text: # -*- coding: utf-8 -*-
  '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
  Copyright (C) 2016, Caleb Bell <Caleb.Andrew.Bell@gmail.com>
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.'''
  
  import pytest
  import numpy as np
  import pandas as pd
  import json
  from math import isnan
  from fluids.numerics import linspace, assert_close, derivative, assert_close1d
  from chemicals.vapor_pressure import *
  from thermo.vapor_pressure import *
  from thermo.vapor_pressure import SANJARI, EDALAT, AMBROSE_WALTON, LEE_KESLER_PSAT, BOILING_CRITICAL, COOLPROP, VDI_PPDS, VDI_TABULAR, WAGNER_MCGARRY, ANTOINE_EXTENDED_POLING, ANTOINE_POLING, WAGNER_POLING, DIPPR_PERRY_8E
  from thermo.utils import TDependentProperty
  from chemicals.identifiers import check_CAS
  import chemicals
  from thermo.coolprop import has_CoolProp
  from math import *
  
  
  @pytest.mark.CoolProp
  @pytest.mark.meta_T_dept
  @pytest.mark.skipif(not has_CoolProp(), reason='CoolProp is missing')
  def test_VaporPressure_CoolProp():
      EtOH = VaporPressure(Tb=351.39, Tc=514.0, Pc=6137000.0, omega=0.635, CASRN='64-17-5')
      assert_close(EtOH.calculate(305.0, COOLPROP), 11592.205263402893, rtol=1e-7)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure():
      # Ethanol, test as many methods asa possible at once
      EtOH = VaporPressure(Tb=351.39, Tc=514.0, Pc=6137000.0, omega=0.635, CASRN='64-17-5')
      
      # Check that assigning a bad method does not change the object and raises a ValueERror
      h0 = hash(EtOH)
      with pytest.raises(ValueError):
          EtOH.method = 'NOTAMETHOD'
      assert hash(EtOH) == h0
      
      # Check valid_methods call
      assert set(EtOH.valid_methods()) == EtOH.all_methods
      # For Ethanol, all methods are valid around 300 K
      assert EtOH.valid_methods(300) == EtOH.valid_methods()
      
      # Check test_property_validity
      assert not EtOH.test_property_validity(1j)
      
      # Check can't calculate with a bad method
      with pytest.raises(ValueError):
          EtOH.calculate(300, 'NOTAMETHOD')
      
      methods = list(EtOH.all_methods)
      methods.remove(VDI_TABULAR)
      if COOLPROP in methods:
          methods.remove(COOLPROP)
  
      Psats_expected = {WAGNER_MCGARRY: 11579.634014300127,
                       WAGNER_POLING: 11590.408779316374,
                       ANTOINE_POLING: 11593.661615921257,
                       DIPPR_PERRY_8E: 11659.154222044575,
                       VDI_PPDS: 11698.02742876088,
                       BOILING_CRITICAL: 14088.453409816764,
                       LEE_KESLER_PSAT: 11350.156640503357,
                       AMBROSE_WALTON: 11612.378633936816,
                       SANJARI: 9210.262000640221,
                       EDALAT: 12081.738947110121}
  
      T = 305.0
      Psat_calcs = {}
      for i in methods:
          EtOH.method = i
          Psat_calcs[i] = EtOH.T_dependent_property(T)
          Tmin, Tmax = EtOH.T_limits[i]
          assert Tmin < T < Tmax
  
      for k, v in Psats_expected.items():
          assert_close(v, Psat_calcs[k], rtol=1e-11)
      assert len(Psats_expected) == len(Psats_expected)
  
      assert_close(EtOH.calculate(305, VDI_TABULAR), 11690.81660829924, rtol=1E-4)
  
      s = EtOH.as_json()
      assert 'json_version' in s
      obj2 = VaporPressure.from_json(s)
      assert EtOH == obj2
  
  
  
      # Use another chemical to get in ANTOINE_EXTENDED_POLING
      a = VaporPressure(CASRN='589-81-1')
  
      Psat_calcs = []
      for i in list(a.all_methods):
          a.method = i
          Psat_calcs.append(a.T_dependent_property(410))
  
  
      Psat_exp = [162944.82134710113, 162870.44794192078, 162865.5380455795]
      assert_close1d(sorted(Psat_calcs), sorted(Psat_exp))
  
      s = a.as_json()
      obj2 = VaporPressure.from_json(s)
      assert a == obj2
  
      # Test that methods return None
      EtOH = VaporPressure(Tb=351.39, Tc=514.0, Pc=6137000.0, omega=0.635, CASRN='64-17-5')
      EtOH.extrapolation = None
      for i in list(EtOH.all_methods):
          EtOH.method = i
          assert EtOH.T_dependent_property(5000) is None
  
      # Test interpolation, extrapolation
      w = VaporPressure(Tb=373.124, Tc=647.14, Pc=22048320.0, omega=0.344, CASRN='7732-18-5')
      Ts = linspace(300, 350, 10)
      Ps = [3533.918074415897, 4865.419832056078, 6612.2351036034115, 8876.854141719203, 11780.097759775277, 15462.98385942125, 20088.570250257424, 25843.747665059742, 32940.95821687677, 41619.81654904555]
      w.add_tabular_data(Ts=Ts, properties=Ps)
      assert_close(w.T_dependent_property(305.), 4715.122890601165)
      w.extrapolation = 'interp1d'
      assert_close(w.T_dependent_property(200.), 0.5364148240126076)
      
