def StockMarket():
X = pd.read_csv('data/TData9313_final5.csv', index_col=0)
X.index = pd.to_datetime(X.index)
X = X['20080101':'20091231']
X = X.resample('B', how='last').ffill().dropna(axis=0,
how='all').dropna(axis=1)
#X = X.dropna(axis=1)
X = np.log(X).diff()[1:]
pd.DataFrame(np.round(np.cov(X.T), 5), columns=X.columns,
index=X.columns).to_latex('covariancetable.tex')
print pd.DataFrame(np.round(np.cov(X.T), 5),
columns=X.columns,
index=X.columns).max()
print pd.DataFrame(np.round(np.cov(X.T), 5),
columns=X.columns,
index=X.columns)
print pd.DataFrame(np.round(np.cov(X.T), 5),
columns=X.columns,
index=X.columns).idxmax()
X /= X.std(axis=0)
covariance_, precision_ = graphical_lasso(X, 0.6)
print pd.DataFrame(precision_)
gephi = pd.DataFrame()
nodes = pd.DataFrame()
nr = 0
artdf = pd.DataFrame(precision_, columns=X.columns, index=X.columns)
artdf2 = pd.DataFrame(covariance_, columns=X.columns, index=X.columns)
print "Create Gephi Files"
for nr1, i in enumerate(artdf.index):
for nr2, j in enumerate(artdf.columns):
if nr1 > nr2:
if abs(artdf.loc[i, j]) > 0:
gephi.loc[nr, 'Source'] = nr1 + 1
gephi.loc[nr, 'Target'] = nr2 + 1
gephi.loc[nr, 'Type'] = 'Undirected'
gephi.loc[nr, 'Id'] = nr + 1
gephi.loc[nr, 'Label'] = nr1 + 1
gephi.loc[nr, 'Weight'] = abs(artdf.loc[i, j])
gephi.loc[nr, 'Weight'] = abs(artdf2.loc[i, j])
gephi.loc[nr, 'name'] = str(i)
gephi.loc[nr, 'toname'] = str(j)
nr += 1
nodes.loc[nr1, 'Nodes'] = nr1 + 1
nodes.loc[nr1, 'Id'] = nr1 + 1
nodes.loc[nr1, 'Label'] = str(i).split('feat')[0]
gephi.to_csv('gephi.csv')
nodes.to_csv('nodes.csv')
def StockMarket():
X = pd.read_csv('data/TData9313_final5.csv',index_col=0)
X.index = pd.to_datetime(X.index)
X = X['20080101':'20091231']
X = X.resample('B',how='last').ffill().dropna(axis=0,how='all').dropna(axis=1)
#X = X.dropna(axis=1)
X = np.log(X).diff()[1:]
pd.DataFrame(np.round(np.cov(X.T),5),columns=X.columns,index=X.columns).to_latex('covariancetable.tex')
print pd.DataFrame(np.round(np.cov(X.T),5),columns=X.columns,index=X.columns).max()
print pd.DataFrame(np.round(np.cov(X.T),5),columns=X.columns,index=X.columns)
print pd.DataFrame(np.round(np.cov(X.T),5),columns=X.columns,index=X.columns).idxmax()
X /= X.std(axis=0)
covariance_,precision_ = graphical_lasso(X,0.6)
print pd.DataFrame(precision_)
gephi = pd.DataFrame()
nodes = pd.DataFrame()
nr = 0
artdf = pd.DataFrame(precision_,columns=X.columns,index=X.columns)
artdf2 = pd.DataFrame(covariance_,columns=X.columns,index=X.columns)
print "Create Gephi Files"
for nr1,i in enumerate(artdf.index):
for nr2,j in enumerate(artdf.columns):
if nr1 > nr2:
if abs(artdf.loc[i,j]) > 0:
gephi.loc[nr,'Source'] = nr1+1
gephi.loc[nr,'Target'] = nr2+1
gephi.loc[nr,'Type'] = 'Undirected'
gephi.loc[nr,'Id'] = nr+1
gephi.loc[nr,'Label'] = nr1+1
gephi.loc[nr,'Weight'] = abs(artdf.loc[i,j])
gephi.loc[nr,'Weight'] = abs(artdf2.loc[i,j])
gephi.loc[nr,'name'] = str(i)
gephi.loc[nr,'toname'] = str(j)
nr += 1
nodes.loc[nr1,'Nodes'] = nr1+1
nodes.loc[nr1,'Id'] = nr1+1
nodes.loc[nr1,'Label'] = str(i).split('feat')[0]
gephi.to_csv('gephi.csv')
nodes.to_csv('nodes.csv')
def Main():
