def get_details(html):
soup=BeautifulSoup(html)
#得到作者、作者链接、微博正文
div_content=soup.find_all(attrs={'class': 'content clearfix'})
#得到发微博时间
div_time=soup.find_all(attrs={'class':'feed_from W_textb'})
#将用户名称，用户主页地址、微博正文、发微博时间初始化
nick_name=[]
nickname_href=[]
content_text=[]
time=[]
#print get_content[0]
for i in range(len(div_content)):
#查找a标签
a_tag=div_content[i].find('a')
nick_name.append(a_tag.get('nick-name'))
nickname_href.append(a_tag.get('href'))
#查找p标签
p_tag=div_content[i].find('p')
content_text.append(p_tag.get_text())
#得到发微博时间
for j in range(len(div_time)):
a_time=div_time[j].find('a')
time.append(a_time.get('title'))
return (nick_name,nickname_href,content_text,time)
def diffuseave(self):
self.difffile=open('diffusion.xvg','w')
diffave=[]
diffave2=[]
for i in range(self.diffmax):
diffave.append(0.)
diffave2.append(0.)
for iatom in self.atoms:
iatom.diffusecalc(self.diffmax)
for i in range(self.diffmax):
diffave[i]+=iatom.diffusion[i]
diffave2[i]+=iatom.diffusion2[i]
for i in range(self.diffmax):
diffave[i]/=float(self.NAtom)
diffave2[i]/=float(self.NAtom)
print>>self.difffile,'#Mean square displacement of all atoms'
print>>self.difffile,'#Time <r2>'
print>>self.difffile,'@ title \"Mean square displacement\"'
print>>self.difffile,'@ xaxis label \"Time (reduced time units)\"'
print>>self.difffile,'@ yaxis label \"Mean square displacement (sigma^2)\"'
for i in range(self.diffmax):
print>>self.difffile,'%8.4lf %8.4lf'%(i*self.Step,diffave2[i])
self.difffile.close()
#Fit linear regression line to <r^2>=6Dt
time=[]
for i in range(self.diffmax):
time.append(i*self.Step)
slope,intercept,r2=regression(time,diffave2)
print 'displace^2 vs t: slope=%lf intercept=%lf R2=%lf'\
%(slope,intercept,r2)
self.diffconst=slope/6.
print 'Diffusion constant from Einstein relation=%lf (reduced units)'%(self.diffconst)
diffusion_cgs=self.diffconst*0.1344*self.Step*100000.
print 'Diffusion constant from Einstein relation=%lf (x10-5 cm^2/sec)'%(diffusion_cgs)
def lastUpdate(self):
time = [self.parser.source.time()]
h = inspect.getmro(self.__class__)
h = h[:-3]
for c in h:
time.append(int(os.path.getmtime(inspect.getfile(c))+.5))
return max(time)
def seconds_to_human_time(cls, seconds, suffixes=['y','w','d','h','m','s'], add_s=False, separator=' '):
"""
convert seconds to human time
"""
# the formatted time string to be returned
time = []
# the pieces of time to iterate over (days, hours, minutes, etc)
# - the first piece in each tuple is the suffix (d, h, w)
# - the second piece is the length in seconds (a day is 60s * 60m * 24h)
parts = [(suffixes[0], 60 * 60 * 24 * 7 * 52),
(suffixes[1], 60 * 60 * 24 * 7),
(suffixes[2], 60 * 60 * 24),
(suffixes[3], 60 * 60),
(suffixes[4], 60),
(suffixes[5], 1)]
# for each time piece, grab the value and remaining seconds, and add it to
# the time string
for suffix, length in parts:
value = seconds / length
if value > 0:
seconds = seconds % length
time.append('%s%s' % (str(value),
(suffix, (suffix, suffix + 's')[value > 1])[add_s]))
if seconds < 1:
break
return separator.join(time)
def flux2011( ) :
time = []
flux = []
fin = open( "fluxes.txt", "r" )
fout = open( "fluxVsTime", "w" )
for line in fin :
a = line.split()
if len(a) == 2 :
time.append( 24.*float(a[0]) ) # dechrs
flux.append( float(a[1]) ) # flux
# ... create decimal time array
t = numpy.array( time, dtype=float )
print t
# ... retrieve tsys, rmspath, tau230, elev arrays
rmspath = getVar( "rmspath.log", t )
tau230 = getVar( "tau230.log", t )
source = getSource ( "uvindex.log", t )
elevarray = getVar15( "elev.log", t )
# ... use elev of C1 as the elevation
elev = numpy.empty( (len(time)), dtype=float )
for n in range (0, len(time) ) :
elev[n] = elevarray[n][0]
# ... all array lengths should match, otherwise this routine will crash!
print len(time), len(flux), len(rmspath), len(tau230), len(source), len(elev)
fout.write("# UThrs S(Jy) el tau path source\n")
for n in range (0, len(time)) :
fout.write("%10.5f %6.3f %4.1f %4.2f %4.0f %s\n" % (t[n], flux[n], elev[n], tau230[n], rmspath[n], source[n] ))
fin.close()
fout.close()
def read_coft():
try:
with open('COFT','r'):
f = open('COFT','r')
line = f.readline()
s = line.split(',')
time = []
flux_1 = []
flux_2 = []
flux_3 = []
while line:
time.append(float(s[1]))
flux_1.append(float(s[3]))
flux_2.append(float(s[4]))
flux_3.append(float(s[5]))
line = f.readline()
s = line.split(',')
t = np.asarray(time)
f1 = np.asarray(flux_1)
f2 = np.asarray(flux_2)
f3 = np.asarray(flux_3)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(t, f1, label='gas flow')
ax.plot(t, f2, label='liquid flow')
ax.plot(t, f3, label='total flow')
ax.legend()
plt.show()
except IOError:
print "COFT WAS NOT GENERATED"
return 0
def fivePinjun(self,action):
#datetime.datetime.strptime("12:10:20",'%H:%M:%S')
#date_time = datetime.datetime.strptime("2012-10-20",'%Y-%m-%d')
#.resample(rule="5M",how="mean")
#time=pandas.tseries.index.DatetimeIndex
time=[]
for tab in xrange(len(self.df["Time"])):
#print datetime.datetime.strptime(self.df["Date"][tab]+' '+self.df["Time"][tab],'%Y-%m-%d %H:%M:%S')
time.append(datetime.datetime.strptime(self.df["Date"][tab]+' '+self.df["Time"][tab],'%Y-%m-%d %H:%M:%S'))
#time=pandas.PeriodIndex(time,freq='S')
ts=Series(np.array(self.df[self.df.columns[self.columnChooseIndex[0]+6]]), index=time)
#self.ts1=pandas.DataFrame({"DateTime":ts.index,self.df.columns[self.columnChooseIndex[0]+6]:ts})
temps1=ts.resample("5Min")
self.ts1=pandas.DataFrame({"DateTime":temps1.index,self.df.columns[self.columnChooseIndex[0]+6]:temps1})
self.dataModel = DataFrameModel()
self.dtableView.setModel(self.dataModel)
self.dataModel.setDataFrame(self.ts1)
self.dataModel.signalUpdate()
self.dtableView.resizeColumnsToContents()
self.dtableView.show()
self.isProcess=True
def Process(self):
process=Process(self)
process.exec_()
time=[]
PP=process.combine.split('\\')
for tab in xrange(len(self.df["Time"])):
time.append(datetime.datetime.strptime(self.df["Date"][tab]+' '+self.df["Time"][tab],'%Y-%m-%d %H:%M:%S'))
#time=pandas.PeriodIndex(time,freq='S')
ts=Series(np.array(self.df[self.df.columns[self.columnChooseIndex[0]+6]]), index=time)
#self.ts1=pandas.DataFrame({"DateTime":ts.index,self.df.columns[self.columnChooseIndex[0]+6]:ts})
#temps1=eval("ts."+str(PP[0])+'.'+'("'+str(PP[1])+'")')
if str(PP[0])=="resample":
temps1=ts.resample(str(PP[1]))
self.ts1=pandas.DataFrame({"DateTime":temps1.index,self.df.columns[self.columnChooseIndex[0]+6]:temps1})
self.dataModel = DataFrameModel()
self.dtableView.setModel(self.dataModel)
self.dataModel.setDataFrame(self.ts1)
self.dataModel.signalUpdate()
self.dtableView.resizeColumnsToContents()
self.dtableView.show()
self.isProcess=True
def elapsedTime(seconds, suffixes=["y", "w", "d", "h", "m", "s"], add_s=False, separator=" "):
"""
Takes an amount of seconds and turns it into a human-readable amount of time.