      Ts_bad = [300, 325, 350]
      Ps_bad = [1, -1, 1j]
      with pytest.raises(ValueError):
          w.add_tabular_data(Ts=Ts_bad, properties=Ps_bad)
      Ts_rev = list(reversed(Ts))
      with pytest.raises(ValueError):
          w.add_tabular_data(Ts=Ts_rev, properties=Ps)
  
      # Get a check for Antoine Extended
      cycloheptane = VaporPressure(Tb=391.95, Tc=604.2, Pc=3820000.0, omega=0.2384, CASRN='291-64-5')
      cycloheptane.method = ('ANTOINE_EXTENDED_POLING')
      cycloheptane.extrapolation = None
      assert_close(cycloheptane.T_dependent_property(410), 161647.35219882353)
      assert None == cycloheptane.T_dependent_property(400)
  
      with pytest.raises(Exception):
          cycloheptane.test_method_validity(300, 'BADMETHOD')
  
      obj = VaporPressure(CASRN="71-43-2", Tb=353.23, Tc=562.05, Pc=4895000.0, omega=0.212, extrapolation="AntoineAB|DIPPR101_ABC", method="WAGNER_MCGARRY")
      assert_close(obj.T_dependent_property_derivative(600.0), 2379682.4349338813, rtol=1e-4)
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting0():
      obj = VaporPressure(CASRN='13838-16-9')
      Tmin, Tmax = obj.WAGNER_POLING_Tmin, obj.WAGNER_POLING_Tmax
      Ts = linspace(Tmin, Tmax, 10)
      Ps = [obj(T) for T in Ts]
      Tc, Pc = obj.WAGNER_POLING_Tc, obj.WAGNER_POLING_Pc
      fitted = obj.fit_data_to_model(Ts=Ts, data=Ps, model='Wagner', use_numba=False,
                            model_kwargs={'Tc': obj.WAGNER_POLING_Tc, 'Pc': obj.WAGNER_POLING_Pc})
      res = fitted
      assert 'Tc' in res
      assert 'Pc' in res
      assert_close(res['a'], obj.WAGNER_POLING_coefs[0])
      assert_close(res['b'], obj.WAGNER_POLING_coefs[1])
      assert_close(res['c'], obj.WAGNER_POLING_coefs[2])
      assert_close(res['d'], obj.WAGNER_POLING_coefs[3])
  
      # Heavy compound fit
      Ts = linspace(179.15, 237.15, 5)
      props_calc = [Antoine(T, A=138., B=520200.0, C=3670.0) for T in Ts]
      res, stats = TDependentProperty.fit_data_to_model(Ts=Ts, data=props_calc, model='Antoine',
                            do_statistics=True, use_numba=False, model_kwargs={'base':10.0},
                            fit_method='lm')
      assert stats['MAE'] < 1e-5
      
      # Fit with very low range and no C
      Ts = linspace(374, 377.0, 5)
      props_calc = [Antoine(T, A=12.852103, B=2942.98, C=0.0) for T in Ts]
      res, stats = TDependentProperty.fit_data_to_model(Ts=Ts, data=props_calc, model='Antoine',
                            do_statistics=True, use_numba=False, model_kwargs={'base':10.0},
                            fit_method='lm')
      assert stats['MAE'] < 1e-5
  
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting1():
      # Ammonia data fitting from chemsep
      ammonia_Ts_Psats = [191.24, 202.546, 213.852, 225.157, 236.463, 239.82, 247.769, 259.075, 270.381, 281.686, 292.992, 304.298, 315.604, 326.909, 338.215, 349.521, 360.827, 372.133, 383.438, 394.744, 406.05]
      ammonia_Psats = [4376.24, 10607.70, 23135.70, 46190.70, 85593.30, 101505.00, 148872.00, 245284.00, 385761.00, 582794.00, 850310.00, 1203550.00, 1658980.00, 2234280.00, 2948340.00, 3821410.00, 4875270.00, 6133440.00, 7621610.00, 9367940.00, 11403600.00]
      res, stats = VaporPressure.fit_data_to_model(Ts=ammonia_Ts_Psats, data=ammonia_Psats, model='DIPPR101',
                            do_statistics=True, use_numba=False, fit_method='lm')
      assert stats['MAE'] < 1e-4
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting2_dippr():
      pts = 10
      fit_issue_CASs = ['75-07-0', '107-02-8', '108-38-3', '7732-18-5', '85-44-9',
                        '67-64-1', '78-87-5', '624-72-6', '118-96-7', '124-18-5',
                        # '526-73-8'
                        ]
      for CAS in fit_issue_CASs:
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.Perrys2_8_Tmin, obj.Perrys2_8_Tmax, pts)
          props_calc = [obj.calculate(T, DIPPR_PERRY_8E) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='DIPPR101',
                                do_statistics=True, use_numba=False, multiple_tries=True, fit_method='lm')
          assert stats['MAE'] < 1e-5
  
  @pytest.mark.slow
  @pytest.mark.fuzz
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting3_WagnerMcGarry():
      for i, CAS in enumerate(chemicals.vapor_pressure.Psat_data_WagnerMcGarry.index):
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.WAGNER_MCGARRY_Tmin, obj.WAGNER_MCGARRY_Tc, 10)
          props_calc = [obj.calculate(T, WAGNER_MCGARRY) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='Wagner_original',
                                do_statistics=True, use_numba=False,
                                fit_method='lm', model_kwargs={'Tc': obj.WAGNER_MCGARRY_Tc, 'Pc': obj.WAGNER_MCGARRY_Pc})
          assert stats['MAE'] < 1e-7
  
