Merge branch 'master' into keepdims

code/numpy/numerical.c

@@ -274,7 +274,7 @@ static mp_obj_t numerical_sum_mean_std_iterable(mp_obj_t oin, uint8_t optype, si
     }
 }
 
-static mp_obj_t numerical_sum_mean_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype, size_t ddof) {
+static mp_obj_t numerical_sum_mean_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, mp_obj_t keepdims, uint8_t optype, size_t ddof) {
     COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
     uint8_t *array = (uint8_t *)ndarray->array;
     shape_strides _shape_strides = tools_reduce_axes(ndarray, axis);
@@ -380,7 +380,7 @@ static mp_obj_t numerical_sum_mean_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t
             bool isStd = optype == NUMERICAL_STD ? 1 : 0;
             results = ndarray_new_dense_ndarray(_shape_strides.ndim, _shape_strides.shape, NDARRAY_FLOAT);
             farray = (mp_float_t *)results->array;
-            // we can return the 0 array here, if the degrees of freedom is larger than the length of the axis
+            // we can return the 0 array here, if the degrees of freedom are larger than the length of the axis
             if((optype == NUMERICAL_STD) && (_shape_strides.shape[0] <= ddof)) {
                 return MP_OBJ_FROM_PTR(results);
             }
@@ -397,11 +397,9 @@ static mp_obj_t numerical_sum_mean_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t
                 RUN_MEAN_STD(mp_float_t, array, farray, _shape_strides, div, isStd);
             }
         }
-        if(results->ndim == 0) { // return a scalar here
-            return mp_binary_get_val_array(results->dtype, results->array, 0);
-        }
-        return MP_OBJ_FROM_PTR(results);
+        return ulab_tools_restore_dims(ndarray, results, keepdims, _shape_strides);
     }
+    // we should never get to this point
     return mp_const_none;
 }
 #endif
@@ -441,7 +439,7 @@ static mp_obj_t numerical_argmin_argmax_iterable(mp_obj_t oin, uint8_t optype) {
     }
 }
 
-static mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype) {
+static mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t keepdims, mp_obj_t axis, uint8_t optype) {
     // TODO: treat the flattened array
     if(ndarray->len == 0) {
         mp_raise_ValueError(MP_ERROR_TEXT("attempt to get (arg)min/(arg)max of empty sequence"));
@@ -521,7 +519,9 @@ static mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t
         int32_t *strides = m_new0(int32_t, ULAB_MAX_DIMS);
 
         numerical_reduce_axes(ndarray, ax, shape, strides);
-        uint8_t index = ULAB_MAX_DIMS - ndarray->ndim + ax;
+        shape_strides _shape_strides = tools_reduce_axes(ndarray, axis);
+
+        uint8_t index = _shape_strides.axis;
 
         ndarray_obj_t *results = NULL;
 
@@ -550,8 +550,9 @@ static mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t
         if(results->len == 1) {
             return mp_binary_get_val_array(results->dtype, results->array, 0);
         }
-        return MP_OBJ_FROM_PTR(results);
+        return ulab_tools_restore_dims(ndarray, results, keepdims, _shape_strides);
     }
+    // we should never get to this point
     return mp_const_none;
 }
 #endif
@@ -560,6 +561,7 @@ static mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_m
     static const mp_arg_t allowed_args[] = {
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, { .u_rom_obj = MP_ROM_NONE} } ,
         { MP_QSTR_axis, MP_ARG_OBJ, { .u_rom_obj = MP_ROM_NONE } },
+        { MP_QSTR_keepdims, MP_ARG_OBJ, { .u_rom_obj = MP_ROM_FALSE } },
     };
 
     mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
@@ -567,6 +569,8 @@ static mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_m
 
