LazyArray examples #257
LazyArray examples #257
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src/blosc2/lazyexpr.py
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| >>> expr.save(urlpath='lazy_array.b2nd', mode='w') | ||
| >>> # Open and load the LazyExpr from disk | ||
| >>> disk_expr = blosc2.open('lazy_array.b2nd') | ||
| >>> f"The first two rows of the loaded LazyExpr data: {disk_expr[:2]} |
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Too verbose. Perhaps simply write >>> disk_expr[:2] ? It should be explanatory enough IMO.
src/blosc2/lazyexpr.py
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| >>> import numpy as np | ||
| >>> # Define a user-defined function that will be applied to each block of data | ||
| >>> def my_function(inputs_tuple, output, offset): | ||
| >>> a, b = inputs_tuple |
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For the sake of locality (which improves readability), I'd move this function definition as close as possible to their use down below.
src/blosc2/lazyexpr.py
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| >>> # Perform the mathematical operation | ||
| >>> expr = a1 + b1 | ||
| >>> output = expr.eval() | ||
| >>> print("Array A: ", a[:]) |
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Why uppercase 'A'? I don't even think printing them is necessary, as linspace() should be easy to grasp for the reader.
src/blosc2/lazyexpr.py
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| [[0. 0.625 1.25 ] | ||
| [1.875 2.5 3.125] | ||
| [3.75 4.375 5. ]] | ||
| >>> print("Result of A + B (Lazy evaluation):", output[:]) |
src/blosc2/lazyexpr.py
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| >>> b1 = blosc2.asarray(b) | ||
| >>> # Perform the mathematical operation | ||
| >>> expr = a1 + b1 # LazyExpr expression | ||
| >>> output = expr.eval(cparams={"typesize": 4}) |
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typesize should be never needed when creating arrays in blosc2.
src/blosc2/lazyexpr.py
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| >>> # Perform the mathematical operation | ||
| >>> expr = a1 + b1 # LazyExpr expression | ||
| >>> output = expr.eval(cparams={"typesize": 4}) | ||
| >>> output[3] |
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You are not checking getitem for blosc2.NDArray here, but rather for np.array.
src/blosc2/lazyexpr.py
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| >>> output = expr.eval(cparams={"typesize": 4}) | ||
| >>> output[3] | ||
| [2.01680672 2.18487395 2.35294118 2.5210084 ] | ||
| >>> output[2:4] |
src/blosc2/lazyexpr.py
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| >>> operands = { 'a': a1, 'b': b1 } | ||
| >>> lazy_expr = blosc2.lazyexpr(expr, operands=operands) | ||
| >>> f"Lazy Expression Created: {lazy_expr}" | ||
| Lazy Expression Created: a1 * b1 + 2 |
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At this point it should be illustrative to get some results out of the lazy expression
src/blosc2/lazyexpr.py
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| Lazy Expression Created: a1 * b1 + 2 | ||
| >>> expr_ = a1 * b1 + 2 | ||
| >>> result = expr_.eval() | ||
| >>> result[:] |
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The above two line can be fused into one: expr_[:]. Remember that a LazyExpr supports the __getitem__ interface.
src/blosc2/lazyexpr.py
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| >>> a_path = 'a_array.b2nd' | ||
| >>> b_path = 'b_array.b2nd' | ||
| >>> a1 = blosc2.asarray(a, urlpath=a_path, mode='w') | ||
| >>> b1 = blosc2.asarray(b, urlpath=b_path, mode='w') |
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As a_path and b_path are going to be used just once, it is better to use the values directly. In this case, the above 5 lines can be reduced to:
a1 = blosc2.asarray(a, urlpath='a_array.b2nd', mode='w')
b1= blosc2.asarray(b, urlpath='b_array.b2nd', mode='w')
and it reads better IMO.
src/blosc2/lazyexpr.py
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| >>> expr = a1 + b1 | ||
| >>> output = expr.eval() | ||
| >>> f"Result of a + b (Lazy evaluation): {output[:]}" | ||
| Result of a + b (Lazy evaluation): |
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Please do not use upper case in the middle of sentences. For example, above "Lazy" -> "lazy".
| >>> import numpy as np | ||
| >>> dtype = np.float64 | ||
| >>> shape = [3, 3] | ||
| >>> size = shape[0] * shape[1] |
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In the future, you can change these kind of lines, for size = np.prod(shape)
src/blosc2/lazyexpr.py
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| >>> expr_ = a1 * b1 + 2 | ||
| >>> expr_[:] | ||
| [[ 2. 2.390625 3.5625 ] | ||
| [ 5.515625 8.25 11.765625] | ||
| [16.0625 21.140625 27. ]] |
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Maybe it would be better to show the results of the lazyexpr created with the blosc2.lazyexpr lazy_expr instead of creating the same expression and showing it (expr_)
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