  @pytest.mark.slow
  @pytest.mark.fuzz
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting4_WagnerPoling():
      for i, CAS in enumerate(chemicals.vapor_pressure.Psat_data_WagnerPoling.index):
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.WAGNER_POLING_Tmin, obj.WAGNER_POLING_Tc, 10)
          props_calc = [obj.calculate(T, WAGNER_POLING) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='Wagner',
                                do_statistics=True, use_numba=False,
                                fit_method='lm', model_kwargs={'Tc': obj.WAGNER_POLING_Tc, 'Pc': obj.WAGNER_POLING_Pc})
          assert stats['MAE'] < 1e-7
      
  @pytest.mark.slow
  @pytest.mark.fuzz
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting5_AntoinePoling():
      for i, CAS in enumerate(chemicals.vapor_pressure.Psat_data_AntoinePoling.index):
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.ANTOINE_POLING_Tmin, obj.ANTOINE_POLING_Tmax, 10)
          props_calc = [obj.calculate(T, ANTOINE_POLING) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='Antoine',
                                do_statistics=True, use_numba=False,
                                multiple_tries=True,
                                model_kwargs={'base': 10.0},  fit_method='lm')
          assert stats['MAE'] < 1e-7
  
  @pytest.mark.slow
  @pytest.mark.fuzz
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting6_VDI_PPDS():
      for i, CAS in enumerate(chemicals.vapor_pressure.Psat_data_VDI_PPDS_3.index):
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.VDI_PPDS_Tm, obj.VDI_PPDS_Tc, 10)
          props_calc = [obj.calculate(T, VDI_PPDS) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='Wagner',
                                do_statistics=True, use_numba=False,
                                fit_method='lm', model_kwargs={'Tc': obj.VDI_PPDS_Tc, 'Pc': obj.VDI_PPDS_Pc})
          assert stats['MAE'] < 1e-7
      
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting7_reduced_fit_params_with_jac():
      obj = VaporPressure(CASRN='13838-16-9')
      Tmin, Tmax = obj.WAGNER_POLING_Tmin, obj.WAGNER_POLING_Tmax
      Ts = linspace(Tmin, Tmax, 10)
      Ps = [obj(T) for T in Ts]
      fit = obj.fit_data_to_model(Ts=Ts, data=Ps, model='Wagner', use_numba=False,
                                  model_kwargs={'Tc': obj.WAGNER_POLING_Tc, 'Pc': obj.WAGNER_POLING_Pc, 'd': -4.60})
      assert fit['d'] == -4.6
  
      fit = obj.fit_data_to_model(Ts=Ts, data=Ps, model='Wagner', use_numba=False,
                                  model_kwargs={'Tc': obj.WAGNER_POLING_Tc, 'Pc': obj.WAGNER_POLING_Pc, 'b': 2.4})
      assert fit['b'] == 2.4
  
      fit = obj.fit_data_to_model(Ts=Ts, data=Ps, model='Wagner', use_numba=False,
                                  model_kwargs={'Tc': obj.WAGNER_POLING_Tc, 'Pc': obj.WAGNER_POLING_Pc, 'd': -4.60, 'a': -8.329, 'b': 2.4})
      assert fit['a'] == -8.329
      assert fit['b'] == 2.4
      assert fit['d'] == -4.6
  
