def math(arith_str):
#替换空格、--、++、-+、+-、/+、*+为去空格、+、+、-、-、/、*,还有去掉行首的 + 号
arith_str = equl_tihuan(arith_str)
# 如果发现括号，则先算括号里面的
if re.findall('[()]+', arith_str):
arith_sub = re.findall('[(][^()]+[)]', arith_str) #取所有最内部括号
n = 0
for i in arith_sub:
# 取括号内的表达式
j = re.search('[^()]+',i).group()
# 算括号内的值
i = calculate(j)
# 将原来的括号替换为算出来的值
arith_str = arith_str.replace(arith_sub[n], i, 1)
n = n + 1
# print("去最外层括号:", arith_str)
# 如果没有括号，则再算加减乘除
elif re.findall('[+*/-]+', arith_str):
# print(arith_str)
if re.search('e-',arith_str) == None:
arith_str = calculate(arith_str)
return arith_str
# 如果就一个字符串，不包含括号和加减乘除则，直接返回
else:
return arith_str
# arith_str = equl_tihuan(arith_str)
# print("asadsad")
return math(arith_str)
def test_whitespaces():
assert calculate('5 + 5') == 10
assert calculate('5+5 -3') == 7
assert calculate('5 + 9') == 14
assert calculate('5\t+6') == 11
assert calculate('8\n+5+ 7') == 20
def main():
args = parse_args()
csv_filename = args.data
result_csv = args.outfile
data_dict = load_data(csv_filename)
calculate(data_dict)
write_csv(data_dict, result_csv)
write_sql(result_csv, args=args)
def train(model, sess, saver, epochs, batch_size, data_train, id2word, id2tag):
"""
模型训练
:param model: model的一些信息
:param sess: TensorFlow会话内容
:param saver: TensorFlow保存器
:param epochs: 所有训练数据forwward+bachword后更新参数的次数
:param batch_size: 一次iteration选取的样本数量
:param data_train: 输入样本
:param id2word: 给每个word映射了一个id, 通过id 查找word
:param id2tag: 给每个word映射了一个tag, 通过id 查找tag
:return:
"""
batch_num = int(data_train.y.shape[0] / batch_size) # 计算一次iteration需要多少轮
for epoch in range(epochs):
for batch in range(batch_num):
x_batch, y_batch = data_train.next_batch(batch_size)
feed_dict = {model.input_data: x_batch, model.labels: y_batch}
predict_train, _ = sess.run(
[model.viterbi_sequence, model.train_op], feed_dict=feed_dict)
acc = 0 # 累计
if batch % 100 == 0:
for i, j in product(range(len(y_batch)),
range(len(y_batch[0]))):
if y_batch[i][j] == predict_train[i][j]:
acc += 1
recall_result = acc * 1.0 / (len(y_batch) * len(y_batch[0]))
print("召回率：", recall_result)
# 保存结果
save_path_name = os.path.join(os.getcwd(), "model",
"model" + str(epoch) + ".ckpt")
print(save_path_name)
if epoch % 5 == 0:
saver.save(sess, save_path_name)
print("in epoch %d" % epoch, "model is saved")
entity_result = [] # 应该是实体结果
entity_all = [] # 和上面一样， 功能未知
for batch in range(batch_num):
x_batch, y_batch = data_train.next_batch(batch_size)
feed_dict = {model.input_data: x_batch, model.labels: y_batch}
predict_train = sess.run([model.viterbi_sequence], feed_dict)
predict_train = predict_train[0] # 未知
entity_result = calculate(x_batch, predict_train, id2word,
id2tag, entity_result)
entity_all = calculate(x_batch, y_train, id2word, id2tag,
entity_all)
and_set = set(entity_result) & set(entity_all)
if len(and_set) > 0:
precision_ratio = len(and_set) / len(entity_result)
recall_ratio = len(and_set) / len(entity_all)
f_value = 2 * precision_ratio * recall_ratio / (
precision_ratio + recall_ratio)
print("precision_ratio is :", precision_ratio)
print("recall_ratio is : ", recall_ratio)
print("f value is :", f_value)
else:
print("something wrong! no and set!!")
def calc_everything(tup):
""""Designed to be run in other tread. Calculates everything"""
global progress
filename = tup[0]
q = tup[1]
filename = filename.rstrip('.png')
_time = time.time()
extracted = c.extract(filename, IMAGES_FOLDER, stepDEGR)
#print('Extract time: ', time.time()-_time, ' File: ', filename)
if extracted is not None:
_time = time.time()
output = []
if filename.startswith('3'):
#pass
for point in extracted:
output.append(c.calculateTOP(point[0], point[1], point[2]))
else:
for point in extracted:
output.append(c.calculate(point[0], point[1], point[2]))
#print('Calculate time: ', time.time()-_time)
q.put(output)
mutex.lock()
progress = progress + 1
print('DONE!!!', filename, "progress: ", progress, '\n')
mutex.unlock()
else:
progress = progress + 1
print("Corrupted PNG file: ", filename, "progress: ", progress, '\n')
def pipeline(args):
pd.options.mode.chained_assignment = None
# Sanity check args
if not parent_dir_exists(args.out):
raise ValueError('--out flag points to an invalid path.')
print('Preparing files for analysis...')
gwas_snps, bed, N1, N2 = prep(args.bfile, args.partition, args.sumstats1,
args.sumstats2, args.N1, args.N2)
print('Calculating LD scores...')
ld_scores = ldscore(args.bfile, gwas_snps)
gwas_snps = gwas_snps[gwas_snps['SNP'].isin(ld_scores['SNP'])]
print('{} SNPs included in our analysis...'.format(len(gwas_snps)))
print('Calculating heritability...')
h_1, h_2 = heritability(gwas_snps, ld_scores, N1, N2)
print(
'The genome-wide heritability of the first trait is {}.\nThe genome-wide heritability of the second trait is {}.'
