from pyhpo import Ontology
from pyhpo.set import HPOSet

# initilize the Ontology ()
_ = Ontology()

# Declare the clinical information of the patients
patient_1 = HPOSet.from_queries([
    'HP:0002943',
    'HP:0008458',
    'HP:0100884',
    'HP:0002944',
    'HP:0002751'
])

patient_2 = HPOSet.from_queries([
    'HP:0002650',
    'HP:0010674',
    'HP:0000925',
    'HP:0009121'
])

# and compare their similarity
patient_1.similarity(patient_2)
#> 0.7594183905785477

from pyhpo import Ontology

# initilize the Ontology ()
_ = Ontology()

term_1 = Ontology.get_hpo_object('Scoliosis')
term_2 = Ontology.get_hpo_object('Abnormal axial skeleton morphology')

path = term_1.path_to_other(term_2)
for t in path[1]:
    print(t)

"""
HP:0002650 | Scoliosis
HP:0010674 | Abnormality of the curvature of the vertebral column
HP:0000925 | Abnormality of the vertebral column
HP:0009121 | Abnormal axial skeleton morphology
"""

from pyhpo import Ontology

# initilize the Ontology ()
_ = Ontology()

# Retrieve a term e.g. via its HPO-ID
term = Ontology.get_hpo_object('Scoliosis')

print(term)
#> HP:0002650 | Scoliosis

# Get information content from Term <--> Omim associations
term.information_content['omim']
#> 2.39

# Show how many genes are associated to the term
# (Note that this includes indirect associations, associations
# from children terms to genes.)
len(term.genes)
#> 947

# Show how many Omim Diseases are associated to the term
# (Note that this includes indirect associations, associations
# from children terms to diseases.)
len(term.omim_diseases)
#> 730

# Get a list of all parent terms
for p in term.parents:
    print(p)
#> HP:0010674 | Abnormality of the curvature of the vertebral column

# Get a list of all children terms
for p in term.children:
    print(p)
"""
HP:0002943 | Thoracic scoliosis
HP:0008458 | Progressive congenital scoliosis
HP:0100884 | Compensatory scoliosis
HP:0002944 | Thoracolumbar scoliosis
HP:0002751 | Kyphoscoliosis
"""

from pyhpo import Ontology
_ = Ontology()
term = Ontology.get_hpo_object('Scoliosis')

# Let's get a second term, this time using it HPO-ID
term_2 = Ontology.get_hpo_object('HP:0009121')

print(term_2)
#> HP:0009121 | Abnormal axial skeleton morphology

# Check if the Scoliosis is a direct or indirect child
# of Abnormal axial skeleton morphology

term.child_of(term_2)
#> True

# or vice versa
term_2.parent_of(term)
#> True

# show all nodes between two term:
path = term.path_to_other(term_2)
for t in path[1]:
    print(t)

"""
HP:0002650 | Scoliosis
HP:0010674 | Abnormality of the curvature of the vertebral column
HP:0000925 | Abnormality of the vertebral column
HP:0009121 | Abnormal axial skeleton morphology
"""

print(f'Steps from Term 1 to Term 2: {path[0]}')
#> Steps from Term 1 to Term 2: 3


# Calculate the similarity between two terms
term.similarity_score(term_2)
#> 0.442

from pyhpo import Ontology, HPOSet

# initilize the Ontology (this must be done only once)
_ = Ontology()

# Get a term based on its name
term = Ontology.get_hpo_object('Scoliosis')
print(term)
#> HP:0002650 | Scoliosis

# ...or based on HPO-ID
term = Ontology.get_hpo_object('HP:0002650')
print(term)
#> HP:0002650 | Scoliosis

# ...or based on its index
term = Ontology.get_hpo_object(2650)
print(term)
#> HP:0002650 | Scoliosis

# shortcut to retrieve a term based on its index
term = Ontology[2650]
print(term)
#> HP:0002650 | Scoliosis

# Search for term
for term in Ontology.search('olios'):
    print(term)

"""
HP:0002211 | White forelock
HP:0002290 | Poliosis
HP:0002650 | Scoliosis
HP:0002751 | Kyphoscoliosis
HP:0002943 | Thoracic scoliosis
HP:0002944 | Thoracolumbar scoliosis
HP:0003423 | Thoracolumbar kyphoscoliosis
HP:0004619 | Lumbar kyphoscoliosis
HP:0004626 | Lumbar scoliosis
HP:0005659 | Thoracic kyphoscoliosis
HP:0008453 | Congenital kyphoscoliosis
HP:0008458 | Progressive congenital scoliosis
HP:0100884 | Compensatory scoliosis
"""

from pyhpo import Ontology, HPOSet

import module2

# initilize the Ontology
_ = Ontology()

if __name__ == '__main__':
    module2.find_term('Compensatory scoliosis')

from pyhpo import Ontology

def find_term(term):
    return Ontology.get_hpo_object(term)

from pyhpo import Ontology, HPOSet

# initilize the Ontology
_ = Ontology()

# create HPOSets, corresponding to 
# e.g. the clinical information of a patient
# You can initiate an HPOSet using either
# - HPO-ID: 'HP:0002943'
# - HPO-Name: 'Scoliosis'
# - HPO-ID (int): 2943

ci_1 = HPOSet.from_queries([
    'HP:0002943',
    'HP:0008458',
    'HP:0100884',
    'HP:0002944',
    'HP:0002751'
])

ci_2 = HPOSet.from_queries([
    'HP:0002650',
    'HP:0010674',
    'HP:0000925',
    'HP:0009121'
])

# Compare the similarity
ci_1.similarity(ci_2)
#> 0.7593552670152157

# Remove all non-leave nodes from a set
ci_leaf = ci_2.child_nodes()
len(ci_2)
#> 4
len(ci_leaf)
#> 1
ci_2
#> HPOSet.from_serialized("925+2650+9121+10674")
ci_leaf
#> HPOSet.from_serialized("2650")

# Check the information content of an HPOSet
ci_1.information_content()
"""
{
    'mean': 6.571224974009769,
    'total': 32.856124870048845,
    'max': 8.97979449089521,
    'all': [5.98406221734122, 8.286647310335265, 8.97979449089521, 5.5458072864100645, 4.059813565067086]
}
"""

from pyhpo import Ontology, HPOSet
from pyhpo.stats import EnrichmentModel

# initilize the Ontology
_ = Ontology()

ci = HPOSet.from_queries([
    'HP:0002943',
    'HP:0008458',
    'HP:0100884',
    'HP:0002944',
    'HP:0002751'
])

gene_model = EnrichmentModel('gene')
genes = gene_model.enrichment(method='hypergeom', hposet=ci)

print(genes[0]['item'])
#> PAPSS2