  @pytest.mark.slow
  @pytest.mark.fuzz
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting8_TRC_AntoineExtended():
      hard_CASs = frozenset(['110-82-7'])
      for i, CAS in enumerate(chemicals.vapor_pressure.Psat_data_AntoineExtended.index):
          obj = VaporPressure(CASRN=CAS)
          Ts = linspace(obj.ANTOINE_EXTENDED_POLING_Tmin, obj.ANTOINE_EXTENDED_POLING_Tmax, 10)
          props_calc = [obj.calculate(T, ANTOINE_EXTENDED_POLING) for T in Ts]
          res, stats = obj.fit_data_to_model(Ts=Ts, data=props_calc, model='TRC_Antoine_extended',
                                do_statistics=True, use_numba=False, multiple_tries=CAS in hard_CASs,# multiple_tries_max_err=1e-4,
                                fit_method='lm', model_kwargs={'Tc': obj.ANTOINE_EXTENDED_POLING_coefs[0], 
                                                               'to': obj.ANTOINE_EXTENDED_POLING_coefs[1]})
          assert stats['MAE'] < 1e-4
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting9_Yaws_Psat():
      A, B, C, D, E, Tmin, Tmax = 53.93890302013294, -788.24, -22.734, 0.051225, 6.1896e-11, 68.15, 132.92
      Ts = linspace(Tmin, Tmax, 10)
      props_calc = [Yaws_Psat(T, A, B, C, D, E) for T in Ts]
      res, stats = VaporPressure.fit_data_to_model(Ts=Ts, data=props_calc, model='Yaws_Psat',
                            do_statistics=True, use_numba=False, 
                            fit_method='lm')        
      assert stats['MAE'] < 1e-5
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting10_example():
      Ts = [203.65, 209.55, 212.45, 234.05, 237.04, 243.25, 249.35, 253.34, 257.25, 262.12, 264.5, 267.05, 268.95, 269.74, 272.95, 273.46, 275.97, 276.61, 277.23, 282.03, 283.06, 288.94, 291.49, 293.15, 293.15, 293.85, 294.25, 294.45, 294.6, 294.63, 294.85, 297.05, 297.45, 298.15, 298.15, 298.15, 298.15, 298.15, 299.86, 300.75, 301.35, 303.15, 303.15, 304.35, 304.85, 305.45, 306.25, 308.15, 308.15, 308.15, 308.22, 308.35, 308.45, 308.85, 309.05, 311.65, 311.85, 311.85, 311.95, 312.25, 314.68, 314.85, 317.75, 317.85, 318.05, 318.15, 318.66, 320.35, 320.35, 320.45, 320.65, 322.55, 322.65, 322.85, 322.95, 322.95, 323.35, 323.55, 324.65, 324.75, 324.85, 324.85, 325.15, 327.05, 327.15, 327.2, 327.25, 327.35, 328.22, 328.75, 328.85, 333.73, 338.95]
      Psats = [58.93, 94.4, 118.52, 797.1, 996.5, 1581.2, 2365, 3480, 3893, 5182, 6041, 6853, 7442, 7935, 9290, 9639, 10983, 11283, 13014, 14775, 15559, 20364, 22883, 24478, 24598, 25131, 25665, 25931, 25998, 26079, 26264, 29064, 29598, 30397, 30544, 30611, 30784, 30851, 32636, 33931, 34864, 37637, 37824, 39330, 40130, 41063, 42396, 45996, 46090, 46356, 45462, 46263, 46396, 47129, 47396, 52996, 52929, 53262, 53062, 53796, 58169, 59328, 66395, 66461, 67461, 67661, 67424, 72927, 73127, 73061, 73927, 79127, 79527, 80393, 79927, 80127, 81993, 80175, 85393, 85660, 85993, 86260, 86660, 92726, 92992, 92992, 93126, 93326, 94366, 98325, 98592, 113737, 136626]
      res, stats = TDependentProperty.fit_data_to_model(Ts=Ts, data=Psats, model='Antoine', do_statistics=True, multiple_tries=True, model_kwargs={'base': 10.0})
      assert stats['MAE'] < 0.014
  
  @pytest.mark.fitting
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fitting11_bad_method():
      Ts = [203.65, 209.55, 212.45, 234.05, 237.04, 243.25, 249.35, 253.34, 257.25, 262.12, 264.5, 267.05, 268.95, 269.74, 272.95, 273.46, 275.97, 276.61, 277.23, 282.03, 283.06, 288.94, 291.49, 293.15, 293.15, 293.85, 294.25, 294.45, 294.6, 294.63, 294.85, 297.05, 297.45, 298.15, 298.15, 298.15, 298.15, 298.15, 299.86, 300.75, 301.35, 303.15, 303.15, 304.35, 304.85, 305.45, 306.25, 308.15, 308.15, 308.15, 308.22, 308.35, 308.45, 308.85, 309.05, 311.65, 311.85, 311.85, 311.95, 312.25, 314.68, 314.85, 317.75, 317.85, 318.05, 318.15, 318.66, 320.35, 320.35, 320.45, 320.65, 322.55, 322.65, 322.85, 322.95, 322.95, 323.35, 323.55, 324.65, 324.75, 324.85, 324.85, 325.15, 327.05, 327.15, 327.2, 327.25, 327.35, 328.22, 328.75, 328.85, 333.73, 338.95]
      Psats = [58.93, 94.4, 118.52, 797.1, 996.5, 1581.2, 2365, 3480, 3893, 5182, 6041, 6853, 7442, 7935, 9290, 9639, 10983, 11283, 13014, 14775, 15559, 20364, 22883, 24478, 24598, 25131, 25665, 25931, 25998, 26079, 26264, 29064, 29598, 30397, 30544, 30611, 30784, 30851, 32636, 33931, 34864, 37637, 37824, 39330, 40130, 41063, 42396, 45996, 46090, 46356, 45462, 46263, 46396, 47129, 47396, 52996, 52929, 53262, 53062, 53796, 58169, 59328, 66395, 66461, 67461, 67661, 67424, 72927, 73127, 73061, 73927, 79127, 79527, 80393, 79927, 80127, 81993, 80175, 85393, 85660, 85993, 86260, 86660, 92726, 92992, 92992, 93126, 93326, 94366, 98325, 98592, 113737, 136626]
      with pytest.raises(ValueError):
          TDependentProperty.fit_data_to_model(Ts=Ts, data=Psats, model='NOTAMETHOD')
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_analytical_derivatives():
      Psat = VaporPressure(CASRN="108-38-3", Tb=412.25, Tc=617.0, Pc=3541000.0, omega=0.331,
                           extrapolation="AntoineAB|DIPPR101_ABC", method=WAGNER_MCGARRY)
      assert_close(Psat.calculate_derivative(T=400.0, method=WAGNER_MCGARRY), 2075.9195652247963, rtol=1e-13)
      assert_close(Psat.calculate_derivative(T=400.0, method=WAGNER_MCGARRY, order=2), 47.61112509616565, rtol=1e-13)
  
      assert_close(Psat.calculate_derivative(T=400.0, method=WAGNER_POLING), 2073.565462948561, rtol=1e-13)
      assert_close(Psat.calculate_derivative(T=400.0, method=WAGNER_POLING, order=2), 47.60007499952595, rtol=1e-13)
  
