def get_columns():
response = jsonify({
'teams':util.get_columns(),
'venues': util.get_venues()
})
response.headers.add('Access-Control-Allow-Origin','*')
return response
def predict():
if request.method == "POST":
print("Achievment")
PR = util.get_PROD_CD()
SL = util.get_SLSMAN_CD()
PROD_CD = str(request.form["PROD_CD"]) #1
SLSMAN_CD = str(request.form["SLSMAN_CD"]) #2
PLAN_MONTH = request.form["PLAN_MONTH"] #3
TARGET_IN_EA = request.form["TARGET_IN_EA"] #4
x = np.zeros(len(util.get_columns()))
x[0] = PR.index(PROD_CD)
x[1] = SL.index(SLSMAN_CD)
x[2] = PLAN_MONTH
x[3] = TARGET_IN_EA
print(x)
# x = np.reshape(x,(-1,1))
prd = model.predict([x])
#prd = np.reshape(prd,(1,)
# response = jsonify({
# "predictions" : str(np.round(prd[0],4))
# })
# response.headers.add('Access-Control-Allow-Origin','*')
if int(prd) > 0:
return render_template('index.html',
prediction_text="Achievment {}".format(
np.round(prd.item(), 3)))
elif int(prd) < 0:
return render_template('index.html',
prediction_text="Achievment ".format(
np.round(-prd.item(), 3)))
# elif int(output)==0:
# return render_template('index.html', prediction_text="Achieved.")
# return response
return render_template('index.html')
def predict():
if request.method == "POST":
print("Getting Flight detail's Flies")
AL = util.get_Airlines()
OA = util.get_Origin()
DA = util.get_Dest()
SD = request.form["SCHEDULED_DEPARTURE"] #1
ST = request.form["SCHEDULED_TIME"] #2
date = request.form["DATE"] #3
AIRLINE = str(request.form["AIRLINE"]) #4
ORIGIN = str(request.form["ORIGIN_AIRPORT"]) #5
DEST = str(request.form["DESTINATION_AIRPORT"]) #6
SDepart = int(SD[:2] + SD[3:5])
print(SDepart)
STime = int(ST[:2] + ST[3:5])
print(STime)
year, month, day = (int(x) for x in date.split('-'))
dow = datetime.date(year, month, day).weekday()
print(day, month, dow)
print("Inputiing")
x = np.zeros(len(util.get_columns()))
x[0] = month
x[1] = day
x[2] = dow
x[3] = AL.index(AIRLINE)
x[4] = OA.index(ORIGIN)
x[5] = DA.index(DEST)
x[6] = SDepart
x[7] = STime
x[8] = float(request.form["ARRIVAL_DELAY"]) #7
print(x)
x = preprocessing.scale(x)
x = np.array([x])
prd = model.predict([x], verbose=0)
prd = np.reshape(prd, (1, ))
output = round(prd[0], 2)
# response = jsonify({
# "predictions" : round(output.item(),2)
# })
# response.headers.add('Access-Control-Allow-Origin','*')
if int(output) > 0:
return render_template(
'single.html',
prediction_text=
"Flight's probable departure will be {} minutes after the schedule"
.format(round(output.item(), 2)))
elif int(output) < 0:
return render_template(
'single.html',
prediction_text=
"Flight's probable departure will be {} minutes before the schedule."
.format(round(-output.item(), 2)))
elif int(output) == 0:
return render_template(
'single.html',
prediction_text="Flight will be departing on time.")
# return response
return render_template('single.html')
def field_cov(fields, models, apps):
columns = util.get_columns(fields, models, apps)
columns = util.make_real(columns)
return np.cov(columns)
def columns():
return get_columns([c_serial])
def columns():
return get_columns()
def _reinitialize(self):
self.matrix = init_random_matrix(self.row_c, self.col_c)
self.matrix.extend(get_columns(self.matrix))
self.reqs = self.row_c + self.col_c
def get_columns(self, table):
return util.get_columns(self.conn, table)
def plot(file_1, file_2, coord, ip, flag): # Here, file_1 is thought as the output of the GetAperture routine and file_2 a MAD-X file
# -----------------------------
# Text and plot characteristics
# -----------------------------
DPI = 300
textwidth = 4
rc('font',**{'family':'serif','serif':['Computer Modern Roman'], 'size':10})
rc('text', usetex=True)
rcParams['figure.figsize']=textwidth, textwidth/1.618
fig = plt.figure()
ax = fig.add_subplot(111)
coord = '%s'%coord
# -----------------------------
# Deal with each possible case
# -----------------------------
# --------------------------------
# Get the data for each coordinate
# --------------------------------
if coord =='x':
var_x, var_y = get_columns('LHCAperture_old.dat', 0, 1)
var_x2, var_y2 = get_columns('LHCAperture_new.dat', 0, 1)
ax.set_ylabel('x [m]')
elif coord =='y':
var_x, var_y = get_columns('LHCAperture_old.dat', 0, 2)
var_x2, var_y2 = get_columns('LHCAperture_new.dat', 0, 2)
ax.set_ylabel('y [m]')
else:
print '>> ERROR: input "x" or "y" in the 3rd argument'
# ---------------
# Choose your IR
# ---------------
y_limits = (0.2, 0.65, 0.2, 0.2, 0.4, 0.2, 0.2, 0.3)
position, ylim = get_ir(ip, y_limits[ip-1])
if ip == 1:
var_x, var_y = get_ip1(var_x, var_y)
var_x2, var_y2 = get_ip1(var_x2, var_y2)
# -------------------------------------
# Choose the elements you want to plot
# -------------------------------------
regex_list = (['VC+', 'T'], ['VC+'], ['VC+'], ['VC+'], ['VC+', 'T'], ['VC+'], ['VC+'], ['VC+'])
heights = (0.12, 0.42, 0.1, 0.13, 0.23, 0.1, 0.1, 0.2)
name, pos, height = get_element('twiss_ip1_b1.tfs', 0, 3, regex_list[ip-1], heights[ip-1])
if flag == True:
# ------------------
# Plot the elements
# ------------------
if ip == 1:
pos, name = get_ip1(pos, name)
for s, n in zip(pos, name):
plt.annotate(n, xy=(s, height), xytext=(s, height), name='Verdana', family='sans-serif',
weight='light', va='bottom', ha='center', rotation=90, size=3)
# plt.bar(s-l, 100, l, 150, color = '#249A27', edgecolor = 'black', linewidth = '1.7', alpha = 0.
else:
print 'No elements were plotted'
# ---------
# Plotting
# ---------
ax.plot(var_x, var_y, 'b-', linewidth=1, label='Old file')
ax.plot(var_x2, var_y2, 'r-', linewidth=0.4, label='New file')
ip = '%s'%ip
ax.set_title('IR' + ip)
ax.set_xlim([position - 150, position + 150])
ax.set_ylim([0, ylim])
ax.set_xlabel('s [m]')
ax.ticklabel_format(style='sci',axis='y',scilimits=(0,0))
ax.grid(b=None, which='major')
ax.legend(loc='upper right', prop={'size':6})
plt.subplots_adjust(left=0.12,bottom=0.16,right=0.94,top=0.88)
# plt.show()
# --------------
# Save the plot
# --------------
plt.savefig('allapert_ip_' + ip + '_' + coord + '_new.png', dpi=DPI)
plt.close()