我们从Python开源项目中，提取了以下21个代码示例，用于说明如何使用matplotlib.pyplot.clabel()。
def saturation_index_countour(lab, elem1, elem2, Ks, labels=False): plt.figure() plt.title('Saturation index %s%s' % (elem1, elem2)) resoluion = 100 n = math.ceil(lab.time.size / resoluion) plt.xlabel('Time') z = np.log10((lab.species[elem1]['concentration'][:, ::n] + 1e-8) * ( lab.species[elem2]['concentration'][:, ::n] + 1e-8) / lab.constants[Ks]) lim = np.max(abs(z)) lim = np.linspace(-lim - 0.1, +lim + 0.1, 51) X, Y = np.meshgrid(lab.time[::n], -lab.x) plt.xlabel('Time') CS = plt.contourf(X, Y, z, 20, cmap=ListedColormap(sns.color_palette( "RdBu_r", 101)), origin='lower', levels=lim, extend='both') if labels: plt.clabel(CS, inline=1, fontsize=10, colors='w') # cbar = plt.colorbar(CS) if labels: plt.clabel(CS, inline=1, fontsize=10, colors='w') cbar = plt.colorbar(CS) plt.ylabel('Depth') ax = plt.gca() ax.ticklabel_format(useOffset=False) cbar.ax.set_ylabel('Saturation index %s%s' % (elem1, elem2)) return ax
def contour_plot_of_rates(lab, r, labels=False, last_year=False): plt.figure() plt.title('{}'.format(r)) resoluion = 100 n = math.ceil(lab.time.size / resoluion) if last_year: k = n - int(1 / lab.dt) else: k = 1 z = lab.estimated_rates[r][:, k - 1:-1:n] # lim = np.max(np.abs(z)) # lim = np.linspace(-lim - 0.1, +lim + 0.1, 51) X, Y = np.meshgrid(lab.time[k::n], -lab.x) plt.xlabel('Time') CS = plt.contourf(X, Y, z, 20, cmap=ListedColormap( sns.color_palette("Blues", 51))) if labels: plt.clabel(CS, inline=1, fontsize=10, colors='w') cbar = plt.colorbar(CS) plt.ylabel('Depth') ax = plt.gca() ax.ticklabel_format(useOffset=False) cbar.ax.set_ylabel('Rate %s [M/V/T]' % r) return ax
def contour_plot_of_delta(lab, element, labels=False, last_year=False): plt.figure() plt.title('Rate of %s consumption/production' % element) resoluion = 100 n = math.ceil(lab.time.size / resoluion) if last_year: k = n - int(1 / lab.dt) else: k = 1 z = lab.species[element]['rates'][:, k - 1:-1:n] lim = np.max(np.abs(z)) lim = np.linspace(-lim - 0.1, +lim + 0.1, 51) X, Y = np.meshgrid(lab.time[k:-1:n], -lab.x) plt.xlabel('Time') CS = plt.contourf(X, Y, z, 20, cmap=ListedColormap(sns.color_palette( "RdBu_r", 101)), origin='lower', levels=lim, extend='both') if labels: plt.clabel(CS, inline=1, fontsize=10, colors='w') cbar = plt.colorbar(CS) plt.ylabel('Depth') ax = plt.gca() ax.ticklabel_format(useOffset=False) cbar.ax.set_ylabel('Rate of %s change $[\Delta/T]$' % element) return ax
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options): """Makes a pseudocolor plot. xs: ys: zs: pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot options: keyword args passed to pyplot.pcolor and/or pyplot.contour """ _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) X, Y = np.meshgrid(xs, ys) Z = zs x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = pyplot.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: pyplot.pcolormesh(X, Y, Z, **options) if contour: cs = pyplot.contour(X, Y, Z, **options) pyplot.clabel(cs, inline=1, fontsize=10)
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options): """Makes a pseudocolor plot. xs: ys: zs: pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot options: keyword args passed to plt.pcolor and/or plt.contour """ _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) X, Y = np.meshgrid(xs, ys) Z = zs x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = plt.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: plt.pcolormesh(X, Y, Z, **options) if contour: cs = plt.contour(X, Y, Z, **options) plt.clabel(cs, inline=1, fontsize=10)