      assert_close(Psat.calculate_derivative(T=400.0, method=DIPPR_PERRY_8E), 2075.1783812355125, rtol=1e-13)
      assert_close(Psat.calculate_derivative(T=400.0, method=DIPPR_PERRY_8E, order=2), 47.566696599306596, rtol=1e-13)
  
      assert_close(Psat.calculate_derivative(T=400.0, method=VDI_PPDS), 2073.5972901257196, rtol=1e-13)
      assert_close(Psat.calculate_derivative(T=400.0, method=VDI_PPDS, order=2), 47.489535848986364, rtol=1e-13)
  
      cycloheptane = VaporPressure(Tb=391.95, Tc=604.2, Pc=3820000.0, omega=0.2384, CASRN='291-64-5')
      cycloheptane.method = ANTOINE_EXTENDED_POLING
      assert_close(cycloheptane.calculate_derivative(T=500.0, method=ANTOINE_EXTENDED_POLING, order=2), 176.89903538855853, rtol=1e-13)
      assert_close(cycloheptane.calculate_derivative(T=500.0, method=ANTOINE_EXTENDED_POLING, order=1), 15046.47337900798, rtol=1e-13)
  
      cycloheptane.method = ANTOINE_POLING
      assert_close(cycloheptane.calculate_derivative(T=400.0, method=ANTOINE_POLING, order=1), 3265.237029987264, rtol=1e-13)
      assert_close(cycloheptane.calculate_derivative(T=400.0, method=ANTOINE_POLING, order=2), 65.83298769903531, rtol=1e-13)
  
  
  
  def test_VaporPressure_no_isnan():
      assert not isnan(VaporPressure(CASRN='4390-04-9').Tmin)
  
  def test_VaporPressure_linear_extrapolation_non_negative():
      ethanol_psat = VaporPressure(Tb=351.39, Tc=514.0, Pc=6137000.0, omega=0.635, CASRN='64-17-5')
  
      # Make sure the constants are set to guard against future changes to defaults
      ethanol_psat.method = WAGNER_MCGARRY
      ethanol_psat.interpolation_T = (lambda T: 1/T)
      ethanol_psat.interpolation_property = (lambda P: log(P))
      ethanol_psat.interpolation_property_inv = (lambda P: exp(P))
      ethanol_psat.extrapolation = 'linear'
  
      assert_close(ethanol_psat(700), 59005875.32878946, rtol=1e-4)
      assert_close(ethanol_psat(100), 1.0475828451230242e-11, rtol=1e-4)
  
      assert ethanol_psat.T_limits['WAGNER_MCGARRY'][0] == ethanol_psat.WAGNER_MCGARRY_Tmin
      assert ethanol_psat.T_limits['WAGNER_MCGARRY'][1] == ethanol_psat.WAGNER_MCGARRY_Tc
  
      assert_close(ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tc),
                   ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tc-1e-6))
      assert_close(ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tc),
                   ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tc+1e-6))
  
      assert_close(ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tmin),
                   ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tmin-1e-6))
      assert_close(ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tmin),
                   ethanol_psat.T_dependent_property(ethanol_psat.WAGNER_MCGARRY_Tmin+1e-6))
  
  
  
      Tmin = ethanol_psat.T_limits[ethanol_psat.method][0]
      Ts = linspace(0.7*Tmin, Tmin*(1-1e-10), 10)
      Ps = [ethanol_psat(T) for T in Ts]
  
      # Confirms it's linear
      # plt.plot(1/np.array(Ts), np.log(Ps))
      # plt.show()
  #    def rsquared(x, y):
  #        import scipy.stats
  #        _, _, r_value, _, _ = scipy.stats.linregress(x, y)
  #        return r_value*r_value
  
  #    assert_close(rsquared(1/np.array(Ts), np.log(Ps)), 1, atol=1e-5)
      assert abs(np.polyfit(1/np.array(Ts), np.log(Ps), 1, full=True)[1][0]) < 1e-13
  
  
      # TODO make work with different interpolation methods
  #    assert ethanol_psat == VaporPressure.from_json(ethanol_psat.as_json())
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_extrapolation_solve_prop():
      cycloheptane = VaporPressure(Tb=391.95, Tc=604.2, Pc=3820000.0, omega=0.2384, CASRN='291-64-5')
      cycloheptane.method = 'ANTOINE_EXTENDED_POLING'
      cycloheptane.extrapolation = 'AntoineAB|DIPPR101_ABC'
      cycloheptane.T_dependent_property(T=4000)
  
      assert_close(cycloheptane.solve_property(1), 187.25621087267422)
  
      assert_close(cycloheptane.solve_property(1e-20), 60.677576120119156)
  
      assert_close(cycloheptane.solve_property(1e5), 391.3576035137979)
      assert_close(cycloheptane.solve_property(1e6), 503.31772463155266)
      assert_close(cycloheptane.solve_property(1e7), 711.8047771523733)
      assert_close(cycloheptane.solve_property(3e7), 979.2026813626704)
  
  def test_VaporPressure_bestfit_derivatives():
      obj = 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]))
      assert_close(obj.T_dependent_property(300), 18601.061401014867, rtol=1e-11)
      assert_close(obj.T_dependent_property_derivative(300), 954.1652489206775, rtol=1e-11)
      assert_close(obj.T_dependent_property_derivative(300, order=2), 41.8787546283273, rtol=1e-11)
      assert_close(derivative(obj.T_dependent_property, 300, dx=300*1e-7), obj.T_dependent_property_derivative(300))
      assert_close(derivative(obj.T_dependent_property_derivative, 300, dx=300*1e-7), obj.T_dependent_property_derivative(300, order=2))
  
