本文整理汇总了Python中matplotlib.pylab.suptitle函数的典型用法代码示例。如果您正苦于以下问题：Python suptitle函数的具体用法？Python suptitle怎么用？Python suptitle使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了suptitle函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: chooseDegree
def chooseDegree(npts, mindegree=0, maxdegree=20, filename=None):
"""Gets noisy data, uses cross validation to estimate error, and fits new data with best model."""
x, y = bv.noisyData(npts)
degrees = numpy.arange(mindegree,maxdegree+1)
errs = numpy.zeros_like(degrees,dtype=numpy.float)
for i,d in enumerate(degrees):
errs[i] = estimateError(x, y, d)
plt.subplot(1,2,1)
plt.plot(degrees,errs,'bo-')
plt.xlabel("Degree")
plt.ylabel("CV Error")
besti = numpy.argmin(errs)
bestdegree = degrees[besti]
plt.subplot(1,2,2)
x2, y2 = bv.noisyData(npts)
plt.plot(x2,y2,'ro')
xs = numpy.linspace(min(x),max(x),150)
fitf = numpy.poly1d(numpy.polyfit(x2,y2,bestdegree))
plt.plot(xs,fitf(xs),'g-',lw=2)
plt.xlim((bv.MIN,bv.MAX))
plt.ylim((-2.,2.))
plt.suptitle('Selected Degree '+str(bestdegree))
bv.outputPlot(filename)示例2: graphical_analysis
def graphical_analysis(strace, comment=None, cutoff=50, threshold=-45, bins=50):
# -----------------------------------------------------------------------------
"""
Graphical report of the trace.
strace - voltage trace of class SimpleTrace
cutoff - first part of the trace cut away from the analysis (in ms)
threshold - cutting voltage
"""
import matplotlib.pylab as pyl
voltage = strace._data
time = np.arange(len(voltage))*strace._dt
pyl.figure()
rate, mean, var, skew, kurt = full_analysis(strace, cutoff, threshold)
if comment:
pyl.suptitle(comment)
sp1 = pyl.subplot(2,1,1)
sp1.plot(time,voltage)
sp1.set_title("Spike rate = {0}".format(rate))
sp1.set_xlabel("time [ms]")
sp1.set_ylabel("V [mV]")
sp2 = pyl.subplot(2,1,2)
cut_trace = voltage[int(cutoff/strace._dt):]
data = cut_trace[cut_trace<threshold]
sp2.hist(data, bins=bins, histtype="stepfilled", normed=1)
xlim = sp2.get_xlim()
pyl.text(xlim[0]+0.7*(xlim[1]-xlim[0]), 0.6,
"mean = {0}\nvar={1}\nskew={2}\nkurt={3}".format(mean, var, skew, kurt))
sp2.plot([mean, mean],[0,1],"r")
sp2.plot([mean-np.sqrt(var)/2., mean+np.sqrt(var)/2.],[0.1,0.1], "r")
sp2.set_xlabel("V [mV]")
sp2.set_ylabel("normalized distribution")示例3: plot
def plot(self,typ='s3',titre='titre',log=False,stem=True,subp=True):
"""
"""
fa = np.linspace(self.Br.fmin,self.Br.fmax,self.Br.Nf)
st = titre+' shape : '+typ
plt.suptitle(st,fontsize=14)
if subp:
plt.subplot(221)
titre = '$\sum_f \sum_m |Br_{l}^{(m)}(f)|$'
self.Br.plot(typ=typ,title=titre, yl=True,color='r',stem=stem,log=log)
else:
self.Br.plot(typ=typ,color='r',stem=stem,log=log)
if subp:
plt.subplot(222)
titre = '$\sum_f \sum_m |Bi_{l}^{(m)}(f)|$'
self.Bi.plot(typ=typ,title=titrei,color='m',stem=stem,log=log)
else:
self.Bi.plot(typ=typ,color='m',stem=stem,log=log)
if subp:
plt.subplot(223)
titre = '$\sum_f \sum_m |Cr_{l}^{(m)}(f)|$'
self.Cr.plot(typ=typ,title=titre, xl=True, yl=True,color='b',stem=stem,log=log)
else:
self.Cr.plot(typ=typ,color='b',stem=stem,log=log)
if subp:
plt.subplot(224)
titre = '$\sum_f \sum_m |Ci_{l}^{(m)}(f)|$'
self.Ci.plot(typ=typ, title = titre, xl=True,color='c',stem=stem,log=log)
else:
self.Ci.plot(typ=typ,xl=True,yl=True,color='c',stem=stem,log=log)
if not subp:
plt.legend(('$\sum_f \sum_m |Br_{l}^{(m)}(f)|$',
'$\sum_f \sum_m |Bi_{l}^{(m)}(f)|$',
'$\sum_f \sum_m |Cr_{l}^{(m)}(f)|$',
'$\sum_f \sum_m |Ci_{l}^{(m)}(f)|$'))示例4: plotFittingResults
def plotFittingResults(self):
"""
Plot results of Rmax optimization procedure and best fit of the experimental data
