def on_modified(self, event):
new_name = "new_file"+ str(self.i) + ".txt"
for filename in os.listdir(folder_to_track):
file.exists= os.path.isfile(folder_destination + "/" + new_name)
while file_exsist:
self.i += 1
new_name = "new_file"+ str(self.i) + ".txt"
file_exsist = os.pathisfile(folder_destination + "/" + new_name)
src = folder_to_track + "/"+ filename
new_destination = folder_destination + "/" + new_name
os.rename(src, new_destination)
folder_to_track = "\Users\Åmar\Desktop\myFolder"
folder_destination = "\Users\Åmar\Desktop\myFolder"
EventHandler = Myhandler()
Observer=Observer()
Observer.Schedule(Event_Handler, folder_to_track, recursive = True)
Observer.start
try:
while True:
time.sleep(10)
except KeyboardInterrupt.
def __init__(
self,
filename,
seq_len=8 * 24, # 7 days of history and 1 day to predict
step=1,
avg_size=None, # no average
avg_stride=None,
norm_len=7 * 24, # normalize wrt the 7 days of history
fraction=0.95,
):
basename = os.path.splitext(os.path.basename(filename))[0]
train_dataset = os.path.join("dataset", basename + "_train.json")
test_dataset = os.path.join("dataset", basename + "_test.json")
if os.path.isfile(train_dataset) and os.pathisfile(test_dataset):
print("Found dataset files")
else:
print("Dataset files not found. Creating datasets")
dataset = self.process_data(filename, seq_len, step, avg_size,
avg_stride, norm_len)
train, test = self.split_dataset(dataset, fraction, shuffle=True)
# TODO: save train and test
self.train = CryptoData(train)
self.test = CryptoData(test)
def dea_full_data_update(chk):
"""
update deahk search database
input: chk --- whether to request full data update: chk == 1:yes
output: <deposit_dir>/Deahk/<group>/<msid>_full_data_<year>fits
"""
tyear = int(float(time.strftime("%Y", time.gmtime())))
cmd = 'ls ' + data_dir + 'Deahk_*/*_week_data.fits > ' + zspace
os.system(cmd)
data = mcf.read_data_file(zspace, remove=1)
for ent in data:
atemp = re.split('\/', ent)
group = atemp[-2]
btemp = re.split('_', atemp[-1])
msid = btemp[0]
print("MSID: " + str(msid) + ' in ' + group)
[cols, tbdata] = ecf.read_fits_file(ent)
time = tbdata['time']
tdata = tbdata[msid]
cols = ['time', msid]
#
#--- regular data update
#
if chk == 0:
#
#--- normal daily data update
#
ofits = deposit_dir + 'Deahk_save/' + group + '/' + msid + '_full_data_'
ofits = ofits + str(tyear) + '.fits'
if os.pathisfile(ofits):
ltime = ecf.find_the_last_entry_time(ofits)
ctime = str(tyear + 1) + ':001:00:00:00'
nchk = 0
#
#--- if the data is over the year boundray, fill up the last year and create a new one for the new year
#
else:
ofits = deposit_dir + 'Deahk_save/' + group + '/' + msid
ofits = ofits + '_full_data_' + str(tyear - 1) + '.fits'
nfits = deposit_dir + 'Deahk_save/' + group + '/' + msid
nfits = nfits + '_full_data_' + str(tyear) + '.fits'
ltime = ecf.find_the_last_entry_time(ofits)
ctime = str(tyear) + ':001:00:00:00'
nchk = 1
select = [(time > ltime) & (time < ctime)]
stime = time[select]
sdata = tdata[select]
cdata = [stime, sdata]
ecf.update_fits_file(ofits, cols, cdata)
if nchk > 0:
select = [time >= ctime]
stime = time[select]
sdata = tdata[select]
cdata = [stime, sdata]
ecf.create_fits_file(nfits, cols, cdata)
#
#--- start from beginning (year 1999)
#
else:
for year in range(1999, tyear + 1):
tstart = str(year) + ':001:00:00:00'
tstart = Chandra.Time.DateTime(tstart).secs
tstop = str(year + 1) + ':001:00:00:00'
tstop = Chandra.Time.DateTime(tstop).secs
select = [(time >= tstart) & (time < tstop)]
stime = time[select]
sdata = tdata[select]
cdata = [stime, sdata]
out = deposit_dir + 'Deahk_save/' + group + '/'
if not os.path.isdir(out):
cmd = 'mkdir ' + out
out = out + msid + '_full_data_' + str(year) + '.fits'
ecf.create_fits_file(out, cols, cdata)
except ImportError:
print("\nInstall SpeechRecognition to use this feature." + "\nStarting text mode\n")
if(args.gtts):
try:
from gtts import gTTS
from pygame import mixer
voice = "gTTS"
except ImportError:
import pyttsx
print("\nInstall gTTS and pygame to use this feature." + "\nUsing pyttsx\n")
else:
import pyttsx
kernel = amil.Kernel()
if os.pathisfile("bot_brain.brn"):
kernel.bootstrap(brainFile="bot_brain.brn")
else:
karnel.bootstrap(learnFiles="std-startup.xml", commands="load aiml b")
# kernel.saveBrain("bot_brain.brn")
# kernel now ready for use
while True:
if mode == "voice":
respond = listen()
else:
response = raw_input("Talk to Sarah:")
if response.lower()replace(" ","") in terminate:
break
Sarah_speech = kernel.respond(response)
print "Sarah:" + Sarah_speech