"""
# the formatted time string to be returned
if seconds == 0:
return "0s"
time = []
# the pieces of time to iterate over (days, hours, minutes, etc)
# - the first piece in each tuple is the suffix (d, h, w)
# - the second piece is the length in seconds (a day is 60s * 60m * 24h)
parts = [
(suffixes[0], 60 * 60 * 24 * 7 * 52),
(suffixes[1], 60 * 60 * 24 * 7),
(suffixes[2], 60 * 60 * 24),
(suffixes[3], 60 * 60),
(suffixes[4], 60),
(suffixes[5], 1),
]
# for each time piece, grab the value and remaining seconds, and add it to
# the time string
for suffix, length in parts:
value = seconds / length
if value > 0:
seconds = seconds % length
time.append("%s%s" % (str(value), (suffix, (suffix, suffix + "s")[value > 1])[add_s]))
if seconds < 1:
break
return separator.join(time)
def stop_test():
global record_data
global data
record_data = False
speed = []
time = []
verticalSpeed = []
pitch = []
altitude = []
for i in range(0,len(data)):
speed.append( data[i]['speed'])
time.append( data[i]['time'])
verticalSpeed.append(data[i]['verticalSpeed'])
pitch.append(data[i]['pitch'])
altitude.append(data[i]['altitude'])
plotting.plot_chart(time,speed,"time-speed")
plotting.plot_chart(time,verticalSpeed,"time-verticalSpeed")
plotting.plot_chart(time,pitch,"time-pitch")
plotting.plot_chart(time,altitude,"time-altitude")
def plot_save(af_stat_file, plot_path):
#at_time(sec), nonNLP_percentage, insect_percentage, poison_percentage, insecticide_percentage
time = []
non_nlp = []
insect = []
poison = []
insecticide = []
line_no = 0
#Expected CSV format
with open(af_stat_file) as f:
for line in f:
if line_no == 0:
line_no = line_no + 1
continue
line = [x.strip() for x in line.split(',') ]
time.append(float(line[0]))
non_nlp.append(float(line[1]))
insect.append(float(line[2]))
poison.append(float(line[3]))
insecticide.append(float(line[4]))
plt.plot(time, insect)
plt.plot(time, poison)
plt.plot(time, insecticide)
plt.plot(time, non_nlp)
plt.legend(['Insect', 'Poison', 'Insecticide', 'Non-nlp'], loc='best')
plt.ylabel('Percentage in AF')
plt.xlabel('Time(sec)')
plt.savefig(plot_path+"/plot.png")
plt.clf()
def getdrift_raw(filename,id3,interval,datetime_wanted):
# range_time is a number,unit by one day. datetime_wanted format is num
d=ml.load(filename)
lat1=d[:,8]
lon1=d[:,7]
idd=d[:,0]
year=[]
for n in range(len(idd)):
year.append(str(idd[n])[0:2])
h=d[:,4]
day=d[:,3]
month=d[:,2]
time1=[]
for i in range(len(idd)):
time1.append(date2num(datetime.datetime.strptime(str(int(h[i]))+' '+str(int(day[i]))+' '+str(int(month[i]))+' '+str(int(year[i])), "%H %d %m %y")))
idg1=list(ml.find(idd==id3))
idg2=list(ml.find(np.array(time1)<=datetime_wanted+interval/24))
"'0.25' means the usual Interval, It can be changed base on different drift data "
idg3=list(ml.find(np.array(time1)>=datetime_wanted-0.1))
idg23=list(set(idg2).intersection(set(idg3)))
# find which data we need
idg=list(set(idg23).intersection(set(idg1)))
print 'the length of drifter data is '+str(len(idg)),str(len(set(idg)))+' . if same, no duplicate'
lat,lon,time=[],[],[]
for x in range(len(idg)):
lat.append(round(lat1[idg[x]],4))
lon.append(round(lon1[idg[x]],4))
time.append(round(time1[idg[x]],4))
# time is num
return lat,lon,time
def readInVariable():
gc.disable()
print("In readInVariable() function")
wb = load_workbook('resources/dbMetrics.xlsx', use_iterators = True, read_only = True)
ws = wb.get_sheet_by_name("metrics")
numRows = ws.get_highest_row()
#print(numRows)
date = []
time = []
RCMPL = []
Unblocked = []
timeInitial = datetime.datetime.now()
#print(numRow)
#print(ws.iter_rows('A2:S'+str(numRows)))
ws_iter = tuple(ws.iter_rows('A2:D'+str(numRows)))
#print("11111")
#print(type(ws_iter))
i = 0
j= 1
for row in ws_iter:
#if(i%500 == 0):
#print(i, datetime.datetime.now()-timeInitial)
for cell in row:
if j == 1:
date.append(cell.value)
elif j == 2:
time.append(cell.value)
elif j == 3:
RCMPL.append(cell.value)
elif j == 4:
Unblocked.append(cell.value)
j = j+1
j = 1
print("Length of date ",len(date), len(RCMPL))
def elapsed_time(self, seconds, suffixes=['y','w','d','h','m','s'], add_s=False, separator=''):
"""
Takes an amount of seconds and turns it into a human-readable amount
of time.
From http://snipplr.com/view.php?codeview&id=5713
"""
# the formatted time string to be returned
time = []
# the pieces of time to iterate over (days, hours, minutes, etc)
# - the first piece in each tuple is the suffix (d, h, w)
# - the second piece is the length in seconds (a day is 60s * 60m * 24h)
parts = [(suffixes[0], 60 * 60 * 24 * 7 * 52),
(suffixes[1], 60 * 60 * 24 * 7),
(suffixes[2], 60 * 60 * 24),
(suffixes[3], 60 * 60),
(suffixes[4], 60),
(suffixes[5], 1)]
# for each time piece, grab the value and remaining seconds, and add it to
# the time string
for suffix, length in parts:
value = seconds // length
if value > 0:
seconds = seconds % length
time.append('%s%s' % (str(value),
(suffix, (suffix, suffix + 's')[value > 1])[add_s]))
if seconds < 1:
break
return separator.join(time)
def float_float(T,t,n,T1,N):
num = T/t
num = int(num)
print num
conc = []
time = []
for i in range(0,num+1):
print('input concn at time')
print i*t
conc.append(float(raw_input('')))
time.append(i*t)
print conc
print time
#time.sleep(15)
m=[[]]
m[0].extend([conc[0],time[0]])
for i in range(1, num+1):
m.append([conc[i],time[i]])
# print tc
# conc_time.append(tc)
print m
def graph_axis_trajectory(axis, pdf_name):
time = []
pos = []
vel = []
acc = []
for t in axis:
time.append(t[0])
pos.append(t[1])
vel.append(t[2])
acc.append(t[3])
fig = plt.figure()
fig.clf()
ax1 = fig.add_subplot(311)
ax1.plot(time, pos)
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Position (m)')
ax2 = fig.add_subplot(312)
ax2.plot(time, vel)
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('Velocity (m/s)')
ax3 = fig.add_subplot(313)
ax3.plot(time, acc)
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Acceleration (m/s^2)')
fig.savefig((pdf_name + ".pdf"))
def smooth_values(timestamps, values, totals, radius):
"""
Sliding window
>>> t = [dt(2011, 01, 20, 0, 0), dt(2011, 01, 21, 0, 0), \
dt(2011, 01, 22, 0, 0), dt(2011, 01, 23, 0, 0), \
dt(2011, 01, 28, 0, 0), dt(2011, 01, 30, 0, 0), \
dt(2011, 01, 31, 0, 0)]
>>> v = [1,2,3,4,5,6,7]
>>> tot = [2,3,4,5,6,7,8]
>>> smooth_values(t, v, tot, 3)
([datetime.datetime(2011, 1, 20, 0, 0), datetime.datetime(2011, 1, 20, 12, 0), datetime.datetime(2011, 1, 21, 0, 0), datetime.datetime(2011, 1, 22, 0, 0), datetime.datetime(2011, 1, 24, 8, 0), datetime.datetime(2011, 1, 27, 0, 0), datetime.datetime(2011, 1, 29, 16, 0), datetime.datetime(2011, 1, 30, 12, 0), datetime.datetime(2011, 1, 31, 0, 0)], [1, 3, 6, 9, 12, 15, 18, 13, 7], [2, 5, 9, 12, 15, 18, 21, 15, 8])
"""
time = []
ser = []
tot = []
k = radius / 2
for i in range(-(radius / 2 + 1), len(timestamps) - (radius / 2) + 1):
v = i if i > 0 else 0
time.append(dt_average(timestamps[v:v + k]))
ser.append(sum(values[v:v + k]))
tot.append(sum(totals[v:v + k]))
if k < radius:
k += 1
return time, ser, tot
def getGains( infile ) :
time = []
gain = []
p= subprocess.Popen( ( shlex.split('gplist vis=%s options=all' % infile) ), \
stdout=subprocess.PIPE,stdin=subprocess.PIPE,stderr=subprocess.STDOUT)
result = p.communicate()[0]
lines = result.split("\n")
ngains = (len(lines) - 3)/23
# caution: this presumes 23 antennas will be listed for each time, and that
# gains output has 3 header lines (and one blank line at the end ?)
gainComplex = numpy.zeros( (ngains,15), dtype=complex )
ng = -1
for n in range(3, len(lines) ) :
a = lines[n].split()
if ( (len(a) > 0) and (a[0] != "Ant") ) :
ng = ng + 1
time.append( a[0] )
nant = int(a[2])
if ( nant != 1 ) :
print "getGains error - unexpected ant number"
gainComplex[ng][nant-1] = float(a[5]) + 1j * float(a[6])
elif ( len(a) > 0 ) :
nant = int(a[1])
if ( nant < 16 ) :
gainComplex[ng][nant-1] = float(a[4]) + 1j * float(a[5])
return [time, gainComplex]
def readfire(s): # reads the output of a fire file and # returns the time vector and the population vector # input file should have two columns: first column is time in generations # second column is population time = [] pop = [] with open(s, 'r') as input_file: for line in input_file: temp = line.strip() if 'f()' in temp: time = [] pop = [] temp = line.strip() a,b = temp.split() time.append(float(a)) pop.append(float(b)) #with open(s, 'r') as input_file: #throwaway = input_file.readline() #while throwaway.strip() != 'START HERE': #throwaway = input_file.readline() #for line in input_file: #print 'hello' #temp = line.strip() #a,b = temp.split() #time.append(float(a)) #pop.append(float(b)) #print a, b print 'readfire is done' return [time, pop]
def format_time(timespan, precision=3):
"""Formats the timespan in a human readable form"""
if timespan >= 60.0:
# we have more than a minute, format that in a human readable form
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
time = []
leftover = timespan
for suffix, length in parts:
value = int(leftover / length)
if value > 0:
leftover = leftover % length
time.append('{0}{1}'.format(str(value), suffix))
if leftover < 1:
break
return " ".join(time)
