def clean_the_input(line):
"""
check the input is numeric and if so, round to two decimal
input: line --- input quantity
output: line --- if it is a numeric value, a value of two decimal,
otherwise, just return the value as it was
"""
if mcf.chkNumeric(line):
line = str(ecf.round_up(float(line)))
return line
def clean_the_input(line):
"""
check the input is numeric and if so, round to two decimal
input: line --- input quantity
output: line --- if it is a numeric value, a value of two decimal,
otherwise, just return the value as it was
"""
if mcf.chkNumeric(line):
line = str(ecf.round_up(float(line)))
return line
def make_table_with_year(hlist, y_list, title, color):
"""
creata a html table for a give list of sub html page with year of violation
input: hlist --- a list of sub html page names
output: line --- a line contain html descriptions of a table
"""
if len(hlist) == 0:
line = ''
else:
line = '<table border=1 cellpadding=5 style="margin-left:auto;margin-right:auto">\n'
line = line + '<tr><th style="color:' + color + '">' + title + '</th><th>Predicted Year</th></tr>\n<tr>'
ecnt = 0
for k in range(0, len(hlist)):
msid = hlist[k]
year = y_list[k]
line = line + '<td><a href="' + web_address + 'Htmls/' + msid + '_plot.html">' + msid + '</a></td>\n'
line = line + '<td>' + str(ecf.round_up(year)) + '</td></tr>\n'
line = line + '</table>\n'
return line
def make_table_with_year(hlist, y_list, title, color):
"""
creata a html table for a give list of sub html page with year of violation
input: hlist --- a list of sub html page names
output: line --- a line contain html descriptions of a table
"""
if len(hlist) == 0:
line = ''
else:
line = '<table border=1 cellpadding=5 style="margin-left:auto;margin-right:auto">\n'
line = line + '<tr><th style="color:' + color +'">' + title + '</th><th>Predicted Year</th></tr>\n<tr>'
ecnt = 0
for k in range(0, len(hlist)):
msid = hlist[k]
year = y_list[k]
line = line + '<td><a href="' + web_address + 'Htmls/' + msid + '_plot.html">' + msid + '</a></td>\n'
line = line + '<td>' + str(ecf.round_up(year)) + '</td></tr>\n'
line = line + '</table>\n'
return line
def unit_modification(val, tchk):
"""
modify value depending on unit
input: val --- original value
tchk --- whether this is temp and need to be converted into k
if degc tchk = 1
degf tchk = 2
psia tchk = 3
output: val --- updated value
"""
tchk = int(float(tchk))
if tchk == 1:
val += 273.15 #--- C to K conversion
elif tchk == 2:
val = ecf.f_to_k(val) #--- F to K conversion
elif tchk == 3:
val /= 0.145038 #--- kp to psia conversion
#
#--- round off the value to 2 dicimal points
#
val = ecf.round_up(val)
return val
def create_violation_notification(msid, pdata, min_a, min_b, max_a, max_b, tmax):
"""
check violation and create notification
input: msid --- msid
pdata --- data set (see "read_data" for details)
min_a --- intercept value of the lower envelope prediction
min_b --- slope value of the lower envelope prediction
max_a --- intercept value of the upper envelope prediction
max_b --- slope value of the upper envelope prediction
tmax --- prediction ending point in time
output: line --- lower violation description (can be empty)
line2 --- upper violation description (can be empty0
pchk_l --- indicator of whether there is lower violation (if 1)
pchk_u --- indicator of whether there is upper violation (if 1)
"""
#
#--- predict violation
#
line = ''
line2 = ''
yl_time = 0.0
yt_time = 0.0
rl_time = 0.0
rt_time = 0.0
pchk_l = 0
pchk_u = 0
#
#--- check lower violations
#
vlimit = pdata[15][-1]
if vlimit not in ['-999', '-998', '998', '999']:
vtime1 = predict_violation(min_a, min_b, tmax, vlimit, side = 0)
if vtime1 == tmax:
