Allow readonly arrays for higuchi_fd

[jvdd]

The current behavior of this method changes the datatype of x as np.asarray is a wrapper for np.array where copy=False. (see here)

I believe that this is (kind of) unexpected behavior, e.g., a user would not expect that the datatype would change when calculating a feature. Therefore, I suggest giving the user the option of not changing the datatype by adding a copy flag to the higuchi_fd function parameters. By default this flag = False, resulting in the same behavior as now (i.e., datatype of x is changed).

When benchmarking the speed of the code, I observed no real difference. Perhaps we should even remove the flag and just use np.array instead of np.asarray?

In [11]: x = np.random.rand(10_000).astype("float32")

In [12]: %timeit ant.higuchi_fd(x)
246 µs ± 5.24 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [13]: x = np.random.rand(10_000).astype("float32")

In [14]: %timeit ant.higuchi_fd(x, copy=True)
242 µs ± 93.4 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)

PS: I really like the fast functions in this library 😄

[raphaelvallat]

Hi @jvdd,

Thanks for opening the issue! Antropy forces x to be float64 (because of Numba compiling), so regardless of whether the array is copied or not the output is always going to be a float64:

antropy/antropy/fractal.py

Line 197 in 275216a

@jit('float64(float64[:], int32)')

[jvdd]

Hi @raphaelvallat,

Indeed, you are completely right. np.asarray returns a new array when it is necessary (i.e., when the datatype is not float64), whereas np.array always makes a copy (by default).

I made a mistake when narrowing down my error; I believed it was due the conversion not happening correctly when the type is not float64 (as you can see in my comment above). But, when reproducing this error, I noticed that the type conversion did not need to happen (as my data is of type float64).

So. the error I got was because the arrays I use are not writable. I believe working with non-writable arrays is a nice way of avoiding unexpected behavior (overriding values in your original data). This is minimal code that reproduces the error;

In [1]: import numpy as np; import antropy as ant                                                       

In [2]: x = np.arange(10_000).astype("float32") 
   ...: x.flags.writeable = False 
   ...: ant.higuchi_fd(x)                                                                               
Out[2]: 1.0003936754393057

In [3]: x = np.arange(10_000).astype("float64") 
   ...: x.flags.writeable = False 
   ...: ant.higuchi_fd(x)                                                                               
---------------------------------------------------------------------------
TypeError   
    ...
TypeError: No matching definition for argument type(s) readonly array(float64, 1d, C), int64

=> I rebased the master and added a different commit that now only changes the @jit signature of the _higuchi_fd function. Now readonly & non-readonly arrays are supported 😄

[raphaelvallat]

Got it — merging now! Thanks for the PR :)