n",
+       "539643002                   121  Scnn1a-Tg3-Cre     250789   \n",
+       "511458599                    86  Rorb-IRES2-Cre     232623   \n",
+       "572499364                   108  Rbp4-Cre_KL100     291865   \n",
+       "576261945                   123  Rbp4-Cre_KL100     292490   \n",
+       "538803517                    78       Nr5a1-Cre     257786   \n",
+       "501929146                   110    Cux2-CreERT2     222426   \n",
+       "550197614                   114       Nr5a1-Cre     261969   \n",
+       "509962140                   103  Rorb-IRES2-Cre     228786   \n",
+       "509517577                    98    Cux2-CreERT2     229105   \n",
+       "552324309                    91  Rorb-IRES2-Cre     268133   \n",
+       "502810282                    97    Cux2-CreERT2     225037   \n",
+       "563226901                   162       Nr5a1-Cre     271732   \n",
+       "554219904                    86       Nr5a1-Cre     271729   \n",
+       "528574532                   155   Emx1-IRES-Cre     237706   \n",
+       "557589954                    94    Cux2-CreERT2     273576   \n",
+       "562660121                   111   Emx1-IRES-Cre     280643   \n",
+       "580878455                   119   Emx1-IRES-Cre     296704   \n",
+       "502741583                    87  Rbp4-Cre_KL100     226219   \n",
+       "557420967                    91       Nr5a1-Cre     273904   \n",
+       "577820172                   110  Rorb-IRES2-Cre     295995   \n",
+       "559869893                    80       Nr5a1-Cre     279437   \n",
+       "504808661                    84    Cux2-CreERT2     228378   \n",
+       "510517609                    75  Rbp4-Cre_KL100     233442   \n",
+       "560802931                    81   Emx1-IRES-Cre     280639   \n",
+       "560916904                    97       Nr5a1-Cre     277933   \n",
+       "\n",
+       "           experiment_container_id  fail_eye_tracking  imaging_depth  \\\n",
+       "id                                                                     \n",
+       "511458874                511511089               True            375   \n",
+       "562296530                561463418              False            175   \n",
+       "571099190                570994450              False            175   \n",
+       "503772253                511510822               True            175   \n",
+       "580124131                580051757              False            375   \n",
+       "570909395                570428250              False            350   \n",
+       "570006683                569287964              False            175   \n",
+       "568775666                565216521              False            175   \n",
+       "548227481                547315012               True            175   \n",
+       "569493514                567919555              False            300   \n",
+       "505696248                511510836              False            275   \n",
+       "571912779                571684731              False            375   \n",
+       "562172003                560876149              False            175   \n",
+       "560689712                560578597              False            350   \n",
+       "553012563                552760668              False            275   \n",
+       "506520696                511510893               True            175   \n",
+       "565293865                565039910              False            375   \n",
+       "573990411                573378109              False            375   \n",
+       "501794235                511507650               True            175   \n",
+       "541206592                540993888               True            350   \n",
+       "575708990                575302106               True            375   \n",
+       "512124564                512124562              False            275   \n",
+       "504593468                511510723              False            275   \n",
+       "531124922                530739574               True            275   \n",
+       "528972913                528792730               True            350   \n",
+       "575232864                573261513              False            275   \n",
+       "563500510                564791561               True            365   \n",
+       "559082739                555040113              False            375   \n",
+       "507304910                511510940              False            275   \n",
+       "567763333                566674370              False            275   \n",
+       "...                            ...                ...            ...   \n",
+       "505687912                511510776              False            275   \n",
+       "502368172                511510670               True            175   \n",
+       "505801925                511510994               True            175   \n",
+       "555018432                554037268              False            350   \n",
+       "506156402                511510998              False            175   \n",
+       "539643002                535575493               True            275   \n",
+       "511458599                511500480               True            275   \n",
+       "572499364                572376866               True            375   \n",
+       "576261945                575771818               True            375   \n",
+       "538803517                538803515               True            350   \n",
+       "501929146                511510715               True            175   \n",
+       "550197614                549855418               True            350   \n",
+       "509962140                511510675               True            275   \n",
+       "509517577                511510681              False            175   \n",
+       "552324309                551888517              False            275   \n",
+       "502810282                511510699               True            275   \n",
+       "563226901                560722730              False            350   \n",
+       "554219904                554219902              False            350   \n",
+       "528574532                527676429               True            275   \n",
+       "557589954                556936291              False            275   \n",
+       "562660121                564791547              False            265   \n",
+       "580878455                575710989              False            275   \n",
+       "502741583                511510896              False            375   \n",
+       "557420967                560821491              False            325   \n",
+       "577820172                576411244              False            275   \n",
+       "559869893                559645337              False            350   \n",
+       "504808661                511510884               True            175   \n",
+       "510517609                511511006               True            375   \n",
+       "560802931                560753319              False            175   \n",
+       "560916904                560809200              False            350   \n",
+       "\n",
+       "                reporter_line     session_type  \\\n",
+       "id                                               \n",
+       "511458874  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "562296530  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "571099190  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "503772253  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "580124131  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "570909395  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "570006683  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "568775666  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "548227481  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "569493514  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "505696248  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "571912779  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "562172003  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "560689712  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "553012563  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "506520696  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "565293865  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "573990411  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "501794235  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "541206592  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "575708990  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "512124564  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "504593468  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "531124922  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "528972913  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "575232864  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "563500510  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "559082739  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "507304910  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "567763333  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "...                       ...              ...   \n",
+       "505687912  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "502368172  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "505801925  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "555018432  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "506156402  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "539643002  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "511458599  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "572499364  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "576261945  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "538803517  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "501929146  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "550197614  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "509962140  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "509517577  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "552324309  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "502810282  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "563226901  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "554219904  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "528574532  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "557589954  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "562660121  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "580878455  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "502741583  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "557420967  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "577820172  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "559869893  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "504808661  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "510517609  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "560802931  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "560916904  Ai93(TITL-GCaMP6f)  three_session_B   \n",
+       "\n",
+       "                                   specimen_name targeted_structure  \n",
+       "id                                                                   \n",
+       "511458874        Rbp4-Cre;Camk2a-tTA;Ai93-234584               VISl  \n",
+       "562296530   Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949              VISpm  \n",
+       "571099190    Cux2-CreERT2;Camk2a-tTA;Ai93-283278              VISam  \n",
+       "503772253    Cux2-CreERT2;Camk2a-tTA;Ai93-225037              VISpm  \n",
+       "580124131  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-300663              VISrl  \n",
+       "570909395       Nr5a1-Cre;Camk2a-tTA;Ai93-286366              VISrl  \n",
+       "570006683    Cux2-CreERT2;Camk2a-tTA;Ai93-282820              VISam  \n",
+       "568775666    Cux2-CreERT2;Camk2a-tTA;Ai93-282817              VISam  \n",
+       "548227481   Emx1-IRES-Cre;Camk2a-tTA;Ai93-260936               VISl  \n",
+       "569493514       Nr5a1-Cre;Camk2a-tTA;Ai93-279430               VISl  \n",
+       "505696248    Cux2-CreERT2;Camk2a-tTA;Ai93-229109              VISpm  \n",
+       "571912779        Rbp4-Cre;Camk2a-tTA;Ai93-288600              VISpm  \n",
+       "562172003   Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643              VISal  \n",
+       "560689712       Nr5a1-Cre;Camk2a-tTA;Ai93-277933               VISl  \n",
+       "553012563  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-268133              VISam  \n",
+       "506520696    Cux2-CreERT2;Camk2a-tTA;Ai93-229470              VISpm  \n",
+       "565293865   Emx1-IRES-Cre;Camk2a-tTA;Ai93-276949              VISpm  \n",
+       "573990411  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291865               VISl  \n",
+       "501794235    Cux2-CreERT2;Camk2a-tTA;Ai93-222424               VISp  \n",
+       "541206592  Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-250789               VISp  \n",
+       "575708990  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291465              VISam  \n",
+       "512124564  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-234831               VISp  \n",
+       "504593468  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-222431              VISal  \n",
+       "531124922  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-249122               VISp  \n",
+       "528972913       Nr5a1-Cre;Camk2a-tTA;Ai93-248894               VISp  \n",
+       "575232864   Emx1-IRES-Cre;Camk2a-tTA;Ai93-296702               VISl  \n",
+       "563500510   Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643              VISal  \n",
+       "559082739        Rbp4-Cre;Camk2a-tTA;Ai93-271750               VISp  \n",
+       "507304910    Cux2-CreERT2;Camk2a-tTA;Ai93-229106              VISpm  \n",
+       "567763333    Cux2-CreERT2;Camk2a-tTA;Ai93-282820               VISl  \n",
+       "...                                          ...                ...  \n",
+       "505687912    Cux2-CreERT2;Camk2a-tTA;Ai93-222424              VISpm  \n",
+       "502368172    Cux2-CreERT2;Camk2a-tTA;Ai93-225037               VISp  \n",
+       "505801925    Cux2-CreERT2;Camk2a-tTA;Ai93-228379               VISl  \n",
+       "555018432       Nr5a1-Cre;Camk2a-tTA;Ai93-270341              VISpm  \n",
+       "506156402    Cux2-CreERT2;Camk2a-tTA;Ai93-229105              VISal  \n",
+       "539643002  Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-250789               VISp  \n",
+       "511458599  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-232623              VISal  \n",
+       "572499364  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-291865              VISal  \n",
+       "576261945  Rbp4-Cre_KL100;Camk2a-tTA;Ai93-292490               VISl  \n",
+       "538803517       Nr5a1-Cre;Camk2a-tTA;Ai93-257786               VISp  \n",
+       "501929146    Cux2-CreERT2;Camk2a-tTA;Ai93-222426              VISal  \n",
+       "550197614       Nr5a1-Cre;Camk2a-tTA;Ai93-261969              VISpm  \n",
+       "509962140  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-228786               VISp  \n",
+       "509517577    Cux2-CreERT2;Camk2a-tTA;Ai93-229105              VISpm  \n",
+       "552324309  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-268133              VISpm  \n",
+       "502810282    Cux2-CreERT2;Camk2a-tTA;Ai93-225037               VISp  \n",
+       "563226901       Nr5a1-Cre;Camk2a-tTA;Ai93-271732              VISam  \n",
+       "554219904       Nr5a1-Cre;Camk2a-tTA;Ai93-271729               VISl  \n",
+       "528574532   Emx1-IRES-Cre;Camk2a-tTA;Ai93-237706               VISp  \n",
+       "557589954    Cux2-CreERT2;Camk2a-tTA;Ai93-273576              VISal  \n",
+       "562660121   Emx1-IRES-Cre;Camk2a-tTA;Ai93-280643              VISal  \n",
+       "580878455   Emx1-IRES-Cre;Camk2a-tTA;Ai93-296704              VISrl  \n",
+       "502741583        Rbp4-Cre;Camk2a-tTA;Ai93-226219               VISp  \n",
+       "557420967       Nr5a1-Cre;Camk2a-tTA;Ai93-273904              VISpm  \n",
+       "577820172  Rorb-IRES2-Cre;Camk2a-tTA;Ai93-295995              VISam  \n",
+       "559869893       Nr5a1-Cre;Camk2a-tTA;Ai93-279437              VISam  \n",
+       "504808661    Cux2-CreERT2;Camk2a-tTA;Ai93-228378               VISp  \n",
+       "510517609        Rbp4-Cre;Camk2a-tTA;Ai93-233442              VISpm  \n",
+       "560802931   Emx1-IRES-Cre;Camk2a-tTA;Ai93-280639              VISal  \n",
+       "560916904       Nr5a1-Cre;Camk2a-tTA;Ai93-277933              VISpm  \n",
+       "\n",
+       "[181 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi_data = mi_data.merge(experiments,left_on='oeid',right_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mi_data['depth'] = mi_data['imaging_depth'].map(lambda x: np.floor(x/100)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff95933fc10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.factorplot('targeted_structure','mutual_info',data=mi_data,\n",
+    "#                    hue='cre_line',\n",
+    "                   row='depth',\n",
+    "                   aspect=3,\n",
+    "                   kind='strip',\n",
+    "                   jitter=True\n",
+    "                  )\n",
+    "for ax in g.axes.flatten():\n",
+    "    ax.axhline(0.0,color='0.5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff959cd2590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask = (\n",
+    "    (mi_data['targeted_structure'].isin(['VISp','VISl','VISpm']))\n",
+    ")\n",
+    "\n",
+    "g = sns.factorplot('targeted_structure','mutual_info',data=mi_data[mask],\n",
+    "#                    hue='cre_line',\n",
+    "                   hue='depth',\n",
+    "                   aspect=3,\n",
+    "                   kind='violin',\n",
+    "#                    jitter=True\n",
+    "                  )\n",
+    "for ax in g.axes.flatten():\n",
+    "    ax.axhline(0.0,color='0.5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff95b4fd810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lmplot('mutual_info_null','mutual_info',data=mi_data,\n",
+    "#                hue='cre_line',\n",
+    "               col='targeted_structure',\n",
+    "               row='depth',\n",
+    "               fit_reg=False,\n",
+    "              )\n",
+    "for ax in g.axes.flatten():\n",
+    "    ax.plot([0,0.007],[0,0.007],color='k')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:mnist_openscope]",
+   "language": "python",
+   "name": "conda-env-mnist_openscope-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sweep_response_to_csv.ipynb b/sweep_response_to_csv.ipynb
new file mode 100644
index 0000000..881cfcf
--- /dev/null
+++ b/sweep_response_to_csv.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## requirements\n",
+    "# allensdk\n",
+    "# scikit-learn > 0.19\n",
+    "# xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boc = BrainObservatoryCache(manifest_file='/local1/data/boc/manifest.json',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 541206592\n",
+    "\n",
+    "# Initializations:\n",
+    "nwb_dataset = boc.get_ophys_experiment_data(oeid)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
+    "\n",
+    "def get_ns_msrx(nwb_dataset):\n",
+    "    ns = NaturalScenes(nwb_dataset)\n",
+    "    mean_sweep_response = ns.mean_sweep_response.copy()\n",
+    "    \n",
+    "    # I don't know what dx is. goodbye!\n",
+    "    mean_sweep_response.drop('dx',axis=1,inplace=True)\n",
+    "    \n",
+    "    # annotate the dataframe with useful indices and columns\n",
+    "    time = pd.Series(\n",
+    "        ns.timestamps[ns.stim_table['start']],\n",
+    "        name='time',\n",
+    "    )\n",
+    "    neurons = pd.Series(\n",
+    "        ns.cell_id,\n",
+    "        name='neuron',\n",
+    "    )\n",
+    "    mean_sweep_response.set_index(time,inplace=True)\n",
+    "    mean_sweep_response.columns = neurons\n",
+    "    \n",
+    "    # convert this to xarray & annotate images\n",
+    "    srx = xr.DataArray(mean_sweep_response)\n",
+    "    srx.coords['natural_image'] = ('time',ns.stim_table['frame'])\n",
+    "    \n",
+    "    return srx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (time: 5950, neuron: 154)>\n",
+      "array([[  2.750398,   3.113332,   3.283231, ...,   3.246232,  17.838305,\n",
+      "         44.883263],\n",
+      "       [  5.472741,   4.520462,   1.848134, ...,   3.700325,  41.864319,\n",
+      "         55.052734],\n",
+      "       [  4.938696,   1.872071,   0.822514, ...,   1.446858,  19.081219,\n",
+      "         -0.731454],\n",
+      "       ..., \n",
+      "       [  0.984122,  -1.035176,   2.519683, ...,  -0.894795,  14.030047,\n",
+      "          1.291967],\n",
+      "       [  1.705522,   1.379635,   1.116845, ...,   0.169055,   6.26528 ,\n",
+      "          1.80034 ],\n",
+      "       [  0.420941,   2.543081,   2.991403, ...,   0.980473,  -0.720777,\n",
+      "          4.779262]])\n",
+      "Coordinates:\n",
+      "  * time           (time) float64 545.2 545.5 545.7 546.0 546.2 546.5 546.7 ...\n",
+      "  * neuron         (neuron) int64 541510267 541510270 541510307 541510405 ...\n",
+      "    natural_image  (time) int64 92 27 52 37 103 1 54 19 54 -1 74 115 44 88 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "msrx = get_ns_msrx(nwb_dataset)\n",
+    "print msrx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def decode(msrx):\n",
+    "    \n",
+    "    # get features and output\n",
+    "    X = msrx.data\n",
+    "    y = msrx['natural_image']\n",
+    "    \n",
+    "    # split training & testing\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,stratify=y)\n",
+    "    \n",
+    "    # do the classification\n",
+    "    lm = LogisticRegression(\n",
+    "        solver='saga',\n",
+    "        multi_class='ovr',\n",
+    "        penalty='l1',\n",
+    "        n_jobs=-1,\n",
+    "    )\n",
+    "    lm.fit(X_train,y_train)\n",
+    "    return lm.score(X_test,y_test) * len(np.unique(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19.9\n",
+      "1 loop, best of 1: 50.1 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print decode(msrx)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:jk]",
+   "language": "python",
+   "name": "conda-env-jk-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/brain_observatory.ipynb b/tutorial/brain_observatory.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brain Observatory\n",
+    "This notebook documents some classes and functions in the AllenSDK that help manipulate files and data structures in the Allen Brain Observatory.  The main entry point in the `BrainObservatoryCache` class.  This class is responsible for downloading any requested data or metadata on request and storing it in well known locations.\n",
+    "\n",
+    "Download this file in .ipynb format <a href='brain_observatory.ipynb'>here</a>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment Containers\n",
+    "The experiment container describes a set of experiments performed with the same targeted area, imaging depth, and Cre line.  The `BrainObservatoryCache` has a number of functions for figuring out what experiment containers are available at the moment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all targeted structures: [u'VISal', u'VISam', u'VISl', u'VISp', u'VISpm', u'VISrl']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "import pprint\n",
+    "\n",
+    "# This class uses a 'manifest' to keep track of downloaded data and metadata.  \n",
+    "# All downloaded files will be stored relative to the directory holding the manifest\n",
+    "# file.  If 'manifest_file' is a relative path (as it is below), it will be \n",
+    "# saved relative to your working directory.  It can also be an absolute path.\n",
+    "boc = BrainObservatoryCache()\n",
+    "\n",
+    "# Download a list of all targeted areas\n",
+    "targeted_structures = boc.get_all_targeted_structures()\n",
+    "print(\"all targeted structures: \" + str(targeted_structures))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all VISp experiment containers: 46\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download experiment containers for VISp experiments\n",
+    "visp_ecs = boc.get_experiment_containers(targeted_structures=['VISp'])\n",
+    "print(\"all VISp experiment containers: %d\" % len(visp_ecs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all imaging depths: [175, 265, 275, 300, 320, 325, 335, 350, 365, 375]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download a list of all imaging depths\n",
+    "depths = boc.get_all_imaging_depths()\n",
+    "print(\"all imaging depths: \" + str(depths))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all stimuli:\n",
+      "\n",
+      "['drifting_gratings',\n",
+      " 'locally_sparse_noise',\n",
+      " 'locally_sparse_noise_4deg',\n",
+      " 'locally_sparse_noise_8deg',\n",
+      " 'natural_movie_one',\n",
+      " 'natural_movie_three',\n",
+      " 'natural_movie_two',\n",
+      " 'natural_scenes',\n",
+      " 'spontaneous',\n",
+      " 'static_gratings']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download a list of all stimuli\n",
+    "stims = boc.get_all_stimuli()\n",
+    "print(\"all stimuli:\\n\")\n",
+    "pprint.pprint(stims)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all cre lines:\n",
+      "\n",
+      "[u'Cux2-CreERT2',\n",
+      " u'Emx1-IRES-Cre',\n",
+      " u'Nr5a1-Cre',\n",
+      " u'Rbp4-Cre_KL100',\n",
+      " u'Rorb-IRES2-Cre',\n",
+      " u'Scnn1a-Tg3-Cre']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download a list of all cre driver lines \n",
+    "cre_lines = boc.get_all_cre_lines()\n",
+    "print(\"all cre lines:\\n\")\n",
+    "pprint.pprint(cre_lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cux2 experiments: 62\n",
+      "\n",
+      "Example experiment container record:\n",
+      "{'cre_line': u'Cux2-CreERT2',\n",
+      " 'donor_name': u'283284',\n",
+      " 'failed': False,\n",
+      " 'id': 566759225,\n",
+      " 'imaging_depth': 275,\n",
+      " 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      " 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
+      " 'tags': [],\n",
+      " 'targeted_structure': u'VISam'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download experiment containers for Cux2 experiments\n",
+    "cux2_ecs = boc.get_experiment_containers(cre_lines=['Cux2-CreERT2'])\n",
+    "print(\"Cux2 experiments: %d\\n\" % len(cux2_ecs))\n",
+    "\n",
+    "print(\"Example experiment container record:\")\n",
+    "pprint.pprint(cux2_ecs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download Experiments for a Container\n",
+    "An experiment container is a group of experiments.  Each experiment has a different stimulus protocol.  For example, one experiment protocol contains the static gratings stimulus and another has the natural scenes stimulus.  The `BrainObservatoryCache` helps you find out which experiment associated with a container has the stimuli you are interested in.  First, let's see what experiments are available for a single container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Experiments for experiment_container_id 566759225: 3\n",
+      "\n",
+      "[{'acquisition_age_days': 129,\n",
+      "  'cre_line': u'Cux2-CreERT2',\n",
+      "  'donor_name': u'283284',\n",
+      "  'experiment_container_id': 566759225,\n",
+      "  'fail_eye_tracking': False,\n",
+      "  'id': 569494121,\n",
+      "  'imaging_depth': 275,\n",
+      "  'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      "  'session_type': u'three_session_C2',\n",
+      "  'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
+      "  'targeted_structure': u'VISam'},\n",
+      " {'acquisition_age_days': 128,\n",
+      "  'cre_line': u'Cux2-CreERT2',\n",
+      "  'donor_name': u'283284',\n",
+      "  'experiment_container_id': 566759225,\n",
+      "  'fail_eye_tracking': False,\n",
+      "  'id': 569407590,\n",
+      "  'imaging_depth': 275,\n",
+      "  'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      "  'session_type': u'three_session_B',\n",
+      "  'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
+      "  'targeted_structure': u'VISam'},\n",
+      " {'acquisition_age_days': 140,\n",
+      "  'cre_line': u'Cux2-CreERT2',\n",
+      "  'donor_name': u'283284',\n",
+      "  'experiment_container_id': 566759225,\n",
+      "  'fail_eye_tracking': False,\n",
+      "  'id': 570305847,\n",
+      "  'imaging_depth': 275,\n",
+      "  'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      "  'session_type': u'three_session_A',\n",
+      "  'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
+      "  'targeted_structure': u'VISam'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find all of the experiments for an experiment container\n",
+    "cux2_ec_id = cux2_ecs[0]['id']\n",
+    "exps = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id])\n",
+    "print(\"Experiments for experiment_container_id %d: %d\\n\" % (cux2_ec_id, len(exps)))\n",
+    "pprint.pprint(exps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `session_type` field indicates which experimental protocol was used.  If you just want to find which experiment contains the static gratings stimulus, you can do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Experiment with static gratings:\n",
+      "{'acquisition_age_days': 101,\n",
+      " 'cre_line': u'Cux2-CreERT2',\n",
+      " 'donor_name': u'225036',\n",
+      " 'experiment_container_id': 511510779,\n",
+      " 'fail_eye_tracking': True,\n",
+      " 'id': 503019786,\n",
+      " 'imaging_depth': 275,\n",
+      " 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      " 'session_type': u'three_session_B',\n",
+      " 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-225036',\n",
+      " 'targeted_structure': u'VISp'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import allensdk.brain_observatory.stimulus_info as stim_info\n",
+    "\n",
+    "# pick one of the cux2 experiment containers\n",
+    "cux2_ec_id = cux2_ecs[-1]['id']\n",
+    "\n",
+    "# Find the experiment with the static static gratings stimulus\n",
+    "exp = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id], \n",
+    "                                stimuli=[stim_info.STATIC_GRATINGS])[0]\n",
+    "print(\"Experiment with static gratings:\")\n",
+    "pprint.pprint(exp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can download the NWB file for this experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'age_days': 101,\n",
+      " 'cre_line': u'Cux2-CreERT2/wt',\n",
+      " 'device': u'Nikon A1R-MP multiphoton microscope',\n",
+      " 'device_name': u'CAM2P.2',\n",
+      " 'excitation_lambda': u'910 nanometers',\n",
+      " 'experiment_container_id': 511510779,\n",
+      " 'fov': u'400x400 microns (512 x 512 pixels)',\n",
+      " 'genotype': u'Cux2-CreERT2/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)',\n",
+      " 'imaging_depth_um': 275,\n",
+      " 'indicator': u'GCaMP6f',\n",
+      " 'ophys_experiment_id': 503019786,\n",
+      " 'pipeline_version': u'2.0',\n",
+      " 'session_start_time': datetime.datetime(2016, 2, 21, 17, 41, 47),\n",
+      " 'session_type': u'three_session_B',\n",
+      " 'sex': u'male',\n",
+      " 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-225036',\n",
+      " 'targeted_structure': u'VISp'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "exp = boc.get_ophys_experiment_data(exp['id'])\n",
+    "\n",
+    "# print out the metadata available in the NWB file\n",
+    "pprint.pprint(exp.get_metadata())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find Cells of Interest\n",
+    "Another way to look for data is to search for cells with interesting tuning properties.  We have pre-computed a set of cell metrics.  Take a look at <a href='http://alleninstitute.github.io/AllenSDK/brain_observatory_metrics.html'>this page</a> to see the full list.\n",
