Fix flake8 errors

arrayfire/__init__.py

@@ -49,11 +49,12 @@
 """
 
 from .algorithm import (
-    accum, all_true, allTrueByKey, any_true, anyTrueByKey, count, countByKey, diff1, diff2, imax, imin, max, maxByKey, min, minByKey, product, productByKey, scan, scan_by_key, set_intersect,
-    set_union, set_unique, sort, sort_by_key, sort_index, sum, sumByKey, where)
+    accum, all_true, allTrueByKey, any_true, anyTrueByKey, count, countByKey, diff1, diff2, imax, imin, max, maxByKey,
+    min, minByKey, product, productByKey, scan, scan_by_key, set_intersect, set_union, set_unique, sort, sort_by_key,
+    sort_index, sum, sumByKey, where)
 from .arith import (
-    abs, acos, acosh, arg, asin, asinh, atan, atan2, atanh, cast, cbrt, ceil, clamp, conjg, cos, cosh, cplx, erf, erfc, exp,
-    expm1, factorial, floor, hypot, imag, isinf, isnan, iszero, lgamma, log, log1p, log2, log10, maxof, minof,
+    abs, acos, acosh, arg, asin, asinh, atan, atan2, atanh, cast, cbrt, ceil, clamp, conjg, cos, cosh, cplx, erf, erfc,
+    exp, expm1, factorial, floor, hypot, imag, isinf, isnan, iszero, lgamma, log, log1p, log2, log10, maxof, minof,
     mod, pow,
     pow2, real, rem, root, round, rsqrt, sigmoid, sign, sin, sinh, sqrt, tan, tanh, tgamma, trunc)
 from .array import (
@@ -64,45 +65,50 @@
 from .blas import dot, matmul, matmulNT, matmulTN, matmulTT, gemm
 from .cuda import get_native_id, get_stream, set_native_id
 from .data import (
-    constant, diag, flat, flip, identity, iota, join, lookup, lower, moddims, pad, range, reorder, replace, select, shift,
-    tile, upper)
+    constant, diag, flat, flip, identity, iota, join, lookup, lower, moddims, pad, range, reorder, replace, select,
+    shift, tile, upper)
 from .library import (
-    BACKEND, BINARYOP, CANNY_THRESHOLD, COLORMAP, CONNECTIVITY, CONV_DOMAIN, CONV_GRADIENT, CONV_MODE, CSPACE, DIFFUSION, ERR, FLUX,
-    HOMOGRAPHY, IMAGE_FORMAT, INTERP, ITERATIVE_DECONV, INVERSE_DECONV, MARKER, MATCH, MATPROP, MOMENT, NORM, PAD, RANDOM_ENGINE, STORAGE, TOPK, VARIANCE, YCC_STD, Dtype, Source, AF_VER_MAJOR, FORGE_VER_MAJOR)
+    BACKEND, BINARYOP, CANNY_THRESHOLD, COLORMAP, CONNECTIVITY, CONV_DOMAIN, CONV_GRADIENT, CONV_MODE, CSPACE,
+    DIFFUSION, ERR, FLUX, HOMOGRAPHY, IMAGE_FORMAT, INTERP, ITERATIVE_DECONV, INVERSE_DECONV, MARKER, MATCH, MATPROP,
+    MOMENT, NORM, PAD, RANDOM_ENGINE, STORAGE, TOPK, VARIANCE, YCC_STD, Dtype, Source, AF_VER_MAJOR, FORGE_VER_MAJOR)
 from .device import (
     alloc_device, alloc_host, alloc_pinned, device_gc, device_info, device_mem_info, eval, free_device, free_host,
     free_pinned, get_device, get_device_count, get_device_ptr, get_manual_eval_flag, info,
-    info_str, init, is_dbl_supported, is_half_supported, is_locked_array, lock_array, lock_device_ptr, print_mem_info, set_device,
-    set_manual_eval_flag, sync, unlock_array, unlock_device_ptr)
+    info_str, init, is_dbl_supported, is_half_supported, is_locked_array, lock_array, lock_device_ptr, print_mem_info,
+    set_device, set_manual_eval_flag, sync, unlock_array, unlock_device_ptr)
 from .graphics import Window
 from .image import (
-    anisotropic_diffusion, bilateral, canny, color_space, confidenceCC, dilate, dilate3, erode, erode3, gaussian_kernel, gradient,
-    gray2rgb, hist_equal, histogram, hsv2rgb, is_image_io_available, iterativeDeconv, inverseDeconv, load_image, load_image_native, maxfilt,
-    mean_shift, minfilt, moments, regions, resize, rgb2gray, rgb2hsv, rgb2ycbcr, rotate, sat, save_image,
-    save_image_native, scale, skew, sobel_derivatives, sobel_filter, transform, translate, unwrap, wrap, ycbcr2rgb)
+    anisotropic_diffusion, bilateral, canny, color_space, confidenceCC, dilate, dilate3, erode, erode3,
+    gaussian_kernel, gradient, gray2rgb, hist_equal, histogram, hsv2rgb, is_image_io_available, iterativeDeconv,
+    inverseDeconv, load_image, load_image_native, maxfilt, mean_shift, minfilt, moments, regions, resize, rgb2gray,
+    rgb2hsv, rgb2ycbcr, rotate, sat, save_image, save_image_native, scale, skew, sobel_derivatives, sobel_filter,
+    transform, translate, unwrap, wrap, ycbcr2rgb)
 from .index import Index, ParallelRange, Seq
 from .interop import AF_NUMBA_FOUND, AF_NUMPY_FOUND, AF_PYCUDA_FOUND, AF_PYOPENCL_FOUND, to_array
 from .lapack import (
-    cholesky, cholesky_inplace, det, inverse, is_lapack_available, lu, lu_inplace, norm, pinverse, qr, qr_inplace, rank, solve,
-    solve_lu, svd, svd_inplace)
+    cholesky, cholesky_inplace, det, inverse, is_lapack_available, lu, lu_inplace, norm, pinverse, qr, qr_inplace,
+    rank, solve, solve_lu, svd, svd_inplace)
 from .library import (
     get_active_backend, get_available_backends, get_backend, get_backend_count, get_backend_id, get_device_id,
-    get_size_of, safe_call, set_backend)
+    get_size_of, safe_call, set_backend, to_str)
 from .ml import convolve2GradientNN
 from .random import (
     Random_Engine, get_default_random_engine, get_seed, randn, randu, set_default_random_engine_type,
     set_seed)
 from .signal import (
-    approx1, approx1_uniform, approx2, approx2_uniform, convolve, convolve1, convolve2, convolve2NN, convolve2_separable, convolve3, dft, fft, fft2, fft2_c2r,
-    fft2_inplace, fft2_r2c, fft3, fft3_c2r, fft3_inplace, fft3_r2c, fft_c2r, fft_convolve, fft_convolve1,
-    fft_convolve2, fft_convolve3, fft_inplace, fft_r2c, fir, idft, ifft, ifft2, ifft2_inplace, ifft3, ifft3_inplace,
-    ifft_inplace, iir, medfilt, medfilt1, medfilt2, set_fft_plan_cache_size)
+    approx1, approx1_uniform, approx2, approx2_uniform, convolve, convolve1, convolve2, convolve2NN,
+    convolve2_separable, convolve3, dft, fft, fft2, fft2_c2r, fft2_inplace, fft2_r2c, fft3, fft3_c2r, fft3_inplace,
+    fft3_r2c, fft_c2r, fft_convolve, fft_convolve1, fft_convolve2, fft_convolve3, fft_inplace, fft_r2c, fir, idft,
+    ifft, ifft2, ifft2_inplace, ifft3, ifft3_inplace, ifft_inplace, iir, medfilt, medfilt1, medfilt2,
+    set_fft_plan_cache_size)
 from .sparse import (
     convert_sparse, convert_sparse_to_dense, create_sparse, create_sparse_from_dense, create_sparse_from_host,
     sparse_get_col_idx, sparse_get_info, sparse_get_nnz, sparse_get_row_idx, sparse_get_storage, sparse_get_values)
 from .statistics import corrcoef, cov, mean, meanvar, median, stdev, topk, var
 from .timer import timeit
-from .util import dim4, dim4_to_tuple, implicit_dtype, number_dtype, to_str, get_reversion, get_version, to_dtype, to_typecode, to_c_type
+from .util import (
+    dim4, dim4_to_tuple, implicit_dtype, number_dtype, get_reversion, get_version, to_dtype, to_typecode,
+    to_c_type)
 
