def therm_calc(the_row): rT = cloud_vars['wt'] S = the_row[-1] temp, press,height = the_row[-4:-1] esat = find_esat(temp) e = S*esat rv = c.eps*e/(press - e) #Thompkins eqn 2.20 Td = find_Td(rv,press) thetae = find_thetaet(Td,rT,temp,press) hm = c.cpd*temp + find_lv(temp)*rv + c.g0*height return (thetae, hm)
def makeSkewWet(ax, corners=[-30, 25], skew=30): """ draw a skew-T lnP diagram on an axis Parameters ---------- ax : matplotlib.axes matplotlib figure axis corners : [float] x axis temperature limits (degC) skew : float adjustable coefficient to make isotherms slope compared to adiabats Returns ------- ax : matplotlib.axes the modified figure axis """ yplot = range(1000, 190, -6) # xcorners = find_corners(corners, skew=skew) xplot = list(np.linspace(xcorners[0], xcorners[1], 45)) pvals = np.size(yplot) tvals = np.size(xplot) temp = np.zeros([pvals, tvals]) theTheta = np.zeros_like(temp) the_rsat = np.zeros_like(temp) theThetae = np.zeros([pvals, tvals]) # lay down a reference grid that labels xplot,yplot points # in the new (skewT-lnP) coordinate system . # Each value of the temp matrix holds the actual (data) # temperature label (in deg C) of the xplot, yplot coordinate. # pairs. The transformation is given by W&H 3.56, p. 78. Note # that there is a sign difference, because rather than # taking y= -log(P) like W&H, I take y= +log(P) and # then reverse the y axis for presshPa in yplot: #loop over pressures for skewed in xplot: #loop over skewed xcoords # Note that we don't have to transform the y # coordinate, as it is still pressure. iInd = yplot.index(presshPa) jInd = xplot.index(skewed) temp[iInd, jInd] = convertSkewToTemp(skewed, presshPa, skew) Tk = c.Tc + temp[iInd, jInd] pressPa = presshPa * 100. theTheta[iInd, jInd] = find_theta(Tk, pressPa) rs = find_rsat(Tk, pressPa) the_rsat[iInd, jInd] = rs theThetae[iInd, jInd] = find_thetaet(Tk,rs,Tk, pressPa) # # Contour the temperature matrix. # # First, make sure that all plotted lines are solid. mpl.rcParams['contour.negative_linestyle'] = 'solid' tempLabels = range(-40, 50, 5) ax.contour(xplot, yplot, temp, tempLabels, \ colors='k') # # contour theta # thetaLabels = list(range(200, 390, 10)) thetaLevs = ax.contour(xplot, yplot, theTheta, thetaLabels, \ colors='b') # # contour rsat # rsLabels = [0.1, 0.25, 0.5, 1, 2, 3] + list(range(4, 28, 2)) #+ [26, 28] rsLevs = ax.contour(xplot, yplot, the_rsat * 1.e3, levels=rsLabels, colors='g', linewidths=.5) thetaeLabels = np.arange(250, 410, 10) thetaeLevs = ax.contour(xplot, yplot, theThetae, thetaeLabels, \ colors='r') # # Customize the plot # ax.set_yscale('log') locs = np.array(range(100, 1100, 100)) labels = locs ax.set_yticks(locs) ax.set_yticklabels(labels) # Conventionally labels semilog graph. ax.set_ybound((200, 1000)) plt.setp(ax.get_xticklabels(), weight='bold') plt.setp(ax.get_yticklabels(), weight='bold') ax.yaxis.grid(True) ax.set_title('skew T - lnp chart') ax.set_ylabel('pressure (hPa)') ax.set_xlabel('temperature (deg C)') # # Crop image to a more usable size # TempTickLabels = range(-30, 40, 5) TempTickCoords = TempTickLabels skewTickCoords = convertTempToSkew(TempTickCoords, 1.e3, skew) ax.set_xticks(skewTickCoords) ax.set_xticklabels(TempTickLabels) skewLimits = convertTempToSkew([-15, 35], 