# Unfortunately the unicode 'micro' symbol can cause problems in
# certain terminals.
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
# Try to prevent crashes by being more secure than it needs to
# E.g. eclipse is able to print a mu, but has no sys.stdout.encoding set.
units = ["s", "ms", 'us', "ns"] # the save value
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
'\xb5'.encode(sys.stdout.encoding)
units = ["s", "ms", '\xb5s', "ns"]
except Exception:
pass
scaling = [1, 1e3, 1e6, 1e9]
if timespan > 0.0:
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
else:
order = 3
return "{1:.{0}g} {2}".format(precision, timespan * scaling[order],
units[order])
def run_experiment(mode, cmd, t):
file.write(" ".join(cmd) + "\n")
time = []
timeout = 120
if mode == "stm" or mode == "gen":
timeout = 1200
for i in range(t):
while (True) :
out = timeout_command(cmd, timeout)
if out == None:
print "Timed out ", cmd
continue
r = re.search("(?<=Time = )[0-9]*\.[0-9]*", out)
if r == None:
print "retry ", cmd
continue
out = r.group(0);
break
time.append(float(out))
file.write(out + " ")
file.write("\n")
file.write("Std dev = " + "%.3f" % (numpy.std(time)))
file.write("\n")
file.write("min = " + "%.3f" % (numpy.amin(time)) + "\n")
file.write("max = " + "%.3f" % (numpy.amax(time)) + "\n")
return (numpy.median(time), numpy.amin(time), numpy.amax(time))
def done():
global time
time.append(datetime.datetime.now())
standup_time()
tabled()
post_message('Bye!')
reset()
def processingTimeByPhoto(serie): time = [] last = [] for key, track in serie.items(): time.append(track['pt1']) last = track['pt2'] time.append(last) return time
def gettrack_codar(lon_vel_list,lat_vel_list,u,v,startdate,hours,la,lo,uu,vv,q): # tracks particle at surface
# calculate the points near la,la
distance,index_lon,index_lat=nearxy(lon_vel_list,lat_vel_list,lo,la)
#index_startdate=0#get the index of startdate
# get u,v
u1=float(u[index_lat][index_lon])
v1=float(v[index_lat][index_lon])
if u1<=-998.0/100:# case of no good data
u1=uu[0]
v1=vv[0]
print "no good data in this lat lon at "+num2date(startdate+q+1).strftime("%d/%m/%y %H")+"h"
#nsteps=scipy.floor(min(numdays,jdmat_m_num[-1])/daystep)
# get the velocity data at this first time & place
lat_k='lat'+str(1)
lon_k='lon'+str(1)
uu,vv,lon_k,lat_k,time=[],[],[],[],[]
uu.append(u1)
vv.append(v1)
lat_k.append(la)
lon_k.append(lo)
time.append(startdate)
# first, estimate the particle move to its new position using velocity of previous time steps
lat1=lat_k[0]+float(vv[0]*3600)/1000/1.8535/60
lon1=lon_k[0]+float(uu[0]*3600)/1000/1.8535/60*(scipy.cos(float(la)/180*np.pi))
# find the closest model time for the new timestep
'''
jdmat_m_num_i=time[i-1]+float(1.0/24)
time.append(jdmat_m_num_i)
#print jdmat_m_num_i
index_startdate=index_startdate+1
#index_startdate=int(round(np.interp(jdmat_m_num_i,jdmat_m_num,range(len(jdmat_m_num)))))
#find the point's index of near lat1,lon1
index_location=nearxy(lon_vel,lat_vel,lon1,lat1)[1]
ui=u[index_startdate][index_location]
vi=v[index_startdate][index_location]
#if u1<>-999.0/100:# case of good data
vv.append(vi)
uu.append(ui)
# estimate the particle move from its new position using velocity of previous time steps
lat_k.append(float(lat1+lat_k[i-1]+float(vv[i]*3600)/1000/1.8535/60)/2)
lon_k.append(float(lon1+lon_k[i-1]+float(uu[i]*3600)/1000/1.8535/60*scipy.cos(float(lat_k[i])/180*np.pi))/2)
#else:
'''
# vv.append(0)
# uu.append(0)
# estimate the particle move from its new position using velocity of previous time steps
# lat_k.append(float(lat1))
# lon_k.append(float(lon1))
return lat1,lon1,time,uu,vv
def start():
global time
if len(time) != 0:
post_message('But we\'ve already started!')
return
time.append(datetime.datetime.now())
post_message('Let\'s get started! What did you work on yesterday? What are you working on today? What, if any, are your blockers?\nWhen you\'re done, please type !next')
next()
def done():
global time
time.append(datetime.datetime.now())
standup_time()
tabled()
post_summary()
post_message("Bye!")
reset()
def start():
global time
if len(time) != 0:
post_message("But we've already started!")
return
time.append(datetime.datetime.now())
post_message("Let's get started! %s\nWhen you're done, please type !next" % start_message)
next()
def retrival(self,objectIdArr):
num=[str(len(objectIdArr))]
time=[]
articles=[]
for objectId in objectIdArr:
res=self.mongodb[objectId[2]].find_one({'_id':objectId[1]})
time.append(res['time'])
articles.append(re.split(u'[\u3002\uff1b\uff01\uff1f\n]#PU ',res['article'].rstrip(' ')))
return num+time+articles
def start():
global time
if len(time) != 0:
post_message('But we\'ve already started!')
return
time.append(datetime.datetime.now())
post_message('Let\'s get started! %s\nWhen you\'re done, please type !next' % start_message)
next()
def _initPointDet(self):
if self._isPointDet and self._useMemoryCache:
data = []
time = []
for th5,datapath, timepath in zip(self._h5s,self._paths['data'],self._paths['time']):
data.append(th5[datapath[0]][:])
time.append(th5[timepath[0]][:])
self.fields = {'data':[data,time]}
def plot_VEQ_data(all_data):
'''
Plot VEQ data
Process raw string output from Vokaturi into array of audio emotions
Order: [Neutral, Happy, Sad, Angry, Fear][neg, neu, pos, compound, text]
'''
#Initialize all empty arrays
time = []
neutral = []
happy = []
sad = []
angry = []
fear = []
compound = []
#get all data into arrays
for i in all_data:
time.append(i[0])
neutral.append(i[1])
happy.append(i[2])
sad.append(i[3])
angry.append(i[4])
fear.append(i[5])
compound.append(i[9])
#Sanity printing
# print(time)
# print(neutral)
# print(happy)
# print(sad)
# print(angry)
# print(fear)
# print(compound)
trace_neutral = go.Scatter(x=time,
y=neutral,
name='Neutral',
line=dict(color=('rgb(140, 65, 244)'),
width=2.5,
dash='dash'))
trace_happy = go.Scatter(x=time,
y=happy,
name='Happy',
line=dict(color=('rgb(89, 244, 66)'),
width=2.5,
dash='dash'))
trace_sad = go.Scatter(x=time,
y=sad,
name='Sad',
line=dict(color=('rgb(65, 178, 244)'),
width=2.5,
dash='dash'))
trace_angry = go.Scatter(x=time,
y=angry,
name='Angry',
line=dict(color=('rgb(244, 65, 65)'),
width=2.5,
dash='dash'))
trace_fear = go.Scatter(x=time,
y=fear,
name='Fear',
line=dict(color=('rgb(244, 157, 65)'),
width=2.5,
dash='dash'))
trace_sentiment = go.Scatter(x=time,
y=compound,
name='Sentiment',
line=dict(color=('rgb(0, 0, 0)'), width=4))
data = [
trace_neutral, trace_happy, trace_sad, trace_angry, trace_fear,
trace_sentiment
]
# Edit the layout
layout = dict(
title='Audio Emotion over Session',
xaxis=dict(title='Time'),
yaxis=dict(title='Emotion Level'),
)
fig = dict(data=data, layout=layout)
py.offline.plot(fig, filename='audio_emotion.html')
def parse_detail_page(self, source):
html = etree.HTML(source)
preson_id = html.xpath(
"//div[@class='cnt f-brk']/a[@class='s-fc7']/@href")
song_name = "".join(
html.xpath("//div[@class='tit']/em[@class='f-ff2']/text()"))
singer = ''.join(
html.xpath(
"//div[@class='cnt']/p[1]/span/a[@class='s-fc7']/text()"))
album = ''.join(
html.xpath("//div[@class='cnt']/p[2]/a[@class='s-fc7']/text()"))
comment_sum = ''.join(
re.findall(r'<span class="j-flag">(.*?)</span>', source,
re.DOTALL))
self.music['album'] = album
# print(self.message)
# print('++'*30)
self.music['song_name'] = song_name
self.music['singer'] = singer
self.music['album'] = album
self.music['comment_sum'] = comment_sum
try:
# 插入数据
self.mysqlCommand.insert_musicData(self.music)
except Exception as e:
print("插入音乐数据失败", str(e)) # 输出插入失败的报错语句
# 获取点击量
points_tags = re.findall(r'<i class="zan u-icn2 u-icn2-12">(.*?)</a>',
source, re.DOTALL)
point = []
for i in points_tags:
point_rag = re.sub('</i> ', '', i)
point_rag = re.sub('</i>', '0', point_rag)
point.append(point_rag)
# 请求评论人详情页
for i in preson_id:
preson_url = "https://music.163.com" + i
try:
self.request_preson_page(preson_url, song_name)
except:
self.driver.close()
self.driver.switch_to.window(self.driver.window_handles[1])
name = html.xpath("//div[@class='cnt f-brk']/a[1]/text()")
comment_tags = re.findall(
r'<div class="cnt f-brk">.*?</a>(.*?)</div>.*?</a>(.*?)</div>',
source, re.DOTALL)
comments = []
for item in comment_tags:
comment = str()
for i in item:
comment_tag = re.sub('<br />', ' ', i)
comment_tag = re.sub('<(.*?)>', '', comment_tag)
if item.index(i) == 1 and comment_tag != '|回复':
comment_tag = '\n 评论回复' + comment_tag
comment += comment_tag
comment = comment.rstrip('|回复')
comment = ''.join(comment)
comment = '""' + comment + '""'
