def Unittest_Kline():
""""""
kline = Guider("600100", "")
print(kline.getData(0).date, kline.getLastData().date)
#kline.myprint()
obv = kline.OBV()
pl.figure
pl.subplot(2,1,1)
pl.plot(kline.getCloses())
pl.subplot(2,1,2)
ma,m2,m3 = kline.MACD()
pl.plot(ma)
pl.plot(m2,'r')
left = np.arange(0, len(m3))
pl.bar(left,m3)
#pl.plot(obv, 'y')
pl.show()
#Unittest_Kstp()
#
#???????????
#----------------------------------------------------------------------
python类bar()的实例源码
def plot_and_save(self, filename="snakemake_stats.png",
outputdir="report"):
import pylab
# If the plot cannot be created (e.g. no valid stats), we create an empty
# axes
try: self.plot()
except:
pylab.bar([0],[0])
if outputdir is None:
pylab.savefig(filename)
else:
pylab.savefig(outputdir + os.sep + filename)
def test_dT_impact(xvals, f, nmpc, sim, start=0.1, end=0.8, step=0.02, ymax=200,
sample_size=100, label=None):
"""Used to generate Figure XX of the paper."""
c = raw_input("Did you remove iter/time caps in IPOPT settings? [y/N] ")
if c.lower() not in ['y', 'yes']:
print "Then go ahead and do it."
return
stats = [Statistics() for _ in xrange(len(xvals))]
fails = [0. for _ in xrange(len(xvals))]
pylab.ion()
pylab.clf()
for (i, dT) in enumerate(xvals):
f(dT)
for _ in xrange(sample_size):
nmpc.on_tick(sim)
if 'Solve' in nmpc.nlp.return_status:
stats[i].add(nmpc.nlp.solve_time)
else: # max CPU time exceeded, infeasible problem detected, ...
fails[i] += 1.
yvals = [1000 * ts.avg if ts.avg is not None else 0. for ts in stats]
yerr = [1000 * ts.std if ts.std is not None else 0. for ts in stats]
pylab.bar(
xvals, yvals, width=step, yerr=yerr, color='y', capsize=5,
align='center', error_kw={'capsize': 5, 'elinewidth': 5})
pylab.xlim(start - step / 2, end + step / 2)
pylab.ylim(0, ymax)
pylab.grid(True)
if label is not None:
pylab.xlabel(label, fontsize=24)
pylab.ylabel('Comp. time (ms)', fontsize=20)
pylab.tick_params(labelsize=16)
pylab.twinx()
yfails = [100. * fails[i] / sample_size for i in xrange(len(xvals))]
pylab.plot(xvals, yfails, 'ro', markersize=12)
pylab.plot(xvals, yfails, 'r--', linewidth=3)
pylab.xlim(start - step / 2, end + step / 2)
pylab.ylabel("Failure rate [%]", fontsize=20)
pylab.tight_layout()
def bar(pl, x, y):
pl.figure
pl.bar(x,y)
pl.show()
pl.close()
def bar(self, *args, **kwargs):
pl.bar(*args, **kwargs)
def feature_importance(rf, save_path=None):
"""Plots feature importance of sklearn RandomForest
Parameters
----------
rf : RandomForestClassifier
save_path : str
"""
importances = rf.feature_importances_
nb = len(importances)
tree_imp = [tree.feature_importances_ for tree in rf.estimators_]
# print "Print feature importance of rf with %d trees." % len(tree_imp)
std = np.std(tree_imp, axis=0) / np.sqrt(len(tree_imp))
indices = np.argsort(importances)[::-1]
# Print the feature ranking
# print("Feature ranking:")
# for f in range(nb):
# print("%d. feature %d (%f)" %
# (f + 1, indices[f], importances[indices[f]]))
# Plot the feature importances of the forest
pl.figure()
pl.title("Feature importances")
pl.bar(range(nb), importances[indices],
color="r", yerr=std[indices], align="center")
pl.xticks(range(nb), indices)
pl.xlim([-1, nb])
if save_path is not None:
pl.savefig(save_path)
pl.close()
def barplot(self,direction,alpha=0.4):
import pylab
pnormalize = pylab.normalize(vmin=1,vmax=len(self.distribution[direction]))
count = 0
# dx = 0.1/(2*len(self.distribution[direction]))
for target,(edges,heights) in self.distribution[direction].items():
count += 1
lefts = edges[:-1] # + count*dx
widths = (edges[1:] - edges[:-1]) # - count*dx
RGB = pylab.cm.jet(pnormalize(count))[:3]
pylab.bar(lefts,heights,width=widths,
alpha=alpha,
color=RGB,
label="%s" % target)
def bootstrap(self, nBoot, nbins = 20):
pops = np.zeros((nBoot, nbins))
#medianpop = [[] for i in data.cat]
pylab.figure(figsize = (20,14))
for i in xrange(3):
pylab.subplot(1,3,i+1)
#if i ==0:
#pylab.title("Bootstrap on medians", fontsize = 20.)
