Python Graphics.adjust_spines方法代码示例

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
	def frequencies(self,str, ax=None, cutoff=30):
		words = nltk.word_tokenize(''.join(ch for ch in str 
											if ch not in exclude 
												and ord(ch)<128 
												and not ch.isdigit()).lower())
		words = [word for word in words if word not in stopwords 
										and word not in emoticons 
										and word  not in ['rt','amp']]
		fdist = nltk.FreqDist(words)
		freqs = fdist.items()[:cutoff]
		word,f =zip(*freqs)
		f = np.array(f).astype(float)
		print f,'kkkkkkk'
		f /= float(f.sum())
		print f,'jjjjjjjjjjjj'
		if not ax:
			fig = plt.figure()
			ax = fig.add_subplot(111)

		ax.plot(-f*np.log(f),'k',linewidth=2)
		artist.adjust_spines(ax)
		ax.yaxis.grid()
		ax.xaxis.grid()
		ax.set_xticks(range(len(word)))
		ax.set_xticklabels(map(format,word),range(len(word)), rotation=45)
		ax.set_ylabel(r'\Large $\log \left(\mathbf{Frequency}\right)$')
		plt.tight_layout()
		plt.show()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def covariance(heatmap,labels,show=False,savename=None,ml=False):
	#Covariance matrix
	fig = plt.figure(figsize=(13,13))
	ax = fig.add_subplot(111)
	cax = ax.imshow(heatmap,interpolation='nearest',aspect='equal')
	artist.adjust_spines(ax)
	ax.set_xticks(range(len(labels)))
	ax.set_xticklabels(map(artist.format,labels),range(len(labels)),rotation=90)

	ax.set_yticks(range(len(labels)))
	ax.set_yticklabels(map(artist.format,labels))

	if ml:
		ax.annotate(r'\LARGE \textbf{Training}', xy=(.2, .2),  xycoords='axes fraction',
		                horizontalalignment='center', verticalalignment='center')


		ax.annotate(r'\LARGE \textbf{Testing}', xy=(.7, .7),  xycoords='axes fraction',
		                horizontalalignment='center', verticalalignment='center')

	plt.colorbar(cax, fraction=0.10, shrink=0.8)
	plt.tight_layout()

	if savename:
		plt.savefig('%s.png'%savename,dpi=200)
	if show:
		plt.show()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def plot_variable(data,basepath=None,dataname='',criterion=None, criterionname=[]):
	fig = plt.figure()
	ax = fig.add_subplot(111)
	x = range(data.shape[1])
	ap('Plotting %s'%dataname)
	if criterion != None:
		if type(criterion) != list:
			median, lq, uq = perc(data[criterion,:])
			ax.plot(x,median,linewidth=2, color='#B22400')
			ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color='#B22400')
		else:
			bmap = brewer2mpl.get_map('Set2', 'qualitative', 7)
			colors = bmap.mpl_colors
			for i,(x_criterion,x_label) in enumerate(itertools.izip_longest(criterion,criterionname,fillvalue='Group')):
				median, lq, uq = perc(data[x_criterion,:])
				ax.plot(x,median,linewidth=2, color=colors[i], label=artist.format(x_label))
				ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color=colors[i])
	
	median, lq, uq = perc(data)
	ax.plot(x,median,linewidth=2, color='#B22400',label=artist.format('Full population'))
	ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color='#B22400')
	
	artist.adjust_spines(ax)
	ax.set_ylabel(artist.format(dataname))
	ax.set_xlabel(artist.format('Time'))
	ax.axvline(data.shape[1]/3,color='r',linewidth=2,linestyle='--')
	ax.axvline(2*data.shape[1]/3,color='r',linewidth=2,linestyle='--')
	plt.legend(frameon=False,loc='lower left')
	plt.tight_layout()
	plt.savefig(os.path.join(basepath,'%s.png'%dataname))

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def snapshots(data, indices,basepath=None, data_label='data'):
		indices = zip(indices,indices[1:])

		for start_idx,stop_idx in indices:
			initial_distribution = data[:,start_idx]
			final_distribution = data[:,stop_idx]

			fig = plt.figure()
			ax = fig.add_subplot(111)
			ax.hist(initial_distribution,color='r',alpha=0.5,bins=20,label='Initial', range=(-1,1))
			ax.hist(final_distribution,color='k',alpha=0.5,bins=20,label='Final',range=(-1,1))
			artist.adjust_spines(ax)
			ax.set_xlabel(artist.format(data_label))
			ax.set_ylabel(artist.format('Prevalence'))

