Simplify documentation of datamodels and usage of plugins

doc/user_guide/concepts.md

@@ -269,83 +269,6 @@ Relations are currently not explored in metadata, but are included because of
 their generality.
 However, relations are heavily used in [collections].
 
-
-### Representing an entity
-Lets start to make a "Person" entity, where we want to describe his/her name, age and skills.
-
-```json
-{
-  "uri": "http://onto-ns.com/meta/0.1/Person",
-  "meta": "http://onto-ns.com/meta/0.3/EntitySchema",
-  "description": "A person.",
-  "dimensions": [
-    {
-      "name": "N",
-      "description": "Number of skills."
-    }
-  ],
-  "properties": [
-    {
-      "name": "name",
-      "type": "string",
-      "description": "Full name."
-    },
-    {
-      "name": "age",
-      "type": "float",
-      "unit": "years",
-      "description": "Age of person."
-    },
-    {
-      "name": "skills",
-      "type": "string",
-      "shape": ["N"],
-      "description": "List of skills."
-    }
-  ]
-}
-```
-
-First we have "uri" identifying the entity, "meta" telling that this is an instance of the entity schema (hence an entity) and a human description.
-Then comes "dimensions".
-In this case one dimension named "N", which is the number of skills the person has.
-Finally we have the properties; "name", "age" and "skills".
-We see that "name" is represented as a string, "age" as a floating point number with unit years and "skills" as an array of strings, one for each skill.
-
-
-### SOFT7 representation
-Based on input from [SOFT7], DLite also supports a slightly shortened representation of entities.
-The "Person" entity from the above example will in this representation, look like:
-
-```json
-{
-  "uri": "http://onto-ns.com/meta/0.1/Person",
-  "description": "A person.",
-  "dimensions": {
-    "N": "Number of skills."
-  },
-  "properties": {
-    "name": {
-      "type": "string",
-      "description": "Full name."
-    },
-      "age": {
-      "type": "float",
-      "unit": "years",
-      "description": "Age of person."
-    },
-      "skills": {
-      "type": "string",
-      "shape": ["N"],
-      "description": "List of skills."
-    }
-  }
-}
-```
-
-In this representation defaults the `meta` field to the entity schema if it is left out.
-Dimensions and Properties are dictionaries (JSON objects) instead of arrays with the dimension or property name as key.
-
 references
 ----------
 

doc/user_guide/datamodels.md

@@ -0,0 +1,69 @@
+Representing a datamodel (entity)
+----------------------------------
+
+The underlying structure of DLite datamodels are described under [concepts].
+
+Here, at set of rules on how to create a datamodel is presented.
+
+Note that several other possibilities are avilable, and this can be seen in the
+examples and tests present in the repository. 
+
+We choose here to present only one method as mixing repsentation methods might 
+be confusing. Note, however that yaml and json representations are interchangable.
+
+A generic example with some comments for clarity can be seen below.
+
+```yaml
+uri: http://namespace/version/name
+description: A description of what this datamodel represents.
+dimensions:  # Named dimensions referred to in the property shapes. Simplest to represent it as a dict, set to {} if there are no dimensions
+  name_of_dimension: description of dimension
+properties:
+  name_of_property1:
+    description: What is this property
+    type: ref # Can be any on string, float, double, int, ref ....
+    unit: unit # can be ommitted, not relevant with type ref
+    shape: [name_of_dimension]  # Can be omitted if the property is a scalar
+    $ref:  http://namespace/version/name_of_referenceddatamodel # only if type is ref
+```
+
+A slightly more realistic example is the "Person" entity, where we want to describe his/her name, age and skills.
+
+```yaml
+uri: http://onto-ns.com/meta/0.1/Person
+description: A person.
+dimensions: 
+  N: Number of skills.
+properties:
+  name:
+    description: Full name.
+    type: string
+  age:
+    description: Age of person.
+    type: float
+    unit: years
+  skills:
+    description: List of skills.
+    type: string
+    shape: [N]
+```
+
+First we have "uri" identifying the entity, and a human description.
+Then comes "dimensions".In this case one dimension named "N", which is the number of skills the person has.
+Finally we have the properties; "name", "age" and "skills".
+We see that "name" is represented as a string, "age" as a floating point number with unit years and "skills" as an array of strings, one for each skill.
+
+
+dlite-validate
+==============
+The dlite-validate tool can be used to check if a specific representation (in a file) is a valid DLite datamodel
+
+This can be run as follows
+```bash
+dlite-validate filename.yaml # or json
+```
+
+It will then return a list of errors if it is not a valid representation.
+
+
+[concepts]: https://sintef.github.io/dlite/user_guide/concepts.html

doc/user_guide/index.rst

@@ -6,6 +6,7 @@ User Guide
    :caption: Contents
 
    concepts
+   datamodels
    type-system
    exceptions
    collections

doc/user_guide/storage_plugins.md

@@ -1,5 +1,5 @@
-Storage plugins
-===============
+Storage plugins / Drivers
+=========================
 
 Content
 -------
@@ -28,6 +28,37 @@ It also comes with a specific `Blob` and `Image` storage plugin, that can load a
 Storage plugins can be written in either C or Python.
 
 
+How to make storage plugins available
+-------------------------------------
+
+As described below it is possible (and most often advisable) to create specific drivers (storage plugins) for your data.
+Additional storage plugins drivers can be made available by setting the environment variables
+`DLITE_STORAGE_PLUGIN_DIRS` or `DLITE_PYTHON_STORAGE_PLUGIN_DIRS` e.g.:
+```bash
+export DLITE_STORAGE_PLUGIN_DIRS=/path/to/new/folder:$DLITE_STORAGE_PLUGIN_DIRS
+```
+
+Within python, the path to the directory containing plugins can be added as follows:
+
+```python
+import dlite
+dlite.python_storage_plugin_path.append("/path/to/plugins/dir")
+```
+
+Often drivers are connected to very specific datamodel (entities). 
+DLite will find these datamodels if the path to their directory is set with the
+environment variable `DLITE_STORAGES` or added within python with `dlite.storage_path.append` similarly to described above for drivers.
+
+
+IMPORTANT:
+Often, during development dlite will fail unexpectedly. This is typically either because of an error in the 
+datamodel or the driver. 
+The variable DLITE_PYDEBUG can be set as `export DLITE_PYDEBUG=` to get python debugging information. 
+This will give information about the driver.
+But is is advisable to first check that the datamodel is valid with the command `dlite-validate datamodelfilename`.
+
+
+
 Using storages implicitly from Python
 -------------------------------------
 For convenience DLite also has an interface for creating storages implicitly.