Compatibility for numpy 2.0 and scipy 1.14

General improvements

.github/workflows/build.yml

@@ -54,23 +54,23 @@ jobs:
         architecture: ${{ matrix.architecture }}
 
     - name: cache Linux
-      uses: actions/cache@v2
+      uses: actions/cache@v4
       if: startsWith(runner.os, 'Linux')
       with:
         path: ~/.cache/pip
         key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements_test.txt') }}
         restore-keys: |
           ${{ runner.os }}-${{ runner.architecture }}-${{ runner.python-version }}pip-
     - name: cache MacOS
-      uses: actions/cache@v2
+      uses: actions/cache@v4
       if: startsWith(runner.os, 'macOS')
       with:
         path: ~/Library/Caches/pip
         key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements_test.txt') }}
         restore-keys: |
           ${{ runner.os }}-${{ runner.architecture }}-${{ runner.python-version }}pip-
     - name: cache Windows
-      uses: actions/cache@v2
+      uses: actions/cache@v4
       if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache

README.rst

@@ -247,5 +247,5 @@ Citation
 
 To cite Thermo in publications use::
 
-    Caleb Bell and Contributors (2016-2023). Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)
+    Caleb Bell and Contributors (2016-2024). Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)
     https://github.com/CalebBell/thermo.

conftest.py

@@ -15,7 +15,7 @@ def pytest_ignore_collect(path):
         return True
     if 'conf.py' in path:
         return True
-    if ver_tup < (3, 7) or ver_tup >= (3, 12) or is_pypy:
+    if ver_tup < (3, 7) or ver_tup >= (3, 13) or is_pypy:
         # numba does not yet run under pypy
         if 'numba' in path:
             return True

docs/activity_coefficients.rst

@@ -158,11 +158,11 @@ Other json libraries can be used besides the standard json library by design.
 
 Storing and recreating objects with Python's :py:func:`pickle.dumps` library is also tested; this can be faster than using JSON at the cost of being binary data.
 
-All models have a :obj:`__hash__ <thermo.activity.GibbsExcess.__hash__>` method that can be used to compare different models to see if they are absolutely identical (including which values have been calculated already).
+All models have a :obj:`__hash__ <thermo.activity.GibbsExcess.exact_hash>` method that can be used to compare different models to see if they are absolutely identical (including which values have been calculated already).
 
 They also have a :obj:`model_hash <thermo.activity.GibbsExcess.model_hash>` method that can be used to compare different models to see if they have identical model parameters.
 
-They also have a :obj:`state_hash <thermo.activity.GibbsExcess.state_hash>` method that can be used to compare different models to see if they have identical temperature, composition, and model parameters.
+They also have a :obj:`state_hash <thermo.activity.GibbsExcess.state_hash>` method that can be used to compare different models to see if they have identical temperature, composition, and model parameters. This is the __hash__ method.
 
 Activity Coefficient Identities
 -------------------------------

docs/cubic_equations_of_state.rst

@@ -203,15 +203,17 @@ True
 >>> PR_case.state_hash() == PR_case2.state_hash()
 False
 
-And finally it is possible to see if two objects are exactly identical, including cached calculation results, by using the  :obj:`__hash__ <thermo.eos.GCEOS.__hash__>` method:
+:obj:`state_hash <thermo.eos.GCEOS.state_hash>` is the __hash__ method of the object.
+
+And finally it is possible to see if two objects are exactly identical, including cached calculation results, by using the  :obj:`exact_hash <thermo.eos.GCEOS.exact_hash>` method:
 
 >>> PR_case3 = PRMIX(T=115, P=1E6, Tcs=[126.1, 190.6], Pcs=[33.94E5, 46.04E5], omegas=[0.04, 0.011], zs=[0.5, 0.5], kijs=[[0,0.41],[0.41,0]])
 >>> PR_case.state_hash() == PR_case3.state_hash()
 True
->>> hash(PR_case) == hash(PR_case3)
+>>> PR_case.exact_hash() == PR_case3.exact_hash()
 True
 >>> _ = PR_case.da_alpha_dT_ijs
->>> hash(PR_case) == hash(PR_case3)
+>>> PR_case.exact_hash() == PR_case3.exact_hash()
 False
 
 Serialization

docs/index.rst

@@ -93,7 +93,7 @@ Citation
 
 To cite Thermo in publications use::
 
-    Caleb Bell and Contributors (2016-2023). Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)
+    Caleb Bell and Contributors (2016-2024). Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)
     https://github.com/CalebBell/thermo.
 
