Remove MATLAB examples and references to API docs

Author: kabilar

docs/src/compute/make.md

@@ -9,8 +9,7 @@ entered using the `insert` method directly.
 [Automation](../tabletiers#automation-imported-and-computed).
 
 The `make` method receives one argument: the *key*, which represents the upstream table
-entries that need populating. The `key` is a `dict` or `struct` in Python and Matlab,
-respectively. 
+entries that need populating. The `key` is a `dict` in Python. 
 
 A `make` function should do three things:
 
@@ -30,20 +29,6 @@ the class method called to run the `make` method on all relevant keys[^2].
 [^2]: For information on reprocessing keys that resulted in an error, see information
 on the [Jobs table](../../ref-integrity/distributed-computing). 
 
-=== "Python"
-
-    ``` python
-    Segmentation.populate()
-    ```    
-
-=== "Matlab"
-
-    In Matlab, the `key` is a `struct`. 
-    ``` matlab
-    populate(Segmentation)
-    ```
-
-For more information on the `populate` options in each language, please visit the 
-[Python and Matlab](../../../) documentation pages.
-
-<!-- TODO: Replace above with respective links when live -->
+``` python
+Segmentation.populate()
+```    

docs/src/design/diagrams.md

@@ -6,8 +6,7 @@ some minor departures fom this standard.
 
 Here, tables are depicted as nodes and [dependencies](../dependencies) as directed edges
 between them. The `draw` method plots the graph, with many other methods (
-[Python](https://datajoint.com/docs/core/datajoint-python/latest/api/datajoint/diagram/),
-[Matlab](https://github.com/datajoint/datajoint-matlab/blob/master/%2Bdj/ERD.m)) to
+[Python](https://datajoint.com/docs/core/datajoint-python/latest/api/datajoint/diagram/)) to
 save or adjust the output.
 
 Because DataJoint pipelines are directional (see [DAG](../../../glossary#dag)), the
@@ -23,8 +22,7 @@ DataJoint uses the following conventions:
 -   [Tables](../table-definitions) are indicated as nodes in the graph. The
     corresponding class name is indicated by each node.
 
--   [Table type](../../reproduce/table-tiers) is indicated by colors and symbols, with some
-    differences across Python and Matlab: 
+-   [Table type](../../reproduce/table-tiers) is indicated by colors and symbols: 
 
     - **Lookup**: gray, rectangle or asterisk
 
@@ -75,6 +73,3 @@ Here, we see ...
 6. Several Computed tables: *Segmentation*, *Trace*, and *RF*
 
 7. A part table: *Field*
- 
-For examples calling `Diagram` in Python and Matlab, please visit the documentation for
-the respective API.

docs/src/design/tables/tiers.md

@@ -32,9 +32,7 @@ Lookup tables contain basic facts that are not specific to an experiment and are
 persistent. In GUIs, lookup tables are often used for drop-down menus or radio buttons.
 In Computed tables, the contents of Lookup tables are often used to specify alternative
 methods for computations. Unlike Manual tables, Lookup tables can specify contents in
-the schema definition. For syntax, please visit the 
-[Python](https://datajoint.com/docs/core/datajoint-python/) and 
-[Matlab](https://datajoint.com/docs/core/datajoint-matlab/) documentation pages.
+the schema definition.
 
 Lookup tables are especially useful for entities with many unique features. Rather than
 adding many primary keys, this information can be retrieved through an index. For an

docs/src/manipulation/insert.md

@@ -49,8 +49,6 @@ call.
 data = query.fetch()
 ```
 
-For more examples in Python or Matlab, please visit the respective API documentation. 
-
 ## Drop
 
 The `drop` method completely removes a table from the database, including its
@@ -78,4 +76,3 @@ dj.Diagram(schema).draw()
 ```
 
 For more information about diagrams, see [this article](../../getting-started/diagrams).
-For more examples in Python or Matlab, please visit the respective API documentation. 

docs/src/query/fetch.md

@@ -10,53 +10,29 @@
 For example, if given a `Session` table, you can
 create a query object to retrieve its entire contents as follows:
 
-=== "Python"
-
-    ``` python
-    query  = Session()
-    ```
-
-=== "Matlab"
-
-    ``` matlab
-    query = Session;
-    ```
+``` python
+query  = Session()
+```
 
 More generally, a query object may be formed as a **query expression**
 constructed by applying [operators](./operators.md) to other query objects.
 