cov = np.array([[0.5,0.2,0.1],[0.2,0.6,0.09],[0.1,0.09,0.7]])
data = np.random.multivariate_normal([0,0,0],cov,5000)
rows = 5
cls = 5
fig1,axs = plt.subplots(rows,cls,figsize=(20,16),facecolor='grey',edgecolor = 'black')
fig1.subplots_adjust(hspace = .5, wspace=.5)
axs = axs.ravel()
for cc in range(rows*cls):
cov = graphical_lasso(data,alpha=0.01*cc)
print cov[0]
seaborn.heatmap(pd.DataFrame(np.array(cov[0])),ax=axs[cc])
axs[cc].set_title(cc)
plt.tight_layout()
plt.show()
def Main():
cov = np.array([[0.5, 0.2, 0.1], [0.2, 0.6, 0.09], [0.1, 0.09, 0.7]])
data = np.random.multivariate_normal([0, 0, 0], cov, 5000)
rows = 5
cls = 5
fig1, axs = plt.subplots(rows,
cls,
figsize=(20, 16),
facecolor='grey',
edgecolor='black')
fig1.subplots_adjust(hspace=.5, wspace=.5)
axs = axs.ravel()
for cc in range(rows * cls):
cov = graphical_lasso(data, alpha=0.01 * cc)
print cov[0]
seaborn.heatmap(pd.DataFrame(np.array(cov[0])), ax=axs[cc])
axs[cc].set_title(cc)
plt.tight_layout()
plt.show()
def StockMarketOLD():
###############################################################################
# Retrieve the data from Internet
# Choose a time period reasonnably calm (not too long ago so that we get
# high-tech firms, and before the 2008 crash)
d1 = datetime.datetime(2005, 1, 1)
d2 = datetime.datetime(2009, 12, 31)
# kraft symbol has now changed from KFT to MDLZ in yahoo
symbol_dict = {
'TOT': 'Total',
'XOM': 'Exxon',
'CVX': 'Chevron',
'COP': 'ConocoPhillips',
'VLO': 'Valero Energy',
'MSFT': 'Microsoft',
'IBM': 'IBM',
'TWX': 'Time Warner',
'CMCSA': 'Comcast',
#'CVC': 'Cablevision',
#'YHOO': 'Yahoo',
#'DELL': 'Dell',
'HPQ': 'HP',
'AMZN': 'Amazon',
'TM': 'Toyota',
'CAJ': 'Canon',
'MTU': 'Mitsubishi',
'SNE': 'Sony',
#'F': 'Ford',
'HMC': 'Honda',
#'NAV': 'Navistar',
'NOC': 'Northrop Grumman',
'BA': 'Boeing',
'KO': 'Coca Cola',
'MMM': '3M',
'MCD': 'Mc Donalds',
#'PEP': 'Pepsi',
'MDLZ': 'Kraft Foods',
'K': 'Kellogg',
'UN': 'Unilever',
'MAR': 'Marriott',
'PG': 'Procter Gamble',
'CL': 'Colgate-Palmolive',
'GE': 'General Electrics',
'WFC': 'Wells Fargo',
'JPM': 'JPMorgan Chase',
#'AIG': 'AIG',
'AXP': 'American Express',
'BAC': 'Bank of America',
'GS': 'Goldman Sachs',
'AAPL': 'Apple',
'SAP': 'SAP',
'CSCO': 'Cisco',
'TXN': 'Texas Instruments',
'XRX': 'Xerox',
#'LMT': 'Lookheed Martin',
'WMT': 'Wal-Mart',
'WBA': 'Walgreen',
'HD': 'Home Depot',
'GSK': 'GlaxoSmithKline',
'PFE': 'Pfizer',
'SNY': 'Sanofi-Aventis',
'NVS': 'Novartis',
'KMB': 'Kimberly-Clark',
'R': 'Ryder',
'GD': 'General Dynamics',
'RTN': 'Raytheon',
'CVS': 'CVS',
'CAT': 'Caterpillar',
'DD': 'DuPont de Nemours',
#'GM': 'General Motors',
#'GOOG' : 'Google',
'ORCL': 'Oracle',
'NVO': 'Novo Nordisk',
'LLY': 'Eli Lilly and Company',
#'FB':'Facebook',
'MRK': 'Merck Co',
}
'''
symbol_dict = {'Danske.CO':'Danske Bank',
'Maersk-B.CO':'Maersk',
'DSV.CO':'DSV',
'FLS.CO':'FLS',
'Gen.CO':'Genmab',
'TDC.CO':'TDC',
'CARL-B.CO':'Carlsberg',
'CHR.CO':'Chr Hansen',
'COLO-B.CO':'Coloplast',
'GN.CO':'GN Store Nord',
'NDA-DKK.co':'Nordea',
'Novo-B.co':'Novo Nordisk',
'NZYM-B.CO':'Novozymes',
'PNDORA.CO':'Pandora',
'Tryg.co':'Tryg',
'VWS.CO':'Vestas',
'WDH.CO':'William Demant',
'G4s.co':'G4S',