 

docs/tutorial_phases_and_flash.rst

@@ -61,11 +61,11 @@ Storing and recreating objects with Python's :py:func:`pickle.dumps` library is
 
 Hashing
 ^^^^^^^
-All models have a :obj:`__hash__ <thermo.phases.Phase.__hash__>` method that can be used to compare different phases to see if they are absolutely identical (including which values have been calculated already).
+All models have a :obj:`__hash__ <thermo.phases.Phase.exact_hash>` method that can be used to compare different phases to see if they are absolutely identical (including which values have been calculated already).
 
 They also have a :obj:`model_hash <thermo.phases.Phase.model_hash>` method that can be used to compare different phases to see if they have identical model parameters.
 
-They also have a :obj:`state_hash <thermo.phases.Phase.state_hash>` method that can be used to compare different phases to see if they have identical temperature, composition, and model parameters.
+They also have a :obj:`state_hash <thermo.phases.Phase.state_hash>` method that can be used to compare different phases to see if they have identical temperature, composition, and model parameters. This is the __hash__ method.
 
 
 

requirements.txt

@@ -1,6 +1,6 @@
 numpy
-scipy
+scipy>=1.6.0
 pandas
 coolprop
-fluids>=1.0.25
+fluids>=1.0.26
 chemicals>=1.1.5

requirements_docs.txt

@@ -3,7 +3,7 @@ scipy
 numpydoc
 pint
 nbsphinx
-fluids>=1.0.25
+fluids>=1.0.26
 chemicals>=1.1.5
 IPython
 ipython
@@ -12,5 +12,10 @@ pandas
 sphinxcontrib-katex
 sphinx-sitemap
 sphinx==4.3.0
+sphinxcontrib-applehelp==1.0.4
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==2.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.5
 matplotlib
 mpmath

requirements_test.txt

@@ -2,7 +2,7 @@ numpy
 scipy
 pandas
 sympy
-fluids>=1.0.25
+fluids>=1.0.26
 chemicals>=1.1.5
 pytest
 pytest-cov

setup.py

@@ -45,6 +45,7 @@
     'Programming Language :: Python :: 3.10',
     'Programming Language :: Python :: 3.11',
     'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
     'Programming Language :: Python :: Implementation :: CPython',
     'Programming Language :: Python :: Implementation :: PyPy',
     'Topic :: Education',
@@ -61,7 +62,7 @@
   version = '0.2.27',
   description = 'Chemical properties component of Chemical Engineering Design Library (ChEDL)',
   author = 'Caleb Bell',
-  install_requires=['fluids>=1.0.25', 'scipy', 'pandas', 'chemicals>=1.1.5'],
+  install_requires=['fluids>=1.0.26', "scipy>=1.6.0", 'pandas', 'chemicals>=1.1.5'],
   extras_require = {
       'Coverage documentation':  ['wsgiref>=0.1.2', 'coverage>=4.0.3']
   },

tests/test_chemical_package.py

@@ -79,7 +79,6 @@ def test_ChemicalConstantsPackage_json_version_exported():
     c2 = ChemicalConstantsPackage.from_json(json.loads(string))
     assert 'py/object' in string
     assert 'json_version' in string
-    assert not hasattr(c2, 'json_version')
 