"""
_listFitQ = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitQ()]
_listFitValues = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitValues()]
_listExpQ = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataQ()]
_listExpValues = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataValues()]
#_listExpStdDev = None
#if self.getDataInput().getExperimentalDataStdDev():
# _listExpStdDev = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataStdDev()]
#if _listExpStdDev:
# pylab.errorbar(_listExpQ, _listExpValues, yerr=_listExpStdDev, linestyle='None', marker='o', markersize=1, label="Experimental Data")
# pylab.gca().set_yscale("log", nonposy='clip')
#else:
# pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label="Experimental Data")
pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label="Experimental Data")
pylab.semilogy(_listFitQ, _listFitValues, label="Fitting curve")
pylab.xlabel('q')
pylab.ylabel('I(q)')
pylab.suptitle("RMax : %3.2f. Fit quality : %1.3f" % (self.getDataInput().getRMax().getValue(), self.getDataOutput().getFitQuality().getValue()))
pylab.legend()
pylab.savefig(os.path.join(self.getWorkingDirectory(), "gnomFittingResults.png"))
pylab.clf()示例5: visualization2
def visualization2(self, sp_to_vis=None):
if sp_to_vis:
species_ready = list(set(sp_to_vis).intersection(self.all_sp_signatures.keys()))
else:
raise Exception('list of driver species must be defined')
if not species_ready:
raise Exception('None of the input species is a driver')
for sp in species_ready:
# Setting up figure
plt.figure()
plt.subplot(313)
mon_val = OrderedDict()
signature = self.all_sp_signatures[sp]
for idx, mon in enumerate(list(set(signature))):
if mon[0] == 'C':
mon_val[self.all_comb[sp][mon] + (-1,)] = idx
else:
mon_val[self.all_comb[sp][mon]] = idx
mon_rep = [0] * len(signature)
for i, m in enumerate(signature):
if m[0] == 'C':
mon_rep[i] = mon_val[self.all_comb[sp][m] + (-1,)]
else:
mon_rep[i] = mon_val[self.all_comb[sp][m]]
# mon_rep = [mon_val[self.all_comb[sp][m]] for m in signature]
y_pos = numpy.arange(len(mon_val.keys()))
plt.scatter(self.tspan[1:], mon_rep)
plt.yticks(y_pos, mon_val.keys())
plt.ylabel('Monomials', fontsize=16)
plt.xlabel('Time(s)', fontsize=16)
plt.xlim(0, self.tspan[-1])
plt.ylim(0, max(y_pos))
plt.subplot(312)
for name in self.model.odes[sp].as_coefficients_dict():
mon = name
mon = mon.subs(self.param_values)
var_to_study = [atom for atom in mon.atoms(sympy.Symbol)]
arg_f1 = [numpy.maximum(self.mach_eps, self.y[str(va)][1:]) for va in var_to_study]
f1 = sympy.lambdify(var_to_study, mon)
mon_values = f1(*arg_f1)
mon_name = str(name).partition('__')[2]
plt.plot(self.tspan[1:], mon_values, label=mon_name)
plt.ylabel('Rate(m/sec)', fontsize=16)
plt.legend(bbox_to_anchor=(-0.1, 0.85), loc='upper right', ncol=1)
plt.subplot(311)
plt.plot(self.tspan[1:], self.y['__s%d' % sp][1:], label=parse_name(self.model.species[sp]))
plt.ylabel('Molecules', fontsize=16)
plt.legend(bbox_to_anchor=(-0.15, 0.85), loc='upper right', ncol=1)
plt.suptitle('Tropicalization' + ' ' + str(self.model.species[sp]))
# plt.show()
plt.savefig('s%d' % sp + '.png', bbox_inches='tight', dpi=400)示例6: scatter
def scatter(title, file_name, x_array, y_array, size_array, x_label, \
y_label, x_range, y_range, print_pdf):
'''
Plots the given x value array and y value array with the specified
title and saves with the specified file name. The size of points on
the map are proportional to the values given in size_array. If
print_pdf value is 1, the image is also written to pdf file.
