fixed dtype errors - package works

Author: shivasankarka

numojo/core/array_creation_routines.mojo

@@ -38,8 +38,7 @@ fn arange[
         Error if both dtype and dtype are integers or if dtype is a float and dtype is an integer.
 
     Parameters:
-        dtype: Input datatype of the input values.
-        dtype: Output datatype of the output NDArray.
+        dtype: Datatype of the output array.
 
     Args:
         start: Scalar[dtype] - Start value.
@@ -64,7 +63,7 @@ fn arange[
     )
     for idx in range(num):
         result.data[idx] = (
-            start.cast[out_dtype]() + step.cast[out_dtype]() * idx
+            start.cast[dtype]() + step.cast[dtype]() * idx
         )
 
     return result
@@ -89,11 +88,10 @@ fn linspace[
     Function that computes a series of linearly spaced values starting from "start" to "stop" with given size. Wrapper function for _linspace_serial, _linspace_parallel.
 
     Raises:
-        Error if both dtype and dtype are integers or if dtype is a float and dtype is an integer.
+        Error if dtype is an integer.
 
     Parameters:
-        dtype: Datatype of the input values.
-        dtype: Datatype of the output NDArray.
+        dtype: Datatype of the output array.
 
     Args:
         start: Start value.
@@ -222,11 +220,10 @@ fn logspace[
     Generate a logrithmic spaced NDArray of `num` elements between `start` and `stop`. Wrapper function for _logspace_serial, _logspace_parallel functions.
 
     Raises:
-        Error if both dtype and dtype are integers or if dtype is a float and dtype is an integer.
+        Error if dtype is an integer.
 
     Parameters:
-        dtype: Datatype of the input values.
-        dtype: Datatype of the output NDArray.
+        dtype: Datatype of the output array.
 
     Args:
         start: The starting value of the NDArray.
@@ -363,11 +360,10 @@ fn geomspace[
     Generate a NDArray of `num` elements between `start` and `stop` in a geometric series.
 
     Raises:
-        Error if both dtype and dtype are integers or if dtype is a float and dtype is an integer.
+        Error if dtype is an integer.
 
     Parameters:
         dtype: Datatype of the input values.
-        dtype: Datatype of the output NDArray.
 
     Args:
         start: The starting value of the NDArray.

numojo/core/array_manipulation_routines.mojo

@@ -75,7 +75,7 @@ fn reshape[
         num_elements_new *= i
         ndim_new += 1
 
-    if array.ndshape._size != num_elements_new:
+    if array.ndshape.ndsize != num_elements_new:
         raise Error("Cannot reshape: Number of elements do not match.")
 
     var shape_new: List[Int] = List[Int]()
@@ -100,9 +100,9 @@ fn ravel[dtype: DType](inout array: NDArray[dtype], order: String = "C") raises:
         return
     else:
         if order == "C":
-            reshape[dtype](array, array.ndshape._size, order="C")
+            reshape[dtype](array, array.ndshape.ndsize, order="C")
         else:
-            reshape[dtype](array, array.ndshape._size, order="F")
+            reshape[dtype](array, array.ndshape.ndsize, order="F")
 
 
 fn where[
@@ -122,7 +122,7 @@ fn where[
         mask: A NDArray.
 
     """
-    for i in range(x.ndshape._size):
+    for i in range(x.ndshape.ndsize):
         if mask.data[i] == True:
             x.data.store(i, scalar)
 
@@ -148,7 +148,7 @@ fn where[
     """
     if x.ndshape != y.ndshape:
         raise Error("Shape mismatch error: x and y must have the same shape")
-    for i in range(x.ndshape._size):
+    for i in range(x.ndshape.ndsize):
         if mask.data[i] == True:
             x.data.store(i, y.data[i])
 