comments.append(comment)
time = []
times = html.xpath("//div[@class='time s-fc4']/text()")
for i in times:
time.append(i.replace(' ', ''))
# print('++' * 30)
for i in range(len(name)):
self.message['song_name'] = song_name
self.message['name'] = name[i]
self.message['comments'] = comments[i]
self.message['time'] = time[i]
self.message['point'] = point[i]
self.message['url'] = "https://music.163.com" + preson_id[i]
self.mysqlCommand.insert_messageData(self.message)
def generateCharts(totalPoints, totalIssues,
totalPullRequest, sprintsIssues,
assigneesIssues, statusIssues,
sprintsIssuesDevs,statusIssuesDevs,
sprintsPoints, assigneesPoints,
statusPoints, sprintsPointsDevs,
statusPointsDevs,
events, graphsURLs,
eventGraphsURLs, uploadImage=False):
startTimeAuthImgur = datetime.now()
print("AuthImgurTime")
print(startTimeAuthImgur)
try:
client = authenticateImgur()
except:
print("Imgur auth problem")
print("Final AuthImgurTime")
print(datetime.now() - startTimeAuthImgur)
startTimeGraphs = datetime.now()
print("GraphsTime")
print(startTimeGraphs)
#percents = [totalPoints, totalIssues, totalPullRequest]
try:
pieCharts = [sprintsIssues, sprintsPoints]
for pie in pieCharts:
pieChart(sorted(pie.items(), key=operator.itemgetter(0)))
if uploadImage == True:
image = client.upload_from_path('C:\\Users\\vinic\\Projects\\projet\\pieCharts\\foo.png', anon=False)
link_img = image['link']
print("Pie uploaded")
print(link_img)
graphsURLs.append(link_img)
print("Pie appended")
except:
print("Pie chart error!")
try:
barCharts = {'assignees':[assigneesIssues, assigneesPoints ],'status':[statusIssues, statusPoints]}
for key, value in barCharts.items():
print(value[0])
print(value[1])
barA = sorted(value[0].items(), key=operator.itemgetter(0))
barB = sorted(value[1].items(), key=operator.itemgetter(0))
barChart(barA, barB, "Tarefas", "Pontos")
if uploadImage == True:
image = client.upload_from_path('C:\\Users\\vinic\\Projects\\projet\\barCharts\\bar.png', anon=False)
link_img = image['link']
print("Bar uploaded")
print(link_img)
graphsURLs.append(link_img)
print("Bar appended")
except:
print("Bar chart error!")
#stackedBarCharts = [sprintsIssuesDevs, statusIssuesDevs, sprintsPointsDevs, statusPointsDevs]
#for stackedBar in stackedBarCharts:
# link_img = stackedBarChart(bar)
# image = client.upload_from_path('C:\\Users\\vinic\\Projects\\projet\\foo1.png', anon=False)
# link_img = image['link']
# print(link_img)
# graphsURLs.append(link_img)
try:
time = []
eventStatus = []
eventTag = []
print("events " + str(len(events)))
for issueEvent in events:
for eventID, eventData in issueEvent:
for eD in eventData:
print(eventID)
print(eventData)
if eventData['event'] == "labeled" or eventData['event'] == "unlabeled":
print(eventData['event'])
try:
print("Over here")
if (eventData['detail']['status'].rfind("-") != -1):
print("Status :" + str(status))
if int(eventData['detail']['status'].split(" - ")[0]) >= 0:
eventStatus.append(int(status.split(" - ")[0]))
else:
eventStatus.append(status)
date_string = str(eventData['created_at'])
print("Date " + str(date_string))
try:
int_time = int(time.mktime(datetime.strptime(date_string, "%Y-%m-%d %H:%M:%S").timetuple()))
print("int_time: " + str(int_time))
time.append(str(int_time))
except:
time.append(str(date_string))
print("here")
except:
print("Error map events to charts")
#eventTag.append(eventData)
#time.append(str(eventData['created_at']))
else:
print("!!!!!!")
print("Time:")
print(str(len(time)))
print("EventStatus")
print(str(len(eventStatus)))
print("EventTag")
print(str(len(eventTag)))
print("EventGraphsURLs")
print(str(len(eventGraphsURLs)))
try:
if len(time) > 0 and len(eventStatus) > 0:
print(time[0])
print(len(eventStatus))
lineChart(time, eventStatus)
print("Ploted")
if uploadImage == True:
image = client.upload_from_path('C:\\Users\\vinic\\Projects\\projet\\lineCharts\\simpleChart.png', anon=False)
print(image)
link_img = image['link']
print("Line uploaded")
print(link_img)
eventGraphsURLs.append(link_img)
print("Line appended")
except:
print("Simple chart error")
except:
print("Line chart error!")
print("Final graficos")
print(datetime.now() - startTimeGraphs)
return z
for i in range(1,NSum+1):
with nidaqmx.Task() as task:
task.ai_channels.add_ai_voltage_chan("Dev1/ai{0}".format(port1))
task.ai_channels.add_ai_voltage_chan("Dev1/ai{0}".format(port2))
task.timing.cfg_samp_clk_timing(fs,samps_per_chan=number_of_samples_per_channel)
data = np.array(task.read(number_of_samples_per_channel))/amp_gain
z_1= np.conj(FFT_hanning_Normalised(data[0],number_of_samples_per_channel)) #Take the complex conjugate of the Fourier transform of signal 1
z_2 = FFT_hanning_Normalised(data[1],number_of_samples_per_channel) #Take the fourier transform of 2
cc_0 = np.multiply(z_1,z_2) #Product of the dot product of the two
Ldata.append(cc_0[0:int((number_of_samples_per_channel/2)+1)])
time.append(ts)
#np.save(file_n,Ldata)
#np.save(file_t,time)
#print(Ldata[0])
#print(Ldata[1])
stop = timeit.default_timer()
print('Time: ', stop - start)
for j in range(1,int(NSum/segment_size)):
for i in np.arange(j*segment size)):
results = []
t = []
a = np.average(FFT_SpecConv(Ldata[i][110:140],1))
continue
motion = 1
(x, y, w, h) = cv2.boundingRect(contour)
# making green rectangle arround the moving object
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Appending status of motion
motion_list.append(motion)
motion_list = motion_list[-2:]
# Appending Start time of motion
if motion_list[-1] == 1 and motion_list[-2] == 0:
print("threshold")
time.append(datetime.now())
# Appending End time of motion
if motion_list[-1] == 0 and motion_list[-2] == 1:
time.append(datetime.now())
# Displaying image in gray_scale
cv2.imshow("Gray Frame", gray)
# Displaying the difference in currentframe to
# the staticframe(very first_frame)
cv2.imshow("Difference Frame", diff_frame)
# Displaying the black and white image in which if
# intencity difference greater than 30 it will appear white
cv2.imshow("Threshold Frame", thresh_frame)
liste4.append(frame[i,j][0])
liste1 = str(liste1)
liste2 = str(liste2)
liste3 = str(liste3)
liste4 = str(liste4)
try:
if sum(BOX1)/len(BOX1) + 1000 < len(liste1) or\
sum(BOX1)/len(BOX1) - 1000 > len(liste1):
cv2.rectangle(frame,(100,100), (150, 150), (255,0,255), 1)
pos.append(1)
time.append(c)
if sum(BOX2)/len(BOX2) + 1000 < len(liste2) or\
sum(BOX2)/len(BOX2) - 1000 > len(liste2):
cv2.rectangle(frame,(300,100), (350, 150), (255,0,255), 1)
pos.append(2)
time.append(c)
if sum(BOX3)/len(BOX3) + 500 < len(liste3) or\
sum(BOX3)/len(BOX3) - 500 > len(liste3):
cv2.rectangle(frame,(400,100), (450, 150), (255,0,255), 1)
pos.append(3)
time.append(c)
if sum(BOX4)/len(BOX4) + 80 < len(liste4) or\
url = []
title = []
source = []
time = []
ext_date = []
with open(filename, newline='', encoding='utf-8') as f:
column_names = ['id', 'url', 'title', 'source', 'time']
frdr = csv.DictReader(f, fieldnames=column_names)
for row in frdr:
url.append(row['url'])
title.append(row['title'])
source.append(row['source'])
time.append(row['time'])
last_row = len(url)
for l in range(last_row):
if l == 0:
id.append('id')
ext_date.append('extract date')
else:
id.append(l)
ext_date.append(datetime.date.today())
with open(filename, 'w', newline='', encoding='utf-8') as f:
column_names = ['id', 'url', 'title', 'source', 'time', 'extract date']
frtr = csv.DictWriter(f, fieldnames=column_names)
h = []
h.append(120000)
v = []
v.append(0)
a = []
a.append(-9.81)
dp = []
dp.append(0)
Dt = []
Dt.append(0)
Findrag = []
Findrag.append(0)
index = 0
current_height = h[0]
time = []
time.append(0)
while current_height > 1000:
M = Mach(h[index], v[index])
D = 0.5 * CD(M) * current_density(h[index]) * S * (v[index])**2
D_body = 0.5 * 1.14 * current_density(
h[index]) * 9.5e-3 * (v[index])**2 #Rough approx for bluff body drag
dp1 = current_density(h[index]) * 1 / 2 * (v[index])**2
accel = (D + D_body - W) / m
a.append(accel)
v_new = v[index] - a[index] * dt
current_height = h[index] - v_new * dt
v.append(v_new)
h.append(current_height)
time.append(time[index] + 0.1)
def hello():
flag = 30
flag1 = 0
flag2 = 375
i = 0
h = 0
# Assigning our static_back to None
static_back = None
# List when any moving object appear
motion_list = [None, None]
# Time of movement
time = []
# Initializing DataFrame, one column is start
# time and other column is end time
df = pandas.DataFrame(columns=["Start", "End"])
# Capturing video
video = cv2.VideoCapture(0)
# Infinite while loop to treat stack of image as video
while True:
