.. _python-cycles:
    
Cycle Calculations
==================

It is also possible to carry out simple thermodynamic cycle calculations with the 
CoolProp classes. These calculations are based on the utility classes 
:py:class:`CoolProp.Plots.SimpleCycles.StatePoint` and 
:py:class:`CoolProp.Plots.SimpleCycles.StateContainer`, which can be used on their 
own as demonstrated below. Note that the utility classes support numerous notations
to access their members and you can chose the one you like best or mix them:

.. ipython::

    In [0]: from __future__ import print_function
    
    In [0]: import CoolProp
    
    In [0]: from CoolProp.Plots import StateContainer
    
    In [0]: T0 = 300.000; p0 = 200000.000; h0 = 112745.749; s0 = 393.035
    
    In [0]: cycle_states = StateContainer()
    
    In [0]: cycle_states[0,'H'] = h0
    
    In [0]: cycle_states[0]['S'] = s0
    
    In [0]: cycle_states[0][CoolProp.iP] = p0
    
    In [0]: cycle_states[0,CoolProp.iT] = T0
    
    In [0]: cycle_states[1,"T"] = 300.064
    
    In [0]: print(cycle_states)

    
The utility classes were designed to work well with the plotting objects described above
and this example illustrates how a simple Rankine cycle can be added to to a :math:`T,s` 
graph, note how the unit conversion is handled:

.. plot::
    :include-source:
    
    import CoolProp
    from CoolProp.Plots import PropertyPlot
    from CoolProp.Plots import SimpleCompressionCycle
    pp = PropertyPlot('HEOS::R134a', 'PH', unit_system='EUR')
    pp.calc_isolines(CoolProp.iQ, num=11)
    cycle = SimpleCompressionCycle('HEOS::R134a', 'PH', unit_system='EUR')
    T0 = 280
    pp.state.update(CoolProp.QT_INPUTS,0.0,T0-10)
    p0 = pp.state.keyed_output(CoolProp.iP)
    T2 = 310
    pp.state.update(CoolProp.QT_INPUTS,1.0,T2+15)
    p2 = pp.state.keyed_output(CoolProp.iP)
    pp.calc_isolines(CoolProp.iT, [T0-273.15,T2-273.15], num=2)
    cycle.simple_solve(T0, p0, T2, p2, 0.7, SI=True)
    cycle.steps = 50
    sc = cycle.get_state_changes()
    pp.draw_process(sc)
    import matplotlib.pyplot as plt
    plt.close(cycle.figure)
    pp.show()