diff --git a/.gitignore b/.gitignore
index 525c175..3a32f66 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,12 +1,14 @@
+# OSX and python intermediate files
 __pycache__/
 .DS_Store
 ~*
 *~
 *.pyc
 .ipynb_checkpoints/
-dj_local_conf.json
-
-.idea/
+debug.log
 
+# DataJoint and setup files
+dj_local_con*.json
 julia/setup.jl
-debug.log
+.idea/
+temp*
diff --git a/00-Getting started.ipynb b/00-Getting_started/00-Getting started.ipynb
similarity index 89%
rename from 00-Getting started.ipynb
rename to 00-Getting_started/00-Getting started.ipynb
index acfd0ff..ab1a38b 100644
--- a/00-Getting started.ipynb	
+++ b/00-Getting_started/00-Getting started.ipynb	
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook!\n",
+    "Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook! These notebooks are oranized with numbers to highlight what should come next. First, we'll walk through the startup process. Next, you'll see an introduction to DataJoint, and how to structure a data pipeline. The Calcium Imaging and Electrophysiology folders show example data structures and analysis techniques from common experimental techniques. We've included some fill-in-the-blank sections for you to code yourself. If you want to see completed notebooks, see the folder of completed exercises.\n",
     "\n",
     "Before we start, remember that all of the edits you make in these tutorial notebooks are ***not persistent*** - they will be reset to the original content everytime you restart the server. However, you can easily download the notebooks that you're interested in keeping the changes."
    ]
@@ -316,18 +316,18 @@
     "The next step in the tutorial is to:\n",
     "1. learn the basic of the DataJoint framework with [01-DataJoint Basics](./01-DataJoint%20Basics%20-%20Interactive.ipynb)\n",
     "2. data ingestion in a pipeline, with practical examples in 2 domains:\n",
-    "    + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
-    "    + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
+    "    + [02-electrophysiology](/02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
+    "    + [02-calcium imaging](./01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
     "3. more advanced topics on built-in automatic computation of the pipeline with DataJoint, also with practical examples from:\n",
-    "    + [03-electrophysiology](./electrophysiology/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)\n",
-    "    + [03-calcium imaging](./calcium_imaging/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)"
+    "    + [03-electrophysiology](/02-Electrophysiology/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)\n",
+    "    + [03-calcium imaging](./01-Calcium_Imaging/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you will find out that these tutorial notebooks are meant to be interactive. There will be empty cells meant for you to write the code yourself, as mini exercises. To access the notebooks with complete solutions, visit this [folder](./completed)."
+    "As you will find out that these tutorial notebooks are meant to be interactive. There will be empty cells meant for you to write the code yourself, as mini exercises. To access the notebooks with complete solutions, visit this [folder](../03-Completed_Exercises)."
    ]
   }
  ],
diff --git a/01-DataJoint Basics - Interactive.ipynb b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb
similarity index 98%
rename from 01-DataJoint Basics - Interactive.ipynb
rename to 00-Getting_started/01-DataJoint Basics - Interactive.ipynb
index 69cf4ee..6482646 100644
--- a/01-DataJoint Basics - Interactive.ipynb	
+++ b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb	
@@ -555,7 +555,7 @@
    "source": [
     "Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! \n",
     "\n",
-    "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)."
+    "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)."
    ]
   },
   {
@@ -590,7 +590,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object"
+    "You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard."
    ]
   },
   {
@@ -599,7 +599,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1535,8 +1535,8 @@
     "In the next session, we are going to extend our data pipeline with tables to represent **imported data** and define new tables to **compute and hold analysis results**.\n",
     "\n",
     "We will use both ephys and calcium imaging as example pipelines:\n",
-    "+ [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
-    "+ [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)"
+    "+ [02-electrophysiology](../02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
+    "+ [02-calcium imaging](../01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb)"
    ]
   },
   {
diff --git a/01-DataJoint Basics - Interactive.py b/00-Getting_started/01-DataJoint Basics - Interactive.py
similarity index 96%
rename from 01-DataJoint Basics - Interactive.py
rename to 00-Getting_started/01-DataJoint Basics - Interactive.py
index d8d173b..298a7f1 100644
--- a/01-DataJoint Basics - Interactive.py	
+++ b/00-Getting_started/01-DataJoint Basics - Interactive.py	
@@ -42,13 +42,13 @@
 #
 # Let's go ahead and build together a pipeline from scratch to better understand what a data pipeline is all about.
 
-# # Building our first pipeline: 
+# # Building our first pipeline:
 
 # Let's build a pipeline to collect, store and process data and analysis for our hypothetical single electrode recording or calcium imaging recording in mice. To help us understand the project better, here is a brief description:
 
 # > * Your lab houses many mice, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender.
 # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on any given day, a mouse undergoes at most one recording session.
-# > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on.  
+# > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on.
 #
 # > * In a session of electrophysiology
 # >> * you record electrical activity from a single neuron. You use recording equipment that produces separate data files for each neuron you recorded.
@@ -64,7 +64,7 @@
 
 # > * Your lab houses many **mice**, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender.
 # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on an any given day, a mouse undergoes at most one recording session.
-# > * For each **experimental session**, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on.  
+# > * For each **experimental session**, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on.
 #
 # > * In a session of electrophysiology
 # >> * you record electrical activity from a **single neuron**. You use recording equipment that produces separate data files for each neuron you recorded.
@@ -92,7 +92,7 @@
 
 # In the current notebook, we will design the tables for mouse and experimental sessions, the rest of the pipeline will be designed in the subdirectory `electrophysiology` and `calcium_imaging`
 
-# In DataJoint data pipeline, we represent these **entities** as **tables**. Different *kinds* of entities become distinct tables, and each row of the table is a single example (instance) of the category of entity. 
+# In DataJoint data pipeline, we represent these **entities** as **tables**. Different *kinds* of entities become distinct tables, and each row of the table is a single example (instance) of the category of entity.
 #
 # For example, if we have a `Mouse` table, then each row in the mouse table represents a single mouse!
 
@@ -229,7 +229,7 @@ class Mouse(dj.Manual):
 
 # ## Create tables with dependencies
 
-# Congratulations! We have successfully created your first table! We are now ready to tackle and include other **entities** in the project into our data pipeline. 
+# Congratulations! We have successfully created your first table! We are now ready to tackle and include other **entities** in the project into our data pipeline.
 #
 # Let's now take a look at representing an **experimental session**.
 
@@ -245,11 +245,11 @@ class Mouse(dj.Manual):
 #
 # to uniquely identify a single experimental session.
 
-# Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! 
+# Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse!
 #
-# With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**).
+# With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**).
 
-# Thus we will need both **mouse** and a new attribute **session_date** to uniquely identify a single session. 
+# Thus we will need both **mouse** and a new attribute **session_date** to uniquely identify a single session.
 #
 # Remember that a **mouse** is already uniquely identified by its primary key - **mouse_id**. In DataJoint, you can declare that **session** depends on the mouse, and DataJoint will automatically include the mouse's primary key (`mouse_id`) as part of the session's primary key, along side any additional attribute(s) you specificy.
 
@@ -266,9 +266,9 @@ class Session(dj.Manual):
     """
 
 
-# You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object
+# You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard.
 
-dj.ERD(schema)
+dj.Diagram(schema)
 
 # Let's try inserting a few sessions manually.
 
@@ -397,17 +397,17 @@ class Session(dj.Manual):
 # ENTER YOUR CODE
 
 
-# In computer science/math lingo, DataJoint operations are said to **satisfy closure property**. Practically speaking, this means that the result of a query can immediately be used in another query, allowing you to build more complex queries from simpler ones. 
+# In computer science/math lingo, DataJoint operations are said to **satisfy closure property**. Practically speaking, this means that the result of a query can immediately be used in another query, allowing you to build more complex queries from simpler ones.
 
 # ### Restriction one table with another
 
 # All mice that have a session
 
-Mouse & Session 
+Mouse & Session
 
 # ### Combining restrictions
 
-# All the above queries could be combined 
+# All the above queries could be combined
 
 # Male mice that had a session
 
@@ -452,7 +452,7 @@ class Session(dj.Manual):
 Mouse * Session & 'session_date > "2017-05-19"'
 
 # ## Projection .proj(): focus on attributes of interest
-# Beside restriction (`&`) and join (`*`) operations, DataJoint offers another type of operation: projection (`.proj()`). Projection is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes. 
+# Beside restriction (`&`) and join (`*`) operations, DataJoint offers another type of operation: projection (`.proj()`). Projection is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes.
 
 # From the ***Mouse*** table, suppose we want to focus only on the `sex` attribute and ignore the others, this can be done as:
 
@@ -556,7 +556,7 @@ class Session(dj.Manual):
 # In the next session, we are going to extend our data pipeline with tables to represent **imported data** and define new tables to **compute and hold analysis results**.
 #
 # We will use both ephys and calcium imaging as example pipelines:
-# + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)
-# + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)
+# + [02-calcium imaging](.../01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb)
+# + [02-electrophysiology](../02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)
 
 
diff --git a/calcium_imaging/02-Imported Tables - Interactive.ipynb b/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb
similarity index 97%
rename from calcium_imaging/02-Imported Tables - Interactive.ipynb
rename to 01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb
index 49786a1..cc8d3fd 100644
--- a/calcium_imaging/02-Imported Tables - Interactive.ipynb	
+++ b/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb	
@@ -67,8 +67,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.mouse_session import schema, Mouse, Session"
    ]
   },
@@ -142,7 +142,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the ERD again."
+    "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the Diagram again."
    ]
   },
   {
@@ -151,7 +151,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -296,7 +296,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -333,9 +333,9 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
-    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses."
+    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]
   },
   {
@@ -344,7 +344,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -676,7 +676,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
diff --git a/calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
similarity index 98%
rename from calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
rename to 01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
index 66e55f9..8756843 100644
--- a/calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb	
+++ b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb	
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. \n",
+    "Welcome back! In this session, we are going to continue working with the pipeline for the mouse calcium imaging example. \n",
     "\n",
     "In this session, we will learn to:\n",
     "\n",
@@ -69,8 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.imaging import schema, Mouse, Session, Scan, AverageFrame"
    ]
   },
@@ -406,7 +406,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n",
+    "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n",
     "\n",
     "This tier indicates that the table will contain information:\n",
     "* that will be referenced by other tables\n",
@@ -435,7 +435,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -501,7 +501,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1006,14 +1006,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have covered most of the building elements of data pipeline design. Using these elements, we could design more sophiscated pipelines that facillitates your experimental recordings and data analyses."
+    "We have covered most of the building elements of data pipeline design. Using these elements, we could design more sophiscated pipelines that facillitates your experimental recordings and data analyses. To see these concepts in the context of another methodology, see the next folder on [Electrophysiology](../02-Electrophysiology)"
    ]
   }
  ],
diff --git a/electrophysiology/02-Imported Tables - Interactive.ipynb b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb
similarity index 81%
rename from electrophysiology/02-Imported Tables - Interactive.ipynb
rename to 02-Electrophysiology/02-Imported Tables - Interactive.ipynb
index aace0ab..e1619c1 100644
--- a/electrophysiology/02-Imported Tables - Interactive.ipynb	
+++ b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb	
@@ -66,8 +66,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.mouse_session import schema, Mouse, Session"
    ]
   },
@@ -305,7 +305,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ENTER YOUR CODE! - plot ERD of the schema\n"
+    "# ENTER YOUR CODE! - plot a Diagram of the schema\n"
    ]
   },
   {
@@ -335,9 +335,9 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
-    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses."
+    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]
   },
   {
@@ -346,7 +346,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -641,76 +641,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg height=\"185pt\" viewBox=\"0.00 0.00 75.50 185.00\" width=\"76pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 181)\">\n",
-       "<title>%3</title>\n",
-       "<polygon fill=\"white\" points=\"-4,4 -4,-181 71.5,-181 71.5,4 -4,4\" stroke=\"none\"/>\n",
-       "<!-- Mouse -->\n",
-       "<g class=\"node\" id=\"node1\"><title>Mouse</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"mouse_id             \r",
-       "------------------------------\r",
-       "dob=null             \r",
-       "sex=&quot;unknown&quot;        \r",
-       "\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"59.75,-177 7.75,-177 7.75,-142 59.75,-142 59.75,-177\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
-       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"15.75\" y=\"-157.4\">Mouse</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- Session -->\n",
-       "<g class=\"node\" id=\"node3\"><title>Session</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ Mouse\r",
-       "session_date         \r",
-       "------------------------------\r",
-       "experiment_setup     \r",
-       "experimenter         \r",
-       "data_path=&quot;&quot;         \r",
-       "\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"63.25,-106 4.25,-106 4.25,-71 63.25,-71 63.25,-106\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
-       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"12.25\" y=\"-86.4\">Session</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- Mouse&#45;&gt;Session -->\n",
-       "<g class=\"edge\" id=\"edge1\"><title>Mouse-&gt;Session</title>\n",
-       "<path d=\"M33.75,-141.797C33.75,-130.949 33.75,-116.867 33.75,-106.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
-       "</g>\n",
-       "<!-- Neuron -->\n",
-       "<g class=\"node\" id=\"node2\"><title>Neuron</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ Session\r",
-       "neuron_id            \r",
-       "------------------------------\r",
-       "activity             \r",
-       "\">\n",
-       "<ellipse cx=\"33.75\" cy=\"-17.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"33.5018\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
-       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"14.75\" y=\"-15.4\">Neuron</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- Session&#45;&gt;Neuron -->\n",
-       "<g class=\"edge\" id=\"edge2\"><title>Session-&gt;Neuron</title>\n",
-       "<path d=\"M33.75,-70.797C33.75,-59.9485 33.75,-45.8669 33.75,-35.0492\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x21e2cbe7358>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dj.ERD(schema)"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -728,7 +663,7 @@
    "formats": "ipynb,py:light"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -742,9 +677,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/electrophysiology/02-Imported Tables - Interactive.py b/02-Electrophysiology/02-Imported Tables - Interactive.py
similarity index 94%
rename from electrophysiology/02-Imported Tables - Interactive.py
rename to 02-Electrophysiology/02-Imported Tables - Interactive.py
index c203da5..93c9d54 100644
--- a/electrophysiology/02-Imported Tables - Interactive.py	
+++ b/02-Electrophysiology/02-Imported Tables - Interactive.py	
@@ -15,7 +15,7 @@
 
 # # Working with automated computations: Imported tables
 
-# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. 
+# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example.
 #
 # In this session, we will learn to:
 #
@@ -34,8 +34,8 @@
 
 # Now we would like to continue working with the tables we defined in the previous notebook. To do so, we would need the classes for each table: `Mouse` and `Session`. We can either redefine it here, but for your convenience, we have included the schema and table class definitions in a package called `tutorial_pipeline.mouse_session`, from which you can import the classes as well as the schema object. We will use the schema object again to define more tables.
 
-import sys
-sys.path.append("..")
+import os
+if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')
 from tutorial_pipeline.mouse_session import schema, Mouse, Session
 
 Mouse()
@@ -57,7 +57,7 @@
 #
 # So `data_100_2017-05-25.npy` is the data for session identified by `mouse_id = 100` and `session_date = "2017-05-25"`.
 
