Merge pull request #5 from fkguo/develop

Develop

Project.toml

@@ -3,24 +3,22 @@ uuid = "beb75e20-2205-47e6-ad51-640e9c2309f1"
 authors = ["Feng-Kun Guo"]
 version = "0.1.0"
 
-
 [deps]
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
-
 [compat]
 PyCall = "1"
-PyPlot = "1, 2"
+Plots = "1"
 julia = "1"
 DataFrames = "0.21"
 Combinatorics = "1"
 ForwardDiff = "0.10"
-# StatsBase = "0.33"
+StatsBase = "0.31,0.32,0.33"
 
 
 [extras]

docs/Manifest.toml

@@ -19,9 +19,9 @@ version = "0.8.2"
 
 [[Documenter]]
 deps = ["Base64", "Dates", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "REPL", "Test", "Unicode"]
-git-tree-sha1 = "395fa1554c69735802bba37d9e7d9586fd44326c"
+git-tree-sha1 = "f3464968c65fc78846dad1c038c474a2c39bbb23"
 uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-version = "0.24.11"
+version = "0.25.0"
 
 [[InteractiveUtils]]
 deps = ["Markdown"]
@@ -52,9 +52,9 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[Parsers]]
 deps = ["Dates", "Test"]
-git-tree-sha1 = "eb3e09940c0d7ae01b01d9291ebad7b081c844d3"
+git-tree-sha1 = "10134f2ee0b1978ae7752c41306e131a684e1f06"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.5"
+version = "1.0.7"
 
 [[Pkg]]
 deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]

src/Data.jl

@@ -1,5 +1,5 @@
 using DataFrames: DataFrame
-using PyPlot: plt
+using Plots: scatter, plot!
 
 """
     Data(x::T, y::T, err::T) where {T<:Vector{Real}}
@@ -25,17 +25,12 @@ end
 Data(df::DataFrame) = Data(df[:,1], df[:,2], df[:,3])
 
 """
-    plt_data(data::Data; xlab = "x", ylab = "y")
+    plt_data(data::Data; xlab = "x", ylab = "y", legend = :topleft)
 
 Make a errorbar plot of the data
 """
-function plt_data(data::Data; xlab = "x", ylab = "y")
-    fig, ax = plt.subplots(figsize = (6, 4))
-    ax.errorbar(data.x, data.y, data.err, fmt = "o", label = "Data" )
-    ax.legend()
-    ax.set_xlabel(xlab); ax.set_ylabel(ylab)
-    ax.minorticks_on(); ax.tick_params(which="both",direction="in", right="on", top="on")
-    ax.grid(which="major", axis="both", alpha=0.25)
+function plt_data(data::Data; xlab = "x", ylab = "y", legend = :best)
+    scatter(data.x, data.y, yerror = data.err, label = "Data", xlab = xlab, ylab = ylab, legend = legend)
 end
 
 
@@ -67,30 +62,21 @@ function chisq(dist::Function, data::Data, par::Tuple; fitrange = ())
 end
 
 """
-    plt_best(dist::Function, fit::Fit, data::Data; xrange = (), xlab = "x", ylab = "y", npt = 100)`
+    plt_best(dist::Function, fit::Fit, data::Data; npts = 100, xrange = (), xlab = "x", ylab = "y", legend = :bes)`
 
 for plotting the comparison of the result from fit with the data.
 
 `xrange`: range of `x` for plotting the best fit; if not given then use the range of `data.x`
 
-`npt`: number of points computed for the best-fit curve, default = 100.
+`npts`: number of points computed for the best-fit curve, default = 100.
 """
-function plt_best(dist::Function, fit::Fit, data::Data; xrange = (), xlab = "x", ylab = "y", npt = 100)
+function plt_best(dist::Function, fit::Fit, data::Data; npts = 100, xrange = (), xlab = "x", ylab = "y", legend = :best)
     paras1 = convert(Array, fit.args)
 
     dis(x) = (length(func_argnames(dist)) > 2 ? dist(x, paras1...) : dist(x, paras1))
 
-    fig, ax = plt.subplots(figsize=(6,4))
-    ax.minorticks_on(); ax.tick_params(which="both",direction="in", right="on", top="on")
-    ax.set_xlabel(xlab)
-    ax.set_ylabel(ylab)
-
-    xrange = (isempty(xrange) && data.x)
-    wv = LinRange(xrange[1], xrange[end], npt)
-    ax.errorbar(data.x, data.y, data.err, c = "C0", fmt="o", label = "Data")
-    ax.plot(wv, dis.(wv), c = "C3", "-", label = "Best fit", lw=1.25)
-    ax.legend();
-
-    ax.set_xlim(wv[1], wv[end]); #ax.set_ylim(0, )
-    ax.grid(true, alpha = 0.3)
+    xrange = (isempty(xrange) ? data.x : xrange)
+    wv = LinRange(xrange[1], xrange[end], npts)
+    scatter(data.x, data.y, yerror = data.err, label = "Data", xlab = xlab, ylab = ylab, legend = legend)
+    plot!(wv, dis.(wv), label = "Best fit", lw=1.5)
 end

src/IMinuit.jl

@@ -1,7 +1,7 @@
 __precompile__() # this module is safe to precompile
 module IMinuit
 
-using PyCall
+using PyCall: PyObject, pycall, PyNULL, PyAny, pyimport_conda, pyimport
 import PyCall: PyObject, pycall
 import PyCall: hasproperty # Base.hasproperty in Julia 1.2
 import Base: convert, ==, isequal, hash,  haskey