+    "\n",
+    "You can use these properties to filter the full set of cells down to the set your are interested in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total cells: 37091\n",
+      "VISp cells: 12127\n",
+      "cells with sig. response to drifting gratings: 6610\n",
+      "direction-selective cells: 686\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Download cells for a set of experiments and convert to DataFrame\n",
+    "cells = boc.get_cell_specimens()\n",
+    "cells = pd.DataFrame.from_records(cells)\n",
+    "print(\"total cells: %d\" % len(cells))\n",
+    "\n",
+    "# find direction selective cells in VISp\n",
+    "visp_ec_ids = [ ec['id'] for ec in visp_ecs ]\n",
+    "visp_cells = cells[cells['experiment_container_id'].isin(visp_ec_ids)]\n",
+    "print(\"VISp cells: %d\" % len(visp_cells))\n",
+    "\n",
+    "# significant response to drifting gratings stimulus\n",
+    "sig_cells = visp_cells[visp_cells['p_dg'] < 0.05]\n",
+    "print(\"cells with sig. response to drifting gratings: %d\" % len(sig_cells))\n",
+    "\n",
+    "# direction selective cells\n",
+    "dsi_cells = sig_cells[(sig_cells['g_dsi_dg'] > 0.9)]\n",
+    "print(\"direction-selective cells: %d\" % len(dsi_cells))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find Experiments for Cells\n",
+    "Once you have found a set of cells you would like to investigate, you can use the `BrainObservatoryCache` to find the experiments for those cells that contain the relevant stimulus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total dsi experiment containers: 45\n",
+      "VISp drifting gratings ophys experiments: 45\n",
+      "Example ophys experiment:\n",
+      "{'acquisition_age_days': 111,\n",
+      " 'cre_line': u'Cux2-CreERT2',\n",
+      " 'donor_name': u'244896',\n",
+      " 'experiment_container_id': 527550471,\n",
+      " 'fail_eye_tracking': True,\n",
+      " 'id': 527745328,\n",
+      " 'imaging_depth': 275,\n",
+      " 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
+      " 'session_type': u'three_session_A',\n",
+      " 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-244896',\n",
+      " 'targeted_structure': u'VISp'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import allensdk.brain_observatory.stimulus_info as stim_info\n",
+    "\n",
+    "# find experiment containers for those cells\n",
+    "dsi_ec_ids = dsi_cells['experiment_container_id'].unique()\n",
+    "print(\"total dsi experiment containers: %d\" % len(dsi_ec_ids))\n",
+    "\n",
+    "# Download the ophys experiments containing the drifting gratings stimulus for VISp experiment containers\n",
+    "dsi_exps = boc.get_ophys_experiments(experiment_container_ids=dsi_ec_ids, stimuli=[stim_info.DRIFTING_GRATINGS])\n",
+    "print(\"VISp drifting gratings ophys experiments: %d\" % len(dsi_exps))\n",
+    "\n",
+    "print(\"Example ophys experiment:\")\n",
+    "pprint.pprint(dsi_exps[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download Experiment Data for a Cell\n",
+    "Once you have some experiments, you can download the NWB files that contain the fluorescence traces for segmented cells in those experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metadata from NWB file:\n",
+      "{'age_days': 93,\n",
+      " 'cre_line': u'Scnn1a-Tg3-Cre/wt',\n",
+      " 'device': u'Nikon A1R-MP multiphoton microscope',\n",
+      " 'device_name': u'CAM2P.2',\n",
+      " 'excitation_lambda': u'910 nanometers',\n",
+      " 'experiment_container_id': 511498742,\n",
+      " 'fov': u'400x400 microns (512 x 512 pixels)',\n",
+      " 'genotype': u'Scnn1a-Tg3-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)',\n",
+      " 'imaging_depth_um': 350,\n",
+      " 'indicator': u'GCaMP6f',\n",
+      " 'ophys_experiment_id': 511534603,\n",
+      " 'pipeline_version': u'2.0',\n",
+      " 'session_start_time': datetime.datetime(2016, 4, 1, 9, 12, 3),\n",
+      " 'session_type': u'three_session_A',\n",
+      " 'sex': u'female',\n",
+      " 'specimen_name': u'Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-231953',\n",
+      " 'targeted_structure': u'VISp'}\n",
+      "stimuli available in this file:\n",
+      "[u'drifting_gratings', u'natural_movie_one', u'natural_movie_three', u'spontaneous']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# pick a direction-selective cell and find its NWB file\n",
+    "dsi_cell = dsi_cells.iloc[0]\n",
+    "\n",
+    "# figure out which ophys experiment has the drifting gratings stimulus for that cell\n",
+    "cell_exp = boc.get_ophys_experiments(cell_specimen_ids=[dsi_cell['cell_specimen_id']],\n",
+    "                                     stimuli=[stim_info.DRIFTING_GRATINGS])[0]\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(cell_exp['id'])\n",
+    "\n",
+    "print(\"Metadata from NWB file:\")\n",
+    "pprint.pprint(data_set.get_metadata())\n",
+    "\n",
+    "print(\"stimuli available in this file:\")\n",
+    "print(data_set.list_stimuli())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fluorescence Traces\n",
+    "Now that we have a data set, we can plot the traces for the cell we care about."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dsi_cell_id = dsi_cell['cell_specimen_id']\n",
+    "time, raw_traces = data_set.get_fluorescence_traces(cell_specimen_ids=[dsi_cell_id])\n",
+    "_, demixed_traces = data_set.get_demixed_traces(cell_specimen_ids=[dsi_cell_id])\n",
+    "_, neuropil_traces = data_set.get_neuropil_traces(cell_specimen_ids=[dsi_cell_id])\n",
+    "_, corrected_traces = data_set.get_corrected_fluorescence_traces(cell_specimen_ids=[dsi_cell_id])\n",
+    "_, dff_traces = data_set.get_dff_traces(cell_specimen_ids=[dsi_cell_id])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S1UFxzgDVrrAH4BtuiCcfRFT9Nt4YuOCCpHNBaVIowIkzAGhuBtLU8KUSzd7q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\nT55sLmh4p2W7iRPNuM3NmceJbPY4Zp14ojvsnXe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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbc0d510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Pb52cYTl69+79Y39DQwMaGhrCZIdqVNhCbSXf4lNNiq2DlpZo55f2wkU+tb6v\nVOt2iQJrftKr1o+7OBTb/5J61TWPC4pDY2MjGmP6r5WiwY+I/AzASlVdICKrAzgIwC0i0lFVZzij\nHQvgS6f/FQBPicidMM3dtgIwzC9tb/BD6VUtJ7ZqyWdaLvorV8aXdjU1exszJtp8UO2qlnNMEtL6\nqutqkaYXHsSxbmtte1H8sitF+vTpE1naQWp+fg6gn4i0gWkm96yqviYij4tIVwCtACYDOAsAVHWs\niAwAMBbASgBn801vVIqwNT+jRgH77mv6kzjRVlub8qTnTxS3NNb8sBBoVGo98BX44fHaQLWuaEtP\nVR2tqruqaldV3VlVb3SGn+x876qqR6tqk2eam1V1K1XtoqpD4lwAql1hT8A33RRPPoio+nXuDFxw\nQdK5oDQpFODEGQA0NwNpavhSiWZv9RxMUvrwr60otcp54UESJ9rBgys/z3LE+T9K8+bFk65X9nbn\n3Up/XC/G998DH35YXhpcl/nxf36CmzoViLAFz4+SeuaHqNow+KGi0n7Bb201n0m/le6OOwqPl2+6\npMTZJGj06GjT9hP1Cxuo9pW7z7PZG8Ul6euBV5ryQhQHBj9UVKVPhKXeyU/6hJ30/MOKs+ZnyZLi\n45TrzTfjnweRVxTHeLWdJ+JS7rkninMX/+SUqD4x+KHUKif4SeLuqq2BKqZSeSs2nzgvtkOHxpd2\nPiw8ULbsfYL7SO2Ie1suWBBv+mkW9hrF44qqDYMfKirtJ7ZqfeYj7vwWS7/a1heV5rPPks5B7WCz\nt2Dmzo33Vfpp98kn4adJ06uu48hLPez3VD0Y/FBqlfPCgyQEnX9aLgJxNntLeluQ6/33k85Bcljz\nUznec8n66wM9e1ZufoUU2uZpORdXO65HqjYMfqiopF8kEHS8pJu9pa1gFWQdRJlnb1qVXBdpCX7T\niuslOlyXwU2ZUvj3SrxeOQoXXQQMHBj/fNJU8xMWX3VN1YbBD9WMagt+arEgVYvLVO3qeZtEXfPD\nZm/5xdV0avz4YPMrR6G83nkn0LdvdPOKU63uW4U89xwwbVrSuaBqw+CHikr7297seEFfOBC1sPlN\nywUqzmZv+QosRNWsngPJqAU59yxcCGy3nf9vSdQup11ari1+4srb8ccDt94aT9pUuxj8UGrV6jM/\naRFnfpctiy/tfKpt/VPlpaHmh4Ljf3lVBx4XVG0Y/FBqlVPzUw13wJK+YMRZ85OEpNdnWtXzeonz\nj3xLVUvo/iD9AAAgAElEQVTHXCHF1lUa1kPQG2dR70d+y57kvlWJbZGG7U1kMfihoqql8FRtz/wk\nrdrym09aav7SKqnmoETlqtUCs9+5Kq6XzyQxfaVVW34peQx+KLXCntCqrZAX9wn7u+8qO/+kg0+i\nbCwUVU6px3zS/3cWRL2dz/jyHqp1DH6oqGp81XUSwuY3DWrpop6m9ZomXC+uNNwRr6VjrpBq2u/S\n8IfQaar5Iap1DH4odUq965T0Mz9Ba57SUviJ8wLZJoEzCy/4mZYvTzoHyUvjMz+UqR7XadzXgG+/\nDTd+Wq5JRJXC4IeKqpaan6SUOv9qzXcQu+4aX9r5JL0+02bAAPPJ9eLiuohPHAXoQmmOGhX9/Col\n7md+XnyxvOnjaPYWZ4BVK8f1ffcB/fsnnYv6UDT4EZF2IvKJiIwUkdEi0ssZvq6IDBGR8SIyWETa\ne6bpKSITRGSciHSLcwGodpXT7C3NLzxIy122Wqv5oUzV9gxcHKI+1libFr1Sz0P/+U/yeUirsMuT\nlmtSvTvnHOC885LORX0oWkRR1eUA9lfVXQB0BXCIiOwJ4AoAQ1V1WwBvAegJACKyPYDjAXQBcAiA\n+0R4aFWztF8Yqu2Zn1qVVPCZlu2fNnYb7LNPsvlIUvY+Ue4+cuqp5U1P4RQ6j1Tzn5zGfX6s9LNt\nQZaHpUBKk0D3Z1V1idPbDsCqABTAUQD6OcP7ATja6T8SQH9VbVbVyQAmANgzqgxT5aW92Zuf77+P\nLh9Bha35SUNhPa4LUppr3urNjjsmnQOqR3E3W2bNZn6VDn6SPvfWUmBVS8uSZoGCHxFpIyIjAcwA\n8Iaqfgqgg6o2AYCqzgCwoTP6xgCmeCaf6gwjCqWcE/DkyZFmJZCZM8ON39wcTz6CiuOCZdNca63o\n0w46bzJ4EY2+5icKYbbL+PFsapdPlNty/vzKzasS80jDWw2z8XxEabJqkJFUtRXALiKyNoAXRWQH\nmNqfjNHCzrx3794/9jc0NKChoSFsElSDSg16/KZrba3c8yfLloUb/667gP33jycvQcR5QV9jjfjS\nrmWLFgGvvgr84Q/RpZmGAj+VZrvtgBtuAK66KumcAMOHA59/njlM1S3U2s/33nN/K6Tc5qqvvhps\nvCDpFwswf/gh2LzKEcVxWq9NgOtteetFY2MjGhsbY0k7UPBjqepCEWkEcDCAJhHpoKpNItIRgL3v\nPRVAZ89knZxhObzBD1G2KF54kOZ24WFriqLmLbhELYmmVrVwAXzySeDss6MJfnintTb2iUoUvIO4\n/HLgrbcyh82dC6y/fuaw/faLbp5p2YeHDw83/k47xZOPoMLu97NnV3Z+REFkV4r06dMnsrSDvO3t\nZ/ZNbiKyOoCDAIwD8AqAU53RTgHwstP/CoDuItJWRDYHsBWAYZHlmCou7c/8+I1XqLZnu+2A664r\nLU+F5h/2f37S0GY9ysKFdztsuGH+8aK2dGnu/MnF9eJKw7pIS4E+rGLnq1KXKw3bpNbyEPbaMndu\nfHmxqnW/rzSup8oI0iDo5wDeFpFRAD4BMFhVXwNwK4CDRGQ8gAMA3AIAqjoWwAAAYwG8BuBs1TSc\nWqjalBP8FDqBjB8PDB1aWp7C5iPK8aMWZ81PJZftjTeinef06cD990eTVlhRbg9eRJM/xmpJsdYn\n1Vxg5n6Siesjv733Bu6+O+lcULmCvOp6tKruqqpdVXVnVb3RGT5XVQ9U1W1VtZuqzvdMc7OqbqWq\nXVR1SJwLQPFL+4mwpSV3WBIFv7A1P59+Ctx6a3z5KSbt2zUpDzxgmp7VCm7n6pbm7RflcypBXHkl\nsNtu5c+znDzEJUge2rcvPk7QtGpJJZf3o4+AV16p3PwoHqGe+aH6lPZmb1bPnm5/sZcc2Idyo1RK\nM7bPPos+H0HFWfOThFq44Ee5PZqaokuLohN2G3/1VTz5iFolziWDBgGjRkWXXlwvCIjrXBR0Hdfb\nq64rrd6Wtxbxf9gpdUq9IHXs6Pbzf2aKq5Vmb1HPM+w6aWkBLrggmXkXMm9edGlVqzS+6jqspF+M\nUkih9Rnl//zYcarlfJX0fpb0/P3U0o22OHE9VQaDHyoqjSdSP2Hy+atfRT9/v+Z3xSR5oov6hQtJ\n7SdRv9kv7DZZuBDo2zeaeUcpTX+mS7VJFViwwLyiPa2Ftnra/+1b2+ppmYHK73v1tn5rEZu9UWqV\nU/Ve7GTYtm34/BRTSjCR5El0wYJoLxpDhrh/LpvmV4ynWRwvPBgzJro0q00aa37CbuM05DkfVWCH\nHYD11vP/LWga+TzxhDuOSHoDrCgEWV/Flv/EE4OnVW5evFasKD5OudvuvfeAPfYAVlst97c0HyOU\nTqz5odQqJ/ip1B+beucbNL9puoBHmZfFi+N5i15QSdX8RCmOeSf5Ug2qbSLA1KnAxInhpw1yvN5w\nQ/h0w+jRw3y2axfvfIKI4vy1ZEk0aYWdfuDA8uYXxH77AQ8+GP98qD4w+KGa4Vfzk68wGeTk3toa\n7pXHafjfnqQlGTgkFfykKZilwqrxDnGa8+y98RPn//zEtQ7sW7viaAkQh7S+8GCVVYqPE8V5srm5\n/DSiEOcxyetJZTD4oaKq5W1v9s8ugWhOINOnh3vlcaHg5803gf/9z/SPHVtevqIU9Yk2rrcnFZL0\nMz9RqqUL3yOPAJMmJZ2L6lcNwQ+Qu+9G+cKDfPOIygsvRJteKdssimZvSZ0/dt65MvNJ87FA1YXB\nD6VOqQXoq692+3/60+jyE1Sh/B54IHDSSab/yy+DTQOY13fX8l2me+8FJkxINg/leuaZpHPgL+lt\ne/rp6Wtyx8JTtCq1PuN+29sHH8STbhhTpxYfZ86cYGlVej/fcMPi40RRa5NvuSq9vDyPVD8GP1RU\ntdT8eEX9Nrcgr5st1m68lOW55ZZgD5OWKsrChHf5gi7ruecCd9wR/fzLEXad9O8fzXxrUdKFhKTn\nX2mtrcBhh1VufmFeMpPPsmX5f0s6gI9LXMsVVS14HP/zM25caXkJMp9aOs5rdZ9PGwY/VFQ1nliK\nnUDCLNPw4UCHDsXHW2edYOl58/b888HzUczQoeEfPI4r+KmkqJvaJfnMT1rTqhVpOJfF+ba3FSuA\n114Ll34UynnmJ0jNb6Gan9deC/bMSZrEvR+msSYkivNRPQQ/VBkMfmJ20EHAKacknYvyJHVi+eST\ncOOfc47bH2Xws2BBsPGKvfCg1KYbQcc/6CDgjDPiSTuIUl/4EEchPYlnjtKWVhokvTy1WihasiQd\nf2IbRc1PoW0UJM3hwwufe6r5D5eTmn8cNT9RYPBDUWHwE7OhQyvzGsg4JXVieffdcOP71c5Ekfeg\nr82O61XXYcYv9aIVxXry3v2t5gJHLbzt7fTT3T88jMJ668Xb/JLy89uvjz0W2GCDyuclW6G/F4jy\nfFgorUr+rUFQxZY97nNGuefCQYOin599ruq778LnJ8x8KiEt+aDSpfC0UXuq/UBJKv9haxLCjB+m\nmj5oLZJ3/l99BVx/vf/41XixLuTpp8tPo1TvvOMWykt55ihtoioUPfJItA9xz5vn/odINSlnP1i5\nEjj44OjyYoXdxn7rfdIkoKUlmvyUI2jNT1NTNMGz3zzSePMhaeX+7YI9pxfSvTvwww+mP8hxZm/G\nfP996fmq1vN6GNyfKyOFxTBKm2oJfvwuxFHkPejJyJvfBx8Err22eHqFAqFKrvdy5vWnP5WfRqka\nGoAnn8ydf5i8RP1yjHKk+cK3cGHSOQgvez8Is18sXAgMHhxtfkrhfTukle85maSe9Sj2zE/HjkCv\nXvmnzfbee6a2MXtcv3mk8WZSMXZ5Hnss3vTj9OyzlX9TZ1qavdVDEFbrqvC0UX2q/UCJO/8rVrh3\nkLzz8wYTS5cC/foVTieufJYS/Pjxu4D/9rel5SlqUay71tbSmtGVW+D3uwPe1BRs2lGjcl9UkeYA\nJIyol2PTTd3/qqoWYZvvlMIvOCmkUvtXayswYEC88yh0nGefD6dPD57Gxx+b2sYg66oagx9r1KjS\npnvwQeCQQ/L/Xu7zYGH/oynpZ34eeKAy86faUcWnjWgtXhxf2tUe/MTtz38G1l47d/jHH7v9r78O\nnHpq4XT8go9Fi/xfWBBH4dw7/0LTeH8r1HQlrj/K8zNtWmnT5Zv3V18Fn67Y+h09Ovz8s/9c8447\ngK+/zp1ml11y8xrVMz+DBgF/+EM0aZUiTFoffAAsX158vPnzS89PGsRx3O+0UzTHD2DOIUG2Qz7e\n5Zs4ETjhBFOD8tJLwBtvAPffHyydb78FZswIN++VK/PnBcgfOPu98fKyy/zT9FONNyvKzXP//uaa\n+M03/uk+8kh56ZfT6iJO5TSZ83r//WifiawGra3AyJFJ5yI9igY/ItJJRN4SkTEiMlpEznOG9xKR\n70VkhNMd7Jmmp4hMEJFxItItzgWIwrhx8f4pZrUHP/nyP2ZMNOmPH+8/j7Fj3f6wD8Xa8fffH9h2\n2/LyF/RCFbT9++9+5/YXupNeif3m//6veD6CUnXzfPfd/uPMmmXWTZBCjbXzzu7F+PvvgU6d/Md7\n9VW3P/viffHF5g9V/WSPG1Xw8/jjppASR+FMpPgzH2Hm++tfm7vJ1quv+k9f7eeyuJTy/E1zc+4f\nP556KrDaaqXnw+9O/Lx5wDHHAN26AWefHSwA2mwzYL/9gs8PMAFTIfne3lkocLQBWKFmb/n283/+\nM3gh/u23g40XVKVqSC++2H940P3xlVeAiy7KHe7drgsXmmChkEqdF8L+lYMfEWDffYFLLw033Ykn\nAtdcY/rjXN5SrhdjxhT/E9mhQ4Fddw3/IqlaFaTmpxnARaq6A4C9AJwrIts5v92hqrs63esAICJd\nABwPoAuAQwDcJ5K+ezMffuieIObOTTYvaed3oH/5JbDjjvHO1+8EXujA9cvnxInBm0BlC/rCA6vY\nhdbedfEWPoIEAUFOtA89VHwcP7bNdlT/wVCsGYS92zZrVvj0R48GOnfO/0/o3ruCfvPPl6dyl937\nfNmSJfkLcyLFXxoQJi/ei93nnxce9+uvi/+DvHdf9N4h9L5VLM3BT5AH6tOQf++NmV//OvO3J54I\nPr2fIMt39tnFxwGCveJ/773zz7dYYczaeONg4+WTr9nbeeeZmsog62TKlPLy4CdIDV6p+6PdB8p9\nscG11wJ33pk73JvurbeaYEEk8waJV7Hl8LbiGDCg9D/iVS1+zWxpySw75AuYwq67p54qv0YtqC+/\nDJY/EfMH7DvuWPz5Mbs//uY3ZWevJhQNflR1hqqOcvp/ADAOgD1d+Z2GjwLQX1WbVXUygAkA9owm\nu9HZZx9z16MS0nDBDct7Z9kv/+U0zQjKb76FHrD0O1l4nyUqVVTBjx9bQFAF2rVzh3/8sfvbm28W\nT+fMM910ShHVMz/F+L2IIl+NjJeqqQEKOv8wbybL13TEzvf114Ol06YNsNde/oU5e1c8TPPaIUOA\n4483BThvnuzdcBGT3nXXAV275k7vnWbrrU0BxmvGjMztYAOe0aPdl3U0NWU2D1E1TQqzA6nFi/2X\nbdky9xj84guTpxNOAEaMyL/cJ5wQ/s1gY8e6x09LS+H9WbX4czrLlwPrr+9+/+c/3f7u3Uu7dnTs\naP6Txt5x/vBDtzakqanw3fUePYLNwy73ggWF/yfNrv8JE/IHKXb/Oeus/MeofY7Hb3sNGVI8vwAw\nZ07xcUp94UGl34jX0OD2F6rBC/pCnqamwueycoOffDdNvAGGdx7Z+2HQZ3722svt798/2B/xXnpp\nbkFdFWjb1t233ngjd7r993efpf3kE2CrrYrPCwBefDH4+oy7TLfTTv7NQf/4R/Oaey97877YtSV9\nVRDJCvXMj4hsBqArAHtaPVdERonIQyLS3hm2MQDvfZSpcIOlVPEWPMvV2gr06QP84he5v1Vj8AMU\nvnAUKrz4WbHCNFf68EN32OLF5oC0B+XDD2dO062bO6/77jP9xQo1S5dGV5ti7bNP5vcpU8zbiLLT\nCvrMj9dGG7nTegsQe+3lPqx86KHB85rvoeIoqBYulHprfooVqFpbc9/+lC9N76cfWxvoHcdvnama\nQvtnn+VPa8YM4JJL3O/ffmseLF62LNif7n7xhf/wzTYrPq3XI4+Y5pHPPZdZCzNjBrDDDu73G2/M\nXI8XX+yu408/zUwz++L485+bQq0tXM2YARxwgGkaYZ13Xm7ettwy95jYYw9gzz3NOr7tNnf444+7\nhY9x48zngAHAbrtl7iM2kLO/n3giMGxY8GYu3prEDTcEzj8/83eb9qRJJt2ddip8jHr382eeyVwP\nzz6bW0MTpKDf1JR5B9yeL/bf3wRG2cGpV7477nPmmGbDlj0GevQATjopf3q77Wb26222Af76V9P/\nwgvmN1vj09Rk7io/8ABw7rmFly2sffZxX0bRr5+pLT33XLOu/RSq2SwU/JT7DFVY77wTbLygNTcd\nOwKnnZb/97BNdh980JzL8rEBm3f/X2WVwmkC7jNafrKbAAZtifHyy7ktPezy2ptJN93k/mbPve+9\n505XKHDMvqYce2z+Z1ULBejPPecfhHmtXOmudxHgrbfc37zN+wH3xtLSpbnpPPOMCdK814SXXzaf\nxco0+faN5ubcslc9CBz8iMhPATwP4AKnBug+AFuoalcAMwDcHnbmvXv3/rFrbGwMO3nZCu0sIsGq\n/QFzZ36VVcwd4nyFnzgsWOAeKMuXFz6plcIWGPyaBBS7E7lsWWbwtGiRyau30GQfnrZ3YZ94wpy0\n7PMidvv06VP85GLH33hjc3ck34FuC2beQkih9PxssolpDz93rpmPLTSU8j8W3tqebKWk5/dQP2CW\npVCb/CB3vJ5/vnB+W1vdbe59i9HTT5sCJ5B5x/O664rP0+rdu/g4Qd621707sPvu+afPd5G7555w\nr8Rubg4eYC9ZYgKHUaPc9XP66e7v9i5mS4sJWLzNdL3H/MUXmxc75OOXn/POcwtXb7xhLsreoOS5\n5/zTyA6kxo0zF/EVK3Lb0tvCTvZF/qyzMgsn3pcpPPecuYu+1VaZN1qK/XfRnDlm/Xz0kTvs4osz\nn+ksVBi2efWeP/74x9zxRo7MrBE744zC+bL8julyLn1bbglst51Z5nPPdY+B7O3mx9ZoPvSQ6bfT\neN9+uPXWpecN8L8JsmiRuQl28snusI03NoG4N3D28hYWsxUq8F97rds0Ly3eecd9bjHIedevGa1t\nDRD2xmqPHoWPIb9jo1BwafPhvZ6KmDLDtGlme3bpkn/60093l+H5582LOSxvKw/7Apvs/cm7/I8+\nmpu+d98QyQyuC627lpbM8469eeW3LY4/3tyonTcv/wsFTjops0WADeYnTsy8mRWU9waVvckVphbQ\nW5N7zTXu+Wu99Ux/9rk6imetStHY2JgRJ0RKVYt2AFYF8DpM4OP3+6YAvnD6rwBwuee31wH80mca\n9XPKKeb+8YIFvj9HBlB99lnT/9575nv27xMmBEvrgQfM+L/6lX86gOpzz4XL37x5xcc54AB3fr/+\ntWqXLuHm4fXqq6qPPup+B1SvucbNf7Z8w602bczvLS2qAwaovvtu7jTff+8Oy9epqh50kPv9wQdN\nXqdNy8yHt9tuO9Xbb89NJ3v8YqZOzRz/5JNVd9nF/X755f7zHzTI/S17vp99ljlu167m9+bm3PHt\nflUsr//9b+5yZU/Xv3/ub97uoINU77678HxuuSU3L5tsUnjb2fntsYfpHz/efJ882X/cRYtUp0xx\nv0+cWHwfsZ132/it+1VWKbw/3HOP6pdfut9nznTzecMN5nPlSrNPex14YPA83nBD5rR+2yHftLvv\nnvl95kzViy5yv++4Y/H5L16s+umnZhmC5tnbnXOO+Vx7bZOfL7/MXI5ly/z3s3z7nbez+5/fb62t\nucdN+/aqZ56pus025pwAqP71r8Xns+++/sfVggVmWFOTarduxdM54ACzv/qlle2NN8w4224bbD37\n7R+qqltt5T/+k0+az2efLW27Aqq//32w7RS2mzEj+H7Qtas5xvKtk733zh12zz3+6x9QXW01//Wa\nb/3m+63Q9l2xwj0nBJnm8MPd33v0yJzOuvxy1c6dzbB99jHXIr/0Dz44+PLY3994I/8y+k3fq1f+\nfXPDDd3hN92kethhpv+SS8z3Qumvv775nD0787wLZF6/L7ggd9pdd1VduFB1v/3cYb/7XeYytbS4\n58djjjGfV1/t/v6nP+Wum7FjVW+80X9Zvd3ee+f+ZvNiffihOW9NnGjKZt7pjzvOjDN2rPm+cqXq\n8uWZ4zz2mP/2s5297thr8GGHqb7+eub4W29trjl77WXKL97p7bXorLPM96FDM39fuFB15EhzPbbL\n9dlnZlhrq//+FTcnbkAUXbCRgMdhXm7gHdbR038hgKed/u0BjATQFsDmAL4GID5pZizUnDmq//63\nu+KPPrr8FbV0aW5BxQJMgVDVLZhbtiB6/fXFN7K3MLvXXm46ffpkFp5/85tweQdM4cZasUL1hx8y\nx9l5Z3d+a6yRuQzz5ql+/nnx+bzzjkl3o43M9P36ufPPd8I799zc4euuq7r66pnjAaq/+EVuWi0t\nqmutpfrii4VPjjZ973d7oSvWZQc/gwblpjVnTv710tKiOm5c5vgdOmR+v+IK/3n/9Kfub9nr47XX\ncse/+WYTvGaP7z0e8pkwITe9O+/MnK65WfXYY/3XZ77t7Cc7+ClWgPYuy+67m34bFH7zTea4222n\nOmSI+33xYjP+Qw8F29755j9nTvG8eTtv4fH9993+k04yn+utZz73399Mv/rq4fLU0GAuhh98kHux\nCdtlX8yCdPaCmR18ltptsIG5EWG/lxP8/O537nksu8sOfqLqvGbPNsP23DP49N78evepu+9W/de/\nTLA/c6Y7ztprB89X9rCHH84//mWXmU/vDauw3XHH5RZ0o+iC3OTydh075l8n++yTOezbb1X/+c/c\nbWkLbKuumjn+uHGZ4+XbF/zWv9ekSW7ZADDne79pdtnFjOctR3h/P+OMzH3b8gZ5dpm/+CI3/W7d\nCi/P4sWma99etbHRDP/Pf8w5qNgxafXpkzvcXse8wY/35ostTwTpNt44d9huuwWb9ic/cfsPOihz\nmfyO40suyfx+222m8G+n22IL/3WQnc5GG+VffxMnqr78sunfYQfzudlmmeMccoiZ3gY/9ka2t3v0\nUdWnnnJvHP7wQ7B1Umgf9hv3uONM/5VXZv5mg05748Zv/6q0igY/APYB0AJglBPUjABwsBMQfeEM\nfwlAB880PZ2gZxyAbnnSzViovn0zV+y++5rhb73l3h0Pv6JMAKNqCmrNzZm/9e/v3on2Zufxx91h\ns2e7wxcuzC0w26gZcE9YNv277nJ/yw5+Bg8uvAMB5sRug7dTTzW1KV5du7ppZAc/9u6mnx49TMHJ\nzgdw7zL57eT5hgGqI0Zk1urMnm0CqKAnvmLd//1f5nfvOi3U3XZb7onEbxkaG/3XUZD5XHpp/t96\n9jSfffpkFg79gh9v19zszvtf/8rdN/32k0JdS4tbOCi0HbPnA5iAu7VVdfp0Myw7+MmucSqUlv3+\n+9+b/g8+yB0/O5gcPz7zLlzYrtCyNjervv127nBv4bFdu8Lp57tDXQ2dN9Ast7vuutxhd98dbX5L\nrakq1i1d6u6nNvjZbrvS0vr662DHZJDu6KMrv0/YGyRRd088EU06qrnBT79+qvfe6/6+1lqqf/mL\naqdO+dP54QdzbvNLv9C5VVV17lzVJUsyhwGqhx4a7Nxq9zHbrb56ZsHX8gY/2bVd3s4W+P3yrKq6\n/fbuTdJrr839vVh+v/3WlKGyh//jH6bfG/xE2fndNC3W7bmnyW+p+5bf8EcfdVsjBZ0m6LG7++6q\nH32U//cHH8yspR4xIli6d91l8mZvRBXqvMdTdvDzl79kfre1et7lr7SK1/zE0dngx17Usu/od+pk\nCkiFCvHWWWeZ4GnyZFN16K4ot2rzhBNMkwHvb/37ZxbgLG9V+ZlnmoO/udk0a1tzzeyN4XbZwY/3\njsl++2VOl104VzV3gr/5xlxEARORr7uu+e2Xv3THu/JKU+Nkm/l486GaWRh55hkz7OCDzYnBO673\n7u+66xY+SJYuDXbgRd2tuWZp02XX/AD5m6mtXOlul2XLzKe3OVEp3VVXuf2DBrn93tocv85b83Df\nfbn7pqq5k2fzWSwf2cdVsdq21lY3eP3hB9U333Tnf+utmXn5298Kp5V9jEybZo7DfOP7NW8opwuy\nfsrpnn463vTj7PI1nyqls7UOcXZhamPCdu++q3r//aqjR5eflrdgXG2d9y56GjtVt4bcdo895p4n\ns2+gFur8CpJeftc7ez7xBol2WNDgx3tj1a+zzZaCLscBB6h+/LHqppua64JffvN1xZoUz5rlP/yc\nc1R/9rN4t3Wh4DWufctvuH2Uwa9buNB/eKFgNbtbZ53g4xYrO9ju1FPN8gRpuuvt/vSnzO9rrVV8\nnVVaTQU/tomZvXtTyor2jmdrepqa3A3qHUfVLWD27++2FQdM9Xx2erabOtWtju/b13/edqf3a3dt\na7JUVefPd++c2M7bfMn7bILNsz0IbbC4xRZu1bA3H5Mm+a87wBTWPvww3AFRrd0ddwQf11647Hoa\nOLD84Mfbtti7j4XpvMeE3a9tHkVMbWIp6Yrk/80+2wKYYNkGn62tbvCzYkVu+2S/TjWzyc/WW1d2\nHxg4MN70s5tQsKvOrtiNnzCdbR7JLvqud2//4Cf7WlpqZ331leqFF+b+bgMi77OF3iDJ1rAUSt97\nfs3XeZvbBunsjdTspsSffFLe+pg+PfltXqnupZf8h+drglmoy24en0SnGq7pYanzqLSaCn7sivTe\n5c63om37+twV4nYXXmgCCftcyjrrZFYtPvqo25/dXOO++3Kr/vIdCNnzLdbZlxH06GG+hzlhL19u\n2pLXAsEAABtpSURBVH0C5sUJdrht6lbsjpd9tuCQQ8rf4aulC9o8zrs9//c/028L+eV03ipj2/43\nbJe9vX7xi+Dtfgt19mUUfp33jp43iH/00cwmYUFO8FOnqnbvntw+0Lt3cvNmx45d/N2//x3dcd7S\nUvimzvz55rNYc9h8narqaacVH2/LLUtLP7s1Cbv67bbfPv55JCHK4EdMepUnIqqqgf8PRTXzNbkz\nZpjXvmb729+Au+7KHLb22sDChbnjbrih+R+DoDp2dP9g0JufUmy3Xe777/PZYw/3dYYHHggMHVr6\nfOvFvfcC55yTdC6MF18EjjkmmrSOOAIYODCatIiIqDLmz898jThRNUsidBARqGokf9ca6k9Ok+T9\n74ihQ90/h8zm9weXfoEPEC7wyVbuv+UGDXyAzD8rZOBTfaIKfAAGPkRE1YiBD1F6VE3Nj9dBBwX7\n00siIiIiIopOtdf8VGXwQ0RERERElVftwU/VNHsjIiIiIiIqB4MfIiIiIiKqC4kGP8uXJzl3IiIi\nIiKqJ4kGP99/n+TciYiIiIioniQa/PBlB0REREREVCmJBj8JvWiOiIiIiIjqUKLBz2efJTl3IiIi\nIiKqJ4kGP8uWJTl3IiIiIiKqJ4kGP234om0iIiIiIqqQRMOPVVZJcu5ERERERFRPigY/ItJJRN4S\nkTEiMlpEzneGrysiQ0RkvIgMFpH2nml6isgEERknIt3ypc3ghygZr7+edA6IiIiIKk+0yCvXRKQj\ngI6qOkpEfgrgMwBHATgNwBxV/buIXA5gXVW9QkS2B/AUgD0AdAIwFMDWmjUjEdGFCxVrrx39QhFR\nYQsXgsdejRszBthoI2DddZPOCRER1ZIk3tYsIlDVSP4kp2jNj6rOUNVRTv8PAMbBBDVHAejnjNYP\nwNFO/5EA+qtqs6pOBjABwJ5+abdrV1beiapW797Jzp+1rtWllOcjt98eWGed8i5Su+8OtG9ffDwK\nbtgwYNSopHMR3qmnJp0DIqJohLqkishmALoC+BhAB1VtAkyABGBDZ7SNAUzxTDbVGeaTXrjMUnXa\nbLOkc5A+vXoBp5yS3PxbW5Obdz0p9xx37rnAwQcDc+ZEk5+wPv0U2HtvYK21yk/rggvM57bblp9W\nNdtjD+AXv3C/d8vbMLywf/0rd9gll5hgV9Vd31bfvvnTuvba4vN79FHzhtbm5uLj/uMfxcepNF6H\n6sOIEUnngKpB4ODHafL2PIALnBqg7PuJoe8vXn99bwC2aww7OcVok02Aww+PJq2nnnL7d9opmjRr\nwWOPJTfv1VZLbt715KWXypv+nnuAQYNMDY61zTbFp8suAPzpT4XHv+oqYP58YIpz2+rgg93f/vMf\nYOpUYL/9guU5n7vuAqZNA+69N3P4nXe6/dkF9lI0NJjPNdfMHN6lS2aN68qV4dJVNc1Fi9WkeQOb\nbBde6PbbvOyyCzBwYLi8HHEE0KMH0NTkDttrLxP8WHfdZfL6yCPm+3nn5a4TAHjgAeDyyzOHZZ8f\nzjzTfLZrZ2qNhw8Hdtghf/46dQq+LJVwww3AvvsmnYt0u+aawr9vtFFp6bZtW/j3IUNKS9frxBPN\n54gR5ngq1c9+Vn5eiin3/y032gg47rjSprXbOMg1JJ+HHwbefLP06cNobGxE7969f+wipapFOwCr\nAngdJvCxw8bB1P4AQEcA45z+KwBc7hnvdQC/9ElTm5vtPSp2aem6djWfM2eq/t//RZPmRx+5/Tfd\nlPwyhu2WLlX9xz+iS69dO/1RUssU9bz/8Ae3f7/9gk/3/vvh5nPbbW7/wQe7/Ycfnvx+cvnlucNe\nesl8Xnut+bzuOvP54IO54260kf92smxa3bqpHnWUar9+ZvjcuWb4/Pn+06mqXnJJ/v3g7LNVW1rc\ncZcvVx0/3k3fam5W/e9/3Wn/9z83z48/rrruusX3OVXVoUPd78cea+alarbhyJG50/31r6Xt2337\nmmPNDr/pJve3DTcsfAz07Vt4W3TvbobNmqW6aFHmeHffXXwdWCNHqv7wQ/jjsbHRTNPamj9tP0cf\nnZnOxx+7v40fb4bdd5/q00+747S2mi7boEHuOJtvnpnuiy9mfvduB0D15JNVTz218DKOGRPNcWnX\nzdy5qu+9F12aUXZduiSfh2L74F/+oioSPt1331X94x/9f5s0SXX27PLyvemmqqNGmf4RI8xyTJ+e\nOc622wZL64or4l+33mM2u3vjjcLp3H676sqVudvKKpaPZ59VPeII1SFDzLoqZVk+/DDYuSYOJmQp\nHrME6YKNBDwO4I6sYbfaIAfA5QBucfq3BzASQFsAmwP4Gs6LFbKmL7gTsFPdeOPSpvvsMxO8ZA//\n9a+LTztihLlAqKoed1zhce+6y3w+84z5bGpSXWc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dbJ8kLSRTx9V9z5e6kYSqGsq/XS1+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\nJEmSJE0Eix9JkiRJE+H/BjXp04t9InoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bc59ac90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FUa7MtXjXLMYnqV+o/JCSZLETvckmtQurXOVrzz3jlyUKqtVrkJLIM9ztLZgs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\nBa8Yd96p+vXXrl/z57sdjVVXza8EunV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+yWhBFZrX7LWXOb7wgjn+4x9uh2XiRNVu3cx5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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbd484d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wYDJP4PET9obV7t2BY48tbx5xr+Ow6Z1ySnlBqz1r553PTz9Fm79buctg7Njy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\n/X+DGugaGSJK/wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbbf7f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# plot raw and corrected ROI trace\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"Raw Fluorescence Trace\")\n",
+    "plt.plot(time, raw_traces[0])\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"Demixed Fluorescence Trace\")\n",
+    "plt.plot(time, demixed_traces[0])\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"Neuropil-corrected Fluorescence Trace\")\n",
+    "plt.plot(time, corrected_traces[0])\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"dF/F Trace\")\n",
+    "# warning: dF/F can occasionally be one element longer or shorter \n",
+    "# than the time stamps for the original traces.\n",
+    "plt.plot(time[:len(dff_traces[0])], dff_traces[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ROI Masks\n",
+    "If you want to take a look at the cell visually, you can open the NWB file and extract a pixel mask.  You can also pull out the maximum intensity projection of the movie for context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rW9mwYQOHH374jI59MDMXNhuNRo2QsCspAjU94STvS2tNU1MTY2NjJl9tcHCw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\n+utYvtUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbc0d090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(510221121)\n",
+    "\n",
+    "# get the specimen IDs for a few cells\n",
+    "cids = data_set.get_cell_specimen_ids()[:15:5]\n",
+    "\n",
+    "# get masks for specific cells\n",
+    "roi_mask_list = data_set.get_roi_mask(cell_specimen_ids=cids)\n",
+    "\n",
+    "# plot each mask\n",
+    "f, axes = plt.subplots(1, len(cids)+2, figsize=(15, 3))\n",
+    "for ax, roi_mask, cid in zip(axes[:-2], roi_mask_list, cids):\n",
+    "    ax.imshow(roi_mask.get_mask_plane(), cmap='gray')\n",
+    "    ax.set_title('cell %d' % cid)\n",
+    "\n",
+    "# make a mask of all ROIs in the experiment    \n",
+    "all_roi_masks = data_set.get_roi_mask_array()\n",
+    "combined_mask = all_roi_masks.max(axis=0)\n",
+    "\n",
+    "axes[-2].imshow(combined_mask, cmap='gray')\n",
+    "axes[-2].set_title('all ROIs')\n",
+    "\n",
+    "# show the movie max projection\n",
+    "max_projection = data_set.get_max_projection()\n",
+    "axes[-1].imshow(max_projection, cmap='gray')\n",
+    "axes[-1].set_title('max projection')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ROI Analysis\n",
+    "The code to compute all of the cell metrics available for download (along with several others we haven't put in our database yet) is in the AllenSDK.  All you need is an NWB file.  For example, this is how you compute metrics for the drifting gratings stimulus and plot some results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done analyzing drifting gratings\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.drifting_gratings import DriftingGratings\n",
+    "\n",
+    "# example loading drifing grating data\n",
+    "data_set = boc.get_ophys_experiment_data(512326618)\n",
+    "dg = DriftingGratings(data_set)\n",
+    "dg_peak = dg.peak\n",
+    "print(\"done analyzing drifting gratings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3gIlblvkKrE4WA89UVFYpsX3p2vmJr8gmsyrjuqWateBKT3BpDLFpwMURcUVu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\n7l1cAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbbcc790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# filter for visually responding, selective cells\n",
+    "vis_cells = (dg_peak.ptest_dg < 0.05) &  (dg_peak.peak_dff_dg > 3)\n",
+    "osi_cells = vis_cells & (dg_peak.osi_dg > 0.5) & (dg_peak.osi_dg <= 1.5)\n",
+    "dsi_cells = vis_cells & (dg_peak.dsi_dg > 0.5) & (dg_peak.dsi_dg <= 1.5)\n",
+    "\n",
+    "# 2-d tf vs. ori histogram\n",
+    "# tfval = 0 is used for the blank sweep, so we are ignoring it here\n",
+    "os = np.zeros((len(dg.orivals), len(dg.tfvals)-1))\n",
+    "ds = np.zeros((len(dg.orivals), len(dg.tfvals)-1))\n",
+    "\n",
+    "for i,trial in dg_peak[osi_cells].iterrows():\n",
+    "    os[trial.ori_dg, trial.tf_dg-1] += 1\n",
+    "    \n",
+    "for i,trial in dg_peak[dsi_cells].iterrows():\n",
+    "    ds[trial.ori_dg, trial.tf_dg-1] += 1\n",
+    "\n",
+    "max_count = max(os.max(), ds.max())\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "\n",
+    "# plot direction selectivity\n",
+    "im = ax1.imshow(ds, clim=[0,max_count], cmap='hot', interpolation='nearest')\n",
+    "ax1.set_xlabel('temporal frequency')\n",
+    "ax1.set_ylabel('direction')\n",
+    "ax1.set_xticks(np.arange(len(dg.tfvals)-1))\n",
+    "ax1.set_xticklabels(dg.tfvals[1:])\n",
+    "ax1.set_yticks(np.arange(len(dg.orivals)))\n",
+    "ax1.set_yticklabels(dg.orivals)\n",
+    "ax1.set_title('direction selective cells')\n",
+    "\n",
+    "# plot orientation selectivity\n",
+    "im = ax2.imshow(os, clim=[0,max_count], cmap='hot', interpolation='nearest')\n",
+    "ax2.set_xlabel('temporal frequency')\n",
+    "ax2.set_ylabel('orientation')\n",
+    "ax2.set_xticks(np.arange(len(dg.tfvals)-1))\n",
+    "ax2.set_xticklabels(dg.tfvals[1:])\n",
+    "ax2.set_yticks(np.arange(len(dg.orivals)))\n",
+    "ax2.set_yticklabels(dg.orivals)\n",
+    "ax2.set_title('orientation selective cells')\n",
+    "\n",
+    "# plot a colorbar\n",
+    "fig.subplots_adjust(right=0.9)\n",
+    "cbar_ax = fig.add_axes([0.95, 0.05, 0.05, 0.85])\n",
+    "cbar = fig.colorbar(im, cax=cbar_ax)\n",
+    "cbar.set_ticks(np.arange(0, max_count, 2)+0.5)\n",
+    "cbar.set_ticklabels(np.arange(0, max_count, 2, dtype=int))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neuropil Correction\n",
+    "All of the raw fluorescence traces are available in NWB files, but some of these signals are contaminated by nearby neuropil signal.  Neuropil correction is performed after the signal from overlapping traces has been demixed.  The code to perform neuropil correction is available in the AllenSDK and can be used as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r = 0.142000\n",
+      "max error = 0.005002\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.r_neuropil import estimate_contamination_ratios\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(569407590)\n",
+    "csid = data_set.get_cell_specimen_ids()[0]\n",
+    "\n",
+    "time, demixed_traces = data_set.get_demixed_traces(cell_specimen_ids=[csid])\n",
+    "_, neuropil_traces = data_set.get_neuropil_traces(cell_specimen_ids=[csid])\n",
+    "\n",
+    "results = estimate_contamination_ratios(demixed_traces[0], neuropil_traces[0])\n",
+    "correction = demixed_traces[0] - results['r'] * neuropil_traces[0]\n",
+    "print(\"r = %f\" % results['r'])\n",
+    "print(\"max error = %f\" % results['err'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The NWB files contain the neuropil traces and `r` values needed to perform this correction, so you don't need to recompute them.  The corrected trace can be computed on the fly when you use the `get_corrected_fluorescence_traces` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S+NByiy2S57xUhs1R8L6o1BtkAabImQ0QVE0aatQwTax7pTpPBwwwLaq1bOkO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\ndp/b8Tao8gY/999vrgOvb75xl7d5swn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vvOBWELfsvu/Uycx7333mpWJhzjnHfF5+uanw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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f575f023090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, corrected_traces = data_set.get_corrected_fluorescence_traces(cell_specimen_ids=[csid])\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"Neuropil-corrected Fluorescence Trace\")\n",
+    "plt.plot(time, corrected_traces[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute dF/F\n",
+    "You can compute dF/F for yourself using the `allensdk.brain_observatory.dff` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PFL8APPWUf24s7aJwxRXxWrXo/088Md4/GvxHb5Ur3C5Jt7EU3paZdLF8883w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\nsp/BayhZIwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f575ffee8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.dff import compute_dff\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.title(\"dF/F Trace\")\n",
+    "dff = compute_dff(np.array(corrected_traces))\n",
+    "plt.plot(time, dff[0,:])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running Speed\n",
+    "We recorded the animal's running speed during the course of the experiment and made it available in the NWB file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rxGPZMj1cKV0y17KlfgfUfvu5p6dHD13KMGiQe7XN776z+ydM0Bn8yy5Lfzbt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\nBkFERERERFRR/h+bikRfGJ6/MQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f575fb16790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(501940850)\n",
+    "\n",
+    "dxcm, dxtime = data_set.get_running_speed()\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.plot(dxtime, dxcm)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motion Correction\n",
+    "X and Y translation values in pixels required to correct for motion artifacts during the experiment are available as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uZF3hqYXRd11fxdnPgzEHkc2zELJ3N70LALiTfActF7dEWlYaAKkgwtL/luLr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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f57bbe7d190>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(501940850)\n",
+    "\n",
+    "mc = data_set.get_motion_correction()\n",
+    "\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.plot(mc.timestamp, mc.x_motion)\n",
+    "plt.plot(mc.timestamp, mc.y_motion)\n",
+    "plt.legend(['x motion','y motion'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Eye Tracking\n",