def plot_2d(self,ax1,ax2,x1,x2,y1,y2): # give 2d plot of potential and electric field self.x=linspace(x1,x2,self.n) self.y=linspace(y2,y1,self.n) self.x,self.y=meshgrid(self.x,self.y) cs=ax1.contour(self.x,self.y,self.V) plt.clabel(cs, inline=1, fontsize=10) ax1.set_title('Equipotential lines',fontsize=18) ax1.set_xlabel('x (m)',fontsize=14) ax1.set_ylabel('y (m)',fontsize=14) for j in range(1,self.n-1,1): for i in range(1,self.n-1,1): ax2.arrow(self.x[j][i],self.y[j][i],self.Ex[j][i]/40,self.Ey[j][i]/40,fc='k', ec='k') ax2.set_xlim(-1.,1.) ax2.set_ylim(-1.,1.) ax2.set_title('Electric field',fontsize=18) ax2.set_xlabel('x (m)',fontsize=14) ax2.set_ylabel('y (m)',fontsize=14)
def plot_Etot_contour(celldmsx,nmesh=(50,50,50),fittype="quadratic",ibrav=4,a=None): """ This function makes a countour plot with matplotlib of Ex(celldmsx) fitted results. The plot type depends on ibrav. """ from .minutils import calculate_fitted_points_anis if a==None: return fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # create an axes object in the figure if (ibrav==4): celldmsxdense, Edensefitted = calculate_fitted_points_anis(celldmsx,nmesh,fittype,ibrav,a) Xd = np.zeros((nmesh[0],nmesh[2])) Yd = np.zeros((nmesh[0],nmesh[2])) Zd = np.zeros((nmesh[0],nmesh[2])) for i in range(0,nmesh[0]): for j in range(0,nmesh[2]): index = i*nmesh[0]+j Xd[i,j] = celldmsxdense[index,0] Yd[i,j] = celldmsxdense[index,2] Zd[i,j] = Edensefitted[index] CS = ax.contour(Xd, Yd, Zd) plt.clabel(CS, inline=1, fontsize=10) CS.ax.set_xlabel("a (a.u.)") CS.ax.set_ylabel("c (a.u.)") plt.show() return fig
def contour_plot(lab, element, labels=False, days=False, last_year=False): plt.figure() plt.title(element + ' concentration') resoluion = 100 n = math.ceil(lab.time.size / resoluion) if last_year: k = n - int(1 / lab.dt) else: k = 1 if days: X, Y = np.meshgrid(lab.time[k::n] * 365, -lab.x) plt.xlabel('Time') else: X, Y = np.meshgrid(lab.time[k::n], -lab.x) plt.xlabel('Time') z = lab.species[element]['concentration'][:, k - 1:-1:n] CS = plt.contourf(X, Y, z, 51, cmap=ListedColormap( sns.color_palette("Blues", 51)), origin='lower') if labels: plt.clabel(CS, inline=1, fontsize=10, colors='w') cbar = plt.colorbar(CS) plt.ylabel('Depth') ax = plt.gca() ax.ticklabel_format(useOffset=False) cbar.ax.set_ylabel('%s [M/V]' % element) if element == 'Temperature': plt.title('Temperature contour plot') cbar.ax.set_ylabel('Temperature, C') if element == 'pH': plt.title('pH contour plot') cbar.ax.set_ylabel('pH') return ax
def annual_cycle_taylor_diagram(parameter): """Calculate annual cycle climatology""" variables = parameter.variables seasons = parameter.season output_path = parameter.output_path var_longname = [ varid_longname[x] for x in variables] for j, variable in enumerate(variables): obs_data = genfromtxt(output_path+'/metrics/'+variable+'_obs_annual_cycle_std_corr.csv') test_data = genfromtxt(output_path+'/metrics/'+variable+'_test_annual_cycle_std_corr.csv') mmm_data = genfromtxt(output_path+'/metrics/'+variable+'_mmm_annual_cycle_std_corr.csv') cmip_data = genfromtxt(output_path+'/metrics/'+variable+'_cmip_annual_cycle_std_corr.csv') mod_num = cmip_data.shape[0] fig = plt.figure(figsize=(8,8)) refstd = obs_data[0] dia = TaylorDiagram(refstd, fig=fig,rect=111, label="Reference") # Add samples to Taylor diagram for i,(stddev,corrcoef) in enumerate(cmip_data): dia.add_sample(stddev, corrcoef, marker='.',ms=10, c='grey') dia.add_sample(test_data[0], test_data[1],marker='.',ms=15, c='red',label='MOD') dia.add_sample(mmm_data[0], mmm_data[1],marker='.',ms=15, c='b',label='MMM') # Add RMS contours, and label them contours = dia.add_contours(colors='0.5') plt.clabel(contours, inline=1, fontsize=10) plt.title(var_longname[j]) # Add a figure legend fig.legend([dia.samplePoints[0],dia.samplePoints[-2],dia.samplePoints[-1]] , [ p.get_label() for p in [dia.samplePoints[0],dia.samplePoints[-2],dia.samplePoints[-1]] ], numpoints=1, loc='upper right',prop={'size':10}) # np.savetxt(basedir+'metrics/'+vas[va_ind]+'_'+mod+'std_corr.csv',mod_sample,fmt='%.3f') fig.savefig(output_path+'/figures/'+variable+'_annual_cycle_taylor_diagram.png') plt.close('all')
def plotBoundary(mytheta, myX, myy, mylambda=0.): """ Function to plot the decision boundary for arbitrary theta, X, y, lambda value Inside of this function is feature mapping, and the minimization routine. It works by making a grid of x1 ("xvals") and x2 ("yvals") points, And for each, computing whether the hypothesis classifies that point as True or False. Then, a contour is drawn with a built-in pyplot function. """ theta, mincost = optimizeRegularizedTheta(mytheta,myX,myy,mylambda) xvals = np.linspace(-1,1.5,50) yvals = np.linspace(-1,1.5,50) zvals = np.zeros((len(xvals),len(yvals))) for i in range(len(xvals)): for j in range(len(yvals)): myfeaturesij = mapFeature(np.array([xvals[i]]),np.array([yvals[j]])) zvals[i][j] = np.dot(theta,myfeaturesij.T) zvals = zvals.transpose() u, v = np.meshgrid( xvals, yvals ) mycontour = plt.contour( xvals, yvals, zvals, [0]) #Kind of a hacky way to display a text on top of the decision boundary myfmt = { 0:'Lambda = %d'%mylambda} plt.clabel(mycontour, inline=1, fontsize=15, fmt=myfmt) plt.title("Decision Boundary") #Build a figure showing contours for various values of regularization parameter, lambda #It shows for lambda=0 we are overfitting, and for lambda=100 we are underfitting
def DrawContourAndMark(contour, x, y, z, level, clipborder, patch, m): # ??????? ------ ?????????? if contour.contour['visible']: matplotlib.rcParams['contour.negative_linestyle'] = 'dashed' if contour.contour['colorline']: CS1 = m.contour(x, y, z, levels=level, linewidths=contour.contour['linewidth']) else: CS1 = m.contour(x, y, z, levels=level, linewidths=contour.contour['linewidth'], colors=contour.contour['linecolor']) # ????????? if contour.contourlabel['visible']: CS2 = plt.clabel(CS1, inline=1, fmt=contour.contourlabel['fmt'], inline_spacing=contour.contourlabel['inlinespacing'], fontsize=contour.contourlabel['fontsize'], colors=contour.contourlabel['fontcolor']) # ??????????? if clipborder.path is not None and clipborder.using: for collection in CS1.collections: # collection.set_clip_on(True) collection.set_clip_path(patch) for text in CS2: if not clipborder.path.contains_point(text.get_position()): text.remove() # print(CS2)
def plot_contour(self): X, Y = np.meshgrid(np.arange(-1.00, 1.01, 2./(len(self.lattice_in) - 1)), np.arange(-1.00, 1.01, 2./(len(self.lattice_in) - 1))) plt.figure(figsize = (8,8)) CS = plt.contour(X, Y, self.lattice_in, 19) plt.clabel(CS, inline=1, fontsize=10) plt.title('Electric potential near two metal plates') #plt.show() return 0