 try:
     # FIXME: pycuda imported but unused
@@ -133,7 +139,7 @@
     "broadcast",
     # blas
     "dot", "matmul", "matmulNT", "matmulTN", "matmulTT", "gemm",
-    #cuda
+    # cuda
     "get_native_id", "get_stream", "set_native_id",
     # data
     "constant", "diag", "flat", "flip", "identity", "iota", "join", "lookup",
@@ -169,7 +175,7 @@
     "norm", "pinverse", "qr", "qr_inplace", "rank", "solve", "solve_lu", "svd", "svd_inplace",
     # library
     "get_active_backend", "get_available_backends", "get_backend", "get_backend_count",
-    "get_backend_id", "get_device_id", "get_size_of", "safe_call", "set_backend",
+    "get_backend_id", "get_device_id", "get_size_of", "safe_call", "set_backend", "to_str",
     # ml
     "convolve2GradientNN",
     # random
@@ -192,6 +198,6 @@
     # timer
     "timeit",
     # util
-    "dim4", "dim4_to_tuple", "implicit_dtype", "number_dtype", "to_str", "get_reversion",
+    "dim4", "dim4_to_tuple", "implicit_dtype", "number_dtype", "get_reversion",
     "get_version", "to_dtype", "to_typecode", "to_c_type"
 ]

arrayfire/algorithm.py

@@ -1,5 +1,5 @@
 #######################################################
-# Copyright (c) 2019, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -14,11 +14,13 @@
 from .array import Array
 from .library import backend, safe_call, BINARYOP, c_bool_t, c_double_t, c_int_t, c_pointer, c_uint_t
 