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_extrapolation_AB():
      obj = VaporPressure(Tb=309.21, Tc=469.7, Pc=3370000.0, omega=0.251, CASRN='109-66-0', load_data=True, extrapolation='AntoineAB')
      obj.method = WAGNER_MCGARRY
      obj.calculate_derivative(300, WAGNER_MCGARRY)
  
      for extrapolation in ('AntoineAB', 'DIPPR101_ABC', 'AntoineAB|AntoineAB', 'DIPPR101_ABC|DIPPR101_ABC',
                            'DIPPR101_ABC|AntoineAB', 'AntoineAB|DIPPR101_ABC'):
          obj.extrapolation = extrapolation
  
          assert_close(obj.T_dependent_property(obj.WAGNER_MCGARRY_Tc),
                       obj.T_dependent_property(obj.WAGNER_MCGARRY_Tc-1e-6))
          assert_close(obj.T_dependent_property(obj.WAGNER_MCGARRY_Tc),
                       obj.T_dependent_property(obj.WAGNER_MCGARRY_Tc+1e-6))
  
          assert_close(obj.T_dependent_property(obj.WAGNER_MCGARRY_Tmin),
                       obj.T_dependent_property(obj.WAGNER_MCGARRY_Tmin-1e-6))
          assert_close(obj.T_dependent_property(obj.WAGNER_MCGARRY_Tmin),
                       obj.T_dependent_property(obj.WAGNER_MCGARRY_Tmin+1e-6))
  
  
  # @pytest.mark.meta_T_dept
  # def test_VaporPressure_fast_Psat_poly_fit():
  #     corr = VaporPressure(exp_poly_fit=(273.17, 647.086, [-2.8478502840358144e-21, 1.7295186670575222e-17, -4.034229148562168e-14, 5.0588958391215855e-11, -3.861625996277003e-08, 1.886271475957639e-05, -0.005928371869421494, 1.1494956887882308, -96.74302379151317]))
      
  #     # Low transition
  #     P_trans = corr.exp_poly_fit_Tmin_value
  #     assert_close(corr.solve_property(P_trans), corr.solve_property_exp_poly_fit(P_trans), rtol=1e-10)
  #     assert_close(corr.solve_property(P_trans + 1e-7), corr.solve_property_exp_poly_fit(P_trans + 1e-7), rtol=1e-10)
      
  #     # High transition
  #     P_trans = corr.exp_poly_fit_Tmax_value
  #     assert_close(corr.solve_property(P_trans), corr.solve_property_exp_poly_fit(P_trans), rtol=1e-10)
  #     assert_close(corr.solve_property(P_trans + 1e-7), corr.solve_property_exp_poly_fit(P_trans + 1e-7), rtol=1e-10)
  
  
  #     # Low temperature values - up to 612 Pa
  #     assert_close(corr.solve_property(1e-5), corr.solve_property_exp_poly_fit(1e-5), rtol=1e-10)
  #     assert_close(corr.solve_property(1), corr.solve_property_exp_poly_fit(1), rtol=1e-10)
  #     assert_close(corr.solve_property(100), corr.solve_property_exp_poly_fit(100), rtol=1e-10)
  
  
  #     # Solver region
  #     assert_close(corr.solve_property(1e5), corr.solve_property_exp_poly_fit(1e5), rtol=1e-10)
  #     assert_close(corr.solve_property(1e7), corr.solve_property_exp_poly_fit(1e7), rtol=1e-10)
  
  #     # High T
  #     assert_close(corr.solve_property(1e8), corr.solve_property_exp_poly_fit(1e8), rtol=1e-10)
  
  #     # Extrapolation
  #     from thermo.vapor_pressure import POLY_FIT, BEST_FIT_AB, BEST_FIT_ABC
  #     obj = VaporPressure(poly_fit=(178.01, 591.74, [-8.638045111752356e-20, 2.995512203611858e-16, -4.5148088801006036e-13, 3.8761537879200513e-10, -2.0856828984716705e-07, 7.279010846673517e-05, -0.01641020023565049, 2.2758331029405516, -146.04484159879843]))
  #     assert_close(obj.calculate(1000, BEST_FIT_AB), 78666155.90418352, rtol=1e-10)
  #     assert_close(obj.calculate(1000, BEST_FIT_ABC), 156467764.5930495, rtol=1e-10)
  
  #     assert_close(obj.calculate(400, POLY_FIT), 157199.6909849476, rtol=1e-10)
  #     assert_close(obj.calculate(400, BEST_FIT_AB), 157199.6909849476, rtol=1e-10)
  #     assert_close(obj.calculate(400, BEST_FIT_ABC), 157199.6909849476, rtol=1e-10)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_generic_polynomial_exp_parameters():
      coeffs = [-1.446088049406911e-19, 4.565038519454878e-16, -6.278051259204248e-13, 4.935674274379539e-10,
                                                  -2.443464113936029e-07, 7.893819658700523e-05, -0.016615779444332356, 2.1842496316772264, -134.19766175812708]
      
      obj_bestfit = VaporPressure(exp_poly_fit=(175.7, 512.49, coeffs))
      obj_polynomial = VaporPressure(exp_polynomial_parameters={'test': {'coeffs': coeffs,
                                                          'Tmin': 175.7, 'Tmax': 512.49}})
      assert_close(obj_bestfit.T_dependent_property(300), 18601.061401014867, rtol=1e-11)
      