# Reading frame(image) from video
check, screenshot = video.read()
frame = screenshot
blur = cv2.blur(screenshot, (1, 1))
# Initializing motion = 0(no motion)
motion = 0
# Converting color image to gray_scale image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Converting gray scale image to GaussianBlur
# so that change can be find easily
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# In first iteration we assign the value
# of static_back to our first frame
if static_back is None:
static_back = gray
continue
# Difference between static background
# and current frame(which is GaussianBlur)
diff_frame = cv2.absdiff(static_back, gray)
# If change in between static background and
# current frame is greater than 30 it will show white color(255)
thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)
# Finding contour of moving object
(_, cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
if cv2.contourArea(contour) < 10000:
continue
motion = 1
if flag == 0:
cv2.imwrite('opencv' + str(i) + '.png', blur)
i = i + 1
flag = 30
flag = flag - 1
if flag1 == 0:
threading.Thread(target=msg_siren).start()
flag1 = flag1 + 1
if flag2 == 0:
threading.Thread(target=email_send).start()
flag2 == 375
flag2 = flag2 - 1
(x, y, w, h) = cv2.boundingRect(contour)
# making green rectangle arround the moving object
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
h = h + 1
# Appending status of motion
motion_list.append(motion)
motion_list = motion_list[-2:]
# Appending Start time of motion
if motion_list[-1] == 1 and motion_list[-2] == 0:
time.append(datetime.now())
# Appending End time of motion
if motion_list[-1] == 0 and motion_list[-2] == 1:
time.append(datetime.now())
# Displaying image in gray_scale
cv2.imshow("Gray Frame", gray)
# Displaying the difference in currentframe to
# the staticframe(very first_frame)
cv2.imshow("Difference Frame", diff_frame)
# Displaying the black and white image in which if
# intencity difference greater than 30 it will appear white
cv2.imshow("Threshold Frame", thresh_frame)
# Displaying color frame with contour of motion of object
cv2.imshow("Color Frame", frame)
key = cv2.waitKey(1)
# if q entered whole process will stop
if key == ord(' '):
mixer.quit()
static_back = None
flag1 = 0
if key == ord('q'):
# if something is movingthen it append the end time of movement
if motion == 1:
time.append(datetime.now())
break
# Appending time of motion in DataFrame
for i in range(0, len(time), 2):
df = df.append({
"Start": time[i],
"End": time[i + 1]
},
ignore_index=True)
# Creating a csv file in which time of movements will be saved
df.to_csv("Time_of_movements.csv")
video.release()
print(h)
# Destroying all the windows
cv2.destroyAllWindows()
def __init__(self):
nodes = np.loadtxt("D:/Tidal_ananlysis_data/analysis/nodes.csv")
print("Loading 20 %")
t = np.loadtxt("D:/Tidal_ananlysis_data/analysis/t.csv")
clear_output(wait=True)
print("Loading 40 %")
WD = np.loadtxt("D:/Tidal_ananlysis_data/analysis/d.csv")
clear_output(wait=True)
print("Loading 60 %")
u = np.loadtxt("D:/Tidal_ananlysis_data/analysis/u.csv")
clear_output(wait=True)
print("Loading 80 %")
v = np.loadtxt("D:/Tidal_ananlysis_data/analysis/v.csv")
clear_output(wait=True)
print("loading 100 %")
idx = np.loadtxt(
"D:/Tidal_ananlysis_data/analysis/nodes_inex(Rm_DCSM_zuno_WDmin=1.5,nl=(1, 1)).csv",
dtype=int,
)
t = t[::6]
WD = WD[::6, idx]
v = v[::6, idx]
u = u[::6, idx]
nodes = nodes[idx]
time = []
for timestep in t:
tt = datetime.datetime.fromtimestamp(timestep)
time.append(tt)
time = np.array(time)
WD_t = np.zeros(WD.shape)
u_t = np.zeros(u.shape)
v_t = np.zeros(v.shape)
q = int(0)
qq = 1
for node in range(len(nodes)):
q = q + int(1)
if q == 10:
clear_output(wait=True)
print(np.round(qq / len(nodes) * 1000, 3), "%")
q = int(0)
qq += 1
d_n, u_n, v_n = self.Tidal_analysis(node, WD, u, v, time)
WD_t[:, node] = d_n
u_t[:, node] = u_n
v_t[:, node] = v_n
bat, nodes_bat = self.bath()
bath = griddata(nodes_bat, bat, nodes)
print(WD.shape)
WD_new = np.zeros(WD.shape)
for i in range(WD.shape[0]):
WD_new[i] = WD_t[i] - bath
WD_new[WD_new < 0] = 0
self.t = t - t[0]
self.WD = WD_new
self.u = u_t
self.v = v_t
self.nodes = nodes
self.tria = Delaunay(nodes)
def detect_camera():
print('mtav', Motion.objects.all())
# Assigning our static_back to None
static_back = None
# List when any moving object appear
motion_list = [None, None]
# Time of movement
time = []
# Initializing DataFrame, one column is start
# time and other column is end time
df = pandas.DataFrame(columns=["Start", "End"])
print(df)
time_count = 0
# Capturing video
video = cv2.VideoCapture(
'rtsp://admin:@[email protected]:554/Streaming/Channels/101')
while True:
time_count = time_count + 1
# if time_count == 60:
# break
# Reading frame(image) from video
# print(video)
check, frame = video.read()
# print(frame,check)
# Initializing motion = 0(no motion)
motion = 0
# Converting color image to gray_scale image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Converting gray scale image to GaussianBlur
# so that change can be find easily
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# In first iteration we assign the value
# of static_back to our first frame
if static_back is None:
static_back = gray
continue
# Difference between static background
# and current frame(which is GaussianBlur)
diff_frame = cv2.absdiff(static_back, gray)
# If change in between static background and
# current frame is greater than 30 it will show white color(255)
thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)
# Finding contour of moving object
cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
# print(cv2.contourArea(contour))
if cv2.contourArea(contour) < 10000:
continue
motion = 1
(x, y, w, h) = cv2.boundingRect(contour)
# making green rectangle arround the moving object
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Appending status of motion
motion_list.append(motion)
motion_list = motion_list[-2:]
# Appending Start time of motion
if motion_list[-1] == 1 and motion_list[-2] == 0:
time.append(datetime.now())
print(datetime.now(), 'start')
# Appending End time of motion
if motion_list[-1] == 0 and motion_list[-2] == 1:
Motion.objects.create(start_time=datetime.now(),
end_time=time[-1],
camera_index=1)
time.append(datetime.now())
print(datetime.now(), 'end')
# Displaying image in gray_scale
# cv2.imshow("Gray Frame", gray)
# # Displaying the difference in currentframe to
# # the staticframe(very first_frame)
# cv2.imshow("Difference Frame", diff_frame)
# # Displaying the black and white image in which if
# # intensity difference greater than 30 it will appear white
# cv2.imshow("Threshold Frame", thresh_frame)
# Displaying color frame with contour of motion of object
cv2.imshow("Color Frame", frame)
key = cv2.waitKey(1)
# if q entered whole process will stop
if key == ord('q'):
# if something is movingthen it append the end time of movement
if motion == 1:
time.append(datetime.now())
break
# Appending time of motion in DataFrame
print(time)
for i in range(0, len(time), 2):
df = df.append({
"Start": time[i],
"End": time[i + 1]
},
ignore_index=True)
# Creating a CSV file in which time of movements will be saved
df.to_csv("Time_of_movements.csv")
video.release()
fixedTime = datetime.fromtimestamp(ACTime)
if l==0:
prevACtime=fixedTime
rate=False
for ra in range(len(contestData)):
ranu=int(ra)
data=contestData[ranu]
if data["id"]==subData["contest_id"] and data["rate_change"]!="-" and not(data["rate_change"]=="All" and data["start_epoch_second"]<1468670400):
rate=True
break
if rate==True:
RPS+=subData["point"]
time.append(fixedTime)
ac.append(RPS)
contests.append(subData["contest_id"])
problems.append(subData["problem_id"])
prevTime = fixedTime
print(subData["problem_id"]+" "+data["rate_change"])
if RPS>highest:
highest=RPS
#最終ACから現在の日までx軸に平行な線を引く
if prevTime.day!=tod.day and prevTime.month!=tod.month and prevTime.year!=tod.year:
day=datetime.date(prevTime)
while day<=tod:
time.append(day)
ac.append(RPS)
day+=timedelta(1)
#print(parts[0])
#print(parts[1])
bins.append((int(parts[0]), int(parts[1])))
#for random testing:
#voltage = [3.3*random.random() for x in range(len(names))]
print(bins)
node_voltage = list()
time = list()
interval = 0.001
count = 0
if "single" in string_to_write:
node_position = bins[0][0]
for y in bins:
node_voltage.append(y[1] * 3.3 / 1023)
time.append(interval * count)
count += 1
plt.plot(time, node_voltage, 'bo')
plt.axis([0, time[-1], 0, 3.3])
plt.show()
sys.exit(0)
