#!/usr/bin/env python
# coding: utf-8

# # Installation

# Note: If you are on Binder, you don't need to execute the following command.

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get_ipython().system('pip install nexusforge[linking_sklearn]')


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import os 

def full_path_relative_to_root(path): 
    return os.path.join(os.path.abspath('../../..'), path)


# # Configuration
# 
# This notebook presents a set of configuation options to set up when creating a knowledge graph forge session. Refer to the [Nexus Forge docs](https://nexus-forge.readthedocs.io/en/latest/interaction.html#forge) to learn more about all the possible configuration options.

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config = dict()


# ## Configure for Demo
# This configuration is for testing Nexus Forge features without using or deploying a persistent store. Not all features are  accessible with the demo configuration. The demo configuration is therefore not recommendeded for production use. 

# ### Model

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config['Model'] = {
    "name": "DemoModel",
    "origin": "directory",
    "source": full_path_relative_to_root("tests/data/demo-model/"),
}


# ### Store

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config["Store"] = {
    "name": "DemoStore",
    "model": {"name": "DemoModel"},
    "versioned_id_template": "{x.id}?_version={x._store_metadata.version}"
}


# ### Resolvers

# #### sourced from a directory

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config["Resolvers"] = {
    "terms": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "sexontology",
                    "bucket": "sex.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root("examples/configurations/demo-resolver/term-to-resource-mapping.hjson")
        }
    ],
    "entities": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "agents",
                    "bucket": "agents.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root("examples/configurations/demo-resolver/entity-to-resource-mapping.hjson")
        }
    ],
    "schemaorg": [
        {
            "resolver": "EntityLinkerSkLearn from kgentitylinkingsklearn",
            "origin": "directory",
            "source": full_path_relative_to_root("examples/data/"),
            "targets": [
                {
                    "identifier": "terms",
                    "bucket": "tfidfvectorizer_model_schemaorg_linking"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root("examples/configurations/entitylinking-resolver/entitylinking-mapper.hjson")
        }
    ]
}


# ## Configure Nexus Forge to use with [Blue Brain Nexus Delta](https://bluebrainnexus.io/docs/delta/api/current/index.html) as a Store

# ### Create a project to work with in the BlueBrainNexus sandbox

# #### Get a token

# The [Nexus sandbox application](https://sandbox.bluebrainnexus.io/web) can be used to login and get a token.
# 
# - Step 1: From the opened web page, click on the login button on the right corner and follow the instructions.
# 
# ![login-ui](https://raw.githubusercontent.com/BlueBrain/nexus-forge/master/examples/notebooks/use-cases/login-ui.png)
# 
# - Step 2: At the end you’ll see a token button on the right corner. Click on it to copy the token.
# 
# ![login-ui](https://raw.githubusercontent.com/BlueBrain/nexus-forge/master/examples/notebooks/use-cases/copy-token.png)
# 

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import getpass

token = getpass.getpass(prompt="Token")


# #### Set a project name

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endpoint = "https://sandbox.bluebrainnexus.io/v1"
org ="github-users"

project = getpass.getpass(prompt="Github username") # Provide here the automatically created project name corresponding to your Github login when you logged in the Nexus sandbox instance.


# ### Model

# #### RDFModel

# This model supports the W3C SHACL schema language. Let use examples of SHACL schemas from https://github.com/INCF/neuroshapes. SHACL schemas can be loaded either from a directory or from a store. 

# ##### sourced from BlueBrainNexus store

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shacl_schema_bucket = "neurosciencegraph/datamodels"


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config['Model'] = {
    "name": "RdfModel",
    "origin": "store",
    "source": "BlueBrainNexus",
    "context": {
        "iri": "https://bbp.neuroshapes.org",
        "bucket": shacl_schema_bucket
    }
}


# For the following tutorials please keep the following Model configuration:

# ##### sourced from a directory

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neuroshapes_path = full_path_relative_to_root("examples/models/neuroshapes")


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get_ipython().system(' rm -Rf $neuroshapes_path')


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get_ipython().system(' git clone https://github.com/INCF/neuroshapes.git $neuroshapes_path')


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get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/dataset $neuroshapes_path/shapes/neurosciencegraph/commons/')
get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/activity $neuroshapes_path/shapes/neurosciencegraph/commons/')
get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/entity $neuroshapes_path/shapes/neurosciencegraph/commons/')
get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/ontology $neuroshapes_path/shapes/neurosciencegraph/commons/')
get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/person $neuroshapes_path/shapes/neurosciencegraph/commons/')
get_ipython().system(' cp -R $neuroshapes_path/shapes/neurosciencegraph/datashapes/core/contribution $neuroshapes_path/shapes/neurosciencegraph/commons/')


