ephys tutorial completed

Author: Thinh Nguyen

electrophysiology/02-Imported and Computed Tables - Completed.ipynb

@@ -60,7 +60,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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 `workshop.session2`, from which you can import the classes as well as the schema object. We will use the schema object again to define more tables."
+    "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."
    ]
   },
   {
@@ -69,7 +69,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from workshop.session2 import schema, Mouse, Session"
+    "import sys\n",
+    "sys.path.append(\"..\")\n",
+    "from tutorial_pipeline.mouse_session import schema, Mouse, Session"
    ]
   },
   {
@@ -94,7 +96,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `session2.py` also fills each table with data to make sure we are all on the same page."
+    "The `mouse_session.py` also fills each table with data to make sure we are all on the same page."
    ]
   },
   {
@@ -258,7 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So this particular file contains a NumPy array of length 1000. This represents a (simulated) recording of raw electric activity from a single neuron over 1000 time bins."
+    "So this particular file contains a NumPy array of size 1 x 1000. This represents a (simulated) recording of raw electric activity from neuron(s) (1st dimension) over 1000 time bins (2nd dimesion)."
    ]
   },
   {
@@ -274,7 +276,7 @@
    "source": [
     "We now would like to have all these recorded `Neuron` represented and stored in our data pipeline.\n",
     "\n",
-    "Since we only record a single neuron from each session, a `Neuron` can be uniquely identified by knowing the `Session` it was recorded in. For each `Neuron`, we want to store the neural activity found in the data file."
+    "Since there may be multiple neurons recorded from each session, a `Neuron` can be uniquely identified by knowing the `Session` it was recorded in, as well as its `neuron_id`. For each `Neuron`, we want to store the neural activity found in the data file."
    ]
   },
   {
@@ -287,6 +289,7 @@
     "class Neuron(dj.Imported):\n",
     "    definition = \"\"\"\n",
     "    -> Session\n",
+    "    neuron_id: int\n",
     "    ---\n",
     "    activity: longblob    # electric activity of the neuron\n",
     "    \"\"\""
@@ -319,7 +322,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that our `Neuron` class inherits from `dj.Imported` instaed of `dj.Manual` like others. This is because **this table's content will depend on data imported from an external file**. The `Manual` vs `Imported` are said to specify the **tier of the table**."
+    "Note that our `Neuron` class inherits from `dj.Imported` instead of `dj.Manual` like others. This is because **this table's content will depend on data imported from an external file**. The `Manual` vs `Imported` are said to specify the **tier of the table**."
    ]
   },
   {
@@ -491,14 +494,18 @@
     "\n",
     "        # load the data\n",
     "        data = np.load(data_file)\n",
+    "        \n",
+    "        for idx, d in enumerate(data):\n",
+    "            # add the index of the 1st dimension as neuron_id\n",
+    "            key['neuron_id'] = idx\n",
+    "            \n",
+    "            # add the loaded data as the \"activity\" column\n",
+    "            key['activity'] = d\n",
     "\n",
-    "        # add the loaded data as the \"activity\" column\n",
-    "        key['activity'] = data\n",
+    "            # insert the key into self\n",
+    "            self.insert1(key)\n",
     "\n",
-    "        # insert the key into self\n",
-    "        self.insert1(key)\n",
-    "\n",
-    "        print('Populated a neuron for mouse_id={mouse_id} on session_date={session_date}'.format(**key))"
+    "            print('Populated neuron={neuron_id} for mouse_id={mouse_id} on session_date={session_date}'.format(**key))"
    ]
   },
   {
@@ -533,6 +540,13 @@
     "Neuron()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can obviously see, in these example datasets, we only have data for one neuron per session. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1524,7 +1538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

…gy/02-Imported and Computed Tables.ipynb … and Computed Tables - Interactive.ipynbelectrophysiology/02-Imported and Computed Tables.ipynb renamed to electrophysiology/02-Imported and Computed Tables - Interactive.ipynb electrophysiology/02-Imported and Computed Tables.ipynb renamed to electrophysiology/02-Imported and Computed Tables - Interactive.ipynb

@@ -0,0 +1,67 @@
+import datajoint as dj
+
+
+# fail-safe user name retrieval
+username = dj.conn().conn_info['user']
+schema = dj.schema('{}_tutorial_pipeline'.format(username))
+
+
+# Table definitions
+
+@schema
+class Mouse(dj.Manual):
+    definition = """
+    # Experimental animals
+    mouse_id             : int                          # Unique animal ID
+    ---
+    dob=null             : date                         # date of birth
+    sex="unknown"        : enum('M','F','unknown')      # sex
+    """
+
+
+@schema
+class Session(dj.Manual):
+    definition = """
+    # Experiment session
+    -> Mouse
+    session_date               : date                         # date
+    ---
+    experiment_setup           : int                          # experiment setup ID
+    experimenter               : varchar(100)                 # experimenter name
+    data_path=''               : varchar(255)                 #
+    """
+    
+
+# Insert the following data into the table
+    
+mouse_data = [
+ {'dob': "2017-03-01", 'mouse_id': 0, 'sex': 'M'},
+ {'dob': "2016-11-19", 'mouse_id': 1, 'sex': 'M'},
+ {'dob': "2016-11-20", 'mouse_id': 2, 'sex': 'unknown'},
+ {'dob': "2016-12-25", 'mouse_id': 5, 'sex': 'F'},
+ {'dob': "2017-01-01", 'mouse_id': 10, 'sex': 'F'},
+ {'dob': "2017-01-03", 'mouse_id': 11, 'sex': 'F'},
+ {'dob': "2017-05-12", 'mouse_id': 100, 'sex': 'F'}
+]
+
+session_data = [
+ {'experiment_setup': 0,
+  'experimenter': 'Edgar Y. Walker',
+  'mouse_id': 0,
+  'session_date': "2017-05-15"},
+ {'experiment_setup': 0,
+  'experimenter': 'Edgar Y. Walker',
+  'mouse_id': 0,
+  'session_date': "2017-05-19"},
+ {'experiment_setup': 1,
+  'experimenter': 'Fabian Sinz',
+  'mouse_id': 5,
+  'session_date': "2017-01-05"},
+ {'experiment_setup': 100,
+  'experimenter': 'Jacob Reimer',
+  'mouse_id': 100,
+  'session_date': "2017-05-25"}
+]
+
+Mouse.insert(mouse_data, skip_duplicates=True)
+Session.insert(session_data, skip_duplicates=True)