      assert_close(obj_polynomial(300), obj_bestfit.T_dependent_property(300), rtol=1e-13)
  
      assert VaporPressure.from_json(obj_bestfit.as_json()) == obj_bestfit
      assert eval(str(obj_bestfit)) == obj_bestfit
  
      assert VaporPressure.from_json(obj_polynomial.as_json()) == obj_polynomial
      assert eval(str(obj_polynomial)) == obj_polynomial
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_generic_polynomial_exp_parameters_complicated():
      coeffs = [-1.446088049406911e-19, 4.565038519454878e-16, -6.278051259204248e-13, 4.935674274379539e-10,
      -2.443464113936029e-07, 7.893819658700523e-05, -0.016615779444332356, 2.1842496316772264, -134.19766175812708]
      T = 300.0
      
      obj2 = VaporPressure(exp_poly_fit=(175.7, 512.49, coeffs))
      assert_close(obj2(T), 18601.061401014867, rtol=1e-13)
      
      assert_close(obj2.T_dependent_property_derivative(T), 954.1652489206775, rtol=1e-14)
      assert_close(obj2.T_dependent_property_derivative(T, order=2), 41.8787546283273, rtol=1e-14)
      assert_close(obj2.T_dependent_property_derivative(T, order=3), 1.496803960985584, rtol=1e-13)
      
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_exp_stablepoly_fit():
      obj2 = VaporPressure(Tc=591.72, exp_stablepoly_fit=((309.0, 591.72, [0.008603558174828078, 0.007358688688856427, -0.016890323025782954, -0.005289197721114957, -0.0028824712174469625, 0.05130960832946553, -0.12709896610233662, 0.37774977659528036, -0.9595325030688526, 2.7931528759840174, 13.10149649770156])))
      assert_close(obj2(400), 157191.01706242564, rtol=1e-13)
      assert_close(obj2.T_dependent_property_derivative(400, order=1), 4056.436943642117, rtol=1e-13)
      
      assert_close(obj2.T_dependent_property_derivative(400, order=2), 81.32645570045084, rtol=1e-13)
      assert_close(obj2.T_dependent_property_derivative(400, order=3), 1.103603650822488, rtol=1e-13)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_exp_cheb_fit():
      obj2 = VaporPressure(Tc=591.72, exp_cheb_fit=((309.0, 591.72, [12.570668791524573, 3.1092695610681673, -0.5485217707981505, 0.11115875762247596, -0.01809803938553478, 0.003674911307077089, -0.00037626163070525465, 0.0001962813915017403, 6.120764548889213e-05, 3.602752453735203e-05])))
      assert_close(obj2(300), 4186.189338463003, rtol=1e-13)
      
      assert_close(obj2.T_dependent_property_derivative(400, order=1), 4056.277312107932, rtol=1e-13)
      
      assert_close(obj2.T_dependent_property_derivative(400, order=2), 81.34302144188977, rtol=1e-13)
      assert_close(obj2.T_dependent_property_derivative(400, order=3),1.105438780935656, rtol=1e-13)
      
  @pytest.mark.meta_T_dept
  def test_VaporPressure_extrapolation_no_validation():
      N2 = VaporPressure(CASRN='7727-37-9', extrapolation='DIPPR101_ABC')
      N2.method = WAGNER_MCGARRY
      assert N2(298.15) is not None
      assert N2(1000.15) is not None
  
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_fast_Psat_poly_fit_extrapolation():
      obj = 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]))
      obj.extrapolation = 'AntoineAB|DIPPR101_ABC'
      assert_close(obj.solve_property(1e-13), 88.65839225764933)
      assert_close(obj.solve_property(300), 237.7793675652309)
      assert_close(1e8, obj.extrapolate(obj.solve_property(1e8), 'EXP_POLY_FIT'))
      assert_close(obj.extrapolate(800, 'EXP_POLY_FIT'), 404793143.0358333)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_Antoine_inputs():
      obj = VaporPressure()
      obj.add_correlation(name='WebBook', model='Antoine', Tmin=177.70, Tmax=264.93,  A=3.45604+5, B=1044.038, C=-53.893)
      assert_close(obj(200), 20.432980367117192, rtol=1e-12)
  
      # json
      hash0 = hash(obj)
  
      obj2 = VaporPressure.from_json(json.loads(json.dumps(obj.as_json())))
      assert obj == obj2
      assert hash(obj) == hash0
      assert hash(obj2) == hash0
  
      obj2 = eval(str(obj))
      assert obj == obj2
      assert hash(obj) == hash0
      assert hash(obj2) == hash0
  
  
      obj2 = VaporPressure(Antoine_parameters={'WebBook': {'A': 8.45604, 'B': 1044.038, 'C': -53.893, 'Tmin': 177.7, 'Tmax': 264.93}})
      assert_close(obj2(200), 20.432980367117192, rtol=1e-12)
      assert obj == obj2
      
      with pytest.raises(ValueError):
          obj.add_correlation(name='WebBook2', model='Antoine', Tmin=177.70, Tmax=264.93,  A=3.45604+5, B=1044.038)
      with pytest.raises(ValueError):
          obj.add_correlation(name='WebBook', model='Antoine', Tmin=177.70, Tmax=264.93,  A=3.45604+5, B=1044.038, C=-53.893)
      with pytest.raises(ValueError):
          obj.add_correlation(name='WebBook4', model='NOTAMODEL', Tmin=177.70, Tmax=264.93,  A=3.45604+5, B=1044.038, C=-53.893)
          
