def update(option):
global flag; global stock_list; global len_stock_list; global num_days
if option!="Clustering":
opt2.configure(state=NORMAL)
num.configure(state=DISABLED)
length.configure(state=NORMAL)
if flag:
stock_list, len_stock_list, num_days = LoadTimeseries.simple_scan(data_path)
opt2['menu'].delete(0, 'end')
for choice in stock_list:
opt2['menu'].add_command(label=choice, command=tk._setit(stock, choice))
label42.config(text='Length of timeseries (1 - '+str(num_days)+'):')
flag = False
label4.config(text='Number of timeseries:')
if option == "Prediction":
label4.config(text='Number of timeseries:')
steps.configure(state=NORMAL)
else:
steps.configure(state=DISABLED)
else:
opt2.configure(state=DISABLED)
num.configure(state=NORMAL)
length.configure(state=NORMAL)
stock_list, len_stock_list, num_days = LoadTimeseries.simple_scan(data_path)
label4.config(text='Number of timeseries (1 - '+str(len_stock_list)+'):')
label42.config(text='Length of timeseries (1 - '+str(num_days)+'):')
steps.configure(state=DISABLED)
def update(option):
global flag
global stock_list
global len_stock_list
global num_days
if option != "Clustering":
opt2.configure(state=NORMAL)
num.configure(state=DISABLED)
length.configure(state=NORMAL)
if flag:
stock_list, len_stock_list, num_days = LoadTimeseries.simple_scan(
data_path)
opt2['menu'].delete(0, 'end')
for choice in stock_list:
opt2['menu'].add_command(label=choice,
command=tk._setit(stock, choice))
label42.config(text='Length of timeseries (1 - ' + str(num_days) +
'):')
flag = False
label4.config(text='Number of timeseries:')
if option == "Prediction":
label4.config(text='Number of timeseries:')
steps.configure(state=NORMAL)
else:
steps.configure(state=DISABLED)
else:
opt2.configure(state=DISABLED)
num.configure(state=NORMAL)
length.configure(state=NORMAL)
stock_list, len_stock_list, num_days = LoadTimeseries.simple_scan(
data_path)
label4.config(text='Number of timeseries (1 - ' + str(len_stock_list) +
'):')
label42.config(text='Length of timeseries (1 - ' + str(num_days) +
'):')
steps.configure(state=DISABLED)
def _Go():
global flag
global num_days
global length
global steps
global num_days
global f
global sa
global plots
global curr_pos
sel_task = task.get()
if (data_path == None):
display_error(1)
elif (sel_task == 'None'):
display_error(2)
elif (sel_task == 'Summarize'):
curr_pos = 0
plots = []
name = str(stock.get())
if name != "None":
lot = length.get()
if lot == "":
display_error(3)
else:
lot = int(lot)
if type(lot) != int:
display_error(4)
elif lot <= 0:
display_error(5)
if lot != "" and type(int(lot)) == int and int(lot) > 0:
data = LoadTimeseries.read_p_timeseries(data_path, name, lot)
original, summarized = Summarize.summarize(data)
plt.clf()
#yearsFmt = mdates.DateFormatter('%b %Y')
f = plt.figure(figsize=(6, 6), dpi=100, facecolor='white')
plt.subplot(411)
plt.title("Initial Timeserie")
original[name].ix[original[name].index].plot(style='b')
## print(summarized[name]["extremas-x"])
## tps = []
## tps_ind = []
## for i in range(len(original[name])):
## if i in summarized[name]["extremas-x"]:
## tps.append(original[name][i])
## tps_ind.append(original[name].index[i])
## print(tps_ind)
## print(tps)
## f = Series(tps, index=tps_ind).ix[tps_ind].plot(style='r',marker='o', linestyle="", markersize=7)
plt.subplot(412)
plt.title("Overall Timeserie Trend")
plt.plot(summarized[name]["trend"]["x"],
summarized[name]["trend"]["r"], 'g')
plt.xlim([0, lot])
plt.subplot(413)
plt.title("Turning Points")
plt.plot(summarized[name]["extremas-x"],
summarized[name]["extremas"],
'o',
mfc='none',
markersize=7)
plt.xlim([0, lot])
plt.subplot(414)
plt.title("Seasonality and Cycle")
plt.plot(summarized[name]["Ds-x"], summarized[name]["Ds"], 'r')
plt.xlim([0, lot])
plt.tight_layout()
canvas.figure = f
canvas.draw()
else:
display_error(6)
elif sel_task == 'Clustering':
n = num.get()
lot = length.get()
if n == "":
display_error(7)
else:
n = int(n)
if type(n) != int:
display_error(8)
elif n <= 0:
display_error(9)
if lot == "":
display_error(10)
else:
lot = int(lot)
if type(lot) != int:
display_error(11)
elif lot <= 0:
display_error(12)
if type(lot) == int and type(n) == int and lot > 0 and n > 0:
curr_pos = 0
plots = []
data, aDates = LoadTimeseries.read_c_timeseries(data_path, n, lot)
original, summarized = Summarize4Clustering.summarize(
data, aDates, lot)
plt.clf()
curr_pos = 0
plots = Clustering.cluster(original, summarized, n)
canvas.figure = plots[0]
canvas.draw()
elif sel_task == 'Prediction':
curr_pos = 0
plots = []
name = str(stock.get())
if name != "None":
lot = length.get()
sp = steps.get()
