stat submodule to desc completed (#870)

Author: shssf

dpnp/dpnp_algo/dpnp_algo_statistics.pyx

@@ -57,18 +57,18 @@ ctypedef void(*fptr_custom_std_var_1in_1out_t)(void *, void * , size_t * , size_
 ctypedef void(*custom_statistic_1in_1out_func_ptr_t)(void *, void * , size_t * , size_t, size_t * , size_t)
 
 
-cdef dparray call_fptr_custom_std_var_1in_1out(DPNPFuncName fptr_name, utils.dpnp_descriptor x1, ddof):
+cdef utils.dpnp_descriptor call_fptr_custom_std_var_1in_1out(DPNPFuncName fptr_name, utils.dpnp_descriptor x1, ddof):
     cdef shape_type_c x1_shape = x1.shape
 
-    """ Convert string type names (dparray.dtype) to C enum DPNPFuncType """
+    """ Convert string type names (array.dtype) to C enum DPNPFuncType """
     cdef DPNPFuncType param_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)
 
     """ get the FPTR data structure """
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, param_type, DPNP_FT_NONE)
 
-    result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
-    """ Create result array with type given by FPTR data """
-    cdef dparray result = dparray((1,), dtype=result_type)
+    # ceate result array with type given by FPTR data
+    cdef shape_type_c result_shape = (1,)
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, kernel_data.return_type, None)
 
     cdef fptr_custom_std_var_1in_1out_t func = <fptr_custom_std_var_1in_1out_t > kernel_data.ptr
 
@@ -92,7 +92,7 @@ cpdef dpnp_average(utils.dpnp_descriptor x1):
     return (return_type(array_sum / x1.size))
 
 
-cpdef dparray dpnp_correlate(utils.dpnp_descriptor x1, utils.dpnp_descriptor x2):
+cpdef utils.dpnp_descriptor dpnp_correlate(utils.dpnp_descriptor x1, utils.dpnp_descriptor x2):
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)
     cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(x2.dtype)
 
@@ -101,9 +101,9 @@ cpdef dparray dpnp_correlate(utils.dpnp_descriptor x1, utils.dpnp_descriptor x2)
 
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_CORRELATE, param1_type, param2_type)
 
-    result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
-
-    cdef dparray result = dparray(1, dtype=result_type)
+    # ceate result array with type given by FPTR data
+    cdef shape_type_c result_shape = (1,)
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, kernel_data.return_type, None)
 
     cdef fptr_2in_1out_t func = <fptr_2in_1out_t > kernel_data.ptr
 
@@ -120,7 +120,7 @@ cpdef utils.dpnp_descriptor dpnp_cov(utils.dpnp_descriptor array1):
     if array1.ndim == 1:
         input_shape.insert(input_shape.begin(), 1)
 
-    # convert string type names (dparray.dtype) to C enum DPNPFuncType
+    # convert string type names (array.dtype) to C enum DPNPFuncType
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)
 
     # get the FPTR data structure
@@ -137,14 +137,14 @@ cpdef utils.dpnp_descriptor dpnp_cov(utils.dpnp_descriptor array1):
     return result
 
 
-cpdef dparray _dpnp_max(dparray input, _axis_, output_shape):
+cdef utils.dpnp_descriptor _dpnp_max(utils.dpnp_descriptor input, _axis_, shape_type_c result_shape):
     cdef shape_type_c input_shape = input.shape
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
 
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MAX, param1_type, param1_type)
 
-    result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
-    cdef dparray result = dparray(output_shape, dtype=result_type)
+    # ceate result array with type given by FPTR data
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, kernel_data.return_type, None)
 
     cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr
     cdef shape_type_c axis
@@ -160,16 +160,16 @@ cpdef dparray _dpnp_max(dparray input, _axis_, output_shape):
 
     func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim, < size_t * > axis_.data(), axis_size)
 
-    dpnp_array = dpnp.array(result, dtype=input.dtype)
-    dpnp_result_array = dpnp_array.reshape(output_shape)
-    return dpnp_result_array
+    return result
 