'JYSK.CO':'Jyske Bank',
'KBHL.CO':'Kobenhavns Lufthavne',
'RBREW.CO':'Royal Unibrew',
'ROCK-B.CO':'Rockwool',
'SYDB.CO':'Sydbank',
'TOP.CO':'Topdanmark',
#'ALMB.CO':'Alm Brand',
'AURI-B.CO':'Auriga',
'Bava.CO':'Bavarian Nordic',
'BO.CO':'Bang Olufsen',
'DFDS.CO':'DFDS',
'DNORD.CO':'DS Norden',
'GES.CO':'Greentech',
'IC.CO':'IC Group',
'JDAN.CO':'Jeudan',
#'JUTBK.CO':'Jutlander Bank',
#'MATAS.CO':'Matas',
'NKT.CO':'NKT',
#'NNIT.CO':'NNIT',
'NORDJB.CO':'Nordjyske Bank',
#'ONXEO.CO':'Onxeo',
#'OSSR.CO':'Ossur',
'PAAL-B.CO':'Per Aarslef',
'RILBA.CO':'Ringkobing Landbobank',
'SAS-DKK.CO':'SAS',
'SCHO.CO':'Schouw Co.',
'SIM.CO':'SimCorp',
'Solar-B.co':'Solar B',
'SPNO.CO':'Spar Nord',
'TIV.CO':'Tivoli',
'UIE.CO':'UIE',
'VELO.CO':'Veloxis',
'ZEAL.CO':'Zealand Pharma'
}
'''
symbols, names = np.array(list(symbol_dict.items())).T
for symbol in symbols:
print symbol
if len(
pd.DataFrame(
np.array([[
q[5] for q in quotes_historical_yahoo(
symbol, d1, d2, True, False)
]]).T)) != 1259:
print symbol, len(
pd.DataFrame(
np.array([[
q[5] for q in quotes_historical_yahoo(
symbol, d1, d2, True, False)
]]).T))
open = pd.DataFrame(
np.array([[
q[5] for q in quotes_historical_yahoo(symbol, d1, d2, True, False)
] for symbol in symbols]).T)
close = pd.DataFrame(
np.array([[
q[6] for q in quotes_historical_yahoo(symbol, d1, d2, True, False)
] for symbol in symbols]).T)
# The daily variations of the quotes are what carry most information
variation = np.array(close - open)
###############################################################################
# Learn a graphical structure from the correlations
#edge_model = covariance.GraphLassoCV()
# standardize the time series: using correlations rather than covariance
# is more efficient for structure recovery
df = pd.read_csv('data/TData9313_final5.csv', index_col=0)
X = variation.copy()
pd.DataFrame(np.round(np.cov(X.T), 3), columns=symbols,
index=symbols).to_latex('covariancetable.tex')
print np.max(np.round(np.cov(X.T), 3))
X /= X.std(axis=0)
covariance_, precision_ = graphical_lasso(X, 0.3)
print pd.DataFrame(precision_)
#edge_model.fit(X)
###############################################################################
# Cluster using affinity propagation
_, labels = cluster.affinity_propagation(covariance_)
n_labels = labels.max()
for i in range(n_labels + 1):
print('Cluster %i: %s' % ((i + 1), ', '.join(symbols[labels == i])))
###############################################################################
# Find a low-dimension embedding for visualization: find the best position of
# the nodes (the stocks) on a 2D plane
# We use a dense eigen_solver to achieve reproducibility (arpack is
# initiated with random vectors that we don't control). In addition, we
# use a large number of neighbors to capture the large-scale structure.
node_position_model = manifold.LocallyLinearEmbedding(n_components=2,
eigen_solver='dense',
n_neighbors=6)
embedding = node_position_model.fit_transform(X.T).T
###############################################################################
# Visualization
plt.figure(1, facecolor='w', figsize=(20, 16))
plt.clf()
ax = plt.axes([0., 0., 1., 1.])
