GFit -> GModelFit

Project.toml

@@ -1,4 +1,4 @@
-name = "GFit"
+name = "GModelFit"
 uuid = "ac42e6ba-c0bf-4fcf-827d-deea44b16255"
 authors = ["Giorgio Calderone <[email protected]>"]
 version = "0.1.0"

README.md

@@ -1,21 +1,21 @@
-# GFit.jl
+# GModelFit.jl
 
 [![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE.md)
-[![DocumentationStatus](https://img.shields.io/badge/docs-stable-blue.svg?style=flat)](https://gcalderone.github.io/GFit.jl/)
+[![DocumentationStatus](https://img.shields.io/badge/docs-stable-blue.svg?style=flat)](https://gcalderone.github.io/GModelFit.jl/)
 
-`GFit` is a general purpose, data-driven model fitting framework for Julia.
+`GModelFit` is a general purpose, data-driven model fitting framework for Julia.
 
 ## Installation
 
 Install with:
 ```julia
-]add GFit
+]add GModelFit
 ```
 
 ## Example
 
 ```julia
-using GFit
+using GModelFit
 
 # Prepare vectors with domain points, empirical measures and uncertainties
 x    = [0.1, 1.1, 2.1, 3.1, 4.1]
@@ -38,7 +38,7 @@ The output is as follows:
 ╭───────────┬────────────┬─────────────┬───────────┬───────────┬───────────┬─────────╮
 │ Component │ Type       │ Eval. count │ Min       │ Max       │ Mean      │ NaN/Inf │
 ├───────────┼────────────┼─────────────┼───────────┼───────────┼───────────┼─────────┤
-│ main      │ GFit.FComp │ 76          │     6.088 │     25.84 │     13.56 │ 0       │
+│ main      │ GModelFit.FComp │ 76          │     6.088 │     25.84 │     13.56 │ 0       │
 ╰───────────┴────────────┴─────────────┴───────────┴───────────┴───────────┴─────────╯
 
 Parameters:

docs/src/api.md

@@ -5,7 +5,7 @@
 ```
 
 ## Exported symbols
-The list of **GFit.jl** exported symbols is as follows:
+The list of **GModelFit.jl** exported symbols is as follows:
 
 ```@docs
 CartesianDomain{N}
@@ -32,14 +32,14 @@ values
 
 
 ## Non-exported symbols
-The following symbols are not exported by the **GFit.jl** package since they are typically not used in every day work, or aimed to debugging purposes.  Still, they can be useful in some case, hence they are documented here.
+The following symbols are not exported by the **GModelFit.jl** package since they are typically not used in every day work, or aimed to debugging purposes.  Still, they can be useful in some case, hence they are documented here.
 
 ```@docs
-GFit.FitStats
-GFit.FunctDesc
-GFit.ModelSnapshot
-GFit.Parameter
-GFit.comptypes
-GFit.mock
-GFit.serialize
+GModelFit.FitStats
+GModelFit.FunctDesc
+GModelFit.ModelSnapshot
+GModelFit.Parameter
+GModelFit.comptypes
+GModelFit.mock
+GModelFit.serialize
 ```

docs/src/builtincomp.md

@@ -5,7 +5,7 @@ include("setup.jl")
 
 # Built-in components
 
-The **GFit.jl** provides several built-in components which may be used to build arbitrarily complex models.
+The **GModelFit.jl** provides several built-in components which may be used to build arbitrarily complex models.
 
 
 ## FComp
@@ -20,15 +20,15 @@ In the first constructor `funct` is the Julia function, `deps` is a vector of de
 
 #### Example
 ```@example abc
-using GFit
+using GModelFit
 
 # Define a simple Julia function to evaluate a linear relationship
 myfunc(x, b, m) = b .+ x .* m
 
 # Prepare domain and a model with a FComp wrapping the previously defined function.
 # Also specify the initial guess parameters.
 dom = Domain(1:5)
-model = Model(dom, :linear => GFit.FComp(myfunc, [:x], b=2, m=0.5))
+model = Model(dom, :linear => GModelFit.FComp(myfunc, [:x], b=2, m=0.5))
 