			H,p =kruskal(initial_distribution,final_distribution)
			effect_size = np.linalg.norm(final_distribution-initial_distribution)
			ax.annotate('\Large $d=%.02f, \; p=%.04f$'%(effect_size,p), xy=(.3, .9),  
				xycoords='axes fraction', horizontalalignment='right', verticalalignment='top')
			plt.tight_layout()
			plt.legend(frameon=False)

			filename = os.path.join(basepath,'%s-compare-%d-%d.png'%(data_label,start_idx,stop_idx))
			plt.savefig(filename,dpi=300)	
			plt.close()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def ecdf(data, show=False,savename=None):
	
 	ecdf = sm.distributions.ECDF(data)

 	x = np.linspace(data.min(),data.max())
 	y = ecdf(x)

 	cutoff = x[y>0.85][0]
	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.plot(x,y,'k',linewidth=3)
	artist.adjust_spines(ax)

	ax.annotate(r'\Large \textbf{Cutoff:} $%.03f$'%cutoff, xy=(.3, .2),  xycoords='axes fraction',
                horizontalalignment='center', verticalalignment='center')
	ax.set_xlabel(artist.format('Absolute Correlation'))
	ax.set_ylabel(artist.format('Percentile'))
	ax.axhline(y=0.85,color='r',linestyle='--',linewidth=2)
	ax.axvline(x=cutoff,color='r',linestyle='--',linewidth=2)
	ax.set_xlim((0,1))
	plt.tight_layout()

	if savename:
		plt.savefig('%s.png'%savename,dpi=200)
	if show:
		plt.show()

	return cutoff

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def coefficients(model,labels,show=False,savename=None,title=None):

	fig = plt.figure(figsize=(8,10))
	ax = fig.add_subplot(111)
	x = -model.coef_.transpose()
	x /= np.absolute(x).max()
	y = np.arange(len(x))+0.5

	cutoff = scoreatpercentile(np.absolute(x),85)
	ax.barh(y,x,color=['r' if datum < 0 else 'g' for datum in x])
	ax.axvline(cutoff,linewidth=2,linestyle='--',color='r')
	ax.axvline(-cutoff,linewidth=2,linestyle='--',color='r')
	artist.adjust_spines(ax)
	ax.grid(True)
	ax.set_ylim(ymax=62)
	ax.set_xlim(xmin=-1.1,xmax=1.1)
	ax.set_yticks(y)
	ax.set_yticklabels(map(format,labels),y)
	ax.set_xlabel(format('Regression coefficient'))

	if title:
		ax.set_title(r'\Large \textbf{%s}'%title)
	plt.tight_layout()
	if show:
		plt.show()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def plot(aList,ax, cutoff=20):
	tokens,frequencies = zip(*sorted(aList,key=lambda item:item[1],reverse=True))

	tokens = tokens[:cutoff][::-1]
	frequencies = frequencies[:cutoff][::-1]

	ax.plot(frequencies,xrange(cutoff),'k--', linewidth=2)
	artist.adjust_spines(ax)
	ax.set_yticks(xrange(cutoff))
	ax.set_yticklabels(map(artist.format,tokens),rotation='horizontal')

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def network_stability(energy_trace,savename):

	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.plot(energy_trace,'k',linewidth=2)
	artist.adjust_spines(ax)
	ax.set_xlabel(artist.format('Time'))
	ax.set_ylabel(artist.format('Stability (energy)'))

	plt.savefig('%s.png'%savename,dpi=200)
	plt.close()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def plot_and_save(frequencies, words, ylabel, savefile):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.semilogy(frequencies,'k--',linewidth=3)
    artist.adjust_spines(ax)

    ax.set_xticks(xrange(len(words)))
    ax.set_xticklabels([r'\textbf{\textsc{%s}'%word for word in words],rotation='vertical')
    ax.set_ylabel(artist.format(ylabel))

    plt.tight_layout()
    plt.show()
    plt.savefig(savefile, bbox_inches="tight")

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def memory_stability(mat,savename):

	fig = plt.figure()
	ax = fig.add_subplot(111)
	cax = ax.imshow(mat,interpolation='nearest',aspect='auto')
	
	artist.adjust_spines(ax)
	ax.set_ylabel(artist.format('Memory'))
	ax.set_xlabel(artist.format('Time'))

	cbar = plt.colorbar(cax)
	cbar.set_label(artist.format('Energy'))
	plt.savefig('%s.png'%savename,dpi=200)
	plt.close()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def hist_compare(data,criterion=None, basepath=None, criterionname='Target population',fieldname='Field'):
	del fig,ax

	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.hist(data,color='k',histtype='step',label=artist.format('Full Population'))
	plt.hold(True)
	if criterion:
		ax.hist(data[criterion],color='r',histtype='stepfilled',label=artist.format(criterionname))
	artist.adjust_spines(ax)
	ax.set_xlabel(artist.format(fieldname))
	ax.set_ylabel(artist.format('No. of people '))
	plt.legend(frameon=False)
	plt.tight_layout()
	plt.savefig(os.path.join(basepath,'hist_compare_full_%s'%('_'.join(criterion.split()))),dpi=300)

