Auto-generated commit

code-blocks/data/data.csv

@@ -803,6 +803,12 @@ base.dists.weibull.stdev,"var v = base.dists.weibull.stdev( 1.0, 1.0 )\nv = base
 base.dists.weibull.variance,"var v = base.dists.weibull.variance( 1.0, 1.0 )\nv = base.dists.weibull.variance( 4.0, 12.0 )\nv = base.dists.weibull.variance( 8.0, 2.0 )\n"
 base.dists.weibull.Weibull,"var weibull = base.dists.weibull.Weibull( 6.0, 5.0 );\nweibull.k\nweibull.lambda\nweibull.entropy\nweibull.kurtosis\nweibull.mean\nweibull.median\nweibull.mode\nweibull.skewness\nweibull.stdev\nweibull.variance\nweibull.cdf( 3.0 )\nweibull.logcdf( 3.0 )\nweibull.logpdf( 1.0 )\nweibull.mgf( -0.5 )\nweibull.pdf( 3.0 )\nweibull.quantile( 0.8 )\n"
 base.ellipe,"var y = base.ellipe( 0.5 )\ny = base.ellipe( -1.0 )\ny = base.ellipe( 2.0 )\ny = base.ellipe( PINF )\ny = base.ellipe( NINF )\ny = base.ellipe( NaN )\n"
+base.ellipj,"var v = base.ellipj( 0.3, 0.5 )\nv = base.ellipj( 0.0, 0.0 )\nv = base.ellipj( Infinity, 1.0 )\nv = base.ellipj( 0.0, -2.0)\nv = base.ellipj( NaN, NaN )\n"
+base.ellipj.assign,"var out = new Float64Array( 4 );\nvar v = base.ellipj.assign( 0.3, 0.5, out, 1, 0 )\nvar bool = ( v === out )\n"
+base.ellipj.sn,"var v = base.ellipj.sn( 0.3, 0.5 )\n"
+base.ellipj.cn,"var v = base.ellipj.cn( 0.3, 0.5 )\n"
+base.ellipj.dn,"var v = base.ellipj.dn( 0.3, 0.5 )\n"
+base.ellipj.am,"var v = base.ellipj.am( 0.3, 0.5 )\n"
 base.ellipk,"var y = base.ellipk( 0.5 )\ny = base.ellipk( -1.0 )\ny = base.ellipk( 2.0 )\ny = base.ellipk( PINF )\ny = base.ellipk( NINF )\ny = base.ellipk( NaN )\n"
 base.epsdiff,"var d = base.epsdiff( 12.15, 12.149999999999999 )\nd = base.epsdiff( 2.4341309458983933, 2.4341309458633909, 'mean-abs' )\nfunction scale( x, y ) { return ( x > y ) ? y : x; };\nd = base.epsdiff( 1.0000000000000002, 1.0000000000000100, scale )\n"
 base.erf,"var y = base.erf( 2.0 )\ny = base.erf( -1.0 )\ny = base.erf( -0.0 )\ny = base.erf( NaN )\n"

