⚡️ Speed up function manual_convolution_1d by 163%

[codeflash-ai]

📄 163% (1.63x) speedup for manual_convolution_1d in src/numpy_pandas/signal_processing.py

⏱️ Runtime : 25.5 milliseconds → 9.68 milliseconds (best of 108 runs)

📝 Explanation and details

Here’s an optimized version of your program.

• The double for-loop can be replaced by numpy's fast vectorized operations, specifically np.dot.
• Loops and array accesses in Python are slow compared to numpy's optimized routines.
• The logic and result of the function remain exactly the same.

This takes full advantage of numpy's efficient vectorized operations for each convolution window.
If you want further acceleration and you are allowed to use more built-in numpy functions, you could also use np.convolve(signal, kernel, mode='valid'), but since the function signature and name suggest a "manual" convolution, the above is the best blend of your logic and numpy speed.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 37 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import numpy as np
# imports
import pytest  # used for our unit tests
from src.numpy_pandas.signal_processing import manual_convolution_1d

# unit tests

# ----------- BASIC TEST CASES -----------

def test_basic_identity_kernel():
    # Convolution with [1] kernel returns the same array
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([1])
    expected = np.array([1, 2, 3, 4])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.50μs -> 7.96μs (56.0% slower)

def test_basic_sum_kernel():
    # Kernel [1, 1] computes running sum of pairs
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([1, 1])
    expected = np.array([3, 5, 7])  # [1+2, 2+3, 3+4]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.00μs -> 6.58μs (39.2% slower)

def test_basic_arbitrary_kernel():
    # Kernel with negative and positive values
    signal = np.array([2, 4, 6, 8])
    kernel = np.array([1, -1])
    expected = np.array([2-4, 4-6, 6-8])  # [-2, -2, -2]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.88μs -> 6.42μs (39.6% slower)

def test_basic_float_signal_and_kernel():
    # Both signal and kernel are floats
    signal = np.array([1.0, 2.0, 3.0])
    kernel = np.array([0.5, 0.5])
    expected = np.array([1.5, 2.5])  # [1*0.5+2*0.5, 2*0.5+3*0.5]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.00μs -> 4.42μs (32.1% slower)

def test_basic_kernel_longer_than_one():
    # Kernel length 3
    signal = np.array([1, 2, 3, 4, 5])
    kernel = np.array([1, 0, -1])
    expected = np.array([1*1+2*0+3*-1, 2*1+3*0+4*-1, 3*1+4*0+5*-1])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.88μs -> 6.54μs (25.5% slower)

# ----------- EDGE TEST CASES -----------

def test_edge_signal_equals_kernel_length():
    # Output should be a single value (dot product)
    signal = np.array([1, 2, 3])
    kernel = np.array([4, 5, 6])
    expected = np.array([1*4 + 2*5 + 3*6])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.83μs -> 4.12μs (31.3% slower)

def test_edge_kernel_length_one():
    # Kernel of length 1 returns original signal
    signal = np.array([5, 6, 7])
    kernel = np.array([2])
    expected = np.array([10, 12, 14])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.83μs -> 6.62μs (57.2% slower)

def test_edge_kernel_all_zeros():
    # Kernel of all zeros returns zeros
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([0, 0])
    expected = np.array([0, 0, 0])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.83μs -> 6.46μs (40.6% slower)

def test_edge_signal_with_negatives():
    # Signal contains negative numbers
    signal = np.array([-1, -2, 3, 4])
    kernel = np.array([1, 2])
    expected = np.array([-1*1 + -2*2, -2*1 + 3*2, 3*1 + 4*2])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.83μs -> 6.38μs (39.9% slower)

def test_edge_kernel_with_negatives():
    # Kernel contains negative numbers
    signal = np.array([1, 2, 3])
    kernel = np.array([-1, 2])
    expected = np.array([1*-1 + 2*2, 2*-1 + 3*2])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.08μs -> 5.42μs (43.1% slower)





