fix markdown formatting

docs/components.md

@@ -2,10 +2,10 @@
 
 This toolkit consists of three components.
 
-The first component ([](/src/rpft/parsers/common)) is RapidPro-agnostic and takes care of reader spreadsheets and converting them into internal data models and other output formats, see [](sheets.md)
+The first component ([/src/rpft/parsers/common]) is RapidPro-agnostic and takes care of reader spreadsheets and converting them into internal data models and other output formats, see [sheets.md]
 
-The second component ([](/src/rpft/parsers/creation)) defines data models for a spreadsheet format for RapidPro flows, and process spreadsheets into RapidPro flows (and back) using the first component.
+The second component ([/src/rpft/parsers/creation]) defines data models for a spreadsheet format for RapidPro flows, and process spreadsheets into RapidPro flows (and back) using the first component.
 
-The third component ([](/src/rpft/rapidpro)) defines internal representations of RapidPro flows and to read and write to a JSON format that can be import to/exported from RapidPro. It is partially entangled with the second component, as it needs to be aware of the data models of the second component to convert RapidPro flows into the spreadsheet format.
+The third component ([/src/rpft/rapidpro]) defines internal representations of RapidPro flows and to read and write to a JSON format that can be import to/exported from RapidPro. It is partially entangled with the second component, as it needs to be aware of the data models of the second component to convert RapidPro flows into the spreadsheet format.
 
-The latter two components are (poorly) documented here: [](rapidpro.md)
+The latter two components are (poorly) documented here: [rapidpro.md]

docs/models.md

@@ -1,13 +1,12 @@
 # Models
 
-`RowModel`s are subclasses of [`pydantic.BaseModel`]
-(https://docs.pydantic.dev/latest/concepts/models/#basic-model-usage), and may
+`RowModel`s are subclasses of [`pydantic.BaseModel`](https://docs.pydantic.dev/latest/concepts/models/#basic-model-usage), and may
 contain basic types, lists and other models as attributes, nested arbirarily
 deep. Every `Sheet` can only be parsed in the context of a given `RowModel`
 (which can, however, be automatically inferred from the sheet headers, if desired).
 
 Technically, there is no `RowModel` class, but instead it is called `ParserModel`
-and is defined in [](/src/rpft/parsers/common/rowparser.py). `ParserModel` attributes have to be
+and is defined in [/src/rpft/parsers/common/rowparser.py]. `ParserModel` attributes have to be
 basic types, lists or `ParserModel`s.
 The only addition to `pydantic.BaseModel` are the (optional) methods `header_name_to_field_name`, `field_name_to_header_name` and (for full row models) `header_name_to_field_name_with_context` that allow remapping
 column header names to different model attributes. 
@@ -46,9 +45,9 @@ How sheets and their column headers correspond to `RowModel`s is specified in
 
 More examples can also be found in the tests:
 
-- [](/src/rpft/parsers/common/tests/test_rowparser.py)
-- [](/src/rpft/parsers/common/tests/test_full_rows.py)
-- [](/src/rpft/parsers/common/tests/test_differentways.py)
+- [/tests/test_rowparser.py]
+- [/tests/test_full_rows.py]
+- [/tests/test_differentways.py]
 
 
 The `header_name_to_field_name` and related `ParserModel` methods can be used to map column headers to fields of a different name, for example:

docs/rapidpro.md

@@ -18,13 +18,13 @@ There are two sheet types of particular interest
 - [`DataSheet`](/src/rpft/parsers/creation/contentindexparser.py): Similar to a [`RowDataSheet`](sheets.md), however, assumed that the `RowModel` has an `ID` field, and rather than storing a list of rows, it stores an ordered dict of rows, indexed by their ID.
 - [`TemplateSheet`](/src/rpft/parsers/creation/contentindexparser.py): Wrapper around `tablib.Dataset`, with template arguments.
 
-Note: It may be worthwhile unifying the data structures used here, to be consistent with `Sheet` and `RowDataSheet` documented in [](sheets.md). Also see the discussion there why `DataSheet`s can be exported to nested JSON, while `TemplateSheet`s can only be exported to flat JSON.
+Note: It may be worthwhile unifying the data structures used here, to be consistent with `Sheet` and `RowDataSheet` documented in [sheets.md]. Also see the discussion there why `DataSheet`s can be exported to nested JSON, while `TemplateSheet`s can only be exported to flat JSON.
 