help/data/data.csv

@@ -802,14 +802,14 @@ base.dists.weibull.skewness,"\nbase.dists.weibull.skewness( k, λ )\n    Returns
 base.dists.weibull.stdev,"\nbase.dists.weibull.stdev( k, λ )\n    Returns the standard deviation of a Weibull distribution.\n\n    If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n    If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n    Parameters\n    ----------\n    k: number\n        Shape parameter.\n\n    λ: number\n        Scale parameter.\n\n    Returns\n    -------\n    out: number\n        Standard deviation.\n\n    Examples\n    --------\n    > var v = base.dists.weibull.stdev( 1.0, 1.0 )\n    1.0\n    > v = base.dists.weibull.stdev( 4.0, 12.0 )\n    ~3.051\n    > v = base.dists.weibull.stdev( 8.0, 2.0 )\n    ~0.279\n\n"
 base.dists.weibull.variance,"\nbase.dists.weibull.variance( k, λ )\n    Returns the variance of a Weibull distribution.\n\n    If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n    If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n    Parameters\n    ----------\n    k: number\n        Shape parameter.\n\n    λ: number\n        Scale parameter.\n\n    Returns\n    -------\n    out: number\n        Variance.\n\n    Examples\n    --------\n    > var v = base.dists.weibull.variance( 1.0, 1.0 )\n    1.0\n    > v = base.dists.weibull.variance( 4.0, 12.0 )\n    ~9.311\n    > v = base.dists.weibull.variance( 8.0, 2.0 )\n    ~0.078\n\n"
 base.dists.weibull.Weibull,"\nbase.dists.weibull.Weibull( [k, λ] )\n    Returns a Weibull distribution object.\n\n    Parameters\n    ----------\n    k: number (optional)\n        Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n    λ: number (optional)\n        Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n    Returns\n    -------\n    weibull: Object\n        Distribution instance.\n\n    weibull.k: number\n        Shape parameter. If set, the value must be greater than `0`.\n\n    weibull.lambda: number\n        Scale parameter. If set, the value must be greater than `0`.\n\n    weibull.entropy: number\n        Read-only property which returns the differential entropy.\n\n    weibull.kurtosis: number\n        Read-only property which returns the excess kurtosis.\n\n    weibull.mean: number\n        Read-only property which returns the expected value.\n\n    weibull.median: number\n        Read-only property which returns the median.\n\n    weibull.mode: number\n        Read-only property which returns the mode.\n\n    weibull.skewness: number\n        Read-only property which returns the skewness.\n\n    weibull.stdev: number\n        Read-only property which returns the standard deviation.\n\n    weibull.variance: number\n        Read-only property which returns the variance.\n\n    weibull.cdf: Function\n        Evaluates the cumulative distribution function (CDF).\n\n    weibull.logcdf: Function\n        Evaluates the natural logarithm of the cumulative distribution function\n        (CDF).\n\n    weibull.logpdf: Function\n        Evaluates the natural logarithm of the probability density function\n        (PDF).\n\n    weibull.mgf: Function\n        Evaluates the moment-generating function (MGF).\n\n    weibull.pdf: Function\n        Evaluates the probability density function (PDF).\n\n    weibull.quantile: Function\n        Evaluates the quantile function at probability `p`.\n\n    Examples\n    --------\n    > var weibull = base.dists.weibull.Weibull( 6.0, 5.0 );\n    > weibull.k\n    6.0\n    > weibull.lambda\n    5.0\n    > weibull.entropy\n    ~1.299\n    > weibull.kurtosis\n    ~0.035\n    > weibull.mean\n    ~4.639\n    > weibull.median\n    ~4.704\n    > weibull.mode\n    ~4.85\n    > weibull.skewness\n    ~-0.373\n    > weibull.stdev\n    ~0.899\n    > weibull.variance\n    ~0.808\n    > weibull.cdf( 3.0 )\n    ~0.046\n    > weibull.logcdf( 3.0 )\n    ~-3.088\n    > weibull.logpdf( 1.0 )\n    ~-7.865\n    > weibull.mgf( -0.5 )\n    ~0.075\n    > weibull.pdf( 3.0 )\n    ~0.089\n    > weibull.quantile( 0.8 )\n    ~5.413\n\n"
-base.ellipe,"\nbase.ellipe( m )\n    Computes the complete elliptic integral of the second kind.\n\n    Parameters\n    ----------\n    m: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.ellipe( 0.5 )\n    ~1.351\n    > y = base.ellipe( -1.0 )\n    ~1.910\n    > y = base.ellipe( 2.0 )\n    NaN\n    > y = base.ellipe( PINF )\n    NaN\n    > y = base.ellipe( NINF )\n    NaN\n    > y = base.ellipe( NaN )\n    NaN\n\n    See Also\n    --------\n    base.ellipk\n"