.format(h_1, h_2))
print('Calculating phenotypic correlation...')
pheno_corr, pheno_corr_var = pheno(gwas_snps, ld_scores, N1, N2, h_1, h_2)
print('Calculating local genetic covariance...')
out = calculate(args.bfile, bed, args.thread, gwas_snps, ld_scores, N1, N2,
pheno_corr, pheno_corr_var)
out.to_csv(args.out, sep=' ', na_rep='NA', index=False)
def pipeline(args):
pd.options.mode.chained_assignment = None
# Sanity check args
if args.save_ld is not None and args.use_ld is not None:
raise ValueError('Both the --save-ld and --use-ld flags were set. '
'Please use only one of them.')
if args.save_ld is not None:
if not parent_dir_exists(args.save_ld):
raise ValueError('--save-ld flag points to an invalid path.')
if args.use_ld is not None:
if not os.path.exists(args.use_ld + '.csv.gz'):
raise ValueError('--use-ld flag points to an invalid path.')
if not parent_dir_exists(args.out):
raise ValueError('--out flag points to an invalid path.')
print('Preparing files for analysis...')
gwas_snps, N1, N2, annots = prep(args.bfile, args.annot, args.sumstats1,
args.sumstats2)
if args.N1 is not None:
N1 = args.N1
if args.N2 is not None:
N2 = args.N2
if args.use_ld is not None:
print('Loading LD scores from {}'.format(args.use_ld))
ld_scores = pd.read_csv(args.use_ld + '.csv.gz', sep=' ')
else:
print('Calculating LD scores...')
ld_scores = ldscore(args.bfile, annots, gwas_snps, args.save_ld)
print('Calculating correlation...')
out = calculate(gwas_snps, ld_scores, annots, N1, N2)
print('\nFinal results:\n{}\n'.format(out))
print('\nView ldsc.log for verbose output.')
out.insert(0, 'annot_name', out.index)
out.to_csv(args.out, sep=' ', na_rep='NA', index=False)
def get(self):
d = self.request.get('astro')
celebbday = datetime.datetime.strptime(d, '%d/%m/%Y').date().strftime('%Y%m%d')
[cellewis, celspiller, celchinese, celmillman] = calculate.calculate(celebbday)
res = []
infile = open("celebs")
for line in infile.readlines():
resline = []
tokens = line.split("|")
name = tokens[0]
resline.append(name)
occup = tokens[1]
resline.append(occup)
tokens = tokens[2].split(":")
if celspiller in tokens: resline.append(celspiller)
if celchinese in tokens: resline.append(celchinese)
for lewi in cellewis:
if lewi in tokens: resline.append(lewi)
if len(resline) > 5: # to avoid unnecessary sorting later
res.append(resline)
# sort according to list size, negative is taken
# so that the sorting is descending
#res.sort(lambda x, y: cmp(-len(x), -len(y)))
res = res[0:100]
self.response.out.write(simplejson.dumps(res))
def text_calculate(string):
numbers = {
'один': '1',
'два': '2',
'три': '3',
'четыре': '4',
'пять': '5',
'шесть': '6',
'семь': '7',
'восемь': '8',
'девять': '9',
'ноль': '0'
}
operators = {'плюс': '+', 'минус': '-', 'умножить': '*', 'делить': '/'}
string = string.split()
word_ind = 0
num_string = ''
while word_ind < len(string):
if string[word_ind] in numbers:
num_string = num_string + numbers[string[word_ind]]
elif string[word_ind] in operators:
num_string = num_string + operators[string[word_ind]]
elif string[
word_ind] == 'и' and word_ind - 1 >= 0 and word_ind + 1 < len(
string) and string[word_ind -
1] in numbers and string[word_ind +
1] in numbers:
num_string = num_string + '.'
