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# Calling scalar UDFs from expressions.
# This is for 1-dim arrays.

from time import time

import numpy as np

import iarray as ia
from iarray import udf

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# Examples on getting orthogonal slices

import iarray as ia
import numpy as np


dtype = np.float32

ia.set_config_defaults(dtype=dtype)

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# Benchmark comparing npy, npz, jdb and blosc2 storage formats

import sys

import numpy as np
import jdata as jd
import blosc2
from time import time

N = 10_000

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import numpy as np
import iarray as ia
from iarray import udf
import math


# Params for array construction
shape = (40_000, 40_000)
ia.set_config_defaults(dtype=np.float32, fp_mantissa_bits=15)

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@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  

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@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  

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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)

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@udf.scalar()
def square_filter(val: udf.float32) -> udf.float32:
    if val >= 0.5:
        return 1.
    return math.nan