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

# # Practical introduction to RDF and SPARQL

# ## Reminder: IRIs and Literals
# 
# **Literals** refer two simple values (numerical values, strings, boolean, dates, etc.)
# 
# **Resource** refers to complex objects identified by an **IRI** (International Resource Identifier == URI allowing international characters). Note that URLs are IRIs pointing to web accessible documents/data. URIs can be shortened with **PREFIX**. As an example `<http://my/super/vocab/my_term>` can be shortened as `ns:my_term` if `ns` is defined as a prefix for `http://my/super/vocab/`. 
# 
# 
# ## Reminder: RDF, triples
# 1. an RDF **statement** represents a **relationship** between two resources: a **subject** and an **object**
# 1. relationships are directional and are called a **predicates** (or RDF properties)
# 1. (logical) statements are called **triple** : {`subject`, `predicate`, `object`}
# 1. a set of triples form a **directed labelled graph** : subject nodes are IRIs, edges are predicate (IRIs only),  object nodes are IRIs or Literals. 
# 
# Go through https://www.w3.org/TR/rdf11-primer/ to have more details on RDF. 
# 
# ## Reminder: Turtle syntax
# - header to define prefix
#    - example: with `@prefix ns: http://my_voc# .`, `http://my_voc#term` can be written as `ns:term` 
# - generally one line per triple with a `.` at the end: `<subject> <predicate> <object> .`
# - possible shortcuts to share the same subject: `;` 
# ```
# s p1 o1 ; 
#     p2 o2 .
# ```
# - possible shortcuts to share the same subject-predicate: `,` 
# ```
# s p o1, o2, o3 .
# ```
# 
# ## Example
# turtle syntax: 
# ```ruby
# <http://HG37> rdf:type <http://human_genome> .
# <http://sample1> <http://is_aligned_with> <http://HG37> .
# <http://sample1> rdfs:comment "Sample 1 from Study X [...]"^^xsd:string .
# ```
# 
# or 
# 
# ```turtle
# <http://HG37> rdf:type <http://human_genome> .
# <http://sample1> <http://is_aligned_with> <http://HG37> ;
#               rdfs:comment "Sample 1 from Study X [...]"^^xsd:string .
# ```

# ## Question 1
#  
# 1. Consider the following RDF properties `family:has_mother`, `family:has_father`, `family:has_sister`
# 2. Only using these predicates, represent with RDF triples the following family:
#     -  *The mother of John is Mary*,
#     -  *Mickael is the son of Mark*,
#     -  *John and Mickael are cousins (because Mark and Mary are siblings)*.
# 3. Go to https://www.ldf.fi/service/rdf-grapher 
# 4. Generate a graphical representation of the RDF graph.

# ## Answer
# 
# ```turtle
# @prefix family: <http://family.org/> .
# 
# <http://John> family:has_mother <http://Mary> .
# <http://Mickael> family:has_father <http://Mark> .
# <http://Mark> family:has_sister <http://Mary> .
# ```
# 
# ![:scale 50%](fig/family.png)

# ---

# # SPARQL hands-on

# SPARQL is the standards language to query multiple data sources expressed in RDF. The principle consists in defining a **graph pattern** to be matched against an RDF graph.

# ## <font color='red'>Definition</font>
# **Triple Patterns** (TPs) are like RDF triples except that each of the *subject*, *predicate* and *object* may be a **variable**. Variables are prefixed with a `?` . 
# 
# ## <font color='green'>Example</font>
# Triple pattern
# ```ruby
# ?x <is_a_variant_of> <RAC1> .
# ```
# 
# RDF graph
# ```ruby
# <SNP:123> <is_a_variant_of> <NEMO> .
# <SNP:rs527330002> <is_a_variant_of> <RAC1> .
# <SNP:rs527330002> <refers_to_organism> <http://www.uniprot.org/taxonomy/9606> .
# <SNP:rs61753123> <is_a_variant_of> <RAC1> .
# ```
# 
# Bindings of variables `?x`
# ```ruby
# ?x = <SNP:rs527330002>
# ?x = <SNP:rs61753123>
# ```
# 