 For example, the following query retrieves information about all
 experiments and scans for mouse 001:
 
-=== "Python"
-
-    ``` python
-    query = Session * Scan & 'animal_id = 001'
-    ```
-
-    Note that for brevity, query operators can be applied directly to class, as
-    `Session` instead of `Session()`.
-
-=== "Matlab"
+``` python
+query = Session * Scan & 'animal_id = 001'
+```
 
-    ``` matlab
-    query = Session * Scan & 'animal_id = 001';
-    ```
+Note that for brevity, query operators can be applied directly to class, as
+`Session` instead of `Session()`.
 
 Alternatively, we could query all scans with a sample rate over 1000, and preview the
 contents of the query simply displaying the object. 
 
-=== "Python"
-
-    ``` python
-    Scan & 'sample_rate > 1000'
-    ```
-
-=== "Matlab"
-
-    ``` matlab
-    Session * Scan & 'sample_rate > 1000';
-    ```
+``` python
+Scan & 'sample_rate > 1000'
+```
 
 The above command shows the following table:
 
@@ -81,25 +57,13 @@ Once the desired query object is formed, the query can be executed using its [fe
 (./fetch) methods. To **fetch** means to transfer the data represented by the query
 object from the database server into the workspace of the host language.
 
-=== "Python"
-    
-    ```python
-    query = Scan & 'sample_rate > 1000'
-    s = query.fetch()
-    ```
-
-    Here fetching from the `query` object produces the NumPy record array
-    `s` of the queried data.
+```python
+query = Scan & 'sample_rate > 1000'
+s = query.fetch()
+```
 
-=== "Matlab"
-
-    ``` matlab
-    query = Session * Scan & 'sample_rate > 1000';
-    s = query.fetch();
-    ```
-
-    Here fetching from the `query` object produces the struct array `s` of
-    the queried data.
+Here fetching from the `query` object produces the NumPy record array
+`s` of the queried data.
 
 ## Checking for entities
 
@@ -109,31 +73,16 @@ returned. It can be useful to know the number of entities returned by a query, o
 whether a query will return any entities at all, without having to fetch all the data
 themselves.
 
-=== "Python"
-
-    The `bool` function applied to a query object evaluates to `True` if the
-    query returns any entities and to `False` if the query result is empty.
+The `bool` function applied to a query object evaluates to `True` if the
+query returns any entities and to `False` if the query result is empty.
 
-    The `len` function applied to a query object determines the number of
-    entities returned by the query.
+The `len` function applied to a query object determines the number of
+entities returned by the query.
 
-    ``` python
-    # number of sessions since the start of 2018.
-    n = len(Session & 'session_date >= "2018-01-01"')
-    ```
-
-=== "Matlab"
-
-    The `exists` method applied to a query object evaluates to `true` if the
-    query returns any entities and to `false` if the query result is empty.
-
-    The `count` method applied to a query object determines the number of
-    entities returned by the query.
-
-    ``` matlab
-    % number of ephys sessions since the start of 2018.
-    n = count(ephys.Session & 'session_date >= "2018-01-01"')
-    ```
+``` python
+# number of sessions since the start of 2018.
+n = len(Session & 'session_date >= "2018-01-01"')
+```
 
 ## Normalization in queries
 

docs/src/query/operators.md

@@ -120,9 +120,6 @@ The condition `cond` may be one of the following:
     -   a `Not` object
     -   a query expression
 
-For more examples on each of these in Python and Matlab, please visit the documentation
-for the respective API.
-
 ??? Warning "Permissive Operators"
 
     To circumvent compatibility checks, DataJoint offers permissive operators for 
@@ -144,9 +141,6 @@ another the coordinator, both referencing the common personnel pool.
 3. Projection can also perform calculations (as available in 
 [MySQL](https://dev.mysql.com/doc/refman/5.7/en/functions.html)) on a single attribute.
 
-For examples of each of these in Python and Matlab, please visit the documentation for
-the respective API.
-
 ## Aggr
 
 **Aggregation** is a special form of `proj` with the added feature of allowing
@@ -156,8 +150,7 @@ the respective API.
   in the matching entities of `other`.
 
 Aggregation functions include `count`, `sum`, `min`, `max`, `avg`, `std`, `variance`,
-and others. For examples in Python and Matlab, please visit the documentation for the
-respective API.
+and others.
 
 ## Union
 
@@ -221,9 +214,6 @@ Universal sets should be used sparingly when no suitable base tables already exi
 some cases, defining a new base table can make queries clearer and more semantically
 constrained.
 
-For examples in Python and Matlab, please visit the documentation for the respective
-API.
-
 The examples below will use the table definitions in [table tiers](../../reproduce/table-tiers).
 
 <!-- ## Join appears here in the general docs -->