typos (#121)

Author: spaette

README.md

@@ -86,7 +86,7 @@ println("x = ", root_fdfsolver_root(solver))
 
 Extra functionality defined in this package:
 
-* Convenice functions `hypergeom` and `hypergeom_e` for the hypergeometric functions.
+* Convenience functions `hypergeom` and `hypergeom_e` for the hypergeometric functions.
 * Function wrapping macros `@gsl_function`, `@gsl_function_fdf`, `@gsl_multiroot_function` and `@gsl_multiroot_function_fdf` that are used for packaging Julia functions so that they can be passed to GSL.
 * Functions `wrap_gsl_vector` and `wrap_gsl_matrix` that return a Julia array or matrix pointing to the data in a `gsl_vector` or `gsl_matrix`.
 

gen/heuristic.jl

@@ -6,7 +6,7 @@
 #
 # TODO: error handler on integer output
 
-# List of functions that herustic should ignore (original name)
+# List of functions that heuristic should ignore (original name)
 heuristic_ignore_list = ["gsl_vector_char_const_ptr", "gsl_matrix_char_const_ptr"]
 
 function secondary_wrappers(functions, docs)

gen/makewrappers.jl

@@ -478,7 +478,7 @@ function match_enum_interior(interior)
         if occursin("=", l)
             name, val = split(l, "=")
             val = trim_whitespace(val)
-            # Catch if unitialized element comes after this one
+            # Catch if uninitialized element comes after this one
             count = nothing 
         else
             name = l
@@ -624,7 +624,7 @@ function gen_julia(structs, typedefs, constants, functions, filename)
         if haskey(docs, s.name)
             gsldoc = "GSL documentation:\n\n### " * docs[s.name]
         end        
-        # Check if theres a typedef pointing to this struct,
+        # Check if there's a typedef pointing to this struct,
         # then write it immediately if there is.
         # This helps is subsequent structs use this typedef
         if haskey(typedefs_lookup, jname)

src/GSL.jl

@@ -39,7 +39,7 @@ end
 # END MODULE C
 
 # BEGIN MODULE Const
-# Phsyical constants
+# Physical constants
 module Const
 using Markdown
 include("gen/gsl_const.jl")

src/gen/direct_wrappers/gsl_integration_h.jl

@@ -1041,7 +1041,7 @@ GSL documentation:
 
 ### `void gsl_integration_fixed_free(gsl_integration_fixed_workspace * w)`
 
-> This function frees the memory assocated with the workspace `w`
+> This function frees the memory associated with the workspace `w`
 
 """
 function integration_fixed_free(w)

src/gen/direct_wrappers/gsl_multifit_h.jl

@@ -702,7 +702,7 @@ GSL documentation:
 
 ### `int gsl_multifit_linear_gcv_curve(const gsl_vector * reg_param, const gsl_vector * UTy, const double delta0, gsl_vector * G, gsl_multifit_linear_workspace * work)`
 
-> This funtion calculates the GCV curve $G(\lambda)$ and stores it in
+> This function calculates the GCV curve $G(\lambda)$ and stores it in
 > `G` on output, which must be the same size as `reg_param`. The inputs
 > `reg_param`, `UTy` and `delta0` are computed in
 > `gsl_multifit_linear_gcv_init`.
@@ -1018,7 +1018,7 @@ GSL documentation:
 > `double sigma`
 >
 > > This contains an estimate of the standard deviation of the final
-> > residuals by attemping to reconcile `sigma_rob` and `sigma_ols` in a
+> > residuals by attempting to reconcile `sigma_rob` and `sigma_ols` in a
 > > reasonable way.
 >
 > `double Rsq`
@@ -1099,7 +1099,7 @@ GSL documentation:
 
 > This function computes the best-fit parameters `c` of the model
 > $y = X c$ for the observations `y` and the matrix of predictor
-> variables `X`, attemping to reduce the influence of outliers using the
+> variables `X`, attempting to reduce the influence of outliers using the
 > algorithm outlined above. The $p$-by-$p$ variance-covariance matrix of
 > the model parameters `cov` is estimated as $\sigma^2 (X^T X)^{-1}$,
 > where $\sigma$ is an approximation of the residual standard deviation
@@ -1153,7 +1153,7 @@ GSL documentation:
 > $r_i = {y_i - (X c)_i \over \sigma \sqrt{1 - h_i}}$ for the
 > observations `y`, coefficients `c` and matrix of predictor variables
 > `X`. The routine `gsl_multifit_robust` must first be called to compute
-> the statisical leverages $h_i$ of the matrix `X` and residual standard
+> the statistical leverages $h_i$ of the matrix `X` and residual standard
 > deviation estimate $\sigma$.
 
 """

src/gen/direct_wrappers/gsl_poly_h.jl

@@ -134,7 +134,7 @@ GSL documentation:
 > arrays `xa` and `ya` of length `size`. Hermite interpolation
 > constructs polynomials which also match first derivatives $dy/dx$
 > which are provided in the array `dya` also of length `size`. The first
-> derivatives can be incorported into the usual divided-difference
+> derivatives can be incorporated into the usual divided-difference
 > algorithm by forming a new dataset $z = \{x_0,x_0,x_1,x_1,...\}$,
 > which is stored in the array `za` of length 2\*`size` on output. On
 > output the divided-differences of the Hermite representation are