Rel 9.5.0 - Fix for matrix input using JSON

Examples/Dyes/dyes.jl

@@ -8,8 +8,7 @@ dyes ="
 data {
   int BATCHES; 
   int SAMPLES; 
-  real y[BATCHES, SAMPLES]; 
-  //vector[SAMPLES] y[BATCHES]; 
+  matrix[BATCHES, SAMPLES] y; 
 } 
 
 parameters {
@@ -45,25 +44,21 @@ generated quantities {
 }
 "
 
-dyesdata = Dict(:BATCHES => 6,
+data = Dict(
+    :BATCHES => 6,
     :SAMPLES => 5,
-    :y => reshape([
-      [1545, 1540, 1595, 1445, 1595]; 
-      [1520, 1440, 1555, 1550, 1440]; 
-      [1630, 1455, 1440, 1490, 1605]; 
-      [1595, 1515, 1450, 1520, 1560]; 
-      [1510, 1465, 1635, 1480, 1580]; 
-      [1495, 1560, 1545, 1625, 1445]
-    ], 6, 5)
+    :y => [
+        1545 1540 1595 1445 1595; 
+        1520 1440 1555 1550 1440; 
+        1630 1455 1440 1490 1605; 
+        1595 1515 1450 1520 1560; 
+        1510 1465 1635 1480 1580; 
+        1495 1560 1545 1625 1445]
   )
 
-#data = (BATCHES=6, SAMPLES=5, y=dyesdata[:y])
-data = joinpath(ProjDir, "dyes.json")
-
 tmpdir = joinpath(@__DIR__, "tmp")
 sm = SampleModel("dyes", dyes, tmpdir);
 
-#rc = stan_sample(sm; data)
 rc = stan_sample(sm; data)
 
 if success(rc)
@@ -79,7 +74,7 @@ if success(rc)
       :internals => names(chns, [:internals])
     )
   )
-  show(chn)
+  chn |> display
   println()
   describe(chn, sections=[:mu]) |> display
   println()

Examples/Dyes/dyes_2.jl

@@ -48,22 +48,20 @@ generated quantities {
 dyesdata = Dict(:BATCHES => 6,
     :SAMPLES => 5,
     :y => [
-      1545  1520  1630  1595  1510  1495;
-      1540  1440  1455  1515  1465  1560;
-      1595  1555  1440  1450  1635  1545;
-      1445  1550  1490  1520  1480  1625;
-      1595  1440  1605  1560  1580  1445;
-    ]
+        1545 1540 1595 1445 1595; 
+        1520 1440 1555 1550 1440; 
+        1630 1455 1440 1490 1605; 
+        1595 1515 1450 1520 1560; 
+        1510 1465 1635 1480 1580; 
+        1495 1560 1545 1625 1445]
   )
 
 data = (BATCHES=6, SAMPLES=5, y=dyesdata[:y])
-#data = joinpath(ProjDir, "dyes.json")
 
 tmpdir = joinpath(@__DIR__, "tmp")
 sm = SampleModel("dyes", dyes, tmpdir);
 
 rc = stan_sample(sm; use_json=true, data)
-#rc = stan_sample(sm; use_json=false, data)
 
 if success(rc)
   chns = read_samples(sm, :mcmcchains; include_internals=true)
@@ -78,7 +76,7 @@ if success(rc)
       :internals => names(chns, [:internals])
     )
   )
-  show(chn)
+  chn |> display
   println()
   describe(chn, sections=[:mu]) |> display
   println()

Examples_Stan_Methods/Generate_Quantities/generate_quantities.jl

@@ -21,14 +21,16 @@ gq = "
   }
 ";
 
-gq_data = Dict(
+data = Dict(
   "N" => 3,
   "y" => [100, 950, 450]
 );
 
 stanmodel = SampleModel("Generate_quantities", gq);
 
-rc = stan_sample(stanmodel; data=gq_data)
+rc = stan_sample(stanmodel; data,
+  use_cpp_chains=true, check_num_chains=false,
+  num_cpp_chains=2, num_julia_chains=2)
 
 if success(rc)
   # Convert to an MCMCChains.Chains object
@@ -41,7 +43,11 @@ if success(rc)
   display(df)
   println()
 
-  StanSample.stan_generate_quantities(stanmodel, 1)
+  available_chains(stanmodel) |> display
+  println()
+
+  gq_df = stan_generate_quantities(stanmodel, 1, "2_3")
+  gq_df |> display
 end
 
 

LocalPreferences.toml

@@ -0,0 +1,2 @@
+[CPUSummary]
+hwloc = false

Project.toml

@@ -3,19 +3,21 @@ uuid = "682df890-35be-576f-97d0-3d8c8b33a550"
 license = "MIT"
 desc = "Illustrate StanJulia's interface options to cmdstan"
 authors = ["Rob J Goedman <[email protected]>"]
-version = "9.4.0"
+version = "9.5.0"
 
 [deps]
 AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CompatHelperLocal = "5224ae11-6099-4aaa-941d-3aab004bd678"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca"
+NamedArrays = "86f7a689-2022-50b4-a561-43c23ac3c673"
 NamedTupleTools = "d9ec5142-1e00-5aa0-9d6a-321866360f50"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StanBase = "d0ee94f6-a23d-54aa-bbe9-7f572d6da7f5"
@@ -35,12 +37,14 @@ AxisKeys = "0.1, 0.2.0"
 CSV = "0.10"
 CompatHelperLocal = "0.1.20"
 DataFrames = "1.3"
+DataStructures = "0.18.11"
 DimensionalData = "0.20"
 Distributions = "0.25"
 DocStringExtensions = "0.8"
 JSON = "0.21"
 MCMCChains = "5.0"
 MonteCarloMeasurements = "1.0"
+NamedArrays = "0.9.6"
 NamedTupleTools = "0.13, 0.14"
 StanBase = "4"
 StanDiagnose = "4"
@@ -54,6 +58,7 @@ Tables = "1.6"
 julia = "1"
 
 [extras]
+CPUSummary = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]

README.md

@@ -18,7 +18,9 @@
 
 ## Purpose
 
-A collection of example Stan Language programs demonstrating the use Stan's cmdstan executable (as an external program) from Julia. 
+A collection of example Stan Language programs demonstrating all methods available in Stan's cmdstan executable (as an external program) from Julia.
+
+For most applications one of the method packages is a better choice for day to day use and for executing a Stan Language program use the most important method (sample) in StanSample.jl.
 