 # Fit model against data
 data = Measures(dom, [4.01, 7.58, 12.13, 19.78, 29.04], 0.4)
@@ -37,7 +37,7 @@ dumpjson("ex_FComp", best, fitstats, data) # hide
 show((best, fitstats)) # hide
 ```
 
-In the second constructor a [`GFit.FunctDesc`](@ref) object is accepted, as generated by the [`@λ`](@ref) macro).  The function is typically a mathematical expression combining any number of parameters and/or other component evaluations within the same model.  The expression should be given in the form:
+In the second constructor a [`GModelFit.FunctDesc`](@ref) object is accepted, as generated by the [`@λ`](@ref) macro).  The function is typically a mathematical expression combining any number of parameters and/or other component evaluations within the same model.  The expression should be given in the form:
 ```
 @λ (x, [y, [further domain dimensions...],]
     [comp1, [comp2, [further components ...],]]
@@ -50,7 +50,7 @@ The previous example can be rewritten as follows:
 
 #### Examples
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and a linear model (with initial guess parameters)
 dom = Domain(1:5)
@@ -105,7 +105,7 @@ println() # hide
 To define the model we will rewrite the equation as `Ax - b = 0`, and define a model as follows
 ```@example abc
 dom = Domain(length(b))  # dummy domain
-model = Model(dom, GFit.FCompv(x -> A*x - b,
+model = Model(dom, GModelFit.FCompv(x -> A*x - b,
                                [1, 1, 1]))
 println() # hide
 ```
@@ -124,8 +124,8 @@ An offset and slope component for 1D and 2D domains.
 
 The constructors are defined as follows:
 
-- 1D: `GFit.Components.OffsetSlope(offset, x0, slope)`;
-- 2D: `GFit.Components.OffsetSlope(offset, x0, y0, slopeX, slopeY)`;
+- 1D: `GModelFit.Components.OffsetSlope(offset, x0, slope)`;
+- 2D: `GModelFit.Components.OffsetSlope(offset, x0, y0, slopeX, slopeY)`;
 
 The parameters are:
 
@@ -143,11 +143,11 @@ The parameters are:
 
 #### Examples
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and a linear model using the OffsetSlope component
 dom = Domain(1:5)
-model = Model(dom, :linear => GFit.OffsetSlope(2, 0, 0.5))
+model = Model(dom, :linear => GModelFit.OffsetSlope(2, 0, 0.5))
 
 # Fit model against data
 data = Measures(dom, [4.01, 7.58, 12.13, 19.78, 29.04], 0.4)
@@ -161,11 +161,11 @@ Note that the numerical results are identical to the previous example (where an
 
 A similar example in 2D is as follows:
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and a linear model using the OffsetSlope component
 dom = CartesianDomain(1:5, 1:5)
-model = Model(dom, :plane => GFit.OffsetSlope(2, 0, 0, 0.5, 0.5))
+model = Model(dom, :plane => GModelFit.OffsetSlope(2, 0, 0, 0.5, 0.5))
 
 # Fit model against data
 data = Measures(dom, [ 3.08403  3.46719  4.07612  4.25611  5.04716
@@ -186,18 +186,18 @@ A *n*-th degree polynomial function (*n > 1*) for 1D domains.
 
 The constructor is defined as follows:
 
-- `GFit.Polynomial(p1, p2, ...)`;
+- `GModelFit.Polynomial(p1, p2, ...)`;
   where `p1`, `p2`, etc. are the guess values for the coefficients of each degree of the polynomial.
 
 The parameters are accessible via the `p` vector, as: `p[1]`, `p[2]`, etc.
 