+    "Many experiments contain pupil position and pupil size from eye tracking. Extreme outliers from the tracking have been removed and replaced with NaN, which will appear as gaps in the plotted data. If an experiment does not have eye tracking data, a NoEyeTrackingException will be raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No eye tracking for experiment 501940850.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zj9zeuV1vrJtISUtBVFIUyj5T1vI+kYmRbk/TbYRSKXYh5kKWnSt/7vyy70a1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ufO7beLGp9cGVBE8bs8BY0bByX7kwonHH+8/rOqAaU2WlXrOXOB3NcnzveL\nI39N5GKecMwxIjKi69V04YW5l70eMMDblgk1ArXLLuERKxuirk1bAzeOQ8M2AiWXlaipsPJ6Nsd5\nY64IywiNqZVUnERJNsvIAhM6bMK/tL2KClHjdO21YlI3bhwwdKh4Lxc3M9kDvancLJNABZE1Bdum\ninHo3z9ePzHORd1WXK8hNc/U1aUlVexp2VJEdU2EDf79+ombJFGo6yDMc1soTIalWnychLg35LhG\n2xNPiPS7sO3362c/KSGpbGLECP9nQ/dv0Q4A91+w2Vy/LVoID7Oc/ZCLCc6mfE9Ie99VA0q+NsaP\nD/9s3bpBQz3J/v3nP6IJd1ySRBJzcT4VUqTn/fdFTeOwYeFOE52MebZEOehso0W2Y+CZZwpjNQ77\n7ae/J9Nv5iJQ6WE8tTjnc8P+8rmTSZk4MagSQwPQXXeJHh6AdzLXqSMGNrV4kIhzo6xVyztRS0r8\naVEXXxzdGFNl2bLga7JBN3Kk97zYb5bZXMBJhSGKlVxEoNati5bJJeicTiIBTdePjqQpQ3XqmKVx\nS0tFepyJadP8DT0LdR2EGYC5gq6p887Tv286xy69NPttxxkX99gjvhf47rvthE3CrqOw4vHjjvP3\n3aEWBEkaQidBjbDloiHwDjvYNy8vNtK+juvX98a7uA6sNWv8EcukyLUySWSwbRk1KjzrxkSHDqIB\nu4lCGlB77QU8+6y9EZKmgzJKhOKGG4TKYhSjRgn12ijuuks4h+IwdKjegKLaMReBSo9ITRLG2EoE\nZcv/BDADwPmc89nBTxUHa9cCV1zhL+qnAUguROzbV0i9AiK39sAD9QXJcS9EGvjVPhwdOsTvbfHh\nh/7/ly3z76N8YRe7AbXXXsk/G1YovikS1eMmCXGaLXIuJqe6VIxsyEUN2Zo18SRY4zTmPeyw6AJv\nWwrh4aPUHlN/ulzuU5zzNyodL1dQnxTimWeAMWNEzY1Ko0bAQw+JSdrMmdHrzub7vPaaaL5Nv9/G\njbmZ4Dz8cLKIR02krMwTE0lj/L3zTq9o3xZZgTfO/XrBgnjbufHGeMsTgweHG/KFbhMB+JWRw0hr\nvGndOtqB+9BD4t4X5SCvVQto1y6d/VJp3FjvcKLomDOg0sNmOvIvAPMBPAWAARgGoCOAzwA8DKB/\nrnYuDdSGdLqJnSrtbJqEqBdilIfSZEAl4bXX/P/LhsSIEfGbUxaSbAa0mnbx5yICFUcBia4HxkRh\nsKzmmA25KFqPq0Y0YYK+0bSONm1E8XMaFMKAUhWjVHJ53eSzCN/EiSfqFbCmTBERL3WbQ4d6KdU6\n8mXkjRvn316uzp2ysk23/0suHYJpjL9ybbQtSVXq4hpQuaIYDKhhwzxluTDSGvtmzQq/Psk5VOie\nnEOGALfe6v2v7o9L4UsPm1Prr5zzMZzzlZzzFZzzBwAcwDkfByCGjzf//PFH8ALTGVB77GG3PnUg\nj1Irk3NwqUFgUsLkux9+2N8fp9gjUNl2cs8l+T52W21l19w2Dttua5ceAHiGzqpVwnhKcnx1KWtJ\nGhinTYsWhdmPQkxUk6oXppESG+ecydX1NXOmiFCq7LVX8pRK7b5+chqwWhQYksGdtoy5w0+uDah8\nH3vVYUvbt6kbjdu4NRfccosQyikUlKYc5RzTyZhnQ9Om4SqeRFhqez4oK9On8NF9qaY5oQuJzaFc\nwxg7mjFWkvk7GgAlCRX1VL1586CnR2dAHXGEJ/EYxiWXeJGgvn39faJ0yBGoOGlVOuJEmGqyAZXr\niz+tNC5b+vQBLrgg/fXapgfQ9UA3oyS/Tf/+wdfkvPvNbXJYCA/fbrsBr4S0NzftUxqRQtum0UDu\nUvjSHvOM+zjjH8BqIVl5zjl52J4jp/ezXKRQR2FSOf3KQtvYNm0tWy69VER4dFx4oZhbFYrbbgNe\nftn+mrGtB06LQhpQVBcfZkC5sSY9bHylxwG4E8B9EAbTRwCGM8bqAjgj7IPFiG7CwJjdgNCihTcx\nVGuSdFCDXcaA996z30cdlL4kR5pM1GQDKteT0zfeEI+5buhYLKRRq3TWWdmvoyZRCAOqtDTcyWLq\nYZJtZHz+fHPKs45c1kAVamKQxnaLJTWrGHnzzdytuxB9+Ezjg000OF/nuCzGU4zYzHFoGZv+TWmx\n445CIKJQUPPj884DevXyvxe3PYkjmkgDKiMSYYq1TE13d3LP3XenI91rA3kiSkrsIlxh0GDw7bf2\nyxYrxRyBonx2UxPGmgZFDyinPclvY2PUb04UY455rhpQxzGeiFxMAj/7LH3Pr81YC6QTxcv2/uBI\nRiFS+LJJ8S3GsaUQNG8u+qeFQddl0nqzJGy7bWHrw87IhDSaNxe1UDLFULdW07BR4WsJ4GQA7eXl\nOed/y91u5Y5u3cRfPiktTe8itkmXOe209FXV0qSYDSjClL5Q02jZUig6JjWgTJL/Mj//HC1yUJPo\n2LHQexCkWNI2cuXcmTUruo9fHBgLT+cdP947ptkYUD17mmX7HbmnECl82RhBaTS+rgnssUd07Tpl\nV+TTgCr0OBv2XTdVEZlixmboeBlAYwBvA3hN+nNEIItI6BSi4hCn2Hubbfwy7cXGpmBAbU40aeL9\nJnF/G+qZE4atEl5NgPPiNKCK5brJVQrfnXcKIYnevdNfN0GpQP36AQcd5L2ejQHlvMKFpZhS+GzI\nhbppTYXqgApt1OSTMAPKRS/Tx8Ymrcc5vzjne1IDoRvugAHiIt5pp+TrKvac5DgUswHVuHHxp0Dm\ngiQG1IUXAvvuG76+uOt05IZWrQq9Bx5xz4f99xcqkWHMnAmsWAFMny6cTWGqpUlR21HQc5rUVlTk\npv+ZI3fkO4Xv0ENFn0mZuCIsDjs2lzpmmbAok4tApY/NIX2VMTaYcz4h53tTQ2nYsNB7UDx07Qoc\ncEDyz+fagOrfHzjhhNxuo5iJM5kI65vUsqWQla5Xr3iiH5sz2TSvTpMkE8DDD49Oc7v77vjNycOI\ncx2QAZVkwjZypGg5sP328T/ryJ58p/C99FLwtTFjgCuvtPu8i0DZQ9HdfBudhTRydREoxoAXX3T3\n4VxgY0CdDeBSxlgFgA0QzXQ559xCEd9BLFwIfPll8s/XFM/TN99k9/lch6ErKjbPtJqkKXxhrFiR\n/jodySiW9I3Zs8U1FocddwS22CJ8mQkTgL//3f+a2kQ9bei8zibqdMIJIlp2/vnp7JMjHjNmFH58\natbMvrappswD8kEhjtWSJdm3rEnK4MHmjJAhQ4DPP8/v/mwORNqknPOGnPMSznldznmjzP/OeIoB\nY8Dixdmtww2cglx7Ub75Jr9Fp8UCTSLSDPPTOuOkqDjSZ/r04ik+X7IkvhPl8suBk04KX2bWLGDt\nWvE8FxPiH380v5ft2LxwYXafdySnZ8/CG1Bx2JT2tdAUYs703nvA8OH53y4gepSG1d+6OWT6WE2X\nGGNNAWwHoHoqxDnPsrPR5kW2qngudC/IRyPdZ58F9tsvt9spRkpLgS23TG99FMnbHA3SYiKXwgpx\nOfbY+Cm8q1Z50UwT1HNPJpvJpvpZ3cSElsl2YrL77vlrreHYtNlmm0LvwaYDzZkaOXe/I0fYyJif\nBJHG1wbAFwD6AvgQwIDc7lrNYp99sgvtOgNK9LLJx2ScPNmbE4zlzkNVqJQGR/GhM3Si2H33ZP2m\n8uVx/fnn7D6fy+bCjpoFYyLtMExi3yGg678YVVELgRtj0se2BqoXgI845/swxroAuCG3u1WzoBM3\nm9oaZ0ABbdvmZzvFku6UT8iA2mcfoH79dNZJXdHTLO53bH7ccUdht790afj7dJ4nxRlQ4XTqFJ5C\nubmxyy7izxFOoeZMgwcXZrtRuBS+9LExoNZxztcxxsAYq805/44x1jnne+bw4eRx88O0aSIvfnOD\nJnBjxqS73vLy4m7q7Kh5pGGMyOto2jR8mXr1stuWM6DCyfb4OjZPCmFAnX46cNhh+d+uozDYGFDz\nGWNNALwE4C3G2DIAc3O7WzWLNG6OLgKVH/r1K/QeFI5ceKjcxNBRKPJ17mU7NjsDKpwbbnA1Yo74\nFCLics89+d+mo3DYqPAdxjlfzjkfDeAKAA8BGJLrHasJ/OMf6a3LRaAcuSRXE7jnn3cN/Bz5JQ1x\nh59+sl82TQPquuuAt97Kbn01jYMOcjLvjvhMmlToPXDUdGJpmnHOp3DOx3PO1+dqh2oSp54qHtOY\nnDoDypFL0lIUUzn44OLpQeTYNGnRIv/b/Ppr+2XTuGbo+rviCuD667Nfn8OxufPnn4Xeg+LC1UCl\nj+tNnEPohHUpfA6Hw7HpEGe8TSMCJfPBB9mtz+FwOKezI/cUtQHFGBvEGPuOMfY/xtjFhd6fuNCN\nMY3eOj17ppsS6HDocF6qzYuGDc0iCcXCI48Ajz2W321u3Gi/bLbXjFoDte++2a3P4dgc2H778BYC\nxdT/rhhwdZbpU7TVCYyxEgD3ABgI4DcAnzDGXuacb1LlpDvtlI4sdqNGwH33Zb8eh8NELntBOYqT\nU04BWrcu9F6Ec/DB+d+mrQH1xBPZpwo5EQmHIz5r1oS/f8opwOLF+dmXTYE4dZ0OO4rWgALQG8AP\nnPO5AMAYewbAoQA2GQPKTUYdmxLOgHLUBMgYWbUq+Tps03+OOw5YuRLYYovk21INKHcNOhzRnHFG\neA82zoGSos6xyi/Nmxd6D2oexWxAbQ1A7rc9H8Ko2mRwnkXHpoQ7Vx01iU8+Sf7Z1avtl23YEDjy\nyOTbku8T9eq5JqkOhw0XXhj+/g47AJdfnp992RQoLy/0HtQ8itmAsmL06NHVz/v374/+/fsXbF9U\nnAHl2NRw3m+HA/j++/xtq7zcaxa7cqW7ZzgcadC6dfGnJ+eTzfXe/u677+Ldd9/NybqL2YD6FUA7\n6f82mdd8yAZUMeJuho5NBZfC56gJpKFYuj7TqOP227NfVxRHHulFsFzKkcPhyAWbq5KzGli5+uqr\nU1t3MQ/XnwDoxBjbhjFWC8AwAOMLvE+xcJNRx6aEM6AcNYE0JgorVojH2rWzX5fD4XAUms3VgMol\nRRuB4pxXMsbOAPAmhKH3EOf82wLvVixcCp9jU8Kdq46aQJoTBXdNOByOmkDDhoXeg5pH0RpQAMA5\nfx1A50LvR1I2bvQ8mQ7HpgDnwB9/AFOmAEccUei9cTjik2YDzVq10luXw+FwFIpevYBfA0Uwjmwo\n5hS+TZ4ZM4Affij0XjgcdlAK37XXZqcq5nAUkjQNqOOPT29dDofDUUi22qrQe1CzcAZUDnG6+45N\nCZeu5KgJpJHCV6eOeHTSvw6Hw+HQ4QyoHHLMMcD8+YXeC4fDHs6dkIRj0yaNCNSgQdmvw+FwOBw1\nF2dA5RDGgK23LvReOBx2OBU+R01g+HDxeO65ydfhap8cDofDEUZRi0g4HI78QQaUM6IcmzIPPQQ8\n+GCh98LhcDgcNRlnQDkcDgBeDVSZGxUcmziuns/hcDgcucSl8Dkcjmo4B0aPBj74oNB74nAUDmeA\nORwOhyMMZ0A5HA4AXgpf48bAbrsVem8cDofD4XA4ihNnQDkcDgBORMLhIFwEyuFwOBxhOAPK4XAA\ncJNGh8PhcDgcDhtcubjD4ajGRaA2L664AigtLfReFB+u/YTD4XA4wmB8E54xMcb4prz/Dkcx0aQJ\nMGcO0LRpoffE4Sgs69cDf/4JtGxZ6D1xOBwOR1owxsA5TyXfxqXwORwOAK4GyuEgatVyxpPD4XA4\nzDgDyuFwAHA1UA6Hw+FwOBw2OAPK4XBU4yJQDofD4XA4HOE4A8rhcAAAhg4VqUsOh8PhcDgcDjNO\nRMLhcDgcDofD4XDUaJyIhMPhcDgcDofD4XAUAGdAORwOh8PhcDgcDoclzoByOBwOh8PhcDgcDkuc\nAeVwOBwOh8PhcDgcljgDyuFwOBwOh8PhcDgscQaUw+FwOBwOh8PhcFjiDCiHw+FwOBwOh8PhsMQZ\nUA6Hw+FwOBwOh8NhiTOgHA6Hw+FwOBwOh8MSZ0A5HA6Hw+FwOBwOhyXOgHI4HA6Hw+FwOBwOSwpm\nQDHGjmSMzWSMVTLGeirvXcIY+4Ex9i1jbP9C7aPD4XA4HA6Hw+FwyJQVcNtfAzgMwBj5RcbY9gCO\nBrA9gDYA/r+9u4+Ro67jOP7+IA+KoBKNFSkPNmC0qKlNqEbAVBMRnygxaojPQY0RUKNRQdGUf1TQ\nGDUmmCioqCiCioIWbQ2eRk2xCrXFIlYRhUaQKD4QlID9+sfOwbbelblyO7N7vF/J5mZ+M3vzm/ne\nd3e/95ud+UGSI6qquu+iJEmSJN2ntxGoqrq+qrYC2WnRKuCiqrqnqm4EtgIruu6fJEmSJO1sHL8D\ndRBw09D8tqZNkiRJkno10lP4kqwDFg03AQWcWVWXj3LbkiRJkjTfRlpAVdXzduNp24CDh+YXN20z\nOuuss+6dXrlyJStXrtyNTUqSJElaKKamppiamhrJ707f12ZI8kPgXVX1y2Z+KXAh8AwGp+6tA2a8\niEQSry0hSZIkaZeSUFU7X3tht/R5GfMTk9wEPBP4TpIrAKpqC3AxsAVYA5xilSRJkiRpHPQ+AvVA\nOAIlSZIk6f4siBEoSZIkSZo0FlCSJEmS1JIFlCRJkiS1ZAElSZIkSS1ZQEmSJElSSxZQkiRJktSS\nBZQkSZIktWQBJUmSJEktWUBJkiRJUksWUJIkSZLUkgWUJEmSJLVkASVJkiRJLVlASZIkSVJLFlCS\nJEmS1JIFlCRJkiS1ZAElSZIkSS1ZQEmSJElSSxZQkiRJktSSBZQkSZIktWQBJUmSJEktWUBJkiRJ\nUksWUJIkSZLUkgWUJEmSJLVkASVJkiRJLVlASZIkSVJLFlCSJEmS1JIFlCRJkiS1ZAElSZIkSS31\nVkAl+UiS65JsTPKNJI8YWvbeJFub5cf11UdJkiRJGtbnCNRa4MiqWgZsBd4LkGQp8ArgycALgHOT\npLde6n5NTU313QVhHMaBMRgPxmE8GIf+GYPxYBwWnt4KqKr6QVVtb2bXA4ub6ROAi6rqnqq6kUFx\ntaKHLqolXxjGg3HonzEYD8ZhPBiH/hmD8WAcFp5x+Q7UycCaZvog4KahZduaNkmSJEnq1Z6j/OVJ\n1gGLhpuAAs6sqsubdc4E7q6qr46yL5IkSZL0QKWq+tt48nrgTcBzq+qupu0MoKrqnGb+e8Dqqrpq\nhuf313lJkiRJE6Oq5uW6Cr0VUEmOBz4GPLuq/jrUvhS4EHgGg1P31gFHWv1E6AAABtJJREFUVJ+V\nniRJkiQx4lP47sengL2Bdc1F9tZX1SlVtSXJxcAW4G7gFIsnSZIkSeOg11P4JEmSJGmSjMtV+OYs\nyfFJfpPkt0lO77s/C1mSG5P8Ksk1SX7etB2QZG2S65N8P8kjh9b3RsjzIMn5SW5Nsmmobc7HPcny\nJJuaXPlE1/sx6WaJw+okNye5unkcP7TMOMyzJIuTXJnk10k2J3lb024+dGiGOLy1aTcfOpJknyRX\nNe/Hm5OsbtrNhQ7tIg7mQseS7NEc68ua+W5yoaom7sGg8PsdcCiwF7AReFLf/VqoD+AG4ICd2s4B\n3tNMnw6c3UwvBa5hcHroYU2c0vc+TOIDOAZYBmx6IMcduAo4qpleAzy/732bpMcscVgNvHOGdZ9s\nHEYSg8cBy5rp/YDrgSeZD2MTB/Oh2zjs2/x8CIP7aK4wF8YmDuZC93F4B/Bl4LJmvpNcmNQRqBXA\n1qr6Y1XdDVwErOq5TwtZ+P/RylXABc30BcCJzbQ3Qp4nVfUT4Padmud03JM8Dti/qjY0631x6Dlq\nYZY4wCAvdrYK4zDvquqWqtrYTN8BXMfg5uvmQ4dmicP0fRrNh45U1Z3N5D4MPgwW5kLnZokDmAud\nSbIYeCFw3lBzJ7kwqQXUzjfbvRlvtjtKxeBiHxuSvLFpW1RVt8LgTRV4bNPujZBH67FzPO4HMciP\naebK/DktycYk5w2dImAcRizJYQxGBNcz99ch4zBPhuIwfYsR86EjzSlL1wC3AOuaD37mQsdmiQOY\nC136OPBu7iteoaNcmNQCSt06uqqWM6jyT01yLDv+sTLDvLrhce/HucCSqlrG4M3zYz3350EhyX7A\n14G3NyMgvg71YIY4mA8dqqrtVfV0BqOwK5IcibnQuRnisBRzoTNJXgTc2oyK7+reTiPJhUktoLYB\nhwzNL27aNAJV9efm523AtxickndrkkUAzfDnX5rVtwEHDz3d2MyvuR534zECVXVbNSdLA5/lvtNU\njcOIJNmTwYf2L1XVt5tm86FjM8XBfOhHVf0TmAKOx1zozXAczIVOHQ2ckOQG4KvAc5N8Cbili1yY\n1AJqA3B4kkOT7A2cBFzWc58WpCT7Nv9tJMnDgeOAzQyO9+ub1V4HTH+guQw4KcneSZ4AHA78vNNO\nLyxhx/+szOm4N8PX/0iyIkmA1w49R+3tEIfmRXnaS4Frm2njMDqfA7ZU1SeH2syH7v1fHMyH7iR5\nzPRpYUkeBjyPwXfRzIUOzRKH35gL3amq91XVIVW1hEEdcGVVvQa4nA5yoc8b6e62qvpvktOAtQyK\nwPOr6rqeu7VQLQIuTVIM/l4urKq1SX4BXJzkZOCPwCsAyhshz5skXwFWAo9O8icGV/c5G7hkjsf9\nVOALwEOBNVX1vS73Y9LNEofnJFkGbAduBN4MxmFUkhwNvArY3HznoID3Mbja0lxfh4zDbtpFHF5p\nPnTmQOCCJHsw+Pzztapak2Q95kKXZovDF82F3p1NB7ngjXQlSZIkqaVJPYVPkiRJkjpnASVJkiRJ\nLVlASZIkSVJLFlCSJEmS1JIFlCRJkiS1ZAElSZIkSS1ZQEmSJElSSxZQkqTeJXlkkrcMzR/Y3PRw\nFNtaleT9u1j+lCSfH8W2JUmTzxvpSpJ6l+Qw4PKqemoH2/op8JKq+tsu1lkLnFxVN4+6P5KkyeII\nlCRpHHwYWJLk6iTnJDk0yWaAJK9LcmmStUluSHJqknc06/4syaOa9ZYkuSLJhiQ/SvLEnTeS5Ajg\nP9PFU5KXJ9mc5JokU0Orfgc4aeR7LUmaOBZQkqRxcAbw+6paXlWnN23Dp0gcCZwIrAA+CNxRVcuB\n9cBrm3U+A5xWVUcB7wY+PcN2jgauHpr/AHBcVT0dOGGo/RfAsQ9slyRJC9GefXdAkqQWflhVdwJ3\nJvk7gxEigM3AU5M8HHgWcEmSNMv2muH3HAjcNjT/E+CC5vtW3xxq/wvw+PncAUnSwmABJUmaBHcN\nTdfQ/HYG72V7ALc3o1K78m/gEff+oqpTkhwFvBj4ZZLlVXU78NBmXUmSduApfJKkcfAvYP/dfXJV\n/Qv4Q5KXTbcledoMq14HHDG0zpKq2lBVqxmMOh3cLHoicO3u9keStHBZQEmSetdc1OGnSTYlOef+\nVp+l/dXAG5JsTHItO36nadqPgWVD8x9ttrkJ+FlVbWranwN8dw67IEl6kPAy5pKkB5UkH2dwyfQr\nZ1m+NzAFHFNV27vsmyRp/DkCJUl6sPkQsO8ulh8CnGHxJEmaiSNQkiRJktSSI1CSJEmS1JIFlCRJ\nkiS1ZAElSZIkSS1ZQEmSJElSSxZQkiRJktTS/wBYIj4Qhh9ceQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f575fb7dbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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teptNSXTvXnQK8nHmmfq5owNYeeVi05K1++8vOgXlMG9e47dpGlsnkddN8ZQp\n+aw3rjIFnkY9wRYAPPpodmmJgyVb1Ai//GXRKWgeL7zQ2HEYozKtHn+8MemIa/hw4Npri05Fc2Gw\nRU0ZbM2YATz1lPf+iSeKufEuSmf6rq1gyJB81lt09RMzMG+Z1BtsNYLdi2BZ00jxnXpq0SmgMPPn\nF52CdK66Sj+XrVfVs88GBgwoOhXNhcEWoVuiblLK4ZJLgN12897vvjtzWqi8/vrXolPQeTRDsDVn\njvc6aenUkUeWt1e1sujXr7ElEwccACy5ZOO2l0ZZj4VG+P3vi05Bfd54o+gUVGvVpid54k9GTXng\nuKpy/eEP3utrrmlcWqjW55+Xv1vytKVCZbppKbpkqUy/hdEMwZadrqT74eDBwKBB2aan1Qwa1Ni2\nd926lTPT0v4NynosNELRbVvr9Ze/lOt62pn3pbSa8DY7HwsXtn4X6EGmTy86Bcm9+KJ+Vspdpe65\nFhykoJnaZ6y9NrD//kWnIh9lqsJZdFrKeNG1g5dzzikuHWHq/d1uvDGbdFA2unUr5/n50ku912U8\nVsuq6OrZLtdfX3QKqB4MtioefBA48MCiU0FJDRrkDVjY6hrdS1q9vvqq6BTk4/LLi06Bp+hS6SWW\nKHb7LvZvUtZhEnjj21q6d6+v+/9G6Mz7XNLgqejzqksR7bY+/rj8+3WzKOEuVYwyHlwUbcyYolPQ\nODzplcOCBUWnwNOjR7bre/jhZPOPGpXt9rNQxlxpP/vGd9as4tLRioYO1c+N3A+6dy9nyZa9b3Xm\nYKvZqxECwOzZjd/m+uu7S9E7876UFkOMima4QFMtdjxQXuPHF52CfJStZ6gs7btvsvnL1iUx0Bzn\ncvtm5T//KS4drWjvvRu/zbKWbNkdqZQxGCyrMp5DzLihjcbMoGww2KpgyRZPxpQNc6EynTc8/TTw\n738Xl56slfGmqiiNyOHs6EhWgj1uXH5pyQpzhvPXyBvmbt30f1rm/7XMaSubMgZbjcb9JVsMMSoY\nbAGLFxedguwsWlR0ClrbwoXu4HzGjOr38+YBp58OnHxyY9LVCEVU5wiS9qagoyObi2kjMmiGDAF6\n944//8CBuSWFyKlrV/1c5usOb54pibDrHPel5BhiVDAno/ZGuZl9+GHRKchemU5wPXq4q3D6x7ZZ\neunWKzG1B6QtWtp9Yv584OCD699+I/7bsWPz3wZRPUywVeaq02W6fpQdM9+9/SXs3phjm8bHXaqC\nBxew2mqgzwWdAAAgAElEQVRFp6B+Dz4IbLVVawZbeXrppeRj98T9jXmRz089mURZtLdqtUCaWkcj\nM1DNtliy1RqY+R5vfzn99PzT0SoYYlTw4MrWpEnFVEscOhR4++3Gb7cR8txHjz8e6Ncvn3W3wkV+\n+eWLTkH25sypfx0MtqhMijrXfOtb+rnMnee0wnm4UXg/GF6yZT7jPhUfg60Klmxlq1cv4KmnGrvN\nLl3KVcUra/5xq15+uZh0JNUKN+TN8lvn6X//q53Gi206/N3yUcTvqhSw7LL69QorNH77cXGfi0cE\nuP/+olNRHmGBZys1PckbQ4wKBlvZ69+/sdvr1w949NHGbrOR/L2sTZyYzXrvuAMYMSKbdYXlgpXN\nKafEn3fllfNLR7NwDRDcCoF0Ecp6TDS7In/XddYpd0+l3Ofie+WVxmwnabfqM2fmk44wrms6z/vJ\nMcSoYLFx9vydJeSt1S8mTz9d/X7atGzWe/jh2awnyPz5+a4/rZdeij/viiu2Zv10kfgXfNfglmW+\nuaTOxx5wPOtretT1pUuXct+Etvr1sRkNH55s/iyqfsdl9hd7rDbDPs4oHgZbFSzZai7HHJNs/lVX\nzScdjeRvN/TnPxeTDr/hw4H33gv+vFV6kzM9jpVJFiW5xx3nbtjvD5Jd8+R9A3fddcA55+jXacZq\nK+N/Rvmxq1pnncnz8ce10/71L+91167lznzorMHW4MFFpyBY0v+kiHbw/iqVIsCLLzY+Hc2OIUaF\nyQU7/HBgt92KTcuiRcA11xSbhrJz5VqGNU5eccX80tIofftWv//2t4tJh9822wA77FB0KvLXqjfu\nd9wBPPRQ7fSoIHmJJYABA/JJk3H11d7rNGO1/epXyZdpxE3p6NH5b6MzevNN7/WUKdmu27VfHHaY\n97rswVaZS93ydOSRRacgWNJzzbx5+aSD8sdgq8Lk2j70UOM7drBNmgQsuSTw298Wl4Zm4Aq2brst\neP5WzNUruurrnXcCzzyjX5vftxUu6Gefrb+b/yLdrVsx6WkEV8l+VGn/brvpgCtP9e5PO+6YTTqy\nZt+kU3bsDpKyPufb6/vhD/WzfYyUMdhaZRX9vMsurXkNbHZJ/5MTTsgnHS5HHNG4bXUGDLYqvv66\n6BRo++9fdAqaQ9KbsFYYd8t/Yi462AK8UmCTtrLdbKRx6KHAL35RO2hzq5ZsAe7AKmz/6tq1MTeX\n9QZbSasbZ2W55YrZbmc3ebL3+vnn89vOJpvo5x49vGllDLbOPBP40Y900MVgK1qj/z97e3HGaHvu\nufzS4vfYY43bVmfAYKvijDOKToFmV4OgYP6e+QCgT5+GJ6Oh/BfLPfYoJh02czPsf25WM2YAm26q\nX/uradrB1s47Ny5NjeAKrMIu/l266JK+PMcVWrw4+c1PnjfYSeRRCjp7tvu8Rx67ndaDD+a3nS5d\n9LZMl+9AOYOtJZfUgaEIg604Gj3Eh329tDMKgpTxP3zyyXKPL1cWDLYqxo/Xz43s7cXPP44SBXPd\nHO69d+PT0Uj+E1qZOv1YuFA/l+Fm46ST9POxxyZfdpllvNc9e+rnpZbSz/Y+ZwYxbRWukq0tt3TP\n+8ADwAsv5H9zucQSwMiRyZYZNSqftJTBgAHAuusWnYpyy7O036x77bWBffetLtUCyhls3XMPcO21\nnTfYSvqdX3stn3TEkXf717z07eseFoSqMdgqkSJ6eGmlE3DUha4sud5pnX129fsyDrRbhpKtrbfW\nz9dem3xZ182amWb3BtlqN/VJemPddlvgBz/QQdfbb+eXpjTKULU2L9OnF52C8svz/GOulS+95M7Y\nK2OwZUpLOmuwlaQq3JNPAqeeml9a/D76qDozqRmq7QXtQ0X0kthsGGyVyM9+1vhtxim6bgZdu0Zf\naJu96pf/ZuuRR4pJR5iODmC11YpNw447Attvn27ZsGBrv/28aZ98km79ZZUkSDHH2aJFwMUX55Oe\ntObObdy2vv3tYmtCUK0828qZKv5Bx0oZgy1zc9xZg60kzTL8vf3m7fvfB045pXramDGNTUNSQfdY\n++8P/O9/jU1Lsyks2BKRT0XkXRF5W0Rer0xbXkSeEJFRIvK4iCxnzX+GiIwRkQ9FpK81fSsRGSEi\no0XksiK+SzMrQ0lEGv7BAE891X2h2333xqSHtPZ2YJ11ik3D+usDr7ySblnXjdQVVwR/5rLnnum2\nXaRmPQ/4pekePq0ytFMoU1XiMshzvEzTO1tQQFXGYOv44/XwByLAUUeV/2Y+a2UZi9LFdT3p3RuY\nNk2/LmNwHHadeP31xqWjGRVZstUBoI9Sakul1HaVaacDeEoptSGAZwCcAQAisgmAQwBsDGBPAFeJ\nfLOrXg2gv1KqN4DeItIyt9eNONj8B08ZD3CXWbOq33fv7r7Q3XUXsOaajUlT3pph4O2OjtbrtW+9\n9ZLNH9WuZtw4PVhvmSQJtsx++MtfApcxe8upUdUZm+V83Sgbbui9vvzyZMu+/3684yBosOQXXyzf\nDecJJwA33ODtj48/Xmx6ivTrX+tn17m3iMymBQvc002VvDJmgIWlqZWrcGehyNs3cWx/fwC3VF7f\nAsBUrNsPwJ1KqTal1KcAxgDYTkRWA7CsUuqNyny3Wss0vSKCrTIe4HEEVSNcbjngu99tfHry4P9+\nW21VTDrCtLcXGxTmUY0oyUVk+HDguOO8966Bp7/zHd3mqUxcGRU/+Un4Msst1xwZAEUw5+4sz+Gu\ndTHYqmYHW0mvZZtvrjuUiBJ0PlhpJV2qXkbmOG3W63sWTIdJv/lN7WdlKKU2zLm4LP/VQQd5r8NK\nbnktCFfkz6MAPCkib4hIJc8BqyqlvgQApdRkAJUh+dALwOfWshMr03oBmGBNn1CZ1hIacbD5L9Zl\nOcCT+PRTfaD7TwT++tCt5NRT9VhQZWNKtq66Kt3y112nO15I64UX0i8bxNxcrbRS9Lxrr11dshfU\nu9Xmm9efrihJxmRxHffPPuv+fMUV9XMZq021MtcN4dSpjU9HmSmlM6HWWAMYMiT+chttpJ9Nr6ph\ngm4qt9suusv/UaP0gOmNxlKH8BoXQZkWN9+cS1JCmXNte7vukXXnnYEpUxqbBvv3sDuGCrs//Nvf\ngD/9Kb80NbscRgOJ7YdKqS9EZGUAT4jIKOgAzJZpvt3AgQO/ed2nTx/0KfnATO3t+YzXYmuFkq11\n1nHf+JmTa+/e+dyEF6lLl3L+V6ZkK03Vzbvu0rmOa64J/PCH8YIbP9fYWPUGBKbr9zg5dyutVH0D\n3Lt3fdt2OeAA4L77oufbaSfge98DRoyInjfqN5o9Wz+vvLK+AQB0NZgZM6LXTdl45pmiU1B+SunA\nYtIk/YjL9C76r38BRx7pXm+U7t2jS0jOOEMfu4ceGj9tWTDp6swloWHn76Br6THHAEcfnUtyApn/\nyGRcDhvW2O2bbfvTA0RfJ558snydJsU1bNgwDMvxxy4s2FJKfVF5nioi9wPYDsCXIrKqUurLShVB\nE89PBLCWtfialWlB053sYKsZNOJmuhWCLQD4+GP9sJlgy65a0sxMgNXRAZx1VjlLFcwFYp99ki9r\nbkCU0jf106dX56qFef11nbM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30Vn49FOv0acR1lg+iKtRcD25P3HaDpk61f5thfUA5TpJ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\nJFso19ERMVHp7MF028eq+j+2aqyjHOz38t0oaVxETFD6kf1hi+szbNj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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f575fdfad50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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jgCeAk3s2NDMz29M6cp/CZcAxwLKI+FvgKGBTj0ZlZmaZ6EhS2BEROwAk9YmI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\ny3FSMDOzHCcFMzPL+f+tBk9itxbuGwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5764b60f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.brain_observatory_exceptions import NoEyeTrackingException\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# example with no eye tracking data\n",
+    "data_set = boc.get_ophys_experiment_data(501940850)\n",
+    "try:\n",
+    "    timestamps, locations = data_set.get_pupil_location()\n",
+    "except NoEyeTrackingException:\n",
+    "    print(\"No eye tracking for experiment %s.\" % data_set.get_metadata()[\"ophys_experiment_id\"])\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(569407590)\n",
+    "    \n",
+    "# looking at azimuth and altitude over time\n",
+    "# by default locations returned are (azimuth, altitude)\n",
+    "# passing as_spherical=False to get_pupil_location will return (x,y) in cm\n",
+    "timestamps, locations = data_set.get_pupil_location()\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.plot(timestamps, locations.T[0])\n",
+    "plt.plot(timestamps, locations.T[1])\n",
+    "plt.title(\"Eye position over time\")\n",
+    "plt.xlabel(\"time (s)\")\n",
+    "plt.ylabel(\"angle (deg)\")\n",
+    "plt.legend(['azimuth', 'altitude'])\n",
+    "plt.show()\n",
+    "\n",
+    "#pupil size over time\n",
+    "timestamps, area = data_set.get_pupil_size()\n",
+    "plt.figure(figsize=(14,4))\n",
+    "plt.plot(timestamps, area)\n",
+    "plt.title(\"Pupil size over time\")\n",
+    "plt.xlabel(\"time (s)\")\n",
+    "plt.ylabel(\"area (px)\")\n",
+    "plt.ylim(0, 20000)\n",
+    "plt.show()\n",
+    "\n",
+    "# scatter of gaze positions over approximate screen area\n",
+    "plt.figure()\n",
+    "plt.scatter(locations.T[0], locations.T[1], s=2, c=\"m\", edgecolor=\"\")\n",
+    "plt.title(\"Eye position scatter plot\")\n",
+    "plt.xlim(-70, 70)\n",
+    "plt.ylim(-60, 60)\n",
+    "plt.xlabel(\"azimuth (deg)\")\n",
+    "plt.ylabel(\"altitude (deg)\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tutorial/brain_observatory_analysis.ipynb b/tutorial/brain_observatory_analysis.ipynb
new file mode 100755
index 0000000..001aa67
--- /dev/null
+++ b/tutorial/brain_observatory_analysis.ipynb
@@ -0,0 +1,881 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Brain Observatory Trace Analysis\n",
+    "This notebook demonstrates how to run the stimulus-specific tuning analysis code in the SDK.  First let's instantiate a `BrainObservatoryCache` instance.\n",
+    "\n",
+    "Download this notebook in .ipynb format <a href='brain_observatory_analysis.ipynb'>here</a>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "boc =  BrainObservatoryCache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drifting Gratings\n",
+    "In this example, we'll show how you can plot a heatmap of a cell's response organized by orientation and temporal frequency.  Here we start with a known experiment ID.  Take a look at the other notebook to see how you can find experiments of interest.  You can run the drifting grating analysis code on that experiment's NWB file as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import MaxNLocator\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from allensdk.brain_observatory.drifting_gratings import DriftingGratings\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(502376461)\n",
+    "dg = DriftingGratings(data_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you know which cell you're interested in, here's how you can find out where it is in the NWB File."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Specimen ID:', 517425074)\n",
+      "('Cell loc:', 97)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "specimen_id = 517425074\n",
+    "cell_loc = data_set.get_cell_specimen_indices([specimen_id])[0]\n",
+    "\n",
+    "print(\"Specimen ID:\", specimen_id)\n",
+    "print(\"Cell loc:\", cell_loc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `response` property of the stimulus-specific analysis objects is 4-D array organized with the following dimensions:\n",
+    "    \n",
+    "    0: num. grating directions\n",
+    "    1: num. grating temporal frequencies + 1 (0=blank sweep)\n",
+    "    2: num. cells + 1 (running speed)\n",
+    "    3: 0=response mean, 1=response standard error of the mean, 2=number of signficant trials\n",
+    "\n",
+    "Dimension 2 of the `response` array has one index per cell in the experiment, plus one.  The final index of that dimension is the running speed (`response[:,:,-1,:]`).  This organization allows users to examine whether the mouse ran more for some specific stimulus conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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l8OxshwJfl7SpmeWOnh4E3Uwly+MJ4Mnal78IDC16vUI61yPU9HkkJ82luNC3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\nmc4DjsWHKL8FkDQA2AWPRHZ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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f844e095510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# skip the blank sweep column of the temporal frequency dimension\n",
+    "plt.imshow(dg.response[:,1:,cell_loc,0], cmap='hot', interpolation='none')\n",
+    "plt.xticks(range(5), dg.tfvals[1:])\n",
+    "plt.yticks(range(8), dg.orivals)\n",
+    "plt.xlabel(\"Temporal frequency (Hz)\", fontsize=20)\n",
+    "plt.ylabel(\"Direction (deg)\", fontsize=20)\n",
+    "plt.tick_params(labelsize=14)\n",
+    "cbar= plt.colorbar()\n",
+    "cbar.set_label(\"DF/F (%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `peak` property of the analysis object is a Pandas DataFrame of peak conditions (direction and temporal frequency) as well as computed response metrics.  For drifting gratings this includes:\n",
+    "\n",
+    "    ori_dg: preferred direction (index into dg.orivals)\n",
+    "    tf_dg: preferred temporal frequency (index into tf.tfvals)\n",
+    "    response_reliability_dg: response reliability \n",
+    "    osi_dg: orientation selectivity index\n",
+    "    dsi_dg: direction selectivity index\n",
+    "    ptest_dg: number of signficant cells\n",
+    "    p_run_dg: K-S statistic comparing running trials to stationary trials\n",
+    "    run_modulation_dg: ratio of mean fluorescence during running vs static\n",
+    "    cv_dg: circular variance "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ori_dg                         4\n",
+       "tf_dg                          2\n",
+       "reliability_dg          0.194066\n",
+       "osi_dg                   1.21454\n",
+       "dsi_dg                  0.340134\n",
+       "peak_dff_dg              11.6263\n",
+       "ptest_dg             3.03061e-21\n",
+       "p_run_dg               0.0638074\n",
+       "run_modulation_dg        -0.6221\n",
+       "cv_os_dg                       1\n",
+       "cv_ds_dg                0.340134\n",
+       "tf_index_dg             0.297836\n",
+       "cell_specimen_id       517425074\n",
+       "Name: 97, dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dg.peak.loc[cell_loc]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next let's plot all trials for a given cell's preferred condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Preferred direction:', 180)\n",
+      "('Preferred temporal frequency:', 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pref_ori = dg.orivals[dg.peak.ori_dg[cell_loc]]\n",
+    "pref_tf = dg.tfvals[dg.peak.tf_dg[cell_loc]]\n",
+    "print(\"Preferred direction:\", pref_ori)\n",
+    "print(\"Preferred temporal frequency:\", pref_tf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temporal_frequency</th>\n",
+       "      <th>orientation</th>\n",
+       "      <th>blank_sweep</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>836</td>\n",
+       "      <td>896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7156</td>\n",
+       "      <td>7216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7337</td>\n",
+       "      <td>7397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9775</td>\n",
+       "      <td>9834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13477</td>\n",
+       "      <td>13536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16548</td>\n",
+       "      <td>16607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>55013</td>\n",
+       "      <td>55073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56819</td>\n",
+       "      <td>56878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59708</td>\n",
+       "      <td>59768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97818</td>\n",
+       "      <td>97878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99082</td>\n",
+       "      <td>99142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>102603</td>\n",
+       "      <td>102663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104499</td>\n",
+       "      <td>104559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>105312</td>\n",
+       "      <td>105371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>2</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113258</td>\n",
+       "      <td>113318</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     temporal_frequency  orientation  blank_sweep   start     end\n",
+       "1                     2          180            0     836     896\n",
+       "71                    2          180            0    7156    7216\n",
+       "73                    2          180            0    7337    7397\n",
+       "100                   2          180            0    9775    9834\n",
+       "141                   2          180            0   13477   13536\n",
+       "175                   2          180            0   16548   16607\n",
+       "271                   2          180            0   55013   55073\n",
+       "291                   2          180            0   56819   56878\n",
+       "323                   2          180            0   59708   59768\n",
+       "435                   2          180            0   97818   97878\n",
+       "449                   2          180            0   99082   99142\n",
+       "488                   2          180            0  102603  102663\n",
+       "509                   2          180            0  104499  104559\n",
+       "518                   2          180            0  105312  105371\n",
+       "606                   2          180            0  113258  113318"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pref_trials = dg.stim_table[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]\n",
+    "pref_trials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sweep_response` is a DataFrame that contains the DF/F of each cell during each stimulus trial. It shares its index with `stim_table`. Each cell contains a timeseries that extends from 1 second prior to the start of the trial to 1 second after the end of the trial. The final column of `sweep_response`, named `dx`, is the running speed of the mouse during each trial. The data in this DataFrame is used to create another DataFrame called `mean_sweep_response` that contains the mean DF/F during the trial for each cell (and the mean running speed in the last column)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = dg.sweep_response[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we look at the mean running speed during trials that presented the preferred condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1       0.920868\n",
+       "71      0.060151\n",
+       "73      0.027268\n",
+       "100     4.897258\n",
+       "141    -0.000856\n",
+       "175    -0.002599\n",
+       "271    38.010730\n",
+       "291     0.000437\n",
+       "323    36.139884\n",
+       "435    -0.012866\n",
+       "449    11.234307\n",
+       "488     5.972907\n",
+       "509     1.967057\n",
+       "518    -0.004841\n",
+       "606     6.747042\n",
+       "Name: dx, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subset_mean = dg.mean_sweep_response[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]\n",
+    "subset_mean['dx']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the response to each trial of the preferred condition, labeled with the mean running speed during the trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mYBmAiEwCbgMygYXAU2LDfxljjDF9NqiJXUSGA1ep6nMAqupU1UZgMfC8p9jz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kiKjiY8OJiP9uxhVUEBqwoKKKur4/KcHK6+6CKWzp5NgL8/a7ZvZ+327Xy4Zw81zc3H3SsOCw4m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SpUtZs2YNs2fPntJzUp/NRGd3u+wCu4hYgGLgOuAk8ClwtzHm+Ig8GtivZBrYp0RrVxf7Skv5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\nadomohO7pmmapm0i/w9E3a6VotWm3QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f844da126d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trial_timestamps = np.arange(-1*dg.interlength, dg.interlength+dg.sweeplength, 1.)/dg.acquisition_rate\n",
+    "plt.figure(figsize=(8,20))\n",
+    "for i in range(len(subset)):\n",
+    "    plt.subplot(len(pref_trials),1,i+1)\n",
+    "    plt.plot(trial_timestamps, subset[str(cell_loc)].iloc[i], color='k', lw=2)\n",
+    "    plt.axvspan(0,2,color='red', alpha=0.3)\n",
+    "    plt.ylabel(\"DF/F (%)\")\n",
+    "    plt.ylim(-10,600)\n",
+    "    plt.yticks(range(0,700,200))\n",
+    "    plt.text(2.5, 300, str(round(subset_mean['dx'].iloc[i],2))+\" cm/s\")\n",
+    "    if i<(len(subset)-1):\n",
+    "        plt.xticks([])\n",
+    "    else:\n",
+    "        plt.xticks([-1,0,1,2,3])\n",
+    "        plt.xlabel(\"Time (s)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Static Gratings\n",
+    "The static gratings analysis object is quite similar to the drifting gratings analysis object.  Here we'll just take a look at the `peak` table, which contains information about the preferred orientation, spatial frequency, phase, as well as a number of other metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.brain_observatory.static_gratings import StaticGratings\n",
+    "\n",
+    "# example loading drifing grating data\n",
+    "data_set = boc.get_ophys_experiment_data(510938357)\n",
+    "\n",
+    "sg = StaticGratings(data_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ori_sg</th>\n",
+       "      <th>sf_sg</th>\n",
+       "      <th>phase_sg</th>\n",
+       "      <th>reliability_sg</th>\n",
+       "      <th>osi_sg</th>\n",
+       "      <th>peak_dff_sg</th>\n",
+       "      <th>ptest_sg</th>\n",
+       "      <th>time_to_peak_sg</th>\n",
+       "      <th>cell_specimen_id</th>\n",
+       "      <th>p_run_sg</th>\n",
+       "      <th>cv_os_sg</th>\n",
+       "      <th>run_modulation_sg</th>\n",
+       "      <th>sf_index_sg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0113189</td>\n",
+       "      <td>0.285308</td>\n",
+       "      <td>3.36311</td>\n",
+       "      <td>0.559699</td>\n",
+       "      <td>0.6634</td>\n",
+       "      <td>517399188</td>\n",
+       "      <td>0.898808</td>\n",
+       "      <td>0.313745</td>\n",
+       "      <td>0.193421</td>\n",
+       "      <td>0.193012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0328444</td>\n",
+       "      <td>0.856061</td>\n",
+       "      <td>17.1593</td>\n",
+       "      <td>5.6698e-07</td>\n",
+       "      <td>0.46438</td>\n",
+       "      <td>517399994</td>\n",
+       "      <td>0.102605</td>\n",
+       "      <td>0.792459</td>\n",
+       "      <td>0.716304</td>\n",
+       "      <td>0.272816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0140488</td>\n",
+       "      <td>0.918923</td>\n",
+       "      <td>4.73641</td>\n",
+       "      <td>0.81548</td>\n",
+       "      <td>0.36487</td>\n",
+       "      <td>517399857</td>\n",
+       "      <td>0.118354</td>\n",
+       "      <td>0.602649</td>\n",
+       "      <td>0.870513</td>\n",
+       "      <td>0.218487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.00940717</td>\n",
+       "      <td>1.05556</td>\n",
+       "      <td>2.29522</td>\n",
+       "      <td>0.0635452</td>\n",
+       "      <td>0.36487</td>\n",
+       "      <td>517399727</td>\n",
+       "      <td>0.259146</td>\n",
+       "      <td>0.914524</td>\n",
+       "      <td>1.05157</td>\n",
+       "      <td>0.145557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.00875107</td>\n",
+       "      <td>0.378599</td>\n",
+       "      <td>3.99094</td>\n",
+       "      <td>0.0794371</td>\n",
+       "      <td>0.86242</td>\n",
+       "      <td>517399442</td>\n",
+       "      <td>0.112734</td>\n",
+       "      <td>0.318078</td>\n",
+       "      <td>0.958211</td>\n",
+       "      <td>0.204495</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ori_sg sf_sg phase_sg reliability_sg    osi_sg peak_dff_sg    ptest_sg  \\\n",
+       "0      4     2        1      0.0113189  0.285308     3.36311    0.559699   \n",
+       "1      5     3        3      0.0328444  0.856061     17.1593  5.6698e-07   \n",
