def dtwrep():
print "-----DTW-patient------------"
labels=[random.choice(lab) for i in range(0,250) ]
p_data= np.stack([np.random.random((3000,4))]*250)
print "originaltransp",p_data.shape
distance_file="dist"
dtw_square_distance_matrix = distance_metric.dtw_distance_matrix(p_data, distance_file + '_dtw', recompute=True)
print 'matrix loaded'
Y_2 = mtsne.distance_tsne(dtw_square_distance_matrix, 2)
print labels
#Y_3 = mtsne.similarity_tsne(similarity_matrix, 3)
graph_plot.graf_with_scale('DTWDataRepeated',Y_2, labels)
#graph_plot.dump_graph(Y_3, labels,'eros_4v_'+patient,labels,3)
Y_2=[]
p_data=[]
labels=[]
def erosrep():
labels=[random.choice(lab) for i in range(0,500) ]
p_data= np.stack([np.random.random((3000,4))]*100)
print "originaltransp",p_data.shape
np.savetxt(similarity_file, eros.eros(p_data).reshape((len(p_data), 1, len(p_data))))
similarity_matrix = np.loadtxt(similarity_file)
print 'matrix loaded' #Y_2_d = mtsne.distance_tsne(similarity_matrix, 2)
Y_2_s = mtsne.similarity_tsne(similarity_matrix, 2)
print labels
#Y_3 = mtsne.similarity_tsne(similarity_matrix, 3)
graph_plot.graf_with_scale('erosDataRepeated',Y_2_s, labels)
#graph_plot.dump_graph(Y_3, labels,'eros_4v_'+patient,labels,3) eros_4v_zoomST7132J0
Y_2_s=[]
p_data=[]
labels=[]
def dtw(patient):
print "-----DTW-patient------------",patient
data,labels=connection.get_data_pp(patient,4)
print "original", data.shape
p_data=[ np.transpose(i) for i in data]
p_data=np.array(p_data)
print "originaltransp",p_data.shape
distance_file="dist"
dtw_square_distance_matrix = distance_metric.dtw_distance_matrix(p_data, distance_file + '_dtw', recompute=True)
print 'matrix loaded'
Y_2 = mtsne.distance_tsne(dtw_square_distance_matrix, 2)
print labels
#Y_3 = mtsne.similarity_tsne(similarity_matrix, 3)
graph_plot.graf_with_scale('Ldtw_4v_'+patient,Y_2, labels)
#graph_plot.dump_graph(Y_3, labels,'eros_4v_'+patient,labels,3)
Y_2=[]
p_data=[]
labels=[]
def eross(patient):
print "------patient------------",patient
data,labels=connection.get_data_pp(patient,4)
#data,labels=read_data(lab,files,3,10,3000)#connection.get_data_1v()#read_data(lab,files,1,20,3000)
print "original", data.shape
p_data=[ np.transpose(i) for i in data]
p_data=np.array(p_data)
print "originaltransp",p_data.shape
np.savetxt(similarity_file, eros.eros(p_data).reshape((len(p_data), 1, len(p_data))))
similarity_matrix = np.loadtxt(similarity_file)
print 'matrix loaded' #Y_2_d = mtsne.distance_tsne(similarity_matrix, 2)
Y_2_s = mtsne.similarity_tsne(similarity_matrix, 2)
print labels
#Y_3 = mtsne.similarity_tsne(similarity_matrix, 3)
graph_plot.graf_with_scale('Leros_4v_scaled'+patient,Y_2_s, labels)
#graph_plot.dump_graph(Y_3, labels,'eros_4v_'+patient,labels,3)
Y_2_s=[]
p_data=[]
labels=[]