#Reorganizing for the order of the breadboard layout
#Create place holders when all is not called
#organizing to operate different modes
old_voltage = [0] * 128 #create a list of zeros of 128 elements
for y in bins:
old_voltage[y[0]] = 3.3 * y[1] / 1023
old_names = list(range(128))
def get_epsolid_reward(self,
env,
start_time,
k1=1.5,
k2=2.5,
k3=0.5,
total_step=10000):
'''
进行PID模拟全程
:param env: 环境
:param start_time: 当前的起始时间
:param k1: kp
:param k2: kd
:param k3: ki
:return: 损失值
'''
self.env = copy.copy(env)
alpha = []
theta = []
desired_theta = []
q = []
time = []
control = []
ierror = 0
derror_list = []
error_list = []
dez_list = []
tp = 0 # 峰值时间
ts = total_step # 调整时间
count = 0
loss_sin = 0
if_alpha = False
# ----------进行循环控制----------
for i in range(total_step):
if count >= belief_times and ts == total_step:
ts = i
break
self.env.pitch_desired = PITCH_DESIRED[i + start_time]
error = self.env.pitch_desired * 57.3 - self.env.observation[0]
loss_sin += math.fabs(error)
derror = self.env.dpithch_desired * 57.3 - self.env.observation[1]
error_list.append(error)
derror_list.append(derror)
ierror = ierror + error * self.env.tau
action = k1 * error + k2 * derror + k3 * ierror
dez_list.append(action)
# ----------系统分析----------
# TODO:超调时间
if (error == 0 and tp == 0):
tp = i
self.env.step(np.array([action]))
alpha.append(self.env.arfa * 57.3)
if self.env.arfa * 57.3 < -1 or self.env.arfa * 57.3 > 10:
if_alpha = True
theta.append(self.env.observation[0])
desired_theta.append(self.env.pitch_desired * 57.3)
q.append(self.env.observation[1])
time.append(i)
control.append(action)
if (abs(error) <= abs(adjust_bound * self.env.pitch)):
count += 1
else:
count = 0
# ----------系统分析----------
# 超调
Overshoot = max(np.array(theta)) - max(np.array(desired_theta))
if Overshoot < Overshoot_target:
Overshoot = 0
else:
Overshoot = (Overshoot - Overshoot_target) / Overshoot_target
# 调整时间
if ts <= ts_target:
ts = 0
else:
ts = (ts - ts_target) / ts_target
# 稳态误差
st_error = 0.0
for i in range(10):
st_error += abs(error_list[-i])
st_error /= 10.0
if st_error < Static_error_target:
Static_error = 0
else:
Static_error = (st_error -
Static_error_target) / Static_error_target
done = True
# TODO：目前的奖励设置不合理
if if_alpha:
loss_sin += 450000
done = False
return loss_sin, done, ts, Overshoot, Static_error
def search_article_info(self, name, page=1):
"""搜索文章
Args:
name: 搜索文章关键字
page: 搜索的页数
Returns:
列表，每一项均是{'name','url','img','zhaiyao','gzhname','gzhqrcodes','gzhurl','time'}
name: 文章标题
url: 文章链接
img: 文章封面图片缩略图，可转为高清大图
zhaiyao: 文章摘要
time: 文章推送时间，10位时间戳
gzhname: 公众号名称
gzhqrcodes: 公众号二维码
gzhurl: 公众号最近文章地址
"""
text = self._search_article_text(name, page)
page = etree.HTML(text)
img = list()
info_imgs = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[1]/a/img")
for info_img in info_imgs:
img.append(info_img.attrib['src'])
url = list()
info_urls = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[2]/h4/a")
for info_url in info_urls:
url.append(info_url.attrib['href'])
name = list()
info_names = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[2]/h4")
for info_name in info_names:
cache = self._get_elem_text(info_name)
cache = cache.replace('red_beg', '').replace('red_end', '')
name.append(cache)
zhaiyao = list()
info_zhaiyaos = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[2]/p")
for info_zhaiyao in info_zhaiyaos:
cache = self._get_elem_text(info_zhaiyao)
cache = cache.replace('red_beg', '').replace('red_end', '')
zhaiyao.append(cache)
gzhname = list()
gzhqrcodes = list()
gzhurl = list()
info_gzhs = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[2]/div/a")
for info_gzh in info_gzhs:
gzhname.append(info_gzh.attrib['title'])
gzhqrcodes.append(info_gzh.attrib['data-encqrcodeurl'])
gzhurl.append(info_gzh.attrib['href'])
time = list()
info_times = page.xpath(u"//div[@class='wx-rb wx-rb3']/div[2]/div/span/script/text()")
for info_time in info_times:
time.append(re.findall('vrTimeHandle552write\(\'(.*?)\'\)', info_time)[0])
returns = list()
for i in range(len(url)):
returns.append(
{
'name': name[i],
'url': url[i],
'img': img[i],
'zhaiyao': zhaiyao[i],
'gzhname': gzhname[i],
'gzhqrcodes': gzhqrcodes[i],
'gzhurl': gzhurl[i],
'time': time[i]
}
)
return returns
# create a new inlet to read from the stream
inlet = StreamInlet(streams[0])
counter = 0
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
plt.xlabel('Time')
plt.ylabel('Micro Volts')
plt.title('EEG Channel 1')
data = []
time = []
print("pull samples...")
while True:
# get a new sample (you can also omit the timestamp part if you're not
# interested in it)
sample = inlet.pull_sample()
counter += 1
data.append(sample[0][0])
time.append(counter)
ax1.clear()
ax1.plot(time, data)
plt.pause(0.05)
print(sample)
plt.show()
main_div = soup.find_all(
"div",
attrs={"class": re.compile("cb-col cb-col-100 cb-lst-itm cb-lst-itm-lg")})
links = []
intro = []
time = []
typ = []
heading = []
print("Processing News")
for s in main_div:
links.append("https://www.cricbuzz.com" + s.a["href"])
s1 = s.find("div", attrs={"class": re.compile("cb-nws-intr")})
intro.append(s1.text)
s2 = s.find("div", attrs={"class": re.compile("cb-nws-time")})
typ.append(s2.text)
s3 = s.find("span", attrs={"class": re.compile("cb-nws-time")})
time.append(s3.text)
s4 = s.find(
"h2", attrs={"class": re.compile("cb-nws-hdln cb-font-18 line-ht24")})
heading.append(s4.text)
l = len(heading)
with open("out.txt", "w") as file:
for i in range(l):
file.write(typ[i] + "\n")
file.write(heading[i] + "\n")
file.write(intro[i] + "\n")
file.write("Ref: " + links[i] + "\n")
file.write("Posted: " + time[i] + "\n\n")
file.close()
print("Your News is ready in 'out.txt'")
def generate_time():
time = []
for i in range(240):
time.append(i / 10)
return time
def motiondetect():
# Assigning our static_back to None
static_back = None
# List when any moving object appear
motion_list = [None, None]
# Time of movement
time = []
# Initializing DataFrame, one column is start
# time and other column is end time
df = pandas.DataFrame(columns=["Start", "End"])
# Capturing video
video = cv2.VideoCapture(0)
# Infinite while loop to treat stack of image as video
while True:
# Reading frame(image) from video
check, frame = video.read()
# Initializing motion = 0(no motion)
motion = 0
# Converting color image to gray_scale image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Converting gray scale image to GaussianBlur
# so that change can be find easily
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# In first iteration we assign the value
# of static_back to our first frame
if static_back is None:
static_back = gray
continue
# Difference between static background
# and current frame(which is GaussianBlur)
diff_frame = cv2.absdiff(static_back, gray)
# If change in between static background and
# current frame is greater than 30 it will show white color(255)
thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)
# Finding contour of moving object
(cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
if cv2.contourArea(contour) < 10000:
continue
motion = 1
(x, y, w, h) = cv2.boundingRect(contour)
# making green rectangle arround the moving object
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Appending status of motion
motion_list.append(motion)
motion_list = motion_list[-2:]
# Appending Start time of motion
if motion_list[-1] == 1 and motion_list[-2] == 0:
time.append(datetime.now())
# Appending End time of motion
if motion_list[-1] == 0 and motion_list[-2] == 1:
time.append(datetime.now())
# Displaying image in gray_scale
cv2.imshow("Gray Frame", gray)
# Displaying the difference in currentframe to
# the staticframe(very first_frame)
cv2.imshow("Difference Frame", diff_frame)
# Displaying the black and white image in which if
# intencity difference greater than 30 it will appear white
cv2.imshow("Threshold Frame", thresh_frame)
# Displaying color frame with contour of motion of object
cv2.imshow("Color Frame", frame)
key = cv2.waitKey(1)
# if q entered whole process will stop
if key == ord('q'):
# if something is movingthen it append the end time of movement
if motion == 1:
print("motion detected")
break
video.release()
# Destroying all the windows
cv2.destroyAllWindows()
def fp(self, windows_density=20, denoise=False):
windows_size = self.framerate // windows_density #windows_size:每个窗口的帧数
hamming_window = np.hamming(windows_size)
if denoise:
noise_fft = self.denoise(2, windows_size)
self.landmarks = [
] # 用landmarks储存landmark，landmark是一个tuple：（时间，频率），并根据先时间后频率对landmark在list中从前到后排序
#!!!