+       "2      4     3        3      0.0140488  0.918923     4.73641     0.81548   \n",
+       "3      5     5        1    -0.00940717   1.05556     2.29522   0.0635452   \n",
+       "4      3     5        0    -0.00875107  0.378599     3.99094   0.0794371   \n",
+       "\n",
+       "  time_to_peak_sg cell_specimen_id  p_run_sg  cv_os_sg run_modulation_sg  \\\n",
+       "0          0.6634        517399188  0.898808  0.313745          0.193421   \n",
+       "1         0.46438        517399994  0.102605  0.792459          0.716304   \n",
+       "2         0.36487        517399857  0.118354  0.602649          0.870513   \n",
+       "3         0.36487        517399727  0.259146  0.914524           1.05157   \n",
+       "4         0.86242        517399442  0.112734  0.318078          0.958211   \n",
+       "\n",
+       "  sf_index_sg  \n",
+       "0    0.193012  \n",
+       "1    0.272816  \n",
+       "2    0.218487  \n",
+       "3    0.145557  \n",
+       "4    0.204495  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sg.peak.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Natural Scenes\n",
+    "The natural scenes analysis object is again similar to the others.  In addition to computing the `sweep_response` and `mean_sweep_response` arrays, `NaturalScenes` reports the cell's preferred scene, running modulation, time to peak response, and other metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done analyzing natural scenes\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(510938357)\n",
+    "\n",
+    "ns = NaturalScenes(data_set)\n",
+    "print(\"done analyzing natural scenes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scene_ns</th>\n",
+       "      <th>reliability_ns</th>\n",
+       "      <th>peak_dff_ns</th>\n",
+       "      <th>ptest_ns</th>\n",
+       "      <th>p_run_ns</th>\n",
+       "      <th>run_modulation_ns</th>\n",
+       "      <th>time_to_peak_ns</th>\n",
+       "      <th>cell_specimen_id</th>\n",
+       "      <th>image_selectivity_ns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0.0183184</td>\n",
+       "      <td>4.91692</td>\n",
+       "      <td>0.241369</td>\n",
+       "      <td>0.0814777</td>\n",
+       "      <td>-1.04747</td>\n",
+       "      <td>0.76291</td>\n",
+       "      <td>517399188</td>\n",
+       "      <td>0.307441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>96</td>\n",
+       "      <td>0.151659</td>\n",
+       "      <td>7.58865</td>\n",
+       "      <td>2.77309e-08</td>\n",
+       "      <td>0.0145825</td>\n",
+       "      <td>-1.24205</td>\n",
+       "      <td>0.36487</td>\n",
+       "      <td>517399994</td>\n",
+       "      <td>0.523847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>103</td>\n",
+       "      <td>-0.0106162</td>\n",
+       "      <td>2.51431</td>\n",
+       "      <td>0.0333866</td>\n",
+       "      <td>0.0991697</td>\n",
+       "      <td>1.01418</td>\n",
+       "      <td>0.3317</td>\n",
+       "      <td>517399857</td>\n",
+       "      <td>-0.000474576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0047568</td>\n",
+       "      <td>1.65234</td>\n",
+       "      <td>0.577975</td>\n",
+       "      <td>0.156953</td>\n",
+       "      <td>1.04387</td>\n",
+       "      <td>0.03317</td>\n",
+       "      <td>517399727</td>\n",
+       "      <td>0.176847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21</td>\n",
+       "      <td>0.000218728</td>\n",
+       "      <td>1.55928</td>\n",
+       "      <td>0.0684794</td>\n",
+       "      <td>0.428506</td>\n",
+       "      <td>0.539678</td>\n",
+       "      <td>0.56389</td>\n",
+       "      <td>517399442</td>\n",
+       "      <td>0.242186</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  scene_ns reliability_ns peak_dff_ns     ptest_ns   p_run_ns  \\\n",
+       "0       48      0.0183184     4.91692     0.241369  0.0814777   \n",
+       "1       96       0.151659     7.58865  2.77309e-08  0.0145825   \n",
+       "2      103     -0.0106162     2.51431    0.0333866  0.0991697   \n",
+       "3       15      0.0047568     1.65234     0.577975   0.156953   \n",
+       "4       21    0.000218728     1.55928    0.0684794   0.428506   \n",
+       "\n",
+       "  run_modulation_ns time_to_peak_ns cell_specimen_id image_selectivity_ns  \n",
+       "0          -1.04747         0.76291        517399188             0.307441  \n",
+       "1          -1.24205         0.36487        517399994             0.523847  \n",
+       "2           1.01418          0.3317        517399857         -0.000474576  \n",
+       "3           1.04387         0.03317        517399727             0.176847  \n",
+       "4          0.539678         0.56389        517399442             0.242186  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ns.peak.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Locally Sparse Noise\n",
+    "The locally sparse noise stimulus object is a bit different from the others.  It does not have a peak condition table, instead providing a method to retrieve the \"on\" and \"off\" receptive fields of all cells.  The receptive field of a cell is computed by averaging responses to trials in which a given sparse noise grid location is on/off."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done analyzing locally sparse noise\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.locally_sparse_noise import LocallySparseNoise\n",
+    "import allensdk.brain_observatory.stimulus_info as stim_info\n",
+    "\n",
+    "specimen_id = 587179530\n",
+    "cell = boc.get_cell_specimens(ids=[specimen_id])[0]\n",
+    "\n",
+    "exp = boc.get_ophys_experiments(experiment_container_ids=[cell['experiment_container_id']],\n",
+    "                                stimuli=[stim_info.LOCALLY_SPARSE_NOISE])[0]\n",
+    "                                         \n",
+    "data_set = boc.get_ophys_experiment_data(exp['id'])\n",
+    "lsn = LocallySparseNoise(data_set)\n",
+    "print(\"done analyzing locally sparse noise\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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McUL7EOCKOe9/3O2TJK2RbetRyczMzN7tXq9Hr9dbj2olqRn9fp9+v79suXHmtI8AZqrq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\nqZApXAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f844cde5410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yVX11qJyTIEjSmFZjEoQHAV+rqiur6hbgVOCoCY4nSRphktC+K3D1vNfXdNskSatk01qc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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f844dfb8cd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "cell_idx = data_set.get_cell_specimen_indices([specimen_id])[0]\n",
+    "\n",
+    "plt.imshow(lsn.receptive_field[:,:,cell_idx,0], interpolation='nearest', cmap='PuRd', origin='lower')\n",
+    "plt.title(\"on receptive field\")\n",
+    "plt.show()\n",
+    "plt.imshow(lsn.receptive_field[:,:,cell_idx,1], interpolation='nearest', cmap='Blues', origin='lower')\n",
+    "plt.title(\"off receptive field\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorial/brain_observatory_monitor.ipynb b/tutorial/brain_observatory_monitor.ipynb
new file mode 100755
index 0000000..0036a3c
--- /dev/null
+++ b/tutorial/brain_observatory_monitor.ipynb
@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Brain Observatory Monitor\n",
+    "This notebook demonstrates how to query an BrainObservatoryDataNwbDataSet object to find out what type of stimulus was on the monitor at a given acquisiton frame during an experiment, and align that stimulus on the monitor with stimulus templates from other parts of a session (or other sessions in a container)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import allensdk.brain_observatory.stimulus_info as si\n",
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "boc =  BrainObservatoryCache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataframe summarizes the epochs of the experiment, and their start and end acquisition frames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stimulus</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>locally_sparse_noise</td>\n",
+       "      <td>742</td>\n",
+       "      <td>22417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>spontaneous</td>\n",
+       "      <td>22567</td>\n",
+       "      <td>31449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>natural_movie_one</td>\n",
+       "      <td>31450</td>\n",
+       "      <td>40479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>locally_sparse_noise</td>\n",
+       "      <td>41385</td>\n",
+       "      <td>63058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>natural_movie_two</td>\n",
+       "      <td>63962</td>\n",
+       "      <td>72991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>spontaneous</td>\n",
+       "      <td>73141</td>\n",
+       "      <td>82023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>locally_sparse_noise</td>\n",
+       "      <td>82024</td>\n",
+       "      <td>105502</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               stimulus  start     end\n",
+       "0  locally_sparse_noise    742   22417\n",
+       "1           spontaneous  22567   31449\n",
+       "2     natural_movie_one  31450   40479\n",
+       "3  locally_sparse_noise  41385   63058\n",
+       "4     natural_movie_two  63962   72991\n",
+       "5           spontaneous  73141   82023\n",
+       "6  locally_sparse_noise  82024  105502"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwb_dataset = boc.get_ophys_experiment_data(527550473)\n",
+    "nwb_dataset.get_stimulus_epoch_table()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the stimulus parameters shown on acquisition frame 1010:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "end                         1013\n",
+       "frame                         35\n",
+       "repeat                       NaN\n",
+       "start                       1006\n",
+       "stimulus    locally_sparse_noise\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params, template = nwb_dataset.get_stimulus(1010)\n",
+    "pd.Series(params[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is what was on the moniter during that acquision frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAADiCAYAAABeKzy5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADMVJREFUeJzt3X+MZeVdx/H3x90SvZSwNK3IsijUEJKGmGJuGrVNbUrb\nbGvj1sQ0bKwBJRn/0EpNk0rrHzPzh0mjtdZEU7OWFYwIMZRaYqp2rW3QBAnDlpaFbQvBLeyysDSb\n2Nb5A5Gvf8wlGTbz695zZu7sM+9Xspl7zn245/vs2f3w7HPPeU6qCknS+e9Hpl2AJKkfBrokNcJA\nl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEbu38mCDwaD27Nmz4fZ79+4d6/OfffbZcUuS\npG3v9OnT36uqN6zXbksDfc+ePczMzGy4/dzc3FifP257STofzM/Pf3cj7ZxykaRGdAr0JPuTfDvJ\nk0lu7asoSdL4Jg70JLuAvwDeC7wJOJjkTX0VJkkaT5cR+luAJ6vqqap6EbgbONBPWZKkcXUJ9MuB\nZ5Ztnxzte5UkM0kWkiwsLi52OJwkaS2b/qVoVR2qqmFVDQeDwWYfTpJ2rC6Bfgq4Ytn2vtE+SdIU\ndAn0h4Crk1yV5ALgBuC+fsqSJI1r4huLquqlJL8D/AuwCzhcVY/1VpkkaSyd7hStqi8BX+qpFklS\nB1t66/+4vJVfkjbOW/8lqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrok\nNcJAl6RGGOiS1AgDXZIaYaBLUiMmDvQkVyT5apLHkzyW5JY+C5MkjafLeugvAR+tqqNJLgIeTnKk\nqh7vqTZJ0hgmHqFX1emqOjp6/QPgOHB5X4VJksbTyxx6kiuB64AHV3hvJslCkoXFxcU+DidJWkHn\nQE/yWuDzwEeq6vvnvl9Vh6pqWFXDwWDQ9XCSpFV0CvQkr2EpzO+sqnv7KUmSNIkuV7kEuA04XlWf\n7q8kSdIkuozQ3wr8OvDOJI+Mfr2vp7okSWOa+LLFqvoPID3WIknqoMt16NKrzM/Pj9V+dnZ2kyqR\ndiZv/ZekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhrhWi7q\njWuzrM51btq2Xc6vI3RJaoSBLkmN6OOZoruSfD3JP/ZRkCRpMn2M0G8BjvfwOZKkDro+JHof8EvA\n5/opR5I0qa4j9M8AHwNeXq1BkpkkC0kWFhcXOx5OkrSaiQM9yfuBM1X18FrtqupQVQ2rajgYDCY9\nnCRpHV1G6G8FfjnJCeBu4J1J/raXqiRJY5s40Kvq41W1r6quBG4A/q2qPtRbZZKksXgduiQ1opdb\n/6vqa8DX+vgsSdJkXMtF2gKuzdK27XJ+nXKRpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakR\nBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqRNeHRO9Jck+SbyU5nuTn+ypMkjSersvn/hnwz1X1\nq0kuAHxoqCRNycSBnuRi4O3ATQBV9SLwYj9lSZLG1WXK5SrgBeCvk3w9yeeSXNhTXZKkMXUJ9N3A\nzwKfrarrgP8Bbj23UZKZJAtJFhYXFzscTpK0li6BfhI4WVUPjrbvYSngX6WqDlXVsKqGg4FT7JK0\nWSYO9Kp6DngmyTWjXdcDj/dSlSRpbF2vcvkwcOfoCpengN/oXpIkaRKdAr2qHgGGPdUiSerAO0Ul\nqRFdp1y0hvn5+bHaz87OblIlknYCR+iS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJek\nRhjoktQIA12SGmGgS1IjXMtlE7k2i6St5AhdkhrRKdCT/F6Sx5IcS3JXkh/tqzBJ0ngmDvQklwO/\nCwyr6lpgF3BDX4VJksbTdcplN/BjSXYDA+DZ7iVJkibR5SHRp4BPAU8Dp4H/rqov91WYJGk8XaZc\nLgEOAFcBe4ELk3xohXYzSRaSLCwuLk5eqSRpTV2mXN4F/FdVvVBV/wvcC/zCuY2q6lBVDatqOBgM\nOhxOkrSWLoH+NPBzSQZJAlwPHO+nLEnSuLrMoT8I3AMcBR4dfdahnuqSJI2p052iVTULeDukJG0D\n3ikqSY1wLRedN+bn58dq71o6/Zmbm9vU9uqHI3RJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANd\nkhphoEtSIwx0SWqEgS5JjTDQJakRqaotO9jevXtrZmZmy44nSdvRuOsSAQ9X1XC9Ro7QJakR6wZ6\nksNJziQ5tmzf65IcSfLE6Oclm1umJGk9Gxmh3w7sP2ffrcBXqupq4CujbUnSFK0b6FV1P3D2nN0H\ngDtGr+8APtBzXZKkMU06h35pVZ0evX4OuLSneiRJE+r8pWgtXSaz6qUySWaSLCRZWFxc7Ho4SdIq\nJg3055NcBjD6eWa1hlV1qKqGVTUcDAYTHk6StJ5JA/0+4MbR6xuBL/ZTjiRpUhu5bPEu4AHgmiQn\nk9wMfBJ4d5IngHeNtiVJU7R7vQZVdXCVt67vuRZJUgfeKSpJjVh3hC5J6tfs7OxY7Te69osjdElq\nhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEZs\n5AEXh5OcSXJs2b4/TvKtJN9M8oUkeza3TEnSejYyQr8d2H/OviPAtVX1M8B3gI/3XJckaUzrBnpV\n3Q+cPWffl6vqpdHmfwL7NqE2SdIY+phD/03gn3r4HElSB50CPckfAC8Bd67RZibJQpKFxcXFLoeT\nJK1h4kBPchPwfuDXqqpWa1dVh6pqWFXDwWAw6eEkSeuY6JmiSfYDHwN+saocdkvSNrCRyxbvAh4A\nrklyMsnNwJ8DFwFHkjyS5C83uU5J0jrWHaFX1cEVdt+2CbVIkjrwTlFJasREc+g71dzc3Ka2l6Qu\nHKFLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNcC2XMey0\ntVlcu0bblX82V+YIXZIasZEHXBxOcibJsRXe+2iSSvL6zSlPkrRRGxmh3w7sP3dnkiuA9wBP91yT\nJGkC6wZ6Vd0PnF3hrT9l6bmiqz4gWpK0dSaaQ09yADhVVd/ouR5J0oTGvsolyQD4BEvTLRtpPwPM\nAFx88cXjHk6StEGTjNB/GrgK+EaSE8A+4GiSn1ipcVUdqqphVQ0Hg8HklUqS1jT2CL2qHgV+/JXt\nUagPq+p7PdYlSRrTRi5bvAt4ALgmyckkN29+WZKkca07Qq+qg+u8f2Vv1UiSJuadopLUCNdy0ap2\nyvoXOv/4Z3NljtAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJA\nl6RGpGrrHgma5AXguyu89XpgJ62nbn/btZP6CvZ3q/xUVb1hvUZbGuirFpEsVNVw2nVsFfvbrp3U\nV7C/241TLpLUCANdkhqxXQL90LQL2GL2t107qa9gf7eVbTGHLknqbruM0CVJHU010JPsT/LtJE8m\nuXWatWyFJCeSPJrkkSQL066nb0kOJzmT5Niyfa9LciTJE6Ofl0yzxj6t0t+5JKdG5/iRJO+bZo19\nSXJFkq8meTzJY0luGe1v8vyu0d9tfX6nNuWSZBfwHeDdwEngIeBgVT0+lYK2QJITwLCqmrxuN8nb\ngR8Cf1NV1472/RFwtqo+Ofqf9iVV9fvTrLMvq/R3DvhhVX1qmrX1LcllwGVVdTTJRcDDwAeAm2jw\n/K7R3w+yjc/vNEfobwGerKqnqupF4G7gwBTrUUdVdT9w9pzdB4A7Rq/vYOkvRRNW6W+Tqup0VR0d\nvf4BcBy4nEbP7xr93damGeiXA88s2z7JefAb1lEB/5rk4SQz0y5mi1xaVadHr58DLp1mMVvkw0m+\nOZqSaWIKYrkkVwLXAQ+yA87vOf2FbXx+/VJ0a72tqt4MvBf47dE/2XeMWprfa/2yqs8CbwTeDJwG\n/mS65fQryWuBzwMfqarvL3+vxfO7Qn+39fmdZqCfAq5Ytr1vtK9ZVXVq9PMM8AWWpp1a9/xoPvKV\neckzU65nU1XV81X1f1X1MvBXNHSOk7yGpXC7s6ruHe1u9vyu1N/tfn6nGegPAVcnuSrJBcANwH1T\nrGdTJblw9OUKSS4E3gMcW/u/asJ9wI2j1zcCX5xiLZvulXAb+RUaOcdJAtwGHK+qTy97q8nzu1p/\nt/v5neqNRaNLfj4D7AIOV9UfTq2YTZbkjSyNygF2A3/XWn+T3AW8g6UV6Z4HZoF/AP4e+EmWVtr8\nYFU18UXiKv19B0v/HC/gBPBby+aYz1tJ3gb8O/Ao8PJo9ydYmldu7vyu0d+DbOPz652iktQIvxSV\npEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNeL/AW/NcTqICKNSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6669b4ff10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = plt.imshow(template, cmap=plt.cm.gray, interpolation='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes it is nice to have this stimulus visualized exactly as it was shown on the monitor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADmCAYAAADiFP9HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6668561c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = si.BrainObservatoryMonitor()\n",
+    "img_screen = m.lsn_image_to_screen(template)\n",
+    "m.show_image(img_screen, warp=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During a Brain Observatory experiment, the image on the screen (for example, above) is warped so that spherical coordinates could be displayed on a flat monitor.  When an stimulus frame is displayed without this warping, it is useful to overlay a mask of the eventual maximal extent of the stimulus that will be shown on the monitor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADmCAYAAADiFP9HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCDJACEEGCCHIACEEGSCEIAOE+A+jWKTRiWiTHAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6669b4ffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = si.BrainObservatoryMonitor()\n",
+    "img_screen = m.lsn_image_to_screen(template)\n",
+    "m.show_image(img_screen, warp=False, mask=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the ```map_stimulus_coordinate_to_monitor_coordinate``` function, we can assosicate a position on the monitor (eitherpre- or post-warp) between multiple types of stimuli, despite the fact the templates for these stimuli might have different sizes and aspect ratios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wgniaOgVFiidbGY4MwxAvv/wydnf3CssAznty4fvaAqFWq6HRaKDRaBiC2G63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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f665eb5d0d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y0, x0 = (5,12)\n",
+    "y1, x1 = m.map_stimulus((y0, x0), si.LOCALLY_SPARSE_NOISE, si.NATURAL_MOVIE_ONE)\n",
+    "ym, xm = si.map_stimulus_coordinate_to_monitor_coordinate((y0, x0), \n",
+    "                                                          (m.n_pixels_r, m.n_pixels_c), \n",
+    "                                                          si.LOCALLY_SPARSE_NOISE)\n",
+    "\n",
+    "img_lsn =  nwb_dataset.get_stimulus_template('locally_sparse_noise')[0,:,:]\n",
+    "img_movie =  nwb_dataset.get_stimulus_template('natural_movie_one')[0,:,:]\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(2,2, figsize=(10,5))\n",
+    "natural_movie_image = m.natural_movie_image_to_screen(img_movie, origin='upper')\n",
+    "m.show_image(natural_movie_image, \n",
+    "             ax=ax[0,1], \n",
+    "             show=False, \n",
+    "             origin='upper', \n",
+    "             mask=True)\n",
+    "ax[0,1].plot([xm], [ym], 'r.')\n",
+    "ax[0,1].set_title('Natural Movie One, Monitor\\n1200x1920')\n",
+    "lsn_image = m.lsn_image_to_screen(img_lsn, origin='upper')\n",
+    "m.show_image(lsn_image, \n",
+    "             ax=ax[0,0], \n",
+    "             show=False, \n",
+    "             origin='upper', \n",
+    "             mask=True)\n",
+    "ax[0,0].plot([xm], [ym], 'r.')\n",
+    "ax[0,0].set_title('Locally Sparse Noise, Monitor\\n1200x1920')\n",
+    "\n",
+    "ax[1,0].imshow(np.flipud(img_lsn), interpolation='none', cmap=plt.cm.gray, \n",
+    "               extent=[0,img_lsn.shape[1],img_lsn.shape[0],0])\n",
+    "ax[1,0].plot([x0], [y0], 'r.')\n",
+    "ax[1,0].axes.get_xaxis().set_visible(False)\n",
+    "ax[1,0].axes.get_yaxis().set_visible(False)\n",
+    "ax[1,0].set_xlim((0, img_lsn.shape[1]-1))\n",
+    "ax[1,0].set_ylim((img_lsn.shape[0]-1, 0))\n",
+    "ax[1,0].set_title('Locally Sparse Noise, Template\\n16x28')\n",
+    "\n",
+    "ax[1,1].imshow(img_movie, interpolation='none', cmap=plt.cm.gray,\n",
+    "               extent=[0,img_movie.shape[1],img_movie.shape[0],0])\n",
+    "ax[1,1].plot([x1], [y1], 'r.')\n",
+    "ax[1,1].axes.get_xaxis().set_visible(False)\n",
+    "ax[1,1].axes.get_yaxis().set_visible(False)\n",
+    "ax[1,1].set_xlim((0, img_movie.shape[1]-1))\n",
+    "ax[1,1].set_ylim((img_movie.shape[0]-1, 0))\n",
+    "ax[1,1].set_title('Natural Movie One, Template\\n304x608')\n",
+    "\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, in the top row of the figure above, the same position on the monitor (pre-warp) is marked in two different stimuli (left: locally sparse noise, right: natural movie).  Below, the same point is reproduced on the template image for reach stimuli. You can see that in both images, the relative location of the marked point (red) is the same in both the pre-warp monitor image and the template image. This example demonstrates how to co-register locations between stimulus sets.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An optional `translation` argument will reposition the stimulus frame relative to a gray background canvas; translation in supplied in units of monitor pixels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VlZUn3p/Gs36fRYioEDebTQkd1URctw/Rcw4cK9sMh+aaOkt15PwBUTWd65dkkuelW25o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wAAAg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\nYHnJYgiEyy7/UTQstdUkqC7W62kKryVdLw74w3US8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e1uHwAAYBgRgwGog056JPdK+qGk\n/2Zm3zKzPzOzaUk73f1cts4rknZm93dLejl6/JlsGQAAAFpHDAZg4EY7fOzbJb3f3b9mZn+kbAhF\n4O5uZl71ic3ssKTDknTVVVd10MT6uO+++yotBwAAKNCTGGwzxl8AeqeTHskzks64+9ey3z+rxkHt\nVTPbJUnZz/PZ389Kuj56/J5s2RXc/Zi73+TuN01NTXXQRAAAgE2nJzEY8ReAKtpOJN39FUkvm9nP\nZItulfSMpBOSDmXLDkl6NLt/QtJBM5sws72S9kl6st3tAwAADCNiMAB10MnQVkl6v6SHzGxc0guS\nfl2N5PQRM7tL0kuS7pQkdz9lZo+ocaBblnS3u690uH0AAIBhRAwGYKA6SiTd/SlJN+X86daC9Y9K\nOtrJNgEAAIYdMRiAQev0OpIAAAAAgCHT6dBWtIjqrBsLVXYBAEA/EHNgo6JHEgAAAABQCYkkAAAA\nAKASEkkAAAAAQCUkkgAAAACASkgkAQAAAACVULUVyEGlNAAA0A/EHNio6JEEAAAAAFRCIgkAAAAA\nqIREEgAAAABQCYkkAAAAAKASEkkAAAAAQCVUbd3kiiqBUSGsnu6///7c5ffee2+fWwIAQDXEHBsL\nMQc6RY8kAAAAAKASEkkAAAAAQCUkkgAAAACASkgkAQAAAACVdJRImtm/NrNTZva0mX3azLaa2TVm\n9piZPZ/9vDpa/x4zO21mz5nZbZ03HwAAYPgQgwEYNHP39h5otlvSX0va7+5zZvaIpC9I2i/pNXf/\niJkdkXS1u/+eme2X9GlJN0u6TtKXJb3F3VfKtnPdddf54cOH22ojgI3t/vvv/4a73zTodgBAnfQj\nBiP+AoZXq/FXp0NbRyVNmtmopClJ/1fSAUnHs78fl3RHdv+ApIfdfcHdX5R0Wo0DGgAAAKohBgMw\nUG0nku5+VtJ/kvS3ks5JesPdvyRpp7ufy1Z7RdLO7P5uSS9HT3EmWwYAAIAWEYMBqIO2E8ls3P0B\nSXvVGCYxbWa/Gq/jjXGzlcfOmtlhMztpZidnZ2fbbSIAAMCm06sYjPgLQBWdDG39JUkvuvsP3X1J\n0uck/T+SXjWzXZKU/TyfrX9W0vXR4/dky67g7sfc/SZ3v2lqaqqDJgIAAGw6PYnBiL8AVNFJIvm3\nkm4xsykzM0m3SnpW0glJh7J1Dkl6NLt/QtJBM5sws72S9kl6soPtAwAADCNiMAADN9ruA939a2b2\nWUnflLQs6VuSjknaJukRM7tL0kuS7szWP5VVFXsmW//u9Sq2bkT3339/7vJ77723zy2pJ/YPAACd\nIQZrDTEH0FttJ5KS5O73Skr/GxfUODOWt/5RSUc72SYAAMCwIwYDMGidXv4DAAAAADBkSCQBAAAA\nAJWQSAIAAAAAKiGRBAAAAABU0lGxHVyJSmDl6rZ/7rvvvkrLAQDAxkDMAfQWPZIAAAAAgEpIJAEA\nAAAAlZBIAgAAAAAqIZEEAAAAAFRCIgkAAAAAqISqrRhqVEoDAAD9QMyBzYYeSQAAAABAJSSSAAAA\nAIBKSCQBAAAAAJWQSAIAAAAAKiGRBAAAAABUQiIJAAAAAKhk3UTSzB4ws/Nm9nS07Boze8zMns9+\nXh397R4zO21mz5nZbdHyd5jZd7K//bGZWfdfDgAAwOZADAagzlrpkXxQ0u3JsiOSHnf3fZIez36X\nme2XdFDSjdljPmZmI9ljPi7pNyXty27pcwIAAOCyB0UMBqCm1k0k3f2rkl5LFh+QdDy7f1zSHdHy\nh919wd1flHRa0s1mtkvSDnd/wt1d0iejxwAAACBBDAagztqdI7nT3c9l91+RtDO7v1vSy9F6Z7Jl\nu7P76XIAAAC0jhgMQC10XGwnO7vlXWhLk5kdNrOTZnZydna2m08NAACwKXQ7BiP+AlBFu4nkq9lQ\nCWU/z2fLz0q6PlpvT7bsbHY/XZ7L3Y+5+03uftPU1FSbTQQAANh0ehaDEX8BqKLdRPKEpEPZ/UOS\nHo2WHzSzCTPbq8aE7iezIRgXzOyWrFLY+6LHAAAAoDXEYABqYXS9Fczs05J+UdK1ZnZG0r2SPiLp\nETO7S9JLku6UJHc/ZWaPSHpG0rKku919JXuq31Gj+tikpC9mNwAAAOQgBgNQZ+smku7+3oI/3Vqw\n/lFJR3OWn5T01kqtAwAAGFLEYADqrONiOwAAAACA4UIiCQAAAACohEQSAAAAAFAJiSQAAAAAoBIS\nSQAAAABAJSSSAAAAAIBKSCQBAAAAAJWQSAIAAAAAKiGRBAAAAABUQiIJAAAAAKiERBIAAAAAUAmJ\nJAAAAACgEhJJAAAAAEAlJJIAAAAAgEpIJAEAAAAAlZBIAgAAAAAqIZEEAAAAAFRCIgkAAAAAqGTd\nRNLMHjCz82b2dLTsD83su2b2bTP7vJm9KfrbPWZ22syeM7PbouXvMLPvZH/7YzOz7r8cAACAzYEY\nDECdtdIj+aCk25Nlj0l6q7v/fUnfk3SPJJnZfkkHJd2YPeZjZjaSPebjkn5T0r7slj4nAAAALntQ\nxGAAamrdRNLdvyrptWTZl9x9Ofv1CUl7svsHJD3s7gvu/qKk05JuNrNdkna4+xPu7pI+KemObr0I\nAACAzYYYDECddWOO5G9I+mJ2f7ekl6O/ncmW7c7up8sBAADQHmIwAAMz2smDzezDkpYlPdSd5jSf\n97Ckw9mvC/fff//TZev3wbWSfkQbJNWjHbThsjq0o5dt+Ls9el4A2NB6EYPVMP6SNv/33EZqg1SP\ndtCGy3rVjpbir7YTSTP7NUnvlnRrNlRCks5Kuj5abU+27KwuD72Il+dy92OSjmXbOenuN7Xbzm6g\nDfVqB22oVzvq0AYAGCa9isHqFn/VpR20oV7toA31aUdbQ1vN7HZJH5L0Hnefjf50QtJBM5sws71q\nTOh+0t3PSbpgZrdklcLeJ+nRDtsOAAAwVIjBANTFuj2SZvZpSb8o6VozOyPpXjUqhE1IeiyrIP2E\nu/+2u58ys0ckPaPGcIu73X0le6rfUaP62KQa4/m/KAAAAOQiBgNQZ+smku7+3pzFnyhZ/6ikoznL\nT0p6a6XWNRxr4zHdRhsuq0M7aMNldWhHHdoAAJvOgGOwuhzb69AO2nBZHdpBGy4baDvs8tB6AAAA\nAADW143LfwAAAAAAhkhtE0kzu93MnjOz02Z2pIfbud7M/srMnjGzU2b2gWz5fWZ21syeym7vih5z\nT9au58zsti625Qdm9p1seyezZdeY2WNm9nz28+petcPMfiZ6vU+Z2QUz+2Cv94WZPWBm583s6WhZ\n5ddtZu/I9t9pM/vjrKhAp+34QzP7rpl928w+b2ZvypbfYGZz0T750260o6ANlfd/D9rwmWj7PzCz\np3q5HwAAgzNsMdig46/sOYc2BqtD/FXSDmKwMu5eu5ukEUnfl/TTksYl/Y2k/T3a1i5Jb8/ub5f0\nPUn7Jd0n6d/lrL8/a8+EpL1ZO0e61JYfSLo2WfYfJR3J7h+R9Ae9bkf0HryixnVkerovJL1T0tsl\nPd3J65b0pKRbJJkahQR+uQvt+MeSRrP7fxC144Z4veR52m5HQRsq7/9utyH5+3+W9B96uR+4cePG\njdtgbhrCGEw1ir+i92BoYrCCNvQ1/ippR+X93+19kfy9VjFYXXskb5Z02t1fcPdFSQ9LOtCLDbn7\nOXf/Znb/oqRnJe0uecgBSQ+7+4K7vyjpdNbeXjkg6Xh2/7ikO/rUjlslfd/dX1qnbR23wd2/Kum1\nnOdu+XWb2S5JO9z9CW/8B30yekzb7XD3L7n7cvbrE1p7La4rdNqOgn1RpCf7oqwN2RmtOyV9uuw5\nuvF+AAAGghjs8rYGEX9JQxaD1SH+KmpHCWIw1Xdo625JL0e/n1H5gaUrzOwGSW+T9LVs0fuzLvUH\nom79XrbNJX3ZzL5hZoezZTu9cQ0oqXF2amcf2iFJB7X2g9rvfVH1de/O7veiLcFvaG3J9L3ZUIL/\nY2a/ELWvF+2osv97uS9+QdKr7v58tKyf+wEA0FvDGIPVKf6SiMFSg4y/JGKwQnVNJPvOzLZJ+gtJ\nH3T3C5I+rsawjp+VdE6NruRe+3l3/1lJvyzpbjN7Z/zH7KxCz8vsmtm4pPdI+h/ZokHsi6Z+ve4y\nZvZhNa7L9VC26Jykv5O9X/9G0p+b2Y4ebX6g+z/xXq39cuvnfgAAbEI1iMFqEX9JxGCpAcdfEjFY\nqbomkmclXR/9vidb1hNmNqbGAewhd/+cJLn7q+6+4u6rkv6rLg8X6Fnb3P1s9vO8pM9n23w166IO\nXdXne90ONQ6k33T3V7P29H1fqPrrPqu1wx661hYz+zVJ75b0L7IDqrKhDD/O7n9DjbHxb+lFO9rY\n/z3ZF2Y2KumfSfpM1La+7QcAQF8MXQxWo/hLIgZrGnT8lW2DGKxEXRPJr0vaZ2Z7szMzByWd6MWG\nsvHGn5D0rLt/NFq+K1rtn0oK1ZNOSDpoZhNmtlfSPjUmtHbajmkz2x7uqzHJ+Olse4ey1Q5JerSX\n7cisOePR730RPXfLrzsbgnHBzG7J3tP3RY9pm5ndLulDkt7j7rPR8p8ys5Hs/k9n7XihF+2ouv97\ntS8k/ZKk77p7c7hEP/cDAKAvhioGq1n8JRGDSapH/JVtgxisjPeggk83bpLepUb1ru9L+nAPt/Pz\nanTZf1vSU9ntXZL+u6TvZMtPSNoVPebDWbueU5eqIKnRbf432e1UeM2S3izpcUnPS/qypGt63I5p\nST+WdFW0rKf7Qo0D5jlJS2qM476rndct6SY1/sG/L+lPJFkX2nFajTHw4bPxp9m6/zx7n56S9E1J\nv9KNdhS0ofL+73YbsuUPSvrtZN2e7Adu3Lhx4za4m4YoBlNN4q/seYcyB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P43sDUiVkZE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\nLCQlSZIkSbVYSEqSJEmSavl/KEDygeqKeqwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6658c02750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "origin = 'upper'\n",
+    "img_movie =  nwb_dataset.get_stimulus_template(si.NATURAL_MOVIE_ONE)[0,:,:]\n",
+    "natural_movie_image = m.natural_movie_image_to_screen(img_movie, origin=origin)\n",
+    "natural_movie_image_translated = m.natural_movie_image_to_screen(img_movie, origin=origin, translation=(100,-300))\n",
+    "\n",
+    "img_lsn =  nwb_dataset.get_stimulus_template(si.LOCALLY_SPARSE_NOISE)[0,:,:]\n",
+    "lsn_image = m.lsn_image_to_screen(img_lsn, origin=origin)\n",
+    "img_lsn_translated = m.lsn_image_to_screen(img_lsn, origin=origin, translation=(-500,0))\n",
+    "\n",
+    "img_grating = m.grating_to_screen(.5,.02,0)\n",
+    "img_grating_translated = m.grating_to_screen(.5,.02,0, translation=(250,250))\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(3,2, figsize=(20,10))\n",
+    "_ = ax[0,0].imshow(natural_movie_image, interpolation='none', cmap=plt.cm.gray,\n",
+    "                   extent=[0,natural_movie_image.shape[1],natural_movie_image.shape[0],0])\n",
+    "_ = ax[0,1].imshow(natural_movie_image_translated, interpolation='none', cmap=plt.cm.gray,\n",
+    "                   extent=[0,natural_movie_image_translated.shape[1],natural_movie_image_translated.shape[0],0])\n",
+    "_ = ax[1,0].imshow(lsn_image, interpolation='none', cmap=plt.cm.gray,\n",
+    "                   extent=[0,lsn_image.shape[1],lsn_image.shape[0],0])\n",
+    "_ = ax[1,1].imshow(img_lsn_translated, interpolation='none', cmap=plt.cm.gray,\n",
+    "                   extent=[0,img_lsn_translated.shape[1],img_lsn_translated.shape[0],0])\n",
+    "_ = ax[2,0].imshow(img_grating, interpolation='none', cmap=plt.cm.gray,)\n",
+    "_ = ax[2,1].imshow(img_grating_translated, interpolation='none', cmap=plt.cm.gray)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/brain_observatory_stimuli.ipynb b/tutorial/brain_observatory_stimuli.ipynb
new file mode 100644
index 0000000..a630d87
--- /dev/null
+++ b/tutorial/brain_observatory_stimuli.ipynb
@@ -0,0 +1,378 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:fbcd58473f0b01a3cb54f57cc3a5a972808faadd42a70c9d8cf38b5739582f7c"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Brain Observatory Stimuli\n",
+      "The next few sections will describe how to understand the various stimulus tables present in the NWB file.  First, here is a function we can use to plot when a given stimulus condition is on the screen.  We'll use this further down.\n",
+      "\n",
+      "Download this notebook in .ipynb format <a href='brain_observatory_stimuli.ipynb'>here</a>."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "import matplotlib.patches as patches\n",
+      "%matplotlib inline\n",
+      "\n",
+      "def plot_stimulus_table(stim_table, title):\n",
+      "    fstart = stim_table.start.min()\n",
+      "    fend = stim_table.end.max()\n",
+      "    \n",
+      "    fig = plt.figure(figsize=(15,1))\n",
+      "    ax = fig.gca()\n",
+      "    for i, trial in stim_table.iterrows():    \n",
+      "        x1 = float(trial.start - fstart) / (fend - fstart)\n",
+      "        x2 = float(trial.end - fstart) / (fend - fstart)            \n",
+      "        ax.add_patch(patches.Rectangle((x1, 0.0), x2 - x1, 1.0, color='r'))\n",
+      "    ax.set_xticks((0,1))\n",
+      "    ax.set_xticklabels((fstart, fend))\n",
+      "    ax.set_yticks(())\n",
+      "    ax.set_title(title)\n",
+      "    ax.set_xlabel(\"frames\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Drifting Gratings\n",
+      "The drifting gratings stimulus describes the temporal frequency and direction of motion of the displayed grating.  We can use this information to figure out when a given stimulus condition (temporal frequency + orientation) is visible during the experiment."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+      "boc = BrainObservatoryCache()\n",
+      "data_set = boc.get_ophys_experiment_data(501940850)\n",
+      "\n",
+      "# this is a pandas DataFrame. find trials with a given stimulus condition.\n",
+      "temporal_frequency = 4\n",
+      "orientation = 225\n",
+      "stim_table = data_set.get_stimulus_table('drifting_gratings')\n",
+      "stim_table = stim_table[(stim_table.temporal_frequency == temporal_frequency) & (stim_table.orientation == orientation)]\n",
+      "\n",
+      "# plot the trials\n",
+      "plot_stimulus_table(stim_table, \"TF %d ORI %d\" % (temporal_frequency, orientation))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 501940850 NWB. This can take some time.\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA24AAAByCAYAAAArrYIaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f8838af1b50>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Static Gratings Stimulus\n",
+      "This stimulus table is very similar to the drifting grating stimulus table.  Static gratings have three parameters: spatial frequency, orientation, and phase.  We can make a similar plot to identify frame ranges with a given paramerization."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(501498760)\n",
+      "\n",
+      "# this is a pandas DataFrame. find trials with a given stimulus condition.\n",
+      "spatial_frequency = 0.02\n",
+      "orientation = 30\n",
+      "phase = 0.0\n",
+      "stim_table = data_set.get_stimulus_table('static_gratings')\n",
+      "stim_table = stim_table[(stim_table.spatial_frequency == spatial_frequency) & \\\n",
+      "                        (stim_table.orientation == orientation) & \\\n",
+      "                        (stim_table.phase == phase) ]\n",
+      "\n",
+      "# plot the trials\n",
+      "plot_stimulus_table(stim_table, \"SF %.02f ORI %d Phase %.02f\" % (spatial_frequency, orientation, phase))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 501498760 NWB. This can take some time.\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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g6CSfBU6uqt+siwFJknRXMrhJkoZkZe/964Grq+rAJOsBN/WW3dx7v7r3eTW3/W0L8Jiq\n+tPINo5M8nXgWcAZSZ5RVRfdZSOQJGkd8B43SdJQbQb8tr1/GbDeLNc/DVgw9SHJ7u3f7atqcVW9\nH/gJsNNdsK+SJK1TBjdJ0lyrMe8/AhyU5Fy6cLViTL/RWlPLFgB7JDkvyWLgFa39tUnOT3Ie8Cfg\nW3d2AJIkrWs+nESSJEmSBs4ZN0mSJEkaOIObJEmSJA2cwU2SJEmSBs7gJkmSJEkDZ3CTJEmSpIEz\nuEmSJEnSwBncJEmSJGngDG6SJEmSNHD/B7xUpA0WdgwqAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f8838af1050>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Natural Scenes Stimulus\n",
+      "The natural scene stimulus is simply a series of static images.  The natural scene template section of the NWB file contains the array of images."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(501498760)\n",
+      "\n",
+      "scene_nums = [4, 83]\n",
+      "\n",
+      "# read in the array of images\n",