Otherwise it is only written to png file.
'''
rc('text', usetex=True)
rc('font', family='serif')
plt.clf() # clear the ploting window, a must.
plt.scatter(x_array, y_array, s = size_array, c = 'b', marker = 'o', alpha = 0.4)
if x_label != None:
plt.xlabel(x_label)
if y_label != None:
plt.ylabel(y_label)
plt.axis ([0, x_range, 0, y_range])
plt.grid(True)
plt.suptitle(title)
Plotter.print_to_png(plt, file_name)
if print_pdf:
Plotter.print_to_pdf(plt, file_name)示例7: plot_fcs
def plot_fcs(normed_df, unnormed_df, pair, basename):
"""
Plot fold changes for normed and unnormed dataframes.
Parameters:
-----------
normed_df: Normalized dataframe of values
unnormed_df: Unnormalized dataframe of values
pair: Tuple containing the two columns to use
to compute fold change. Fold change is first
sample divided by second.
"""
if (pair[0] not in normed_df.columns) or \
(pair[1] not in normed_df.columns):
raise Exception, "One of the pairs is not in normed df."
if (pair[0] not in unnormed_df.columns) or \
(pair[1] not in unnormed_df.columns):
raise Exception, "One of the pairs is not in unnormed.df"
normed_fc = \
np.log2(normed_df[pair[0]]) - np.log2(normed_df[pair[1]])
unnormed_fc = \
np.log2(unnormed_df[pair[0]]) - np.log2(unnormed_df[pair[1]])
fc_df = pandas.DataFrame({"normed_fc": normed_fc,
"unnormed_fc": unnormed_fc})
# Remove nans/infs etc.
pandas.set_option('use_inf_as_null', True)
fc_df = fc_df.dropna(how="any", subset=["normed_fc",
"unnormed_fc"])
plt.figure()
fc_df.hist(color="k", bins=40)
plt.suptitle("%s vs. %s" %(pair[0], pair[1]))
plt.xlabel("Fold change (log2)")
save_fig(basename)
pandas.set_option('use_inf_as_null', False) 示例8: update
def update(frame):
global _counter
centroid = np.random.uniform(-0.5, 0.5, size=2)
width = np.random.uniform(0, 0.01)
length = np.random.uniform(0, 0.03) + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 50
model = mock.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = mock.make_mock_shower_image(geom, model.pdf,
intensity=intens,
nsb_level_pe=5000)
# alternate between cleaned and raw images
if _counter > 20:
plt.suptitle("Image Cleaning ON")
cleanmask = reco.cleaning.tailcuts_clean(geom, image, pedvars=80)
for ii in range(3):
reco.cleaning.dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
if _counter >= 40:
plt.suptitle("Image Cleaning OFF")
_counter = 0
disp.image = image
disp.set_limits_percent(100)
_counter += 1示例9: q6
def q6(abreviation):
'''#Q6: PLOTS polling data for a given state'''
#get STATE; connect to db
state = abreviation.upper()
connection = sqlite3.connect(path+"/poll.db")
cursor = connection.cursor()
#query db, get names, rankings for Rep, Dem, Ind candidates
sql_cmd = "SELECT candidate_names.democrat, candidate_names.republican, candidate_names.independent, rankings.day, rankings.dem, rankings.rep, rankings.indep from rankings left join statetable on rankings.state = statetable.fullname\
left join candidate_names on statetable.abrev = candidate_names.state where statetable.abrev = '%s' order by rankings.day ASC" % state
cursor.execute(sql_cmd)
dbinfo = cursor.fetchall()
#'new' is the name of the record array corresponding to the output from the sql query
new = N.array(dbinfo, dtype= [('demname', '|S25'),('repname', '|S25'),('indname', '|S25'),('day', N.int16), ("dem", N.int16), ("rep", N.int16), ("ind", N.int16)])
demname= new['demname'][0] #name of democrat
repname= new['repname'][0] # name of rep
indname= new['indname'][0] #" of ind
# 2 (+1) lines for dem, rep, candiates ind if he has more than 1 point at the first datapoint, indicating there is an independent candidate
plt.plot(new['day'],new['dem'], color='blue', label='%s' % demname)