 
-cpdef dparray dpnp_max(dparray input, axis):
+cpdef utils.dpnp_descriptor dpnp_max(utils.dpnp_descriptor input, axis):
     cdef shape_type_c shape_input = input.shape
+    cdef shape_type_c output_shape
+
     if axis is None:
         axis_ = axis
-        output_shape = 1
+        output_shape.push_back(1)
     else:
         if isinstance(axis, int):
             if axis < 0:
@@ -185,12 +185,13 @@ cpdef dparray dpnp_max(dparray input, axis):
                     _axis_.append(axis[i])
             axis_ = tuple(_axis_)
 
-        output_shape = dparray(len(shape_input) - len(axis_), dtype=numpy.int64)
+        output_shape.resize(len(shape_input) - len(axis_), 0)
         ind = 0
         for id, shape_axis in enumerate(shape_input):
             if id not in axis_:
                 output_shape[ind] = shape_axis
                 ind += 1
+
     return _dpnp_max(input, axis_, output_shape)
 
 
@@ -407,29 +408,29 @@ cpdef utils.dpnp_descriptor dpnp_min(utils.dpnp_descriptor input, axis):
     return _dpnp_min(input, axis_, shape_output)
 
 
-cpdef dparray dpnp_nanvar(utils.dpnp_descriptor arr, ddof):
-    cdef dparray mask_arr = dpnp.isnan(arr)
-    n = sum(mask_arr)
+cpdef utils.dpnp_descriptor dpnp_nanvar(utils.dpnp_descriptor arr, ddof):
+    cdef utils.dpnp_descriptor mask_arr = dpnp_isnan(arr)
+    n = sum(mask_arr.get_pyobj())
     res_size = arr.size - n
 
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(arr.dtype)
 
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_NANVAR, param1_type, param1_type)
 
-    result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
-    cdef dparray without_nan_arr = dparray((res_size, ), dtype=result_type)
+    # ceate result array with type given by FPTR data
+    cdef shape_type_c result_shape = utils._object_to_tuple(res_size)
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, kernel_data.return_type, None)
 
     cdef fptr_custom_nanvar_t func = <fptr_custom_nanvar_t > kernel_data.ptr
 
-    func(arr.get_data(), mask_arr.get_data(), without_nan_arr.get_data(), arr.size)
+    func(arr.get_data(), mask_arr.get_data(), result.get_data(), arr.size)
 
-    without_nan_arr_desc = dpnp.get_dpnp_descriptor(without_nan_arr)
-    return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR, without_nan_arr_desc, ddof)
+    return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR, result, ddof)
 
 
-cpdef dparray dpnp_std(utils.dpnp_descriptor a, size_t ddof):
+cpdef utils.dpnp_descriptor dpnp_std(utils.dpnp_descriptor a, size_t ddof):
     return call_fptr_custom_std_var_1in_1out(DPNP_FN_STD, a, ddof)
 
 
-cpdef dparray dpnp_var(utils.dpnp_descriptor a, size_t ddof):
+cpdef utils.dpnp_descriptor dpnp_var(utils.dpnp_descriptor a, size_t ddof):
     return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR, a, ddof)

dpnp/dpnp_iface_statistics.py

@@ -231,7 +231,7 @@ def correlate(x1, x2, mode='valid'):
         elif mode != 'valid':
             pass
         else:
-            return dpnp_correlate(x1_desc, x2_desc)
+            return dpnp_correlate(x1_desc, x2_desc).get_pyobj()
 
     return call_origin(numpy.correlate, x1, x2, mode=mode)
 
@@ -384,7 +384,7 @@ def max(x1, axis=None, out=None, keepdims=False, initial=None, where=True):
         elif where is not True:
             pass
         else:
-            result_obj = dpnp_max(x1, axis=axis)
+            result_obj = dpnp_max(x1_desc, axis).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
@@ -562,7 +562,7 @@ def nanvar(x1, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
         elif keepdims:
             pass
         else:
-            result_obj = dpnp_nanvar(x1_desc, ddof)
+            result_obj = dpnp_nanvar(x1_desc, ddof).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
@@ -620,7 +620,7 @@ def std(x1, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
         elif keepdims:
             pass
         else:
-            result_obj = dpnp_std(x1_desc, ddof)
+            result_obj = dpnp_std(x1_desc, ddof).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result
@@ -678,7 +678,7 @@ def var(x1, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
         elif keepdims:
             pass
         else:
-            result_obj = dpnp_var(x1_desc, ddof)
+            result_obj = dpnp_var(x1_desc, ddof).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
             return result