bump to v0.3.1

Project.toml

@@ -1,7 +1,7 @@
 name = "ChemistryQuantitativeAnalysis"
 uuid = "3ed48883-6809-43ec-b77a-d23b0650d8c4"
 authors = ["Yu-Fong Peng <[email protected]>"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

README.md

@@ -132,7 +132,7 @@ value3
 .
 .
 ```
-The config file in `batch_name.batch` must contain property `delim` which determines the default delimiter for all `table.txt` in this directory and subdirectories.
+The property `delim` determines the default delimiter for `table.txt` in this directory and subdirectories.
 
 `data.at` and `calibration` is not necessary for initializing a batch. The former can be added to the batch directly in julia, and the latter will be generated after calibration.
 
@@ -144,6 +144,9 @@ Config file for `ColumnDataTable` needs at least the following two properties.
 [Type]
 C
 
+[delim]
+\t
+
 [Sample]
 sample_col_name
 
@@ -159,6 +162,9 @@ Config file for `RowDataTable` needs at least the following two properties.
 [Type]
 R
 
+[delim]
+\t
+
 [Analyte]
 analyte_col_name
 
@@ -169,7 +175,6 @@ sample_col_name_2
 .
 .
 ``` 
-User can also provide `delim` to overwrite the default one defined in `batch_name.batch/config.txt`.
 
 ### *.mt
 It must contain two `*dt` files. `true_concentrstion.dt` contains true concentration for each analyte and level. The sample names must be integers.
@@ -180,6 +185,9 @@ Config file for `method.mt` needs two properties, `signal` and `pointlevel`.
 [signal]
 area
 
+[delim]
+\t
+
 [pointlevel]
 level_for_1st_point
 level_for_2nd_point
@@ -208,13 +216,13 @@ It can contain multiple `*.dt`. The file names must start from an integer, `_` a
 ### Reading and writing Batch
 To read a batch into julia, call `ChemistryQuantitativeAnalysis.read`.
 ```julia-repl
-julia> batch = ChemistryQuantitativeAnalysis.read("batch_name.batch", T; table_type, analytetype)
+julia> batch = ChemistryQuantitativeAnalysis.read("batch_name.batch", T; table_type, analytetype, delim)
 ```
-`T` is the sink function for tabular data; it should create an object following `Tables.jl` interface. `table_type` is `T` parameter in the type signature of `Batch` which determines the underlying table type, and `analytetype` is a concrete type for `analyte` which msut have a method for string input.
+`T` is the sink function for tabular data; it should create an object following `Tables.jl` interface. `table_type` is `T` parameter in the type signature of `Batch` which determines the underlying table type, `analytetype` is a concrete type for `analyte` which msut have a method for string input, and `delim` specifies delimiter for tabular data if `config[:delim]` does not exist.
 
-To write project to disk, call `ChemistryQuantitativeAnalysis.write`. There is a keyword argument `delim` controling whether using "\t" or "," as delim for tables.
+To write batch to disk, call `ChemistryQuantitativeAnalysis.write`. There is a keyword argument `delim` controling delimiter of tables.
 ```julia-repl
-julia> ChemistryQuantitativeAnalysis.write("batch_name.batch", batch; delim = "\t")
+julia> ChemistryQuantitativeAnalysis.write("batch_name.batch", batch; delim = '\t')
 ```
 There will be a folder `calibration` containing multiple `*.mcal` or `*.scal` folders. The former is for `MultipleCalibration` and the latter is for `SingleCalibration`.
 

test/runtests.jl

@@ -160,14 +160,18 @@ end
         @test all(isapprox.(cbatch.calibration[2].table.accuracy[1:3], [1, sqrt(1.1), sqrt(0.9)]))
         @test all(isapprox.(rbatch.calibration[1].table.accuracy[1:3], [1, 1.1, 0.9]))
         @test all(isapprox.(rbatch.calibration[2].table.accuracy[1:3], [1, sqrt(1.1), sqrt(0.9)]))
+        @test isapprox(update_calibration!(SingleCalibration((method.conctable.analyte[2], ), 100.0), method).conc, first(getanalyte(method.conctable, 2)))
     end
     @testset "Quantification" begin
         @test all(isapprox_nan.(set_quantification!(rbatch.data, rbatch).estimated_concentration.S1, update_inv_predict!(rbatch, set_relative_signal(rbatch.data, rbatch)).data.estimated_concentration.S1))
         @test all(isapprox.(quantification(cbatch, cbatch.data).var"G1(drug_a)", inv_predict(cbatch.calibration[1], relative_signal(cbatch, cbatch.data))))
         @test all(isapprox_nan.(quantification(rbatch, rbatch.data).S2, update_quantification!(rbatch, rbatch.data).data.estimated_concentration.S2))
         @test all(isapprox.(update_inv_predict!(update_relative_signal!(cbatch)).data.estimated_concentration.var"G1(drug_b)", set_quantification(cbatch.data, cbatch).estimated_concentration.var"G1(drug_b)"))
+        @test all(isapprox.(set_inv_predict(cbatch.data, cbatch).estimated_concentration.var"G1(drug_b)", cbatch.data.estimated_concentration.var"G1(drug_b)"))
         set!(cbatch.data, :true_concentration, deepcopy(cbatch.data.estimated_concentration))
         @test all(isapprox.(update_accuracy!(cbatch).data.accuracy.var"G1(drug_b)", [1.0, 1.0, 1.0]))
+        @test all(isapprox.(set_accuracy!(cbatch.data).accuracy.var"G1(drug_b)", [1.0, 1.0, 1.0]))
+        @test all(isapprox.(quantification(cbatch.calibration[1], cbatch.data.area), quantification(cbatch.calibration[1], cbatch.data.area, cbatch.calibration[1].analyte...)))
     end
     @testset "Utils" begin
         @test getanalyte(cdata.area, AnalyteG1("G1(drug_b)")) == getanalyte(cdata.area, Symbol("G1(drug_b)"))