def calc(msg):
text = (re.findall(r'(\-*\w+\.*\w*)(?:\s+)*', msg.text))
cmd = (text.pop(0))
if len(text) == 0:
send_mess = 'Чтобы воспользоваться коммандой {0} укажите после нее набор чисел'.format(
cmd)
bot.send_message(msg.chat.id, send_mess)
else:
if True not in (list(map(str.isalpha, text))):
d = list(map(float, text))
if cmd == 'median':
bot.send_message(msg.chat.id,
"Медиана: {0}".format(str(mymath.median(d))))
if cmd == 'mean':
bot.send_message(
msg.chat.id,
"Среднее значение: {0}".format(str(mymath.mean(d))))
if cmd == 'sko':
bot.send_message(msg.chat.id,
"СКО: {0}".format(str(mymath.sko(d))))
if cmd == 'cv':
bot.send_message(msg.chat.id,
"CV: {0} %".format(str(mymath.cv(d))))
if cmd == 'sum':
bot.send_message(msg.chat.id, "Сумма: {0}".format(str(sum(d))))
def testMean(self): # - - - - - - - - - - - - - - - - - - - - - - - - - - -
"""Test 'mean' routine"""
for i in self.vectors:
m = mymath.mean(i[0])
assert isinstance(m, float), 'Value returned from "mean" is not a float: ' + str(m)
assert m == i[1], 'Wrong "mean" with data: ' + str(i[0]) + " (should be: " + str(i[1]) + "): " + str(m)
def any_text(msg):
cmd = (re.findall(r'(\-*\w+\.*\w*)(?:\s+)*', msg.text))
if True not in (list(map(str.isalpha, cmd))):
d = list(map(float, cmd))
bot.send_message(msg.chat.id, 'N : {0}'.format(str(len(d))))
bot.send_message(msg.chat.id,
'Среднее значение: {0}'.format(str(mymath.mean(d))))
bot.send_message(msg.chat.id, 'СКО: {0}'.format(str(mymath.sko(d))))
bot.send_message(
msg.chat.id,
"Коэффициент вариации: {0} %".format(str(mymath.cv(d))))
bot.send_message(msg.chat.id,
"Медиана: {0}".format(str(mymath.median(d))))
def testMean(
self): # - - - - - - - - - - - - - - - - - - - - - - - - - - -
"""Test 'mean' routine"""
for i in self.vectors:
m = mymath.mean(i[0])
assert (isinstance(m,float)), \
'Value returned from "mean" is not a float: '+str(m)
assert m == i[1], \
'Wrong "mean" with data: '+str(i[0])+' (should be: '+str(i[1])+ \
'): '+str(m)
def ss_curve_pos():
global s, e, properties
if 'filetosave' in request.files:
print('File is come!')
for file in request.files:
f = request.files[file]
extension = re.findall(r'(?:\w+.)(\w+)', f.filename)[0]
if f and mkxlsx.allowed_file(extension):
f.save(
os.path.join(app.config['UPLOAD_FOLDER'],
'upload.' + extension))
if extension == 'xlsx':
try:
s, e = readxlsx.mk_df(UPLOAD_FOLDER + 'upload.xlsx')
except:
return json.dumps({'key': 'no_data'})
for key in s.keys():
properties[key] = mymath.s_s_prop(
e[key], s[key], 100, 200)
print(properties.keys())
return json.dumps({'properties': properties, 'status': 'uploaded'})
elif request.form['key'] == 'request_data':
properties[request.form['sample']] = mymath.s_s_prop(
e[request.form['sample']], s[request.form['sample']],
int(request.form['begin']), int(request.form['end']))
stress = readxlsx.less_lenght(s[request.form['sample']], 100)
strain = readxlsx.less_lenght(e[request.form['sample']], 100)
stress_reg = np.linspace(0, max(stress) * 1.1, 10)
strain_reg = ((stress_reg - properties[request.form['sample']]['intercept']) \
/ properties[request.form['sample']]['slope'])
strain_reg = list(strain_reg)
stress_reg = list(stress_reg)
return json.dumps({
'properties': properties,
'strain': strain,
'stress': stress,
'strain_reg': strain_reg,
'stress_reg': stress_reg,
'key': 'data'
})
elif request.form['key'] == 'reload_data':
for sample in s.keys():
properties[sample] = mymath.s_s_prop(e[sample], s[sample],
int(request.form['begin']),
int(request.form['end']))
return json.dumps({'properties': properties, 'key': 'properties'})
elif request.form['key'] == 'stats':
stats = {}
props = {}
for sample in properties.keys():
for p in properties[sample]:
if p in props.keys():
props[p].append(properties[sample][p])
else:
props[p] = []
props[p].append(properties[sample][p])
for prop in [
'ultimate', 'modulus', 'proportional', 'yield', 'extension'
]:
stats[prop] = {}
stats[prop]['Макс'] = mymath.round_step(max(props[prop]), 0.1)
stats[prop]['Мин'] = mymath.round_step(min(props[prop]), 0.1)
stats[prop]['Сред.'] = mymath.round_step(mymath.mean(props[prop]),
0.1)
stats[prop]['СКО'] = mymath.round_step(mymath.sko(props[prop]),
0.1)
stats[prop]['CV, %'] = mymath.round_step(mymath.cv(props[prop]),
0.1)
return json.dumps({'stats': stats, 'key': 'stats'})
file = open('maria.txt', 'r')
except FileNotFoundError:
print('file not found!')
else:
s = file.read()
file.close()
#
from mymath import arithmetic
arithmetic.add(1, 3)
import mymath
mymath.add(1, 3)
mymath.mean([1, 2, 3, 4, 5])
from mymath import pi
pi
#
joinstr_list = ['^(N_)?(\(주\)|주식회사|\(?주\)한무쇼핑|주\)|한무쇼핑\(주\))?(.*)(매장|-)', \
'^(N_)?(\(주\)|주식회사|\(?주\)한무쇼핑|주\)|한무쇼핑\(주\))?(.*)']
id_list = ['로라메르시에', '엘리자베스아덴', '르라보', '킬리안', '하이코스', '비디비치', \
'숨', '산타마리아노벨라', '오리진스', '라메르', '달팡', '그라운드플랜', '데코르테', \
'동인비', '톰포드뷰티', '에르메스퍼퓸', '라페르바', '불리1803', '끌레드뽀보떼', 'RMK', '구딸파리', \
'시슬리화장품', '지방시뷰티', '라프레리']
complete_str_list = []