line = 'The data are aready in Red Lower Violation'
rl_time = -1.0
else:
vlimit = pdata[13][-1]
vtime2 = predict_violation(min_a, min_b, tmax, vlimit, side = 0)
if vtime2 == tmax:
line = 'The data are aready in Yellow Lower Violation'
yl_time = -1.0
elif (vtime2 > 0) and (vtime2 < tmax + 5):
line = 'The data may violate the lower yellow limit at Year: ' + str(ecf.round_up(vtime2))
yl_time = vtime2
pchk_l = 1
#
#--- check upper violations
#
vlimit = pdata[16][-1]
if vlimit not in ['-999', '-998', '998', '999']:
vtime1 = predict_violation(min_a, min_b, tmax, vlimit, side = 1)
if vtime1 == tmax:
if line == '':
line = 'The data are aready in Red Upper Violation'
line2 = ''
else:
line2 = 'The data are aready in Red Upper Violation'
rt_time = -1.0
else:
vlimit = pdata[14][-1]
vtime2 = predict_violation(max_a, max_b, tmax, vlimit, side = 1)
if (vtime2 > 0) and (vtime2 < tmax + 5):
if line == '':
if vtime2 == tmax:
line = 'The data are aready in Yellow Upper Violation'
yt_time = -1.0
else:
line = 'The data may violate the upper yellow limit at Year: ' + str(ecf.round_up(vtime2))
line2 = ''
yt_time = vtime2
pchk_u = 1
else:
if vtime2 == tmax:
line2 = 'The data are aready in Yellow Upper Violation'
yt_time = -1.0
else:
line2 = 'The data may violate the upper yellow limit at Year: ' + str(ecf.round_up(vtime2))
yt_time = vtime2
pchk_u = 1
#
#-- update violation time estimate database
#
vtdata = [yl_time, yt_time, rl_time, rt_time]
ved.incert_data(msid, vtdata)
return [line, line2, pchk_l, pchk_u]
def compute_stats(data, y_min, y_max, r_min, r_max):
"""
compute basic statistics for the given data
input: data --- data
y_min --- yellow lower limit
y_max --- yellow upper limit
r_min --- red lower limit
r_max --- red upper limit
output: a list of stat results:
cnt --- number of the data points
avg --- mean
med --- median
dmin --- min
dmax --- max
y_min_cnt --- the number of times the data violoated the lower yellow limit
y_max_cnt --- the number of times the data violoated the upper yellow limit
r_min_cnt --- the number of times the data violoated the lower red limit
r_max_cnt --- the number of times the data violoated the upper red limit
"""
#
#--- remove out layers
#
data.sort()
dlen = len(data)
cut = int(0.025 * dlen)
ndata = data[cut:dlen - cut]
if len(ndata) == 0:
ndata = data
darray = numpy.array(ndata)
#
#--- compute data statistics
#
cnt = len(data)
avg = ecf.round_up(numpy.mean(darray))
med = ecf.round_up(numpy.median(darray))
std = ecf.round_up(numpy.std(darray))
dmin = ecf.round_up(min(ndata))
dmax = ecf.round_up(max(ndata))
#
#--- fine the numbers of times the data violated limits
#--- if they is no limit (998, 999 etc), just use huge numbers as limit
#
if r_min in [-999, -998, -99]:
r_min = -1.0e12
if y_min in [-999, -998, -99]:
y_min = -1.0e12
if r_max in [999, 998, 99]:
r_max = 1.0e12
if y_max in [999, 998, 99]:
y_max = 1.0e12
y_min_cnt = 0
y_max_cnt = 0
r_min_cnt = 0
r_max_cnt = 0
for k in range(0, cnt):
if data[k] < r_min:
r_min_cnt += 1
elif data[k] < y_min:
y_min_cnt += 1
if data[k] > r_max:
r_max_cnt += 1
elif data[k] > y_max:
y_max_cnt += 1
return [
cnt, avg, med, std, dmin, dmax, y_min_cnt, y_max_cnt, r_min_cnt,
r_max_cnt
]
def create_violation_notification(msid, pdata, min_a, min_b, max_a, max_b,
tmax):
"""
check violation and create notification
input: msid --- msid
pdata --- data set (see "read_data" for details)
min_a --- intercept value of the lower envelope prediction
min_b --- slope value of the lower envelope prediction
max_a --- intercept value of the upper envelope prediction
max_b --- slope value of the upper envelope prediction
tmax --- prediction ending point in time
output: line --- lower violation description (can be empty)