word_ind += 1
return calculate(num_string)
def get(self):
d = self.request.get('astro')
celebbday = datetime.datetime.strptime(
d, '%d/%m/%Y').date().strftime('%Y%m%d')
[cellewis, celspiller, celchinese,
celmillman] = calculate.calculate(celebbday)
res = []
infile = open("celebs")
for line in infile.readlines():
resline = []
tokens = line.split("|")
name = tokens[0]
resline.append(name)
occup = tokens[1]
resline.append(occup)
tokens = tokens[2].split(":")
if celspiller in tokens: resline.append(celspiller)
if celchinese in tokens: resline.append(celchinese)
for lewi in cellewis:
if lewi in tokens: resline.append(lewi)
if len(resline) > 5: # to avoid unnecessary sorting later
res.append(resline)
# sort according to list size, negative is taken
# so that the sorting is descending
#res.sort(lambda x, y: cmp(-len(x), -len(y)))
res = res[0:100]
self.response.out.write(simplejson.dumps(res))
def test_speed_increase_calculate_valid(self):
calc = calculate()
car_speed_in = 10.4673
road_speed_in = 15
result = calc.speed_increase(car_speed_in, road_speed_in) #valid speed
answer = -4.5327 #10.4673 - 15 = -4.5327
self.assertEqual(answer, result)
def test_average_speed_calculate_invalid2(self): calc = calculate() time_1_in = 170 time_2_in = 120 dist_in = 4 result = calc.average_speed(time_1_in, time_2_in, dist_in)#invalid speed self.assertFalse(result)
def test_speed_increase_calculate_valid(self): calc = calculate() car_speed_in = 10.4673 road_speed_in = 15 result = calc.speed_increase(car_speed_in, road_speed_in)#valid speed answer = -4.5327 #10.4673 - 15 = -4.5327 self.assertEqual(answer, result)
def pipeline(args):
pd.options.mode.chained_assignment = None
# Sanity check args
if not parent_dir_exists(args.out):
raise ValueError('--out flag points to an invalid path.')
print('Preparing files for analysis...')
gwas_snps, reversed_alleles_ref, bed, N1, N2 = prep(args.bfile1, args.bfile2, args.partition, args.sumstats1, args.sumstats2, args.N1, args.N2)
print('Calculating LD scores for the first population...')
ld_scores1 = ldscore(args.bfile1, gwas_snps)
print('Calculating LD scores for the second population...')
ld_scores2 = ldscore(args.bfile2, gwas_snps)
ld_scores1 = ld_scores1[ld_scores1['SNP'].isin(ld_scores2['SNP'])]
ld_scores2 = ld_scores2[ld_scores2['SNP'].isin(ld_scores1['SNP'])]
subset_index = gwas_snps['SNP'].isin(ld_scores1['SNP'])
gwas_snps = gwas_snps[subset_index]
reversed_alleles_ref = reversed_alleles_ref[subset_index]
print('{} SNPs included in our analysis...'.format(len(gwas_snps)))
#print('Calculating heritability...')
#h_1, h_2 = heritability(gwas_snps, ld_scores1, ld_scores2, N1, N2)
#print('The genome-wide heritability of the first trait is {}.\nThe genome-wide heritability of the second trait is {}.'.format(h_1, h_2))
print('Calculating local genetic covariance...')
out = calculate(args.bfile1, args.bfile2, bed, args.thread, gwas_snps, reversed_alleles_ref, N1, N2, args.genome_wide, ld_scores1, ld_scores2)
out.to_csv(args.out, sep=' ', na_rep='NA', index=False)
def update_text(self):
"""Called whenever the text is updated"""
# Blank value for answer
answer = ''
# Get the text
# Get the variable (and start value if applicable)
# Get the answer
# Give the text the extra d/dx, int, or sum
if self.mode == 'Derivative':
text = self.ui.text_entry_d.text()
if len(text) > 0:
variable = self.ui.variable_d.currentText()
answer = calculate(self.mode, text, variable)
text = f'd/d{variable} ({text})'
elif self.mode == 'Integral':
text = self.ui.text_entry_i.text()
if len(text) > 0:
variable = self.ui.variable_i.currentText()
answer = calculate(self.mode,
text,
variable,
start=self.ui.lower_bound.text(),
end=self.ui.upper_bound.text())
text = f'int {text}d{variable}'
else:
text = self.ui.text_entry_s.text()
if len(text) > 0:
variable = self.ui.variable_s.currentText()
value = self.ui.value_s.text()
answer = calculate(self.mode,
text,
variable,
value=value,
do_it=self.ui.button_value.isChecked())
text = fr'sum_({variable}={value})^infty {text}'
try:
# Blank input
if len(text) == 0:
load_svg(self.ui.input, to_svg(BLANK))
load_svg(self.ui.output, to_svg(BLANK))
# Not blank input
else:
load_svg(self.ui.input, to_svg(to_latex(text)))
load_svg(self.ui.output, to_svg(to_latex(answer)))
except ValueError:
# If there's an input error
pass
def test_average_speed_calculate_valid(self): calc = calculate() time_1_in = 120 time_2_in = 160 dist_in = 56 result = calc.average_speed(time_1_in, time_2_in, dist_in)#valid speed answer = 1.4 self.assertEqual(answer, result)