 def test_ChemicalConstantsPackage_json_export_does_not_change_hashes():
     # There was a nasty bug where the hashing function was changing its result
@@ -208,3 +207,56 @@ def test_chemical_package_recreation():
     obj = ChemicalConstantsPackage.correlations_from_IDs(IDs=['water', 'hexane', 'toluene'])
     copy = eval(str(obj))
     assert obj == copy
+
+def test_chemical_package_recreation_another_issue():
+    constants = ChemicalConstantsPackage(atom_fractions={'H': 0.6666666666666666, 'C': 0.16666666666666666, 'O': 0.16666666666666666}, atomss=[{'H': 4, 'C': 1, 'O': 1}],
+                                        CASs=['67-56-1'], charges=[0], conductivities=[4.4e-05], dipoles=[1.7],
+                                        formulas=['CH4O'], Gfgs=[-162000.13000000003], Gfgs_mass=[-5055890.325967345], Hcs=[-764464.0], Hcs_lower=[-676489.1], Hcs_lower_mass=[-21112666.368306957], Hcs_mass=[-23858290.373904638],
+                                        Hfgs=[-200700.0], Hfgs_mass=[-6263681.321870828], Hfus_Tms=[3215.0000000000005], Hfus_Tms_mass=[100337.49601302797], Hvap_298s=[37486.47944178592], Hvap_298s_mass=[1169922.078237216],
+                                        Hvap_Tbs=[35170.873821707064], Hvap_Tbs_mass=[1097653.9383702152], LFLs=[0.06], logPs=[-0.74], molecular_diameters=[3.7995699999999997],
+                                        MWs=[32.04186], names=['methanol'], omegas=[0.5589999999999999], Parachors=[1.574169046057019e-05], Pcs=[8084000.0], phase_STPs=['l'], Psat_298s=[16905.960312551426], PSRK_groups=[{15: 1}],
+                                        Pts=[0.1758862695025245], PubChems=[887], rhocs=[8547.008547008547], rhocs_mass=[273.86205128205125], rhol_STPs=[24494.78614922483], rhol_STPs_mass=[784.8585085234012], RIs=[1.3288], S0gs=[239.9],
+                                        S0gs_mass=[7487.080962216301], Sfgs=[-129.7999999999999], Sfgs_mass=[-4050.950849919446], similarity_variables=[0.18725504699165404], Skins=[True], smiless=['CO'], STELs=[(250.0, 'ppm')],
+                                        StielPolars=[0.027243902847492674], Stockmayers=[685.96], Tautoignitions=[713.15], Tbs=[337.65], Tcs=[512.5], Tflashs=[282.15], Tms=[175.15], Tts=[175.59], UFLs=[0.36],
+                                        UNIFAC_Dortmund_groups=[{15: 1}], UNIFAC_groups=[{15: 1}], Van_der_Waals_areas=[358000.0], Van_der_Waals_volumes=[2.1709787e-05], Vcs=[0.000117], Vml_STPs=[4.0825014511573776e-05],
+                                        Vml_Tms=[3.541058756059562e-05], Zcs=[0.22196480200068586], UNIFAC_Rs=[1.4311], UNIFAC_Qs=[1.432], rhos_Tms=[1013.0221439813405], Vms_Tms=[3.162996997683616e-05], solubility_parameters=[29315.58469262365],
+                                        Vml_60Fs=[4.033573571273147e-05], rhol_60Fs_mass=[794.3789652976724], rhol_60Fs=[24791.91174599953])
+
+    VaporPressures = [VaporPressure(exp_poly_fit=(175.7, 512.49, [-1.446088049406911e-19, 4.565038519454878e-16, -6.278051259204248e-13, 4.935674274379539e-10,
+                                                            -2.443464113936029e-07, 7.893819658700523e-05, -0.016615779444332356, 2.1842496316772264, -134.19766175812708])), ]
+    HeatCapacityGases = [HeatCapacityGas(poly_fit=(50.0, 1000.0, [2.3511458696647882e-21, -9.223721411371584e-18, 1.3574178156001128e-14, -8.311274917169928e-12,
+                                                                4.601738891380102e-10, 1.78316202142183e-06, -0.0007052056417063217, 0.13263597297874355, 28.44324970462924])), ]
+    HeatCapacityLiquids = [HeatCapacityLiquid(poly_fit=(180.0, 503.1, [-5.042130764341761e-17, 1.3174414379504284e-13, -1.472202211288266e-10, 9.19934288272021e-08,
+                                                                    -3.517841445216993e-05, 0.008434516406617465, -1.2381765320848312, 101.71442569958393, -3508.6245143327947])),]
+    VolumeLiquids = [VolumeLiquid(poly_fit=(175.7, 502.5, [3.5725079384600736e-23, -9.031033742820083e-20, 9.819637959370411e-17, -5.993173551565636e-14,
+                                                        2.2442465416964825e-11, -5.27776114586072e-09, 7.610461006178106e-07, -6.148574498547711e-05, 0.00216398089328537])), ]
