Searching and Downloading Data from the Blue Brain Knowledge Graph using the Knowledge Graph Forge¶

Initialize and configure¶

Get an authentication token¶

For now, the Nexus web application can be used to get a token. We are looking for other simpler alternatives.

Step 1: From the opened web page, click on the login button on the right corner and follow the instructions.

login-ui

Step 2: At the end you’ll see a token button on the right corner. Click on it to copy the token.

login-ui

Once a token is obtained then proceed to paste it below.

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import getpass

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TOKEN = getpass.getpass()

Configure a client (forge) to access the knowledge graph¶

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from kgforge.core import KnowledgeGraphForge

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# Let target the sscx dissemination project in Nexus
ORG = "public"
PROJECT = "sscx"

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forge = KnowledgeGraphForge("prod-forge-nexus.yml",bucket=f"{ORG}/{PROJECT}",token=TOKEN)

Search and Download¶

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forge.types()

For ontologies¶

Set filters¶

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# Supported filters for the time being are:
from kgforge.core.commons.strategies import ResolvingStrategy
text = "somatosensory"
limit=10

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# other Search strategy can be ResolvingStrategy.BEST_MATCH, ResolvingStrategy.EXACT_MATCH
brain_region = forge.resolve(text, scope="ontology", target="terms", strategy=ResolvingStrategy.ALL_MATCHES, limit=limit)

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forge.as_dataframe(brain_region).head(100)

For Morphologies¶

Set filters¶

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# Supported filters for the time being are:
_type = "ReconstructedCell"
classification_type="nsg:MType"
mType="L4_NBC"
brainRegion = "primary somatosensory cortex"
layer = "layer 4"
encodingFormat="application/swc"
limit=2

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forge.template("Dataset")

Run Query¶

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path = forge.paths("Dataset") # to have autocompletion on the properties
data = forge.search(path.type.id == _type,
                    path.annotation.hasBody.type ==classification_type,
                    path.annotation.hasBody.label ==mType,
                    path.brainLocation.brainRegion.label == brainRegion,
                    path.brainLocation.layer.label == layer,
                    path.distribution.encodingFormat == encodingFormat,
                    limit=limit)

print(str(len(data))+" dataset of type '"+_type+"' found.")

Display the results¶

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DISPLAY_LIMIT = 10
reshaped_data = forge.reshape(data, keep=["id","name","subject","brainLocation.brainRegion.id","brainLocation.brainRegion.label","brainLocation.layer.id","brainLocation.layer.label", "contribution","brainLocation.layer.id","brainLocation.layer.label","distribution.name","distribution.contentUrl","distribution.encodingFormat"])

forge.as_dataframe(reshaped_data[:DISPLAY_LIMIT])

Dowload¶

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dirpath = "./downloaded/"
forge.download(data, "distribution.contentUrl", dirpath)

For Trace¶

Set filters¶

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# Supported filters for the time being are:
_type = "Trace"
classification_type="nsg:EType"
eType="cADpyr"
brainRegion = "primary somatosensory cortex"
layer = "layer 5"
encodingFormat="application/nwb"
limit=10

Run Query¶

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path = forge.paths("Dataset") # to have autocompletion on the properties
data = forge.search(path.type.id == _type,
                    path.annotation.hasBody.type ==classification_type,
                    path.annotation.hasBody.label ==eType,
                    path.brainLocation.brainRegion.label == brainRegion,
                    path.brainLocation.layer.label == layer,
                    path.distribution.encodingFormat == encodingFormat,
                    limit=limit)

print(str(len(data))+" data of type '"+_type+"' found.")