def test_average_speed_calculate_invalid2(self):
calc = calculate()
time_1_in = 170
time_2_in = 120
dist_in = 4
result = calc.average_speed(time_1_in, time_2_in,
dist_in) #invalid speed
self.assertFalse(result)
def test_average_speed_calculate_valid(self):
calc = calculate()
time_1_in = 120
time_2_in = 160
dist_in = 56
result = calc.average_speed(time_1_in, time_2_in,
dist_in) #valid speed
answer = 1.4
self.assertEqual(answer, result)
def test_basic_operators():
assert calculate('5+5') == 10
assert calculate('5*5') == 25
assert calculate('10/2') == 5
assert calculate('10-4') == 6
assert calculate('10%4') == 2
assert calculate('6/3') == 2
assert calculate('5-10') == -5
assert calculate('55+20') == 75
def __init__(self):
super().__init__()
self.setupUi(self)
self.pushButton.clicked.connect(self.compute)
self.comboBox.addItem('xgboost')
self.comboBox.addItem('naive_bayes')
self.comboBox.addItem('kmeans')
self.calc = calculate.calculate()
self.graphicsView = QGraphicsView()
self.graphicsView_2 = QGraphicsView()
def get(self):
bday = self.request.get('bday')
btype = self.request.get('btype')
[lewis, spiller, chinese, millman] = calculate.calculate(bday)
logging.debug(lewis)
lewis_int = [int(x) for x in lewis]
mb = calculate.calculate_mbti_full([lewis, spiller, chinese, millman])
cycle, now_year, str_d = calculate.calculate_cycle(bday)
res = {"lewis": lewis_int, "spiller": spiller, "chinese": chinese, "millman": millman, "mb": mb, "cycle": cycle}
self.response.out.write(simplejson.dumps(res))
def run(self):
while True:
future_ticks = getFuturesTick(self.future_codes['IF'] +
self.future_codes['IH'] +
self.future_codes['IC'])
index_ticks = getIndexTick(self.index_codes)
result = calculate(future_ticks, index_ticks)
self.update_date.emit(result)
# print(result)
time.sleep(1)
def analyze(recalculate=False):
basename = os.path.basename(os.path.normpath(data_folder))
all_trajectories_filepath = basename + "_" + all_trajectories_file
if (not recalculate) and os.path.exists(all_trajectories_filepath):
df_all = pd.read_csv(all_trajectories_filepath, sep='\t')
# print('A1', ~recalculate)
else:
data_files_list = [
f for f in glob.iglob(os.path.join(data_folder, r"*" + extension),
recursive=False)
]
file = data_files_list[0]
df_all = pd.read_csv(file, sep='\t')
for i in trange(1,
np.min([max_N, len(data_files_list)]),
desc='Loading data files'):
# for file in data_files_list[1:]: # , desc='Loading data files'):
file = data_files_list[i]
df = pd.read_csv(file, sep='\t')
df_all = df_all.append(df, ignore_index=True)
df_all.to_csv(all_trajectories_filepath, sep='\t') # , na_rep='nan')
# folder, _ = os.path.split(file_list[0])
results_path = results_folder
reinit_folder(results_path)
# Process
from calculate import calculate
calculate(csv_file=all_trajectories_filepath,
results_folder=results_path,
bl_produce_maps=True,
snr_label=snr_label,
sigma=sigma,
recalculate=recalculate,
ticks=True,
txt_extension=extension,
pdf=True,
png=True,
**kwargs)
def call_calculate(bot, update):
equation = update.message.text.replace('/calculate', '').strip()
if equation == "":
q_equation = ""
custom_keyboard = [['7', '8', '9', '*'], ['4', '5', '6', '-'],
['1', '2', '3', '+'], [',', '0', '=', '/']]
reply_markup = telegram.ReplyKeyboardMarkup(custom_keyboard)
bot.send_message(chat_id=update.message.chat_id,
text="Калькулятор",
reply_markup=reply_markup)
return
update.message.reply_text(calculate(equation))
def pipeline(args):
pd.options.mode.chained_assignment = None
# Sanity check args
if not parent_dir_exists(args.out):
raise ValueError('--out flag points to an invalid path.')
print('Preparing files for analysis...')
gwas_snps, N1, N2 = prep(args.bfile, args.chr, args.start, args.end,
args.sumstats1, args.sumstats2, args.N1, args.N2)
print('Calculating local TWAS...')
out = calculate(args.bfile, gwas_snps, N1, N2, args.h1, args.h2,
args.shrinkage)
out.to_csv(args.out, sep=' ', na_rep='NA', index=False)
def __init__(self, request_data):
self.request_data = request_data
self.valid = validate()
site_id = self.valid.site(request_data['site_id'])
self.site_cam_id = self.valid.cam(request_data['camera_id'])
self.uuid = self.valid.uuid(request_data['uuid'])
self.time_2 = self.valid.time(request_data['epoch_time'])
self.sqlite = database()
self.calc = calculate()
self.s_id, self.s_limit = self.sqlite.find_site(site_id)
self.cam_id = self.sqlite.get_cam_id(self.site_cam_id, self.s_id)