def Contour(obj, pcolor=False, contour=True, imshow=False, **options): """Makes a contour plot. d: map from (x, y) to z, or object that provides GetDict pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot imshow: boolean, whether to use pyplot.imshow options: keyword args passed to pyplot.pcolor and/or pyplot.contour """ try: d = obj.GetDict() except AttributeError: d = obj _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) xs, ys = zip(*d.keys()) xs = sorted(set(xs)) ys = sorted(set(ys)) X, Y = np.meshgrid(xs, ys) func = lambda x, y: d.get((x, y), 0) func = np.vectorize(func) Z = func(X, Y) x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = pyplot.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: pyplot.pcolormesh(X, Y, Z, **options) if contour: cs = pyplot.contour(X, Y, Z, **options) pyplot.clabel(cs, inline=1, fontsize=10) if imshow: extent = xs[0], xs[-1], ys[0], ys[-1] pyplot.imshow(Z, extent=extent, **options)
def Contour(obj, pcolor=False, contour=True, imshow=False, **options): """Makes a contour plot. d: map from (x, y) to z, or object that provides GetDict pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot imshow: boolean, whether to use plt.imshow options: keyword args passed to plt.pcolor and/or plt.contour """ try: d = obj.GetDict() except AttributeError: d = obj _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) xs, ys = zip(*d.keys()) xs = sorted(set(xs)) ys = sorted(set(ys)) X, Y = np.meshgrid(xs, ys) func = lambda x, y: d.get((x, y), 0) func = np.vectorize(func) Z = func(X, Y) x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = plt.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: plt.pcolormesh(X, Y, Z, **options) if contour: cs = plt.contour(X, Y, Z, **options) plt.clabel(cs, inline=1, fontsize=10) if imshow: extent = xs[0], xs[-1], ys[0], ys[-1] plt.imshow(Z, extent=extent, **options)
def show_2D_GPSA(raw, GPSA_data_list, key_x='axis0', key_y='axis1', clim=None, cmap='jet', mutiplier=1.0, Z_cut=None, Z_contour=([],[]), ): x = raw[key_x] y = raw[key_y] Z = raw['mixture fraction'] xx = sorted(list(set(x))) yy = sorted(list(set(y))) n_x = len(xx) n_y = len(yy) print 'n_x = '+str(n_x) print 'n_y = '+str(n_y) if n_x*n_y == 0: print 'cannot build as n_x = '+str(n_x)+', n_y = '+str(n_y) print 'raw = '+str(raw) return print 'building matrix' data = np.zeros((n_y,n_x)) Zmat = np.zeros((n_y,n_x)) for i in range(len(x)): i_y = yy.index(y[i]) i_x = xx.index(x[i]) #if raw['active species'][i]>5: if Z_cut is not None and Z[i]<Z_cut: data[i_y, i_x] = float('nan') else: data[i_y, i_x] = GPSA_data_list[i] * mutiplier Zmat[i_y, i_x] = Z[i] print 'plotting' colors = ['k','w'] lw = [1,3] if len(Z_contour)>0: for i_v in range(len(Z_contour)): CS = plt.contour(Zmat, [Z_contour[0][i_v]], colors=colors[i_v], linewidths=lw[i_v]) #plt.clabel(CS, fontsize=10, inline=1, fmt=Z_contour[1][i_v]) #plt.legend(Z_contour[1]) #imsave(key_show+'.png',data) if clim is None: plt.imshow(data, cmap=cmap) else: plt.imshow(data, clim=clim, cmap=cmap) plt.colorbar() #plt.show()
