diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index ca0f0fb5e61c0ddc2c3d41a18c43820a93dd339b..105be729e3afc02495b8c5c36604a2412d0e5487 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -104,10 +104,7 @@ compile-cerngetdp:
- md $CI_PROJECT_DIR/compiled_getdp
- copy getdp.exe $CI_PROJECT_DIR/compiled_getdp
tags:
- - windows
- - mpi
- except:
- - tags
+ - wingetdpmpi
artifacts:
paths:
- $CI_PROJECT_DIR/compiled_getdp
@@ -155,7 +152,7 @@ test-cerngetdp:
package-win64:
stage: package
- needs: ["compile-shared-lib-win64"]
+ needs: ["compile-shared-lib-win64", "compile-mex"]
image: gitlab-registry.cern.ch/steam/ci-deployer-docker:latest
script:
- export RELEASE_NAME=$(expr match "$CI_COMMIT_TAG" '\(.*\)-release$')
diff --git a/README.md b/README.md
index 4c45c0f5a03532856ce7accb9dd20c24958735a3..bbbd11fd3b8956321373d48c33a328176a8a4e24 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,15 @@ https://steam-material-library.docs.cern.ch/
Some of this documentation can also be accessed through the `docs` folder.
+## Adding a material function:
+
+- Add the c-function to the models/c/general folder.
+- Make sure it has 'return_test_yaml' and the 'eval' function inside of it.
+- Add the function name to the all_material_functions.csv file.
+- Make sure that testing and make_ref is set to TRUE -> this means that a reference file will be made and the output of the function will be tested against this rerefence file.
+- Run utils/generate_ref_data.py
+- Run tests/test_dll.py -> this will build a local dll library and will test all c-functions, it requires you to have installed CMake and its requirements.
+
## Implementation in CERN simulating interfaces
### CERNGetDP
diff --git a/bindings/python/src/steammaterials/STEAM_materials.py b/bindings/python/src/steammaterials/STEAM_materials.py
index 2328da346d4bac2b62f559e6127c72c949fd3478..6417678085599859f233cfdb6cd464a6d0ee685e 100644
--- a/bindings/python/src/steammaterials/STEAM_materials.py
+++ b/bindings/python/src/steammaterials/STEAM_materials.py
@@ -66,7 +66,10 @@ class STEAM_materials:
:param numpy2d: Numpy array with number of columns corresponding to number of function arguments and points to evaluate in rows
:return: Numpy array with values calculated by .dll function
"""
- numpy2d = numpy2d.astype(np.float64)
+ if not isinstance(numpy2d, np.ndarray):
+ numpy2d = np.array(numpy2d, dtype=np.float64, order='C')
+ else:
+ numpy2d = np.ascontiguousarray(numpy2d, dtype=np.float64)
inReal = (numpy2d.__array_interface__['data'][0] + np.arange(numpy2d.shape[0]) * numpy2d.strides[0]).astype(np.uintp)
error_out = self.eval(self.func_name, self.n_arg, inReal, inReal, self.n_points, self.RealPtr, self.Int_Ptr)
if error_out == 1:
diff --git a/requirements.txt b/requirements.txt
index 7a754c27f6d61ad0e652c3355946aaadaca31c47..1560f6e2e18d59ba97992674c446e575a46b8857 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-STEAM-materials~=2024.11.2
+STEAM-materials~=2024.11.3
numpy==1.26.4
pandas==2.2.3
wget==3.2
diff --git a/utils/generate_ref_data.py b/utils/generate_ref_data.py
index d64b45f7273a9b7b56831a0a997e60f49abfac2e..f5db0a75cc2382445c5aeb0acfb14a6d1dc13c71 100644
--- a/utils/generate_ref_data.py
+++ b/utils/generate_ref_data.py
@@ -1,164 +1,66 @@
-import itertools
-import yaml
-import numpy as np
-from steammaterials.STEAM_materials import STEAM_materials
-from datetime import datetime
-from pathlib import Path
-import os
-import pandas as pd
+#!/usr/bin/env python3
+import itertools
+import yaml
+import numpy as np
+from steammaterials.STEAM_materials import STEAM_materials
+from datetime import datetime
+from pathlib import Path
+import os
+import sys
+import pandas as pd
-################ FILL THIS IN #################
-csv_file = pd.read_csv(Path('../all_material_functions.csv'))
+def create_all_ref_files(output_path=None):
+ repository_root = Path(os.path.dirname(__file__)).parent
+ if output_path is None:
+ output_path = repository_root / 'tests' / 'ref_data'
+ csv_file = pd.read_csv(repository_root / 'all_material_functions.csv')
+
+ errors = 0
+ for i, row in csv_file.iterrows():
+ if row['make_ref']:
+ try:
+ create_ref_file(row, output_path)
+ except Exception as e:
+ print(f'Failed to generate reference file for {row["ref_name"]}')
+ print(f'Failed with error: {e}')
+ errors += 1
+ if errors:
+ print(f'!!! During generation, {errors} errors occurred !!!')
-for i in range(len(csv_file)):
- if csv_file.iloc[i]['make_ref']:
- input_parameters = int(csv_file.iloc[i]['input_parameters'])
- gen_by = csv_file.iloc[i]['ref_generated_by']
- ref_name = csv_file.iloc[i]['ref_name']
+def create_ref_file(row, output_path):
+ """
+ Creates a file to act as reference for the expected output of the given material function.