      
      # Test with the new 'coefficients' input method
      obj = VaporPressure(Antoine_parameters={'WebBook': {'coefficients': [8.45604, 1044.038, -53.893],
                                                      'Tmin': 177.7, 'Tmax': 264.93}})
      assert_close(obj(220), 148.15143004993493, rtol=1e-13)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_DIPPR101_inputs():
      obj = VaporPressure()
      # From Perry's 8th edition
      obj.add_correlation(name='Eq101test', model='DIPPR101', Tmin=175.47, Tmax=512.5,  A=82.718, B=-6904.5, C=-8.8622, D=7.4664E-6, E=2.0)
      assert_close(obj(298.15), 16825.750567754883, rtol=1e-13)
      assert_close(obj.T_dependent_property_derivative(298.15, order=1), 881.6678722199089, rtol=1e-12)
      assert_close(obj.T_dependent_property_derivative(298.15, order=2), 39.36139219676838, rtol=1e-12)
      assert_close(obj.T_dependent_property_derivative(298.15, order=3), 1.4228777458080808, rtol=1e-12)
  
      # json
      hash0 = hash(obj)
      obj2 = VaporPressure.from_json(json.loads(json.dumps(obj.as_json())))
      assert obj == obj2
      assert hash(obj) == hash0
      assert hash(obj2) == hash0
  
      obj2 = eval(str(obj))
      assert obj == obj2
      assert hash(obj) == hash0
      assert hash(obj2) == hash0
  
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_extrapolate_derivatives():
      obj = VaporPressure(extrapolation='DIPPR101_ABC|AntoineAB', exp_poly_fit=(273.17, 647.086, [-2.8478502840358144e-21, 1.7295186670575222e-17, -4.034229148562168e-14, 5.0588958391215855e-11, -3.861625996277003e-08, 1.886271475957639e-05, -0.005928371869421494, 1.1494956887882308, -96.74302379151317]))
      assert_close(obj.T_dependent_property_derivative(230), 1.4571835115958078, rtol=1e-11)
      assert_close(obj.T_dependent_property_derivative(230, order=2), 0.14109259717547848, rtol=1e-11)
      assert_close(obj.T_dependent_property_derivative(230, order=3), 0.012236116569167774, rtol=1e-11)
      
      assert_close(obj.T_dependent_property_derivative(700, order=1), 403088.9468522063, rtol=1e-8)
      assert_close(obj.T_dependent_property_derivative(700, order=2), 2957.4520772886904, rtol=1e-8)
  
  @pytest.mark.meta_T_dept
  def test_VaporPressure_weird_signatures():
      from thermo.utils import PROPERTY_TRANSFORM_LN, PROPERTY_TRANSFORM_DLN, PROPERTY_TRANSFORM_D2LN, PROPERTY_TRANSFORM_D_X, PROPERTY_TRANSFORM_D2_X
  
      obj = VaporPressure(extrapolation='DIPPR101_ABC|AntoineAB', exp_poly_fit=(273.17, 647.086, [-2.8478502840358144e-21, 1.7295186670575222e-17, -4.034229148562168e-14, 5.0588958391215855e-11, -3.861625996277003e-08, 1.886271475957639e-05, -0.005928371869421494, 1.1494956887882308, -96.74302379151317]))
      
      # Within range
      assert_close(obj.T_dependent_property_transform(300, PROPERTY_TRANSFORM_LN),
                   log(obj.T_dependent_property(300)))
      
      assert_close(obj.T_dependent_property_transform(300, PROPERTY_TRANSFORM_D_X),
                   obj.T_dependent_property_derivative(300)/obj.T_dependent_property(300))
      
      assert_close(obj.T_dependent_property_transform(300, PROPERTY_TRANSFORM_D2_X),
                   obj.T_dependent_property_derivative(300, 2)/obj.T_dependent_property(300))
      
      dln = derivative(lambda T: log(obj(T)), 300, dx=300*1e-6)
      assert_close(dln, obj.T_dependent_property_transform(300, PROPERTY_TRANSFORM_DLN))
      
      dln = derivative(lambda T: log(obj(T)), 300, n=2, dx=300*1e-5)
      assert_close(dln, obj.T_dependent_property_transform(300, PROPERTY_TRANSFORM_D2LN), rtol=1e-5)
  
      # Extrapolations
      for extrapolation in ('interp1d', 'DIPPR101_ABC', 'AntoineAB'):
          obj.extrapolation = extrapolation
          for T in (100, 1000):
              assert_close(obj.T_dependent_property_transform(T, PROPERTY_TRANSFORM_LN),
                           log(obj.T_dependent_property(T)))
          
              assert_close(obj.T_dependent_property_transform(T, PROPERTY_TRANSFORM_D_X),
                           obj.T_dependent_property_derivative(T)/obj.T_dependent_property(T))
          
              assert_close(obj.T_dependent_property_transform(T, PROPERTY_TRANSFORM_D2_X),
                           obj.T_dependent_property_derivative(T, 2)/obj.T_dependent_property(T))
          
              dln = derivative(lambda T: log(obj(T)), T, dx=T*1e-6)
              assert_close(dln, obj.T_dependent_property_transform(T, PROPERTY_TRANSFORM_DLN))
          
              dln = derivative(lambda T: log(obj(T)), T, n=2, dx=T*1e-5)
              assert_close(dln, obj.T_dependent_property_transform(T, PROPERTY_TRANSFORM_D2LN), rtol=4e-4)
      
  