1.e3, skew) ax.axis([skewLimits[0], skewLimits[1], 300, 1.e3]) # # Create line labels # fntsz = 9 # Handle for 'fontsize' of the line label. ovrlp = True # Handle for 'inline'. Any integer other than 0 # creates a white space around the label. #tempLevs.clabel(inline=ovrlp, inline_spacing=0,fmt='%2d', fontsize=fntsz,use_clabeltext=True) thetaLevs.clabel(inline=ovrlp, inline_spacing=0, fmt='%3d', fontsize=fntsz, use_clabeltext=True) rsLevs.clabel(inline=ovrlp, inline_spacing=0, fmt='%3.2g', fontsize=fntsz, use_clabeltext=True) thetaeLevs.clabel(thetaeLabels, inline_spacing=0, inline=ovrlp, fmt='%5g', fontsize=fntsz, use_clabeltext=True) ax.invert_yaxis() #ax.figure.canvas.draw() xcorners = find_corners(corners, skew=skew) ax.set(ylim=(1000, 300), xlim=xcorners) return ax, skew
""" return the_string.format_map(the_tup._asdict()) get_ipython().magic('matplotlib inline') pa2hPa = 1.e-2 A_press = 1.e5 #Pa B_press = 4.e4 #Pa A_temp = 300 #K RH = 0.8 e = find_esat(A_temp)*RH A_rv = c.eps*e/(A_press - e) A_Td = find_Td(A_rv,A_press) print('tropical surface dewpoint: {} K'.format(A_Td)) A_thetae=find_thetaet(A_Td,A_rv,A_temp,A_press) print('tropical surface thetae: {} K'.format(A_thetae)) A_temp,A_rv,A_rl = tinvert_thetae(A_thetae,A_rv,A_press) fields=['id','temp','rv','rl','press'] A_dict = dict(zip(fields,('A',A_temp,A_rv,A_rl,A_press))) A_tup = calc_enthalpy(A_dict) print(format_tup(A_tup)) # ### B. Lift to 400 hPa and remove 80% of the liquied water # In[201]: plt.close('all') fig,ax = plt.subplots(1,1,figsize=[10,8]) ax,skew = makeSkewWet(ax,corners=[-15,35])
def makeSkewWet(ax, corners=[-30, 25], skew=30): """ draw a skew-T lnP diagram on an axis Parameters ---------- ax : matplotlib.axes matplotlib figure axis corners : [float] x axis temperature limits (degC) skew : float adjustable coefficient to make isotherms slope compared to adiabats Returns ------- ax : matplotlib.axes the modified figure axis """ yplot = range(1000, 190, -6) # xcorners = find_corners(corners, skew=skew) xplot = list(np.linspace(xcorners[0], xcorners[1], 45)) pvals = np.size(yplot) tvals = np.size(xplot) temp = np.zeros([pvals, tvals]) theTheta = np.zeros_like(temp) the_rsat = np.zeros_like(temp) theThetae = np.zeros([pvals, tvals]) # lay down a reference grid that labels xplot,yplot points # in the new (skewT-lnP) coordinate system . # Each value of the temp matrix holds the actual (data) # temperature label (in deg C) of the xplot, yplot coordinate. # pairs. The transformation is given by W&H 3.56, p. 78. Note # that there is a sign difference, because rather than # taking y= -log(P) like W&H, I take y= +log(P) and # then reverse the y axis for presshPa in yplot: #loop over pressures for skewed in xplot: #loop over skewed xcoords # Note that we don't have to transform the y # coordinate, as it is still pressure. iInd = yplot.index(presshPa) jInd = xplot.index(skewed) temp[iInd, jInd] = convertSkewToTemp(skewed, presshPa, skew) Tk = c.Tc + temp[iInd, jInd] pressPa = presshPa * 100. theTheta[iInd, jInd] = find_theta(Tk, pressPa) rs = find_rsat(Tk, pressPa) the_rsat[iInd, jInd] = rs theThetae[iInd, jInd] = find_thetaet(Tk, rs, Tk, pressPa) # # Contour the temperature matrix. # # First, make sure that all plotted lines are