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config['Model'] = {
    "name": "RdfModel",
    "origin": "directory",
    "source": f"{neuroshapes_path}/shapes/neurosciencegraph/commons/",
    "context": {
        "iri": full_path_relative_to_root("examples/models/neuroshapes_context.json")
    },
}


# ### Store

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config["Store"] = {
    "name": "BlueBrainNexus",
    "endpoint": endpoint,
    "model": {"name": "RdfModel"}, 
    "searchendpoints":{
        "sparql":{
            "endpoint":"https://bluebrain.github.io/nexus/vocabulary/defaultSparqlIndex"
        },
        "elastic":{
            "endpoint":"https://bluebrain.github.io/nexus/vocabulary/defaultElasticSearchIndex"
        }
    },
    "bucket": f"{org}/{project}",
    "token": token,
    "vocabulary":{
        "metadata":{
            "iri": "https://bluebrain.github.io/nexus/contexts/metadata.json",
           "local_iri": "https://bluebrainnexus.io/contexts/metadata.json"
        }, 
        "namespace": "https://bluebrain.github.io/nexus/vocabulary/",
        "deprecated_property": "https://bluebrain.github.io/nexus/vocabulary/deprecated",
        "project_property": "https://bluebrain.github.io/nexus/vocabulary/project"
    },
    "max_connection": 50,
    "versioned_id_template": "{x.id}?rev={x._store_metadata._rev}",
    "file_resource_mapping": full_path_relative_to_root(
        "examples/configurations/nexus-store/file-to-resource-mapping.hjson"
    )
}


# ### Resolvers

# #### sourced from a store

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ontology_bucket = "neurosciencegraph/datamodels"


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config["Resolvers"] = {
    "terms": [
        {
            "resolver": "OntologyResolver",
            "origin": "store",
            "source": "BlueBrainNexus",
            "targets": [
                {
                    "identifier": "sexontology",
                    "bucket": ontology_bucket
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/nexus-resolver/term-to-resource-mapping.hjson"
            )
        }
    ],
    "ontology": [
        {
            "resolver": "OntologyResolver",
            "origin": "store",
            "source": "BlueBrainNexus",
            "targets": [
                {
                    "identifier": "cells",
                    "bucket": ontology_bucket
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/demo-resolver/term-to-resource-mapping.hjson"
            )
        }
    ],
    "entities": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "agents",
                    "bucket": "agents.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/demo-resolver/entity-to-resource-mapping.hjson"
            )
        }
    ],
    "schemaorg": [
        {
            "resolver": "EntityLinkerSkLearn from kgentitylinkingsklearn",
            "origin": "directory",
            "source": full_path_relative_to_root("examples/data/"),
            "targets": [
                {
                    "identifier": "terms",
                    "bucket": "tfidfvectorizer_model_schemaorg_linking"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/entitylinking-resolver/entitylinking-mapper.hjson"
            )
        }
    ]
}


# #### sourced from a directory

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config["Resolvers"] = {
    "terms": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "sexontology",
                    "bucket": "sex.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/demo-resolver/term-to-resource-mapping.hjson"
            )
        }
    ],
    "ontology": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "cells",
                    "bucket": "cell_types.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/demo-resolver/term-to-resource-mapping.hjson"
            )
        }
    ],
    "entities": [
        {
            "resolver": "DemoResolver",
            "origin": "directory",
            "source": full_path_relative_to_root("tests/data/demo-resolver/"),
            "targets": [
                {
                    "identifier": "agents",
                    "bucket": "agents.json"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/demo-resolver/entity-to-resource-mapping.hjson"
            )
        }
    ],
    "schemaorg": [
        {
            "resolver": "EntityLinkerSkLearn from kgentitylinkingsklearn",
            "origin": "directory",
            "source": full_path_relative_to_root("examples/data/"),
            "targets": [
                {
                    "identifier": "terms",
                    "bucket": "tfidfvectorizer_model_schemaorg_linking"
                }
            ],
            "result_resource_mapping": full_path_relative_to_root(
                "examples/configurations/entitylinking-resolver/entitylinking-mapper.hjson"
            )
        }
    ]
}


# ## Configure formatters

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config["Formatters"] = {
    "identifier": "https://kg.example.ch/{}/{}",
}


# ## Save configuration

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import yaml


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with open(full_path_relative_to_root("examples/configurations/forge.yml"), "w") as f:
    yaml.dump(config, f)