plt.axis('off')
plt.annotate('From %s to %s' %
(d1.strftime('%Y-%m-%d'), d2.strftime('%Y-%m-%d')),
xy=(0.11, -0.37),
size=25)
print X.shape
for i in range(n_labels + 1):
plt.annotate('Cluster %i: %s' %
((i + 1), ', '.join(symbols[labels == i])),
xy=(-0.43, 0.02 - i * 0.02),
size=18)
pass
# Display a graph of the partial correlations
#partial_correlations = edge_model.precision_.copy()
partial_correlations = precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)
# Plot the nodes using the coordinates of our embedding
plt.scatter(embedding[0],
embedding[1],
s=200 * d**2,
c=labels,
cmap=plt.cm.spectral)
# Plot the edges
start_idx, end_idx = np.where(non_zero)
#a sequence of (*line0*, *line1*, *line2*), where::
# linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[embedding[:, start], embedding[:, stop]]
for start, stop in zip(start_idx, end_idx)]
values = np.abs(partial_correlations[non_zero])
lc = LineCollection(segments,
zorder=0,
cmap=plt.get_cmap('Greys'),
norm=plt.Normalize(0, .7 * values.max()))
lc.set_array(values)
lc.set_linewidths(15 * values)
ax.add_collection(lc)
# Add a label to each node. The challenge here is that we want to
# position the labels to avoid overlap with other labels
for index, (name, label,
(x, y)) in enumerate(zip(names, labels, embedding.T)):
dx = x - embedding[0]
dx[index] = 1
dy = y - embedding[1]
dy[index] = 1
this_dx = dx[np.argmin(np.abs(dy))]
this_dy = dy[np.argmin(np.abs(dx))]
if this_dx > 0:
horizontalalignment = 'left'
x = x + .002
else:
horizontalalignment = 'right'
x = x - .002
if this_dy > 0:
verticalalignment = 'bottom'
y = y + .002
else:
verticalalignment = 'top'
y = y - .002
plt.text(x,
y,
name,
size=22,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
bbox=dict(facecolor='w',
edgecolor=plt.cm.spectral(label / float(n_labels)),
alpha=.6))
plt.xlim(
embedding[0].min() - .25 * embedding[0].ptp(),
embedding[0].max() + .20 * embedding[0].ptp(),
)
plt.ylim(embedding[1].min() - .20 * embedding[1].ptp(),
embedding[1].max() + .20 * embedding[1].ptp())
plt.savefig('Graphs/StockCluster.pdf', bbox_inches='tight')
plt.savefig('Graphs/StockCluster.svg', bbox_inches='tight')
plt.show()
def StockMarketOLD():
###############################################################################
# Retrieve the data from Internet
# Choose a time period reasonnably calm (not too long ago so that we get
# high-tech firms, and before the 2008 crash)
d1 = datetime.datetime(2005, 1, 1)
d2 = datetime.datetime(2009, 12, 31)
# kraft symbol has now changed from KFT to MDLZ in yahoo
symbol_dict = {
'TOT': 'Total',
'XOM': 'Exxon',
'CVX': 'Chevron',
'COP': 'ConocoPhillips',
'VLO': 'Valero Energy',
'MSFT': 'Microsoft',
'IBM': 'IBM',
'TWX': 'Time Warner',
'CMCSA': 'Comcast',
#'CVC': 'Cablevision',
#'YHOO': 'Yahoo',
#'DELL': 'Dell',
'HPQ': 'HP',
'AMZN': 'Amazon',
'TM': 'Toyota',
'CAJ': 'Canon',
'MTU': 'Mitsubishi',
'SNE': 'Sony',
#'F': 'Ford',
'HMC': 'Honda',
#'NAV': 'Navistar',
'NOC': 'Northrop Grumman',
'BA': 'Boeing',
'KO': 'Coca Cola',
'MMM': '3M',
'MCD': 'Mc Donalds',
#'PEP': 'Pepsi',
'MDLZ': 'Kraft Foods',
'K': 'Kellogg',
'UN': 'Unilever',
'MAR': 'Marriott',
'PG': 'Procter Gamble',
'CL': 'Colgate-Palmolive',