 ## Background info
 

docs/src/VERSIONS.md

@@ -18,6 +18,10 @@ This version of the package has primarily been tested with GitHub workflows and
 
 ## Versions
 
+### Version 9.4.1 (Not yet published)
+
+1. Doc updates
+
 ### Version 9.4.0
 
 1. Updated redcradsstudy results for cmdstan-2.29.0.

test/Examples_Test_Cases/MatrixInput/input_array.jl

@@ -0,0 +1,44 @@
+using StanSample, Random, Statistics
+
+stan_chris = "
+data { 
+    int n_rows;
+    int<lower=1> n_cols;
+    matrix<lower=0>[n_rows,n_cols] x; 
+}
+
+parameters {
+   real mu;
+} 
+
+model {
+    mu ~ normal(0, 1);
+}";
+
+seed = 65445
+Random.seed!(seed)
+
+n_rows = 10
+n_cols = 2
+data = [(x = rand(n_rows, n_cols), n_rows = n_rows, n_cols = n_cols) for i in 1:4]
+
+tmpdir = joinpath(@__DIR__, "tmp")
+sm = SampleModel("chris", stan_chris, tmpdir);
+rc = stan_sample(sm; data, seed);
+
+if success(rc)
+	df = read_samples(sm , :dataframe)
+    #df |> display
+    [mean(df.mu), std(df.mu)] |> display
+end
+
+tmpdir = joinpath(@__DIR__, "tmp")
+sm = SampleModel("chris", stan_chris, tmpdir);
+rc = stan_sample(sm; data=data[1:3], seed);
+
+if success(rc)
+    df = read_samples(sm , :dataframe)
+    #df |> display
+    [mean(df.mu), std(df.mu)] |> display
+end
+

test/Examples_Test_Cases/MatrixInput/input_dict_string.jl

@@ -0,0 +1,34 @@
+using JSON, StanSample, Random, Statistics
+
+stan_chris = "
+data { 
+    int n_rows;
+    int<lower=1> n_cols;
+    matrix<lower=0>[n_rows,n_cols] x; 
+}
+
+parameters {
+   real mu;
+} 
+
+model {
+    mu ~ normal(0, 1);
+}";
+
+seed = 65445;
+Random.seed!(seed)
+
+n_rows = 10
+n_cols = 2
+x = fill(0, n_rows, n_cols)
+data = Dict("x" => x, "n_rows" => n_rows, "n_cols" => n_cols)
+
+tmpdir = joinpath(@__DIR__, "tmp")
+sm = SampleModel("chris", stan_chris, tmpdir);
+rc = stan_sample(sm; data, seed);
+
+if success(rc)
+	df = read_samples(sm , :dataframe)
+    #df |> display
+    [mean(df.mu), std(df.mu)] |> display
+end

test/Examples_Test_Cases/MatrixInput/input_dict_symbol.jl

@@ -0,0 +1,34 @@
+using JSON, StanSample, Random, Statistics
+
+stan_chris = "
+data { 
+    int n_rows;
+    int<lower=1> n_cols;
+    matrix<lower=0>[n_rows,n_cols] x; 
+}
+
+parameters {
+   real mu;
+} 
+
+model {
+    mu ~ normal(0, 1);
+}";
+
+seed = 65445;
+Random.seed!(seed)
+
+n_rows = 10
+n_cols = 2
+x = fill(0, n_rows, n_cols)
+data = Dict(:x => x, :n_rows => n_rows, :n_cols => n_cols)
+
+tmpdir = joinpath(@__DIR__, "tmp")
+sm = SampleModel("chris", stan_chris, tmpdir);
+rc = stan_sample(sm; data, seed);
+
+if success(rc)
+	df = read_samples(sm , :dataframe)
+    #df |> display
+    [mean(df.mu), std(df.mu)] |> display
+end

test/Examples_Test_Cases/MatrixInput/input_nt.jl

@@ -0,0 +1,34 @@
+using StanSample, Random, Statistics
+
+stan_chris = "
+data { 
+    int n_rows;
+    int<lower=1> n_cols;
+    matrix<lower=0>[n_rows,n_cols] x; 
+}
+
+parameters {
+   real mu;
+} 
+
+model {
+    mu ~ normal(0, 1);
+}";
+
+seed = 65445;
+Random.seed!(seed)
+
+n_rows = 10
+n_cols = 2
+x = fill(0, n_rows, n_cols)
+data = (x = x, n_rows = n_rows, n_cols = n_cols)
+
+tmpdir = joinpath(@__DIR__, "tmp")
+sm = SampleModel("chris", stan_chris, tmpdir);
+rc = stan_sample(sm; data, seed);
+
+if success(rc)
+	df = read_samples(sm , :dataframe)
+    #df |> display
+    [mean(df.mu), std(df.mu)] |> display
+end