 #### Examples
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and a linear model using the Polynomial component
 dom = Domain(1:5)
-model = Model(dom, GFit.Polynomial(2, 0.5))
+model = Model(dom, GModelFit.Polynomial(2, 0.5))
 
 # Fit model against data
 data = Measures(dom, [4.01, 7.58, 12.13, 19.78, 29.04], 0.4)
@@ -208,7 +208,7 @@ show((best, fitstats)) # hide
 
 Note again that the numerical results are identical to the previous examples.  Also note that the default name for a component (if none is provided) is `:main`.  To use a 2nd degree polynomial we can simply replace the `:main` component with a new one:
 ```@example abc
-model[:main] = GFit.Polynomial(2, 0.5, 1)
+model[:main] = GModelFit.Polynomial(2, 0.5, 1)
 best, fitstats = fit(model, data)
 dumpjson("ex_Polynomial2", best, fitstats, data) # hide
 show((best, fitstats)) # hide
@@ -222,9 +222,9 @@ A normalized Gaussian component for 1D and 2D domains.
 
 The constructors are defined as follows:
 
-- 1D: `GFit.Components.Gaussian(norm, center, sigma)`;
-- 2D: `GFit.Components.Gaussian(norm, centerX, centerY, sigma)` (implies `sigmaX=sigmaY`, `angle=0`);
-- 2D: `GFit.Components.Gaussian(norm, centerX, centerY, sigmaX, sigmaY, angle)`;
+- 1D: `GModelFit.Components.Gaussian(norm, center, sigma)`;
+- 2D: `GModelFit.Components.Gaussian(norm, centerX, centerY, sigma)` (implies `sigmaX=sigmaY`, `angle=0`);
+- 2D: `GModelFit.Components.Gaussian(norm, centerX, centerY, sigmaX, sigmaY, angle)`;
 
 The parameters are:
 
@@ -247,11 +247,11 @@ The parameters are:
 
 #### Examples
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and model
 dom = Domain(1:0.5:5)
-model = Model(dom, GFit.Gaussian(1, 3, 0.5))
+model = Model(dom, GModelFit.Gaussian(1, 3, 0.5))
 
 # Fit model against data
 data = Measures(dom, [0, 0.3, 6.2, 25.4, 37.6, 23., 7.1, 0.4, 0], 0.6)
@@ -263,15 +263,15 @@ show((best, fitstats)) # hide
 
 A very common problem is to fit the histogram of a distribution with a Gaussian model. In the following example we will generate such distribution with `Random.randn`, and use the [Gnuplot.jl](https://github.com/gcalderone/Gnuplot.jl/) package to calculate the histogram and display the plot:
 ```@example abc
-using Random, GFit, Gnuplot
+using Random, GModelFit, Gnuplot
 
 # Calculate histogram of the distribution
 hh = hist(randn(10000), bs=0.25)
 
 # Prepare domain and data and fit a model
 dom = Domain(hh.bins)
 data = Measures(dom, hh.counts, 1.)
-model = Model(dom, GFit.Gaussian(1e3, 0, 1))
+model = Model(dom, GModelFit.Gaussian(1e3, 0, 1))
 best, fitstats = fit(model, data)
 dumpjson("ex_Gaussian2", best, fitstats, data) # hide
 show((best, fitstats)) # hide
@@ -289,15 +289,15 @@ saveas("gaussian") # hide
 A similar problem in 2D can be handled as follows:
 
 ```@example abc
-using Random, GFit, Gnuplot
+using Random, GModelFit, Gnuplot
 
 # Calculate histogram of the distribution
 hh = hist(1 .+ randn(10000), 2 .* randn(10000))
 
 # Prepare domain and data and fit a model
 dom = CartesianDomain(hh.bins1, hh.bins2)
 data = Measures(dom, hh.counts, 1.)
-model = Model(dom, GFit.Gaussian(1e3, 0, 0, 1, 1, 0))
+model = Model(dom, GModelFit.Gaussian(1e3, 0, 0, 1, 1, 0))
 best, fitstats = fit(model, data)
 dumpjson("ex_Gaussian2D", best, fitstats, data) # hide
 show((best, fitstats)) # hide
@@ -324,7 +324,7 @@ The `SumReducer` component has no parameter.
 
 #### Example
 ```@example abc
-using GFit
+using GModelFit
 
 # Prepare domain and a linear model (with initial guess parameters)
 dom = Domain(1:5)