     mp_obj_t oin = args[0].u_obj;
     mp_obj_t axis = args[1].u_obj;
+    mp_obj_t keepdims = args[2].u_obj;
+
     if((axis != mp_const_none) && (!mp_obj_is_int(axis))) {
         mp_raise_TypeError(MP_ERROR_TEXT("axis must be None, or an integer"));
     }
@@ -598,11 +602,11 @@ static mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_m
             case NUMERICAL_ARGMIN:
             case NUMERICAL_ARGMAX:
                 COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
-                return numerical_argmin_argmax_ndarray(ndarray, axis, optype);
+                return numerical_argmin_argmax_ndarray(ndarray, keepdims, axis, optype);
             case NUMERICAL_SUM:
             case NUMERICAL_MEAN:
                 COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
-                return numerical_sum_mean_std_ndarray(ndarray, axis, optype, 0);
+                return numerical_sum_mean_std_ndarray(ndarray, axis, keepdims, optype, 0);
             default:
                 mp_raise_NotImplementedError(MP_ERROR_TEXT("operation is not implemented on ndarrays"));
         }
@@ -1385,6 +1389,7 @@ mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_arg
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } } ,
         { MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
         { MP_QSTR_ddof, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 0} },
+        { MP_QSTR_keepdims, MP_ARG_OBJ, { .u_rom_obj = MP_ROM_FALSE } },
     };
 
     mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
@@ -1393,6 +1398,8 @@ mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_arg
     mp_obj_t oin = args[0].u_obj;
     mp_obj_t axis = args[1].u_obj;
     size_t ddof = args[2].u_int;
+    mp_obj_t keepdims = args[2].u_obj;
+
     if((axis != mp_const_none) && (mp_obj_get_int(axis) != 0) && (mp_obj_get_int(axis) != 1)) {
         // this seems to pass with False, and True...
         mp_raise_ValueError(MP_ERROR_TEXT("axis must be None, or an integer"));
@@ -1401,7 +1408,7 @@ mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_arg
         return numerical_sum_mean_std_iterable(oin, NUMERICAL_STD, ddof);
     } else if(mp_obj_is_type(oin, &ulab_ndarray_type)) {
         ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(oin);
-        return numerical_sum_mean_std_ndarray(ndarray, axis, NUMERICAL_STD, ddof);
+        return numerical_sum_mean_std_ndarray(ndarray, axis, keepdims, NUMERICAL_STD, ddof);
     } else {
         mp_raise_TypeError(MP_ERROR_TEXT("input must be tuple, list, range, or ndarray"));
     }

code/ulab.c

@@ -33,7 +33,7 @@
 #include "user/user.h"
 #include "utils/utils.h"
 
-#define ULAB_VERSION 6.7.0
+#define ULAB_VERSION 6.7.1
 #define xstr(s) str(s)
 #define str(s) #s
 

code/ulab_tools.c

@@ -162,6 +162,15 @@ void *ndarray_set_float_function(uint8_t dtype) {
 }
 #endif /* NDARRAY_BINARY_USES_FUN_POINTER */
 
+int8_t tools_get_axis(mp_obj_t axis, uint8_t ndim) {
+    int8_t ax = mp_obj_get_int(axis);
+    if(ax < 0) ax += ndim;
+    if((ax < 0) || (ax > ndim - 1)) {
+        mp_raise_ValueError(MP_ERROR_TEXT("axis is out of bounds"));
+    }
+    return ax;
+}
+
 shape_strides tools_reduce_axes(ndarray_obj_t *ndarray, mp_obj_t axis) {
     // TODO: replace numerical_reduce_axes with this function, wherever applicable
     // This function should be used, whenever a tensor is contracted;
@@ -172,38 +181,36 @@ shape_strides tools_reduce_axes(ndarray_obj_t *ndarray, mp_obj_t axis) {
     }
     shape_strides _shape_strides;
 
+    _shape_strides.increment = 0;
+    // this is the contracted dimension (won't be overwritten for axis == None)
+    _shape_strides.ndim = 0;
+
+    if(axis == mp_const_none) {
+        _shape_strides.shape = ndarray->shape;
+        _shape_strides.strides = ndarray->strides;
+        return _shape_strides;
+    }
+
     size_t *shape = m_new(size_t, ULAB_MAX_DIMS + 1);
     _shape_strides.shape = shape;
     int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS + 1);
     _shape_strides.strides = strides;
 
-    _shape_strides.increment = 0;
-    // this is the contracted dimension (won't be overwritten for axis == None)
-    _shape_strides.ndim = 0;
-
     memcpy(_shape_strides.shape, ndarray->shape, sizeof(size_t) * ULAB_MAX_DIMS);
     memcpy(_shape_strides.strides, ndarray->strides, sizeof(int32_t) * ULAB_MAX_DIMS);
 