+
 def _parallel_dim(a, dim, c_func):
     out = Array()
     safe_call(c_func(c_pointer(out.arr), a.arr, c_int_t(dim)))
     return out
 
+
 def _reduce_all(a, c_func):
     real = c_double_t(0)
     imag = c_double_t(0)
@@ -29,11 +31,13 @@ def _reduce_all(a, c_func):
     imag = imag.value
     return real if imag == 0 else real + imag * 1j
 
+
 def _nan_parallel_dim(a, dim, c_func, nan_val):
     out = Array()
     safe_call(c_func(c_pointer(out.arr), a.arr, c_int_t(dim), c_double_t(nan_val)))
     return out
 
+
 def _nan_reduce_all(a, c_func, nan_val):
     real = c_double_t(0)
     imag = c_double_t(0)
@@ -44,6 +48,7 @@ def _nan_reduce_all(a, c_func, nan_val):
     imag = imag.value
     return real if imag == 0 else real + imag * 1j
 
+
 def _FNSD(dim, dims):
     if dim >= 0:
         return int(dim)
@@ -55,20 +60,26 @@ def _FNSD(dim, dims):
             break
     return int(fnsd)
 
+
 def _rbk_dim(keys, vals, dim, c_func):
     keys_out = Array()
     vals_out = Array()
     rdim = _FNSD(dim, vals.dims())
     safe_call(c_func(c_pointer(keys_out.arr), c_pointer(vals_out.arr), keys.arr, vals.arr, c_int_t(rdim)))
     return keys_out, vals_out
 
+
 def _nan_rbk_dim(a, dim, c_func, nan_val):
     keys_out = Array()
     vals_out = Array()
+    # FIXME: vals is undefined
     rdim = _FNSD(dim, vals.dims())
-    safe_call(c_func(c_pointer(keys_out.arr), c_pointer(vals_out.arr), keys.arr, vals.arr, c_int_t(rdim), c_double_t(nan_val)))
+    # FIXME: keys is undefined
+    safe_call(c_func(
+        c_pointer(keys_out.arr), c_pointer(vals_out.arr), keys.arr, vals.arr, c_int_t(rdim), c_double_t(nan_val)))
     return keys_out, vals_out
 
+
 def sum(a, dim=None, nan_val=None):
     """
     Calculate the sum of all the elements along a specified dimension.
@@ -88,18 +99,16 @@ def sum(a, dim=None, nan_val=None):
          The sum of all elements in `a` along dimension `dim`.
          If `dim` is `None`, sum of the entire Array is returned.
     """
-    if nan_val is not None:
-        if dim is not None:
+    if nan_val:
+        if dim:
             return _nan_parallel_dim(a, dim, backend.get().af_sum_nan, nan_val)
         return _nan_reduce_all(a, backend.get().af_sum_nan_all, nan_val)
 
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_sum)
     return _reduce_all(a, backend.get().af_sum_all)
 
 
-
-
 def sumByKey(keys, vals, dim=-1, nan_val=None):
     """
     Calculate the sum of elements along a specified dimension according to a key.
@@ -122,10 +131,10 @@ def sumByKey(keys, vals, dim=-1, nan_val=None):
     values: af.Array or scalar number
          The sum of all elements in `vals` along dimension `dim` according to keys
     """
-    if (nan_val is not None):
+    if nan_val:
         return _nan_rbk_dim(keys, vals, dim, backend.get().af_sum_by_key_nan, nan_val)
-    else:
-        return _rbk_dim(keys, vals, dim, backend.get().af_sum_by_key)
+    return _rbk_dim(keys, vals, dim, backend.get().af_sum_by_key)
+
 
 def product(a, dim=None, nan_val=None):
     """
@@ -146,12 +155,12 @@ def product(a, dim=None, nan_val=None):
          The product of all elements in `a` along dimension `dim`.
          If `dim` is `None`, product of the entire Array is returned.
     """
-    if nan_val is not None:
-        if dim is not None:
+    if nan_val:
+        if dim:
             return _nan_parallel_dim(a, dim, backend.get().af_product_nan, nan_val)
         return _nan_reduce_all(a, backend.get().af_product_nan_all, nan_val)
 
