How to use the fluids.numba function in fluids

def test_Clamond_numba():
    assert_close(fluids.numba.Clamond(10000.0, 2.0), 
                 fluids.Clamond(10000.0, 2.0), rtol=5e-15)
    assert_close(fluids.numba.Clamond(10000.0, 2.0, True),
                 fluids.Clamond(10000.0, 2.0, True), rtol=5e-15)
    assert_close(fluids.numba.Clamond(10000.0, 2.0, False),
                 fluids.Clamond(10000.0, 2.0, False), rtol=5e-15)
    
    Res = np.array([1e5, 1e6])
    eDs = np.array([1e-5, 1e-6])
    fast = np.array([False]*2)
    assert_close1d(fluids.numba_vectorized.Clamond(Res, eDs, fast), 
                   fluids.vectorized.Clamond(Res, eDs, fast), rtol=1e-14)

def test_misc_compressible():
    assert_close(fluids.numba.isentropic_work_compression(P1=1E5, P2=1E6, T1=300, k=1.4, eta=0.78),
                 fluids.isentropic_work_compression(P1=1E5, P2=1E6, T1=300, k=1.4, eta=0.78),)

    assert_close(fluids.numba.isentropic_efficiency(1E5, 1E6, 1.4, eta_p=0.78),
                 fluids.isentropic_efficiency(1E5, 1E6, 1.4, eta_p=0.78))

    assert_close(fluids.numba.polytropic_exponent(1.4, eta_p=0.78),
                 fluids.polytropic_exponent(1.4, eta_p=0.78))
    
    assert_close(fluids.numba.Panhandle_A(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
                 fluids.Panhandle_A(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
    
    assert_close(fluids.numba.Panhandle_B(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
                 fluids.Panhandle_B(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
    
    assert_close(fluids.numba.Weymouth(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
                 fluids.Weymouth(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
    
    assert_close(fluids.numba.Spitzglass_high(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
                 fluids.Spitzglass_high(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))

    assert_close(fluids.numba.Spitzglass_high(P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15, Q=30),
             Spitzglass_high(P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15, Q=30))
    
    assert_close(fluids.numba.Spitzglass_low(D=0.154051, P1=6720.3199, P2=0, L=54.864, SG=0.6, Tavg=288.7),
                 fluids.Spitzglass_low(D=0.154051, P1=6720.3199, P2=0, L=54.864, SG=0.6, Tavg=288.7))

def infer_dictionary_types(d):
    if not d:
        raise ValueError("Empty dictionary cannot infer")
    keys = list(d.keys())
    type_keys = type(keys[0])
    for k in keys:
        if type(k) != type_keys:
            raise ValueError("Inconsistent key types in dictionary")
    values = list(d.values())
    type_values = type(values[0])
    for v in values:
        if type(v) != type_values:
            raise ValueError("Inconsistent value types in dictionary")
            
    return numba.typeof(keys[0]), numba.typeof(values[0])

from __future__ import division
import sys
import importlib.util
import types
import numpy as np

import fluids
import fluids.numba
import ht as normal_ht
import ht.numba
orig_file = __file__
normal = normal_ht
__all__ = []
__funcs = {}
numerics = fluids.numba.numerics
replaced = fluids.numba.numerics_dict.copy()

ht.numba.transform_complete_ht(replaced, __funcs, __all__, normal, vec=True)


globals().update(__funcs)
globals().update(replaced)

__name__ = 'ht.numba_vectorized'
__file__ = orig_file

from __future__ import division
import sys
import importlib.util
import types
import numpy as np

import fluids
import fluids.numba
import ht as normal_ht
import ht.numba
orig_file = __file__
normal = normal_ht
__all__ = []
__funcs = {}
numerics = fluids.numba.numerics
replaced = fluids.numba.numerics_dict.copy()

ht.numba.transform_complete_ht(replaced, __funcs, __all__, normal, vec=True)


globals().update(__funcs)
globals().update(replaced)