+      "scenes = data_set.get_stimulus_template('natural_scenes')\n",
+      "\n",
+      "# display a couple of the scenes\n",
+      "fig, axes = plt.subplots(1,len(scene_nums))\n",
+      "for ax,scene in zip(axes, scene_nums):\n",
+      "    ax.imshow(scenes[scene,:,:], cmap='gray')\n",
+      "    ax.set_axis_off()\n",
+      "    ax.set_title('scene %d' % scene)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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np4fVq1fT399PJBJRmmyhkcRZ3XzzzdjtdvLz8/nwww85evQoBoOB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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f8838880ad0>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The natural scenes stimulus table just describes when a given scene is on the screen.  We can use this to find when the images above are visible."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(501498760)\n",
+      "\n",
+      "# the natural scenes stimulus table describes when each scene is on the screen\n",
+      "stim_table = data_set.get_stimulus_table('natural_scenes')\n",
+      "\n",
+      "# build up a mask of trials for which one of a list of scenes is visible\n",
+      "trial_mask = stim_table.frame == -2\n",
+      "for scene in scene_nums:\n",
+      "    trial_mask |= (stim_table.frame == scene)\n",
+      "stim_table = stim_table[trial_mask]\n",
+      "\n",
+      "# plot the trials\n",
+      "plot_stimulus_table(stim_table, \"scenes %s \" % scene_nums)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA3EAAAByCAYAAAD9CFh/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSpA4xxEmSJElShxjiJEmSJKlD/gd4ESIObKMXmgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f883cda2150>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Natural Movie Stimulus\n",
+      "The natural movie stimulus is very similar to the natural scene stimulus in terms of data structures.  Let's take a look at one frame of the \"natural_movie_one\" clip."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(501498760)\n",
+      "\n",
+      "# read in the natural movie one clip\n",
+      "movie = data_set.get_stimulus_template('natural_movie_one')\n",
+      "\n",
+      "# display a random frame for reference\n",
+      "frame = 200\n",
+      "plt.imshow(movie[frame,:,:], cmap='gray')\n",
+      "plt.axis('off')\n",
+      "plt.title('frame %d' % frame)\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ciOswouu9DQkSZeY6fd8Lb9oImo9QfhgxEsBcNxgMivNBv7w2NZz9VZRengdH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OAAAg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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f88181eefd0>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can now take a look at the stimulus table to see when a given frame (or range of frames) is played to the mouse.  Here you can see that there are 10 repetitions of the video."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(501498760)\n",
+      "\n",
+      "# read in the stimulus table, which describes when a given frame is displayed\n",
+      "stim_table = data_set.get_stimulus_table('natural_movie_one')\n",
+      "\n",
+      "# find out when a particular frame range is displayed\n",
+      "frame_range = [ 100, 120 ]\n",
+      "stim_table = stim_table[(stim_table.frame >= frame_range[0]) & (stim_table.frame <= frame_range[1])]\n",
+      "\n",
+      "plot_stimulus_table(stim_table, \"frames %d -> %d \" % (frame_range[0], frame_range[1]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA3EAAAByCAYAAAD9CFh/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADNBJREFUeJzt3X2wXVV9xvHvw2spCS8FS6mUIBUQqJJWXqaOhUCdYRxs\nxXFAtAJaWrSIcXBaFaYdLc40gFOV8iLWUgXbiIXWQDtIcHhrRW2Q94KDgjBYiLFgIFAwweTXP866\nZnt6L/ckubk3O3w/M2fu2mutvfbaezJz5pm1zk6qCkmSJElSP2wx0xOQJEmSJI3OECdJkiRJPWKI\nkyRJkqQeMcRJkiRJUo8Y4iRJkiSpRwxxkiRJktQjhjhJeglLsl+Su5KsSHL6TM9HkiRNzhAnSS9t\nHwJuqKodqurCmZ7MqJIcn+QbSf43yU3jtM9Ncntr/3aSg4baz0iyNMnTSS5Nss1MzjnJvkmuTvKj\nJE8muS7JvjM9Z0nSpskQJ0kvbXOA+ydqTLKpfk88CXwSOGe4oYWbq4HLgZ2Ay4Crk2zd2o8GPgwc\nxeD+9wb+ctQLJ9k1SaZyzsCOwCJgX2A3YEm7h7FrbtCcJUmbl031y1mStJEluRGYB1zYtlPuk+QL\nST6T5NokzwLzkhyT5M62AvRoko92xtgryZok72ptTyZ5b5JDktyTZHmSC4au+4dJ7k/y47bitGen\n7VNJlrVr3ZPkwPHmXlU3VNVVwNJxmucBW1bV+VX1QlVdAAQ4srWfDPxdVX2nqp4CzgbetQ6P7hTg\n+0k+lmSvUU96sTlX1W1V9fmqeqqqfgp8Gtgvyc5TNGdJ0mbEECdJL1FVdRTwH8D72nbK77WmtwMf\nr6pZwK3As8A7q2pH4BjgT5K8eWi4Q4FXAicA5wNnMVg1OhA4PsnhAO28M4G3ALu263+ptR0N/A6w\nT7vWcQxWr9bVgcA9Q3V3t3qAA9rxmHuA3TqB6UVV1bkM7vOXgW8nuTHJO5Nstx5zncjhwNKqWj4V\nc5YkbV4McZKk7tbAAhZV1TcBqmplVd1SVfe143uBK4Ajhsb4eFWtqqqvAc8AC6vqiap6nEFQm9v6\nvRdYUFUPVNUaYAEwt63GrQJmA/sn2aL1+eF63M8s4OmhuhVt7PHaV7S/sxlRVf1nVZ0G7A58hkHw\nfSzJ59Zjvj8nyR7AhcAHO9UbPGdJ0ubDECdJqqHjH3QPkhyW5Kb20o2ngPcAuwyds6xTfn6c41mt\nPAc4v22zXM7albZfraqbGISXi4BlST6bZH1CyjPADkN1O7V6GKwsdtt37Jz3c5KcleSZ9rl4uL2q\nXgDuBe4CVrJ2tW+9JHkZcD1wUVV9udM08pwlSZs/Q5wkaTILGbx0Y4+q2gm4hPX//ngUOLWqdu58\ntq+qbwFU1QVVdTCD7YP7An82yXjDARTgPuA1Q3WvbvVj7XM7bQcByzpbF9cOXvVXVTW7fU4bq0+y\nS5LTkywBbmDwPOZV1esmme9Ec6ZtjbyewUrognHuaaQ5S5I2f4Y4SVImKI+ZBSyvqlVJDgXewQRB\nZIRrXAKcleQAgCQ7JjmulQ9uq35bA88BPwFWjztYskWSXwC2BrZIsu3Y2yeBm4HVSea3+vnAGuDG\n1n45cEqS/Vtw+gvg8yPfSHIK8DCD3+99lEG4PbOqHpjkvAnnnGQHYDHw9ao6a5zTN2jOkqTNiyFO\nklRD5eGAdhpwdpIVDMLDl4faRwl0BVBVi4BzgSuSPM1gK+LRrc8OwN8CPwYeAZ4APjHBeCcxCHoX\nMwhTzwOfbdd4ATi29Vne/h7b3vpIVS0GzgNuatd5iEEYG9U3gD2r6m1V9dWqGjXQTjhnBi96ORh4\nd2f75or2+7ipmLMkaTOS0b97JEmSJEkzzZU4SZIkSeoRQ5wkSZIk9YghTpIkSZJ6xBAnSZIkST2y\n1cYaOIlvTJEkSZL0klZV4/33PRtko4U4GHrntG/B3PjS+ffh854ePvPp5zOfXj7v6eczn34+8+nn\nM59eGcoQPvONrz3zKU9vjdspJUmSJKlHDHGSJEmS1COGOEmSJEnqEUOcJEmSJPWIIU6SJEmSesQQ\nJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmSJKlHDHGSJEmS1COGOEmSJEnqEUOcJEmSJPWIIU6S\nJEmSesQQJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmSJKlHDHGSJEmS1COGOEmSJEnqEUOcJEmS\nJPWIIU6SJEmSesQQJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmSJKlHDHGSJEmS1COGOEmSJEnq\nEUOcJEmSJPWIIU6SJEmSesQQJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmSJKlHDHGSJEmS1COG\nOEmSJEnqEUOcJEmSJPWIIU6SJEmSesQQJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmSJKlHDHGS\nJEmS1COGOEmSJEnqEUOcJEmSJPWIIU6SJEmSesQQJ0mSJEk9YoiTJEmSpB4xxEmSJElSjxjiJEmS\nJKlHDHGSJEmS1COGOEmSJEnqEUOcJEmSJPWIIU6SJEmSesQQJ0mSJEk9kqraOAMnG2dgSZIkSeqJ\nqspUj7nRQpwkSZIkaeq5nVKSJEmSesQQJ0mSJEk98qIhLsl+Se7sfJ5OMj/JLyX5WpLvJrk+yU6t\n/15Jnu/0v7gz1nVJ7kpyX5JLk2zd6uckuSHJ3UluSvLyjXvLkiRJkjSaCTLRB5IcmuS2VndbkkNa\n/z8Y6r86yWta22uT3Jvke0nO71zjk53+DyRZ/qJzGvU3cUm2AB4DDgXeDzxRVecl+TCwc1V9JMle\nwL9W1avHOX9WVT3bylcBi6rqH5JcCVxTVV9MciTw7qo6aaRJSZIkSdI06WSiw4DLgQVVtTjJG4EP\nVdWRQ/1/g0HueWU7XgKcXlVLklwL/E1VXTd0zunA3Kr6o4nmsS7bKd8APFhVPwB+H7is1V8GHDvZ\nyZ0AtzWwDfBEa9ofuLGVbwbevA5zkiRJkqTpMpaJHgWWAju2+p0YhLth7wC+BJBkd2B2VS1pbZcz\nfo762TkTWZcQd0JnsN2qalkrLwN26/R7RVsGvDnJ67sDJFnc+j/fSZx3A29t5bcAs5PsvA7zkiRJ\nkqTp0M1EHwH+OsmjwCeAM8fpf3yn/8uB/+60PdbqfibJHGAv1i5yjWukEJdkG+D3gCuH22qwH3Ns\nT+bjwK9V1W8CHwQWJpnd6Xs0sDuwbZKTW/WfAkckuQM4vN3M6lHmJUmSJEnTYZxMdCkwv6r2BM4A\n/n6o/2HAc1V1/zpc5gTgyprkN2+jrsS9Ebi9qv6nHS9L8ittcrsDPwKoqlVVtbyV7wAeAvbpDlRV\nK4F/Bg5px0ur6q1V9VvAn7e6FSPOS5IkSZKmw3AmOrSqvtLKVzF4d0jXCcDCzvFjwB6d4z34/1sw\n38YkWylh9BD39qHBrgHGVtJOBhYBJNk1yZatvDeDAPf9JNu3sEeSrYA3AXe2413aDwRhsAR56Yhz\nkiRJkqTpMpyJHkxyRCsfBXx3rKHlm+OAK8bqqmopsCLJYUkCnEjLUe2cVzF4YeS3JpvIVpN1SLI9\ngx/w/XGn+hzgn5KcAjzCYK8nDLZDnp3kBWAN8J6qeirJbsDVSbYFAixm7XLjPGBBkgJuAd432Zwk\nSZIkabpMkIlOBS5qGef5djzmcODRqnpkaKjTgC8A2wHXDr2ZcqRVOFiH/2JAkiRJkjTz1uXtlJIk\nSZKkGWaIkyRJkqQeMcRJkiRJUo8Y4iRJkiSpRwxxkiRJktQjhjhJkiRJ6hFDnCRpk5VkfpL7k3xx\npuciSdKmwv8nTpK0yUryHeB3q+rxTt1WVfXTGZyWJEkzypU4SdImKcklwN7AdUmeSnJ5kq8DlyWZ\nk+Tfk9zePr/dzpmX5JYki5I8lOScJCcmWZLkniR7t34vS3JVq1+S5HWt/ogkd7bPHUlmzdgDkCRp\nAq7ESZI2WUkeBl4LvB94E/D6qlqZZDtgTSvvAyysqkOSzAO+ArwKWA48DHyuqj6WZD7wiqo6I8lC\n4KKqujXJnsB1VXVAkmuABVX1zSS/CKysqtXTfuOSJL2IrWZ6ApIkTSLt7zVVtbKVtwEuTHIQsBrY\np9P/tqpaBpDkQWBxq/8v4MhWfgOwfzI2NLOTbA/cCnwqyT8C/1JVj22MG5IkaUMY4iRJffFcp3wG\nsLSqTkyyJfCTTtvKTnlN53gNa7/3AhxWVauGrnFukn8DjgFuTXJ0VT0wZXcgSdIU8DdxkqQ+2gH4\nYSufBGy5judfD8wfO0gyt/399aq6r6rOA24D9puCuUqSNKUMcZKkTVlNUL4YODnJXQyC1rMT9Bse\na6xtPnBwkruT3Aec2uo/kOTeJHcDq4CvbugNSJI01XyxiSRJkiT1iCtxkiRJktQjhjhJkiRJ6hFD\nnCRJkiT1iCFOkiRJknrEECdJkiRJPWKIkyRJkqQeMcRJkiRJUo8Y4iRJkiSpR/4PYqayQ7XaHn0A\nAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f88387afb10>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Locally Sparse Noise Stimulus\n",
+      "This is essentially a 28x16 pixel movie, so it is again similar to the natural movie and natural scene stimuli.  One extra complication is that a nontrivial number of pixels from the movie are pushed off screen after warping the stimulus, so we provide some helper utilities to identify these pixels."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data_set = boc.get_ophys_experiment_data(505693621)\n",
+      "\n",
+      "# read in the locally sparse noise stimulus movie.  \n",
+      "# the 'mask_offscreen' argument will set off-screen grid locations to LocallySparseNoise.LSN_OFF_SCREEN\n",
+      "lsn_movie, offscreen_mask = data_set.get_locally_sparse_noise_stimulus_template('locally_sparse_noise',\n",
+      "                                                                                mask_off_screen=True)\n",
+      "\n",
+      "# show a single frame of the stimulus for reference\n",
+      "frame = 200\n",
+      "plt.imshow(lsn_movie[frame,:,:], cmap='gray', interpolation='nearest')\n",
+      "plt.axis('off')\n",
+      "plt.title('frame %d' % frame)\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAADqCAYAAACGAX/IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/m29wAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f883cd75e90>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can now take a look at the movie to determine the frame numbers for which a given grid location is 'on' or 'off' in the movie."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "from allensdk.brain_observatory.locally_sparse_noise import LocallySparseNoise\n",
+      "\n",
+      "# find frames at a given grid location that are 'on'\n",
+      "loc = (10,15)\n",
+      "on_frames = np.where(lsn_movie[:,loc[0],loc[1]] == LocallySparseNoise.LSN_ON)[0]\n",
+      "\n",
+      "# pull these trials out of the stimulus table\n",
+      "stim_table = data_set.get_stimulus_table('locally_sparse_noise')\n",
+      "stim_table = stim_table.loc[on_frames]\n",
+      "\n",
+      "plot_stimulus_table(stim_table, \"loc (%d,%d) \" % loc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAByCAYAAADqKsi2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe3e779c210>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/tutorial/cell_specimen_mapping.ipynb b/tutorial/cell_specimen_mapping.ipynb
new file mode 100644
index 0000000..45fdad8
--- /dev/null
+++ b/tutorial/cell_specimen_mapping.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cell Specimen Mapping\n",
+    "The cross-session alignment algorithm has been updated and re-run just prior to release 0.13.2 (June 15th, 2017). As a result all cell specimen IDs have changed. We have built a mapping table to help map from previous cell IDs to new cell IDs. The table is available as a `WellKnownFile` and the `BrainObservatoryApi` provides a method for downloading it and loading it as a DataFrame.\n",
+    "\n",
+    "Download this file in .ipynb format <a href='cell_specimen_mapping.ipynb'>here</a>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downloading the Mapping Table\n",
+    "The `BrainObservatoryApi` provides a `get_cell_specimen_id_mapping` method for downloading the mapping table as a CSV file. The method requires a file name to save the table to, and will return the table as a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 27124 entries, 0 to 27123\n",
+      "Data columns (total 4 columns):\n",
+      "old_cell_id              27124 non-null int64\n",
+      "session_A_new_cell_id    18492 non-null float64\n",
+      "session_B_new_cell_id    17674 non-null float64\n",
+      "session_C_new_cell_id    16868 non-null float64\n",
+      "dtypes: float64(3), int64(1)\n",
+      "memory usage: 847.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.api.queries.brain_observatory_api import BrainObservatoryApi\n",
+    "\n",
+    "api = BrainObservatoryApi()\n",
+    "\n",
+    "table = api.get_cell_specimen_id_mapping(\"./cell_mapping_table.csv\")\n",
+    "\n",
+    "table.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Using the Mapping Table\n",
+    "The cell specimen mapping table contains 4 columns, `old_cell_id`, `session_A_new_cell_id`, `session_B_new_cell_id`, `session_C_new_cell_id`.  In some instances, multiple ROIs that were assigned to a single cell specimen may now be considered distinct cells after reprocessing, so an old cell specimen ID may map to different cell specimens in different sessions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>old_cell_id</th>\n",
+       "      <th>session_A_new_cell_id</th>\n",
+       "      <th>session_B_new_cell_id</th>\n",
+       "      <th>session_C_new_cell_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>517394938</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>517396084.0</td>\n",
+       "      <td>517396679.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    old_cell_id  session_A_new_cell_id  session_B_new_cell_id  \\\n",
+       "17    517394938                    NaN            517396084.0   \n",
+       "\n",
+       "    session_C_new_cell_id  \n",
+       "17            517396679.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's assume I did some previous analysis on a cell and\n",
+    "# want to get the new session B id for use with the SDK\n",
+    "old_cell_id = 517394938\n",
+    "\n",
+    "session_B_id = int(table[table.old_cell_id == old_cell_id].session_B_new_cell_id)\n",
+    "\n",
+    "# Looking at the output of the table I can see that this ID\n",
+    "# was actually split to two cells in reprocessing\n",
+    "table[table.old_cell_id == old_cell_id]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FqEEQWOEt31EZRr9PNe3ANDlXK9KZaH/rvZQ+p6jTZg0b34GKrv9nn7KfAVgE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\nl8kBe/pc/ng+kK/xSSxi6u2mSs5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ArFKphFDv7u4GQ/Pee+/pjTfeCIXGIgwPD0doDCMG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xvxOhaLWV+i9fV14+OQNtje3g406CSS9pshyaqhw8gFgoODRrRFI1NmAcOb7O7u1sbGRuCQ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Q0InQKoDSfsAF4JL3YKQArz7ix8CTSpKqHWgoVJmOgdBXkqXfpbJG7J133tGxY8dMD8mB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\nsWLlmfL/AJUPQeAbhosAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdc317b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "import numpy as np\n",
+    "import copy\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "%matplotlib inline\n",
+    "\n",
+    "boc = BrainObservatoryCache()\n",
+    "\n",
+    "cell_ids = {}\n",
+    "cell_ids[\"three_session_B\"] = int(table[table.old_cell_id == old_cell_id].session_B_new_cell_id)\n",
+    "cell_ids[\"three_session_C\"] = int(table[table.old_cell_id == old_cell_id].session_C_new_cell_id)\n",
+    "\n",
+    "datasets = {}\n",
+    "for session, cell_id in cell_ids.iteritems():\n",
+    "    # Find and download the session experiment\n",
+    "    exp = boc.get_ophys_experiments(cell_specimen_ids=[cell_id],\n",
+    "                                    session_types=[session])[0]\n",
+    "    datasets[session] = boc.get_ophys_experiment_data(exp['id'])\n",
+    "\n",
+    "# set up a color map for overlay\n",
+    "overlay_map = ListedColormap([\"g\"])\n",
+    "overlay_map.set_bad(color='k', alpha=0)\n",
+    "\n",
+    "# overlay the cell ROIs on the max projection\n",
+    "plt.figure(figsize=(11, 5))\n",
+    "for i, key in enumerate(sorted(datasets.keys())):\n",
+    "    plt.subplot(1, 2, i+1)\n",
+    "    dataset = datasets[key]\n",
+    "    cell_id = cell_ids[key]\n",
+    "    overlay_mask = dataset.get_roi_mask(cell_specimen_ids=[cell_id])[0].get_mask_plane().astype(float)\n",
+    "    overlay_mask[overlay_mask == 0] = np.nan\n",
+    "    plt.imshow(dataset.get_max_projection(), cmap='gray')\n",
+    "    plt.imshow(overlay_mask, cmap=overlay_map, alpha=0.3)\n",
+    "    plt.title(key)\n",
+    "    plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>old_cell_id</th>\n",
+       "      <th>session_A_new_cell_id</th>\n",
+       "      <th>session_B_new_cell_id</th>\n",
+       "      <th>session_C_new_cell_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>517394947</td>\n",
+       "      <td>517396395.0</td>\n",
+       "      <td>517396395.0</td>\n",
+       "      <td>517396395.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    old_cell_id  session_A_new_cell_id  session_B_new_cell_id  \\\n",
+       "19    517394947            517396395.0            517396395.0   \n",
+       "\n",
+       "    session_C_new_cell_id  \n",
+       "19            517396395.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# I found an interesting cell on the brain observatory website and\n",
+    "# want to see if it's a cell I previously did some analysis on\n",
+    "new_cell_id = 517396395\n",
+    "\n",
+    "old_cell_id = int(table[(table.session_A_new_cell_id==new_cell_id) | \n",
+    "                        (table.session_B_new_cell_id==new_cell_id) |\n",
+    "                        (table.session_C_new_cell_id==new_cell_id)].old_cell_id)\n",
+    "\n",
+    "# I can see that this cell is found in all sessions as well.\n",
+    "table[(table.session_A_new_cell_id==new_cell_id) | \n",
+    "      (table.session_B_new_cell_id==new_cell_id) |\n",
+    "      (table.session_C_new_cell_id==new_cell_id)]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/receptive_fields.ipynb b/tutorial/receptive_fields.ipynb
new file mode 100644
index 0000000..88b842a
--- /dev/null
+++ b/tutorial/receptive_fields.ipynb
@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Receptive Field Analysis\n",
+    "This notebook demonstrates how to run the `brain_observatory.receptive_field_analysis` module.  This module uses a cell's responses to the locally sparse noise stimulus to characterize the spatial receptive field, including on and off subunits.  We highly recommend reading through the the stimulus analysis <a href='http://help.brain-map.org/display/observatory/Documentation]'>whitepaper</a> to understand the locally sparse noise stimulus and the analysis methodology.\n",
+    "\n",
+    "Download this file in .ipynb format <a href='receptive_fields.ipynb'>here</a>.\n",
+    "\n",
+    "First we import packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
+    "import allensdk.brain_observatory.receptive_field_analysis.visualization as rfvis\n",
+    "import allensdk.brain_observatory.receptive_field_analysis.receptive_field as rf\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given a cell of interest, we now identify the experiment that contains the locally sparse noise stimulus and download its NWB file.  We also look in the NWB file to figure out the position/index of the cell that has the ID we're interested in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 501474098 NWB. This can take some time.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cell 587377366 has index 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_specimen_id = 587377366\n",
+    "\n",
+    "boc = BrainObservatoryCache()\n",
+    "\n",
+    "exps = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id],\n",
+    "                                 stimuli=['locally_sparse_noise'])\n",
+    "\n",
+    "data_set = boc.get_ophys_experiment_data(exps[0]['id'])\n",
+    "\n",
+    "cell_index = data_set.get_cell_specimen_indices([cell_specimen_id])[0]\n",
+    "\n",
+    "print(\"cell %d has index %d\" % (cell_specimen_id, cell_index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute receptive fields\n",
+    "The following method in the `receptive_field_analysis` module will characterize on and off receptive fields and perform a per-pixel significance test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rf_data = rf.compute_receptive_field_with_postprocessing(data_set, \n",
+    "                                                         cell_index, \n",
+    "                                                         'locally_sparse_noise', \n",
+    "                                                         alpha=0.5, \n",
+    "                                                         number_of_shuffles=10000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chi^2 significance map\n",
+    "Per-pixel chi-square tests identify cells that show non-uniform distributions of responses across pixels.  The `receptive_field_analysis.visualization` module has function to plot that significance as a heat map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WD13RtcxI43HLCYE+69MEzroRNAFgDVkXJ4IAYMIMEzQBoDoyTQAosNbPS5MQNAHUAxNB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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6bf54a13d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rfvis.plot_chi_square_summary(rf_data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Response-triggered stimulus field\n",
+    "The response-triggered stimulus field shows, for a given pixel, how many trials contained a detected calcium event."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OAk/Vo4Z9CjbXFuhsvhYKG2nvRiPG+FhiRHJcCTkR9HfW/3St8wSEFCxYXE5ITLB5lZLU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\nZgacaNEgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6bf4997710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_rts_summary(rf_data, ax1, ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Blurred response-triggered stimulus field\n",
+    "The RTS field is convolved with a Gaussian to pool the contributions of neighboring stimulus pixels.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4RNsqKfGwy3nDGVrOgSgbK40AcN2O9JpFjnUDfV0jETpMr6Jnix3LAx1mJSbCZkyWHq8I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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6be75b5a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_rts_blur_summary(rf_data, ax1, ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## p value field\n",
+    "\n",
+    "Per-pixel p-values are estimated from the blurred RTS field to understand the significance of the response to each pixel.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hYk/fpLd4WFp2oHn88AWvqdjWikIV+8sSe9VLpbHCYG15pZk7PTlVxQplLzP3hCZ6Un7Q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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6bf501e610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_p_values(rf_data, ax1, ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Significance mask\n",
+    "\n",
+    "The significance mask is p-value field after applying a binary threshold to remove insignificant pixels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAB2CAYAAAAawO4tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAXCOFvVT0AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6be35c1890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_mask(rf_data, ax1, ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gaussian fit\n",
+    "Each identified subunit of the on and off receptive fields are fit with a Gaussian in the `receptive_field_analysis.postprocessing` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q9XhS+j0vSzxeKioKinCOnWFs6tmucpbShKVmKOp2kpA27U+Tk9tUAYZem7OS4mdu155e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\nwBhjDBYGxhhjsDAwxhiDhYExxhjg/wE+KOMon02OFQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6bd745d350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_gaussian_fit(rf_data, ax1, ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.65 vs 9.3 degree sparse noise stimuli\n",
+    "Newer experiments switched from using a single locally sparse noise stimulus with 4.54 visual-degree pixels to two blocks of stimuli with different pixel sizes (a 4.65 degree block and an 9.3 degree block that are each half the length of the original 4.65-degree-only stimulus).  You can characterize the receptive fields from reponses to each stimulus block separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['locally_sparse_noise_4deg', 'locally_sparse_noise_8deg', 'natural_movie_one', 'natural_movie_two', 'spontaneous']\n",
+      "['drifting_gratings', 'natural_movie_one', 'natural_movie_three', 'spontaneous']\n",
+      "['static_gratings', 'natural_scenes', 'natural_movie_one', 'spontaneous']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_specimen_id = 559109414\n",
+    "exps = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id])\n",
+    "for exp in exps:\n",
+    "    print(boc.get_ophys_experiment_stimuli(exp['id']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This cell comes from an experiment that has the new 4.65 degree and 9.3 degree stimulus blocks.  Let's find the experiment that contains the 9.3 degree stimulus.\n",
+    "\n",
+    "**Note:** the NWB files refer to these stimuli as `locally_sparse_noise_4deg` and `locally_sparse_noise_8deg` respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:allensdk.api.queries.brain_observatory_api:Downloading ophys_experiment 558599066 NWB. This can take some time.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cell 559109414 has index 152\n"
+     ]
+    }
+   ],
+   "source": [
+    "exp = boc.get_ophys_experiments(cell_specimen_ids=[cell_specimen_id],\n",
+    "                                stimuli=['locally_sparse_noise_8deg'])\n",
+    "data_set = boc.get_ophys_experiment_data(exps[0]['id'])\n",
+    "cell_index = data_set.get_cell_specimen_indices([cell_specimen_id])[0]\n",
+    "print(\"cell %d has index %d\" % (cell_specimen_id, cell_index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the receptive field analysis as before and see what this looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rf_data = rf.compute_receptive_field_with_postprocessing(data_set, \n",
+    "                                                         cell_index, \n",
+    "                                                         'locally_sparse_noise_8deg', \n",
+    "                                                         alpha=0.5, \n",
+    "                                                         number_of_shuffles=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oVTtsseL+yZff2K8E51u6Yx+kdNXc/UouAEnQHMZQ6+n6CntyWW1FxXr1fWwTPBgThnup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\nAAUImgBQgKAJAAUImgBQ4P8Aqm1lQ0hecfYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6bde031f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rfvis.plot_chi_square_summary(rf_data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAB2CAYAAAAkyUMmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACTZJREFUeJzt3V2IXVcZxvHnmYxttWknubBKMkmmClKKYDFtKZbSYhop\nqW1uFKtisQreKC0WaqQXalEvgogWciWxhZRIhaCGtiL9NBiRpCltDfYj0sIkM/nAaNNQ2sz5er04\nhzQzzdmz98zs2evM/v/g0JyTtc5aObPO05c9e+3tiBAAoB6Gqp4AAGDxEPoAUCOEPgDUCKEPADVC\n6ANAjRD6AFAjhD6AWdm+yPZjtk/Z/n3vtZ/Z/o/to1XPD/kNVz0BAAPhS5I+KmllRITtNZLulbQm\nIv5b7dRQBJU+gDzWSToU7+/mXCfpJIE/eAj9ObB9he3nbL9l+6Dt23qvP2x7m+3HbZ+2/Q/bl1c9\nXyCv861t2z+R9CNJd/TW9XckPSlpVe/5Q5VOGoVweKcg28OSHpO0XdJGSTdI+pPtq3tNviLpFkkv\nStoh6eeSvlbBVIFC+qzt3ZLWSwpJn4yIO3ttX5f0SESsrWi6mCMq/eKuk3RxRGyNiFZEPCfpcb0f\n7H+MiBcioiNpp6SrqpooUND51vZjomhZUgj94lZJOjLjtcOSVvf+fPyc19+VtHwxJgUsgH5re1UF\nc0FJCP3ijkpaM+O1tZImKpgLsJD6re3JCuaCkhD6xe2T9K7tH9getn2TpC9KerTaaQHzxtquAUK/\noIhoSrpN0iZJJyVtk/SNiDhU6cSAeWJt14O5iQoA1AeVPgDUCKEPADVC6ANAjWTuyLXNAX+UKiJc\nxbisbZRtsda27e9L+rakjqSDku6KiEbf9lm/yO1+MZblGnj1yOdzT3Ly7Wdzt0V5RldsyN12/dmr\nTGTb/dbWAjNoVxr6q0c25mpb9XodtO/W5pVbcrcttl4WXnmf7eKsbdurJO2VdEVENHqXvX4iInb0\n68O1dwBgsC2TdLHtjqSPqLvJri+O6QPAgIqIo5J+qe7lMiYlnYqIp7P6EPoAkICxsY+H7dke517b\nS7ZXSNqs7v0NVklabjvzAnkc3gGABIyPn1Br6qnMNsMXbvzYjJdulvRmRPxPkmz/QdLnJP2u73vM\nc54AgIXSbBbtcVjSdbYvkjQlaYOk57M6EPoAkIozZwo1j4j9tnepe9OmZu+/v8nqQ+gDQCJcvNJX\nRDwg6YG87Ql9AEhFs++eqgVD6ANAKhrFK/2illTol7G77olr78v9njfvzbfDU5IuvOALudum4IVO\n5u+GBlIKu1fzKDLPIt+BsuZQ9S7bQTaXwztFLanQB4CBRqUPADUyNVX6EIQ+AKSCSh8AaqTZKn0I\nQh8AUkGlDwA1QqUPADXSIPQBoD6mOLwDAPUxh0rf9oik7ZI+re59cr8VEfv6tSf0ASAR0WjPpduD\nkv4cEV+2PazuLRP7WrDQv3romvyNR/I3LbL9u4xt9bfu/0X+xhcUaJuAiVPPLPh7FrsURvYNI1Ix\nSDcmr3p8zFOzWOjbvlTSDRHxTUmKiJak01l9qPQBIBFzqPQvl3TS9sOSPiPpgKR7IuK9fh0IfQBI\nxYzQ/+sbx7TnjWNZPYYlfVbSdyPigO1fS/qhpB9ndQAAJCCmpof+jaOX6cbRy84+/+nTL83sMiHp\nSEQc6D3fJWlL1hiEPgAkIhpRrH3ECdtHbH8qIg6pe4/cV7L6EPoAkIhoFgv9nrsl7bT9IUlvSror\nqzGhDwCJ6BSs9CUpIl6WlPv0SUIfABIR5d8il9AHgFS0y7+HCqEPAKnoNF36GIQ+ACSiPUihv/ut\nrQv1VgNrkLbrS+XMN4V/V16bV2aeznxWkbWd9zMdpM+pTHxe07VbQ6WPQaUPAIkg9AGgRlqEPgDU\nR7NZfiQT+gCQiFabSh8AaqPZIfQBoDZa7WWlj0HoA0Ai5lrp2x5S9wYqExFxe1ZbQh8AEjGPwzv3\nqHtJ5Utna1j+ASQAQC6N9lDm43xsj0raJGl7njGo9AEgEXOs9H8l6T5JI3kaL1joF9nSX0TV268H\n7dIKRRSZbxk/38m3n1rw9yyijEuHlLEGWIPlsfNf66bYz2Fua3tm6L98elz/PH24b3vbt0o6EREv\n2b5J0qz/ICp9AEhEM6Zn9pWXjOnKS8bOPt85uXdml+sl3W57k6QPS7rE9o6IuLPfGBzTB4BENDvO\nfMwUEfdHxNqI+ISkOyQ9mxX4EpU+ACSjweYsAKiP81XzeUXEHkl7ZmtH6ANAImYe0y8DoQ8AiZhP\npZ8XoQ8AiWhG+WMQ+gCQiBaVPgDUx1Sn/DEIfQBIRHOphn5ZW6+X8nb1quX9vEZXbCh5JvXDWi3P\nxKlnqp7CNC3O3gGA+liylT4A4IOanfJP3yH0ASARLSp9AKgPzt4BgBppLcLhHS6tDACJaHayHzPZ\nHrX9rO1/2T5o++7ZxqDSB4BEzOEXuS1J9/bunLVc0gu2n4yI1/p1IPQBIBFFD+9ExHFJx3t/fsf2\nq5JWSyL0ASB18zll0/aYpKsk7ctqt2ChP2i7bDev3JKrXZGbZ5c110HaaZzaDsfFlvdnNWjfl7KM\n5pzv+qFrcr/ngc7zudum8Bmcq9GZfuD+ZHNcJ1vjs/brHdrZJemeiHgnqy2VPgAkohntac9Hhkc1\nMjx69vmhM3/7QB/bw+oG/iMRsXu2MQh9AEhEM+Z0ov5Dkl6JiAfzNCb0ASARTbUKtbd9vaSvSzpo\n+0VJIen+iPhLvz6EPgAkolEw9CPi75KWFelD6ANAIhqeKn0MQh8AEtFwo/QxCH0ASERLVPoAUBtN\nDu8AQH204kzpYxD6AJCIViRQ6Ve9rbys9y1yeYW8UtvSjWxlrG3WQDETOT+v9SvzX4ZhkH8GjXiv\n9DGo9AEgEe1OApU+AGBxEPoAUCMtQh8A6qPdKX9zFvfIBYBEtDuNzMf52L7F9mu2D9me9UYhVPoA\nkIh2p9h5+raHJG2TtEHSUUnP297NPXIBYABE8fP0r5X074gYlyTbj0raLO6RCwDpi2gW7bJa0pFz\nnk+o+z+Cvgh9AEjDeERj3SxtTsx3EEIfABIQEWNz6DYpae05z0d7r/XliOj/l3b/vwQWQES4inFZ\n2yjbYqxt28skva7uL3KPSdov6asR8Wq/PpmVflVfSKBsrG0sBRHRtv09SU+qewr+b7MCX5ql0gcA\nLC1szgKAGiH0AaBGCH0AqBFCHwBqhNAHgBoh9AGgRgh9AKiR/wPiFGJAJVQXZAAAAABJRU5ErkJg\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6c38326a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
+    "rfvis.plot_rts_summary(rf_data, ax1, ax2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tutorial/requirements.txt b/tutorial/requirements.txt
new file mode 100644
index 0000000..ef21088
--- /dev/null
+++ b/tutorial/requirements.txt
@@ -0,0 +1,3 @@
+allensdk>=0.14.5
+scikit-learn>=0.19.2
+seaborn
diff --git a/tutorial/runtime.txt b/tutorial/runtime.txt
new file mode 100644
index 0000000..16e8214
--- /dev/null
+++ b/tutorial/runtime.txt
@@ -0,0 +1 @@
+python-2.7
diff --git a/umap.ipynb b/umap.ipynb
new file mode 100644
index 0000000..6546326
--- /dev/null
+++ b/umap.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boc = BrainObservatoryCache(manifest_file='/local1/data/boc/manifest.json',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oeid = 541206592\n",
+    "\n",
+    "# Initializations:\n",
+    "nwb_dataset = boc.get_ophys_experiment_data(oeid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time, traces = nwb_dataset.get_dff_traces()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traces = traces.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from umap import UMAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reducer = UMAP()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reduced = reducer.fit_transform(traces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/justink/anaconda3/envs/umap/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
+      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
+      "/home/justink/anaconda3/envs/umap/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
+      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x7febd0cae8d0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(reduced[:,0],reduced[:,1],kind='hex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(114118, 154)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "traces.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:umap]",
+   "language": "python",
+   "name": "conda-env-umap-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ff248a0df86e2380dae5f953a7b66915326ea7bf Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:10:53 -0800
Subject: [PATCH 03/10] move to gallery