plt.plot(new['day'],new['rep'], color='red', label='%s' % repname)
if new['ind'][0] > 1:
plt.plot(new['day'],new['ind'], color='green', label='%s' % indname)
#plot info
plt.suptitle('Election Polls for the State of %s'%state)
plt.xlabel('Day of the year')
plt.ylabel('Points')
plt.legend()
plt.show()示例10: plot_ss_scatter
def plot_ss_scatter(steadies):
""" Plot scatter plots of steady states
"""
def do_scatter(i, j, ax):
""" Draw single scatter plot
"""
xs, ys = utils.extract(i, j, steadies)
ax.scatter(xs, ys)
ax.set_xlabel(r"$S_%d$" % i)
ax.set_ylabel(r"$S_%d$" % j)
cc = utils.get_correlation(xs, ys)
ax.set_title(r"Corr: $%.2f$" % cc)
dim = steadies.shape[1]
fig, axarr = plt.subplots(1, int((dim ** 2 - dim) / 2), figsize=(20, 5))
axc = 0
for i in range(dim):
for j in range(dim):
if i == j:
break
do_scatter(i, j, axarr[axc])
axc += 1
plt.suptitle("Correlation overview")
plt.tight_layout()
save_figure("images/correlation_scatter.pdf", bbox_inches="tight")
plt.close()示例11: plotErrorAndOrder
def plotErrorAndOrder(schemesName, spaceErrorList,temporalErrorList,
spaceOrderList, temporalOrderList, Ntds):
legendList = []
lstyle = ['b', 'r', 'g', 'm']
fig , axarr = plt.subplots(2, 2, squeeze=False)
for k, scheme_name in enumerate(schemesName):
axarr[0][0].plot(np.log10(np.asarray(spaceErrorList[k])),lstyle[k])
axarr[0][1].plot(np.log10(np.asarray(temporalErrorList[k])),lstyle[k])
axarr[1][0].plot(spaceOrderList[k],lstyle[k])
axarr[1][1].plot(temporalOrderList[k],lstyle[k])
legendList.append(scheme_name)
plt.suptitle('test_MES_convergence(): Results from convergence test using Method of Exact Solution')
axarr[1][0].axhline(1.0, xmin=0, xmax=Ntds-2, linestyle=':', color='k')
axarr[1][0].axhline(2.0, xmin=0, xmax=Ntds-2, linestyle=':', color='k')
axarr[1][1].axhline(1.0, xmin=0, xmax=Ntds-2, linestyle=':', color='k')
axarr[1][1].axhline(2.0, xmin=0, xmax=Ntds-2, linestyle=':', color='k')
axarr[1][0].set_ylim(0, 5)
axarr[1][1].set_ylim(0, 5)
axarr[0][0].set_ylabel('rms Error')
axarr[0][0].set_title('space Error')
axarr[1][0].set_ylabel('rms Error')
axarr[0][1].set_title('temporal Error')
axarr[1][0].set_ylabel('order')
axarr[1][0].set_title('space order')
axarr[1][1].set_ylabel('order')
axarr[1][1].set_title('temporal order')
axarr[0][1].legend(legendList, frameon=False)示例12: report
def report(path='history.cpkl', tn=0, sns=True):
if sns:
import seaborn as sns
sns.set_style('whitegrid')
sns.set_style('whitegrid', {'fontsize': 50})
sns.set_context('poster')
with open(path) as f:
logged_data = pickle.load(f)
history = util.NestedDict()
for name, val in logged_data.iteritems():
history.set_nested(name, val)
num_subplots = len(history)
cols = 2 # 2 panels for Objective and Accuracy
rows = 1
fig = plt.figure(figsize=(12, 8))
fig.subplots_adjust(wspace=0.3, hspace=0.2) # room for labels [Objective, Accuracy]
colors = [sns.xkcd_rgb['blue'], sns.xkcd_rgb['red']]
# Here we assume that history is only two levels deep
for k, (subplot_name, trend_lines) in enumerate(history.iteritems()):
plt.subplot(rows, cols, k + 1)
plt.ylabel(subplot_name.capitalize())
plt.xlabel('Epoch')
for i, (name, (timestamps, values)) in enumerate(trend_lines.iteritems()):
plt.plot(timestamps, values, label=name, color=colors[i])
plt.suptitle('Task number %d' % tn)
plt.legend(loc='best')
plt.show()示例13: plotAstrometry
def plotAstrometry(dist, mag, snr, brightSnr=100,
outputPrefix=""):
"""Plot angular distance between matched sources from different exposures.