def save_contour(netD, filename, cuda=False): #import warnings #warnings.filterwarnings("ignore", category=FutureWarning) #import numpy as np #import matplotlib #matplotlib.use('Agg') #import matplotlib.cm as cm #import matplotlib.mlab as mlab #import matplotlib.pyplot as plt matplotlib.rcParams['xtick.direction'] = 'out' matplotlib.rcParams['ytick.direction'] = 'out' matplotlib.rcParams['contour.negative_linestyle'] = 'solid' # gen grid delta = 0.1 x = np.arange(-25.0, 25.0, delta) y = np.arange(-25.0, 25.0, delta) X, Y = np.meshgrid(x, y) # convert numpy array to to torch variable (h, w) = X.shape XY = np.concatenate((X.reshape((h*w, 1, 1, 1)), Y.reshape((h*w, 1, 1, 1))), axis=1) input = torch.Tensor(XY) input = Variable(input) if cuda: input = input.cuda() # forward output = netD(input) # convert torch variable to numpy array Z = output.data.cpu().view(-1).numpy().reshape(h, w) # plot and save plt.figure() CS1 = plt.contourf(X, Y, Z) CS2 = plt.contour(X, Y, Z, alpha=.7, colors='k') plt.clabel(CS2, inline=1, fontsize=10, colors='k') plt.title('Simplest default with labels') plt.savefig(filename) plt.close()
def mslponly(): # plot MSLP only #create figure plt.figure(figsize=(8,8)) x, y = m(lons, lats) psfchpa = conv.pa_to_hpa(psfc[time]) #convert Pa to hPa mslp = calc.calc_mslp(psfchpa, thgt[0], t2[time]) # get mslp mslp = gaussian_filter(mslp, sigma=3) #smooth wiggles #find local min and local max local_min, local_max = funcs.extrema(mslp, mode='wrap', window=50) clevs = np.arange(900,1055,2.) cs = m.contour(x,y,mslp,clevs,colors='k',linewidths=2.) plt.clabel(cs, inline=True, fmt='%1.0f', fontsize=12, colors='k') xlows = x[local_min]; xhighs = x[local_max] ylows = y[local_min]; yhighs = y[local_max] lowvals = mslp[local_min]; highvals = mslp[local_max] # plot lows as blue L's, with min pressure value underneath. xyplotted = [] # don't plot if there is already a L or H within dmin meters. yoffset = 0.022*(m.ymax-m.ymin) dmin = yoffset for x,y,p in zip(xlows, ylows, lowvals): if x < m.xmax and x > m.xmin and y < m.ymax and y > m.ymin: dist = [np.sqrt((x-x0)**2+(y-y0)**2) for x0,y0 in xyplotted] if not dist or min(dist) > dmin: plt.text(x,y,'L',fontsize=14,fontweight='bold', ha='center',va='center',color='b') plt.text(x,y-yoffset,repr(int(p)),fontsize=12, ha='center',va='top',color='b', bbox = dict(boxstyle="square",ec='None',fc=(1,1,1,0.5))) xyplotted.append((x,y)) # plot highs as red H's, with max pressure value underneath. xyplotted = [] for x,y,p in zip(xhighs, yhighs, highvals): if x < m.xmax and x > m.xmin and y < m.ymax and y > m.ymin: dist = [np.sqrt((x-x0)**2+(y-y0)**2) for x0,y0 in xyplotted] if not dist or min(dist) > dmin: plt.text(x,y,'H',fontsize=14,fontweight='bold', ha='center',va='center',color='r') plt.text(x,y-yoffset,repr(int(p)),fontsize=12, ha='center',va='top',color='r', bbox = dict(boxstyle="square",ec='None',fc=(1,1,1,0.5))) xyplotted.append((x,y)) title = "MSLP (hPa)" ftitle = 'mslp-' cblabel = '' clevs = False # no color bar levels cbticks = False makeplot(cs,title,cblabel,clevs,cbticks,ftitle)
def upperair(): # plot upper air chart for given level. geopotential height, wind bards and temp pb = nc.variables['PB'][time] #base state pressure, Pa p = nc.variables['P'][time] # perturbation pressure, Pa totalp = pb + p # total pressure in Pa U = nc.variables['U'][time] # U wind component V = nc.variables['V'][time] # V wind component Unew = funcs.unstagger(U,'U') # unstagger u Vnew = funcs.unstagger(V,'V') # unstagger v ph = nc.variables['PH'][time] #perturbation geopotential phb = nc.variables['PHB'][time] #base state geopotential totalgp = phb + ph # total geopotential totalgp = funcs.unstagger(totalgp,'Z') #total geopotential unstaggered theta = nc.variables['T'][time] #perturbation