Display the results¶

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DISPLAY_LIMIT = 10
reshaped_data = forge.reshape(data, keep=["id","name","subject","brainLocation.brainRegion.id","brainLocation.brainRegion.label","brainLocation.layer.id","brainLocation.layer.label", "contribution","brainLocation.layer.id","brainLocation.layer.label","distribution.name","distribution.contentUrl","distribution.encodingFormat"])

forge.as_dataframe(reshaped_data[:DISPLAY_LIMIT])

Dowload¶

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dirpath = "./downloaded/"
forge.download(data, "distribution.contentUrl", dirpath)

For LayerThickness¶

Set filters¶

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# Supported filters for the time being are:
_type = "LayerThickness"
brainRegion = "primary somatosensory cortex"
layer = "layer 2"
encodingFormat="application/xlsx"
limit=10

Run query¶

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path = forge.paths("Dataset") # to have autocompletion on the properties
data = forge.search(path.type.id == _type,
                    path.brainLocation.layer.label == layer,
                    path.brainLocation.brainRegion.label == brainRegion,
                    path.distribution.encodingFormat == encodingFormat,
                    limit=limit)

print(str(len(data))+" data of type '"+_type+"' found.")

Display Results¶

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DISPLAY_LIMIT = 10
reshaped_data = forge.reshape(data, keep=["id","name","subject","brainLocation.brainRegion.id","brainLocation.brainRegion.label","brainLocation.layer.id","brainLocation.layer.label", "contribution","brainLocation.layer.id","brainLocation.layer.label","distribution.name","distribution.contentUrl","distribution.encodingFormat"])

forge.as_dataframe(reshaped_data[:DISPLAY_LIMIT])

Dowload¶

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dirpath = "./downloaded/"
forge.download(data, "distribution.contentUrl", dirpath)

For Neuron Density¶

Set filters¶

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# Supported filters for the time being are:
_type = "NeuronDensity"
brainRegion = "primary somatosensory cortex"
layer = "layer 2"
encodingFormat="application/xlsx"
limit=10

Run query¶

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path = forge.paths("Dataset") # to have autocompletion on the properties
data = forge.search(path.type.id == _type,
                    path.brainLocation.layer.label == layer,
                    path.brainLocation.brainRegion.label == brainRegion,
                    path.distribution.encodingFormat == encodingFormat,
                    limit=limit)

print(str(len(data))+" data of type '"+_type+"' found.")

Display Results¶

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DISPLAY_LIMIT = 10
reshaped_data = forge.reshape(data, keep=["id","name","subject","brainLocation.brainRegion.id","brainLocation.brainRegion.label","brainLocation.layer.id","brainLocation.layer.label", "contribution","brainLocation.layer.id","brainLocation.layer.label","distribution.name","distribution.contentUrl","distribution.encodingFormat"])

forge.as_dataframe(reshaped_data[:DISPLAY_LIMIT])

Dowload¶

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dirpath = "./downloaded/"
forge.download(data, "distribution.contentUrl", dirpath)

For Atlas Release¶

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# Let target the bbp/atlas project in Nexus

forge_atlas = KnowledgeGraphForge("prod-forge-nexus.yml", bucket="bbp/atlas", token=TOKEN)

Atlas related types: AtlasRelease CellPositions BrainParcellationDataLayer CellDensityDataLayer GeneExpressionVolumetricDataLayer GliaCellDensity NISSLImageDataLayer

Set filters¶

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# Supported filters for the time being are:
_type = "CellPositions"
limit=10

Run query¶

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path = forge_atlas.paths("Dataset") # to have autocompletion on the properties
data = forge_atlas.search(path.type.id == _type,
                    limit=limit)

print(str(len(data))+" data of type '"+_type+"' found.")

Display Results¶

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DISPLAY_LIMIT = 10
reshaped_data = forge_atlas.reshape(data, keep=["id","name","brainLocation.brainRegion.id","brainLocation.brainRegion.label", "contribution","distribution.name","distribution.contentUrl","distribution.encodingFormat"])

forge_atlas.as_dataframe(reshaped_data[:DISPLAY_LIMIT])

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dirpath = "./downloaded/"
forge_atlas.download(data, "distribution.contentUrl", dirpath)