Creates a file containing the plot with a filename beginning with `outputPrefix`.
Parameters
----------
dist : list or numpy.array
Separation from reference [mas]
mag : list or numpy.array
Mean magnitude of PSF flux
snr : list or numpy.array
Median SNR of PSF flux
brightSnr : float, optional
Minimum SNR for a star to be considered "bright".
outputPrefix : str, optional
Prefix to use for filename of plot file. Will also be used in plot titles.
E.g., outputPrefix='Cfht_output_r_' will result in a file named
'Cfht_output_r_check_astrometry.png'
"""
bright, = np.where(np.asarray(snr) > brightSnr)
numMatched = len(dist)
dist_median = np.median(dist)
bright_dist_median = np.median(np.asarray(dist)[bright])
fig, ax = plt.subplots(ncols=2, nrows=1, figsize=(18, 12))
ax[0].hist(dist, bins=100, color=color['all'],
histtype='stepfilled', orientation='horizontal')
ax[0].hist(np.asarray(dist)[bright], bins=100, color=color['bright'],
histtype='stepfilled', orientation='horizontal',
label='SNR > %.0f' % brightSnr)
ax[0].set_ylim([0., 500.])
ax[0].set_ylabel("Distance [mas]")
ax[0].set_title("Median : %.1f, %.1f mas" %
(bright_dist_median, dist_median),
x=0.55, y=0.88)
plotOutlinedLinesHorizontal(ax[0], dist_median, bright_dist_median)
ax[1].scatter(snr, dist, s=10, color=color['all'], label='All')
ax[1].scatter(np.asarray(snr)[bright], np.asarray(dist)[bright], s=10,
color=color['bright'],
label='SNR > %.0f' % brightSnr)
ax[1].set_xlabel("SNR")
ax[1].set_xscale("log")
ax[1].set_ylim([0., 500.])
