Update line numbers in modelling tutorials after header changes

doc/modelling/tutorial/hello_world.rst

@@ -143,9 +143,9 @@ for running code in TRAC, both on the platform and using the local development s
     :caption: src/tutorial/hello_world.py
     :name: hello_world_py_part_1
     :language: python
-    :lines: 15 - 20
+    :lines: 16 - 20
     :linenos:
-    :lineno-start: 15
+    :lineno-start: 16
 
 The model can define any parameters it is going to need. In this example there is only a
 single parameter so it can be declared in code (more complex models may wish to manage
@@ -155,9 +155,9 @@ are defined in the correct format.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/hello_world.py
     :name: hello_world_py_part_2
     :language: python
-    :lines: 21 - 27
+    :lines: 22 - 27
     :linenos:
-    :lineno-start: 21
+    :lineno-start: 22
 
 The model can also define inputs and outputs. In this case since all we are going to do
 is write a message in the log, no inputs and outputs are needed. Still, these methods are
@@ -166,9 +166,9 @@ required in order for the model to be valid.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/hello_world.py
     :name: hello_world_py_part_3
     :language: python
-    :lines: 28 - 33
+    :lines: 29 - 33
     :linenos:
-    :lineno-start: 28
+    :lineno-start: 29
 
 To write the model logic, implement the :py:meth:`run_model() <tracdap.rt.api.TracModel.run_model>` method.
 When :py:meth:`run_model() <tracdap.rt.api.TracModel.run_model>` is called it receives a
@@ -178,9 +178,9 @@ TRAC platform.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/hello_world.py
     :name: hello_world_py_part_4
     :language: python
-    :lines: 34 - 40
+    :lines: 35 - 40
     :linenos:
-    :lineno-start: 34
+    :lineno-start: 35
 
 There are two useful features of :py:class:`TracContext <tracdap.rt.api.TracContext>`
 that can be seen in this example:
@@ -191,7 +191,7 @@ that can be seen in this example:
         datasets. Log outputs are available even if a job fails so they can be used for debugging.
 
     *   :py:meth:`get_parameter() <tracdap.rt.api.TracContext.get_parameter>` allows models to access any
-        parameters defined in the :py:meth:`define_parameters()<tracdap.rt.api.TracModel.define_parameters>`
+        parameters defined in the :py:meth:`define_parameters() <tracdap.rt.api.TracModel.define_parameters>`
         method. They are returned as native Python objects, so integers use the Python integer type,
         date and time values use the Python datetime classes and so on.
 
@@ -243,9 +243,9 @@ this, but the model will fail to deploy)!
     :caption: src/tutorial/hello_world.py
     :name: hello_world_py_launch
     :language: python
-    :lines: 42-
+    :lines: 43-
     :linenos:
-    :lineno-start: 42
+    :lineno-start: 43
 
 Paths for the system and job config files are resolved in the following order:
 

doc/modelling/tutorial/inputs_and_outputs.rst

@@ -23,19 +23,19 @@ Here is an example of defining an optional input, using schemas read from schema
     :caption: src/tutorial/optional_io.py
     :language: python
     :name: optional_io_part_1
-    :lines: 38 - 48
+    :lines: 39 - 49
     :linenos:
-    :lineno-start: 38
+    :lineno-start: 39
 
 Schemas defined in code can also be marked as optional, let's use that approach to define an
 optional output:
 
 .. literalinclude:: ../../../examples/models/python/src/tutorial/optional_io.py
     :language: python
     :name: optional_io_part_2
-    :lines: 50 - 66
+    :lines: 51 - 67
     :linenos:
-    :lineno-start: 50
+    :lineno-start: 51
 