if sp == "":
display_error(13)
else:
sp = int(sp)
if type(sp) != int:
display_error(14)
elif sp <= 0:
display_error(15)
if lot != "":
lot = int(lot)
if type(lot) != int:
display_error(11)
elif lot <= 0:
display_error(12)
if lot + sp > num_days:
display_error(16)
if type(lot) == int and lot > 0 and type(
sp) == int and sp > 0 and lot + sp <= num_days:
data = LoadTimeseries.read_p_timeseries(
data_path, name, lot + sp)
original, summarized = Summarize4Prediction.summarize(data, sp)
orig_pre, orig_r_pre, sum_pre = Prediction.predict(
original, summarized[name]["Ds"], summarized[name]["AL"],
sp)
plt.clf()
# print(original)
f = plt.figure(figsize=(6, 6), dpi=100, facecolor='white')
original[name].ix[original[name].index[0]:].plot(
style='b', label="Actual")
orig_pre.ix[orig_pre.index[0]:].plot(style='r', label="ARMA")
orig_r_pre.ix[orig_r_pre.index[0]:].plot(style='purple',
label="r-ARMA")
sum_pre.ix[orig_pre.index[0]:].plot(style='g',
label="Summarized")
plt.legend(loc=2, prop={'size': 10})
canvas.figure = f
canvas.draw()
else:
display_error(3)
def _Go():
global flag;global num_days;global length;global steps;
global num_days;global f;global sa;global plots;global curr_pos
sel_task = task.get()
if (data_path == None):
display_error(1)
elif (sel_task == 'None'):
display_error(2)
elif (sel_task == 'Summarize'):
curr_pos = 0
plots = []
name = str(stock.get())
if name != "None":
lot = length.get()
if lot=="":
display_error(3)
else:
lot = int(lot)
if type(lot) != int:
display_error(4)
elif lot <= 0:
display_error(5)
if lot != "" and type(int(lot)) == int and int(lot) > 0:
data = LoadTimeseries.read_p_timeseries(data_path, name, lot)
original, summarized = Summarize.summarize(data)
plt.clf()
#yearsFmt = mdates.DateFormatter('%b %Y')
f = plt.figure(figsize=(6,6), dpi=100, facecolor='white')
plt.subplot(411)
plt.title("Initial Timeserie")
original[name].ix[original[name].index].plot(style='b')
## print(summarized[name]["extremas-x"])
## tps = []
## tps_ind = []
## for i in range(len(original[name])):
## if i in summarized[name]["extremas-x"]:
## tps.append(original[name][i])
## tps_ind.append(original[name].index[i])
## print(tps_ind)
## print(tps)
## f = Series(tps, index=tps_ind).ix[tps_ind].plot(style='r',marker='o', linestyle="", markersize=7)
plt.subplot(412)
plt.title("Overall Timeserie Trend")
plt.plot(summarized[name]["trend"]["x"],summarized[name]["trend"]["r"],'g')
plt.xlim([0,lot])
plt.subplot(413)
plt.title("Turning Points")
plt.plot(summarized[name]["extremas-x"], summarized[name]["extremas"], 'o', mfc='none', markersize=7)
plt.xlim([0,lot])
plt.subplot(414)
plt.title("Seasonality and Cycle")
plt.plot(summarized[name]["Ds-x"], summarized[name]["Ds"], 'r')
plt.xlim([0,lot])
plt.tight_layout()
canvas.figure = f
canvas.draw()
else:
display_error(6)
elif sel_task == 'Clustering':
n = num.get()
lot = length.get()
if n == "":
display_error(7)
else:
n = int(n)
if type(n) != int:
display_error(8)
elif n <= 0:
display_error(9)
if lot == "":
display_error(10)
else:
lot = int(lot)
if type(lot) != int:
display_error(11)
elif lot <= 0:
display_error(12)
if type(lot) == int and type(n) == int and lot > 0 and n > 0:
curr_pos = 0
plots = []
data, aDates = LoadTimeseries.read_c_timeseries(data_path, n, lot)
original, summarized = Summarize4Clustering.summarize(data, aDates, lot)
plt.clf()
curr_pos = 0
plots = Clustering.cluster(original, summarized, n)
canvas.figure = plots[0]
canvas.draw()
elif sel_task == 'Prediction':
curr_pos = 0
plots = []
name = str(stock.get())
if name != "None":
lot = length.get()
sp = steps.get()
if sp == "":
display_error(13)
else:
sp = int(sp)
if type(sp) != int:
display_error(14)
elif sp <= 0:
display_error(15)
if lot != "":
lot = int(lot)
if type(lot) != int:
display_error(11)
elif lot <= 0:
display_error(12)
if lot+sp > num_days:
display_error(16)
if type(lot) == int and lot > 0 and type(sp) == int and sp > 0 and lot+sp<=num_days:
data = LoadTimeseries.read_p_timeseries(data_path, name, lot+sp)
original, summarized = Summarize4Prediction.summarize(data,sp)
orig_pre, orig_r_pre, sum_pre = Prediction.predict(original, summarized[name]["Ds"], summarized[name]["AL"], sp)
plt.clf()
# print(original)
f = plt.figure(figsize=(6,6), dpi=100, facecolor='white')
original[name].ix[original[name].index[0]:].plot(style='b', label="Actual")
orig_pre.ix[orig_pre.index[0]:].plot(style='r',label="ARMA")
orig_r_pre.ix[orig_r_pre.index[0]:].plot(style='purple',label="r-ARMA")
sum_pre.ix[orig_pre.index[0]:].plot(style='g', label="Summarized")
plt.legend(loc=2,prop={'size':10})
canvas.figure = f
canvas.draw()
else:
display_error(3)
