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@jit()
def udf_iarray(out: Array(float32, 3),
               p1: Array(float32, 3),
               p2: Array(float32, 3),
               p3: Array(float32, 3)) -> int:

    l = p1.window_shape[0]
    m = p1.window_shape[1]
    n = p1.window_shape[2]

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precip_udf_expr = ia.expr_from_udf(udf_iarray, [precip1, precip2, precip3], favor=ia.Favor.SPEED)
precip_out = precip_udf_expr.eval()

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precip_expr = ia.expr_from_string("(p1 + p2 + p3) / 3",
                                  {'p1': precip1, 'p2': precip2, 'p3': precip3},
                                  )
precip_mean = precip_expr.eval()

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precip_expr = ia.expr_from_string("(p1 + p2 + p3) / 3",
                                  {'p1': precip1, 'p2': precip2, 'p3': precip3},
                                  )
precip_mean = precip_expr.eval(urlpath="mean-3m.iarr", mode="w")

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precip_trans_expr = ia.tan(precip1) * (ia.sin(precip1) * ia.sin(precip2) + ia.cos(precip2)) + ia.sqrt(precip3) * 2
precip_trans = precip_trans_expr.eval(urlpath="trans-3m.iarr", mode="w")

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nrows = 100_000 # number of rows in matrix am
ncols = 25000   # number of columns in first matrix
ncols2 = 1000   # number of columns in second matrix

shape = (nrows, ncols, ncols2)
amshape = (shape[0], shape[1])
bmshape = (shape[1], shape[2])

# Obtain optimal chunk and block shapes
mparams = ia.matmul_params(amshape, bmshape, dtype=np.float64)

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# Random values with lossy compression to get some decent compression ratio
am = ia.random.normal(amshape, 3, 2, chunks=amchunks, blocks=amblocks, urlpath=filename, fp_mantissa_bits=10)

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from iarray.udf import jit, Array, float64, int64

# Create second array via an UDF
@jit()
def tri(out: Array(float64, 2), x: Array(float64, 2), k: int64) -> int64:
    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):

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c = ia.matmul(am, bm)