Performing a Combination

We’ll demonstrate how a combination works by combining everything we’ve learned so far.

Loading the Workspace¶

To do so, we’ll use a simple workspace to demonstrate functionality of combinations.

import json

import pyhf

with open("data/2-bin_1-channel.json") as serialized:
    spec = json.load(serialized)

workspace = pyhf.Workspace(spec)

Combine Workspaces¶

Let’s just try to combine naively right now.

pyhf.Workspace.combine(workspace, workspace)

As we can see, we can’t just combine a workspace with itself if it has some channel names in common. We try very hard in pyhf to make sure a combination “makes sense”.

Let’s go ahead and rename the channel (as well as the measurement). Then try to combine.

other_workspace = workspace.rename(
    channels={"singlechannel": "othersinglechannel"},
    modifiers={"uncorr_bkguncrt": "otheruncorr_bkguncrt"},
    measurements={"Measurement": "OtherMeasurement"},
)

combined_workspace = pyhf.Workspace.combine(workspace, other_workspace)

And did we combine?

print(f"    channels: {combined_workspace.channels}")
print(f"       nbins: {combined_workspace.channel_nbins}")
print(f"     samples: {combined_workspace.samples}")
print(f"   modifiers: {combined_workspace.modifiers}")
print(f"measurements: {combined_workspace.measurement_names}")

Indeed. And at this point, we can just use all the same functionality we expect of pyhf, such as performing a fit:

model = workspace.model()
data = workspace.data(model)
test_poi = 1.0

pyhf.infer.hypotest(test_poi, data, model, test_stat="qtilde")

other_model = other_workspace.model()
other_data = other_workspace.data(other_model)

pyhf.infer.hypotest(test_poi, other_data, other_model, test_stat="qtilde")

combined_model = combined_workspace.model()
combined_data = combined_workspace.data(combined_model)

pyhf.infer.hypotest(test_poi, combined_data, combined_model, test_stat="qtilde")