-    if(axis == mp_const_none) {
-        return _shape_strides;
-    }
-
-    uint8_t index = ULAB_MAX_DIMS - 1; // value of index for axis == mp_const_none (won't be overwritten)
+    _shape_strides.axis = ULAB_MAX_DIMS - 1; // value of index for axis == mp_const_none (won't be overwritten)
 
     if(axis != mp_const_none) { // i.e., axis is an integer
-        int8_t ax = mp_obj_get_int(axis);
-        if(ax < 0) ax += ndarray->ndim;
-        if((ax < 0) || (ax > ndarray->ndim - 1)) {
-            mp_raise_ValueError(MP_ERROR_TEXT("index out of range"));
-        }
-        index = ULAB_MAX_DIMS - ndarray->ndim + ax;
+        int8_t ax = tools_get_axis(axis, ndarray->ndim);
+        _shape_strides.axis = ULAB_MAX_DIMS - ndarray->ndim + ax;
         _shape_strides.ndim = ndarray->ndim - 1;
     }
 
     // move the value stored at index to the leftmost position, and align everything else to the right
-    _shape_strides.shape[0] = ndarray->shape[index];
-    _shape_strides.strides[0] = ndarray->strides[index];
-    for(uint8_t i = 0; i < index; i++) {
+    _shape_strides.shape[0] = ndarray->shape[_shape_strides.axis];
+    _shape_strides.strides[0] = ndarray->strides[_shape_strides.axis];
+    for(uint8_t i = 0; i < _shape_strides.axis; i++) {
         // entries to the right of index must be shifted by one position to the left
         _shape_strides.shape[i + 1] = ndarray->shape[i];
         _shape_strides.strides[i + 1] = ndarray->strides[i];
@@ -213,16 +220,37 @@ shape_strides tools_reduce_axes(ndarray_obj_t *ndarray, mp_obj_t axis) {
         _shape_strides.increment = 1;
     }
 
+    if(_shape_strides.ndim == 0) {
+        _shape_strides.ndim = 1;
+        _shape_strides.shape[ULAB_MAX_DIMS - 1] = 1;
+        _shape_strides.strides[ULAB_MAX_DIMS - 1] = ndarray->itemsize;
+    }
+
     return _shape_strides;
 }
 
-int8_t tools_get_axis(mp_obj_t axis, uint8_t ndim) {
-    int8_t ax = mp_obj_get_int(axis);
-    if(ax < 0) ax += ndim;
-    if((ax < 0) || (ax > ndim - 1)) {
-        mp_raise_ValueError(MP_ERROR_TEXT("axis is out of bounds"));
+mp_obj_t ulab_tools_restore_dims(ndarray_obj_t *ndarray, ndarray_obj_t *results, mp_obj_t keepdims, shape_strides _shape_strides) {
+    // restores the contracted dimension, if keepdims is True
+    if((ndarray->ndim == 1) && (keepdims != mp_const_true)) {
+        // since the original array has already been contracted and 
+        // we don't want to keep the dimensions here, we have to return a scalar
+        return mp_binary_get_val_array(results->dtype, results->array, 0);
     }
-    return ax;
+
+    if(keepdims == mp_const_true) {
+        results->ndim += 1;
+        for(int8_t i = 0; i < ULAB_MAX_DIMS; i++) {
+            results->shape[i] = ndarray->shape[i];
+        }
+        results->shape[_shape_strides.axis] = 1;
+
+        results->strides[ULAB_MAX_DIMS - 1] = ndarray->itemsize;
+        for(uint8_t i = ULAB_MAX_DIMS; i > 1; i--) {
+            results->strides[i - 2] = results->strides[i - 1] * results->shape[i - 1];
+        }
+    }
+
+    return MP_OBJ_FROM_PTR(results);
 }
 