__name__ = 'ht.numba_vectorized'
__file__ = orig_file

import fluids
import fluids.numba
normal_fluids = fluids
normal = ht

orig_file = __file__
caching = False
'''


'''
__all__ = []
__funcs = {}

numerics = fluids.numba.numerics
replaced = fluids.numba.numerics_dict.copy()


def transform_complete_ht(replaced, __funcs, __all__, normal, vec=False):
    cache_blacklist = set(['h_Ganguli_VDI', 'fin_efficiency_Kern_Kraus', 'h_Briggs_Young', 
                           'h_ESDU_high_fin', 'h_ESDU_low_fin', 'Nu_Nusselt_Rayleigh_Holling_Herwig',
                           'DBundle_for_Ntubes_Phadkeb', 'Thome', 'to_solve_q_Thome',
                       'temperature_effectiveness_air_cooler', 'factorial', 
                       'size_bundle_from_tubecount', 'crossflow_effectiveness_to_int', 
                       'temperature_effectiveness_basic', '_NTU_from_P_solver',
                       'NTU_from_P_basic', '_NTU_from_P_erf',
                       'NTU_from_P_G', 'NTU_from_P_J', 'NTU_from_P_E', 
                       'NTU_from_P_H', 'NTU_from_P_plate'])
    __funcs.update(normal_fluids.numba.numbafied_fluids_functions.copy())
    new_mods = normal_fluids.numba.transform_module(normal, __funcs, replaced, vec=vec,
                                                    cache_blacklist=cache_blacklist)
    if vec:

import ht
import fluids
import fluids.numba
normal_fluids = fluids
normal = ht

orig_file = __file__
caching = False
'''


'''
__all__ = []
__funcs = {}

numerics = fluids.numba.numerics
replaced = fluids.numba.numerics_dict.copy()


def transform_complete_ht(replaced, __funcs, __all__, normal, vec=False):
    cache_blacklist = set(['h_Ganguli_VDI', 'fin_efficiency_Kern_Kraus', 'h_Briggs_Young', 
                           'h_ESDU_high_fin', 'h_ESDU_low_fin', 'Nu_Nusselt_Rayleigh_Holling_Herwig',
                           'DBundle_for_Ntubes_Phadkeb', 'Thome', 'to_solve_q_Thome',
                       'temperature_effectiveness_air_cooler', 'factorial', 
                       'size_bundle_from_tubecount', 'crossflow_effectiveness_to_int', 
                       'temperature_effectiveness_basic', '_NTU_from_P_solver',
                       'NTU_from_P_basic', '_NTU_from_P_erf',
                       'NTU_from_P_G', 'NTU_from_P_J', 'NTU_from_P_E', 
                       'NTU_from_P_H', 'NTU_from_P_plate'])
    __funcs.update(normal_fluids.numba.numbafied_fluids_functions.copy())
    new_mods = normal_fluids.numba.transform_module(normal, __funcs, replaced, vec=vec,
                                                    cache_blacklist=cache_blacklist)

SOFTWARE.'''

from __future__ import division
import sys
import importlib.util
import types
import numpy as np
import fluids as normal_fluids
import numba
import fluids.numba

__all__ = []
__funcs = {}


numerics = fluids.numba.numerics
#replaced, NUMERICS_SUBMOD = fluids.numba.create_numerics(replaced, vec=True)
normal = normal_fluids
replaced = fluids.numba.numerics_dict.copy()


fluids.numba.transform_complete(replaced, __funcs, __all__, normal, vec=True)

globals().update(__funcs)
globals().update(replaced)

import sys
import importlib.util
import types
import numpy as np
import fluids as normal_fluids
import numba
import fluids.numba

__all__ = []
__funcs = {}


numerics = fluids.numba.numerics
#replaced, NUMERICS_SUBMOD = fluids.numba.create_numerics(replaced, vec=True)
normal = normal_fluids
replaced = fluids.numba.numerics_dict.copy()


fluids.numba.transform_complete(replaced, __funcs, __all__, normal, vec=True)

globals().update(__funcs)
globals().update(replaced)