 Now let's see how to use optional inputs and outputs in :py:meth:`run_model() <tracdap.rt.api.TracModel.run_model>`.
 Since the input is optional we will need to check if it is available before we can use it.
@@ -47,9 +47,9 @@ dataset with some stats on the filtered accounts. Here is what that looks like:
 .. literalinclude:: ../../../examples/models/python/src/tutorial/optional_io.py
     :language: python
     :name: optional_io_part_3
-    :lines: 76 - 85
+    :lines: 77 - 86
     :linenos:
-    :lineno-start: 76
+    :lineno-start: 77
 
 In this example the optional output is only produced when the optional input is
 supplied - that is not a requirement and the model can decide whether to
@@ -106,9 +106,9 @@ some basic information about the schema and content:
     :caption: src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_schema_inspection_1
-    :lines: 21 - 39
+    :lines: 22 - 40
     :linenos:
-    :lineno-start: 21
+    :lineno-start: 22
 
 The source data is defined as a dynamic input. Notice that there are no fields in the
 schema definition - dynamic inputs or outputs cannot define any fields, doing so will
@@ -122,9 +122,9 @@ Now let's see how to use these datasets in the model.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_schema_inspection_2
-    :lines: 41 - 60
+    :lines: 42 - 61
     :linenos:
-    :lineno-start: 41
+    :lineno-start: 42
 
 The model gets the source dataset as normal, but it also gets the schema of the dataset using
 :py:meth:`get_schema() <tracdap.rt.api.TracContext.get_schema>` which returns aTRAC
@@ -185,9 +185,9 @@ Let's see how to define this model:
 .. literalinclude:: ../../../examples/models/python/src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_data_generation_1
-    :lines: 63 - 80
+    :lines: 64 - 81
     :linenos:
-    :lineno-start: 63
+    :lineno-start: 64
 
 In real life more parameters would often be needed to control the generated data, e.g. range limits
 or distribution parameters, but for this simple example those are not needed.
@@ -197,9 +197,9 @@ Now let's look at the model code:
 .. literalinclude:: ../../../examples/models/python/src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_data_generation_2
-    :lines: 82 - 100
+    :lines: 83 - 101
     :linenos:
-    :lineno-start: 82
+    :lineno-start: 83
 
 The model creates a :py:class:`SchemaDefinition <tracdap.rt.metadata.SchemaDefinition>` and adds a
 :py:class:`FieldSchema <tracdap.rt.metadata.FieldSchema>` for each column. The same helper functions
@@ -218,7 +218,7 @@ as part of the call to :py:meth:`put_schema() <tracdap.rt.api.TracContext.put_sc
 
 .. note::
     Calling :py:meth:`put_schema() <tracdap.rt.api.TracContext.put_schema>` creates a copy of
-    the TRAC schema object. Any changes made to the schema after it is saved will noo be picked
+    the TRAC schema object. Any changes made to the schema after it is saved will not be picked
     up by TRAC. Calling :py:meth:`get_schema() <tracdap.rt.api.TracContext.get_schema>` after
     a schema has been set will always return the schema as it was saved.
 
@@ -237,19 +237,19 @@ Let's see an example model definition that can help us do that:
 .. literalinclude:: ../../../examples/models/python/src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_dynamic_filtering_1
-    :lines: 103 - 117
+    :lines: 104 - 118
     :linenos:
-    :lineno-start: 63
+    :lineno-start: 104
 
 Here both the input and the output are dynamic, and the model is controlled only by the two filter
 parameters. Now let's see the implementation:
 
 .. literalinclude:: ../../../examples/models/python/src/tutorial/dynamic_io.py
     :language: python
     :name: dynamic_io_dynamic_filtering_2
-    :lines: 119 - 130
+    :lines: 120 - 131
     :linenos:
-    :lineno-start: 130
+    :lineno-start: 120
 
 The original input schema is used directly as the output schema, so the schema of the input
 and output will be the same.

doc/modelling/tutorial/using_data.rst

@@ -24,9 +24,9 @@ the top-level class or function as parameters, as shown in this example.
     :caption: src/tutorial/using_data.py
     :name: using_data_py_part_1
     :language: python
-    :lines: 21 - 48
+    :lines: 22 - 49
     :linenos:
-    :lineno-start: 21
+    :lineno-start: 22
 