-# ## Looking at the data 
+# ## Looking at the data
 
 # Let's take a quick peak at the data file content.
 
@@ -68,7 +68,7 @@
 
 # Any item in this list of keys can be used to uniquely identify a single session!
 
-# ENTER YOUR CODE! - restrict session using keys and any element inside keys. 
+# ENTER YOUR CODE! - restrict session using keys and any element inside keys.
 
 
 # Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convetion!
@@ -113,7 +113,7 @@ class Neuron(dj.Imported):
 
 # Let's check the state of our pipeline.
 
-# ENTER YOUR CODE! - plot ERD of the schema
+# ENTER YOUR CODE! - plot a Diagram of the schema
 
 
 # We defined `activity` as a `longblob` so that it can store a NumPy array holding the electric activity over time. This NumPy array will be imported from the file corresponding to each neuron.
@@ -122,13 +122,13 @@ class Neuron(dj.Imported):
 
 # ## DataJoint table tiers
 
-# In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. 
+# In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session.
 #
-# DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.
+# DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.
 #
-# On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses.
+# On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses.
 
-dj.ERD(schema)
+dj.Diagram(schema)
 
 # ## Importing data into the `Imported` table
 
@@ -188,11 +188,11 @@ def make(self, key):
 
         # load the data
         data = np.load(data_file)
-        
+
         for idx, d in enumerate(data):
             # add the index of the 1st dimension as neuron_id
             key['neuron_id'] = idx
-            
+
             # add the loaded data as the "activity" column
             key['activity'] = d
 
@@ -210,7 +210,7 @@ def make(self, key):
 
 Neuron()
 
-# As you can obviously see, in these example datasets, we only have data for one neuron per session. 
+# As you can obviously see, in these example datasets, we only have data for one neuron per session.
 
 # What happens if we call `Neuron.populate` again?
 
@@ -240,7 +240,7 @@ def make(self, key):
 
 # Congratulations! You have successfully extended your pipeline with a table to represent recorded data (`Neuron` as `Imported` table), learned and implemented the `make()` and `populate()` call to load external data to your tables.
 
-dj.ERD(schema)
+dj.Diagram(schema)
 
 # At this point, our pipeline contains the core elements with data populated, ready for further downstream analysis.
 #
diff --git a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
similarity index 70%
rename from electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
rename to 02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb
index f67d6ba..c086bde 100644
--- a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb	
+++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb	
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,271 +65,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please enter DataJoint username: root\n",
-      "Please enter DataJoint password: ········\n",
-      "Connecting root@localhost:3306\n",
-      "Populating neuron(s) for mouse_id=0 on session_date=2017-05-15\n",
-      "\tPopulated neuron 0\n",
-      "Populating neuron(s) for mouse_id=0 on session_date=2017-05-19\n",
-      "\tPopulated neuron 0\n",
-      "Populating neuron(s) for mouse_id=5 on session_date=2017-01-05\n",
-      "\tPopulated neuron 0\n",
-      "Populating neuron(s) for mouse_id=100 on session_date=2017-05-25\n",
-      "\tPopulated neuron 0\n",
-      "Populating neuron(s) for mouse_id=100 on session_date=2017-06-01\n",
-      "\tPopulated neuron 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        \n",
-       "        <style type=\"text/css\">\n",
-       "            .Relation{\n",
-       "                border-collapse:collapse;\n",
-       "            }\n",
-       "            .Relation th{\n",
-       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "            }\n",
-       "            .Relation td{\n",
-       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "            }\n",
-       "            .Relation tr:nth-child(odd){\n",
-       "                background: #ffffff;\n",
-       "            }\n",
-       "            .Relation tr:nth-child(even){\n",
-       "                background: #f3f1ff;\n",
-       "            }\n",
-       "            /* Tooltip container */\n",
-       "            .djtooltip {\n",
-       "            }\n",
-       "            /* Tooltip text */\n",
-       "            .djtooltip .djtooltiptext {\n",
-       "                visibility: hidden;\n",
-       "                width: 120px;\n",
-       "                background-color: black;\n",
-       "                color: #fff;\n",
-       "                text-align: center;\n",
-       "                padding: 5px 0;\n",
-       "                border-radius: 6px;\n",
-       "                /* Position the tooltip text - see examples below! */\n",
-       "                position: absolute;\n",
-       "                z-index: 1;\n",
-       "            }\n",
-       "            #primary {\n",
-       "                font-weight: bold;\n",
-       "                color: black;\n",
-       "            }\n",
-       "\n",
-       "            #nonprimary {\n",
-       "                font-weight: normal;\n",
-       "                color: white;\n",
-       "            }\n",
-       "\n",
-       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "            .djtooltip:hover .djtooltiptext {\n",
-       "                visibility: visible;\n",
-       "            }\n",
-       "        </style>\n",
-       "        \n",
-       "        <b>Experiment session</b>\n",
-       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "            <table border=\"1\" class=\"Relation\">\n",
-       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                                <p id=\"primary\">mouse_id</p>\n",
-       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"primary\">session_date</p>\n",
-       "                                <span class=\"djtooltiptext\">date</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"nonprimary\">experiment_setup</p>\n",
-       "                                <span class=\"djtooltiptext\">experiment setup ID</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"nonprimary\">experimenter</p>\n",
-       "                                <span class=\"djtooltiptext\">experimenter name</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"nonprimary\">data_path</p>\n",
-       "                                <span class=\"djtooltiptext\"></span>\n",
-       "                            </div> </th> </tr> </thead>\n",
-       "                <tbody> <tr> <td>0</td>\n",
-       "<td>2017-05-15</td>\n",
-       "<td>0</td>\n",
-       "<td>Edgar Y. Walker</td>\n",
-       "<td></td></tr><tr><td>0</td>\n",
-       "<td>2017-05-19</td>\n",
-       "<td>0</td>\n",
-       "<td>Edgar Y. Walker</td>\n",
-       "<td></td></tr><tr><td>5</td>\n",
-       "<td>2017-01-05</td>\n",
-       "<td>1</td>\n",
-       "<td>Fabian Sinz</td>\n",
-       "<td></td></tr><tr><td>100</td>\n",
-       "<td>2017-05-25</td>\n",
-       "<td>100</td>\n",
-       "<td>Jacob Reimer</td>\n",
-       "<td></td></tr><tr><td>100</td>\n",
-       "<td>2017-06-01</td>\n",
-       "<td>1</td>\n",
-       "<td>Jacob Reimer</td>\n",
-       "<td></td> </tr> </tbody>\n",
-       "            </table>\n",
-       "            \n",
-       "            <p>Total: 5</p></div>\n",
-       "            "
-      ],
-      "text/plain": [
-       "*mouse_id    *session_date  experiment_set experimenter   data_path    \n",
-       "+----------+ +------------+ +------------+ +------------+ +-----------+\n",
-       "0            2017-05-15     0              Edgar Y. Walke              \n",
-       "0            2017-05-19     0              Edgar Y. Walke              \n",
-       "5            2017-01-05     1              Fabian Sinz                 \n",
-       "100          2017-05-25     100            Jacob Reimer                \n",
-       "100          2017-06-01     1              Jacob Reimer                \n",
-       " (Total: 5)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Session()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        \n",
-       "        <style type=\"text/css\">\n",
-       "            .Relation{\n",
-       "                border-collapse:collapse;\n",
-       "            }\n",
-       "            .Relation th{\n",
-       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "            }\n",
-       "            .Relation td{\n",
-       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "            }\n",
-       "            .Relation tr:nth-child(odd){\n",
-       "                background: #ffffff;\n",
-       "            }\n",
-       "            .Relation tr:nth-child(even){\n",
-       "                background: #f3f1ff;\n",
-       "            }\n",
-       "            /* Tooltip container */\n",
-       "            .djtooltip {\n",
-       "            }\n",
-       "            /* Tooltip text */\n",
-       "            .djtooltip .djtooltiptext {\n",
-       "                visibility: hidden;\n",
-       "                width: 120px;\n",
-       "                background-color: black;\n",
-       "                color: #fff;\n",
-       "                text-align: center;\n",
-       "                padding: 5px 0;\n",
-       "                border-radius: 6px;\n",
-       "                /* Position the tooltip text - see examples below! */\n",
-       "                position: absolute;\n",
-       "                z-index: 1;\n",
-       "            }\n",
-       "            #primary {\n",
-       "                font-weight: bold;\n",
-       "                color: black;\n",
-       "            }\n",
-       "\n",
-       "            #nonprimary {\n",
-       "                font-weight: normal;\n",
-       "                color: white;\n",
-       "            }\n",
-       "\n",
-       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "            .djtooltip:hover .djtooltiptext {\n",
-       "                visibility: visible;\n",
-       "            }\n",
-       "        </style>\n",
-       "        \n",
-       "        <b></b>\n",
-       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "            <table border=\"1\" class=\"Relation\">\n",
-       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                                <p id=\"primary\">mouse_id</p>\n",
-       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"primary\">session_date</p>\n",
-       "                                <span class=\"djtooltiptext\">date</span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"primary\">neuron_id</p>\n",
-       "                                <span class=\"djtooltiptext\"></span>\n",
-       "                            </div></th><th><div class=\"djtooltip\">\n",
-       "                                <p id=\"nonprimary\">activity</p>\n",
-       "                                <span class=\"djtooltiptext\">electric activity of the neuron</span>\n",
-       "                            </div> </th> </tr> </thead>\n",
-       "                <tbody> <tr> <td>0</td>\n",
-       "<td>2017-05-15</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
-       "<td>2017-05-19</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td></tr><tr><td>5</td>\n",
-       "<td>2017-01-05</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
-       "<td>2017-05-25</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
-       "<td>2017-06-01</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "            </table>\n",
-       "            \n",
-       "            <p>Total: 5</p></div>\n",
-       "            "
-      ],
-      "text/plain": [
-       "*mouse_id    *session_date  *neuron_id    activity  \n",
-       "+----------+ +------------+ +-----------+ +--------+\n",
-       "0            2017-05-15     0             =BLOB=    \n",
-       "0            2017-05-19     0             =BLOB=    \n",
-       "5            2017-01-05     0             =BLOB=    \n",
-       "100          2017-05-25     0             =BLOB=    \n",
-       "100          2017-06-01     0             =BLOB=    \n",
-       " (Total: 5)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Neuron()"
    ]
@@ -559,7 +317,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD."
+    "Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram."
    ]
   },
   {
@@ -568,7 +326,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -842,7 +600,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n",
+    "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n",
     "\n",
     "This tier indicates that the table will contain information:\n",
     "* that will be referenced by other tables\n",
@@ -870,7 +628,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -923,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -959,14 +717,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. "
+    "In the Diagram, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. "
    ]
   },
   {
@@ -1158,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1167,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1176,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1324,7 +1082,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1340,7 +1098,7 @@
    "formats": "ipynb,py:light"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1354,9 +1112,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py
similarity index 94%
rename from electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py
rename to 02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py
index e6bd377..29442a9 100644
--- a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py	
+++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py	
@@ -15,7 +15,7 @@
 
 # # Working with automated computations: Computed tables
 
-# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. 
+# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example.
 #
 # In this session, we will learn to:
 #
@@ -35,10 +35,10 @@
 import matplotlib.pyplot as plt
 # %matplotlib inline
 
-# Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module. 
+# Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module.
 
-import sys
-sys.path.append("..")
+import os
+if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')
 from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron
 
 Session()
@@ -49,7 +49,7 @@
 
 # # Computations in data pipeline
 
-# Now we have successfully imported all data we have into our pipeline, it's time for us to start analyzing them! 
+# Now we have successfully imported all data we have into our pipeline, it's time for us to start analyzing them!
 #
 # When you perform computations in the DataJoint data pipeline, you focus and design tables in terms of **what** is it that you are computing rather than the **how**. You should think in terms of the "things" that you are computing!
 
@@ -120,9 +120,9 @@ class ActivityStatistics(dj.Computed):
     """
 
 
-# Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD.
+# Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram   .
 
-dj.ERD(schema)
+dj.Diagram (schema)
 
 
 # Just like the `Imported` tables, `Computed` tables make use of the same `make` and `populate` logic for defining new entries in the table. Let's go ahead and implement `make` method.
@@ -139,7 +139,7 @@ class ActivityStatistics(dj.Computed):
     stdev: float   # standard deviation of activity
     max: float     # maximum activity
     """
-    
+
     def make(self, key):
         activity = (Neuron() & key).fetch1('activity')    # fetch activity as NumPy array
 
@@ -198,7 +198,7 @@ def make(self, key):
 threshold = 0.5
 
 # find activity above threshold
-above_thrs = (activity > threshold).astype(int)   
+above_thrs = (activity > threshold).astype(int)
 
 plt.plot(activity)
 plt.plot(above_thrs)
@@ -232,7 +232,7 @@ def make(self, key):
 threshold =     # enter different threshold values here
 
 # find activity above threshold
-above_thrs = (activity > threshold).astype(int) 
+above_thrs = (activity > threshold).astype(int)
 
 rising = (np.diff(above_thrs) > 0).astype(int)   # find rising edge of crossing threshold
 spikes = np.hstack((0, rising))    # prepend 0 to account for shortening due to np.diff
@@ -255,7 +255,7 @@ def make(self, key):
 
 # ## Parameter `Lookup` table
 
-# Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.
+# Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram .
 #
 # This tier indicates that the table will contain information:
 # * that will be referenced by other tables
@@ -270,7 +270,7 @@ class SpikeDetectionParam(dj.Lookup):
     """
 
 
-dj.ERD(schema)
+dj.Diagram (schema)
 
 
 # ### Defining `Spikes` table
@@ -292,7 +292,7 @@ class Spikes(dj.Computed):
 
 # ### Define the `Waveform` part table
 
-# We would also like to store the waveform at each detected spike, for a particular neuron. For simplicity, let's define the waveform to be 40 sample points before and after the onset of the detected spike. 
+# We would also like to store the waveform at each detected spike, for a particular neuron. For simplicity, let's define the waveform to be 40 sample points before and after the onset of the detected spike.
 # To accomplish this, we will define a `dj.Part` table `Waveform` which will contain the waveform per spike. The table definition is something like:
 
 @schema
@@ -304,7 +304,7 @@ class Spikes(dj.Computed):
     spikes: longblob     # detected spikes
     count: int           # total number of detected spikes
     """
-    
+
     class Waveform(dj.Part):
         definition = """
         -> master
@@ -316,10 +316,10 @@ class Waveform(dj.Part):
 
 # The `-> master` syntax denotes that the `Waveform` part table is foreign key constrained by `Spike` table - i.e. the master table. The master table drives the ***populate*** logic, and the content of the part table is generally ingested together with the content of the master table, all in one step (i.e. one `make()` call).
 
-dj.ERD(schema)
+dj.Diagram (schema)
 
 
-# In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. 
+# In the Diagram   , we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table.
 