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_product)
     return _reduce_all(a, backend.get().af_product_all)
 
@@ -178,10 +187,10 @@ def productByKey(keys, vals, dim=-1, nan_val=None):
     values: af.Array or scalar number
          The product of all elements in `vals` along dimension `dim` according to keys
     """
-    if (nan_val is not None):
+    if nan_val:
         return _nan_rbk_dim(keys, vals, dim, backend.get().af_product_by_key_nan, nan_val)
-    else:
-        return _rbk_dim(keys, vals, dim, backend.get().af_product_by_key)
+    return _rbk_dim(keys, vals, dim, backend.get().af_product_by_key)
+
 
 def min(a, dim=None):
     """
@@ -200,7 +209,7 @@ def min(a, dim=None):
          The minimum value of all elements in `a` along dimension `dim`.
          If `dim` is `None`, minimum value of the entire Array is returned.
     """
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_min)
     return _reduce_all(a, backend.get().af_min_all)
 
@@ -227,6 +236,7 @@ def minByKey(keys, vals, dim=-1):
     """
     return _rbk_dim(keys, vals, dim, backend.get().af_min_by_key)
 
+
 def max(a, dim=None):
     """
     Find the maximum value of all the elements along a specified dimension.
@@ -244,7 +254,7 @@ def max(a, dim=None):
          The maximum value of all elements in `a` along dimension `dim`.
          If `dim` is `None`, maximum value of the entire Array is returned.
     """
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_max)
     return _reduce_all(a, backend.get().af_max_all)
 
@@ -271,6 +281,7 @@ def maxByKey(keys, vals, dim=-1):
     """
     return _rbk_dim(keys, vals, dim, backend.get().af_max_by_key)
 
+
 def all_true(a, dim=None):
     """
     Check if all the elements along a specified dimension are true.
@@ -288,7 +299,7 @@ def all_true(a, dim=None):
          Af.array containing True if all elements in `a` along the dimension are True.
          If `dim` is `None`, output is True if `a` does not have any zeros, else False.
     """
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_all_true)
     return _reduce_all(a, backend.get().af_all_true_all)
 
@@ -315,6 +326,7 @@ def allTrueByKey(keys, vals, dim=-1):
     """
     return _rbk_dim(keys, vals, dim, backend.get().af_all_true_by_key)
 
+
 def any_true(a, dim=None):
     """
     Check if any the elements along a specified dimension are true.
@@ -332,10 +344,10 @@ def any_true(a, dim=None):
          Af.array containing True if any elements in `a` along the dimension are True.
          If `dim` is `None`, output is True if `a` does not have any zeros, else False.
     """
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_any_true)
-    else:
-        return _reduce_all(a, backend.get().af_any_true_all)
+    return _reduce_all(a, backend.get().af_any_true_all)
+
 
 def anyTrueByKey(keys, vals, dim=-1):
     """
@@ -359,6 +371,7 @@ def anyTrueByKey(keys, vals, dim=-1):
     """
     return _rbk_dim(keys, vals, dim, backend.get().af_any_true_by_key)
 
+
 def count(a, dim=None):
     """
     Count the number of non zero elements in an array along a specified dimension.
@@ -376,10 +389,9 @@ def count(a, dim=None):
          The count of non zero elements in `a` along `dim`.
          If `dim` is `None`, the total number of non zero elements in `a`.
     """
-    if dim is not None:
+    if dim:
         return _parallel_dim(a, dim, backend.get().af_count)
-    else:
-        return _reduce_all(a, backend.get().af_count_all)
+    return _reduce_all(a, backend.get().af_count_all)
 
 
 def countByKey(keys, vals, dim=-1):
@@ -404,6 +416,7 @@ def countByKey(keys, vals, dim=-1):
     """
     return _rbk_dim(keys, vals, dim, backend.get().af_count_by_key)
 
+
 def imin(a, dim=None):
     """
     Find the value and location of the minimum value along a specified dimension
@@ -422,7 +435,7 @@ def imin(a, dim=None):
                 `idx` contains the location of where `val` occurs in `a` along `dim`.
                 If `dim` is `None`, `val` and `idx` value and location of global minimum.
     """
-    if dim is not None:
+    if dim:
         out = Array()
         idx = Array()
         safe_call(backend.get().af_imin(c_pointer(out.arr), c_pointer(idx.arr), a.arr, c_int_t(dim)))
@@ -456,7 +469,7 @@ def imax(a, dim=None):
                 `idx` contains the location of where `val` occurs in `a` along `dim`.
                 If `dim` is `None`, `val` and `idx` value and location of global maximum.
     """
-    if dim is not None:
+    if dim:
         out = Array()
         idx = Array()
         safe_call(backend.get().af_imax(c_pointer(out.arr), c_pointer(idx.arr), a.arr, c_int_t(dim)))