mirror of
https://github.com/CoolProp/CoolProp.git
synced 2026-04-01 03:00:13 -04:00
Added more description regarding the documentation builders
This commit is contained in:
Jorrit Wronski
@@ -13,14 +13,14 @@ Build Sphinx documentation
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2. Make a virtualenv::
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virtualenv ~/env/py27
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virtualenv --distribute ~/env/py27
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source ~/env/py27/bin/activate # Turn on this virtual env, should see (py27) in your command shell next to the prompt to tell you that environment is active
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3. Then install prerequisites into this virtualenv::
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pip install --upgrade setuptools cython
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pip install cython numpy scipy matplotlib python-dateutil pytz pandas
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pip install sphinx ipython sphinxcontrib-bibtex sphinxcontrib-doxylink sphinxcontrib-napoleon cloud-sptheme
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pip install --upgrade cython sphinx
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4. To build the documentation, go into the CoolProp/Web folder and run::
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@@ -49,11 +49,15 @@ Creating a Linux documentation slave
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1. Make sure you have what you need. For Linux::
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sudo aptitude install build-essential git gfortran cmake doxygen
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sudo aptitude install python-dev ipython python-pip
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sudo aptitude install python-numpy python-matplotlib python-scipy python-pandas
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sudo aptitude build-deb cython
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sudo pip install --upgrade cython
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sudo aptitude install build-essential git gfortran cmake doxygen ipython python-pip python-virtualenv
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sudo aptitude install libatlas-base-dev libatlas3-base # numpy
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sudo aptitude install gcc gfortran python-dev libblas-dev liblapack-dev libblas3 liblapack3 # scipy
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sudo aptitude install python-dev libpng-dev tk libfreetype6-dev # matplotlib
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sudo aptitude install libxml2-dev libxslt1-dev libxslt1.1 python-all-dev # pandas
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sudo aptitude install cython python-matplotlib python-scipy python-numpy python-pandas
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sudo aptitude install buildbot buildbot-slave
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2. Create a new buildbot slave (from http://docs.buildbot.net/current/tutorial/firstrun.html)::
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@@ -5,68 +5,104 @@ import CoolProp as CP
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import matplotlib.pyplot as plt
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import scipy.interpolate
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# Prepare the constants
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Water = CP.AbstractState("HEOS", "Water")
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pc = Water.keyed_output(CP.iP_critical)
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Tc = Water.keyed_output(CP.iT_critical)
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Tmin = 200
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Tmax = 1000
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pmax = Water.keyed_output(CP.iP_max)
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pt = 611.657
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Tt = 273.16
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fillcolor = 'g'
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T_min = 200
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T_max = 1000
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p_max = Water.keyed_output(CP.iP_max)
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p_triple = 611.657
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T_triple = 273.16
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# Prepare the data for the melting line
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steps = 2000
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fig = plt.figure(figsize = (3,3))
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ax = fig.add_axes((0.02,0.00,0.98,1))
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lw = 3
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melt_args = dict(lw = lw, solid_capstyle = 'round')
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TT = []
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PP = list(np.logspace(np.log10(pt), np.log10(pmax),1000))
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PP = list(np.logspace(np.log10(p_triple), np.log10(p_max),steps))
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for p in PP:
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TT.append(Water.melting_line(CP.iT, CP.iP, p))
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#Zone VI
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for T in np.linspace(max(TT), 355):
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for T in np.linspace(max(TT), 355, int(steps/10)):
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TT.append(T)
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theta = T/273.31
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pi = 1-1.07476*(1-theta**4.6)
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p = pi*632.4e6
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PP.append(p)
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#~ #Zone VII
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#~ for T in np.linspace(355, 715):
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#~ TT.append(T)
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#~ theta = T/355
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#~ lnpi = 0.173683e1*(1-1/theta)-0.544606e-1*(1-theta**5)+0.806106e-7*(1-theta**22)
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#~ p = np.exp(lnpi)*2216e6
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#~ PP.append(p)
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plt.plot(TT,PP,'darkblue',**melt_args)
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#Zone VII
