def CuSumPlots():
#=============================================================================
cusum_data = PD._cusum(data)
def change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col):
plt.subplot(row,col,plot_num)
plt.plot(dates,cusum_data[:,y_col])
date = np.int(np.floor(len(dates) * DateLine))
plt.axvline(dates[date], color='red')
plt.grid(True)
plt.title(headings[y_col])
plt.xticks(rotation = 90)
fig = plt.gcf()
fig.subplots_adjust(top=0.95)
fig.subplots_adjust(bottom=0.12)
fig.subplots_adjust(left=0.08)
fig.subplots_adjust(right=0.97)
ax = plt.gca()
ax.xaxis.label.set_color([0.75,0.75,0.75])
ax.tick_params(axis='x', colors=[0.75,0.75,0.75])
plt.figure('CuSum plots')
plt.clf()
row = 7
col = 1
DateLine = 0.85
y_col = 3
plot_num = 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 4
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 5
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 8
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 12
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 10
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
y_col = 11
plot_num = plot_num + 1
change(dates,headings,cusum_data,DateLine,row,col,plot_num,y_col)
ax = plt.gca()
ax.xaxis.label.set_color([0,0,0])
ax.tick_params(axis='x', colors=[0,0,0])
plt.show()
for i in range(len(data)):
if (data[i,2] > 0 and
data[i,2] > 0):
data1[m,] = data[i,]
ave_data1[m,] = ave_data[i,]
m = m + 1
data = np.zeros((m,a[1]))
ave_data = np.zeros((m,a[1]))
dates = range(m)
for i in range(m):
data[i,] = data1[i,]
ave_data[i,] = ave_data1[i,]
dates[i] = datetime.datetime(*xlrd.xldate_as_tuple(data[i,0],0))
cusum_data = PD._cusum(data)
"""
==============================================================================
=================================== PCA ======================================
"""
def PCA():
#=============================================================================
#select which elements of the "data" matirx you wish to analyse
data_set = [2,3,8,12,30,17,34,35]
#define the variable column for which predictors need to be identified