---
 hyptertools.ipynb => gallery/hyptertools.ipynb                    | 0
 mutual_info.ipynb => gallery/mutual_info.ipynb                    | 0
 .../sweep_response_to_csv.ipynb                                   | 0
 umap.ipynb => gallery/umap.ipynb                                  | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 rename hyptertools.ipynb => gallery/hyptertools.ipynb (100%)
 rename mutual_info.ipynb => gallery/mutual_info.ipynb (100%)
 rename sweep_response_to_csv.ipynb => gallery/sweep_response_to_csv.ipynb (100%)
 rename umap.ipynb => gallery/umap.ipynb (100%)

diff --git a/hyptertools.ipynb b/gallery/hyptertools.ipynb
similarity index 100%
rename from hyptertools.ipynb
rename to gallery/hyptertools.ipynb
diff --git a/mutual_info.ipynb b/gallery/mutual_info.ipynb
similarity index 100%
rename from mutual_info.ipynb
rename to gallery/mutual_info.ipynb
diff --git a/sweep_response_to_csv.ipynb b/gallery/sweep_response_to_csv.ipynb
similarity index 100%
rename from sweep_response_to_csv.ipynb
rename to gallery/sweep_response_to_csv.ipynb
diff --git a/umap.ipynb b/gallery/umap.ipynb
similarity index 100%
rename from umap.ipynb
rename to gallery/umap.ipynb

