Auto-generated commit

Author: stdlib-bot

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,6 +2,6 @@
 
 We are excited about your pull request, but unfortunately we are not accepting pull requests against this repository, as all development happens on the [main project repository](https://github.com/stdlib-js/stdlib). We kindly request that you submit this pull request against the [respective directory](https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/repl) of the main repository where we’ll review and provide feedback. 
 
-If this is your first stdlib contribution, be sure to read the [contributing guide](https://github.com/stdlib-js/stdlib/blob/develop/CONTRIBUTING.md) which provides guidelines and instructions for submitting contributions. You may also consult the [development guide](https://github.com/stdlib-js/stdlib/blob/develop/docs/development.md) for help on developing stdlib.
+If this is your first stdlib contribution, be sure to read the [contributing guide](https://github.com/stdlib-js/stdlib/blob/develop/CONTRIBUTING.md) which provides guidelines and instructions for submitting contributions. You may also consult the [development guide](https://github.com/stdlib-js/stdlib/blob/develop/docs/contributing/development.md) for help on developing stdlib.
 
 We look forward to receiving your contribution! :smiley:

CHANGELOG.md

@@ -4,7 +4,7 @@
 
 <section class="release" id="unreleased">
 
-## Unreleased (2025-02-22)
+## Unreleased (2025-02-23)
 
 <section class="packages">
 
@@ -219,6 +219,7 @@ A total of 4 people contributed to this release. Thank you to the following cont
 
 <details>
 
+-   [`29a7783`](https://github.com/stdlib-js/stdlib/commit/29a77831faaa86ca3512d0a1248e06a5f3653e7c) - **docs:** update REPL namespace documentation [(#5386)](https://github.com/stdlib-js/stdlib/pull/5386) _(by stdlib-bot)_
 -   [`0625d8e`](https://github.com/stdlib-js/stdlib/commit/0625d8e45c3f5ad868b15c0999e4c1da32326dd7) - **docs:** update REPL namespace documentation [(#5365)](https://github.com/stdlib-js/stdlib/pull/5365) _(by stdlib-bot, Philipp Burckhardt)_
 -   [`5875a2c`](https://github.com/stdlib-js/stdlib/commit/5875a2c8878cf2b966d61e886efd99b91e6793f3) - **docs:** update repl help _(by Athan Reines)_
 -   [`79dbcc6`](https://github.com/stdlib-js/stdlib/commit/79dbcc60d4d9fa8b51460b64c1961e4b985830ff) - **refactor:** update path _(by Athan Reines)_

CONTRIBUTORS

@@ -5,6 +5,7 @@
 Aadish Jain <jain.aadishj@gmail.com>
 Aayush Khanna <aayushiitbhu23@gmail.com>
 Abhijit Raut <abhijitmraut8010@gmail.com>
+Abhishek Jain <40913033+abhishekblue@users.noreply.github.com>
 Adarsh Palaskar <adarshpalaskar99@gmail.com>
 Aditya Sapra <adityaework@gmail.com>
 Ahmed Atwa <Ahmedatwa866@yahoo.com>
@@ -57,6 +58,7 @@ Justin Dennison <justin1dennison@gmail.com>
 Karan Anand <119553199+anandkaranubc@users.noreply.github.com>
 Karthik Prakash <116057817+skoriop@users.noreply.github.com>
 Kohantika Nath <145763549+kohantikanath@users.noreply.github.com>
+Krishnam Agarwal <83017176+888krishnam@users.noreply.github.com>
 Krishnendu Das <86651039+itskdhere@users.noreply.github.com>
 Kshitij-Dale <152467202+Kshitij-Dale@users.noreply.github.com>
 Lovelin Dhoni J B <100030865+lovelindhoni@users.noreply.github.com>