+base.ellipe,"\nbase.ellipe( m )\n    Computes the complete elliptic integral of the second kind.\n\n    Parameters\n    ----------\n    m: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.ellipe( 0.5 )\n    ~1.351\n    > y = base.ellipe( -1.0 )\n    ~1.910\n    > y = base.ellipe( 2.0 )\n    NaN\n    > y = base.ellipe( PINF )\n    NaN\n    > y = base.ellipe( NINF )\n    NaN\n    > y = base.ellipe( NaN )\n    NaN\n\n    See Also\n    --------\n    base.ellipj, base.ellipk\n"
 base.ellipj,"\nbase.ellipj( u, m )\n    Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n    amplitude am.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: Array\n        Jacobi elliptic functions and Jacobi amplitude.\n\n    Examples\n    --------\n    > var v = base.ellipj( 0.3, 0.5 )\n    [ ~0.293, ~0.956, ~0.978, ~0.298 ]\n    > v = base.ellipj( 0.0, 0.0 )\n    [ ~0.0, ~1.0, ~1.0, ~0.0 ]\n    > v = base.ellipj( Infinity, 1.0 )\n    [ ~1.0, ~0.0, ~0.0, ~1.571 ]\n    > v = base.ellipj( 0.0, -2.0)\n    [ ~0.0, ~1.0, ~1.0, NaN ]\n    > v = base.ellipj( NaN, NaN )\n    [ NaN, NaN, NaN, NaN ]\n\n\nbase.ellipj.assign( u, m, out, stride, offset )\n    Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n    amplitude am and assigns results to a provided output array.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    out: Array|TypedArray|Object\n        Output array.\n\n    stride: integer\n        Output array stride.\n\n    offset: integer\n        Output array index offset.\n\n    Returns\n    -------\n    out: Array|TypedArray|Object\n        Jacobi elliptic functions and Jacobi amplitude.\n\n    Examples\n    --------\n    > var out = new Float64Array( 4 );\n    > var v = base.ellipj.assign( 0.3, 0.5, out, 1, 0 )\n    <Float64Array>[ ~0.293, ~0.956, ~0.978, ~0.298 ]\n    > var bool = ( v === out )\n    true\n\n\nbase.ellipj.sn( u, m )\n    Computes the Jacobi elliptic function sn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function sn.\n\n    Examples\n    --------\n    > var v = base.ellipj.sn( 0.3, 0.5 )\n    ~0.293\n\n\nbase.ellipj.cn( u, m )\n    Computes the Jacobi elliptic functions cn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function cn.\n\n    Examples\n    --------\n    > var v = base.ellipj.cn( 0.3, 0.5 )\n    ~0.956\n\n\nbase.ellipj.dn( u, m )\n    Computes the Jacobi elliptic function dn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function dn.\n\n    Examples\n    --------\n    > var v = base.ellipj.dn( 0.3, 0.5 )\n    ~0.978\n\n\nbase.ellipj.am( u, m )\n    Computes the Jacobi amplitude am.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function am.\n\n    Examples\n    --------\n    > var v = base.ellipj.am( 0.3, 0.5 )\n    ~0.298\n\n    See Also\n    --------\n    base.ellipe, base.ellipk"
 base.ellipj.assign,"\nbase.ellipj.assign( u, m, out, stride, offset )\n    Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n    amplitude am and assigns results to a provided output array.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    out: Array|TypedArray|Object\n        Output array.\n\n    stride: integer\n        Output array stride.\n\n    offset: integer\n        Output array index offset.\n\n    Returns\n    -------\n    out: Array|TypedArray|Object\n        Jacobi elliptic functions and Jacobi amplitude.\n\n    Examples\n    --------\n    > var out = new Float64Array( 4 );\n    > var v = base.ellipj.assign( 0.3, 0.5, out, 1, 0 )\n    <Float64Array>[ ~0.293, ~0.956, ~0.978, ~0.298 ]\n    > var bool = ( v === out )\n    true"
 base.ellipj.sn,"\nbase.ellipj.sn( u, m )\n    Computes the Jacobi elliptic function sn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function sn.\n\n    Examples\n    --------\n    > var v = base.ellipj.sn( 0.3, 0.5 )\n    ~0.293"
 base.ellipj.cn,"\nbase.ellipj.cn( u, m )\n    Computes the Jacobi elliptic functions cn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function cn.\n\n    Examples\n    --------\n    > var v = base.ellipj.cn( 0.3, 0.5 )\n    ~0.956"
 base.ellipj.dn,"\nbase.ellipj.dn( u, m )\n    Computes the Jacobi elliptic function dn.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function dn.\n\n    Examples\n    --------\n    > var v = base.ellipj.dn( 0.3, 0.5 )\n    ~0.978"
 base.ellipj.am,"\nbase.ellipj.am( u, m )\n    Computes the Jacobi amplitude am.\n\n    Parameters\n    ----------\n    u: number\n        Input value.\n\n    m: number\n        Modulus m, equivalent to k².\n\n    Returns\n    -------\n    out: number\n        Jacobi elliptic function am.\n\n    Examples\n    --------\n    > var v = base.ellipj.am( 0.3, 0.5 )\n    ~0.298\n\n    See Also\n    --------\n    base.ellipe, base.ellipk"