 #if ULAB_MAX_DIMS > 1

code/ulab_tools.h

@@ -17,6 +17,7 @@
 
 typedef struct _shape_strides_t {
     uint8_t increment;
+    uint8_t axis;
     uint8_t ndim;
     size_t *shape;
     int32_t *strides;
@@ -34,6 +35,7 @@ void *ndarray_set_float_function(uint8_t );
 
 shape_strides tools_reduce_axes(ndarray_obj_t *, mp_obj_t );
 int8_t tools_get_axis(mp_obj_t , uint8_t );
+mp_obj_t ulab_tools_restore_dims(ndarray_obj_t * , ndarray_obj_t * , mp_obj_t , shape_strides );
 ndarray_obj_t *tools_object_is_square(mp_obj_t );
 
 uint8_t ulab_binary_get_size(uint8_t );

docs/ulab-change-log.md

@@ -1,3 +1,15 @@
+Mon, 30 Dec 2024
+
+version 6.7.1
+
+    add keepdims keyword argument to numerical functions
+
+Sun, 15 Dec 2024
+
+version 6.7.0
+
+    add scipy.integrate module
+
 Sun, 24 Nov 2024
 
 version 6.6.1

tests/2d/numpy/sum.py

@@ -0,0 +1,23 @@
+try:
+    from ulab import numpy as np
+except ImportError:
+    import numpy as np
+
+for dtype in (np.uint8, np.int8, np.uint16, np.int8, np.float):
+    a = np.array(range(12), dtype=dtype)
+    b = a.reshape((3, 4))
+
+    print(a)
+    print(b)
+    print()
+
+    print(np.sum(a))
+    print(np.sum(a, axis=0))
+    print(np.sum(a, axis=0, keepdims=True))
+
+    print()
+    print(np.sum(b))
+    print(np.sum(b, axis=0))
+    print(np.sum(b, axis=1))
+    print(np.sum(b, axis=0, keepdims=True))
+    print(np.sum(b, axis=1, keepdims=True))

tests/2d/numpy/sum.py.exp

@@ -0,0 +1,80 @@
+array([0, 1, 2, ..., 9, 10, 11], dtype=uint8)
+array([[0, 1, 2, 3],
+       [4, 5, 6, 7],
+       [8, 9, 10, 11]], dtype=uint8)
+
+66
+66
+array([66], dtype=uint8)
+
+66
+array([12, 15, 18, 21], dtype=uint8)
+array([6, 22, 38], dtype=uint8)
+array([[12, 15, 18, 21]], dtype=uint8)
+array([[6],
+       [22],
+       [38]], dtype=uint8)
+array([0, 1, 2, ..., 9, 10, 11], dtype=int8)
+array([[0, 1, 2, 3],
+       [4, 5, 6, 7],
+       [8, 9, 10, 11]], dtype=int8)
+
+66
+66
+array([66], dtype=int8)
+
+66
+array([12, 15, 18, 21], dtype=int8)
+array([6, 22, 38], dtype=int8)
+array([[12, 15, 18, 21]], dtype=int8)
+array([[6],
+       [22],
+       [38]], dtype=int8)
+array([0, 1, 2, ..., 9, 10, 11], dtype=uint16)
+array([[0, 1, 2, 3],
+       [4, 5, 6, 7],
+       [8, 9, 10, 11]], dtype=uint16)
+
+66
+66
+array([66], dtype=uint16)
+
+66
+array([12, 15, 18, 21], dtype=uint16)
+array([6, 22, 38], dtype=uint16)
+array([[12, 15, 18, 21]], dtype=uint16)
+array([[6],
+       [22],
+       [38]], dtype=uint16)
+array([0, 1, 2, ..., 9, 10, 11], dtype=int8)
+array([[0, 1, 2, 3],
+       [4, 5, 6, 7],
+       [8, 9, 10, 11]], dtype=int8)
+
+66
+66
+array([66], dtype=int8)
+
+66
+array([12, 15, 18, 21], dtype=int8)
+array([6, 22, 38], dtype=int8)
+array([[12, 15, 18, 21]], dtype=int8)
+array([[6],
+       [22],
+       [38]], dtype=int8)
+array([0.0, 1.0, 2.0, ..., 9.0, 10.0, 11.0], dtype=float64)
+array([[0.0, 1.0, 2.0, 3.0],
+       [4.0, 5.0, 6.0, 7.0],
+       [8.0, 9.0, 10.0, 11.0]], dtype=float64)
+
+66.0
+66.0
+array([66.0], dtype=float64)
+
+66.0
+array([12.0, 15.0, 18.0, 21.0], dtype=float64)
+array([6.0, 22.0, 38.0], dtype=float64)
+array([[12.0, 15.0, 18.0, 21.0]], dtype=float64)
+array([[6.0],
+       [22.0],
+       [38.0]], dtype=float64)