 @schema
 class Spikes(dj.Computed):
@@ -330,7 +330,7 @@ class Spikes(dj.Computed):
     spikes: longblob     # detected spikes
     count: int           # total number of detected spikes
     """
-    
+
     class Waveform(dj.Part):
         definition = """
         -> master
@@ -338,7 +338,7 @@ class Waveform(dj.Part):
         ---
         waveform: longblob  # waveform extracted from this spike
         """
-        
+
     def make(self, key):
         print('Populating for: ', key)
 
@@ -353,18 +353,18 @@ def make(self, key):
         print('Detected {} spikes!\n'.format(count))
 
         # create and insert a new dictionary containing `key` and additionally `spikes` and `count`
-        self.insert1(dict(key, spikes=spikes, count=count))  
-        
+        self.insert1(dict(key, spikes=spikes, count=count))
+
         # extract waveform for the `Waveform` part-table
         before_spk, after_spk = 40, 40  # extract 40 sample points before and after a spike as the waveform
         for spk_id, spk in enumerate(np.where(spikes==1)[0]):
-            
+
             # For simplicity, skip the spikes too close to the beginning or the end
-            if spk - before_spk < 0 or spk + after_spk > len(activity) + 1: 
+            if spk - before_spk < 0 or spk + after_spk > len(activity) + 1:
                 continue
-                
-            wf = activity[spk - before_spk: spk + after_spk] 
-            
+
+            wf = activity[spk - before_spk: spk + after_spk]
+
             # create and insert a new dictionary containing `key` and additionally `spike_id` and `waveform`
             self.Waveform.insert1(dict(key, spike_id=spk_id, waveform=wf))
 
@@ -449,6 +449,6 @@ def make(self, key):
 
 # Congratulations! You have successfully extended your pipeline with a table to represent recorded data (`Neuron` as `Imported` table), tables that performs and represents computation results (`ActivityStatistics` and `Spikes` as `Computed` tables) and a table to hold computation parameters (`SpikeDetectionParam` as `Lookup` table).
 
-dj.ERD(schema)
+dj.Diagram (schema)
 