-base.ellipk,"\nbase.ellipk( m )\n    Computes the complete elliptic integral of the first kind.\n\n    Parameters\n    ----------\n    m: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.ellipk( 0.5 )\n    ~1.854\n    > y = base.ellipk( -1.0 )\n    ~1.311\n    > y = base.ellipk( 2.0 )\n    NaN\n    > y = base.ellipk( PINF )\n    NaN\n    > y = base.ellipk( NINF )\n    NaN\n    > y = base.ellipk( NaN )\n    NaN\n\n    See Also\n    --------\n    base.ellipe\n"
+base.ellipk,"\nbase.ellipk( m )\n    Computes the complete elliptic integral of the first kind.\n\n    Parameters\n    ----------\n    m: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.ellipk( 0.5 )\n    ~1.854\n    > y = base.ellipk( -1.0 )\n    ~1.311\n    > y = base.ellipk( 2.0 )\n    NaN\n    > y = base.ellipk( PINF )\n    NaN\n    > y = base.ellipk( NINF )\n    NaN\n    > y = base.ellipk( NaN )\n    NaN\n\n    See Also\n    --------\n    base.ellipe, base.ellipj\n"
 base.epsdiff,"\nbase.epsdiff( x, y[, scale] )\n    Computes the relative difference of two real numbers in units of double-\n    precision floating-point epsilon.\n\n    By default, the function scales the absolute difference by dividing the\n    absolute difference by the maximum absolute value of `x` and `y`. To scale\n    by a different function, specify a scale function name.\n\n    The following `scale` functions are supported:\n\n    - 'max-abs': maximum absolute value of `x` and `y` (default).\n    - 'max': maximum value of `x` and `y`.\n    - 'min-abs': minimum absolute value of `x` and `y`.\n    - 'min': minimum value of `x` and `y`.\n    - 'mean-abs': arithmetic mean of the absolute values of `x` and `y`.\n    - 'mean': arithmetic mean of `x` and `y`.\n    - 'x': `x` (*noncommutative*).\n    - 'y': `y` (*noncommutative*).\n\n    To use a custom scale function, provide a function which accepts two numeric\n    arguments `x` and `y`.\n\n    If computing the relative difference in units of epsilon will result in\n    overflow, the function returns the maximum double-precision floating-point\n    number.\n\n    If the absolute difference of `x` and `y` is `0`, the relative difference is\n    always `0`.\n\n    If `|x| = |y| = infinity`, the function returns `NaN`.\n\n    If `|x| = |-y| = infinity`, the relative difference is `+infinity`.\n\n    If a `scale` function returns `0`, the function returns `NaN`.\n\n    Parameters\n    ----------\n    x: number\n        First number.\n\n    y: number\n        Second number.\n\n    scale: string|Function (optional)\n        Scale function. Default: `'max-abs'`.\n\n    Returns\n    -------\n    out: number\n        Relative difference in units of double-precision floating-point epsilon.\n\n    Examples\n    --------\n    > var d = base.epsdiff( 12.15, 12.149999999999999 )\n    ~0.658\n    > d = base.epsdiff( 2.4341309458983933, 2.4341309458633909, 'mean-abs' )\n    ~64761.512\n\n    // Custom scale function:\n    > function scale( x, y ) { return ( x > y ) ? y : x; };\n    > d = base.epsdiff( 1.0000000000000002, 1.0000000000000100, scale )\n    ~44\n\n    See Also\n    --------\n    base.absdiff, base.reldiff\n"
 base.erf,"\nbase.erf( x )\n    Evaluates the error function.\n\n    If provided `NaN`, the function returns `NaN`.\n\n    As the error function is an odd function (i.e., `erf(-x) == -erf(x)`), if\n    provided `-0`, the function returns `-0`.\n\n    Parameters\n    ----------\n    x: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.erf( 2.0 )\n    ~0.9953\n    > y = base.erf( -1.0 )\n    ~-0.8427\n    > y = base.erf( -0.0 )\n    -0.0\n    > y = base.erf( NaN )\n    NaN\n\n    See Also\n    --------\n    base.erfc, base.erfinv, base.erfcinv\n"
 base.erfc,"\nbase.erfc( x )\n    Evaluates the complementary error function.\n\n    If provided `NaN`, the function returns `NaN`.\n\n    Parameters\n    ----------\n    x: number\n        Input value.\n\n    Returns\n    -------\n    y: number\n        Function value.\n\n    Examples\n    --------\n    > var y = base.erfc( 2.0 )\n    ~0.0047\n    > y = base.erfc( -1.0 )\n    ~1.8427\n    > y = base.erfc( 0.0 )\n    1.0\n    > y = base.erfc( PINF )\n    0.0\n    > y = base.erfc( NINF )\n    2.0\n    > y = base.erfc( NaN )\n    NaN\n\n    See Also\n    --------\n    base.erf, base.erfinv, base.erfcinv, base.erfcx\n"