self.curr_cam_m = self.sqlite.get_cam_m(self.cam_id, self.s_id)
for plate_no in range(0,len(self.request_data['results'])):
self.plate(plate_no)
def get(self):
res = []
bdays = self.request.get('bdays')
for bday in bdays.split(","):
bday_date = datetime.datetime.strptime(bday, '%B %d %Y').date()
bday_pb_date = bday_date.strftime('%Y%m%d')
[lewis, spiller, chinese, millman] = calculate.calculate(bday_pb_date)
logging.debug(lewis)
lewis_int = [int(x) for x in lewis]
mb_astro_res = calculate.calculate_mb([lewis, spiller, chinese, millman])
cycle, now_year, str_d = calculate.calculate_cycle(bday_pb_date)
res.append({"lewis": lewis_int, "spiller": spiller, "chinese": chinese, "millman": millman, "mb": mb_astro_res, "cycle": cycle, "bday": bday_pb_date})
self.response.out.write(simplejson.dumps(res))
def main(arg_str):
"""
Main analysis file that uses TRamWAy
"""
# Define arguments
arg_parser = argparse.ArgumentParser(
description='TRamWAy wrapper for analysis of a random walk trajectory')
arg_parser.add_argument('-v',
'--version',
action='version',
version='%(prog)s ' + str(version))
arg_parser.add_argument('-f',
'--file',
required=True,
action='store',
type=str,
help='Path to a trajectory')
# Analyze arguments
input_args = arg_parser.parse_args(arg_str.split())
# Use the analyzed arguments
file = input_args.file
print(file)
# Tesselate and perform inference
# Calculate Bayes factors and output results
_, output_folder = folders()
calculate(file,
output_folder,
bl_produce_maps=False,
snr_label=snr_label,
sigma=sigma,
extension=extension,
**kwargs)
def get(self):
bday = self.request.get('bday')
btype = self.request.get('btype')
[lewis, spiller, chinese, millman] = calculate.calculate(bday)
logging.debug(lewis)
lewis_int = [int(x) for x in lewis]
mb = calculate.calculate_mbti_full([lewis, spiller, chinese, millman])
cycle, now_year, str_d = calculate.calculate_cycle(bday)
res = {
"lewis": lewis_int,
"spiller": spiller,
"chinese": chinese,
"millman": millman,
"mb": mb,
"cycle": cycle
}
self.response.out.write(simplejson.dumps(res))
def get(self):
d = self.request.get('d')
btype = self.request.get('btype')
try:
lewis, spiller, chinese, millman = calculate.calculate(d)
lewi_summary = self.get_lewi(lewis[0])
spiller_summary = self.get_spiller(spiller)
chinese_summary = self.get_chinese(chinese)
millman_summary = self.get_millman(millman[0])
mbti_summary = self.get_mbti()
cycle, now_year, str_d = calculate.calculate_cycle(d)
mb_astro_res = calculate.calculate_mbti_full(
[lewis, spiller, chinese, millman])
logging.debug(mb_astro_res)
path = os.path.join(os.path.dirname(__file__), 'reading.html')
template_values = {
'code_date': d,
'date': str_d,
'lewi_main': lewis[0],
'lewis': lewis,
'spiller': spiller,
'spiller_converted': convertSpiller(spiller),
'chinese': chinese,
'btype': btype,
'millman': millman,
'millman_main': millman[0],
'mb': mb_astro_res,
'lewi_summary': lewi_summary,
'spiller_summary': spiller_summary,
'chinese_summary': chinese_summary,
'millman_summary': millman_summary,
'mbti_summary': mbti_summary,
'cycle': cycle,
'now_year': now_year
}
except Exception, e:
logging.debug(e)
path = os.path.join(os.path.dirname(__file__), 'explore.html')
template_values = {
'error': 'Wrong date',
}
def get(self):
d = self.request.get('d')
btype = self.request.get('btype')
try:
lewis, spiller, chinese, millman = calculate.calculate(d)
lewi_summary = self.get_lewi(lewis[0])
spiller_summary = self.get_spiller(spiller)
chinese_summary = self.get_chinese(chinese)
millman_summary = self.get_millman(millman[0])
mbti_summary = self.get_mbti()
cycle, now_year, str_d = calculate.calculate_cycle(d)
mb_astro_res = calculate.calculate_mbti_full([lewis, spiller, chinese, millman])
logging.debug(mb_astro_res)
path = os.path.join(os.path.dirname(__file__), 'reading.html')
template_values = {
'code_date': d,
'date': str_d,
'lewi_main': lewis[0],
'lewis': lewis,
'spiller': spiller,
'spiller_converted': convertSpiller(spiller),
'chinese': chinese,
'btype': btype,
'millman': millman,
'millman_main': millman[0],
'mb': mb_astro_res,
'lewi_summary': lewi_summary,
'spiller_summary': spiller_summary,
'chinese_summary': chinese_summary,
'millman_summary': millman_summary,
'mbti_summary': mbti_summary,
'cycle': cycle,
'now_year': now_year
}
except Exception, e:
logging.debug(e)
path = os.path.join(os.path.dirname(__file__), 'explore.html')
template_values = {
'error': 'Wrong date',
}
def result(self,master):
try:
# calculate
rate = evaluate(self.rateEntry.get())