pop = self.angles[(self.categories == i)]# & (self.GFP > 2000)]
for index in xrange(nBoot):
newpop = np.random.choice(pop, size=len(pop), replace=True)
#medianpop[i].append(np.median(newpop))
newhist, binedges = np.histogram(newpop, bins = nbins)
pops[index,:] = newhist/1./len(pop)
#pylab.hist(medianpop[i], bins = nbins, label = "{2} median {0:.1f}, std {1:.1f}".format(np.median(medianpop[i]), np.std(medianpop[i]), data.cat[i]), color = data.colors[i], alpha =.2, normed = True)
meanpop = np.sum(pops, axis = 0)/1./nBoot
stdY = np.std(pops, axis = 0)
print "width", binedges[1] - binedges[0]
pylab.bar(binedges[:-1], meanpop, width = binedges[1] - binedges[0], label = "mean distribution", color = data.colors[i], alpha = 0.6)
pylab.fill_between((binedges[:-1]+binedges[1:])/2., meanpop-stdY, meanpop+stdY, alpha = 0.3)
pylab.legend()
pylab.title(data.cat[i])
pylab.xlabel("Angle(degree)", fontsize = 15)
pylab.ylim([-.01, 0.23])
pylab.savefig("/users/biocomp/frose/frose/Graphics/FINALRESULTS-diff-f3/distrib_nBootstrap{0}_bins{1}_GFPsup{2}_{3}.png".format(nBoot, nbins, 'all', randint(0,999)))
def _plotVirtualTime(accessList, archName, fgname, rd_bin_width = 250):
"""
Plot data access based on virtual time
"""
print "converting to num arrary for _plotVirtualTime"
stt = time.time()
x, y, id, ad, yd = _raListToVTimeNA(accessList)
print ("Converting to num array takes %d seconds" % (time.time() - stt))
fig = pl.figure()
ax = fig.add_subplot(211)
ax.set_xlabel('Access sequence number (%s to %s)' % (id.split('T')[0], ad.split('T')[0]), fontsize = 9)
ax.set_ylabel('Observation sequence number', fontsize = 9)
ax.set_title('%s archive activity ' % (archName), fontsize=10)
ax.tick_params(axis='both', which='major', labelsize=8)
ax.tick_params(axis='both', which='minor', labelsize=6)
ax.plot(x, y, color = 'b', marker = 'x', linestyle = '',
label = 'access', markersize = 3)
#legend = ax.legend(loc = 'upper left', shadow=True, prop={'size':7})
ax1 = fig.add_subplot(212)
ax1.set_xlabel('Access sequence number (%s to %s)' % (id.split('T')[0], ad.split('T')[0]), fontsize = 9)
ax1.set_ylabel('Reuse distance (in-between accesses)', fontsize = 9)
ax1.tick_params(axis='both', which='major', labelsize=8)
ax1.tick_params(axis='both', which='minor', labelsize=6)
ax1.plot(x, yd, color = 'k', marker = '+', linestyle = '',
label = 'reuse distance', markersize = 3)
pl.tight_layout()
fig.savefig(fgname)
pl.close(fig)
y1d = yd[~np.isnan(yd)]
num_bin = (max(y1d) - min(y1d)) / rd_bin_width
hist, bins = np.histogram(y1d, bins = num_bin)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
fig1 = pl.figure()
#fig1.suptitle('Histogram of data transfer rate from Pawsey to MIT', fontsize=14)
ax2 = fig1.add_subplot(111)
ax2.set_title('Reuse distance Histogram for %s' % archName, fontsize = 10)
ax2.set_ylabel('Frequency', fontsize = 9)
ax2.set_xlabel('Reuse distance (# of observation)', fontsize = 9)
ax2.tick_params(axis='both', which='major', labelsize=8)
ax2.tick_params(axis='both', which='minor', labelsize=6)
pl.bar(center, hist, align='center', width=width)
fileName, fileExtension = os.path.splitext(fgname)
fig1.savefig('%s_rud_hist%s' % (fileName, fileExtension))
pl.close(fig1)