'GE': 'General Electrics',
'WFC': 'Wells Fargo',
'JPM': 'JPMorgan Chase',
#'AIG': 'AIG',
'AXP': 'American Express',
'BAC': 'Bank of America',
'GS': 'Goldman Sachs',
'AAPL': 'Apple',
'SAP': 'SAP',
'CSCO': 'Cisco',
'TXN': 'Texas Instruments',
'XRX': 'Xerox',
#'LMT': 'Lookheed Martin',
'WMT': 'Wal-Mart',
'WBA': 'Walgreen',
'HD': 'Home Depot',
'GSK': 'GlaxoSmithKline',
'PFE': 'Pfizer',
'SNY': 'Sanofi-Aventis',
'NVS': 'Novartis',
'KMB': 'Kimberly-Clark',
'R': 'Ryder',
'GD': 'General Dynamics',
'RTN': 'Raytheon',
'CVS': 'CVS',
'CAT': 'Caterpillar',
'DD': 'DuPont de Nemours',
#'GM': 'General Motors',
#'GOOG' : 'Google',
'ORCL' : 'Oracle',
'NVO':'Novo Nordisk',
'LLY':'Eli Lilly and Company',
#'FB':'Facebook',
'MRK':'Merck Co',
}
'''
symbol_dict = {'Danske.CO':'Danske Bank',
'Maersk-B.CO':'Maersk',
'DSV.CO':'DSV',
'FLS.CO':'FLS',
'Gen.CO':'Genmab',
'TDC.CO':'TDC',
'CARL-B.CO':'Carlsberg',
'CHR.CO':'Chr Hansen',
'COLO-B.CO':'Coloplast',
'GN.CO':'GN Store Nord',
'NDA-DKK.co':'Nordea',
'Novo-B.co':'Novo Nordisk',
'NZYM-B.CO':'Novozymes',
'PNDORA.CO':'Pandora',
'Tryg.co':'Tryg',
'VWS.CO':'Vestas',
'WDH.CO':'William Demant',
'G4s.co':'G4S',
'JYSK.CO':'Jyske Bank',
'KBHL.CO':'Kobenhavns Lufthavne',
'RBREW.CO':'Royal Unibrew',
'ROCK-B.CO':'Rockwool',
'SYDB.CO':'Sydbank',
'TOP.CO':'Topdanmark',
#'ALMB.CO':'Alm Brand',
'AURI-B.CO':'Auriga',
'Bava.CO':'Bavarian Nordic',
'BO.CO':'Bang Olufsen',
'DFDS.CO':'DFDS',
'DNORD.CO':'DS Norden',
'GES.CO':'Greentech',
'IC.CO':'IC Group',
'JDAN.CO':'Jeudan',
#'JUTBK.CO':'Jutlander Bank',
#'MATAS.CO':'Matas',
'NKT.CO':'NKT',
#'NNIT.CO':'NNIT',
'NORDJB.CO':'Nordjyske Bank',
#'ONXEO.CO':'Onxeo',
#'OSSR.CO':'Ossur',
'PAAL-B.CO':'Per Aarslef',
'RILBA.CO':'Ringkobing Landbobank',
'SAS-DKK.CO':'SAS',
'SCHO.CO':'Schouw Co.',
'SIM.CO':'SimCorp',
'Solar-B.co':'Solar B',
'SPNO.CO':'Spar Nord',
'TIV.CO':'Tivoli',
'UIE.CO':'UIE',
'VELO.CO':'Veloxis',
'ZEAL.CO':'Zealand Pharma'
}
'''
symbols, names = np.array(list(symbol_dict.items())).T
for symbol in symbols:
print symbol
if len(pd.DataFrame(np.array([[q[5] for q in quotes_historical_yahoo(symbol,d1,d2,True,False)]]).T)) != 1259:
print symbol, len(pd.DataFrame(np.array([[q[5] for q in quotes_historical_yahoo(symbol,d1,d2,True,False)]]).T))
open = pd.DataFrame(np.array([[q[5] for q in quotes_historical_yahoo(symbol,d1,d2,True,False)] for symbol in symbols]).T)
close = pd.DataFrame(np.array([[q[6] for q in quotes_historical_yahoo(symbol,d1,d2,True,False)] for symbol in symbols]).T)
# The daily variations of the quotes are what carry most information
variation = np.array(close - open)
###############################################################################
# Learn a graphical structure from the correlations
#edge_model = covariance.GraphLassoCV()
# standardize the time series: using correlations rather than covariance
# is more efficient for structure recovery
df = pd.read_csv('data/TData9313_final5.csv',index_col=0)
X = variation.copy()
pd.DataFrame(np.round(np.cov(X.T),3),columns=symbols,index=symbols).to_latex('covariancetable.tex')
print np.max(np.round(np.cov(X.T),3))
X /= X.std(axis=0)
covariance_,precision_ = graphical_lasso(X,0.3)
print pd.DataFrame(precision_)
#edge_model.fit(X)