 # Our pipeline is still fairly simple but completely capable of handling analysis!
diff --git a/completed/01-DataJoint Basics - Completed.ipynb b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb
similarity index 99%
rename from completed/01-DataJoint Basics - Completed.ipynb
rename to 03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb
index 9f8588f..51adf41 100644
--- a/completed/01-DataJoint Basics - Completed.ipynb	
+++ b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb	
@@ -1064,7 +1064,7 @@
    "source": [
     "Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! \n",
     "\n",
-    "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)."
+    "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)."
    ]
   },
   {
@@ -1099,7 +1099,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object"
+    "You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard."
    ]
   },
   {
@@ -5078,7 +5078,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -5092,9 +5092,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/completed/02-calcium_imaging - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb
similarity index 99%
rename from completed/02-calcium_imaging - Imported Tables - Completed.ipynb
rename to 03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb
index 85315e8..110991d 100644
--- a/completed/02-calcium_imaging - Imported Tables - Completed.ipynb	
+++ b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb	
@@ -75,8 +75,8 @@
     }
    ],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.mouse_session import schema, Mouse, Session"
    ]
   },
@@ -365,7 +365,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the ERD again."
+    "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the Diagram again."
    ]
   },
   {
@@ -448,7 +448,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -464,7 +464,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we have prepared two tif files of scanning in the [data](../data/calcium_imaging) folder `example_scan_01.tif` and `example_scan_02.tif` "
+    "Here we have prepared two tif files of scanning in the [data](./data/) folder `example_scan_01.tif` and `example_scan_02.tif` "
    ]
   },
   {
@@ -476,12 +476,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example_scan_01.tif  example_scan_02.tif  example_scan_03.tif\r\n"
+      "\u001b[31m./data/example_scan_01.tif\u001b[m\u001b[m* \u001b[31m./data/example_scan_03.tif\u001b[m\u001b[m*\n",
+      "\u001b[31m./data/example_scan_02.tif\u001b[m\u001b[m*\n"
      ]
     }
    ],
    "source": [
-    "ls ../calcium_imaging/data"
+    "ls ./data/*tif"
    ]
   },
   {
@@ -658,7 +659,7 @@
    ],
    "source": [
     "from skimage import io\n",
-    "im = io.imread('../calcium_imaging/data/example_scan_01.tif')\n",
+    "im = io.imread('../01-Calcium_Imaging/data/example_scan_01.tif')\n",
     "print(im.shape)"
    ]
   },
@@ -838,7 +839,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -875,9 +876,9 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
-    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses."
+    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]
   },
   {
@@ -977,7 +978,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1261,7 +1262,7 @@
     "    \"\"\"\n",
     "    def make(self, key):  # key is the primary key of one of the entries in the table `Scan`\n",
     "        # fetch data directory from table Session\n",
-    "        data_path = '../calcium_imaging/' + (Session & key).fetch1('data_path')\n",
+    "        data_path = '../01-Calcium_Imaging/' + (Session & key).fetch1('data_path')\n",
     "        \n",
     "        # fetch data file name from table Scan\n",
     "        file_name = (Scan & key).fetch1('file_name')\n",
@@ -1879,7 +1880,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1894,7 +1895,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1908,9 +1909,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/completed/02-electrophysiology - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb
similarity index 99%
rename from completed/02-electrophysiology - Imported Tables - Completed.ipynb
rename to 03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb
index b3b0e07..7abc090 100644
--- a/completed/02-electrophysiology - Imported Tables - Completed.ipynb	
+++ b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb	
@@ -76,8 +76,8 @@
     }
    ],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.mouse_session import schema, Mouse, Session"
    ]
   },
@@ -556,7 +556,7 @@
     }
    ],
    "source": [
-    "filename = '../electrophysiology/data/data_{mouse_id}_{session_date}.npy'.format(**key)\n",
+    "filename = './data/data_{mouse_id}_{session_date}.npy'.format(**key)\n",
     "filename"
    ]
   },
@@ -1082,8 +1082,8 @@
     }
    ],
    "source": [
-    "# ENTER YOUR CODE! - plot ERD of the schema\n",
-    "dj.ERD(schema)"
+    "# ENTER YOUR CODE! - plot Diagram of the schema\n",
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1113,9 +1113,9 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
-    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses."
+    "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]
   },
   {
@@ -1189,7 +1189,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1469,7 +1469,7 @@
     "    \"\"\"\n",
     "    def make(self, key):\n",
     "        # use key dictionary to determine the data file path\n",
-    "        data_file = \"../electrophysiology/data/data_{mouse_id}_{session_date}.npy\".format(**key)\n",
+    "        data_file = \"./data/data_{mouse_id}_{session_date}.npy\".format(**key)\n",
     "\n",
     "        # load the data\n",
     "        data = np.load(data_file)\n",
@@ -2020,7 +2020,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -2035,7 +2035,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2049,9 +2049,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb
similarity index 99%
rename from completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb
rename to 03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb
index 4cb9b6e..5419379 100644
--- a/completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb	
+++ b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb	
@@ -77,8 +77,8 @@
     }
    ],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.imaging import schema, Mouse, Session, Scan, AverageFrame"
    ]
   },
@@ -1036,7 +1036,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n",
+    "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n",
     "\n",
     "This tier indicates that the table will contain information:\n",
     "* that will be referenced by other tables\n",
@@ -1169,7 +1169,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1380,7 +1380,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -2732,7 +2732,7 @@
     "        \n",
     "        print('Populating: {}'.format(key))\n",
     "        # fetch data directory from table Session\n",
-    "        data_path = '../calcium_imaging/' + (Session & key).fetch1('data_path')\n",
+    "        data_path = '../01-Calcium_Imaging/' + (Session & key).fetch1('data_path')\n",
     "        \n",
     "        # fetch data file name from table Scan\n",
     "        file_name = (Scan & key).fetch1('file_name')\n",
@@ -3051,7 +3051,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -3064,7 +3064,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3078,9 +3078,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb
similarity index 77%
rename from completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb
rename to 03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb
index a78ad1f..26fb81c 100644
--- a/completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb	
+++ b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb	
@@ -89,8 +89,8 @@
     }
    ],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
     "from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron"
    ]
   },
@@ -1470,7 +1470,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD."
+    "Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram."
    ]
   },
   {
@@ -1561,7 +1561,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -1808,7 +1808,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1152x288 with 5 Axes>"
       ]