distance = evaluate(self.distanceEntry.get())
price = evaluate(self.priceEntry.get())
rate = c.rate(rate,self.rateUnit.type)
distance = c.distance(distance,self.distanceUnit.type)
price = c.price(price,self.priceUnit.type)
calculated = c.calculate(rate,distance,price)
self.showRes(f"Total Cost: ${calculated:.2f}")
except ZeroDivisionError:
print ("Error: Dividing by Zero")
self.showRes("Error: Dividing by Zero")
def GetMoodIndex(N, day):
AA4_LIST = [] #存放AA4数据的列表
AA5_LIST = [] #存放AA5数据的列表
AA6_LIST = []
c = calculate(N, day)
highlist = c.GetHighpxList()
lowlist = c.GetLowhpxList()
HHV = c.HHV(highlist)
LLV = c.LLV(lowlist)
for i in range(12, -1, -1):
AA4_LIST.append(c.RSV(c.GetClosepx(i), LLV, HHV))
#计算最近心情指数
i = 0
j = 0
SMA(AA4_LIST, i, 13, 8, AA5_LIST)
SMA(AA5_LIST, j, 13, 8, AA6_LIST)
INDEX = math.ceil(AA6_LIST[-1])
return INDEX
def GetMoodIndex(N,day):
AA4_LIST = [] #存放AA4数据的列表
AA5_LIST = [] #存放AA5数据的列表
AA6_LIST = []
c = calculate(N,day)
highlist = c.GetHighpxList()
lowlist = c.GetLowhpxList()
HHV = c.HHV(highlist)
LLV = c.LLV(lowlist)
for i in range(12,-1,-1):
AA4_LIST.append(c.RSV(c.GetClosepx(i), LLV, HHV))
#计算最近心情指数
i = 0
j = 0
SMA(AA4_LIST,i,13,8,AA5_LIST)
SMA(AA5_LIST,j,13,8,AA6_LIST)
INDEX = math.ceil(AA6_LIST[-1])
return INDEX
def handleImageProcessing(msg):
nmsg = msg[22:]
cb = (base64.urlsafe_b64decode(nmsg + '=' * (4 - len(nmsg) % 4)))
imgarr = idc.bytes_to_np_array(cb)
imgarr = imgarr[:, :, 0:3]
imgarr = np.stack([imgarr[:, :, 2], imgarr[:, :, 1], imgarr[:, :, 0]],
axis=2)
imgarr = np.transpose(imgarr, (2, 0, 1)) / 255
#bb: 416*416 dm:
detections, classes, depthmap = calculate.calculate(imgarr)
dist = np.sum(depthmap[64 - 10:64 + 10, 80 - 40:80 + 40])
print(dist)
if dist < 1800:
emit('vibrate', '6')
elif dist < 2000:
emit('vibrate', '5')
elif dist < 2100:
emit('vibrate', '4')
elif dist < 2300:
emit('vibrate', '2')
elif dist < 2700:
emit('vibrate', '1')
print(detections, '\n')
for x1, y1, x2, y2, _, _, c in detections:
cl = classes[int(c)]
object_width = int(x2 - x1)
object_length = int(y2 - y1)
if object_width <= 0 or object_length <= 0:
break
x1, x2 = x1 / 416 * 128, x2 / 416 * 160
# depthmap = depthmap[int(x1):int(x2), int(y1):int(y2)]
# dist = numpy.sum(depthmap)/(x2-x1)/(y2-y1)
print(dist)
left, right = x1, 416 - x2
if abs(left - right) > 20:
if left > right and dist < 2100:
emit('playsound',
cl + ' is on your right side. And it\'s pretty close')
if left < right and dist < 2100:
emit('playsound',
cl + ' is on your right side. And it\'s pretty close')
def auto_calculate(bot, update):
if update.message.text.startswith('Когда ближайшее полнолуние после'):
date_pol = re.search('\d{4}-\d{2}-\d{2}', update.message.text)
if date_pol is None:
update.message.reply_text('Введите дату формата гггг-мм-дд')
else:
date_pol = date_pol.group(0)
date_pol = ephem.next_full_moon(date_pol)
update.message.reply_text("{}".format(date_pol))
return
global q_equation
if update.message.text != '=':
q_equation = q_equation + update.message.text
else:
reply_markup = telegram.ReplyKeyboardRemove()
bot.send_message(chat_id=update.message.chat_id,
text=calculate(q_equation),
reply_markup=reply_markup)
def get(self):
d = self.request.get('astro')
filter = self.request.get('filter')
celebbday = datetime.datetime.strptime(d, '%d/%m/%Y').date().strftime('%Y%m%d')
[cellewis, celspiller, celchinese, celmillman] = calculate.calculate(celebbday)
res = []
infile = open("celeb_life_goal.txt")
for line in infile.readlines():
try:
tokens = line.split(":")
if filter == '':
if celmillman[0] == tokens[2].replace("\n",""):
res.append([tokens[0], tokens[1]])
else:
#logging.debug(tokens[1])
if (filter.lower() in tokens[1].lower()) and (celmillman[0] == tokens[2].replace("\n","")):
res.append([tokens[0], tokens[1]])
except Exception, e:
pass
def GetJValue(N, day):
KLIST = [] #存放K值的列表
RSV_LIST = [] #存放RSV值的列表
DLIST = [] #存放D值的列表
c = calculate(N, day)
highlist = c.GetHighpxList()
lowlist = c.GetLowhpxList()
HHV = c.HHV(highlist)
LLV = c.LLV(lowlist)
#计算最近5天内的RSV值
for i in range(4, -1, -1):
#print i
RSV_LIST.append(c.RSV(c.GetClosepx(i), LLV, HHV))
#计算K值
i = 0
j = 0
SMA(RSV_LIST, i, 5, 1, KLIST)
SMA(KLIST[2:], j, 3, 1, DLIST)
K = KLIST[-1]
D = DLIST[-1]
J = 3 * K - 2 * D
return J
def get(self):
d = self.request.get('astro')
filter = self.request.get('filter')
celebbday = datetime.datetime.strptime(
d, '%d/%m/%Y').date().strftime('%Y%m%d')
[cellewis, celspiller, celchinese,
celmillman] = calculate.calculate(celebbday)
res = []
infile = open("celeb_life_goal.txt")