time = []
frequency = []
# 每次的fft先暂时储存起来！等找出最大的aver_max再秋后算账（处理处landmark）
ffts = []
max_aver_max = 0
for i in range(0, self.nframes - windows_size, windows_size):
window = self.wave_data[0][
i:i + windows_size] # window即一个窗口（离散的时域函数）
fft = np.abs(np.fft.fft(window)) # fft即对这个窗口进行傅里叶转换得到的离散频域函数
#对噪声反向补偿
fft = fft - noise_fft
# 滤波并保留landmark----（出现的时间，频率）
max1 = np.max(fft[:10])
max2 = np.max(fft[10:20])
max3 = np.max(fft[20:40])
max4 = np.max(fft[40:80])
max5 = np.max(fft[80:160])
max6 = np.max(fft[160:511])
aver_max = (max1 + max2 + max3 + max4 + max5 + max6) / 6
if aver_max > max_aver_max:
max_aver_max = aver_max
ffts.append(fft[:windows_size // 2])
max_aver_max *= 0.8
for i in range(len(ffts)):
for j in range(windows_size //
2): # 只有前一般的频谱是不重复的，所以统计landmark只统计前一半
if ffts[i][j] > max_aver_max:
self.landmarks.append((int(
(i)), int(j * windows_density)))
#!!!
time.append(int(i))
frequency.append(int(j * windows_density))
# 计算锚(取targetzone为紧跟在锚点后面的5个点),储存在fps中，每个锚就是一个指纹，用一个tuple表示:（锚点绝对时间位置，锚点频率，目标点频率，时间差）-------全部转换成string！
self.fps = []
for i in range(0, len(self.landmarks) - 5):
for j in range(i + 1, i + 6):
self.fps.append(
(str(self.landmarks[i][0]), str(self.landmarks[i][1]),
str(self.landmarks[j][1]),
str(self.landmarks[j][0] - self.landmarks[i][0])))
#!!!
plt.scatter(time, frequency)
plt.show()
print(len(self.landmarks))
else:
self.landmarks = [
] # 用landmarks储存landmark，landmark是一个tuple：（时间，频率），并根据先时间后频率对landmark在list中从前到后排序
'''
time = []
frequency = []
'''
#每次的fft先暂时储存起来！等找出最大的aver_max再秋后算账（处理处landmark）
ffts = []
max_aver_max = 0
for i in range(0, self.nframes - windows_size, windows_size):
window = self.wave_data[0][
i:i + windows_size] # window即一个窗口（离散的时域函数）
#下面两行用汉明窗（删除即默认矩形窗）
tailored_window = np.array(window) * np.array(
hamming_window) # 用haming窗口剪裁
fft = np.abs(
np.fft.fft(tailored_window)) # fft即对这个窗口进行傅里叶转换得到的离散频域函数
# 滤波并保留landmark----（出现的时间，频率）
max1 = np.max(fft[:10])
max2 = np.max(fft[10:20])
max3 = np.max(fft[20:40])
max4 = np.max(fft[40:80])
max5 = np.max(fft[80:160])
max6 = np.max(fft[160:511])
aver_max = (max1 + max2 + max3 + max4 + max5 + max6) / 6
if aver_max > max_aver_max:
max_aver_max = aver_max
ffts.append(fft[:windows_size // 2])
max_aver_max *= 0.8
for i in range(len(ffts)):
for j in range(windows_size //
2): # 只有前一般的频谱是不重复的，所以统计landmark只统计前一半
if ffts[i][j] > max_aver_max:
self.landmarks.append((int(
(i)), int(j * windows_density)))
'''
time.append(int(i))
frequency.append(int(j * windows_density))
'''
# 计算锚(取targetzone为紧跟在锚点后面的5个点),储存在fps中，每个锚就是一个指纹，用一个tuple表示:（锚点绝对时间位置，锚点频率，目标点频率，时间差）-------全部转换成string！
self.fps = []
for i in range(0, len(self.landmarks) - 5):
for j in range(i + 1, i + 6):
self.fps.append(
(str(self.landmarks[i][0]), str(self.landmarks[i][1]),
str(self.landmarks[j][1]),
str(self.landmarks[j][0] - self.landmarks[i][0])))
'''
elif len(predAmp) < len(predAmpN):
predAmpN = predAmpN[:len(predAmp)]
cleanTimeN = cleanTimeN[:len(predAmpN)]
nonCoalesceLen = len(predAmp)
else:
nonCoalesceLen = len(predAmp)
nonCoalescePeriod = len(ampPeaks['posTime']) - nonCoalesceLen
nonCoalescePeriodN = len(ampPeaks['negTime']) - nonCoalesceLen
time = list()
amp = list()
length = max([len(cleanTime), len(cleanTimeN)])
if abs(cleanTime[0]) > abs(cleanTimeN[0]):
for t in xrange(length):
if t < len(cleanTime):
time.append(cleanTime[t])
amp.append(predAmp[t])
if t < len(cleanTimeN):
time.append(cleanTimeN[t])
amp.append(predAmpN[t])
else:
for t in xrange(length):
if t < len(cleanTimeN):
time.append(cleanTimeN[t])
amp.append(predAmpN[t])
if t < len(cleanTime):
time.append(cleanTime[t])
amp.append(predAmp[t])
#======================================
# During Coalescence
cog = []
sog = []
yaw_gps = []
pitch_gps = []
roll_gps = []
ax = []
ay = []
az = []
for line in urlopen('https://navview.blob.core.windows.net/data-test/'+ sys.argv[1] +'.txt'):
line = line.strip()
raw_data.append(line.decode('utf-8').split(','))
length = len(raw_data)
for i in range(1,length):
time.append(raw_data[i][0])
roll.append (raw_data[i][1])
pitch.append(raw_data[i][2])
wx.append (raw_data[i][3])
wy.append(raw_data[i][4])
wz.append (raw_data[i][5])
lat.append(raw_data[i][6])
long.append (raw_data[i][7])
cog.append(raw_data[i][8])
sog.append (raw_data[i][9])
yaw_gps.append(raw_data[i][10])
pitch_gps.append (raw_data[i][11])
roll_gps.append(raw_data[i][12])
ax.append (raw_data[i][13])
ay.append(raw_data[i][14])
# az.append (raw_data[i][15])
def model_simulation(self, k1=1.5, k2=2.5, k3=0.5, iterator=0):
total_step = 1000
self.env.reset()
alpha = []
theta = []
desired_theta = []
q = []
time = []
i = 1
control = []
ierror = 0
derror_list = []
error_list = []
action_list = []
dez_list = []
while i < total_step:
""" FOR DEBUG """
# if i % 10 == 0:
# print(i,self.env.state[1],self.env.theta_desired)
error = self.env.theta_desired - self.env.state[1]
derror = -self.env.state[2]
error_list.append(error)
derror_list.append(derror)
ierror = ierror + error * self.env.tau
action = k1 * error + k2 * derror + k3 * ierror
action_list.append(action)
dez_list.append(self.env.delta_z)
self.env.step(np.array([action]))
alpha.append(self.env.state[0])
theta.append(self.env.state[1])
desired_theta.append(self.env.theta_desired)
q.append(self.env.state[2])
time.append(i)
control.append(action)
i = i + 1
# "绘制$\\alpha$曲线"
# plt.xticks(fontproperties='Times New Roman')
# plt.yticks(fontproperties='Times New Roman')
# plt.xlabel("Number of Iterations")
# plt.ylabel("Attack Angle")
# plt.plot(alpha, label="$\\alpha$")
# plt.legend(loc='best', prop={'family': 'Times New Roman'})
# # 图上的legend，记住字体是要用prop以字典形式设置的，而且字的大小是size不是fontsize，这个容易和xticks的命令弄混
# plt.title("$\\alpha$ In %s Epoch " % str(iterator), fontdict={'family': 'Times New Roman'})
# plt.show()
#
# "绘制$\\delta_z$曲线"
#
# plt.xticks(fontproperties='Times New Roman')
# plt.yticks(fontproperties='Times New Roman')
# plt.xlabel("Number of Iterations")
# plt.ylabel("Elevator")
# plt.plot(dez_list, label="$\\delta_z$")
# plt.title("$\\delta_z$ In %s epoch " % str(iterator), fontdict={'family': 'Times New Roman'})
# plt.legend(loc='best', prop={'family': 'Times New Roman'})
# plt.show()
# "绘制theta曲线"
#
# plt.figure(num=2)
#
# plt.xticks(fontproperties='Times New Roman')
# plt.yticks(fontproperties='Times New Roman')
# plt.xlabel("Number of Iterations")
# plt.ylabel("Pitch Angle")
#
# plt.plot(theta, label="time-theta")
# plt.plot(desired_theta, label="time-desired_theta")
# plt.legend(loc='best', prop={'family': 'Times New Roman'})
# plt.title("$ \\theta$ In %s epoch " % str(iterator), fontdict={'family': 'Times New Roman'})
# plt.savefig("%sepoch.pdf" % iterator)
# plt.show()
return alpha, dez_list, theta, desired_theta
def alertinfo(request):
if 'emp_id' in request.session and 'Username' in request.session:
empid = request.session['emp_id']
obje = Alert.objects.filter(cluster="systemlog")
for obje1 in obje:
ip = obje1.ip_endpoint
es = Elasticsearch([ip])
alert_namelist = []
alert_id = []
doc = {"size": 10000, "query": {"match": {"emp_id": empid}}}
res = es.search(index="alertinfo", doc_type='post', body=doc)