+ Expects a dictionary-like `row` containing the number of 'input_parameters', the material name
+ 'ref_name', author 'ref_generated_by' and inputs with 'input{i}'.
+ """
+ parameter_count = int(row['input_parameters'])
+ ref_name = row['ref_name']
+ if parameter_count < 1:
+ raise ValueError('Incorrect number of input parameters for {ref_name}')
- # you = 'Tim Mulder'
- # # material_function = 'CFUN_rhoHast_v2'
- # # input_parameters = 1
- # material_function = 'CFUN_rhoCu_NIST_v2'
- # input_parameters = 3
- ###############################################
+ inputs = [eval(row[f'input{i+1}']) for i in range(parameter_count)]
+ input_mat = np.array(list(itertools.product(*inputs))).T
+ out = STEAM_materials(ref_name, input_mat.shape[0], input_mat.shape[1]).evaluate(input_mat)
- if input_parameters == 1:
- input1 = eval(csv_file.iloc[i]['input1'])
- numpy2d = input1.reshape((1, len(input1)))
- input_list = np.array([[i] for i in input1])
- elif input_parameters == 2:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input_list = np.array([i for i in itertools.product(input1, input2)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1]))
- elif input_parameters == 3:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2]))
- elif input_parameters == 4:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3]))
- elif input_parameters == 5:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4]))
- elif input_parameters == 6:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5]))
- elif input_parameters == 7:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6]))
- elif input_parameters == 8:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input8 = eval(csv_file.iloc[i]['input8'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7, input8)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6], input_list[:, 7]))
- elif input_parameters == 9:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input8 = eval(csv_file.iloc[i]['input8'])
- input9 = eval(csv_file.iloc[i]['input9'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7, input8, input9)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6], input_list[:, 7], input_list[:, 8]))
- elif input_parameters == 10:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input8 = eval(csv_file.iloc[i]['input8'])
- input9 = eval(csv_file.iloc[i]['input9'])
- input10 = eval(csv_file.iloc[i]['input10'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7, input8, input9, input10)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6], input_list[:, 7], input_list[:, 8], input_list[:, 9]))
- elif input_parameters == 11:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input8 = eval(csv_file.iloc[i]['input8'])
- input9 = eval(csv_file.iloc[i]['input9'])
- input10 = eval(csv_file.iloc[i]['input10'])
- input11 = eval(csv_file.iloc[i]['input11'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7, input8, input9, input10, input11)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6], input_list[:, 7], input_list[:, 8], input_list[:, 9], input_list[:, 10]))
- elif input_parameters == 12:
- input1 = eval(csv_file.iloc[i]['input1'])
- input2 = eval(csv_file.iloc[i]['input2'])
- input3 = eval(csv_file.iloc[i]['input3'])
- input4 = eval(csv_file.iloc[i]['input4'])
- input5 = eval(csv_file.iloc[i]['input5'])
- input6 = eval(csv_file.iloc[i]['input6'])
- input7 = eval(csv_file.iloc[i]['input7'])
- input8 = eval(csv_file.iloc[i]['input8'])
- input9 = eval(csv_file.iloc[i]['input9'])
- input10 = eval(csv_file.iloc[i]['input10'])
- input11 = eval(csv_file.iloc[i]['input11'])
- input12 = eval(csv_file.iloc[i]['input12'])
- input_list = np.array([i for i in itertools.product(input1, input2, input3, input4, input5, input6, input7, input8, input9, input10, input11, input12)])
- numpy2d = np.vstack((input_list[:, 0], input_list[:, 1], input_list[:, 2], input_list[:, 3], input_list[:, 4], input_list[:, 5], input_list[:, 6], input_list[:, 7], input_list[:, 8], input_list[:, 9], input_list[:, 10], input_list[:, 11]))
- else:
- print('Wrong amount of input parameters')
+ dictionary_output = {
+ 'author': row['ref_generated_by'],
+ 'date': datetime.today().strftime('%d.%m.%Y'),
+ 'material_function': ref_name,
+ 'input': input_mat.T.tolist(),
+ 'output': [i.item() for i in out],
+ 'input_parameters': parameter_count,
+ }
+ try:
+ with (output_path / f'{ref_name}.yaml').open("x") as f:
+ yaml.dump(dictionary_output, f, default_flow_style=False)
+ except FileExistsError:
+ print(f'Reference file for {ref_name} already exists. You cannot overwrite it.')
- ###############################################
- if input_parameters >= 1:
- out = STEAM_materials(ref_name, numpy2d.shape[0], numpy2d.shape[1]).evaluate(numpy2d)
- else:
- print('Wrong amount of input parameters')
- ############### Making the yaml ################
- dictionary_output = {
- 'author': gen_by,
- 'date': datetime.today().strftime('%d.%m.%Y'),
- 'material_function': ref_name,
- 'input': [[i.item() for i in k] for k in input_list],
- 'output': [i.item() for i in out],
- 'input_parameters': input_parameters,
- }
- try:
- with Path(os.path.dirname(os.path.dirname(__file__)) + os.sep + 'tests' + os.sep + 'ref_data' + os.sep + ref_name+".yaml").open("x") as f:
- yaml.dump(dictionary_output, f, default_flow_style=False)
- except:
- print('Reference file for ' + ref_name + ' already excists. You cannot overwrite it.')
+if __name__ == '__main__':
+ if len(sys.argv) > 1:
+ create_all_ref_files(Path(sys.argv[1]))
+ else:
+ create_all_ref_files()