+    EnthalpyVaporizations = [EnthalpyVaporization(Tc=512.5, poly_fit_ln_tau=(175.7, 512.499, 512.5, [-0.004536133852590396, -0.2817551666837462, -7.344529282245696, -104.02286881045083,
+                                                                                    -860.5796142607192, -4067.8897875259267, -8952.300062896637, 2827.0089241465225, 44568.12528999141])),]
+    HeatCapacitySolids = [HeatCapacitySolid(poly_fit=(1.0, 5000.0, [-4.2547351607351175e-26, 9.58204543572984e-22, -8.928062818728625e-18, 4.438942190507877e-14,
+                                                                    -1.2656161406049876e-10, 2.0651464217978594e-07, -0.0001691371394823046, 0.2038633833421581, -0.07254973910767148])),]
+    SublimationPressures = [SublimationPressure(exp_poly_fit=(61.5, 179.375, [-1.9972190661146383e-15, 2.1648606414769645e-12, -1.0255776193312338e-09, 2.7846062954442135e-07,
+                                                                        -4.771529410705124e-05, 0.005347189071525987, -0.3916553642749777, 18.072103851054266, -447.1556383160345])),]
+    EnthalpySublimations = [EnthalpySublimation(poly_fit=(1.7515, 175.59, [2.3382707698778188e-17, -2.03890965442551e-12, 1.5374109464154768e-09, -4.640933157748743e-07,
+                                                                        6.931187040484687e-05, -0.004954625422589015, 0.045058888152305354, 32.52432385785916, 42213.605713250145])),]
+    VolumeSolids = [VolumeSolid(poly_fit=(52.677, 175.59, [3.9379562779372194e-30, 1.4859309728437516e-27, 3.897856765862211e-24, 5.012758300685479e-21, 7.115820892078097e-18,
+                                                        9.987967202910477e-15, 1.4030825662633013e-11, 1.970935889948393e-08, 2.7686131179275174e-05])),]
+
+    SurfaceTensions = [SurfaceTension(CASRN='67-56-1', method="SOMAYAJULU2")]
+
+    ThermalConductivityLiquids=[ThermalConductivityLiquid(poly_fit=(390.65, 558.9, [-1.7703926719478098e-31, 5.532831178371296e-28, -7.157706109850407e-25, 4.824017093238245e-22, -1.678132299010268e-19, 1.8560214447222824e-17, 6.274769714658382e-15, -0.00020340000228224661, 0.21360000021862866])),]
+    ThermalConductivityGases=[ThermalConductivityGas(poly_fit=(390.65, 558.9, [1.303338742188738e-26, -5.948868042722525e-23, 1.2393384322893673e-19, -1.5901481819379786e-16, 1.4993659486913432e-13, -1.367840742416352e-10, 1.7997602278525846e-07, 3.5456258123020795e-06, -9.803647813554084e-05])),]
+    ViscosityLiquids=[ViscosityLiquid(extrapolation_min=0, exp_poly_fit=(190.0, 391.9, [1.8379049563136273e-17, -4.5666126233131545e-14, 4.9414486397781785e-11, -3.042378423089263e-08, 1.166244931040138e-05, -0.0028523723735774113, 0.4352378275340892, -37.99358630363772, 1456.8338572042996])),]
+    ViscosityGases=[ViscosityGas(extrapolation_min=0, poly_fit=(390.65, 558.9, [4.166385860107714e-29, -1.859399624586853e-25, 3.723945144634823e-22, -4.410000193606962e-19, 3.412270901850386e-16, -1.7666632565075753e-13, 5.266250837132718e-11, 1.8202807683935545e-08, -3.7907568022643496e-07])),]
+
+    correlations = PropertyCorrelationsPackage(constants, VaporPressures=VaporPressures, HeatCapacityGases=HeatCapacityGases, HeatCapacityLiquids=HeatCapacityLiquids, VolumeLiquids=VolumeLiquids,
+                                            EnthalpyVaporizations=EnthalpyVaporizations, HeatCapacitySolids=HeatCapacitySolids, SublimationPressures=SublimationPressures,
+                                            SurfaceTensions=SurfaceTensions,
+                                            ViscosityLiquids=ViscosityLiquids, ViscosityGases=ViscosityGases,
+                                            ThermalConductivityGases=ThermalConductivityGases, ThermalConductivityLiquids=ThermalConductivityLiquids,
+                                            EnthalpySublimations=EnthalpySublimations, VolumeSolids=VolumeSolids)
+    correlations2 = eval(repr(correlations))
+    for name in correlations.correlations:
+        assert getattr(correlations, name) == getattr(correlations2, name)
+        assert hash_any_primitive(getattr(correlations, name)) == hash_any_primitive(getattr(correlations2, name))
+
+    assert correlations == correlations2

