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FrancescAlted / big-calculation2.py
Last active September 23, 2025 05:11
A calculation of a 1000x1000x1000 cube using NumPy and Blosc2
import os
import psutil
from time import time
import numpy as np
import blosc2
# --- Experiment Setup ---
n_frames = 1000 # Raise this for more frames
width, height = np.array((n_frames, n_frames)) # Size of the grid
@FrancescAlted
FrancescAlted / blosc2-jit-microbench.ipynb
Created March 10, 2025 18:08
Micro-benchmark for blosc2, using numpy functions, reductions and broadcasting
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area_circle = ia.nansum(circle)
area_square = ia.nansum(square)
print(f"PI estimate: {4 * area_circle / area_square}")
expr = ia.expr_from_udf(filter_func,
[rand_data],
[shape[0], shape[1], True])
circle = expr.eval()
expr = ia.expr_from_udf(filter_func,
[rand_data],
[shape[0], shape[1], False])
square = expr.eval()
@udf.jit()
def filter_func(out: udf.Array(udf.float32, 2),
vals: udf.Array(udf.float32, 2),
nrows: udf.int64, ncols: udf.int64,
iscircle: udf.bool) -> udf.int32:
n = out.window_shape[0]
m = out.window_shape[1]
row_start = out.window_start[0]
col_start = out.window_start[1]
for i in range(n):
@udf.scalar()
def square_filter(val: udf.float32) -> udf.float32:
if val >= 0.5:
return 1.
return math.nan
import iarray as ia
shape = (40_000, 40_000)
ia.set_config_defaults(dtype=np.float32, fp_mantissa_bits=15)
rand_data = ia.random.random_sample(shape)
@udf.scalar()
def circle_filter(val: udf.float32, row: udf.int64, col: udf.int64,
nrows: udf.int64, ncols: udf.int64) -> udf.float32:
x = (2. * row / nrows) - 1.
y = (2. * col / ncols) - 1.
if ((x ** 2 + y ** 2) <= 1) and val >= 0.5:
return 1.
return math.nan
@FrancescAlted
FrancescAlted / circle_filter.py
Created September 12, 2022 07:07
Computing π using ironArray
@udf.scalar()
def circle_filter(val: udf.float32, row: udf.int64, col: udf.int64,
nrows: udf.int64, ncols: udf.int64)
-> udf.float32:
x = (2. * row / nrows) - 1.
y = (2. * col / ncols) - 1.
if ((x ** 2 + y ** 2) <= 1) and val >= 0.5:
return 1.
return math.nan