info/data/data.csv

@@ -803,6 +803,12 @@ base.dists.weibull.stdev,"\nbase.dists.weibull.stdev( k:number, λ:number )\n
 base.dists.weibull.variance,"\nbase.dists.weibull.variance( k:number, λ:number )\n    Returns the variance of a Weibull distribution.\n"
 base.dists.weibull.Weibull,"\nbase.dists.weibull.Weibull( [k:number, λ:number] )\n    Returns a Weibull distribution object.\n"
 base.ellipe,"\nbase.ellipe( m:number )\n    Computes the complete elliptic integral of the second kind.\n"
+base.ellipj,"\nbase.ellipj( u:number, m:number )\n    Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n    amplitude am.\n"
+base.ellipj.assign,"\nbase.ellipj.assign( u:number, m:number, out:Array|TypedArray|Object, \n  stride:integer, offset:integer )\n    Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n    amplitude am and assigns results to a provided output array.\n"
+base.ellipj.sn,"\nbase.ellipj.sn( u:number, m:number )\n    Computes the Jacobi elliptic function sn.\n"
+base.ellipj.cn,"\nbase.ellipj.cn( u:number, m:number )\n    Computes the Jacobi elliptic functions cn.\n"
+base.ellipj.dn,"\nbase.ellipj.dn( u:number, m:number )\n    Computes the Jacobi elliptic function dn.\n"
+base.ellipj.am,"\nbase.ellipj.am( u:number, m:number )\n    Computes the Jacobi amplitude am.\n"
 base.ellipk,"\nbase.ellipk( m:number )\n    Computes the complete elliptic integral of the first kind.\n"
 base.epsdiff,"\nbase.epsdiff( x:number, y:number[, scale:string|Function] )\n    Computes the relative difference of two real numbers in units of double-\n    precision floating-point epsilon.\n"
 base.erf,"\nbase.erf( x:number )\n    Evaluates the error function.\n"

signature/data/data.csv

@@ -803,6 +803,12 @@ base.dists.weibull.stdev,"base.dists.weibull.stdev( k, λ )"
 base.dists.weibull.variance,"base.dists.weibull.variance( k, λ )"
 base.dists.weibull.Weibull,"base.dists.weibull.Weibull( [k, λ] )"
 base.ellipe,"base.ellipe( m )"
+base.ellipj,"base.ellipj( u, m )"
+base.ellipj.assign,"base.ellipj.assign( u, m, out, stride, offset )"
+base.ellipj.sn,"base.ellipj.sn( u, m )"
+base.ellipj.cn,"base.ellipj.cn( u, m )"
+base.ellipj.dn,"base.ellipj.dn( u, m )"
+base.ellipj.am,"base.ellipj.am( u, m )"
 base.ellipk,"base.ellipk( m )"
 base.epsdiff,"base.epsdiff( x, y[, scale] )"
 base.erf,"base.erf( x )"

typed-signature/data/data.csv

@@ -803,6 +803,12 @@ base.dists.weibull.stdev,"base.dists.weibull.stdev( k:number, λ:number )"
 base.dists.weibull.variance,"base.dists.weibull.variance( k:number, λ:number )"
 base.dists.weibull.Weibull,"base.dists.weibull.Weibull( [k:number, λ:number] )"
 base.ellipe,"base.ellipe( m:number )"
+base.ellipj,"base.ellipj( u:number, m:number )"
+base.ellipj.assign,"base.ellipj.assign( u:number, m:number, out:Array|TypedArray|Object, stride:integer, offset:integer )"
+base.ellipj.sn,"base.ellipj.sn( u:number, m:number )"
+base.ellipj.cn,"base.ellipj.cn( u:number, m:number )"
+base.ellipj.dn,"base.ellipj.dn( u:number, m:number )"
+base.ellipj.am,"base.ellipj.am( u:number, m:number )"
 base.ellipk,"base.ellipk( m:number )"
 base.epsdiff,"base.epsdiff( x:number, y:number[, scale:string|Function] )"
 base.erf,"base.erf( x:number )"