tests/test_eos.py

@@ -2345,7 +2345,7 @@ def test_model_encode_json_eos():
     for eos in eos_iter:
         e = eos(Tc=Tc, Pc=Pc, omega=omega, T=300, P=1E5)
         s = e.as_json()
-        assert 'json_version' in s
+        assert 'json_version' in str(s)
         assert type(s) is dict
         e1 = eos.from_json(s)
         assert e.__dict__ == e1.__dict__

tests/test_eos_mix.py

@@ -4392,7 +4392,7 @@ def test_model_encode_json_gceosmix():
     for eos in eos_mix_no_coeffs_list:
         obj1 = eos(T=T, P=P, zs=zs, omegas=omegas, Tcs=Tcs, Pcs=Pcs, kijs=kijs)
         s = obj1.as_json()
-        assert 'json_version' in s
+        assert 'json_version' in str(s)
         assert type(s) is dict
         obj2 = GCEOSMIX.from_json(s)
         assert obj1.__dict__ == obj2.__dict__
@@ -4675,8 +4675,8 @@ def test_eos_mix_one_minus_kijs():
     import pickle
     eos = PRSV2MIX(Tcs=[507.6], Pcs=[3025000], omegas=[0.2975], zs=[1], T=299., P=1E6, kappa1s=[0.05104], kappa2s=[0.8634], kappa3s=[0.460])
     json_copy = eos.as_json()
-    assert 'kijs' in json_copy
-    assert 'one_minus_kijs' not in json_copy
+    assert 'kijs' in str(json_copy)
+    assert 'one_minus_kijs' not in str(json_copy)
     json_stuff = pickle.dumps(json_copy)
     new_eos = GCEOSMIX.from_json(pickle.loads(json_stuff))
 

tests/test_eos_volume.py

@@ -107,6 +107,7 @@ def validate_volume(args, solver, rtol=1e-15):
 (138.48863713938846, 3.0538555088340556e-06, 2.2081238710001263e-05, 2.2081238710001263e-05, 0.0, 0.14823101466889332),
 (242.01282647944032, 6.892612104351147e-21, 2.208123871000126e-05, 2.208123871000126e-05, 0.0, 0.09892412606156903),
 (4714.866363457429, 0.01, 2.8986924337681703e-05, 4.923467294821763e-05, -8.306951335097421e-10, 1.810368573677903e-13),
+(222.04, 120.66, 12.736158671821732, 25.472317343643464, -162.20973771381992, 580349.6463695838),
 ]
 
 hard_solutions = {}
@@ -137,7 +138,7 @@ def validate_volume(args, solver, rtol=1e-15):
 hard_solutions[(138.48863713938846, 3.0538555088340556e-06, 2.2081238710001263e-05, 2.2081238710001263e-05, 0.0, 0.14823101466889332)] = ((5.228993310247619e-05+0j), (5.436208452435612e-05+0j), (377050778.3368337+0j))
 hard_solutions[(242.01282647944032, 6.892612104351147e-21, 2.208123871000126e-05, 2.208123871000126e-05, 0.0, 0.09892412606156903)] = ((1.3540386918605232e-05-3.003691156915976e-05j), (1.3540386918605232e-05+3.003691156915976e-05j), (2.919367241929447e+23+0j))
 hard_solutions[(4714.866363457429, 0.01, 2.8986924337681703e-05, 4.923467294821763e-05, -8.306951335097421e-10, 1.810368573677903e-13)] = ((-6.252127279461262e-05+0j), (1.328659984639961e-05+0j), (3920158.012884476+0j))
-
+hard_solutions[(222.04, 120.66, 12.736158671821732, 25.472317343643464, -162.20973771381992, 580349.6463695838)] = ((-5.6505121319507525-63.703933152753j), (-5.6505121319507525+63.703933152753j), (13.865240859233307+0j))
 
 # TODO important make mpmath cache answers here