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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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# 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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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) |
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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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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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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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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") |