mirror of
https://github.com/CoolProp/CoolProp.git
synced 2026-01-17 01:48:06 -05:00
19a4875879
* autopep8 rule-groups E101,W1,W2,W3 * autopep8 with rule group E3 (blank lines) autopep8 --in-place --recursive --max-line-length=200 --exclude="externals" --select="E101,E3,W1,W2,W3" . * tabs and space W191 * autopep8 aggressive
152 lines
4.4 KiB
Python
152 lines
4.4 KiB
Python
import matplotlib
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matplotlib.use('Agg') # use a non-interactive backend
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import CoolProp
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import os.path
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web_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),'..'))
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tar_fil = os.path.join(web_dir,'_static','CoolPropLogo.png')
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tar_fil_long = os.path.join(web_dir,'_static','CoolPropLogoLong.png')
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tar_fil_long_large = os.path.join(web_dir,'_static','CoolPropLogoLongLarge.png')
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import matplotlib
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import numpy as np
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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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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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TT = []
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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, 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, 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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# Changes number of points
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steps = int(steps/10.0)
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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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#
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# Prepare the data for the saturation line
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T_sat = np.linspace(T_triple, Tc, len(T_melt))
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p_sat = CP.CoolProp.PropsSI('P','T',T_sat,'Q',[0]*len(T_sat),'Water')
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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(np.min(p_melt)), np.log10(np.max(p_melt)), 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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if p > p_max: pin = p_max
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else: pin = p
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D = CP.CoolProp.PropsSI('D','T',T,'P',pin,'Water')
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TT.append(T)
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DD.append(np.log10(D))
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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 = (1.0,1.0))
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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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delta_x = np.min(T_melt)*0.01
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x_lim = [np.min(T_melt)-delta_x, np.max(T_melt)+delta_x]
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y_lim = [np.min(p_melt)*0.875 , np.max(p_melt)*1.125]
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#y_lim = [np.power(np.log10(np.min(p_melt))*1.01,10),
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# np.power(np.log10(np.max(p_melt))*1.01,10)]
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ax.set_xlim(x_lim)
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ax.set_ylim(y_lim)
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ax.set_yscale('log')
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ax.axis('off')
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plt.savefig(os.path.basename(__file__)+'.pdf')
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plt.savefig(tar_fil, transparent = True, dpi = 100)
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plt.close()
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fig = plt.figure(figsize = (9,3))
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ax = fig.add_axes((0.66,0.01,0.333,0.98))
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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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#ax.text(0.4,800, "CoolProp", size = 12)
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ax.annotate('CoolProp', xy=(-0.1, 0.5), xycoords='axes fraction', fontsize=88, ha='right', va='center') # va='baseline')
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ax.set_xlim(x_lim)
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ax.set_ylim(y_lim)
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ax.set_yscale('log')
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ax.axis('off')
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plt.savefig(tar_fil_long_large, transparent = True)
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plt.close()
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# # Just a hack to resize the image
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# import Image
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# size = 288, 288
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# try:
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# im = Image.open(tar_fil_long_large)
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# im.thumbnail(size, Image.ANTIALIAS)
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# im.save(tar_fil_long)
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# #im.resize(size, Image.ANTIALIAS).save(tar_fil_long)
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# except IOError:
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# print "Error resizing '%s'" % tar_fil_long_large
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