@@ -172,6 +172,6 @@ fn flip[dtype: DType](array: NDArray[dtype]) raises -> NDArray[dtype]:
     var result: NDArray[dtype] = NDArray[dtype](
         shape=array.ndshape, order=array.order
     )
-    for i in range(array.ndshape._size):
-        result.data.store(i, array.data[array.ndshape._size - i - 1])
+    for i in range(array.ndshape.ndsize):
+        result.data.store(i, array.data[array.ndshape.ndsize - i - 1])
     return result

numojo/core/ndarray.mojo

@@ -1792,17 +1792,13 @@ struct NDArray[dtype: DType = DType.float64](
         """
         Itemwise equivelence between scalar and Array.
         """
-        var other_array: Self = NDArray[dtype](self.shape(), fill=other)
-        return math.equal[dtype](self, other_array)
         return math.equal[dtype](self, other)
 
     @always_inline("nodebug")
     fn __ne__(self, other: SIMD[dtype, 1]) raises -> NDArray[DType.bool]:
         """
         Itemwise nonequivelence.
         """
-        var other_array: Self = NDArray[dtype](self.shape(), fill=other)
-        return math.not_equal[dtype](self, other_array)
         return math.not_equal[dtype](self, other)
 
     @always_inline("nodebug")
@@ -1817,8 +1813,6 @@ struct NDArray[dtype: DType = DType.float64](
         """
         Itemwise less than.
         """
-        var other_array: Self = NDArray[dtype](self.shape(), fill=other)
-        return math.less[dtype](self, other_array)
         return math.less[dtype](self, other)
 
     @always_inline("nodebug")

numojo/math/calculus/differentiation.mojo

@@ -19,7 +19,7 @@ fn gradient[
     dtype: DType
 ](x: NDArray[dtype], spacing: Scalar[dtype]) raises -> NDArray[dtype]:
     """
-    Compute the integral of y over x using the trapezoidal rule.
+    Compute the gradient of y over x using the trapezoidal rule.
 
     Parameters:
         dtype: Input data type.
@@ -48,11 +48,11 @@ fn gradient[
     var space: NDArray[dtype] = core.arange[dtype](
         1, x.num_elements() + 1, step=spacing.cast[dtype]()
     )
-    var hu: Scalar[out_dtype] = space.get_scalar(1)
-    var hd: Scalar[out_dtype] = space.get_scalar(0)
+    var hu: Scalar[dtype] = space.get_scalar(1)
+    var hd: Scalar[dtype] = space.get_scalar(0)
     result.store(
         0,
-        (x.get_scalar(1).cast[out_dtype]() - x.get_scalar(0).cast[out_dtype]())
+        (x.get_scalar(1).cast[dtype]() - x.get_scalar(0).cast[dtype]())
         / (hu - hd),
     )
 
@@ -61,23 +61,23 @@ fn gradient[
     result.store(
         x.num_elements() - 1,
         (
-            x.get_scalar(x.num_elements() - 1).cast[out_dtype]()
-            - x.get_scalar(x.num_elements() - 2).cast[out_dtype]()
+            x.get_scalar(x.num_elements() - 1).cast[dtype]()
+            - x.get_scalar(x.num_elements() - 2).cast[dtype]()
         )
         / (hu - hd),
     )
 
     for i in range(1, x.num_elements() - 1):
-        var hu: Scalar[out_dtype] = space.get_scalar(i + 1) - space.get_scalar(
+        var hu: Scalar[dtype] = space.get_scalar(i + 1) - space.get_scalar(
             i
         )
-        var hd: Scalar[out_dtype] = space.get_scalar(i) - space.get_scalar(
+        var hd: Scalar[dtype] = space.get_scalar(i) - space.get_scalar(
             i - 1
         )
-        var fi: Scalar[out_dtype] = (
-            hd**2 * x.get_scalar(i + 1).cast[out_dtype]()
-            + (hu**2 - hd**2) * x.get_scalar(i).cast[out_dtype]()
-            - hu**2 * x.get_scalar(i - 1).cast[out_dtype]()
+        var fi: Scalar[dtype] = (
+            hd**2 * x.get_scalar(i + 1).cast[dtype]()
+            + (hu**2 - hd**2) * x.get_scalar(i).cast[dtype]()
+            - hu**2 * x.get_scalar(i - 1).cast[dtype]()
         ) / (hu * hd * (hu + hd))
         result.store(i, fi)