for line in infile.readlines():
try:
tokens = line.split(":")
if filter == '':
if celmillman[0] == tokens[2].replace("\n", ""):
res.append([tokens[0], tokens[1]])
else:
#logging.debug(tokens[1])
if (filter.lower() in tokens[1].lower()) and (
celmillman[0] == tokens[2].replace("\n", "")):
res.append([tokens[0], tokens[1]])
except Exception, e:
pass
def GetJValue(N,day):
KLIST = [] #存放K值的列表
RSV_LIST = [] #存放RSV值的列表
DLIST = [] #存放D值的列表
c = calculate(N,day)
highlist = c.GetHighpxList()
lowlist = c.GetLowhpxList()
HHV = c.HHV(highlist)
LLV = c.LLV(lowlist)
#计算最近5天内的RSV值
for i in range(4,-1,-1):
#print i
RSV_LIST.append(c.RSV(c.GetClosepx(i),LLV,HHV))
#计算K值
i = 0
j = 0
SMA(RSV_LIST,i,5,1,KLIST)
SMA(KLIST[2:],j,3,1,DLIST)
K = KLIST[-1]
D = DLIST[-1]
J = 3*K-2*D
return J
def answer(text):
try:
dni, date, cp = _check_input_data(text)
except OnVotarError as e:
res = str(e)
logger.info('%s', e.__class__.__name__)
else:
result = calculate(dni, date, cp)
if result:
res = ('{}\n{}\n{}\n\n'
'Districte: {}\n'
'Secció: {}\n'
'Mesa: {}').format(*result)
logger.info('OK - %s %s', date[:4], cp)
else:
res = ('Les dades introduides no han retornat cap resultat.\n\n'
'Si has canviat recentment de domicili, prova el '
'codi postal anterior.\n'
'Si tens més dubtes, contacta amb el correu electrònic '
'oficial de la Generalitat:\n'
'*****@*****.**')
logger.info('DADES_INCORRECTES')
return res
def run(self):
"""
Opens file based on filename given by the user on the start screen.
Calls the calculate function from the calculate.py file to compute the normalised value for tastes/emotions.
Sends this data in an array to the main thread.
If the value for taste/emotion is above 60, it is classified as predominant.
"""
name = getName()
with open('../GUI/files/' + str(name) + '.csv', 'r') as f:
self.rows = sum(1 for line in f)
time.sleep(0.8)
while self.row < self.rows:
values = calculate(self.row, name)
# Thresholds for predominancy
for i in range(0, 5):
if values[i] > 55:
External.pred.append(i)
for i in range(5, 11):
if values[i] > 60:
External.pred.append(i)
self.row += 1
if self.row == self.rows:
values.clear()
values.append(1)
self.countChanged.emit(values)
# OpenFace reads the frames of the video every 0.33 seconds therefore, sleep time = 0.33
time.sleep(0.033)
values.clear()
import sys
from datetime import datetime
sys.path.append('..')
import calculate
file = open ("../famousbday.txt")
for f in file.readlines():
s = f.split(":")[2].replace("\n","")
name = f.split(":")[0].replace("\n","")
occup = f.split(":")[1].replace("\n","")
try:
d = datetime.strptime(s, '%d/%m/%Y').date()
res = calculate.calculate(d.strftime('%Y%m%d'))
if "mathematician" in occup:
if "194" in res[0]:
print name, res, occup, d
#if res[0][0] == '22' and res[3][0] == '303':
# print name, res, occup, d
except Exception, e:
pass
# some experimental code to determine to full type based on
# the celeb database.
import sys; sys.path.append('..')
import calculate
import math
if __name__ == '__main__':
res = calculate.calculate('19730424')
print calculate.calculate_mbti_full(res)
print res
#res = calculate.calculate('19451005')
#res = calculate.calculate('19490222')
#res = calculate.calculate('19820108')
#res = calculate.calculate('19100422') # Norman Steenrod,
#res = calculate.calculate('19160430') # Claude Shannon
#res = calculate.calculate('19230521') # Armand Borel
#res = calculate.calculate('19170617') # Atle Selberg
#res = calculate.calculate('19090821') # Nikolay Bogolyubov
#res = calculate.calculate('19640825') # Maxim Kontsevich
#res = calculate.calculate('19341012') # Albert Shiryaev
#res = calculate.calculate('19061024') # Alexander Gelfond
#res = calculate.calculate('19381103') # Martin Dunwoody
#res = calculate.calculate('19610804') # barack obama
#res = calculate.calculate('19730326') # larry page
#res = calculate.calculate('19730821') # sergey brin
#res = calculate.calculate('19540226') # tayyip erdogan
#res = calculate.calculate('19590226') # ahmet davutoglu
#res = calculate.calculate('19501029') # abdullah gul
def test_speed_increase_calculate_invalid(self): calc = calculate() car_speed_in = '10.4673' road_speed_in = 'a0hbv' result = calc.speed_increase(car_speed_in, road_speed_in)#valid speed self.assertFalse(result)
def test_floating_point():
assert calculate('10/4') == 2.5
assert calculate('5.5*2') == 11
assert calculate('100/3') - 33.33 < 0.1
assert calculate('5.5+3.5') == 9
assert calculate('3.4-1.4') == 2
import pickle
import sys
sys.path.append('..')