code-blocks/data/data.csv

@@ -1878,14 +1878,14 @@ base.strided.dnanvariance,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\
 base.strided.dnanvariance.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariance.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dnanvariance.ndarray( N, 1, x, 2, 1 )\n"
 base.strided.dnanvariancech,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancech( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dnanvariancech( N, 1, x, stride )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dnanvariancech( N, 1, x1, stride )\n"
 base.strided.dnanvariancech.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancech.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dnanvariancech.ndarray( N, 1, x, 2, 1 )\n"
-base.strided.dnanvariancepn,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancepn( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dnanvariancepn( N, 1, x, stride )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dnanvariancepn( N, 1, x1, stride )\n"
-base.strided.dnanvariancepn.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancepn.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dnanvariancepn.ndarray( N, 1, x, 2, 1 )\n"
+base.strided.dnanvariancepn,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancepn( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancepn( 4, 1, x, 2 )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dnanvariancepn( 4, 1, x1, 2 )\n"
+base.strided.dnanvariancepn.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancepn.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancepn.ndarray( 4, 1, x, 2, 1 )\n"
 base.strided.dnanvariancetk,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancetk( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancetk( 4, 1, x, 2 )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dnanvariancetk( 4, 1, x1, 2 )\n"
 base.strided.dnanvariancetk.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancetk.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancetk.ndarray( 4, 1, x, 2, 1 )\n"
-base.strided.dnanvariancewd,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancewd( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dnanvariancewd( N, 1, x, stride )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dnanvariancewd( N, 1, x1, stride )\n"
-base.strided.dnanvariancewd.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancewd.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dnanvariancewd.ndarray( N, 1, x, 2, 1 )\n"
-base.strided.dnanvarianceyc,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvarianceyc( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dnanvarianceyc( N, 1, x, stride )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dnanvarianceyc( N, 1, x1, stride )\n"
-base.strided.dnanvarianceyc.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvarianceyc.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dnanvarianceyc.ndarray( N, 1, x, 2, 1 )\n"
+base.strided.dnanvariancewd,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancewd( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancewd( 4, 1, x, 2 )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dnanvariancewd( 4, 1, x1, 2 )\n"
+base.strided.dnanvariancewd.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvariancewd.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvariancewd.ndarray( 4, 1, x, 2, 1 )\n"
+base.strided.dnanvarianceyc,"var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dnanvarianceyc( x.length, 1, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvarianceyc( 4, 1, x, 2 )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dnanvarianceyc( 4, 1, x1, 2 )\n"
+base.strided.dnanvarianceyc.ndarray,"var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nbase.strided.dnanvarianceyc.ndarray( x.length, 1, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\nbase.strided.dnanvarianceyc.ndarray( 4, 1, x, 2, 1 )\n"
 base.strided.dnrm2,"var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dnrm2( x.length, x, 1 )\nx = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nbase.strided.dnrm2( 3, x, 2 )\nvar x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dnrm2( 3, x1, 2 )\n"
 base.strided.dnrm2.ndarray,"var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dnrm2.ndarray( x.length, x, 1, 0 )\nvar x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nbase.strided.dnrm2.ndarray( 3, x, 2, 1 )\n"
 base.strided.dramp,"var x = new Float64Array( [ 1.1, 2.5, -3.5, 4.0 ] );\nvar y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nbase.strided.dramp( x.length, x, 1, y, 1 )\nvar N = base.floor( x.length / 2 );\ny = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nbase.strided.dramp( N, x, 2, y, -1 )\nvar x0 = new Float64Array( [ 1.1, 2.5, -3.5, 4.0 ] );\nvar y0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nvar y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*2 );\nN = base.floor( x0.length / 2 );\nbase.strided.dramp( N, x1, -2, y1, 1 )\ny0\n"