 
 Defining model requirements
@@ -38,9 +38,9 @@ so we can use the same syntax. We'll define the three parameters needed by the m
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_2
     :language: python
-    :lines: 51-65
+    :lines: 52-66
     :linenos:
-    :lineno-start: 51
+    :lineno-start: 52
 
 The example model function has one data input, which is a table called *customer_loans*.
 The function :py:func:`define_output_table() <tracdap.rt.api.define_output_table>` in the
@@ -79,9 +79,9 @@ lenient type handling for input files.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_3
     :language: python
-    :lines: 67 - 76
+    :lines: 68 - 77
     :linenos:
-    :lineno-start: 67
+    :lineno-start: 68
 
 To define the model outputs we can use :py:func:`define_output_table() <tracdap.rt.api.define_output_table>`,
 which is identical to :py:func:`define_input_table() <tracdap.rt.api.define_input_table>` save for the fact it
@@ -93,9 +93,9 @@ Models are free to define multiple outputs if required, but this example only ha
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_4
     :language: python
-    :lines: 78 - 84
+    :lines: 79 - 85
     :linenos:
-    :lineno-start: 78
+    :lineno-start: 79
 
 Now the parameters, inputs and outputs of the model are defined, we can implement the
 :py:meth:`run_model() <tracdap.rt.api.TracModel.run_model>` method.
@@ -115,9 +115,9 @@ schema for this input.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_5
     :language: python
-    :lines: 86 - 92
+    :lines: 87 - 93
     :linenos:
-    :lineno-start: 86
+    :lineno-start: 87
 
 Once all the inputs and parameters are available, we can call the model function. Since all the inputs
 and parameters are supplied using the correct native types there is no further conversion necessary,
@@ -126,9 +126,9 @@ they can be passed straight into the model code.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_6
     :language: python
-    :lines: 94 - 96
+    :lines: 95 - 97
     :linenos:
-    :lineno-start: 94
+    :lineno-start: 95
 
 The model code has produced a Pandas dataframe that we want to record as an output. To do this, we can use
 :py:meth:`put_pandas_table() <tracdap.rt.api.TracContext.put_pandas_table>`. The dataframe should match
@@ -147,18 +147,18 @@ columns will be dropped.
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_7
     :language: python
-    :lines: 98
+    :lines: 99
     :linenos:
-    :lineno-start: 98
+    :lineno-start: 99
 
 The model can be launched locally using :py:func:`launch_model() <tracdap.rt.launch.launch_model()>`.
 
 .. literalinclude:: ../../../examples/models/python/src/tutorial/using_data.py
     :name: using_data_py_part_8
     :language: python
-    :lines: 101-103
+    :lines: 102-104
     :linenos:
-    :lineno-start: 101
+    :lineno-start: 102
 
 Configure local data
 --------------------
@@ -265,9 +265,9 @@ Now we can re-write our model to use the new schema files. First we need to impo
     :caption: src/tutorial/schema_files.py
     :name: using_data_part_9
     :language: python
-    :lines: 19
+    :lines: 20
     :linenos:
-    :lineno-start: 19
+    :lineno-start: 20
 
 Then we can load schemas from the schemas package in the
 :py:meth:`define_inputs() <tracdap.rt.api.TracModel.define_inputs>` and
@@ -276,9 +276,9 @@ Then we can load schemas from the schemas package in the
 .. literalinclude:: ../../../examples/models/python/src/tutorial/schema_files.py
     :name: using_data_part_10
     :language: python
-    :lines: 46 - 56
+    :lines: 47 - 57
     :linenos:
-    :lineno-start: 46
+    :lineno-start: 47
 
 Notice that the :py:func:`load_schema() <tracdap.rt.api.load_schema>` method is the same
 for input and output schemas, so we need to use