###############################################################################
# Cluster using affinity propagation
_, labels = cluster.affinity_propagation(covariance_)
n_labels = labels.max()
for i in range(n_labels + 1):
print('Cluster %i: %s' % ((i + 1), ', '.join(symbols[labels == i])))
###############################################################################
# Find a low-dimension embedding for visualization: find the best position of
# the nodes (the stocks) on a 2D plane
# We use a dense eigen_solver to achieve reproducibility (arpack is
# initiated with random vectors that we don't control). In addition, we
# use a large number of neighbors to capture the large-scale structure.
node_position_model = manifold.LocallyLinearEmbedding(
n_components=2, eigen_solver='dense', n_neighbors=6)
embedding = node_position_model.fit_transform(X.T).T
###############################################################################
# Visualization
plt.figure(1, facecolor='w', figsize=(20, 16))
plt.clf()
ax = plt.axes([0., 0., 1., 1.])
plt.axis('off')
plt.annotate('From %s to %s' % (d1.strftime('%Y-%m-%d'),d2.strftime('%Y-%m-%d')),xy=(0.11,-0.37),size=25)
print X.shape
for i in range(n_labels + 1):
plt.annotate('Cluster %i: %s' % ((i + 1), ', '.join(symbols[labels == i])),xy=(-0.43,0.02-i*0.02),size=18)
pass
# Display a graph of the partial correlations
#partial_correlations = edge_model.precision_.copy()
partial_correlations = precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)
# Plot the nodes using the coordinates of our embedding
plt.scatter(embedding[0], embedding[1], s=200 * d ** 2, c=labels,
cmap=plt.cm.spectral)
# Plot the edges
start_idx, end_idx = np.where(non_zero)
#a sequence of (*line0*, *line1*, *line2*), where::
# linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[embedding[:, start], embedding[:, stop]]
for start, stop in zip(start_idx, end_idx)]
values = np.abs(partial_correlations[non_zero])
lc = LineCollection(segments,
zorder=0, cmap=plt.get_cmap('Greys'),
norm=plt.Normalize(0, .7 * values.max()))
lc.set_array(values)
lc.set_linewidths(15 * values)
ax.add_collection(lc)
# Add a label to each node. The challenge here is that we want to
# position the labels to avoid overlap with other labels
for index, (name, label, (x, y)) in enumerate(
zip(names, labels, embedding.T)):
dx = x - embedding[0]
dx[index] = 1
dy = y - embedding[1]
dy[index] = 1
this_dx = dx[np.argmin(np.abs(dy))]
this_dy = dy[np.argmin(np.abs(dx))]
if this_dx > 0:
horizontalalignment = 'left'
x = x + .002
else:
horizontalalignment = 'right'
x = x - .002
if this_dy > 0:
verticalalignment = 'bottom'
y = y + .002
else:
verticalalignment = 'top'
y = y - .002
plt.text(x, y, name, size=22,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
bbox=dict(facecolor='w',
edgecolor=plt.cm.spectral(label / float(n_labels)),
alpha=.6))
plt.xlim(embedding[0].min() - .25 * embedding[0].ptp(),
embedding[0].max() + .20 * embedding[0].ptp(),)
plt.ylim(embedding[1].min() - .20 * embedding[1].ptp(),
embedding[1].max() + .20 * embedding[1].ptp())
plt.savefig('Graphs/StockCluster.pdf',bbox_inches='tight')
plt.savefig('Graphs/StockCluster.svg',bbox_inches='tight')
plt.show()