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def sensitivity(x,y, show=False, savename=None):
	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.plot(x,y,'ko--',linewidth=2,clip_on=False)

	artist.adjust_spines(ax)
	ax.set_xlabel(r'\Large \textbf{Mixing fraction,} $\; \alpha$')
	ax.set_ylabel(r'\Large \textbf{Maximum accuracy,}$\; q_{\max}$')
	ax.set_ylim((-1.1))
	plt.tight_layout()

	if savename:
		plt.savefig('%s.png'%savename,dpi=300)
	if show:
		plt.show()
	plt.close()
	return pearsonr(x,y)

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def mvr_coefficients(model,labels,show=False,savename=None):

	fig = plt.figure()
	ax = fig.add_subplot(111)
	cax = ax.imshow(model.coef_.transpose(),interpolation='nearest',aspect='auto',
		vmin=-0.5,vmax=0.5)
	artist.adjust_spines(ax)
	ax.set_yticks(range(len(labels)))
	ax.set_yticklabels(map(artist.format,[name for name in labels if 'EVD' not in name]))
	ax.set_xticks(range(3))
	ax.set_xticklabels(map(artist.format,range(1,4)))
	ax.set_xlabel(artist.format('Placement grade'))
	plt.colorbar(cax)
	plt.tight_layout()
	if savename:
		plt.savefig('%s.png'%savename,dpi=200)
	if show:
		plt.show()

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def population_summary(basepath=None, criterion = None, criterionname=''):

	yvars = open(directory['variables'],READ).read().splitlines()
	yvars.remove('past month drinking')
	ncols = np.ceil(np.sqrt(len(yvars))).astype(int)
	nrows = np.ceil(len(yvars)/ncols).astype(int)
	MALE = 0.5
	FEMALE = 0.3
	fig,axs = plt.subplots(nrows=nrows,ncols=ncols,sharey=True)

	for i,col in enumerate(axs):
		for j,row in enumerate(col):
			filename = 'initial-distribution-%s.txt'%(yvars[i*ncols+j].replace(' ','-'))
			data = np.loadtxt(os.path.join(basepath,filename),delimiter=TAB)
			if criterion:
				weights = np.ones_like(data[criterion])/len(data[criterion])
				_,_,patches1 = axs[i,j].hist(data[criterion],color='r',alpha=0.5,
					label=artist.format(criterionname),histtype='step',weights=weights)
				plt.hold(True)
			weights = np.ones_like(data)/len(data)
			_,_,patches2 = axs[i,j].hist(data, color='k',label=artist.format('Full population'), 
				histtype='stepfilled' if not criterion else 'step',weights=weights)
			fig.canvas.mpl_connect('draw_event', artist.on_draw)
			artist.adjust_spines(axs[i,j])
			if 'attitude' not in yvars[i*ncols+j]: 	
				axs[i,j].set_xlabel(artist.format(yvars[i*ncols+j].replace('drink','use')))
				if 'gender' in yvars[i*ncols+j]:
					axs[i,j].set_xticks([FEMALE,MALE])
					axs[i,j].set_xticklabels(map(artist.format,['Female','Male']))
			elif 'psychological' in yvars[i*ncols+j]:
				label = '\n'.join(map(artist.format,['Attitude to','psychological','consequences']))
				axs[i,j].set_xlabel(label)
			elif 'medical' in yvars[i*ncols+j]:
				label = '\n'.join(map(artist.format,['Attitude','to medical','consequences']))
				axs[i,j].set_xlabel(label)
				#axs[i,j].set_xlim([-50,50])

	plt.tight_layout()
	if criterion:
		fig.legend((patches1[0], patches2[0]), (artist.format(criterionname),artist.format('Full population')),
		loc='lower right', frameon=False, ncol=2)

	filename = os.path.join(os.getcwd(),basepath,'dashboard.png' if criterionname == '' else 'dashboard-%-s.png'%criterionname)
	plt.savefig(filename,dpi=300)

# 需要导入模块: import Graphics [as 别名]
# 或者: from Graphics import adjust_spines [as 别名]
def correlation_visualization(data, show=False,savename=None):
	correlations = ['Quu','Qru','Qvu']
	#Analyze correlations

	fig,axs = plt.subplots(nrows=3,ncols=1,sharex=True)
	for ax,data,label in zip(axs,map(dq,[data[correlation] for correlation in correlations]),correlations):
		ax.plot(data,'k',linewidth=2,label=artist.format(label))

		artist.adjust_spines(ax)

		ax.set_ylabel(r'\Large $\mathbf{\partial \left(\det %s_{%s}\right)}$'%(label[0],label[1:]),rotation='horizontal')
		ax.set_xlabel(artist.format('Time'))

	plt.tight_layout()
	if savename:
		plt.savefig('%s.png'%savename,dpi=200)
	if show:
		plt.show()
	plt.close()