import calculate
lewis, spiller, chinese, millman = calculate.calculate('19730424')
#res = calculate.calculate('19451005')
#res = calculate.calculate('19820108')
#res = calculate.calculate('19490222')
#res = calculate.calculate('19730326') #larry page
#res = calculate.calculate('19730821') #brin
#res = calculate.calculate('19540226')
#res = calculate.calculate('19730423')
#res = calculate.calculate('19480331') # al gore
#res = calculate.calculate('19420108') # hawking
#res = calculate.calculate('19610804') # obama
print lewis, spiller, chinese, millman
def GetTradeDate(i):
c = calculate()
return c.tradedate[i]
# some experimental code to determine to full type based on
# the celeb database.
import sys
import calculate
import math
if __name__ == '__main__':
res = calculate.calculate('19730424')
print calculate.calculate_mbti_full(res)
print res
#res = calculate.calculate('19451005')
#res = calculate.calculate('19490222')
#res = calculate.calculate('19820108')
#res = calculate.calculate('19610804') # barack obama
#res = calculate.calculate('19730326') # larry page
#res = calculate.calculate('19730821') # sergey brin
#res = calculate.calculate('19540226') # tayyip erdogan
#res = calculate.calculate('19590226') # ahmet davutoglu
#res = calculate.calculate('19501029') # abdullah gul
#res = calculate.calculate('19560120') # bill maher
#res = calculate.calculate('19700921') # jen
#res = calculate.calculate('19180511') # richard feynman
#res = calculate.calculate('19691228') # linus torvalds
#res = calculate.calculate('19200102') # isaac asimov
#res = calculate.calculate('19690312') # acun
#res = calculate.calculate('19270622') # cetin altan
#res = calculate.calculate('19560101') # mumtaz turkone
#res = calculate.calculate('19550224') # steve jobs
#res = calculate.calculate('19370611') # mumford
def test_operator_precedence():
# Test Precedence of operators
assert calculate('2+7*2') == 16
assert calculate('4-6/3') == 2
assert calculate('4+5%3') == 6
def test_parentheses():
assert calculate('7+(5*2)') == 17
assert calculate('7*(5+2)') == 49
assert calculate('(7*3)+(9/3)') == 24
assert calculate('7+(7*(6+10/(1*2)))') == 84
__author__ = 'srtoosnye'
# -*- coding: utf-8 -*-
from Expr import Expr
from calculate import calculate
import data
import sys
conn = data.link_to_mysql()
cursor = conn.cursor()
data.create_table(cursor)
expr = raw_input('请输入表达式：')
if data.is_used_data(cursor, expr):
sys.exit()
s = Expr(expr)
s.check_value()
s.opt_value()
data.store_result(cursor, calculate(s.get_postfix()), expr)
conn.commit()
conn.close()
# some experimental code to determine to full type based on
# the celeb database.
import sys
import calculate
import math
if __name__ == '__main__':
res = calculate.calculate('19730424')
print calculate.calculate_mbti_full(res); print res
#res = calculate.calculate('19451005')
#res = calculate.calculate('19490222')
#res = calculate.calculate('19820108')
#res = calculate.calculate('19610804') # barack obama
#res = calculate.calculate('19730326') # larry page
#res = calculate.calculate('19730821') # sergey brin
#res = calculate.calculate('19540226') # tayyip erdogan
#res = calculate.calculate('19590226') # ahmet davutoglu
#res = calculate.calculate('19501029') # abdullah gul
#res = calculate.calculate('19560120') # bill maher
#res = calculate.calculate('19700921') # jen
#res = calculate.calculate('19180511') # richard feynman
#res = calculate.calculate('19691228') # linus torvalds
#res = calculate.calculate('19200102') # isaac asimov died 19920406
#res = calculate.calculate('19690312') # acun
#res = calculate.calculate('19270622') # cetin altan
#res = calculate.calculate('19560101') # mumtaz turkone
#res = calculate.calculate('19550224') # steve jobs
#res = calculate.calculate('19370611') # mumford
#res = calculate.calculate('19470412') # tom clancy
records=[]
insertQuery='insert into __table_name__(type,property,native_type,native_property,hit,total) VALUES(%s,%s,%s,%s,%s,%s);'
insertQuery = insertQuery.replace('__table_name__',tableName)
if lang !="FA":
#print insertQuery
filepath = 't.xml'
tree = xml.parse(filepath)
xmlRoot = tree.getroot()
for c in xmlRoot.findall('.//Type'):
typeName = c.attrib['name']
#typeName = 'http://dbpedia.org/ontology/University'
native_type = findLabel(originTree,typeName,slang,0)
if native_type == None:
native_type = 'None'
properties = calculate.calculate(typeName,lang)
#print properties
total = properties['__total__']
for property in properties:
native_property = ''
if property == '__total__':
native_property = '__total__'
else:
native_property = findLabel(originTree,property[1:-1],slang,1)
if native_property == None:
native_property = findLabel(originTree,property[1:-1],slang,2)
if native_property == None:
native_property = 'None'
hit = properties[property]
record = (typeName,property,native_type,native_property,hit,total)