@@ -1913,7 +1913,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9d5gkV3n2fZ8KncPkDbNJuyvtKrKSVgkZtIABmSQb870WBuH4imAwvPDiwAcY7M8GGwcMwsiyBdiARDYWSEJGSCAUV6vV7mqTNofZmZ0807m7wvn+OHWqq3uqOs9Md+v8rmuvnemuqaruqjrPuZ90CKUUAoFAIBBwpOU+AYFAIBC0F8IwCAQCgaAEYRgEAoFAUIIwDAKBQCAoQRgGgUAgEJSgLPcJVGJgYIBu2LBhuU9DIBAIOobnnntuilI62Mw+2towbNiwAbt27Vru0xAIBIKOgRByutl9CFeSQCAQCEoQhkEgEAgEJQjDIBAIBIIShGEQCAQCQQlNGwZCyFpCyKOEkEOEkAOEkA+6bLODEDJPCNlj/ftks8cVCAQCweLQiqwkHcBHKKW7CSFRAM8RQn5KKT1Ytt0vKaVvasHxBAKBQLCINK0YKKVjlNLd1s9JAIcADDe7X4FAIBAsDy2NMRBCNgC4EsAzLm/fQAjZSwh5kBByaYV93E4I2UUI2TU5OdnK01sU9pydw/5z88t9GgKBQNAyWmYYCCERAN8H8CFKaaLs7d0A1lNKXwbgiwB+6LUfSuldlNLtlNLtg4NNFe8tCX99/0F89sHDy30aAoFA0DJaYhgIISqYUfgmpfQH5e9TShOU0pT18wMAVELIQCuOvdzkNBNZzVju0xAIBIKW0YqsJALgbgCHKKX/6LHNSms7EEKutY473eyx2wHNMFHQzeU+DYFAIGgZrchKuhHAbQBeIITssV77GIB1AEApvRPA2wC8lxCiA8gCuJV2yZqiuklBqTAMAoGge2jaMFBKHwdAqmxzB4A7mj1WO6IZJgxS8eMLBAJBR9HW3VU7Ad2gALpC/AgEAgEAYRiaRjNMmMIuCASCLkIYhibRDBOGsAwCgaCLEIahSXSDQjNF8FkgEHQPortqkxQME3ndRJckWQkEAoEwDM3C0lXZ/wKBQNANCMPQBKZJ7fiCKHITCATdgjAMTeCMLQjDIBAIugVhGJqA1TAwCoYwDAKBoDsQhqEJSgyDUAwCgaBLEIahCZwqIS8Mg0Ag6BKEYWgCXcQYBAJBFyIMQxOIGINAIOhGhGFoAqcxEIpBIBB0C8IwNIEIPgsEgm5EGIYm0JyKwRDLewoEgu5AGIYm0IQrSSAQdCHCMDSBsz+SSFcVCATdgjAMTaDpQjEIBILuQxiGJtCEYhAIBF2IMAxNoIsYg0Ag6EKEYWiC0qwkYRgEAkFtfOq+A3j313ct92l4Ipb2bAJN1DEIBIIGOD6ZwtmZzHKfhidCMTSB6JUkEAgaIa+bSOb05T4NT4RhaAJNF72SBAJB/XS9YSCErCWEPEoIOUQIOUAI+aDLNoQQ8gVCyDFCyD5CyFXNHrcdECu4CQSCRshrBgqGiZzWnh0TWqEYdAAfoZReDOB6AH9ECLmkbJtfA3Ch9e92AF9uwXGXHV7HIEtEpKsKBIKa4RPJdlUNTRsGSukYpXS39XMSwCEAw2Wb3QLgPynjaQA9hJBVzR57ueGVz2GfLBSDQLDEfOPp07j1rqeW+zQaIm8bBm2Zz8SdlsYYCCEbAFwJ4Jmyt4YBnHX8PoKFxoPv43ZCyC5CyK7JyclWnl7L4VlJEb8iYgwCwRJz+HwCe87OLfdpNEReZy6krlUMHEJIBMD3AXyIUpoof9vlT6jLa6CU3kUp3U4p3T44ONiq01sUeB1DyK+goLenr1Ag6FZ0gyKnmaDUdShpa/Jal7uSAIAQooIZhW9SSn/gsskIgLWO39cAGG3FsZcT3TBBCBBUhStJIFhquGLvxPhe17uSCCEEwN0ADlFK/9Fjs/sAvMvKTroewDyldKzZYy83mkmhShJ8iiRcSQLBEsPriNo1s8cLSqk9XrSrYmhF5fONAG4D8AIhZI/12scArAMASumdAB4A8AYAxwBkAPxeC4677Gi6CUUm8MlSRcXwB197FhetjOJPb966hGcnEHQ3fAXFrGagZ5nPpR6cCifRpoqhacNAKX0c7jEE5zYUwB81e6x2QzcpVJkphkzG2/IfmUgu4VkJBC8N+Kw7p3WWWncahnZVDKLyuQk0w4QqE/gUqaKfs6CbSBfa8wYQdBeGSUu6/nYzutGZrqS8I1FFGIYuRDNMKJIEv7LQlbRvZA6mVedQ0E1kCp118wo6k4/94AX80T27l/s0lgReR9RxhkFzKob2dCUJw9AEukGhKgQBVS65OU9MpvCWO57AL49NAWCGIZVvz5mBoLs4O5vBmZnscp/GksDTxbOdZhiEK6m7KRgmVElCQJWQKwkosYs9ncrb22XynXXzCjoT3aDId9hA2Sg8+JzvuBiDw5WUF4qh69ANFnwOqjKyDlcR931mCgZMk0IzqIgxCJaEgmF2ZF5/I2id6kqyro9EhGLoSnSTpasGVBk53bArMHnhTaag25kTmYLRkRWags5CN9u3Y2er0TvVlWQpnL6wXxiGbqRgUCiyhIAqg9Ji+hwvvEnnDfs1w6QvmZmcYPnQ9JfOfcZdSZ2XrsoM2UDEJ4LP3YhumPBZigEAcgXLMDgUg+Z4SNMiAC1YZDTTLPFhdzNax6arsvNeFQ9gNqPZ2YvthDAMTaAbFIoVfAaAnPVAag73kbNVhkhZFSw2mmFCMyiMNhxsWsG3nz2DvVZHVb5QVqe5knhq++qeIAyTYi7bfqpBGIYmKBgsxhC0FAMPQPP86kzBKKlvEAFowWJjZ+p0qWr4zIOHce/OMwCcWUmd9Vm5YhjuDQIApqzsxXZCGIYm0E0TPivGACxUDOm8XmoYRMqqYJHROrRNRK1ojmJRnuSR67CYCjfawz3CMHQlukGtrCT2NdqKwSgqBmcgMCMUg2CR0bpcMWgmtZ8jnuSR7TAXLc9KWm0ZhulUYTlPxxVhGJqAuZIcikErzUpypqsCIvgsWHy6WTFQSqEZRcVQzErqMMOglxoGoRi6DN2gpa4krVTiLogxCFeSYJHp5hiDYVJQCqQLpS7bTnUlrYj6IUtEKIZuQzdMKFIx+MwNAy+8SRdKYwzClSRYTJwLwHRam4ha4BOurO1K6lzFoMoEiiyhL+wTiqHbcBa4AcXgs52VlC/PSuqsG1jQWThTVDttsKwFzVE4aprFlNxO+6x5zYRfYWPGQMSPKaEYuguWleQMPrMbt8SV5KxjEDEGwSLC7zugM9dBrgYvFs1qhm0kgA40DLoBv8LGjIGIUAxdB1vaU/J0JWU1o+SmTYkYg2AR6eTBsha44UvndTuWAnReoD2vmw7D4Md0WhiGrkKzlvbkriRegak5JP1cpljVKGIMgsXE2X6lKxUDj5/opR1kO63yOa+b8FtjRn/Yh6mkcCV1Fbq1tCe3/vkyxQDALneP+BURYxAsKnq3xxgcz5Wz+VynfdaCw5XUF/Et8Cy0A8IwNIhhUpgUUCQJhLA4A5+56CWKgc0GekKqiDEIFpVC1yuG4nM1b024JNLZrqRQWTuddkEYhgbhsxdVIQBgLe9plrwHALOWK6k35HNd3nP/ufmubXgmWFqcE5LuNAzFzzTvUOLtNtuuhjMrKegrdUO3C8IwNAh/CFWJfYVBx7rPzsAYVwy9Yd+C7qpj81m86YuP46cHx5filAVdjnPg7LTBshbcDEM0oHbcZ83rBvxWJmN5fLJdEIahQXigT5GLiqHoSnLEGDIaZIkgFlAWdFflqzdx4yEQNINz4OxOxbDQlRTxK9BNWvLZ2528zppvAljQmbldEIahQXhqoCoXLX/RlVS8gWczBfhkCWGfgkxZuqozy0IgaJaSOoY2m4G2Aufgn8iySVU8qALorD5kfK14AAva6bQLwjA0CHcXqbZikErqGCT2MuYyGnyKhJBfXqAYOrUJmKA90bteMSx0Ja3qCQAAJpPtVwvghW6akK1xg8cY2i2A3hLDQAj5CiFkghCy3+P9HYSQeULIHuvfJ1tx3OWE36SKS4xBMyli1kwmldfhU5hiSOd1UFqc1XGXUzc+xIKlp9D1MYbis5Ow0lX5mgYTHWQYDJNCsWaOwS6PMXwNwM1VtvklpXSb9e8vW3TcZYPfpKpSlIRZh2LoD/vsbX0yUwwmLTUC3d47v9t5+OA4/u939y73adjo3d4Sw0Ux8NbVnaUYKGSpGJsEutQwUEofAzDTin11Cna6quTmSqIIqDJ6Q0w1+BUJEb8CoNQXWnQldd9D/FLgqRPT+O8955b7NGxKg8/tNdC0gtIYQ7liyC3LOTWCUzHY68W7BJ8/+t29+PgPX1jSc+MsZYzhBkLIXkLIg4SQS702IoTcTgjZRQjZNTk5uYSnVx/FGINL8NlkXVf7I34AYDEGHzMMzpRVzXYldd9D/FLAMCk0g8JskzoUrkBliXTlZKPElWQZhnhIRVCVMZHoNMVQmpWUcxkDjkykcHgsuaTnxlkqw7AbwHpK6csAfBHAD702pJTeRSndTindPjg4uESnVz98UHemqzqDz6pEbHcSizGwG8AZgBaKobPhM9hCm6RK8vOJ+JWunGy4uZJUScJQzN+5MQafd7qqbpjL1kZnSQwDpTRBKU1ZPz8AQCWEDCzFsRcLXsegOvKRsw5XkiITDHDFIEsIubqSRPC5k+EV6+1y/XgyA6sGbo9zaiX8eZFI0TAoMsFQ1N9RriTdMIsxBsU7xqAb1F6UaKlZEsNACFlJCCHWz9dax51eimMvFnbls2UY+qzK5kxBh2aaUGUJ/REXxZB3upKsgaXNAk+C2uD3QKFNDIOms/OJBrpTMRQshR0LqkhYxaGqLGEoGuhYxSBJBD5FcjUMmrl8ikFpxU4IIfcC2AFggBAyAuAvAKgAQCm9E8DbALyXEKIDyAK4lTrzNjsQO13VciWttvKpR+dyTDFIBP1hphgkQhD28xjDQsXQaWvWChhcMbSNK8mhGHhX326CP3PxoGq3s1dlgsGoH48d6RzDoJvUrmMAmLfBbSlW3aDL1nizJYaBUvr2Ku/fAeCOVhyrXbDTVa0g0nBPCAAwOpeFZphW8JkphmReR9jHXUnFGYC9cLtQDB2J3maKj7s3IwEF4x3kWqkV/vn6wz6cns4AABSZxRiSeR3ZgmH77NsZp2IALDe0R4whoxmglMJyuCwZovK5Qfhsn3dX5Yrh3FwWukmhygQD3DDkNIT87IZ1KgY+wxOKoTPR2yz4zA1VLKAuaL/SDXDXK8/2A1i6+KD1eyfEGSilJVlJAAtAu7uSKChdnuQUYRgahN+kvPJ5RSwAiTDFoBsmFEmyg8/JXFExpIRi6BqKiqE9DAM3UKt6ApjJFDqqsVwtaAZrPsfrgwCmGHi/JN6Usp3hmc1OxeAsjnXCJx7LsfKjMAwNUsxKItb/ElbGAjg3l4VmZSX124ZBQ0CVQEiZYuAzTqEYOpJ2izHwicaa3hAoRVsuMt8MbI11gp5QsauAIhPXGqF2hWeOySWGQXJtYcKv53J8LmEYGkQv664KsPL8c7NZ6KYJVSrGGHKaCUKI1S/JoRhM0USvk2m7rCTDBCHAsOXWPD/f/q6VetCtNda5QgBYjC/oUiPUrhi2p6E0xuA2BnBXszAMHQRPnVMc2QXDvUGMzmftOoaolYm0ZUUUABD2y65ZSe2SBy+oD6PNKtc1q53zUJQZhvEOqgauhYLB0sB7yxRD2N+eaxq4wScTcnnwuYJiWA6D15KspJcidvBZKlUM9+8bQ1CVocpsLej73n8j1vSyjKWwTynJSy420ROGoRPhD247KQZVIlgRY4ahE4Kx9aDpJlSZoMcZY5AIQmrnuJIMY6FiCPgWZiXxIDWwPAZPGIYG0cu6qwLAqngAukmRzOv2hb9iTY/9fsgvl1Y+86wk4UrqSPR2q3w2TKiKhP6wD4pEutaV1ONwJRFCHG0l2t+VZCsGhws6oMgLMo+c63cvxyJEwpXUIDzg6LT8sUDxhnUWsHBC1poMHG5cdJOWLLIi6AzazTAUDApFkiBJrE1Ed7qSCOIOxQAAITvG0P4TLNcYg29h8NnZQn05WnILw9Ag5d1VASAWLAowp4uJE/bJpd1Vu7x/frfDYwzt4krSDRM+a0IyFAt0qStJKslKAoodSjvBlWRnJZHKMQbNsW58ehlqUoRhaBDdZMt3OoNI0YAzv3qhYgj7ldLuqo6LLwxD59GOMQZF5nU1fownusww2MHnUsUgScSqHm5/V5JRIfhcsrqjY9Io6hg6iILjIeQ4XUmqvPCrjQdVTCbydhZLqWJo/9mOoJR2666qWRX3ACu47M4YA7EVgpNQmRpvV2xXkmPi6Fdl0LLVHZ2uZZGu2kHoBoWvbPCPBoquJKcPkfO6S1cimdfx04Pj1j6ca/S2x+AiqJ22q2OwXC0AMBT1I5HTu2rCUdDZZMytb1CwwwxDuWIASpNQNNOpGIRh6Bh0w1zgLooFS0v1y3nF5gEM9wTxrZ1n2T5MoRg6Gb3N6hh41g5QdGt2QpuIWuEtMdwI+5RlcbnUi+4afOaGwUsxCFdSx8AzQJyEHZ0dVZcYgyQRvGXbajxxfAoF3Sxdo1coho7DaMsYA7vvuHpNdZFh0B2usnI6TzE4muipCyu3NUMoho7EmQHCcUrccqPBWRUPgFK2ApUzwCRqGToPvc16JfHgLNCdiqHgcJWVE3IpEmtH3BRDn7UE8Ey64NhOKIaORHMJPjtxy0oCYPd5mc9qIiupwzHarLsqa4nB7ruI1Y4lmeueBXuchu/5T7wWz3zsNfZ7IZ/clnUMO0/OYMOf3Y99I3MAiinOzhgDr1R3ZpHpQjF0JppJXQd/Lgu9JK/TMGgGtW8QoRg6j3ZTDHqJYrAMwzKtALYYOF1JvWGfPaACQNCntGW66uNHJwEAjxyeAFAc8JUSw8C6MDsLEp3xx+VYW0MYhgbRPQJhvArTy5VUNAwF6KZpz+xu//pz+MbTpxfpbAWLgd5mbdOdca9YF7qStAqupPLi0XYhwg20dR3cspLiQRU+RcJEiWJg95RPlpalid5LwjAsxvLSfM2FcgJ1KgZuGADg4z/c3/LzFCwexZYY7TEg6VbLCMA5IHWPK6lgUE/3bbBNYww81sOTAHSXOgZCWAuTiWRRMfDgczykLotx73rDMDafxSWffAj7z823dL+atUpbOTUrhowG3TDtlsEAcPlwvKXnKFhc2q3AzTCLrslo2Uy1G9BcEj44LMagL8oksBn4eJDMMwPtlpUEsDhDSYzBikVs6A9hbD675L3UXgKGIYesZuDQWKKl+3UrcAOKOcnVgs9zWQ26WaoYnL2WBO2Nsy1y2xgGWjQMqiwhoEpIdVOMwaiUlaTApO1zLcpJliuGsgLY8hYmPBaxeSgCzaAYW+Iq9q43DKZ1ISZbvMyh5lLgBhSDz7JL5TPACt8ifsV2JTn7K3VTznm344gNtk2MwakYACDiV7vKlaRVcCWFfO25WA8f4IsxhoVZSQAwFA1gIuF0JbHtNg+xRb5OTacX/VyddL1h4BZ6KlmosmV9aI4qUydcMVQaLOJB1apjMBHyyXj4wzfhtZes6KoMkm7HWZzYVobBUUsTCyhIdMlkg1KKQhVXEgBk2iy7j7uOuHLzUgxDMT+Sed2uWeDbbR6KAABOTWeW5Hw5XW8YjEVSDM5AnxP7Bq0wc4kHVRZjMNkMaPNQBP1hn1AMHYThkAzNBJ9f+4+/wG13P9OKU1qgGKIBpWtiDPz79nIlBX3WKm5tNrniAzxXbm5ZSQCwwlqOlasGPvEY7gkgoEo4PSUUQ0uxDUOLe9N7BZ9XxoIAvF1JQFEx8KUYAVaQ1E3+4G7HmWfeTB3D0YkUfnl0qhWnBJNSSE5XUkBBqktcScWVzzwUQ5uuycBdR3ZWkl3HsDD4DBSL3JzrvWzoD3emYiCEfIUQMkEIcc23JIwvEEKOEUL2EUKuasVxa4EbhqlUa11JukFLlvXkfPT1W/DxN16M11+60vNvi66kYspr2K8gUzBKZqKC9sV5ndrVlRT1L0+q42LADYPbAlhAbUq9FcxnNBwZT9a8PT9vXpVteBi4IV7kZqWs8qwkRZawvj+E0x0aY/gagJsrvP9rAC60/t0O4MstOm5VdFsxtNaVVHDM9p0EfTL+8BUbqyqGOaslhlJWqbocxSyC+tEdQcR2yYTRu9mVZLi7YDhhK7tvsddHfs83nsPr/umxmicDzgmEbpjeWUm2K4kpBl7HoEoEa3tDODu7tIqhJfmRlNLHCCEbKmxyC4D/pCzJ+GlCSA8hZBWldKwVx6+EYVJsJWcQzWVQOBFiKaYrrwD8kcZ3OnkEl2oHsDkXB07Xf8EuM07jbHYMPkXGpsw4cDqJC9LjuIacQOF4CIj4gfhaoGdt4+coaA2UAudfAAqpkpelVB7XkMMIqTIyWQPf/t4sfvPqYeYiIBKwahugBjx2WqQPCWwio8DpXvZCZAXQv6mhU+01Z7EhPQ2cZjU7l2gnMZ6fBE4HAdkPrL4S8Jhxtzu6aWIdGcfqRB44vXDYGJjL4hpyGP5RnX2Hq68EtCxACKAEgNw8EBlq+jzOTMxgOzmMM3tUbF67BlhxScXtNd3EpeQkQsgjcyyC/ulpXENOwj8aAWaLGYkxSvFy5UX4RxPA6fPomxrDNeQUAmMRXKyN43L9LAonovCFe4EVlzb9OapBWlUQYhmGH1NKL3N578cAPkspfdz6/WcA/pRSuqvSPrdv30537aq4SVV+/sQT2PHTN5S+ePXvAW/+fGM7TE8Dn9sEYJFdPrFh4MMHF/cYguqM7QX+9ZX1/92rPg7c9NGqmz3zyetwnXS4+ILsB/78LKD46z7knr+4GtvIMe8NfuubwMVvqnu/7cDEbAKxz29EgNQYM/nVTwMjzzLDsOZa4MkvAh892vR53Pf59+Mtc18vvvDHe4C+Czy3/95/fQ9v2/sHTR+3hA/srjh5IIQ8Rynd3swhlqqiyk3/uY6shJDbwdxNWLduXdMHlvKsq+HfarfibW95MzY98adAbq7xHeYTACjuxi1QNr8Gv/Py9XXv4uFD4/jKE6cAAG+6fBV++7p12HN2Dn/30Iv41JsvxUVnvwsc/Wnj5yhoHVnrXnn935TM1Mbmc/jId/diVSyAMUv+/8nrt2Db2h7gnltrvsfiSGOnuQVbb/0bxE48AOy6G9BzDRmGOFI4Hbsa63/9kwCAB14YwzeeOYN//831CP3o3c3d98uMoeURIBqOrft/sHnHOxe8n9MM/P5/7MLbr1mLN7/wAfZZUxOAJAPpCSA92ZLzGJCzSNEAfjH423jj1FeYEqmAorHC2k9ov4vb3vRaHJtIsmvyru12XITz6fsOQJYJPv7GS3Df3lF869mz+OrvXIOdp2bw5V8cx5deSdH79GeK9+QislSGYQSA0y+yBsCo24aU0rsA3AUwxdD0kU3mc9xLN2J77BpsCsTs1xrbHwsiHTLXo6fnGmBjZSnpRj41hidN5sq6sm8TsHErDHkGT5oqxvqvxUWpZ5s7R0Hr4NdheDuw7jr75fREEk+aBgZ0P6ZMFr86Hd+GbRuH2aBe4/VTYGCC9mJw6AbEkiesYzYWQJVhIukbAjbuAAAkp8/gSTOG2YGLEXJ+lg7E0JlSSEU32Z/PiZ9SPIM8rgxtxJtlH/uspg6AWt8nBUyzeVca1ZGDD79IrsYbgerXyvrOd5sXYUfPNThTSOFJMwaycQdQZhgmBmM4dD6Bj2/cgdMnj+JJMw5p8w7kjEk8aQYx12Oi17HPxWSpHI73AXiXlZ10PYD5pYgvAICpWxWHVGYBaElu+MGzdggAKJiSa1ZSLfSGHEuAWjdqxO9otiUpHf0QdxX8XpFK51BuQcT5rFbctsbrJ8OADgmJrMbuTaCha08phQwD1HGeW1fGAAAHxtMN77ddMAx