line2 --- upper violation description (can be empty0
pchk_l --- indicator of whether there is lower violation (if 1)
pchk_u --- indicator of whether there is upper violation (if 1)
"""
#
#--- predict violation
#
line = ''
line2 = ''
yl_time = 0.0
yt_time = 0.0
rl_time = 0.0
rt_time = 0.0
pchk_l = 0
pchk_u = 0
#
#--- check lower violations
#
vlimit = pdata[15][-1]
if vlimit not in ['-999', '-998', '998', '999']:
vtime1 = predict_violation(min_a, min_b, tmax, vlimit, side=0)
if vtime1 == tmax:
line = 'The data are aready in Red Lower Violation'
rl_time = -1.0
else:
vlimit = pdata[13][-1]
vtime2 = predict_violation(min_a, min_b, tmax, vlimit, side=0)
if vtime2 == tmax:
line = 'The data are aready in Yellow Lower Violation'
yl_time = -1.0
elif (vtime2 > 0) and (vtime2 < tmax + 5):
line = 'The data may violate the lower yellow limit at Year: ' + str(
ecf.round_up(vtime2))
yl_time = vtime2
pchk_l = 1
#
#--- check upper violations
#
vlimit = pdata[16][-1]
if vlimit not in ['-999', '-998', '998', '999']:
vtime1 = predict_violation(min_a, min_b, tmax, vlimit, side=1)
if vtime1 == tmax:
if line == '':
line = 'The data are aready in Red Upper Violation'
line2 = ''
else:
line2 = 'The data are aready in Red Upper Violation'
rt_time = -1.0
else:
vlimit = pdata[14][-1]
vtime2 = predict_violation(max_a, max_b, tmax, vlimit, side=1)
if (vtime2 > 0) and (vtime2 < tmax + 5):
if line == '':
if vtime2 == tmax:
line = 'The data are aready in Yellow Upper Violation'
yt_time = -1.0
else:
line = 'The data may violate the upper yellow limit at Year: ' + str(
ecf.round_up(vtime2))
line2 = ''
yt_time = vtime2
pchk_u = 1
else:
if vtime2 == tmax:
line2 = 'The data are aready in Yellow Upper Violation'
yt_time = -1.0
else:
line2 = 'The data may violate the upper yellow limit at Year: ' + str(
ecf.round_up(vtime2))
yt_time = vtime2
pchk_u = 1
#
#-- update violation time estimate database
#
vtdata = [yl_time, yt_time, rl_time, rt_time]
ved.incert_data(msid, vtdata)
return [line, line2, pchk_l, pchk_u]
def compute_stats(data, y_min, y_max, r_min, r_max):
"""
compute basic statistics for the given data
input: data --- data
y_min --- yellow lower limit
y_max --- yellow upper limit
r_min --- red lower limit
r_max --- red upper limit
output: a list of stat results:
cnt --- number of the data points
avg --- mean
med --- median
dmin --- min
dmax --- max
y_min_cnt --- the number of times the data violoated the lower yellow limit
y_max_cnt --- the number of times the data violoated the upper yellow limit
r_min_cnt --- the number of times the data violoated the lower red limit
r_max_cnt --- the number of times the data violoated the upper red limit
"""
#
#--- remove out layers
#
data.sort()
dlen = len(data)
cut = int(0.025 * dlen)
ndata = data[cut:dlen-cut]
if len(ndata) == 0:
ndata = data
darray = numpy.array(ndata)
#
#--- compute data statistics
#
cnt = len(data)
avg = ecf.round_up(numpy.mean(darray))
med = ecf.round_up(numpy.median(darray))
std = ecf.round_up(numpy.std(darray))
dmin = ecf.round_up(min(ndata))
dmax = ecf.round_up(max(ndata))
#
#--- fine the numbers of times the data violated limits
#--- if they is no limit (998, 999 etc), just use huge numbers as limit
#
if r_min in [-999, -998, -99]:
r_min = -1.0e12
if y_min in [-999, -998, -99]:
y_min = -1.0e12
if r_max in [999, 998, 99]:
r_max = 1.0e12
if y_max in [999, 998, 99]:
y_max = 1.0e12
y_min_cnt = 0
y_max_cnt = 0
r_min_cnt = 0
r_max_cnt = 0
for k in range(0, cnt):
if data[k] < r_min:
r_min_cnt += 1
elif data[k] < y_min:
y_min_cnt += 1
if data[k] > r_max:
r_max_cnt += 1
elif data[k] > y_max:
y_max_cnt += 1
return [cnt, avg, med, std, dmin, dmax, y_min_cnt, y_max_cnt, r_min_cnt, r_max_cnt]