import os, time, sys, hashlib
from datetime import datetime
from fairgraph import brainsimulation, KGClient, base, uniminds
from fairgraph.analysis import AnalysisActivity, AnalysisScript, AnalysisConfiguration, AnalysisResult, Person
# needs to have HBP_AUTH_TOKEN set as a bash variable
client = KGClient(os.environ["HBP_AUTH_TOKEN"])
if sys.argv[-1] == 'Fetch':
analysis = AnalysisActivity.by_name(
'parameter configuration of toy analysis in demo notebook', client)
# data = uniminds.Dataset.list(client, size=20)[-1]
# print(data)
# analysis_config = AnalysisConfiguration.by_name('parameter configuration of toy analysis in demo notebook', client)
# analysis_config.download('./', client)
# print(analysis_config.config_file)
elif sys.argv[-1] == 'Fetch-Pipeline':
result = AnalysisResult.by_name('Result 2', client)
print(result)
def provenance_tracking_of_result(analysis_result, with_activities=True):
"""
"""
Provenance_loop_continues = True
GENERATING_ENTITIES_BY_LAYER = [[analysis_result]]
while Provenance_loop_continues:
import requests
import numpy as np
import matplotlib.pyplot as plt
from fairgraph.commons import BrainRegion, Species, CellType
from fairgraph.electrophysiology import PatchedCell, Trace
from fairgraph import KGClient
from fairgraph.base import KGQuery
from fairgraph.minds import Dataset
import pytest
token = os.environ.get("HBP_AUTH_TOKEN", None)
if token:
client = KGClient(token,
nexus_endpoint="https://nexus.humanbrainproject.org/v0")
@pytest.mark.skip("comment this out to run the test")
@pytest.mark.skipif(
token is None,
reason="No token provided. Please set environment variable HBP_AUTH_TOKEN")
def test_all():
## Search based on brain region
cells_in_ca1 = PatchedCell.list(
client, brain_region=BrainRegion("hippocampus CA1"))
pprint([cell.brain_location for cell in cells_in_ca1])
### Download the recorded data for one of these cells
import os, time
from datetime import datetime
from fairgraph import brainsimulation, KGClient, base, uniminds
# needs to have HBP_AUTH_TOKEN set as a bash variable
dev = False
if dev:
client = KGClient(
os.environ["HBP_AUTH_TOKEN"],
nexus_endpoint='https://nexus-int.humanbrainproject.org/v0')
else:
client = KGClient(os.environ["HBP_AUTH_TOKEN"])
###############################################
### Downloading the (toy) Model of the demo ###
###############################################
# The model source code is available in a public container at CSCS
container_url = 'https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/simulation_result_demo'
if not os.path.isfile('model_script.py'):
os.system(
'wget https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/simulation_result_demo/model/model_script.py'
)
###############################################
### Documenting Model Metadata ################
###############################################
## --> building a model instance (version) from those metadata
my_model = brainsimulation.ModelInstance(
name='Toy model#%s of neural network dynamics for demo purpose' %
"""
import os, time
from datetime import datetime
import csv
import logging
import pandas as pd
from fairgraph import uniminds, KGClient, minds
logging.basicConfig(filename="to_knowledge_graph.log",
filemode='a',
level=logging.DEBUG)
logger = logging.getLogger("nar")
token = "eyJhbGciOiJSUzI1NiIsImtpZCI6ImJicC1vaWRjIn0.eyJleHAiOjE1ODQwMzEwMTgsInN1YiI6IjMwODEwNSIsImF1ZCI6WyIzMjMxNDU3My1hMjQ1LTRiNWEtYjM3MS0yZjE1YWNjNzkxYmEiXSwiaXNzIjoiaHR0cHM6XC9cL3NlcnZpY2VzLmh1bWFuYnJhaW5wcm9qZWN0LmV1XC9vaWRjXC8iLCJqdGkiOiI2N2Y2OTMwNy1lNzc5LTQzMTItOWMyYS1hNTM2M2E2ODgyNTYiLCJpYXQiOjE1ODQwMTY2MTgsImhicF9rZXkiOiJhN2QwNmFlZTMyOGI3YjkzNTc3OTUxNzU2Mjk2ODc3YjE4YzRmYTFjIn0.A04N8tLAr-UW6R7W3b-yzuNrW7DI4vsDULH0SJNr7cTvrvMTtkfoQkvWM-JFz3BvegXjXAxzs4lF5wMQhqSWtCShkpwnNsqa9elr2b-xfGVoYluAaQc2TU2ubNbI0VD72kZ5ircliV00L60dXyYuLGdNUz5reM9f0QS4Sw0hiDA"
client = KGClient(token,
nexus_endpoint='https://nexus-int.humanbrainproject.org/v0')
# # Load the Metadata Form
# > Metadata is loaded from a CSV file and saved as key: value entries in a dictionary
# In[26]:
reader = csv.reader(open('test_dataset.csv', 'r'))
data = {}
for row in reader:
k, v = row
data[k.lower().strip().replace(" ", "_")] = v
data = {k: None if not v else v
for k, v in data.items()} # replace empty values with None
A sample of brain tissue.
"""
_path = "/core/tissuesample/v1.0.0"
type = ["uniminds:Tissuesample"]
fields = (
Field("alternatives", KGObject, "https://schema.hbp.eu/inference/alternatives", required=False, multiple=True),
Field("identifier", basestring, "http://schema.org/identifier", required=False, multiple=True),
Field("name", basestring, "http://schema.org/name", required=False, multiple=False),
Field("subject", Subject, "https://schema.hbp.eu/uniminds/subject", required=False, multiple=False))
def list_kg_classes():
"""List all KG classes defined in this module"""
classes = [obj for name, obj in inspect.getmembers(sys.modules[__name__])
if inspect.isclass(obj) and issubclass(obj, KGObject) and obj.__module__ == __name__]
classes.remove(UnimindsObject)
classes.remove(UnimindsOption)
return classes
if __name__=='__main__':
import os
from fairgraph import uniminds, KGClient
token = os.environ['HBP_token']
client = KGClient(token)
for cls in uniminds.list_kg_classes():
print(cls.__name__)
# for f in cls.fields:
# print(' - %s' % f.name)
"""
Script to retrieve/update test data
"""
import os.path
from itertools import cycle
import json
from fairgraph import KGClient, core, brainsimulation, electrophysiology, minds, uniminds
core.use_namespace("neuralactivity")
client = KGClient()
def save_nexus_query_result(module, cls):
path = "/data/{}/?fields=all&size=10&deprecated=False".format(cls.path)
response = client._nexus_client._http_client.get(path)
fix_fields(response)
filename = "{}_list_0_10.json".format(cls.__name__.lower())
local_path = os.path.join("nexus",
module.__name__.split(".")[1],
filename)
if "next" in response["links"]:
del response["links"]["next"]
with open(local_path, "w") as fp:
json.dump(response, fp, indent=4)
def save_kg_query_result(module, cls, query_label):
query_id = {"simple": cls.query_id,
"resolved": cls.query_id_resolved}[query_label]