def test_edge_higher_dimensional_arrays():
    # 2D arrays should raise ValueError
    signal = np.array([[1, 2], [3, 4]])
    kernel = np.array([1, 2])
    with pytest.raises(ValueError):
        manual_convolution_1d(signal, kernel)
    with pytest.raises(ValueError):
        manual_convolution_1d(np.array([1, 2, 3]), np.array([[1, 2], [3, 4]]))

def test_edge_single_element_signal_and_kernel():
    # Both signal and kernel are single element
    signal = np.array([42])
    kernel = np.array([2])
    expected = np.array([84])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.83μs -> 5.54μs (48.9% slower)

def test_edge_signal_and_kernel_of_zeros():
    # Both signal and kernel are zeros
    signal = np.zeros(5)
    kernel = np.zeros(3)
    expected = np.zeros(3)
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.92μs -> 6.54μs (24.9% slower)

# ----------- LARGE SCALE TEST CASES -----------

def test_large_scale_signal_and_kernel():
    # Large signal and kernel, but under 1000 elements
    signal = np.arange(1000, dtype=np.float64)
    kernel = np.arange(10, dtype=np.float64)
    # Use numpy's correlate for reference (valid mode, no kernel flip)
    expected = np.correlate(signal, kernel, mode='valid')
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.72ms -> 1.42ms (91.3% faster)

def test_large_scale_kernel_length_one():
    # Large signal, kernel of length 1
    signal = np.arange(999, dtype=np.int32)
    kernel = np.array([3], dtype=np.int32)
    expected = signal * 3
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 382μs -> 1.59ms (75.9% slower)

def test_large_scale_kernel_equals_signal():
    # Both signal and kernel are large and equal in length
    signal = np.arange(500, dtype=np.float32)
    kernel = np.arange(500, dtype=np.float32)
    expected = np.array([np.dot(signal, kernel)])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 169μs -> 5.21μs (3154% faster)

def test_large_scale_random_values():
    # Random values in signal and kernel
    rng = np.random.default_rng(42)
    signal = rng.standard_normal(800)
    kernel = rng.standard_normal(50)
    expected = np.correlate(signal, kernel, mode='valid')
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 10.2ms -> 1.21ms (746% faster)

def test_large_scale_performance():
    # Test that large input runs in reasonable time and produces correct output
    signal = np.ones(1000)
    kernel = np.ones(10)
    expected = np.full(991, 10.0)
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.74ms -> 904μs (203% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import numpy as np
# imports
import pytest  # used for our unit tests
from src.numpy_pandas.signal_processing import manual_convolution_1d

# unit tests

# 1. BASIC TEST CASES

def test_basic_identity():
    # Identity kernel (delta function)
    signal = np.array([1, 2, 3, 4, 5])
    kernel = np.array([1])
    expected = np.array([1, 2, 3, 4, 5])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.08μs -> 9.04μs (54.8% slower)

def test_basic_sum_kernel():
    # Simple sum kernel
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([1, 1])
    expected = np.array([3, 5, 7])  # [1+2, 2+3, 3+4]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.17μs -> 6.88μs (39.4% slower)

def test_basic_arbitrary_kernel():
    # Arbitrary kernel
    signal = np.array([2, 4, 6, 8])
    kernel = np.array([1, 0, -1])
    expected = np.array([2*1 + 4*0 + 6*-1, 4*1 + 6*0 + 8*-1])  # [-4, -4]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.00μs -> 5.79μs (30.9% slower)

def test_basic_float_types():
    # Test with float types
    signal = np.array([0.5, 1.5, 2.5])
    kernel = np.array([2.0, 0.0])
    expected = np.array([0.5*2.0 + 1.5*0.0, 1.5*2.0 + 2.5*0.0])  # [1.0, 3.0]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.00μs -> 4.96μs (39.5% slower)

def test_basic_negative_values():
    # Test with negative values
    signal = np.array([-1, -2, -3, -4])
    kernel = np.array([1, -1])
    expected = np.array([-1*1 + -2*-1, -2*1 + -3*-1, -3*1 + -4*-1])  # [1, 1, 1]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.92μs -> 6.67μs (41.3% slower)

def test_basic_kernel_equals_signal():
    # Kernel same length as signal
    signal = np.array([2, 3, 4])
    kernel = np.array([1, 2, 3])
    expected = np.array([2*1 + 3*2 + 4*3])  # [20]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.75μs -> 3.58μs (23.2% slower)

