Migrate from NumPy

Use this page when converting existing NumPy code to flopscope.

You will learn:

How to convert NumPy imports and operations to flopscope equivalents
Which NumPy behaviors stay the same and which change
How to avoid common pitfalls when migrating

Prerequisites

The basics

Change your import and wrap computation in a BudgetContext:

Before (NumPy):

import numpy as np

W = np.random.randn(256, 256)
x = np.random.randn(256)
h = np.dot(W, x)
h = np.maximum(h, 0)

After (flopscope) — simplest form:

import flopscope as flops
import flopscope.numpy as fnp

# No setup needed — global default budget tracks FLOPs automatically
W = fnp.random.randn(256, 256)
x = fnp.random.randn(256)
h = fnp.dot(W, x)
h = fnp.maximum(h, 0)

# No setup needed — global default budget tracks FLOPs automatically
W = fnp.random.randn(256, 256)
x = fnp.random.randn(256)
h = fnp.dot(W, x)
h = fnp.maximum(h, 0)

flops.budget_summary()  # see what you spent

After (flopscope) — with explicit budget control:

import flopscope as flops
import flopscope.numpy as fnp

with flops.BudgetContext(flop_budget=20_000_000) as budget:
    W = fnp.random.randn(256, 256)
    x = fnp.random.randn(256)
    h = fnp.dot(W, x)
    h = fnp.maximum(h, 0)

What stays the same

Function signatures match NumPy for supported operations
Broadcasting rules are identical
Array indexing, slicing, and assignment work normally

What changes

NumPy	flopscope	Notes
`import numpy as np`	`import flopscope.numpy as fnp`	Use `import flopscope as flops` alongside it for budgets, symmetry, and accounting helpers
Call ops anywhere	Works anywhere too	A global default budget auto-activates; use explicit `BudgetContext` for limits and namespacing
`np.linalg.svd(A)`	`fnp.linalg.svd(A, k=10)`	Truncated SVD with explicit `k`
Plain `ndarray` only	`SymmetricTensor` available	Wrap with `flops.as_symmetric()` for cost savings
All NumPy ops available	Most available, 32 blacklisted	I/O and config ops raise `AttributeError`
No cost tracking	Automatic FLOP counting	Every counted op deducts from budget

Common pitfalls

Symptom: AttributeError when calling an I/O or config function (e.g., fnp.save, fnp.seterr)

Fix: 32 operations are blacklisted because they are I/O, configuration, or datetime functions with no FLOP cost. See Operation Categories for the full list. Use numpy directly for these.

Symptom: Using np.linalg.svd instead of fnp.linalg.svd

Fix: If you import NumPy alongside flopscope, make sure to use fnp. for counted operations. Operations called through np. bypass FLOP counting entirely.

Operation Categories — what's supported and what isn't
API Reference — full list of all operations