ax[1].set_title("# of matches : %d, %d" % (len(bright), numMatched))
ax[1].legend(loc='upper left')
ax[1].axvline(brightSnr, color='red', linewidth=4, linestyle='dashed')
plotOutlinedLinesHorizontal(ax[1], dist_median, bright_dist_median)
plt.suptitle("Astrometry Check : %s" % outputPrefix.rstrip('_'), fontsize=30)
plotPath = outputPrefix+"check_astrometry.png"
plt.savefig(plotPath, format="png")
plt.close(fig)示例14: plot_generated_toy_batch
def plot_generated_toy_batch(X_real, generator_model, discriminator_model, noise_dim, gen_iter, noise_scale=0.5):
# Generate images
X_gen = sample_noise(noise_scale, 10000, noise_dim)
X_gen = generator_model.predict(X_gen)
# Get some toy data to plot KDE of real data
data = load_toy(pts_per_mixture=200)
x = data[:, 0]
y = data[:, 1]
xmin, xmax = -1.5, 1.5
ymin, ymax = -1.5, 1.5
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = stats.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
# Plot the contour
fig = plt.figure(figsize=(10,10))
plt.suptitle("Generator iteration %s" % gen_iter, fontweight="bold", fontsize=22)
ax = fig.gca()
ax.contourf(xx, yy, f, cmap='Blues', vmin=np.percentile(f,80), vmax=np.max(f), levels=np.linspace(0.25, 0.85, 30))
# Also plot the contour of the discriminator
delta = 0.025
xmin, xmax = -1.5, 1.5
ymin, ymax = -1.5, 1.5
# Create mesh
XX, YY = np.meshgrid(np.arange(xmin, xmax, delta), np.arange(ymin, ymax, delta))
arr_pos = np.vstack((np.ravel(XX), np.ravel(YY))).T
# Get Z = predictions
ZZ = discriminator_model.predict(arr_pos)
ZZ = ZZ.reshape(XX.shape)
# Plot contour
ax.contour(XX, YY, ZZ, cmap="Blues", levels=np.linspace(0.25, 0.85, 10))
dy, dx = np.gradient(ZZ)
# Add streamlines
# plt.streamplot(XX, YY, dx, dy, linewidth=0.5, cmap="magma", density=1, arrowsize=1)
# Scatter generated data
plt.scatter(X_gen[:1000, 0], X_gen[:1000, 1], s=20, color="coral", marker="o")
l_gen = plt.Line2D((0,1),(0,0), color='coral', marker='o', linestyle='', markersize=20)
l_D = plt.Line2D((0,1),(0,0), color='steelblue', linewidth=3)
l_real = plt.Rectangle((0, 0), 1, 1, fc="steelblue")
# Create legend from custom artist/label lists
# bbox_to_anchor = (0.4, 1)
ax.legend([l_real, l_D, l_gen], ['Real data KDE', 'Discriminator contour',
'Generated data'], fontsize=18, loc="upper left")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax + 0.8)
plt.savefig("../../figures/toy_dataset_iter%s.jpg" % gen_iter)
plt.clf()
plt.close()示例15: plot_contours
def plot_contours(obj, top_bottom=True):
'''A function that plots the BRF as an azimuthal projection
with contours over the TOC and soil.
Input: rt_layers object, top_bottom - True if only TOC plot, False
if both TOC and soil.
Output: contour plot of brf.
'''
sun = ((np.pi - obj.sun0[0]) * np.cos(obj.sun0[1] + np.pi), \
(np.pi - obj.sun0[0]) * np.sin(obj.sun0[1] + np.pi))
theta = obj.views[:,0]
x = np.cos(obj.views[:,1]) * theta
y = np.sin(obj.views[:,1]) * theta
z = obj.I_top_bottom # * -obj.mu_s
if top_bottom == True:
if np.max > 1.:
maxz = np.max(z)
else:
maxz = 1.
else:
maxz = np.max(z[:obj.n/2])
minz = 0. #np.min(z)
space = np.linspace(minz, maxz, 11)
x = x[:obj.n/2]
y = y[:obj.n/2]
zt = z[:obj.n/2]
zb = z[obj.n/2:]
fig = plt.figure()
if top_bottom == True:
plt.subplot(121)
plt.plot(sun[0], sun[1], 'ro')
triang = tri.Triangulation(x, y)
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zt, space, vmax=maxz, vmin=minz)
plt.title('TOC BRF')
plt.ylabel('Y')
plt.xlabel('X')
if top_bottom == True:
plt.subplot(122)
plt.plot(sun[0], sun[1], 'ro')
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zb, space, vmax=maxz, vmin=minz)
plt.title('Soil Absorption')
plt.ylabel('Y')
plt.xlabel('X')
s = obj.__repr__()
if top_bottom == True:
cbaxes = fig.add_axes([0.11,0.1,0.85,0.05])
plt.suptitle(s,x=0.5,y=0.93)
plt.colorbar(orientation='horizontal', ticks=space,\
cax = cbaxes, format='%.3f')
else:
plt.suptitle(s,x=0.5,y=0.13)
plt.colorbar(orientation='horizontal', ticks=space,\
format='%.3f')
#plt.tight_layout()
plt.show()