From f9a6a480abec4e0ec749f1ffd52b25a3fed6ec24 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:24:56 -0800
Subject: [PATCH 04/10] repo2docker ready

---
 .gitignore                | 2 ++
 requirements.txt          | 2 ++
 runtime.txt               | 1 +
 tutorial/requirements.txt | 2 --
 4 files changed, 5 insertions(+), 2 deletions(-)
 create mode 100644 requirements.txt
 create mode 100644 runtime.txt

diff --git a/.gitignore b/.gitignore
index 747fbe5..3a2ee4d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
 *.json
 *.nwb
 *-checkpoint.ipynb
+.local/
+.ipython/
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..683dfc2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+-r gallery/requirements.txt
+-r tutorial/requirements.txt
diff --git a/runtime.txt b/runtime.txt
new file mode 100644
index 0000000..16e8214
--- /dev/null
+++ b/runtime.txt
@@ -0,0 +1 @@
+python-2.7
diff --git a/tutorial/requirements.txt b/tutorial/requirements.txt
index ef21088..a56a909 100644
--- a/tutorial/requirements.txt
+++ b/tutorial/requirements.txt
@@ -1,3 +1 @@
 allensdk>=0.14.5
-scikit-learn>=0.19.2
-seaborn

From d55b894b039a0b7da8d4c6e562f327ed35d84012 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:27:06 -0800
Subject: [PATCH 05/10] more2ignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 3a2ee4d..3222127 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 *-checkpoint.ipynb
 .local/
 .ipython/
+.jupyter/