code-blocks/data/data.json

@@ -1878,14 +1878,14 @@ base.strided.dnanvariance,"\nbase.strided.dnanvariance( N:integer, correction:nu
 base.strided.dnanvariance.ndarray,"\nbase.strided.dnanvariance.ndarray( N:integer, correction:number, \n  x:Float64Array, stride:integer, offset:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using alternative indexing semantics.\n"
 base.strided.dnanvariancech,"\nbase.strided.dnanvariancech( N:integer, correction:number, x:Float64Array, \n  stride:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass trial mean algorithm.\n"
 base.strided.dnanvariancech.ndarray,"\nbase.strided.dnanvariancech.ndarray( N:integer, correction:number, \n  x:Float64Array, stride:integer, offset:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass trial mean algorithm and\n    alternative indexing semantics.\n"
-base.strided.dnanvariancepn,"\nbase.strided.dnanvariancepn( N:integer, correction:number, x:Float64Array, \n  stride:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a two-pass algorithm.\n"
-base.strided.dnanvariancepn.ndarray,"\nbase.strided.dnanvariancepn.ndarray( N:integer, correction:number, \n  x:Float64Array, stride:integer, offset:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a two-pass algorithm and alternative\n    indexing semantics.\n"
+base.strided.dnanvariancepn,"\nbase.strided.dnanvariancepn( N:integer, correction:number, x:Float64Array, \n  strideX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a two-pass algorithm.\n"
+base.strided.dnanvariancepn.ndarray,"\nbase.strided.dnanvariancepn.ndarray( N:integer, correction:number, \n  x:Float64Array, strideX:integer, offsetX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a two-pass algorithm and alternative\n    indexing semantics.\n"
 base.strided.dnanvariancetk,"\nbase.strided.dnanvariancetk( N:integer, correction:number, x:Float64Array, \n  strideX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass textbook algorithm.\n"
 base.strided.dnanvariancetk.ndarray,"\nbase.strided.dnanvariancetk.ndarray( N:integer, correction:number, \n  x:Float64Array, strideX:integer, offsetX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass textbook algorithm and\n    alternative indexing semantics.\n"
-base.strided.dnanvariancewd,"\nbase.strided.dnanvariancewd( N:integer, correction:number, x:Float64Array, \n  stride:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using Welford's algorithm.\n"
-base.strided.dnanvariancewd.ndarray,"\nbase.strided.dnanvariancewd.ndarray( N:integer, correction:number, \n  x:Float64Array, stride:integer, offset:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using Welford's algorithm and alternative indexing\n    semantics.\n"
-base.strided.dnanvarianceyc,"\nbase.strided.dnanvarianceyc( N:integer, correction:number, x:Float64Array, \n  stride:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and\n    Cramer.\n"
-base.strided.dnanvarianceyc.ndarray,"\nbase.strided.dnanvarianceyc.ndarray( N:integer, correction:number, \n  x:Float64Array, stride:integer, offset:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and\n    Cramer and alternative indexing semantics.\n"
+base.strided.dnanvariancewd,"\nbase.strided.dnanvariancewd( N:integer, correction:number, x:Float64Array, \n  strideX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using Welford's algorithm.\n"
+base.strided.dnanvariancewd.ndarray,"\nbase.strided.dnanvariancewd.ndarray( N:integer, correction:number, \n  x:Float64Array, strideX:integer, offsetX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using Welford's algorithm and alternative indexing\n    semantics.\n"
+base.strided.dnanvarianceyc,"\nbase.strided.dnanvarianceyc( N:integer, correction:number, x:Float64Array, \n  strideX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and\n    Cramer.\n"
+base.strided.dnanvarianceyc.ndarray,"\nbase.strided.dnanvarianceyc.ndarray( N:integer, correction:number, \n  x:Float64Array, strideX:integer, offsetX:integer )\n    Computes the variance of a double-precision floating-point strided array\n    ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and\n    Cramer and alternative indexing semantics.\n"
 base.strided.dnrm2,"\nbase.strided.dnrm2( N:integer, x:Float64Array, stride:integer )\n    Computes the L2-norm of a double-precision floating-point vector.\n"
 base.strided.dnrm2.ndarray,"\nbase.strided.dnrm2.ndarray( N:integer, x:Float64Array, stride:integer, \n  offset:integer )\n    Computes the L2-norm of a double-precision floating-point vector using\n    alternative indexing semantics.\n"
 base.strided.dramp,"\nbase.strided.dramp( N:integer, x:Float64Array, strideX:integer, y:Float64Array, \n  strideY:integer )\n    Evaluates the ramp function for each element in a double-precision floating-\n    point strided array `x` and assigns the results to elements in a double-\n    precision floating-point strided array `y`.\n"

help/data/data.csv

@@ -1879,14 +1879,14 @@ base.strided.dnanvariance,"base.strided.dnanvariance( N, correction, x, stride )
 base.strided.dnanvariance.ndarray,"base.strided.dnanvariance.ndarray( N, correction, x, stride, offset )"
 base.strided.dnanvariancech,"base.strided.dnanvariancech( N, correction, x, stride )"
 base.strided.dnanvariancech.ndarray,"base.strided.dnanvariancech.ndarray( N, correction, x, stride, offset )"
-base.strided.dnanvariancepn,"base.strided.dnanvariancepn( N, correction, x, stride )"
-base.strided.dnanvariancepn.ndarray,"base.strided.dnanvariancepn.ndarray( N, correction, x, stride, offset )"
+base.strided.dnanvariancepn,"base.strided.dnanvariancepn( N, correction, x, strideX )"
+base.strided.dnanvariancepn.ndarray,"base.strided.dnanvariancepn.ndarray( N, correction, x, strideX, offsetX )"
 base.strided.dnanvariancetk,"base.strided.dnanvariancetk( N, correction, x, strideX )"
 base.strided.dnanvariancetk.ndarray,"base.strided.dnanvariancetk.ndarray( N, correction, x, strideX, offsetX )"
-base.strided.dnanvariancewd,"base.strided.dnanvariancewd( N, correction, x, stride )"
-base.strided.dnanvariancewd.ndarray,"base.strided.dnanvariancewd.ndarray( N, correction, x, stride, offset )"
-base.strided.dnanvarianceyc,"base.strided.dnanvarianceyc( N, correction, x, stride )"
-base.strided.dnanvarianceyc.ndarray,"base.strided.dnanvarianceyc.ndarray( N, correction, x, stride, offset )"
+base.strided.dnanvariancewd,"base.strided.dnanvariancewd( N, correction, x, strideX )"
+base.strided.dnanvariancewd.ndarray,"base.strided.dnanvariancewd.ndarray( N, correction, x, strideX, offsetX )"
+base.strided.dnanvarianceyc,"base.strided.dnanvarianceyc( N, correction, x, strideX )"
+base.strided.dnanvarianceyc.ndarray,"base.strided.dnanvarianceyc.ndarray( N, correction, x, strideX, offsetX )"
 base.strided.dnrm2,"base.strided.dnrm2( N, x, stride )"
 base.strided.dnrm2.ndarray,"base.strided.dnrm2.ndarray( N, x, stride, offset )"
 base.strided.dramp,"base.strided.dramp( N, x, strideX, y, strideY )"