style 30Jan2021 (#549)

Author: shssf

dpnp/backend/include/dpnp_iface.hpp

@@ -415,8 +415,7 @@ INP_DLLEXPORT void dpnp_std_c(
  * @param [in]  size    Number of elements in the input array.
  */
 template <typename _DataType>
-INP_DLLEXPORT void dpnp_take_c(
-    void* array, void* indices, void* result, size_t size);
+INP_DLLEXPORT void dpnp_take_c(void* array, void* indices, void* result, size_t size);
 
 /**
  * @ingroup BACKEND_API

dpnp/backend/include/dpnp_iface_random.hpp

@@ -204,8 +204,7 @@ INP_DLLEXPORT void dpnp_rng_logistic_c(void* result, const double loc, double co
  * @param [out] result Output array.
  */
 template <typename _DataType>
-INP_DLLEXPORT void dpnp_rng_lognormal_c(void* result, const _DataType mean, const _DataType stddev,
-    const size_t size);
+INP_DLLEXPORT void dpnp_rng_lognormal_c(void* result, const _DataType mean, const _DataType stddev, const size_t size);
 
 /**
  * @ingroup BACKEND_RANDOM_API
@@ -218,8 +217,8 @@ INP_DLLEXPORT void dpnp_rng_lognormal_c(void* result, const _DataType mean, cons
  * @param [out] result        Output array.
  */
 template <typename _DataType>
-INP_DLLEXPORT void dpnp_rng_multinomial_c(void* result, const int ntrial, const double* p_vector,
-    const size_t p_vector_size, const size_t size);
+INP_DLLEXPORT void dpnp_rng_multinomial_c(
+    void* result, const int ntrial, const double* p_vector, const size_t p_vector_size, const size_t size);
 
 /**
  * @ingroup BACKEND_RANDOM_API

dpnp/backend/kernels/dpnp_krnl_indexing.cpp

@@ -31,7 +31,6 @@
 #include "dpnp_utils.hpp"
 #include "queue_sycl.hpp"
 
-
 template <typename _DataType>
 class dpnp_take_c_kernel;
 
@@ -51,7 +50,6 @@ void dpnp_take_c(void* array1_in, void* indices1, void* result1, size_t size)
     return;
 }
 
-
 void func_map_init_indexing_func(func_map_t& fmap)
 {
     fmap[DPNPFuncName::DPNP_FN_TAKE][eft_INT][eft_INT] = {eft_INT, (void*)dpnp_take_c<int>};

dpnp/backend/kernels/dpnp_krnl_random.cpp

@@ -407,11 +407,8 @@ void dpnp_rng_lognormal_c(void* result, const _DataType mean, const _DataType st
 }
 
 template <typename _DataType>
-void dpnp_rng_multinomial_c(void* result,
-                            const int ntrial,
-                            const double* p_vector,
-                            const size_t p_vector_size,
-                            const size_t size)
+void dpnp_rng_multinomial_c(
+    void* result, const int ntrial, const double* p_vector, const size_t p_vector_size, const size_t size)
 {
     if (!size)
     {

dpnp/backend/tests/test_random.cpp

@@ -43,7 +43,8 @@ bool check_statistics(_DataType* r, double tM, double tD, double tQ, size_t size
     /***** Sample moments *****/
     sum = 0.0;
     sum2 = 0.0;
-    for (size_t i = 0; i < size; i++) {
+    for (size_t i = 0; i < size; i++)
+    {
         sum += (double)r[i];
         sum2 += (double)r[i] * (double)r[i];
     }
@@ -175,7 +176,8 @@ TEST(TestBackendRandomUniform, test_seed)
     }
 }
 
-TEST(TestBackendRandomUniform, test_statistics) {
+TEST(TestBackendRandomUniform, test_statistics)
+{
     const size_t size = 256;
     size_t seed = 10;
     long a = 1;
@@ -187,10 +189,10 @@ TEST(TestBackendRandomUniform, test_statistics) {
     double tD = ((b - a) * (b - a)) / 12.0;
     double tQ = ((b - a) * (b - a) * (b - a) * (b - a)) / 80.0;
 
-    auto QueueOptionsDevices = std::vector<QueueOptions>{ QueueOptions::CPU_SELECTOR,
-        QueueOptions::GPU_SELECTOR };
+    auto QueueOptionsDevices = std::vector<QueueOptions>{QueueOptions::CPU_SELECTOR, QueueOptions::GPU_SELECTOR};
 
-    for (auto device_selector : QueueOptionsDevices) {
+    for (auto device_selector : QueueOptionsDevices)
+    {
         dpnp_queue_initialize_c(device_selector);
         double* result = (double*)dpnp_memory_alloc_c(size * sizeof(double));
         dpnp_rng_srand_c(seed);
@@ -201,8 +203,8 @@ TEST(TestBackendRandomUniform, test_statistics) {
     }
 }
 
-TEST(TestBackendRandomSrand, test_func_ptr) {
-
+TEST(TestBackendRandomSrand, test_func_ptr)
+{
     void* fptr = nullptr;
     DPNPFuncData kernel_data = get_dpnp_function_ptr(
         DPNPFuncName::DPNP_FN_RNG_SRAND, DPNPFuncType::DPNP_FT_DOUBLE, DPNPFuncType::DPNP_FT_DOUBLE);

dpnp/dpnp_algo/dpnp_algo_mathematical.pyx

@@ -278,15 +278,15 @@ cpdef dparray dpnp_gradient(dparray y1, int dx=1):
     size = y1.size
 
     cdef dparray result = dparray(size, dtype=dpnp.float64)
-   
+
     cur = (y1[1] - y1[0]) / dx
 
     result._setitem_scalar(0, cur)
 
     cur = (y1[-1] - y1[-2]) / dx
 
     result._setitem_scalar(size - 1, cur)
-  
+
     for i in range(1, size - 1):
         cur = (y1[i + 1] - y1[i - 1]) / (2 * dx)
         result._setitem_scalar(i, cur)

dpnp/dpnp_iface_mathematical.py

@@ -809,7 +809,7 @@ def gradient(y1, *varargs, **kwargs):
         Keyword arguments ``kwargs`` are currently unsupported.
         Otherwise the functions will be executed sequentially on CPU.
         Input array data types are limited by supported DPNP :ref:`Data types`.
-   
+
     Example
     -------
     >>> import dpnp as np

tests/test_special.py

@@ -17,6 +17,7 @@ def test_erf():
 
     numpy.testing.assert_array_equal(result, expected)
 
+
 def test_erf_fallback():
     a = numpy.linspace(2.0, 3.0, num=10)