potential temperature (theta-t0) theta0 = nc.variables['T00'][0] #base state theta totalTheta = theta + theta0 # total potential temp totalT = conv.k_to_c(calc.theta_to_temp(totalTheta, totalp)) # calc temps in C levels = opt.lvl.split(',') # get list of levels for level in levels: plt.figure(figsize=(8,8)) #create fig for each plot level = int(level) # make it int #interp data for level gphgt = funcs.linear_interp(totalgp,totalp,level) totalTfinal = funcs.linear_interp(totalT,totalp,level) uinterp = funcs.linear_interp(Unew,totalp,level) vinterp = funcs.linear_interp(Vnew,totalp,level) Ufinal = conv.ms_to_kts(uinterp) #convert to kts Vfinal = conv.ms_to_kts(vinterp) #speed = calc.calc_wspeed(Ufinal, Vfinal) gphgt = conv.gphgt_to_hgt(gphgt) # convert to height (m) gphgt = gaussian_filter(gphgt, sigma=3) # smooth wiggles totalTfinal = gaussian_filter(totalTfinal, sigma=2) # set gpheight levels for common pressure levels if level == 250: gpclevs = np.arange(np.min(gphgt),np.max(gphgt),60) elif level == 500: gpclevs = np.arange(np.min(gphgt),np.max(gphgt),60) elif level == 700: gpclevs = np.arange(np.min(gphgt),np.max(gphgt),30) elif level == 850: gpclevs = np.arange(np.min(gphgt),np.max(gphgt),30) elif level == 925: gpclevs = np.arange(np.min(gphgt),np.max(gphgt),30) else: # use generic 30m spacing gpclevs = np.arange(np.min(gphgt),np.max(gphgt),30) #plot all this up cs = m.contour(x,y,gphgt,gpclevs,colors='k',linewidths=2.) plt.clabel(cs, inline=True, fmt='%1.0f', fontsize=12, colors='k') tclevs = np.arange(np.min(totalTfinal),np.max(totalTfinal),4) cs2 = m.contour(x,y,totalTfinal,tclevs,colors='r',linestyles='-',linewidths=2.) plt.clabel(cs2,inline=True,fmt='%1.0f',fontsize=12,colors='r') m.barbs(x[::thin,::thin], y[::thin,::thin], Ufinal[::thin,::thin], Vfinal[::thin,::thin],length=opt.barbsize) #plot barbs level = str(level) title = level+'mb Height (m), Temp (C), Wind Barbs (kts)' ftitle = level+'mb-' cblabel = 'kts' clevs = False cbticks = False makeplot(cs,title,cblabel,clevs,cbticks,ftitle)
def plot_ts(self, name_temp): """ Graph of T-S. :param name_temp: Key of the *data* variable that contains the temperature values. :return: figure """ try: temp = self.data[name_temp] salt = self.data['psal'] except KeyError: return "Error: Not enough data." # Figure out boundaries (mins and maxs) smin = salt.min() - (0.01 * salt.min()) smax = salt.max() + (0.01 * salt.max()) tmin = temp.min() - (0.1 * temp.max()) tmax = temp.max() + (0.1 * temp.max()) # Calculate how many grid cells we need in the x and y dimensions xdim = round((smax - smin) / 0.1 + 1, 0) ydim = round((tmax - tmin) + 1, 0) # Create empty grid of zeros dens = np.zeros((np.int(ydim), np.int(xdim))) # Create temp and salt vectors of appropriate dimensions ti = np.linspace(1, ydim - 1, ydim) + tmin si = np.linspace(1, xdim - 1, xdim) * 0.1 + smin # Loop to fill in grid with densities for j in range(0, int(ydim)): for i in range(0, int(xdim)): dens[j, i] = gsw.rho(si[i], ti[j], 0) # Subtracts 1000 to convert to sigma-t dens -= 1000 # Plot data *********************************************** fig_ts = plt.figure() ax1 = fig_ts.add_subplot(111) cs = plt.contour(si, ti, dens, linestyles='dashed', colors='k') plt.clabel(cs, fontsize=12, inline=1, fmt='%1.1f') # Label every second level ax1.plot(salt, temp, 'or', markersize=9) ax1.set_xlabel('PSU') ax1.set_ylabel('ºC') ax1.set_title("T-S diagram") return fig_ts
