Feat: save and load for alpha and conditions

pyproject.toml

@@ -4,3 +4,8 @@ requires = [
     "wheel"
 ]
 build-backend = "setuptools.build_meta"
+[tool.pytest.ini_options]
+log_cli=true
+log_level='NOTSET'
+log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)"
+log_cli_date_format = "%Y-%m-%d %H:%M:%S"

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="hsbalance",
-    version="0.5.3",
+    version="0.5.4",
     author="Maged M.Eltorkoman",
     author_email="newmaged@gmail.com",
     description="Python tools for Practical Modeling and Solving High Speed Rotor Unbalance Problem",

src/hsbalance/CI_matrix.py

@@ -3,6 +3,7 @@
 import numpy as np
 import hsbalance.tools as tools
 import pandas as pd
+import pickle
 
 _logger = logging.getLogger(__name__)
 _logger.propagate = False
@@ -256,17 +257,24 @@ def __repr__(self):
 
         return ''.join(formatter.info())
 
-def test_save():
-    alpha = Alpha(name='Model_IC')
-    alpha.add(np.random.rand(3, 3))
-    alpha.save('my_alpha')
-
+    def save(self, file:str):
+        '''
+        Method to save condition instance.
+        '''
+        if isinstance(file, str):
+            self.file = file
+        with open(self.file, 'wb') as f:
+            pickle.dump(self, f)
 
-if __name__ == '__main__':
-    test_save()
-    my_alpha = Alpha()
-    my_alpha.load('my_alpha')
-    print(my_alpha)
 
 
+    def load(self, file:str):
+        '''
+        Method to load condition instance.
+        '''
+        if isinstance(file, str):
+            self.file = file
+        with open(self.file, 'rb') as f:
+            _loaded_instance = pickle.load(f)
+            self.add(_loaded_instance.alpha, _loaded_instance.A)
 

src/hsbalance/model.py

@@ -8,6 +8,7 @@
 
 class _Model:
     """Abstract class for models"""
+    # TODO init model by only name to be able to load from previous saved instance
 
     def __init__(self, A:'initial_vibration'=None, alpha: 'instance of Alpha class'=None,
                  conditions: 'list of condition instances'=None, name:'string'=''):
@@ -291,6 +292,7 @@ def info(self):
         return ''.join(formatter.info())
 
 
+
 class LeastSquares(_Model):
     """subclass of Model
     solving the model using Least squares method, The objective function

test/test_ALPHA.py

@@ -5,6 +5,10 @@
 import test_tools
 import warnings
 import hsbalance as hs
+import tempfile
+import logging
+logger = logging.getLogger(__name__)
+temp_file = tempfile.NamedTemporaryFile()
 
 '''This module is for testing ALPHA class'''
 # Reading the test cases from config.yaml file
@@ -150,3 +154,11 @@ def test_info():
     condition1 = hs.Condition(name='Speed 2500')
     condition1.add(alpha2, A=np.random.rand(m,1))
     print(alpha1, alpha2, condition1)
+
+def test_alpha_save_load(test_alpha):
+    np.random.seed(42)
+    test_alpha.add(np.random.rand(6,4))
+    test_alpha.save(temp_file.name)
+    loaded_alpha = hs.Alpha()
+    loaded_alpha.load(temp_file.name)
+    np.testing.assert_allclose(loaded_alpha.value, test_alpha.value)

test/test_model.py

@@ -1,13 +1,14 @@
 import time
-from scipy.interpolate import make_interp_spline
-import matplotlib.pyplot as plt
-import scipy.optimize as opt
 import numpy as np
 import sys
 import yaml
 import pytest
 import test_tools
 import hsbalance as hs
+import tempfile
+import logging
+logger = logging.getLogger(__name__)
+temp_file = tempfile.NamedTemporaryFile()
 
 def test_model_with_no_argument():
     with pytest.raises(TypeError) as e_info:
@@ -195,8 +196,23 @@ def test_info_model():
     split2.update(confirm=True)
     print(model.info())
 
+# Testing save and load conditions
+
+def test_Condition_save_load(test_A, test_alpha):
+    condition = hs.Condition()
+    condition.add(test_alpha, test_A)
+    condition.save(temp_file.name)
+    loaded_condition = hs.Condition()
+    loaded_condition.load(temp_file.name)
+    np.testing.assert_allclose(loaded_condition.alpha.value, test_alpha.value)
+    np.testing.assert_allclose(loaded_condition.A, test_A)
+
 
 if __name__ == '__main__':
+    from scipy.interpolate import make_interp_spline
+    import matplotlib.pyplot as plt
+    import scipy.optimize as opt
+
     def test_performance(n):
         alpha = hs.Alpha()
         real = np.random.uniform(0, 10, [n, n])