27kRyn8sSQljKt/38GEXjwD93Cz4/MQ0YkDCWrG2f1DpvHRKOT6Y86xgAlplk1zE47i87MYUfqlMMAyHkXgBPAdhCCBkhhPwBIeQ9hJD3WJs8AOAEgGMA/g3A+1px3FqgBntYdUiWYWhy0LX+Nm8S16ykWugJ+eyfVTtdlQ0KqbzGzpEaLPApWF74vSKVzu54nvlHX78FX/3dawCwVEa2be33mAITBmRmVPig18D9aVKmPigpDpyXro4h4lfw7BmrT1gzE6JlxtRZujmRvZ0cEb/CugdIsmUQjJYbBonq0CFDR41G3Hp/ZW8EO0/OuK7HwFkRCyCV15HK62xNeYmAEIdh0EjLPkc1WpWV9PYq71MAf9SKY9UNtRQDZEylWmcYdEh42dqehnbBl/IDUExX5YohbzgGCAOo8CAIlgDbMLgrht6wildtHULIJ2OuTsVAKYVMDOhm84bBMClkmCUGTJElbN/Qi2dOTze833ahmmIAWHpuieI2dQBySw0DTB0GlWBQqaZ9UoMZ48vW9OHrR2awZWUUhKCkEJEzFGVxpYlEzuqKwLbha2ukdNq6z1GFzsxdqwMu5YIBH2uL0bRhYBe6NxrEji2Npb/1lLiSyhRDTm/KpSBoMR6GodgMjT1CPVbRItu2NnelYVJLMUgtUAyUKYay87x+Yz+OTGYb3m+7YGjsu62mGFL5MsPAlQPQIsVg1KkY2Hlfsa4fiZyOg6MJV7UAOKuf81ZXhPIaJ77PxVd+XW8YYBmGnkio6EoyGr9BZlPMX/uKi1ZVLGKrBF/MByj2flFkCUFVLrqSgI5+kLsGL8XgKEACgFiJYVAAo3papW5SK8YgI5FzxBgauD91F8UAAFesicPgg1gN59SumEYNhoEX9PFn3NTY9eOfu0WuJAMyDNSmGIj1/hXr+gGwNaC9xg2+xOdEMlfSFYG35k/xy2cu/nXsesNArQvTGwmythiS3NQNki8ws90bDTZ1XnzW4GzdHQk4ZjzAktwAgsqMzDD//Hd2l+ZKlAcReWNEADWrUs0wocJoiWIwTAoVOkBKB85YQIVuD2IdHGPgriRZ9dwmGlCt52fxYgyEsmSBeqb3wIUAACAASURBVGMMQ3GWhZguGJ6LePWFmWGYSRdKuiIosoSQT0ayIFxJLYMbhr4oVwxqU18sT5uTK9ygtcDlobP3S9RvzXj4vjv4Qe4W0lmWJfJPj5xEztHSuXyJxp6QWndWkm44FEOzriRLMdAyxRANKKCQQEE6WoGaOlcM3s9dhD8//Bk3dUs5tDL4zNx19RoGWfHBb2UxeimGmDUm8LVa1LL2Jna7N2EYWoA10+iLhZDK6zBIc4pBt3ydktKcYeBr8joVQzSoYi7TXNqioMU40g0fOnDeflkvizHEna4kWanJqGumCQUmdJ6VZE8IGlEMJlRiFPdhwXsImaSzU6C5K0mq4EqKBpSiK3aRYgyEMkVfs2GgPN1Zdiz7672IVzSgYM5a9te5KFHEryBhu5JEjKF5rAs3EA0DYGmmzXyx/AaVmzUMVhaS8+Kv6Q2yZlkixtA2GLwOBjKmHR16y9MOWWNE6/0GFMN4Io90Ew++YbjXW0Rsw9DchGi54dk91YLPOc1STa51DM0PqJLJsgaNWt1zjhhVyFozolJskitPzZGVBDA3WUK4kloHdyX1x0IAgJwhtcaV1KRhKLqSihd/fV8I52azxWBhBz/I3YLpqINxdio1ylxJ8aCKnGayRXtqNQy6CYWwrKTnTs/iY/99yDpoA4rBui/LYwx+RYIiEcswdK5rsqbgs2UEDThjDEZrYwzghqG2Z5SUGIbKy/4C1gQjU2ALgTndzAEF87xBtDAMzUOsh6Evyg1Dc75WPoOUm6wviFo3MXH0z1/fH4JuUszlrJa+Hfwgdws86GlALlk/ubzyOW4VLdpB5JoUA9vfVRsG8ZqtQzhfYzVtpfOEXBpjIIQg7FeKg2WHwtPOq7mSAG4YNJaNZOrFJI4WxRhMIsOn1uj2c1TOh/w1KIagb8FaLQBLIpjP83FBGIbmsW6KftswFF9rBF5JrSi+KltWhscYMo71F9b1MXfXZLp1PlFBc1CDuYd0yK6KwRljAKzq5xoz3/gsfzAexs2XrXRkD9V/f/IZdbliANhM2h4sO5RijKFSVlK5YiiLMbQgXVemOiiR4fP5a9qn5Kic56vMecUYACAeYmu1aNayvxymGCxXUhPp9rXyEjAMVrpqNAhCgIzeXIzB4DEGtTWuJOdynuv7mfGayFivdXDeebfAZ+KxoB/JfAXFwA1DPYpBs9o8SApWxYMO90QDMQbd29US8SvM6HTwRIPWYBh43E6HxAZPWp6u2oIYg6UY/H5fbfukOotHEFKTK6mHrwdfthAYMwxCMbQO68Kpqh+xgNp0jIE35VOajDFssRqcOfsmrYwF4FMkjKeEYmgXuAsjFg6WKAbdKK7gBhQbI7JamdoMA3dLSrKClXG/QzHUf935wFkefAZYVT2bRXeua9I0+XdVwTBYky0dMmBYDnlqtLbADQZMosDv44ahWozBgG51HuKuJK86BsCxuqOxMPjccb2S2hln8KcnpCLbZIzBtF1JzX1177xuHdb2BnHTRYP2a5JEsLY3iPFk62Y4guYwDR06ldAfDSDhNAxlimHzUAQSAQ6OzuPmWg2DUWwMtyIWaCrpoFgA5qIYAio02tmKgbtP5ArPHQ8+a1QCdMdSvtb33IrPL1PWqNB2JVUzDFRngX/AdiVVy0oyTIq5bMFukQEwg1FrtXUreAkoButLJDJ6giqyOpo0DNYNqta/kIoTQgh2bBkqCT4DwPr+MEaTrZvhCJrEZC0Q+sM+pBzB52JWEnuEQj4FW1fG8PzZuWLb5yroWnEWHA2oUNXaZqGup2krhoUz6ohfhtbhwWfTqK4YeHPKvCmxxY44/OdWKQZJRtBfo2EwDZiWwQ9Z/dCcSqCcniD7DFOpQkksYtNgBBqfx4s6huYhlLUcgCQhHvIhYzQXY+CuBbXJGIMXF62I4ux862Y4guaghgYdEuJBtdSV5NJX/8p1PdhzZo41sqsj+Ewst2QPb7PSwP1Jde88/7BP6XjFQGtQDH1hH1bE/Cyrz6kYNG4Ymh9QZWqAQkagxhgDoVZRLVBbuqrlkpxJF0qCz5esjsHE0rmSut4wsBlfsQNmpmnFwF1Ji2MYLlkdQ95YOskoqAw1DRiQEeUN2ix4d1WlxDD0IpnXWU+bmlxJpdW8fRGWfNBQHYNRDGSXEwko0Eypo12TvB6pWseBy4d7MJszAS1bfFFvXXdZyepgG/LXpu54sBqAXeBWKSupJ1j8fKpDWfSFfcUEBWEYmodYrgCA+e8yGprK9qGLbRhWxWovtxcsPoYGg8iI+FVkNQOaFXTWDHfFAADTWbOmlELTrolh91KfVYTZyP3JB0639TsifqYYaCdnufFswCo9yq5YE8d8noLqTsNgqYcWpOsq1AAlMkJ+FTqVqu5TonrRlVSHYgAWBqntsUF0V20eQh2GIagirZOmVkezXUm+5uoYvLhgIFyUyx08w+sarImFnV5sqQbDZWnPdX1sYE9rqM+VZA3mA1atjdlAnrrdMsI1K4n19jE72DDUrBjWxKFDBtUcrqQWxxiopCBiZXrRKteKOBRD2Fc9K4nHGICFsYhLV8egUQm6LgxD0xDTsH188ZAPGm08Vxwo3qCLpRhkiWC4P85+6eCCpG6BlhkG7k5yizGosoTeUO3uymLfLbZvXoSZzuU8/8ZzXxWWvuR1DMYSDCiLRo3P3RXDcehUhkQd338L6xhka/nUkF+BBrnqIM1cSeyaBGupY3AohpetKV0hcsvKGGvRns6W/1nL6f50VWqUKIYpp5umkbYW1sxMbTJdtRLrB6PAHIQrqQ0gJpvxRa3lFRNWZpJhFtfkdTIY9TN3ZS2GQS8t2uKupHQ2j2id50krZO3wymdeN9GJ2J+vimHoj/i922a0Il0VBiDJVpsRCZpWQKUzkqgO0+qWzFdprBRjCKgy/vjVm7FpKIJbtg2XvNcbUqFDhqYtvoF/CRiGoispHlSbKiICAGpqKFAZPnnxxFY8xLJTqKHD+xYSLAkmy0OPlVWq6yZ1nfkNRPxIztaW+WanYFrtVfpirCVKOlu/YuBK1jUrya9Ao53tSqpVMQBWVwK3x7sldQwmqKQg7GPLe5KaFAMbf4Jq9V5JAPDh121xfZ27BI0lMAwvCVcSvzA9IbXpzqXUEcxeLIIBliPNV4sTLB98YsEVg+1KMqjrzG8w6mdLMNbkSuIpmGzfPMaQyeU9/8YLPqN2zUqyFEMjsYu2wV7wpvpc1q4H8dhHMyhgwWdWcCajUOUZlaxgNVAMPleqY6gENwwixtACJFosMOkJtWCZQ8NYdMMQDjLDkMvXP0AIWozJHuxijIG7krwVQ6rGdNXyGMOApRiaMgwVYgzVAqXtDO+STFwK+MpRvVRFy1xJCoZ7g9AhVb1WvIUGUHQlyRWCz5WI+BUYVIIhmug1j+QoMIkHfQ7F0KDVNTV7f4tFKMANg1AMyw0xNZhEcQk+m1Bd3ImDUT+yBgGt4f6yFYMVFwgHGp8Q2N1HXQbFSIArhg6+n/j36aKIylF9Hoah2QHVNCETCkgK1vSGYFAZ2SqGQaa6vdxqsIY6hkqE/cx9xRMNFpOuNwzOXiUsxtBkjYCjYG6xCAdZj5SccCUtO4RyxcAGm9PTGRwaS+DoeMpTMRhUBqEmYBXBeUHLVwOUJBiQGlOKFWIMq+IBKyupcxVDcV2D6pMy/yK5krixZ+mqCqgkV71WEkzblRSuISupEiGfZeCX4Dp2ffCZuZLYx/QpUrFApgnDYC6yKylkGYZq/kvB4iNRA6akwKdIeNMVq/D1p0/h60+fgmZQrIoHFmw/GPXjJL8/qIFKcy+3Ng8mpIaUop3n7+JqCagyVNXX8emqGpWhkuqDqs/v0cesScNgGta0kqsWWa16rWRqwCTsPglWWfO5GrK1Et9SFCp2vWJwZgUAqL2PugfOuojFImrFGPIixrDsELMYPPyrWy7DYMSPuFWENDa/MHtoMOKvuQumW3sVkyiNJR2YlZe+DPh9HZ2VREwdBqltuPJ7FZ82aRh03bGmN5gLsNq14gVxAOCT2TKrjSoGAKBkaZIIWmIYCCE3E0JeJIQcI4T8mcv7Owgh84SQPda/T7biuLVQbhi4/77hm4QuvmKIBlm6akETimG5kahuBw97wz787CM78POP7vDcfiDqq9ld6VZ7QEn1TJdK+/LK2gn5/R0dfAatPRsw4KEYaLOKwWpUyOufZEWBpmkwTe8uCjI17FX1CCHoCfkQ9jfuqKGS0vTnqIWmXUmEEBnAlwC8FsAIgGcJIfdRSg+WbfpLSumbmj1evTgLTAAgGgwASTRsGNjMZXE9cJGQcCW1C8QqaOJwd8Dbr12HbGHhPdQf9hcVZTXDwAOqTsMgKdALGgq6CZ9S+7ytWMfgPlsOBgMgswbSeb2pgWm5IGbt2YB259Mysrk8Qk2cg6GXNipUVB8kqmMimcdKF7cipZRVSjvun6/93jWu29YKlZQlMfCtuEOuBXCMUnoCAAgh3wJwC4Byw7AsEEdJOsBW4gLQuGEoUyCLAb+xl6LCUVAZyVqYpZzPvPVy1+1liSDk9wMGqroreX+jkkwbSYECA5OpPIZ7grWfqLUv2cOVFAkEIMPA6ekMLlkdq32/7UId9UO8DqicdNOGobi2C8Ba78vQcXY24zrYGyaF7Ag+A8Blw/EmzgA1rw7YLK1wJQ0DOOv4fcR6rZwbCCF7CSEPEkIu9doZIeR2QsguQsiuycnJpk+O9zbhxMLsAjbqvyemvuiGgc9IdOFKWnZkqteUCeOE32PVuqQWl+Ms7l9RVCgw8OALY3Udkw8WXi0jIiE/VBh4wxd+ie88e9Z1m3ZGqsOFG/JwJTVSUe6kvB+V3+eDCgPHJ1Ku2+smhQq9phTbmpGUjumu6hZJKXe67QawnlL6MgBfBPBDr51RSu+ilG6nlG4fHBz02qxm5LIZftxSDDPJTEP7c6a/LhqEQIcErZOzSLoEiRp1P9g1q1JzoWJQVR9WxVTc9dgJ5PU6EiTsNZHdzzUaCkAGS5+987HjoA12F142TKPm4LOXK6mRwsHSUyhtbe73+eCTTBw+n3Td3lYMLTQMRFLsYr/FpBWGYQTAWsfvawCMOjeglCYopSnr5wcAqISQgWo7pmz7pk6OLcVXvDA91mIos6nGOhSyLJXF99EaUJakJ4qgMsxHXF8n3R7LMFQ17I71yG0kGVsGg5hI5vHk8emaj8ljDF7rFfhUH0IKxftftRknJtM46jHLbVckqsOo0fMtK94xhmawW1FY9wORVYRV4EUPw6AbFAoxWmsY5M5ZqOdZABcSQi4ghPgA3ArgPucGhJCVxGpDSQi51jpu1bv+0FgC99crqctw9ioBgF5r+cTZVGOKoXx/i4VJlqYnisAbSqmlGOq73r1Wz6PJuXTl/bsaBgU9ASbCz87UcY/y4LNXOwhJgUwNvOvl60EI8MALY3jxfBLf3dUhbqV60sQ9BuJsk+nfZnk/KklhhmE86TqB1U2TqbSWGgaV3ZOLTNOGgVKqA3g/gIcAHALwHUrpAULIewgh77E2exuA/YSQvQC+AOBWWoMUMEyKY03ObMpjDL2WYphrVDEsQfAZYIahowuSugDNoFBgetYGeNEbYZOP83OV713iEmOApMAvUfgVqS7DQKo1mbOClkPRAK4YjmPnyRl87clT+PgP99d8jOVEqseF6zEQ55tsMcPrQOz7QVIQUihm0gW87p8ew/37SiexhkmhoH5XZCUkWWFjUIUU2VbQkjO23EMPlL12p+PnOwDc0ci+E9nmZJNES9PFeB3DfLqxQBTbX+PpZrVCiWz36xcsDwXDhEzqf7D5gjsT81UmNdRdMRDTwJreIEZm65i8VGtLLSkAKGCaWNMXwqHRBPyKhLxuQjPc+z61E8TRDLMqHgqv2RYzdisKWzHICMhsgD46kcI9O0/jjVessrfXTYoA6leclZBkFQQG0gXdbtOyGLT33QBgPqshkdOQLTQmn2SUun64DEyks8hpRn1yHWzmshQxBkqUzm6T3AVouskUQ52GgbuSJuYq31vUDj47HnCZzezX9IYaMgxuC/XY+7W2WxkLYGw+Z1dup/Ptf5+VF6pW3tj9emlaoamZNq87sb9jSUFAKu6vvDUWUwytdSVJigoZBtL54nj4Z9/fhw996/mWHQPoAMOQyGn4/a8+i0//6ADOzWWx69RMXX+/QMpZPyczWdz9+Em8/vOP1WV0ygtWFg1Jhmnqiy4ZBd4UDBMK9LpdSTz4OZ2sHGMghrtigKlhbV8QZ2friTEYMCiBLHvcm/wYpoZV8QCymoGTU+z8eMfYdqYVriSZ6pjJNK4aytfoZnEbHYf+8mbcsm31guulW66keicWlZBlFSox7AWjTJPiwf3n8dyZ2ZYdA+gAwzCf1XByKo1T02nc8chRvO+bu+v6e7mswI3fNOlcHmemM8gUDOwfna95f14FTy1HZoVOZ+pUNILWUWhQMXDXQdW8eduVVBpj4IphLqPZ6z9Ug5g6dMjwbMMjFRXDiphVy6OzKW7apYK73WiFYpBhItWEEaR6WQdbSQFMA0GfjLW9IYzN5/CtnWfsOhHDNO31G1qFrCiQYdoq7+hECvNZDePz+ZZOItveMMxlCpjNFDCb1jCZzGMuU5/ffcGFsWRgJpvDZIplKew5M1fz/qQW+wy9CPpZpeqP9o5W31iwKBQM9mB7Zvp4Yd1vmVyV2Slv4e7sGGoNNmt7mTuqVncSbxlRvgZ1+TnBNBZ0he0EV1J5B4OKeCg8xfLNN4pZ7q5zVCGv6Q3CMCk+/aODuPvxkwAcisHLvdcAslUAya/Zs5YHpWCYTamhctraMARVGWdnsjApMJspYDpdQMEw6yr8YQUmzhkZ+9nQdZyaZlL6+bO1yzC5gYKnRlAUFUNhBd/bPYLv7DqLgl65t7+g9WgGVwyNGYZcvrJicO3/I8mWYmCZTTXHGWiVdUL4M+BQDJzOcCW1RjFkGoxVAo5K9RLFwA0DM+RZzcDIbAaUUui6YS/s0ypU1QcZBuay7FyedbjWz7t0+22UtjYMYb+CrMYu5FxGw0yaWcR6bmQZxe6GAIotc0nRx/p8HYqh1dLQE0nBuh4Vp6cz+JPv7cMDTdZzCOqnoDPF4FVN7Il1f2TzBXzp0WN4578/47qZaxW9Ndis72cDzYnJ2tK1SbVeQi6uJI4zkNmuOLvcVt/YWzGkmlBH9lKsLoZhbV+xr1W6YGA2o9lLcNYbo6pEPByAAhMHRxOglOKZEzP2sd3awDdKWxsGp7+0YJgYm2MfvFY/IaVMytHy4B4ABSYoZau6jc3n8PjRKQDAZDIP3XCfnZsmtQzD0gSfh2MqfvC+lyMWUPD0idqrYAWtQdMNKKT+OgZ+fxQKBTx3ehY7T824FkARt0WfrMGmJ+TD6ngAh8YSNR2SUL3Y7tv1nIqGwadIGIgUq4NT+fZPiy7PLqwI/6yOFhpUUiDDQKYJI1hcV9vpSmL7WxUPlngER2YzdtO9VhoGRVGhEhN7R+bw4ngS5xM5/K+rWeOJ8/ON1Wa50daGoXxBi4I1YNdq9YsFJmXBPQAK2D7+9ysuwIVDEbz/3t04O5PBjs89iu89N+K6P800LUOzePnDzvMkpoGr1vXi2gv6hWFYBvh6GJ4poF5Y94dMdZycSqOgm5jPLhx8JWosbOEuKfbaxJesjuFgrYahWmUwv2etWe/KeMCeeKU6QjE0YBgk1f6Zyn6oxGgqnsJdSbaClGS7oZ1PkTDcE7Q74o7MZotN91rpYbCykl44N49HD7Mmo7959RooEnnpKAavlY4SNWZq6IYJhZTlEVtGQrEaim0YCOOvf+NyzGU0/GD3OaQLBk57ZAJpBlMMrZwBeOKQqddv7MOp6QzGWjgjEFRH13jH0sZcSTJMnLbiWOcTCx9a1yp6x3W/ZFUMxyfTyGkGsgUDOc17ACfVFrKxYwxsH+v6QrhgIAygdgW+nMioI/jMP6ukFJ99hTURbCr4XO4aKmuB/eV3XI1/e9d2AEwxLKiUbgWSAokamMtouGfnaWxdGcXqniBWxAIvnRiDl2Go9UY2PXrRAIBKmGEYjPixZUUUAPDYUWaBZ9Pu0X3dCkYuVYyhaBj6AQA7T9ZXwyFoDs1SDF6N6TyxVakBnkE4nljYp6dSjAFgisEwKY6MJ/H+e3bjw9/Z433MagvZOFxJAPCJN12Cf71tOwKq1DHpqvUrhqJhIIq/JJunoeac5Y0KywzD5WviuGR1DPGgypJmynsrtQJJgQQTBCbOzmTxxstZpfXKeOAlpBg8Uu9qDT7zJnSlMQZ2UaOWi3Uw6kc8pKI3pOJ5q0hk1iPty05fXArDIBf9lzwQOeEyuAgWD37/eK1x4Ik1Y+VtrgFgvEwxUEohmdUUA1vU5eBoAntH5nFyyrumRaqalVRqGFbFg9g8FEHEr3REVpJM6+hSyt1mkmxfC6IGoRAT6YKBTEHHtX/zM/xkf30JHWZ5jEFWAWouKHlm7UwyjoK4Frqerc/z29eswWfeejne96rNAJhhcFOlDR+mZXtaBGSJnV48WPrF1hxjsNpWExfFEPMzozMYZb2TNgyE7dndrEetRKNN1RrCMUCEfQokUrsLTdAaklY/LbnBOgaFFF0/E46HVjdM7Pj7nyOvFTwMA/u7Nb1BRP0Knjk5g6lUHnMV8tQJrRZjKDUMnIhf6Yg6BqmejgP8s8rFGAPUIFRiIpPXcXQ8hclkHscnK1emL6C8USE/n7Jup2t6gzg7m7V7K9Udo6qE9Xn++i1b8fZr19lelVWxAMbmsy1bZ6PNDQP7f+NguCTi71UNOpXKl7iBTNeWA+xixnwEAVVCxFr/dkN/2N7E6wHUl1IxOAyDJBHEgqprAFOwOFBK8aM9rIK1L1rngpCOGAPHOZubz2o4PZ2BAtMlK0kuue4Xr4rhpwfHAXgrWaCGJnOOAjcnkYDSVArnUlHeJbkiJTEGa1BW/PARE6m8YXdsztTpQuNZSfZEwcPYrogFMJnML4xJtAKPY66MB5DT3JMcGjpMS/aySHBrOBjxI2Z1ElRlgqTHjfzebzyHj3x3r/27UV6QArAqUyJjMCxjy8qYXSnqNAzeisG0KhmXyjAUzyMWEIZhKdl9ZhYvjjLXYqN1DArYIOxXpJIYA3fdrImrdsO9kr91PPSXrI7ZA3dOM0sC0Om8jnd/fRfOzWUhmXoVxVAscHMS9pUahvv3jdV9n/3ljw7iA/e2tolbOXXVDzm6nzqDzwoxkSnoODbJDUOd2Vhm2UDvMUj3hX2Yz2rQdJ7VtviGYVW8tbUMbW0YJEKgygT9ET96QypiAQXxoOrqE6WU4sBoArvPzNpyyvbxld9QkoIbNsTx3XffYL+0YYA9oIQwxeAmyQoaq2RcGsMgl8zu4kEVCWEYlownjk1DIS6Ksxas7UMyUwwXr4qVuJL4QLw6qsCnqgv/1jEhuGRVrORtp2o4fD6Bhw6M44ljU9XXRHY00XMSDSiYSRdw/74xnJhM4Y/u2Y379pyz33/q+DTu3Xmm4sc9MDqPwzWm1TaK0nDw2fobha15ncrrRcVQb5quXeDmKz1O2dre/WH2/py1fPCiKAZjoWIAWlf93NaGAQD++NUX4jeuHEZv2Ie+sA8Rv+KalXQ+kUOmwNK4Rq0vh9pL8S00DBI14FOKH3/TYAQAcNFQFJpBXeW1YS8GvjR1DM5ZQVy4kpaURFZDlF/meg2DNRBEVAKfImHzUMRVMajEBMprYmS1ZEJwyepSwzDjcJXOptn9MJHIWTGGCufJ79lyxeBXcGyCGYSvPXkKADCVKh7jP548hb/9yeEKH5YZulw961M3QGOKQSlTDAYyBYcrqUL6rxu8TbqsliuG0v30R1jcci7N0ssXI8awUDEww/CSUAwA8IHXXIhrL+jD1pUxbF0ZQzSg2jGGgm7ir+8/iNG5LI5PFANJ+8+xbqmeJemysuCLvXR1DP986za884b1AODarO+5U6w6uu70xUaQ1JJzjAUVJDoge6RbSOQ0xPzW49GgYgj7gKGoH6vjAUwkc/Z9y/9fUGMDlMQYAGDzUASKRBBU2czXeV9y9TCRzFfvJeQVY/AXj88LO+cyBbzrKzvxD//zIs4ncpjLaBX98cmcjpxWXy+vkdkMfucrO2tOqGA9z2q8DrKHYYCJ2UzBri3J1htjsJvolSkGF1cSUFw+uO6stkp4HHMw6odEWlf93PaGgfOZt16OO2+7mikGazb/2JFJ/NsvT+Lbz57FsYnigtwHRpms5ZWHKB/IpYWGgRCCW7YNY6XVR2Y2U4BusNWtACap//7BAwCANQPRln++BXSgYnjghTE8cWxquU+jZjIFHV/++fGSZIPHjkziJ/vHkMjqiPutFxs0DD1+gjW9Qdy0ZRAmBR46wILI/P51rYkpu+4BVcat167Fb1+3DkCpK4kbifFEjhmGmgrcSu97v1L8G+5zn8tq2HNmFrtOzdousNE575loKq8jV6e/fufJGfziyKTd8+fPf/ACfnFkEn/yvb2u8YqmFYMahAzWH41nH3rFGM7NZXHb3c9gPJHDW//lCTx04Dx7w85Kqhx85q6kyXlmgHyqDy3D45iqLGEw6n/pKIZyooFi3jW/YE+fmMbxyTSiAQWbhyI4aK2vYBrswktVHj4nvSF20WczGj7+w/343/+5CwBwdCJpZ5moSgsvtBdlMYZYoP1jDO/75m68w6Nh3FJwejqNLz16rKaUvblMAbfe9TT+9ieH8d97iq3N/+Xnx/CPPz2CZF5D1NecYnj9xYP4x/+1DVet68XaviD+2/Ld24aBuPTd4vem4zP8f79+Od79yo0AShMjuJEYT+Srr0XuMaActSZUAxG//dpEIo9ETsfZ2QwmkswFVqnqPpWr35XEXWIz6QJeHE/i3p1n8JHv7MHh80kcOZ8s2dY0XXqeVcIjxiDDsL/W/rDP0zB8+Nt78MujVk07lAAAIABJREFUU/jR3lHsPjNXLCw1dJiUQJEdWU+Ap2I4M8k+RzTUwqWAPZQfAKyMB1tWy9BxhiFiGQbdMPHwoXEQwrqjHhidx6bBCK5YE8fuM3MwTVrMSqrDMPSErMBRpoBDYwm7A+t0qmBnmSxNEz2lzJWkIq+beMe/P40//8ELAIAnjk3hE22ymPt0avmL777x9Gl87qEXq7aq3jcyh1f+3aO2y/GAY6GmiWQesxkNiayOGLf/9RoGq3lb1Aes7gmCEILf2DaMJ45NYSKRsyc2ri3c+e+01DXD78s7f34cb7njcVBKbSMxYSmGRuoYbtk2DAB4z00b7deOW1k7I7NZ6Nb0enTO/TvNaQYKhgnNoHbzyblMAX987/OeHQQAYNp6bzpdwEP7mZLaNBjBbKawQBkbppX00YxikP3s+wZLMLl0OG6v3HhuLmu7l3KagWcsQ8Bn35PJPO7deQZHz89BhwSJd2TwGKR7Qj4Q4my618rgs7vyA3gtw0vUMMQCKlJ5HXtH5jGb0fDWK9egYJjYfWYOW1ZE8coLBzGTLmDfuXl7xaUF6WJls3EntmJIFzCeyNsP8WymgN5AgzPIRnAxDADLlrl35xk8fnQKd/7iOL7+9Gl7UDZMii89egxnppd+1bdDY8nqGy0ye8+yAf7EVOXCpW88fRqUAg988BXYsWUQ+0aKhmEyyWphEjkNUR8fAOqcCBCy4PrdcuUwTArct3cUyZwOnyxBcjUM7g++T5EQ9sk4N5fFvpF5JLK67QKbTOXZWuQN1DG87eo1OPmZN+DXrxzG9Rv7cNW6HlslODlnuZIopfjl0Ul7TRRnkkbOWjPki48cw317R/H93SOepzNjBbinU3lb+auyhLmMtiDuwLuU1nwdiLOOQWGGWlbZIltgBWh9IRUZqxfWjZ99BDd97ucAgJ/sP2/v5sg4u6cnk3n822MnMJ3MwIAMRSq7L8qulSwR9IZ8kHmBY4tbYrgdE2CZSfWuYe95mJbsZQlhJfwaXrTk5h++4gKEfTIuG47hQ6+9EK+8aBCEAF9/6jQePsBuzAUWW1IWpJhxeJX1dLqAyVQeyZwGSimm0wUMhpbYMBilMQYnH/uvF+yOq0fG2Qzvu7vO4nMPvYg7HzsOAPj5ixNLto7DwTE2uIZ9S6CmXNANEy9YCuD4hPcaBoZJ8bNDE9ixdQhbV8ZwxXAcR8aTdpO6ZE6HblKMzecQ9Vl+h0aud9k9xtXsD/ecQzKnIRKw0lK9FIPL/Rlz3APn5rK2K0kzKGBWWROZD2Iu+yWEYCDix7duvwFXrOlx/fMxSzF89sHDuO3uncV4iSMhgtdYnLIMM689coMrhmMTKbuD7FSKTcQyBcOO7QGAzmOFNSsGiRkDSS5WP0uKrRguHIoi6FOQLRglacQA8P3dI1jTG0TIJ9uGYSKZw+h8FioM6JCLPdw8UoAB5qpSsZiGYeExb9oyiJ5QawLdHWcYVsQDMCnw+LFJ+BUJW1ZE8cj/3YEfvPdGrIoH0Rf24WVrevD93SP46X7mO3arY/ByJSmyhJ6QihfPJ2GYFJpBkdNMzKYL6AsupWGQFwSfOe/bsQlnZjJsQACb2aTyOv7p4SMAAJ9VMn7HI8fwzw8freuwZ2cy+P2vPbvgganGQSvgr8jLc0sdGU/Zizodd1ncZjKZx2Qyj+fPzGI6XcDrLlkBALhiTQ9MytxJzl5UBd1ERC0bAOrB0dqC8+vbhrH/XAIvnJtn2UCmR4wBcL0/nf7jsfks5jKaPUhRQ4dZaUZdYb9O3AaWVfEARuezODaRxL8+dgIA7GBz0sUw8O7EXoWoADCTZt81XySrJ8QWpeI442mG1kA9CVcLvMhNUiBRHQDF5qEIQj4ZmYKBnx4at/9kPJHDE8em8BtXDmMw6rdTjM/MZJDTWNcDA5KLYVj4OfvCbKU1tl0LJ0sVYgyv2jKEZz72q605TEv2soRcZuV1P3xoAhsHI5AkghWxQElNwm3Xs5RTHhNYsGZvWSpoORv6wyVL5iVzbPW43qB1gZciXVUuS1cNFB+Kd79yE65a14OBiB/xoIrD5xP4yHf22PnnfDZ2bi67wF9bqXUzwFTGI4cn8H4rM4RSWjWYa5jUdsek8nrL+rU8d3oWv/fVnZW7ilrsHWEDzGDU72oYPvq9vfjwd/bgsaNTkAibXQHAFWtZo7qvPXlqQYA1wi9zI9fbZfJx7QV9AFg6dTRgvb8gY47XGyy8Ts6vdXQui5l0wW6dXXWFM8m9jqGcnjJlSghwxZo4xuZy+J+DxUE0pxu45Y7H8XlrMgKwe0s3TNuVWSmLbsZxjwLA5cNx27Dzv6WUYjyRs+uHvNZydkVSHcahGGuQQLF5kBmGrGbY7UYA4Fs7z8KkLO7CM4sA2BMwBQY0yMXmnrL3teqP+BwxyRaOF3JtBr5ZOs4wXLwqBlkiKOgmNg2GXbf5zavX4PlPvBay1Vp7QYFJhRgDwHozObM/Ejkd0yWKYemDz1wxDER8iIdU3PWu7fjOu6/HlhVRfH/3OTx0YBx//mtbsX19L6aSeWiGifFEDnPZAjTDxGQyj8PnE7jsLx7CbquL7K13PYUvPXqs5LB5y0+88+QMjown8ep/+IU9S/TiE/+9Hyem0tg4GIZh0rpz2r34+A/347GjU/jB7nN46vg0vv/cCAxzodGhlOKp49PoD/tw00WDrs3RxuZyODmVxshsBitjAdvNMRQN4COvvQg/3jeGv7r/YMnfhO0Ctwaut4thWNvHqutNatUPmHrNMQaA3fsAawszMscUw5aVLHVaJmYVw1DbgNJrDYg+RUJvSMVAxI/1/WGMzGXxyKEJexnJbMHAofNJPO5IT85pJo5OpOwFteYr9HaaLgtMXzYcL/l9Pqvhn392FNf9zc9wfi5d+hlqQVJK3Eh8QFVg4NLhGII+GZQCL4zMQ5XZQP/UiSnEgyo2D0XsIjUnMkwYkB3BZ+9rxRSDWf951/K5PI7ZSlpiGAghNxNCXiSEHCOE/JnL+4QQ8gXr/X2EkKsaPVZAlbHZqlLm1cpu9IaLFts1xlDhiy3fbyKnYTZdQE+gCddCvUgK69poTRO5f3njADu3gYgfGwcjuGhlBAXdxNXre/EHv3IBBiJ+TKfzOD+fg0nZw/q1J05hx+cexWNHJqGbFPftGcWJyRSePjGDbz59umSG73xg7983hpNTaXzl8ZMlPl8n81kN9zxzBu+4bh1+78YLAADJGpaK/Mn+Mdz5i+Oe789nNBw+n8Bt16+HKhP8zld34iPf3YsvPnIUv/vVnbb/9/337MZv3fU0fnZoHK+7dAU2D0UwmcwvCGDOZQuYSOQxOpe12wdwPvCaC/GytT3Yf660rUOYX+aGXUml91g8qDKlABQVg1eMweX+/M67r8fO//c1WBkP4PhEGgXDtNcSUaotfVm2UI8XPPtpIOzDcG8QQ1E/fvXiFSjoJnadnsWrtwwBYOsaF3TTnkgAVkaPY6VBL8VQ0M0SF1Rf2IfVPcGSbRI53b4/xufYta4ru4e33C5TDA9+4OW4dHUcYR/7fTpdsA3uvpF5rOll5+FUDBzFijEUj+F9rbasiC6O67lTDAMhRAbwJQC/BuASAG8nhFxSttmvAbjQ+nc7gC83c8xLh9mF3DTkbRgA2D6+hVlJ1QxDqRIZm8tBN+kSZyWVPshcMWwaKj23K4Z7IBHg42+8GIQQ9Ed8mEoVSlI295ydQ7pg4Ae7WR79QwfO42eHJgAAo/M57HVk5cykChiI+BFQJfzsMJPZE8m8vX05k1YGy7UX9CFqVdHWspDSPTvP4s5fHEdBN0vSRTnPnpoBpcDNl63Ea7aygSkeVPH5h4/i5y9O2mt0/3jfGHaenEG6YOANl6/CemtWXp6dMZ/VUDBMHBpL2g3HnFy9rnfBayG1yeCzyyC8tpedXzSg1h1jiAZUDEUDWB0P2rU6K2J+RAMKqwxuIF21HO5KGoj68X9+9SJ86FcvwjUberFtLQtK/8qFg/ApkmsqalYz8ON9Y9i6MorLh+OY8zAM3I3EB981vUE7G5AzlynYynMmye5lUo9LxiXGAACb+pkSCDqSJLZaqitTMIqGwVoT25lMIRMTBnUMmRW+03dctx6fetMWa7vFiDEsrmFoxQh3LYBjlNITAEAI+RaAWwA4dfktAP6Tsqnp04SQHkLIKkpp5ZSZQgYY2bXg5VdFRnGCnMRlNAyMjLr8IePq4DjYks8uhiE747pvALjETGMbKbpYsicL2EbOYG1hiRUDAJx9xmoABnzqqgyuWztZct5vXWHiptviWCEdB0aAS+kZHMyeRfp4wf4MytgotpEMMA5cKQE0ATz12It4fVzCVKqAPU9lsI2w2X585hBuDORAggSnRtPYRthD9NwTM7i552L81/Mj+I8nT+N7770BiiQhd24e28gxrM/6UdBNbCPHYJwN4cQ5gkcOT+APbrzA7mDrJDL1PDZkc7jv/jTu2XkWX/nd7egJFmdpI/tPYrsyhiulAax+WQ6vjWu4bHUQf/8/JzCZzCM0WYB5Zsr+jNGAghv8gzg0nWTncMYPmD14cP8YLloRxVb9CEAA5ICr5NXASKkCenV0ErvJMfSGVMzndJgmRW8mUXot6rp+MpA6v+Aee0XwFHxkBlv1FKBlvRXD6PNAetJ11zf4TyKfmMQQAdZnFbwieAr+jFabK2n2lOd9DwArEjlsI8dwtdKD10QtZXXuLD59dRb/kR/DywMncY1yHLHpaWwjpeuQJ48Z0M+cxB9evw77z80jkzCAEQmfe+gwspqB99y0GUNRPzJTKWwjx3BpTwwHMgncGOzH2kyu5Jkb3Z/ENsImJtJ5dl/U1e7eWcPg/HlkFxCIY3Vq0j7eK4IGjpCTAIBr1QwwAmzVR7GNnMSW/ghePM9iVgOYd1cME4cAX+kkVQIgzZ4o3a4VcCMzcRgIuGeQtQLSbKCQEPI2ADdTSv/Q+v02ANdRSt/v2ObHAD5LKX3c+v1nAP6UUup9hwLYvlqmu26vrApq4Zk3P4zrrr6m+MI9vwUc+UnjO/z9h4B11zd9XhV59m7g/g8v7jEEtfHhQ0BsdX1/c+evAOdfqL7dy/8YeN1fFX9/8UHg3lvrO5bFA73vxBs++CX3Nw0d+MwwoLdula+lZv8rvozLXvPbtW385RuB9TeymfXIs8D17wN++J6mz2GvuREv+0urZcfEYeBfrqvyFwT46HEg3N/0sQEAU8eAO66ufMRPJ56jlG5v5jCtMGVu62+WW5tatmEbEnI7mLsJm9cOAe/4RsMnNpHI4e7nZvHBy68sfePNXwDO76v4t3/yvb2Ih1QcHU/hohVRHBlP4i/efAkuWL0CWHNtw+dUM1e+E+i7oKpPuJxnT83gS48ew4aBsJ1P7uSd163Hq7cO4fRMBqt7AjgynsI//M+L+MNf2YhfuXAAf/b9fVjbH8KqWAD37R3FcE8Q77huHf7uoRfxgVdvZu2Zp9K4/ZUb8fJNA3j44Di+8cxp/POtV2I2U8Cn7juAD7x6Mx47MoW9I3PsOxuIYD6r4YPfeh6vvGgQb71qGB/6Vmmm0btv2ojLh+P4wL3P29k3H3j1Zly9vm/BZ/jT7+/D+v4Q3nD5KnzqvgN4/6s2Y/sGtt10Ko+PfHcvfvflG/CytT34P9/eYy21WHStvXfHJlx3wcIH9cPf3oN1/SFMJvM4N5fF539rG3r6V9ZvFADgt74JTB1Z8PL/HDyPe575/9u70+A4yjOB4/9nLs0hjUb36UOyZfk+ZWNjDMbCBlwE2BwUmwSoJJWEbDhyUFlIqljCfmFzsJVK9oJka7Mh2RybzYbKknAkKJCExDhcBnziGPAlH7Isy7J1eN790N2jGWk0kjwjz+HnV6XSzLg106/f6X76vZ5+h/evaOS6xQ0wbcR3qWUT3PaLlCfwrfu6+Naze1jcWMqn1s/m28/t5cV3uqmsuILNY/2R2wMffxZ6Doy1BWAN5N/+2J+5ekEt713emHSbL/z3q3jdrtiMIkfY7+X0wBDfvrWN/3xhH1vfPsFX37+E2x/7M2Ddl2LNrAo6dlotoQ+vnsFjf3ybW9bMYGljhM//5FWK/R57dpMhEvRyun+I4iIvh/sMD86/NuW+J/jQT8AXssboBs9AsALCdbF1HNsP9cSyxj54/UIefmYn3X2D3NXewvLpZbxx8CRffXInNy6t5zc7jjKtPMAbB3vYHW3g985nVLXCR5+C/hQpx0OVmQsKAJWz4WNPw9nR3a8xX96U9sdkIjDsB6bFPW8ERvbvTGQbAIwxjwCPALS1tRlaNp73jlUD9yULriU1UJL6fT/w1ysI+Txc983n2XHaz+HoWf6+9Uoon+TdvM6XpwhmbZj0nxlPFx2/LoYj1gwmZwqriHWM3L5oNa7mCprs7RfONex/6Tke3ufhss1r+WW/4Ybqehqml9Hx8itcU1dLy2XLeLnjGX7QVUNP5Sw6jnQya6iJS1vm89pfdvKcKaN44bV0n+ijI+rlPWVL+Fnvbt6J9vHB8AqaWmp5e383HVHDwGAFSyOtdIwYy17pa4VAGc/aUwPv2TSHFRtakpZxT2mId3ExN9JER9TL3a2Xgj1GEBgYouNHwuqSuVRXVdERBV+3i4G4+/Le2boGkgScW29bSXGRhy/+bBtburoomncNnO+CvbIZ1s8IMthJxwtbaa9fAC0zR/+dyw1N61K+dVsLPLohitdeM9K5axvP7XuHGzzjfDdr5ls/KQjwmU+tpqEsAGMsUHstUGQtAI1a40tOYks5Y02KkDlX0bl3B0/s3Ms9NevoiJ5jdXM5HXu7+P1eD71RKw3H/WuuYPebW6hvayNYEaLjR0JzMETP2SGO9fazvqGK3Z29sQBUVz6J5JXxwTxgd7k0r4+9NODvpiNqdZX557ez58VyXu/t4Qvz1kF9GHdJDx2/DNA+fSG3XVJCacDHPz382xH/WQLTx2sxTIGRFxNTIBOB4UWgRUSagAPAzcDI9t7jwB32+MMlwMlxxxeybKV9BVpc5IktLHIGpHJZ/D6umFEWW6F6w5J6hqImNojoEBE2zq/h0ef20ts/xMkzg5SHfDTbA/BNVSG8bhfLpkfY1XmKcMD6yrz0zgme332Uo70D1tQ8l8RSOB8/3c+7J6zB385Ydk7r4H73RF/SXEb7T/TFBvq2fLGd6vDYicfKQz7e7erj8EnrxBQ/yyjgdeN1C919gxy3F1ENjJhRVZtk8BmI/d+UB3143datXzPNWXeQbDrkZHjjFhJWhKz3ytDykdgsnbEEfG5OHBkefK4qKbLXrwwPXkeCXs5FDfvsNQ3tc2v4494uevuH+MI1rdy4tIH6SIDf3zt88VPksRaXiliroOfWhuk6PcCB7jOU+D0JKcLTFbS/ayLW98lJIthgDz7PrirmI2tnctW86qSTFQpd2t98Y8wQcAfwJLAd+LEx5g0RuV1EnE69J4C9wB7gUeBv0v3cC6XEvmqylslfgEHnNFWGhk84H1/XHFuluWJmOd/64HL83tFXwEsaIwxFDX+w56SXh3zMqSlhcWMp61oqrdeCPk70DcSmGb78Tje3fGcLT2w7FDsxFdtTMV8/0BM7STlJvZxcOwe7z/K23cU1p8YaP3IJvNt1hp2dp4gEvVSVpD5pVoR8HD89wOGes4gkZgYVEUoDPk6eGeB4b+LMGSdnffU4718X8VNd4k86aJ6u2dXF/ODjl7DRXnmdCZV2eS5UWna/xx1LrgdWy9ThrJx2ZtE5N8W5pLk8dk+JNc0Vo6anOn8bCfpiU7Pn1ZXEZi7VZ/jk7MxKKgv68Lhd1Ib9lAW9sf32uF383XsWXJRBATLTYsAY8wTWyT/+tX+Ne2yAT2fisy40Z975gvrUV1G5wrmiB6vFEPZ7ONE3SGWSedmOJfbq345dVt9veciH3+vm8Tsui21TGvTS3TeI1+3C45LYieHkmUEW2tOHizzW1bqzClnESuPQc3YwlmvnXNSwZV8XFSEfc2vD7OrsZfn0Mvaf6OP0wBCtNSXjnpDLQz4ryeHJs1QWFyVcPQOUBjycPDM4ahHVvLowuw6fGrX9SHe3t3Drmpkpt0nHpbMqM/p+Tt12p1hQlknxUz3Dfg8NkQAvu7oZiprYOohSe4aZswq9JuxnYUOYNw72jFrM5rhxWQNNFSF+ZSfVm1sbpjxkXazURTKYuhpiF3lOULuzvYUPtCUfU7kY5f4lcJa57JPUgvrkX+ZcIyI89N5FLGwoRUSIBH1WYEhxlVwb9lNVUkTHDmutQnlwdBApC/ro7R/CJXDTymnce+1crv/m79h3vC/WYgCr6+3t432IWAf287uPsezBp2M3QAJ4fvcxFjeWsqqpnDcP9bBiZhnfef4vdPb0c9MEDs7ykI+hqGH3kVPUhEeXKxL0WV1JcanAReD+6+ZPKD14JOiLneDygVO3J5LcdXAqBOJanf/x0VU0VYR4ZvsRevuHYl1JI1sM5SEfd2xo4WD3mTED833XzgPgD28dx+sWmqtCsRN3pq/cna6kWBdSJEBDklbMxUoDwzicga98aTEA3Lxqeuyxc4AmW8npEBGWNJbyjL2IrTzJWIrTRdBzdoiw30vY72VRY4R9x/sSunKK7RbKrKpimiqDbLczZx7oPkNzZSiWEnvt7Eo+vHoGH149g+//6W2Gooah6Dlaa8f/f3bGUd481MNls0dffUcCXg73nE24P3LYb6U6mD3Oosh85NRtqvsfZFJ8d+TMihBlIR9+r4ve/uHvifN779FeyoJevG4XV8ypmtD737isntnVxXjdrlhd15dmtsVQ5HHhGtENqYblXa6kC83pt82XFsNIzgGaqsUAcL19wxYgoQUw/D7DwcLpXltkdyHFD3gP2bOKNs2voWbEAPLyGcOriz9y6czY42XTrC6vdS2VtM+rTrmfAOX2/p0djCa9yisNWN1ex3oHYnlwMpWOOBc5dZsqm2kmBXzDpw3nytsJFs73xBnHOWavpJ+MDXNruKvdmpHm1PXINCbpErGSb86smPgswxfu28Azn7sio/uRq7TFMI4bl9bzv68cTNplkQ9KA158blcsXcVYrl9ST1NFiO2HepIO/sZn3QzHAoM1i6cq7sB3BpuvXlAbu1+E01KYVhZkSWMpTZWhhFlH8+vDvPbA1RMuU3zrZ9OC2lH/Xhq07o/ddbqf2dUlbD/UM+p+FoXEqVsnncNUc7qSRKwrb4gPDHYLtbiIeXVhth/qSeuq3OmCnDYF08R//um1scklE3ExDURrYBjH129aykPvWzwlM1QuhLWzKjGGCe3/osZSFjUmbxmVJbQYrIOpbWYZd7W3cFXcDBsnz/3ixlKO9fbjEnjwhoV88ntbWVAf5q722WmWaPieugCrm0cvHioNWHf56+zpZ9n0CLs7TxV0YBARfvzJNcyYxNVvOpwgEPC6Y98rZ2pvfFqTK1ur2H6oJ61pv5fOquDfblnBJU2j152kK9WU6IudBoZxuF2C+0Kk2Z4iN62cxk0rp42/4Tjiu2KcmU9et4vPbZyTsN2Tn7mcs4PnEBE2zK3m+b/dQEMkwEv3b8TndmUkwDpdV1fMqRq+aUr8vtpB4ED3GTYtsLq0Uo2xFIJVU3DiHIsTGIJxs5MCI1oMAFfOreafO95ix+Hzv+2ryyVcnaRVqKaWBgY1IfEHfKrmd3yTX0RiYwBFnswF1yKPm2fvWT/mLJLSuH2tCPn4xs1LdZAxg5wgED9t1QkW8S2zZdMitNaU8NkRFw8q92lgUBNSXOSJrV8o8Wf/a+OsIE4mvjujqbI4lkdJZYYTEOKnrTqBvyyuZeZxu3jys5df2J1TGaGzktSEWGsirKvBVDd5zwXV9kSBdS2VbF6k3RCZNtxiGL5AcILFyFuDqvyU/Us/lTciQSspXy60GFKZXxfmF3dexry6cN5OGshlzmByMK7F4Pe48LolYdxB5a/cPsJVTokErARnoRzPGSUiY6ZdUOnzJxljaK4qprV2/HQmKj/k9hGuckok6KO4yDN8M3R1UUo2+Pyp9bO4/YrmbO2SyjANDGrCZlWFYmm01cUr2eAzTGytjMoPGhjUhH1+Uyt3R6Pjb6gKWiDJOgZVWDQwqAnzeVz4dCLbRS/ZGIMqLHqUK6UmJT4lhipMGhiUUpNS4vcQ9LkT7rGhCot2JSmlJsXvdfObz6/Pi3ugq/OjgUEpNWmZvj+Cyi3alaSUUiqBBgallFIJNDAopZRKoIFBKaVUAg0MSimlEmhgUEoplUADg1JKqQRprWMQkXLgR8BMYB9wkzHmRJLt9gGngHPAkDGmLZ3PVUopNXXSbTHcC/zaGNMC/Np+PpYrjTFLNSgopVRuSzcw3AB81378XeDGNN9PKaVUlqUbGGqMMYcA7N/VY2xngKdE5M8i8olUbyginxCRrSKy9ejRo2nunlJKqckad4xBRJ4BapP805cm8TlrjTEHRaQaeFpEdhhjnku2oTHmEeARgLa2NjOJz1BKKZUBYsz5n3tFZCew3hhzSETqgA5jTOs4f/MA0GuM+doE3v8UsPO8dzC3VQLHsr0TU0jLl9+0fPmr1RhTks4bpJtd9XHgNuAh+/fPR24gIiHAZYw5ZT/eBDw4wfffWaiD1SKytVDLBlq+fKfly18isjXd90h3jOEhYKOI7AY22s8RkXoRecLepgb4nYi8CmwB/s8Y86s0P1cppdQUSavFYIw5DrQnef0gsNl+vBdYks7nKKWUunByfeXzI9negSlUyGUDLV++0/Llr7TLltbgs1JKqcKT6y0GpZRSF5gGBqWUUglyMjCIyDUislNE9ohIqvxLeUNE9onINhF5xZlOJiLlIvK0iOy2f5dlez8nSkT+XUSOiMjrca+NWR4Ruc+uz50icnV29nrixijfAyJywK7DV0Rkc9y/5U35RGSaiDwrIttF5A0Rudt+vSDqL0X5CqX+/CKyRURetcvpGdhHAAAD3ElEQVT3Zfv1zNWfMSanfgA38BbQDPiAV4H52d6vDJRrH1A54rWvAPfaj+8F/iHb+zmJ8lwOLAdeH688wHy7HouAJrt+3dkuw3mU7wHgniTb5lX5gDpguf24BNhll6Eg6i9F+Qql/gQoth97gT8BqzNZf7nYYlgF7DHG7DXGDAA/xErWV4jyNgmhsVKadI14eazy3AD80BjTb4z5C7AHq55z1hjlG0telc8Yc8gY85L9+BSwHWigQOovRfnGkm/lM8aYXvup1/4xZLD+cjEwNADvxj3fT+pKzRfJEglONAlhvhirPIVUp3eIyGt2V5PTVM/b8onITGAZ1lVnwdXfiPJBgdSfiLhF5BXgCPC0MSaj9ZeLgUGSvFYIc2rXGmOWA9cCnxaRy7O9QxdQodTpvwCzgKXAIeDr9ut5WT4RKQZ+CnzGGNOTatMkr+Vj+Qqm/owx54wxS4FGYJWILEyx+aTLl4uBYT8wLe55I3AwS/uSMcZaDY4x5gjwM6ymXKedfBD795Hs7WFGjFWegqhTY0ynfUBGgUcZbo7nXflExIt10vy+MeZ/7JcLpv6Sla+Q6s9hjOkGOoBryGD95WJgeBFoEZEmEfEBN2Ml68tbIhISkRLnMVYiwdcZTkIIYyQhzDNjledx4GYRKRKRJqAFK29WXnEOOttfYdUh5Fn5RESA7wDbjTEPx/1TQdTfWOUroPqrEpGI/TgAXAXsIJP1l+0R9jFG3TdjzSR4C/hStvcnA+VpxpoV8CrwhlMmoALrlqi77d/l2d7XSZTpv7Ca44NYVyQfS1UerPt3vIWVRv3abO//eZbve8A24DX7YKvLx/IBl2F1JbwGvGL/bC6U+ktRvkKpv8XAy3Y5Xgfut1/PWP1pSgyllFIJcrErSSmlVBZpYFBKKZVAA4NSSqkEGhiUUkol0MCglFIqQVq39lSqUIiIM9UPoBY4Bxy1n/cZYy7Nyo4plQU6XVWpEUTkAaDXGPO1bO+LUtmgXUlKjUNEeu3f60XktyLyYxHZJSIPiciH7Nz420Rklr1dlYj8VERetH/WZrcESk2OBgalJmcJcDewCLgFmGOMWQV8G7jT3uYbwD8aY1YC77P/Tam8oWMMSk3Oi8ZObSwibwFP2a9vA660H18FzLdS9gAQFpESY90bQKmcp4FBqcnpj3scjXseZfh4cgFrjDFnLuSOKZUp2pWkVOY9BdzhPBGRpVncF6UmTQODUpl3F9Bm3ynsTeD2bO+QUpOh01WVUkol0BaDUkqpBBoYlFJKJdDAoJRSKoEGBqWUUgk0MCillEqggUEppVQCDQxKKaUS/D+7HISbQqgmYwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2024,7 +2024,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2076,7 +2076,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n",
+    "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n",
     "\n",
     "This tier indicates that the table will contain information:\n",
     "* that will be referenced by other tables\n",
@@ -2197,7 +2197,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -2416,14 +2416,14 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. "
+    "In the Diagram, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. "
    ]
   },
   {
@@ -4001,7 +4001,7 @@
     }
    ],
    "source": [
-    "dj.ERD(schema)"
+    "dj.Diagram(schema)"
    ]
   },
   {
@@ -4014,7 +4014,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -4028,9 +4028,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..f01e87f
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,21 @@
+# Changelog
+
+## [unreleased]
+### Needs
++ Calcium Imaging .py files to mirror Electrophysiology
+
+## [0.0.0b2] - 2022-05-07
+### Added
++ Folder structure to to imply NB order
++ ERD->Diagram
++ Change working directory to /home/notebooks
++ Per above, move data to /home/notebooks, collapse modalities
+
+## [0.0.0b1] - 2021-06-11
+### Fixed 
++ Notebook structure, polish
+
+
+## [0.0.0a] - 2020-04-13
+### Added 
++ Major initial commits
diff --git a/electrophysiology/data/data_0_2017-05-15.npy b/data/data_0_2017-05-15.npy
similarity index 100%
rename from electrophysiology/data/data_0_2017-05-15.npy
rename to data/data_0_2017-05-15.npy
diff --git a/electrophysiology/data/data_0_2017-05-19.npy b/data/data_0_2017-05-19.npy
similarity index 100%
rename from electrophysiology/data/data_0_2017-05-19.npy
rename to data/data_0_2017-05-19.npy
diff --git a/electrophysiology/data/data_100_2017-05-25.npy b/data/data_100_2017-05-25.npy
similarity index 100%
rename from electrophysiology/data/data_100_2017-05-25.npy
rename to data/data_100_2017-05-25.npy
diff --git a/electrophysiology/data/data_100_2017-06-01.npy b/data/data_100_2017-06-01.npy
similarity index 100%
rename from electrophysiology/data/data_100_2017-06-01.npy
rename to data/data_100_2017-06-01.npy
diff --git a/electrophysiology/data/data_5_2017-01-05.npy b/data/data_5_2017-01-05.npy
similarity index 100%
rename from electrophysiology/data/data_5_2017-01-05.npy
rename to data/data_5_2017-01-05.npy
diff --git a/calcium_imaging/data/example_scan_01.tif b/data/example_scan_01.tif
similarity index 100%
rename from calcium_imaging/data/example_scan_01.tif
rename to data/example_scan_01.tif
diff --git a/calcium_imaging/data/example_scan_02.tif b/data/example_scan_02.tif
similarity index 100%
rename from calcium_imaging/data/example_scan_02.tif
rename to data/example_scan_02.tif
diff --git a/calcium_imaging/data/example_scan_03.tif b/data/example_scan_03.tif
similarity index 100%
rename from calcium_imaging/data/example_scan_03.tif
rename to data/example_scan_03.tif