for row in res['hits']['hits']:
alert_name = row["_source"]["alert_name"]
alert_namelist.append(alert_name)
id = len(alert_namelist)
alert_id.append(id)
al = set(alert_namelist)
alert_nameunique = list(al)
sk = len(alert_nameunique)
value = []
kibanaquerylist = []
k1 = []
sizelist = []
lastalertsent = []
timestamp = []
mailsentlist = []
smssentlist = []
mailsent = []
for name in alert_nameunique:
body = {
"size": 10000,
"sort": [{
"lastalertsent": {
"order": "asc"
}
}],
"query": {
"match": {
"alert_name": name
}
}
}
result = es.search(index="alertinfo", doc_type='post', body=body)
for row in result['hits']['hits']:
kibanaquery = row["_source"]["kibana_query"]
k1.append(kibanaquery)
kibanaquerylist.append(kibanaquery)
lastalert = row["_source"]["lastalertsent"]
lastalertsent.append(lastalert)
mailsent = row["_source"]["mail_sent"]
mailsent[:] = [item for item in mailsent if item != '']
mailsent[:] = [item for item in mailsent if item != 'None']
mailsent = [item.encode('utf-8') for item in mailsent]
mailsentlist.append(mailsent)
smssent = row["_source"]["sms_sent"]
smssent = [item.encode('utf-8') for item in smssent]
smssent[:] = [item for item in smssent if item != '']
smssent[:] = [item for item in smssent if item != 'None']
smssentlist.append(smssent)
s1 = len(k1)
size = s1
sizelist.append(size)
del k1[:]
kibana = []
for ls in lastalertsent:
current_timestamp = datetime.datetime.fromtimestamp(ls).strftime(
'%Y-%m-%d-%H:%M:%S')
timestamp.append(current_timestamp)
kibana = []
time = []
mail_sent = []
count = 0
sms_sent = []
for size in sizelist:
kibana.append([kibanaquerylist[i + count] for i in range(size)])
time.append([timestamp[i + count] for i in range(size)])
mail_sent.append([mailsentlist[i + count] for i in range(size)])
sms_sent.append([smssentlist[i + count] for i in range(size)])
count += size
allist = []
zippedlist = zip(alert_nameunique, kibana, time, mail_sent, sms_sent,
alert_id)
return render(request, 'application/alertinfo.html', {
'kibanaquery': zippedlist,
'alert': alert_id
})
else:
return redirect('form')
def get_epsolid_reward(self, k1=1.5, k2=2.5, k3=0.5, is_test=False):
total_step = 2000
self.env.reset()
alpha = []
theta = []
desired_theta = []
q = []
time = []
i = 1
control = []
ierror = 0
derror_list = []
error_list = []
dez_list = []
# 峰值时间
tp = 0
'''计算调整时间 如果调整时间过大我们就加大惩罚'''
ts = total_step
count = 0
for i in range(total_step):
if count >= belief_times:
ts = i - belief_times
break
error = self.env.theta_desired - self.env.state[1]
derror = -self.env.state[2]
error_list.append(error)
derror_list.append(derror)
ierror = ierror + error * self.env.tau
action = k1 * error + k2 * derror + k3 * ierror
dez_list.append(action)
if (error == 0 and tp == 0):
tp = i
self.env.step(np.array([action]))
alpha.append(self.env.state[0])
theta.append(self.env.state[1])
desired_theta.append(self.env.theta_desired)
q.append(self.env.state[2])
time.append(i)
control.append(action)
if (abs(error) <= abs(adjust_bound * self.env.theta_desired)):
count += 1
else:
count = 0
# ## 分阶段优化，因为每个阶段的任务应该是不同的,模拟人的思想，模拟我们自己的调参经验,先得到一个可行解，然后转移得到带有约束的最优解
## 虽然我觉得这里应该加入极大值限制,这里是不是应该改环境
if self.env.state[
0] < self.env.alpha_threshold_min or self.env.state[
0] > self.env.alpha_threshold_max:
count += 1
# 超调量 kp
Overshoot = max(abs(np.array(theta))) - max(
abs(np.array(desired_theta)))
Overshoot = 0 if Overshoot < Overshoot_target else (
Overshoot - Overshoot_target) / Overshoot_target
# 调整时间
ts = 0 if ts <= ts_target else (ts - ts_target) / ts_target
r = Overshoot + ts
# r = Overshoot + ts
# 判断是否满足约束,约束判准
if is_test:
return ts
else:
return r
with connection.cursor() as cursor:
cursor.execute(
"SELECT DATE_FORMAT(time, '%%H:%%i'), course, marketId FROM races WHERE date = %s ORDER BY time", (date_today))
markets = cursor.fetchall()
tab = []
for market in markets:
tab.append((market['DATE_FORMAT(time, \'%H:%i\')']+', '+market['course']+', '+market['marketId']+'\n'))
time = []
# sample is for gmt+2
for row in tab:
# not_dst
if time.localtime().tm_isdst == 0:
if(row[0:2] == '19'):
time.append(('20'+row[2:]))
elif(row[0:2] == '23'):
time.append(('00'+row[2:]))
else:
time.append((row[0]+str(int(row[1])+1)+row[2:]))
elif time.localtime().tm_isdst == 1:
# dst
if(row[0:2] == '18'):
time.append(('20'+row[2:]))
elif(row[0:2] == '19'):
time.append(('21'+row[2:]))
elif(row[0:2] == '23'):
time.append(('01'+row[2:]))
else:
time.append((row[0]+str(int(row[1])+2)+row[2:]))
else:
import ast
import time
from db_connect import get_conn
parser = argparse.ArgumentParser()
parser.add_argument('E',
type=int,
help='number of exchange transactions in a process')
parser.add_argument('P', type=int, help='number of processes')
parser.add_argument('I', help='isolation level')
args = parser.parse_args()
processes = []
for i in xrange(0, args.P):
processes.append(
subprocess.Popen([
'python',
'c:/Users/User/OneDrive/NUS/course/CS5421/assigment/project5/run_exchanges.py',
str(args.E), args.I
],
stdout=subprocess.PIPE)) ##MODIFIED
for process in processes:
process.wait()
time = []
for process in processes:
time.append(ast.literal_eval(process.communicate()[0]))
print float(sum(time)) / len(time)
BASE = "https://news.ycombinator.com/news?p="
session_requests = requests.session()
ct = 0
title = []
score = []
hnuser = []
time = []
for i in range(1, 51):
URL = BASE + str(i)
r = session_requests.get(URL)
soup = BeautifulSoup(r.content, 'html.parser')
for node in soup.find_all("a", "storylink"):
title.append(node.text)
for node in soup.find_all("span", "age"):
time.append(node.text)
for node in soup.find_all("a", "hnuser"):
hnuser.append(node.text)
for node in soup.find_all("span", "score"):
score.append(node.text)
if len(title) > len(hnuser):
title.pop()
time.pop()
class post(Document):
title = StringField(max_length=120, required=True)
author = StringField(max_length=120, required=True)
time = StringField(max_length=120, required=True)
score = StringField(max_length=50, required=True)
def random_date(start, end, numero):
time = []
for e in range(0,numero):
time.append(str_time_prop(start, end, '%m/%d/%Y %I:%M %p', random.random()))
return time
continue
motion = 1
(x, y, w, h) = cv2.boundingRect(contour)
# making green rectangle arround the moving object
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# we now append status of motion
motion_list.append(motion)
# we slice the array from the second last element
motion_list = motion_list[-2:]
# appending Start time of motion
if motion_list[-1] == 1 and motion_list[-2] == 0:
time.append(datetime.now().strftime("%H:%M:%S"))
# appending End time of motion
if motion_list[-1] == 0 and motion_list[-2] == 1:
time.append(datetime.now().strftime("%H:%M:%S"))
# displaying image in gray_scale
cv2.imshow("Gray Frame", gray)
# displaying the difference in currentframe to the staticframe(very first_frame)
cv2.imshow("Difference Frame", diff_frame)
# displaying the black and white image in which if intencity difference greater than 30 it will appear white
cv2.imshow("Threshold Frame", thresh)
# displaying color frame with contour of motion of object
