fix some docstrings and docs issues (#203)

* fix some docstrings and docs issues

* Update index.md

* Update show.jl

* make sure coverage gets processed in travis

Author: mileslucas

.travis.yml

@@ -1,4 +1,5 @@
 language: julia
+
 os:
   - linux
   - osx
@@ -28,3 +29,12 @@ jobs:
         - julia --project=docs/ -e 'using Pkg; Pkg.instantiate(); Pkg.develop(PackageSpec(path=pwd()))'
         - julia --project=docs/ docs/make.jl
       after_success: skip
+
+after_success:
+    - |
+      julia -e '
+        using Pkg
+        Pkg.add("Coverage")
+        using Coverage
+        Codecov.submit(process_folder())'
+        

docs/src/index.md

@@ -153,11 +153,13 @@ Fit a polynomial (of degree `deg`) to `x` and `y` using polynomial interpolation
 ```@example
 using Plots, Polynomials
 xs = range(0, 10, length=10)
-ys = exp.(-xs)
-f = fit(xs, ys)  # fit(xs, ys, k)  for fitting a kth degreee polynomial
+ys = @. exp(-xs)
+f = fit(xs, ys) # degree = length(xs) - 1 
+f2 = fit(xs, ys, 2) # degree = 2
 
-scatter(xs, ys, label="Data");
-plot!(f, extrema(xs)..., label="Fit");
+scatter(xs, ys, markerstrokewidth=0, label="Data")
+plot!(f, extrema(xs)..., label="Fit")
+plot!(f2, extrema(xs)..., label="Quadratic Fit")
 savefig("polyfit.svg"); nothing # hide
 ```
 
@@ -193,7 +195,7 @@ julia> convert(ChebyshevT, Polynomial([1.0, 2,  3]))
 ChebyshevT(2.5⋅T_0(x) + 2.0⋅T_1(x) + 1.5⋅T_2(x))
 ```
 
-!!! Note
+!!! warning
     The older  `Poly` type that this package used prior to `v0.7`  is implemented as an alternate basis  to provide support for older code bases. As of `v1.0`,  this type will be only available by executing `using Polynomials.PolyCompat`.
 
 ### Iteration

src/common.jl

@@ -64,8 +64,9 @@ fromroots(A::AbstractMatrix{T}; var::SymbolLike = :x) where {T <: Number} =
     fromroots(Polynomial, eigvals(A), var = var)
 
 """
-    fit(x, y; [weights], deg=length(x) - 1, var=:x)
-    fit(::Type{<:AbstractPolynomial}, x, y; [weights], deg=length(x)-1, var=:x)
+    fit(x, y, deg=length(x) - 1; [weights], var=:x)
+    fit(::Type{<:AbstractPolynomial}, x, y, deg=length(x)-1; [weights], var=:x)
+
 Fit the given data as a polynomial type with the given degree. Uses linear least squares. When weights are given, as either a `Number`, `Vector` or `Matrix`, will use weighted linear least squares. The default polynomial type is [`Polynomial`](@ref). This will automatically scale your data to the [`domain`](@ref) of the polynomial type using [`mapdomain`](@ref)
 """
 function fit(P::Type{<:AbstractPolynomial},

src/pade.jl

@@ -91,12 +91,12 @@ Evaluate the Pade approximant at the given point.
 ```jldoctest pade
 julia> using SpecialFunctions, Polynomials
 
+
 julia> p = Polynomial(@.(1 // BigInt(gamma(1:17))));
 
 
 julia> pade = Pade(p, 8, 8);
 
-
 julia> pade(1.0) ≈ exp(1.0)
 true