 `DataSheet`s are often used to instantiate `TemplateSheet`s, and the ContentIndexParser has mechanisms for this, see [New features documentation]. Furthermore, `DataSheet`s can also be concatenated, filtered and sorted via the `operation` column, see [here](https://docs.google.com/document/d/1Onx2RhNoWKW9BQvFrgTc5R5hcwDy1OMsLKnNB7YxQH0/edit#heading=h.c93jouk7sqq)
 
 
 
-Relevant code: `parse_all_flows` in [](/src/rpft/parsers/creation/contentindexparser.py).
+Relevant code: `parse_all_flows` in [/src/rpft/parsers/creation/contentindexparser.py].
 
 Examples:
 

docs/sheets.md

@@ -47,7 +47,7 @@ details of the three conversion steps below.
 
 ## Conversion between spreadsheet files and `Sheet`s
 
-SheetReaders and `Sheet`s are defined in [](/src/rpft/parsers/sheets.py)
+SheetReaders and `Sheet`s are defined in [/src/rpft/parsers/sheets.py]
 
 The `Sheet` class wraps [`tablib.Dataset`]
 (https://tablib.readthedocs.io/en/stable/api.html#dataset-object) (which is
@@ -95,7 +95,7 @@ instances. `RowModel`s are subclasses of [`pydantic.BaseModel`]
 (https://docs.pydantic.dev/latest/concepts/models/#basic-model-usage), and may
 contain basic types, lists and other models as attributes, nested arbirarily
 deep. How sheets and their column headers correspond to `RowModel`s is
-documented in more detail in [](models.md).
+documented in more detail in [models.md].
 
 
 ### Forward direction
@@ -127,7 +127,7 @@ row into a `RowModel` instance containing the provided data.
 corresponding entry of the spreadsheet in this row, and is provided
 by the `SheetParser`. Column headers determine which field of the
 model the column contains data for, and different ways to address fields
-in the data models are supported, see [](models.md).
+in the data models are supported, see [models.md].
 
 The `RowParser` interprets the column headers and if the column contains
 a non-basic type (e.g. a list or a submodel), it invokes the `CellParser`
@@ -148,7 +148,7 @@ Examples:
 - `a\;b` --> 'a;b'
 - `a,b|1,2` --> [['a','b'],['1','2']]
 
-More examples can be found in [](/tests/test_cellparser.py).
+More examples can be found in [/tests/test_cellparser.py].
 
 
 #### Templating
@@ -160,7 +160,7 @@ With a given templating context mapping variable names to values
 (e.g. {"user_name": "Chris"), such a string can be evaluated,
 e.g. to `Hello Chris!`.
 
-More examples can be found in [](/tests/test_cellparser.py).
+More examples can be found in [/tests/test_cellparser.py].
 
 
 ##### Instantiating templated sheets
@@ -253,7 +253,7 @@ or a reference to an already defined model).
 
 
 The CLI command `save_data_sheets` implements exporting all data sheets referenced in
-a content index as (a single) nested JSON. This is implemented in `save_data_sheets` in [](/src/rpft/converters.py), using the [`ContentIndexParser`](rapidpro.md). However, it its
+a content index as (a single) nested JSON. This is implemented in `save_data_sheets` in [/src/rpft/converters.py], using the [`ContentIndexParser`](rapidpro.md). However, it its
 own `DataSheet` class via its `to_dict` method. It would be good to unify `DataSheet`
 and `RowDataSheet`, and provide this as standalone functionality, once it's decided which meta-data describing the underlying model needs to be stored.
 
@@ -262,6 +262,8 @@ Below is some (untested) code outlining roughly how this could look like:
 
 ```python
 from converters import create_sheet_reader
+from rpft.parsers.common.model_inference import model_from_headers
+import importlib
 
 def convert_to_nested_json(input_file, sheet_format, user_data_model_module_name=None):
     """

src/rpft/converters.py

@@ -3,7 +3,6 @@
 import shutil
 from pathlib import Path
 
-from rpft.parsers.common.model_inference import model_from_headers
 from rpft.parsers.creation.contentindexparser import ContentIndexParser
 from rpft.parsers.creation.tagmatcher import TagMatcher
 from rpft.parsers.sheets import (