docs: update docs

Author: sathvikbhagavan

README.md

@@ -55,12 +55,11 @@ corresponding to `(u,t)` pairs.
   - `QuadraticSpline(u,t)` - A quadratic spline interpolation.
   - `CubicSpline(u,t)` - A cubic spline interpolation.
   - `AkimaInterpolation(u, t)` - Akima spline interpolation provides a smoothing effect and is computationally efficient.
-  - `BSplineInterpolation(u,t,d,pVec,knotVec)` - An interpolation B-spline. This is a B-spline which hits each of the data points. The argument choices are:
+  - `BSplineInterpolation(u,t,d,knotVec)` - An interpolation B-spline. This is a B-spline which hits each of the data points. The argument choices are:
 
       + `d` - degree of B-spline
-      + `pVec` - Symbol to Parameters Vector, `pVec = :Uniform` for uniform spaced parameters and `pVec = :ArcLen` for parameters generated by chord length method.
       + `knotVec` - Symbol to Knot Vector, `knotVec = :Uniform` for uniform knot vector, `knotVec = :Average` for average spaced knot vector.
-  - `BSplineApprox(u,t,d,h,pVec,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h<length(t)` which is the number of control points to use, with smaller `h` indicating more smoothing.
+  - `BSplineApprox(u,t,d,h,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h<length(t)` which is the number of control points to use, with smaller `h` indicating more smoothing.
   - `CubicHermiteSpline(du, u, t)` - A third order Hermite interpolation, which matches the values and first (`du`) order derivatives in the data points exactly.
   - `PCHIPInterpolation(u, t)` - a type of `CubicHermiteSpline` where the derivative values `du` are derived from the input data in such a way that the interpolation never overshoots the data.
   - `QuinticHermiteSpline(ddu, du, u, t)` - A fifth order Hermite interpolation, which matches the values and first (`du`) and second (`ddu`) order derivatives in the data points exactly.

docs/src/clarification.md

@@ -9,7 +9,7 @@ using DataInterpolations, Plots
 using DataInterpolations: derivative
 u = [2.0, 1.0, 5.0, 4.0, 5.0, 4.0]
 t = [0.0, 2.0, 3.5, 4.0, 5.0, 6.5]
-bspline = BSplineInterpolation(u, t, 0, :Uniform, :Uniform; extrapolation_left=ExtrapolationType.Extension,  extrapolation_right=ExtrapolationType.Extension)
+bspline = BSplineInterpolation(u, t, 0, :Uniform; extrapolation_left=ExtrapolationType.Extension,  extrapolation_right=ExtrapolationType.Extension)
 plot(bspline)
 ```
 
@@ -19,7 +19,7 @@ Thus, the plot for B-Spline interpolation does not appear the same as the plot f
 # Derivative behavior of quadratic B-Spline 
 
 ```@example interpclarity
-bspline = BSplineInterpolation(u, t, 2, :Uniform, :Uniform; extrapolation_left=ExtrapolationType.Extension,  extrapolation_right=ExtrapolationType.Extension)
+bspline = BSplineInterpolation(u, t, 2, :Uniform; extrapolation_left=ExtrapolationType.Extension,  extrapolation_right=ExtrapolationType.Extension)
 plot(t->derivative(bspline, t))
 ```
 

docs/src/index.md

@@ -25,12 +25,11 @@ corresponding to `(u,t)` pairs.
   - `QuadraticSpline(u,t)` - A quadratic spline interpolation.
   - `CubicSpline(u,t)` - A cubic spline interpolation.
   - `AkimaInterpolation(u, t)` - Akima spline interpolation provides a smoothing effect and is computationally efficient.
-  - `BSplineInterpolation(u,t,d,pVec,knotVec)` - An interpolation B-spline. This is a B-spline that hits each of the data points. The argument choices are:
+  - `BSplineInterpolation(u,t,d,knotVec)` - An interpolation B-spline. This is a B-spline that hits each of the data points. The argument choices are:
 
       + `d` - degree of B-spline
-      + `pVec` - Symbol to Parameters Vector, `pVec = :Uniform` for uniformly spaced parameters, and `pVec = :ArcLen` for parameters generated by the chord length method.
       + `knotVec` - Symbol to Knot Vector, `knotVec = :Uniform` for uniform knot vector, `knotVec = :Average` for average spaced knot vector.
-  - `BSplineApprox(u,t,d,h,pVec,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h<length(t)` which is the number of control points to use, with smaller `h` indicating more smoothing.
+  - `BSplineApprox(u,t,d,h,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h<length(t)` which is the number of control points to use, with smaller `h` indicating more smoothing.
   - `CubicHermiteSpline(du, u, t)` - A third order Hermite interpolation, which matches the values and first (`du`) order derivatives in the data points exactly.
   - `PCHIPInterpolation(u, t)` - a type of `CubicHermiteSpline` where the derivative values `du` are derived from the input data in such a way that the interpolation never overshoots the data.
   - `QuinticHermiteSpline(ddu, du, u, t)` - a fifth order Hermite interpolation, which matches the values and first (`du`) and second (`ddu`) order derivatives in the data points exactly.

docs/src/methods.md

@@ -124,10 +124,10 @@ that every data point is taken into account for each point of the curve.
 The interpolating B-spline is the version which hits each of the points. This
 method is described in more detail [here](https://pages.mtu.edu/%7Eshene/COURSES/cs3621/NOTES/INT-APP/CURVE-INT-global.html).
 Let's plot a cubic B-spline (3rd order). Since the data points are not close to
-uniformly spaced, we will use the `:ArcLen` and `:Average` choices:
+uniformly spaced, we will use the `:Average` knot vector:
 
 ```@example tutorial
-A = BSplineInterpolation(u, t, 3, :ArcLen, :Average)
+A = BSplineInterpolation(u, t, 3, :Average)
 plot(A)
 ```
 
@@ -138,7 +138,7 @@ is a least square approximation. This has a natural effect of smoothing the
 data. For example, if we use 4 control points, we get the result:
 
 ```@example tutorial
-A = BSplineApprox(u, t, 3, 4, :ArcLen, :Average)
+A = BSplineApprox(u, t, 3, 4, :Average)
 plot(A)
 ```