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# ## <font color='red'>Definition</font>
# **Basic Graph Patterns** (BGPs) consist in a set of triple patterns to be matched against an RDF graph.
# ## <font color='green'>Example</font>
# Basic graph pattern
# ```ruby
# ?x <is_a_variant_of> <RAC1> .
# ?x <is_a_risk_factor_for> ?z
# ```
# ![:scale 60%](fig/bgp.png)
# 
# 

# # 4 Types of SPARQL queries
# - **SELECT** : returns the variables values (i.e. bound variables) for each graph pattern match ;
# - **CONSTRUCT** : returns an RDF graph constructed by substituting variables in a set of triple patterns ;
# - **ASK** : returns a boolean (true/false) indicating whether a query pattern matches or not ;
# - **DESCRIBE** : returns an RDF graph that describes the resources found (resources neighborhood).
# 
# <br>
# <br>
# Additional features: Optional BGPs, union, filters, aggregate functions, negation, service, *etc.*
# 
# # Anatomy of a SPARQL query
# 
# ![:scale 95%](fig/anat.png)

# ## Question 2
# We will now use the RDFlib package to parse RDF Data and do some very basic SPARQL queries. 

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from rdflib import Graph

# RDF graph, in turtle syntax, stored in a string
my_rdf_data = """
@prefix ns: <http://my_voc/> .
@prefix snp: <http://my_snps/> .

snp:123 ns:is_a_variant_of "NEMO" .
snp:rs527330002 ns:is_a_variant_of "RAC1" .
snp:rs527330002 ns:refers_to_organism <http://www.uniprot.org/taxonomy/9606> .
snp:rs61753123 ns:is_a_variant_of "RAC1" .
"""

# Initialization of the in-memory RDF graph, RDFlib Graph object
kg = Graph()

# Parsing of the RDF data
kg.parse(data=my_rdf_data, format='turtle')

# Printing the size of the graph and serializing it again. 
print(f'the knowledge graph contains {len(kg)} triples\n')
print(kg.serialize(format="turtle"))


# We now execute a simple query to search for all "variants" of `RAC1`. 

# In[ ]:


q = """

"""

res = kg.query(q)
for row in res:
    print(f"{row['x']} is a variant of RAC1")


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# SOLUTION:
get_ipython().system('echo "ClNFTEVDVCA/eCBXSEVSRSB7CiAgICA/eCBuczppc19hX3ZhcmlhbnRfb2YgUkFDMSAuCn0K" | base64 --decode')


# ## Question 3 
# Generalize this query to show all *is a variant of* relations. You can use two variables `?x` and `?y`. 

# In[ ]:


q = """

"""

res = kg.query(q)
for row in res:
    print(f"{row['x']} is a variant of {row['y']}")


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# SOLUTION:
get_ipython().system('echo "ClNFTEVDVCA/eCA/eSBXSEVSRSB7CiAgICA/eCBuczppc19hX3ZhcmlhbnRfb2YgP3kgLgp9Cg==" | base64 --decode')


# ## Question 4
# Search for the name of the gene who has a variant refering to the `http://www.uniprot.org/taxonomy/9606` organism

# In[ ]:


q = """

"""

res = kg.query(q)
for row in res:
    print(row['y'])


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# SOLUTION:
get_ipython().system('echo "ClNFTEVDVCA/eSBXSEVSRSB7CiAgICA/eCBuczpyZWZlcnNfdG9fb3JnYW5pc20gPGh0dHA6Ly93d3cudW5pcHJvdC5vcmcvdGF4b25vbXkvOTYwNj4gLgogICAgP3ggbnM6aXNfYV92YXJpYW50X29mID95IC4KfQo=" | base64 --decode')


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