# 2. EDGE TEST CASES




def test_edge_non_1d_signal():
    # Non-1D signal should raise ValueError
    signal = np.array([[1, 2], [3, 4]])
    kernel = np.array([1, 2])
    with pytest.raises(ValueError):
        manual_convolution_1d(signal, kernel)


def test_edge_single_element_signal_and_kernel():
    # Both signal and kernel are single elements
    signal = np.array([7])
    kernel = np.array([3])
    expected = np.array([21])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.42μs -> 6.17μs (44.6% slower)

def test_edge_kernel_all_zeros():
    # Kernel is all zeros
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([0, 0])
    expected = np.array([0, 0, 0])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.42μs -> 7.33μs (39.8% slower)

def test_edge_signal_all_zeros():
    # Signal is all zeros
    signal = np.zeros(5)
    kernel = np.array([1, 2])
    expected = np.zeros(4)
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 5.12μs -> 10.1μs (49.4% slower)

def test_edge_kernel_with_zeros_and_nonzeros():
    # Kernel contains zeros and non-zeros
    signal = np.array([1, 2, 3, 4])
    kernel = np.array([0, 1])
    expected = np.array([2, 3, 4])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 4.04μs -> 6.79μs (40.5% slower)

def test_edge_signal_with_zeros_and_nonzeros():
    # Signal contains zeros and non-zeros
    signal = np.array([0, 1, 0, 2])
    kernel = np.array([1, 1])
    expected = np.array([1, 1, 2])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 3.96μs -> 6.71μs (41.0% slower)

def test_edge_large_kernel_of_ones():
    # Kernel is all ones, length almost equal to signal
    signal = np.arange(1, 11)  # [1,2,...,10]
    kernel = np.ones(9)
    expected = np.array([sum(range(1,10)), sum(range(2,11))])  # [45, 54]
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 8.75μs -> 6.04μs (44.8% faster)

# 3. LARGE SCALE TEST CASES

def test_large_signal_and_kernel():
    # Large signal and kernel
    signal = np.arange(1000, dtype=np.float64)
    kernel = np.arange(10, dtype=np.float64)
    # Compute expected result using numpy's convolve (valid mode)
    expected = np.convolve(signal, kernel, mode='valid')
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 2.74ms -> 1.54ms (77.9% faster)

def test_large_signal_small_kernel():
    # Large signal, small kernel
    signal = np.arange(999)
    kernel = np.array([1, -1])
    expected = np.convolve(signal, kernel, mode='valid')
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 716μs -> 1.52ms (52.8% slower)

def test_large_kernel_equal_signal():
    # Large kernel, same size as signal
    signal = np.arange(1000)
    kernel = np.ones(1000)
    expected = np.array([np.sum(signal)])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 362μs -> 7.62μs (4657% faster)

def test_large_signal_kernel_all_ones():
    # Both signal and kernel are all ones
    signal = np.ones(500)
    kernel = np.ones(500)
    expected = np.array([500])
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 138μs -> 4.25μs (3156% faster)

def test_large_random_signal_and_kernel():
    # Random large arrays
    rng = np.random.default_rng(42)
    signal = rng.integers(-100, 100, size=800)
    kernel = rng.integers(-10, 10, size=20)
    expected = np.convolve(signal, kernel, mode='valid')
    codeflash_output = manual_convolution_1d(signal, kernel); result = codeflash_output # 5.10ms -> 1.21ms (321% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-manual_convolution_1d-mc5ni55n and push.