solid. mpl.rcParams['contour.negative_linestyle'] = 'solid' tempLabels = range(-40, 50, 10) ax.contour(xplot, yplot, temp, tempLabels, \ colors='k') # # contour theta # thetaLabels = list(range(200, 390, 10)) thetaLevs = ax.contour(xplot, yplot, theTheta, thetaLabels, \ colors='b') # # contour rsat # rsLabels = [0.1, 0.25, 0.5, 1, 2, 3] + list(range(4, 28, 2)) #+ [26, 28] rsLevs = ax.contour(xplot, yplot, the_rsat * 1.e3, levels=rsLabels, colors='g', linewidths=.5) thetaeLabels = np.arange(250, 410, 10) thetaeLevs = ax.contour(xplot, yplot, theThetae, thetaeLabels, \ colors='r') # # Customize the plot # ax.set_yscale('log') locs = np.array(range(100, 1100, 100)) labels = locs ax.set_yticks(locs) ax.set_yticklabels(labels) # Conventionally labels semilog graph. ax.set_ybound((200, 1000)) plt.setp(ax.get_xticklabels(), weight='bold') plt.setp(ax.get_yticklabels(), weight='bold') ax.yaxis.grid(True) ax.set_title('skew T - lnp chart') ax.set_ylabel('pressure (hPa)') ax.set_xlabel('temperature (deg C)') # # Crop image to a more usable size # TempTickLabels = range(-30, 40, 5) TempTickCoords = TempTickLabels skewTickCoords = convertTempToSkew(TempTickCoords, 1.e3, skew) ax.set_xticks(skewTickCoords) ax.set_xticklabels(TempTickLabels) skewLimits = convertTempToSkew([-15, 35], 1.e3, skew) ax.axis([skewLimits[0], skewLimits[1], 300, 1.e3]) # # Create line labels # fntsz = 9 # Handle for 'fontsize' of the line label. ovrlp = True # Handle for 'inline'. Any integer other than 0 # creates a white space around the label. #tempLevs.clabel(inline=ovrlp, inline_spacing=0,fmt='%2d', fontsize=fntsz,use_clabeltext=True) thetaLevs.clabel(inline=ovrlp, inline_spacing=0, fmt='%3d', fontsize=fntsz, use_clabeltext=True) rsLevs.clabel(inline=ovrlp, inline_spacing=0, fmt='%3.2g', fontsize=fntsz, use_clabeltext=True) thetaeLevs.clabel(thetaeLabels, inline_spacing=0, inline=ovrlp, fmt='%5g', fontsize=fntsz, use_clabeltext=True) ax.invert_yaxis() #ax.figure.canvas.draw() xcorners = find_corners(corners, skew=skew) ax.set(ylim=(1000, 300), xlim=xcorners) return ax, skew
end_z = np.searchsorted(z, 1200.0) bot_z = np.searchsorted(z, 1000.0) qv_sound = qv.mean(axis=(1, 2))[bot_z:end_z] * 1.0e-3 # kg/kg temp_sound = temp.mean(axis=(1, 2))[bot_z:end_z] press_sound = np.squeeze(press)[bot_z:end_z] z_sound = z[bot_z:end_z] # print(qv_sound.shape,temp_sound.shape,z_sound.shape,press_sound.shape) thetal_sound = [ find_thetal(the_press, the_temp, the_qv) for the_press, the_temp, the_qv in zip(press_sound, temp_sound, qv_sound) ] Td_sound = find_Td(qv_sound, press_sound) thetae_sound = [ find_thetaet(the_Td, the_qv, the_temp, the_press) for the_Td, the_press, the_temp, the_qv in zip(Td_sound, press_sound, temp_sound, qv_sound) ] fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(16, 8)) ax1.plot(qv_sound * 1.0e3, z_sound) ax2.plot(temp_sound, press_sound * 1.0e-2) ax3.plot(thetal_sound, press_sound * 1.0e-2) ax4.plot(thetae_sound, press_sound * 1.0e-2) axes = [ax2, ax3, ax4] [ax.invert_yaxis() for ax in axes] p0 = press_sound[0] * 1.0e-2 [ax.set(ylim=(p0, 750)) for ax in axes] axes = [ax1, ax2, ax3, ax4] ax1.set(ylabel="heigth (m)") ax2.set(ylabel="pressure (hPa)") titles = ["$q_v\ (g/kg)$", "$temperature\ (K)$", r"$\theta_l\ (K)$", r"$\theta_e\ (K)$"]