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for T in np.linspace(355, 715, int(steps/10)):
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TT.append(T)
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theta = T/355
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lnpi = 0.173683e1*(1-1/theta)-0.544606e-1*(1-theta**5)+0.806106e-7*(1-theta**22)
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p = np.exp(lnpi)*2216e6
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PP.append(p)
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Ts = np.linspace(273.16, Tc, 1000)
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ps = CP.CoolProp.PropsSI('P','T',Ts,'Q',[0]*len(Ts),'Water',[1])
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# Changes number of points
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steps = int(steps/10.0)
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# Supercritical
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pe = 1.3e10
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p_melt = np.logspace(np.log10(np.min(PP)), np.log10(np.max(PP)),steps)
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T_melt_f = scipy.interpolate.interp1d(np.log10(PP),TT)
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#T_melt_f = scipy.interpolate.spline(np.log10(PP),TT,np.log10(p_melt))
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T_melt = T_melt_f(np.log10(p_melt))
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#T_melt = np.array(TT)
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#p_melt = np.array(PP)
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plt.plot(Ts,ps,'orange',lw = lw, solid_capstyle = 'round')
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#
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# Prepare the data for the saturation line
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T_sat = np.linspace(273.16, Tc, len(T_melt))
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p_sat = CP.CoolProp.PropsSI('P','T',T_sat,'Q',[0]*len(T_sat),'Water',[1])
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TD,DD,PD = [], [], []
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for T in np.linspace(240,1000,200):
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for p in np.logspace(np.log10(612),np.log10(1e9),200):
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Tm = scipy.interpolate.interp1d(PP,TT)(p)
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#
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# Prepare density data
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TT,DD,PP = [], [], []
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for T in np.linspace(T_min, T_max, steps):
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for p in np.logspace(np.log10(p_triple), np.log10(p_max), steps):
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Tm = scipy.interpolate.interp1d(p_melt, T_melt)(p)
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if T < Tm: continue
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D = CP.CoolProp.PropsSI('D','T',T,'P',p,'Water')
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TD.append(T)
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TT.append(T)
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DD.append(np.log10(D))
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PD.append(p)
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nm = matplotlib.colors.Normalize(min(DD), max(DD))
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plt.scatter(TD, PD, c = DD, edgecolor = 'none', s = 6, cmap=plt.cm.get_cmap('Blues'), norm = nm)
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PP.append(p)
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tt = np.linspace(T_min, T_max, steps)
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pp = np.logspace(np.log10(p_triple), np.log10(p_max), steps)
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tt, pp = np.meshgrid(tt, pp)
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dd = np.empty(tt.shape)
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dd[:][:] = np.NAN
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nr,nc = tt.shape
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for i in range(nr):
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for j in range(nc):
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Tm = T_melt_f(np.log10(pp[i][j]))
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if tt[i][j] < Tm: continue
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D = CP.CoolProp.PropsSI('D','T',tt[i][j],'P',pp[i][j],'Water')
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dd[i][j] = np.log10(D)
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#
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# Define colours etc
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lw = 3
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melt_args = dict(color = 'orange', lw = lw, solid_capstyle = 'round')
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sat_args = melt_args.copy()
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nm = matplotlib.colors.Normalize(min(DD), max(DD))
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rho_args = dict(cmap=plt.cm.get_cmap('Blues'), norm = nm)
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fig = plt.figure(figsize = (3,3))
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ax = fig.add_axes((0.0,0.0,1.0,1.0))
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plt.plot(T_melt, p_melt, **melt_args)
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plt.plot(T_sat, p_sat, **sat_args )
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plt.scatter(TT, PP, c=DD, edgecolor = 'none', s = 6, **rho_args )
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#plt.contourf(tt, pp, dd, steps, **rho_args )
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#ax.set_ylim(p_triple,p_max)
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ax.set_yscale('log')
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ax.set_xlim(T_min, T_max)
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ax.axis('off')
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plt.ylim(611,1e9)
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plt.gca().set_yscale('log')
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#plt.gca().set_xlim(Tmin*0.99, Tmax)
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plt.gca().set_xlim(240, 1000)
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plt.gca().axis('off')
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plt.savefig('WaterPhaseDiagram.pdf')
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plt.savefig('WaterPhaseDiagram.png', transparent = True)
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plt.close()
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