From 6956744baf3b949d5822252e222702c5a66f1588 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 15:49:08 -0800
Subject: [PATCH 06/10] fix start

---
 start | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 start

diff --git a/start b/start
new file mode 100644
index 0000000..715a551
--- /dev/null
+++ b/start
@@ -0,0 +1,5 @@
+#!/bin/bash
+export BRAIN_OBSERVATORY_MANIFEST=~/brain_observatory_data/manifest.json
+
+exec "$@"
+

From b504072ab09fa974ce7ef7859c3de496caf68c04 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 16:17:24 -0800
Subject: [PATCH 07/10] move to binder folder

---
 runtime.txt => binder/runtime.txt | 0
 start => binder/start             | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename runtime.txt => binder/runtime.txt (100%)
 rename start => binder/start (100%)

diff --git a/runtime.txt b/binder/runtime.txt
similarity index 100%
rename from runtime.txt
rename to binder/runtime.txt
diff --git a/start b/binder/start
similarity index 100%
rename from start
rename to binder/start

From 996b1481ae882bd1e4437de820d101346aa13261 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 16:17:52 -0800
Subject: [PATCH 08/10] move

---
 requirements.txt => binder/requirements.txt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename requirements.txt => binder/requirements.txt (100%)

diff --git a/requirements.txt b/binder/requirements.txt
similarity index 100%
rename from requirements.txt
rename to binder/requirements.txt

From 0a9dfeab4049c964863635e6c5e4b6f34998d822 Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Thu, 15 Nov 2018 16:18:27 -0800
Subject: [PATCH 09/10] relative path

---
 binder/requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/binder/requirements.txt b/binder/requirements.txt
index 683dfc2..c7cce5b 100644
--- a/binder/requirements.txt
+++ b/binder/requirements.txt
@@ -1,2 +1,2 @@
--r gallery/requirements.txt
--r tutorial/requirements.txt
+-r ../gallery/requirements.txt
+-r ../tutorial/requirements.txt

From 148bc57447b88e27cb12bbdae0be479797d14e2f Mon Sep 17 00:00:00 2001
From: Justin Kiggins <justink@alleninstitute.org>
Date: Fri, 16 Nov 2018 12:59:20 -0800
Subject: [PATCH 10/10] chmod on start

---
 binder/start | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 binder/start

diff --git a/binder/start b/binder/start
old mode 100644
new mode 100755