def __init__(self, schedule_section): """Initialize a Schedule object.""" # schedule settings self.poll_frequency = to_int(schedule_section.poll_frequency) self.daily_at = self.format_daily_times(schedule_section.daily_at) self.hourly_at = to_int(to_list(schedule_section.hourly_at)) # skip conditions self.skip_hours_of_day = to_int(to_list(schedule_section.hours_of_day)) self.skip_days_of_week = to_list(schedule_section.days_of_week) self.skip_days_of_month = to_int(to_list(schedule_section.days_of_month)) self.skip_days_of_year = to_list(schedule_section.days_of_year) # lowercase skip days of week self.skip_days_of_week = [day.lower() for day in self.skip_days_of_week] # track state self.start_time = None self.last_time = None self.poll_count = 0 self.run_count = 0 # optional poll_message self.poll_message = ''
def _parse_2010_or_2014(sheet, num_col, num_col_name,
total_name, num_footers):
# Check header row.
headers = [
['Detailed Years of School', num_col_name],
['', 'Number'],
]
type_col = 0
if not np.all(sheet[4:6, [type_col, num_col]] == headers):
raise ValueError('Unexpected row headers.')
schooling_types = sheet[6:, type_col]
schooling_numbers = sheet[6:, num_col]
if schooling_types[0] != total_name:
raise ValueError('Unexpected name for "total"')
total_people = to_int(schooling_numbers[0])
schooling_types = schooling_types[1:]
schooling_numbers = schooling_numbers[1:]
# Remove footnotes, intentionally leave a blank row.
if not np.all(schooling_numbers[-num_footers:] == ''):
raise ValueError('Expected footer rows')
schooling_numbers = schooling_numbers[:-num_footers]
schooling_types = schooling_types[:-num_footers]
result = []
separator_rows, = np.where(schooling_types == '')
num_sections = len(separator_rows) - 1
for section in xrange(num_sections):
begin_index = separator_rows[section] + 1
end_index = separator_rows[section + 1] - 1
# Determine if the section has a category.
category = None
if schooling_numbers[begin_index] == '':
category = schooling_types[begin_index]
begin_index += 1
for index in xrange(begin_index, end_index + 1):
result.append((
schooling_types[index],
category,
to_int(schooling_numbers[index]),
))
if np.abs(sum([val[-1] for val in result]) - total_people) > 5:
raise ValueError('Total does not match observed.')
return total_people, result
def checkAddr(addr):
ip, port = addr.split(':')
if not to_int(port):
return None
if not checkIp(ip):
return None
return True
def test_to_int():
assert to_int('012') == 12
assert to_int('01234.4321') == 1234
assert to_int("01234.9") == 1234
assert to_int('-1234') == -1234
assert to_int('abc', 'FailedToConvert', False) == 'FailedToConvert'
assert to_int(-32.54e7, 'FailedToConvert', False) == -325400000
assert to_int(70.2e5, 'FailedToConvert', False) == 7020000
def is_mm_dd(mm_dd_value):
"""
Return True if mm_dd_value matches a legal month-day value.
Accepts 02-29 as a leap year day as well.
"""
mm_value, separator, dd_value = mm_dd_value.partition('-')
mm_value = to_int(mm_value, 0)
dd_value = to_int(dd_value, 0)
# allow 02-29 (leap year) as legal value
if mm_value == 2 and dd_value == 29:
is_valid = True
else:
try:
# year value does not matter; 2000 chosen as an arbitrary year
# print(f'datetime(2000, {mm_value}, {dd_value}) = {datetime.datetime(2000, mm_value, dd_value)}')
datetime.datetime(2000, mm_value, dd_value)
is_valid = True
except ValueError:
is_valid = False
return is_valid
import common
nums = common.to_int(common.read_file('2021/01/data.txt').splitlines())
# part 1
acc = 0
for i in range(1, len(nums)):
if nums[i - 1] < nums[i]:
acc += 1
print(acc)
# part 2
acc = 0
for i in range(3, len(nums)):
if nums[i - 3] < nums[i]:
acc += 1
print(acc)
import common
year_common = common.import_year_common(2017)
data = common.read_file('2017/10/data.txt').strip()
ints = common.to_int(data.split(','))
# NOTE: implementation of knot hash was moved to common
# part 1
result = year_common.knot_hash(ints)
print(result[0] * result[1])
# part 2
_, representation = year_common.knot_hash_full(data)
print(representation)
from collections import deque
import common
deck1 = common.to_int(common.read_file('2020/22/p1.txt').splitlines())
deck2 = common.to_int(common.read_file('2020/22/p2.txt').splitlines())
# part 1
p1 = deque(deck1)
p2 = deque(deck2)
while len(p1) > 0 and len(p2) > 0:
play1 = p1.popleft()
play2 = p2.popleft()
if play1 > play2:
p1.append(play1)
p1.append(play2)
else:
p2.append(play2)
p2.append(play1)
winner = p1 if len(p1) > 0 else p2
def calculate_score(cards):
acc = 0
for i in range(len(cards)):
acc += cards[-i-1] * (i+1)
return acc
print(calculate_score(winner))
import common
data = common.read_file('2017/6/data.txt')
cells = data.split('\t')
state = common.to_int(cells)
steps = 0
seen = set()
seen_at_step = dict()
def state_to_int():
acc = 0
for i in range(0, len(state)):
acc *= 100
acc += state[i]
return acc
while True:
state_val = state_to_int()
if state_val in seen:
break
seen.add(state_val)
seen_at_step[state_val] = steps
steps += 1
# find max
max_val = 0
max_i = 0
def is_hh_mm(hh_mm_value):
"""Return True hh_mm_value matches a legal hour:minute value."""
hh_value, separator, mm_value = hh_mm_value.partition(':')
hh_value = to_int(hh_value, -1)
mm_value = to_int(mm_value, -1)
return (0 <= hh_value <= 23) and (0 <= mm_value <= 59)
from collections import defaultdict
import common
nums = common.to_int(common.read_file('2021/06/data.txt').split(','))
fish = defaultdict(lambda: 0)
for num in nums:
fish[num] += 1
# part 1 and 2
curr_fish = fish
for day in range(256):
new_fish = defaultdict(lambda: 0)
for i in range(1, 9):
if i in curr_fish:
new_fish[i - 1] = curr_fish[i]
new_fish[6] += curr_fish[0]
new_fish[8] += curr_fish[0]
curr_fish = new_fish
if day == 79:
print(sum(curr_fish.values()))
print(sum(curr_fish.values()))
from collections import defaultdict
import common
lines = common.read_file('2021/13/data.txt').splitlines()
board = defaultdict(lambda: 0)
for i in range(len(lines)):
line = lines[i]
if line == '':
break
x, y = common.to_int(line.split(','))
board[(x, y)] = 1
folds = []
for line in lines[i+1:]:
axis, value = line.split()[2].split('=')
folds.append((axis, int(value)))
# part 1 and 2
for i in range(len(folds)):
axis, middle = folds[i]
new_board = defaultdict(lambda: 0)
if axis == 'x':
for x, y in board:
reflected = 2*middle - x
if reflected < middle:
new_board[(reflected, y)] = 1
else:
new_board[(x, y)] = 1
else:
for x, y in board:
from collections import deque, defaultdict
from queue import PriorityQueue
import common
lines = common.read_file('2021/15/data.txt').splitlines()
board = [common.to_int(x) for x in lines]
w = len(lines[0])
h = len(lines)
# part 1
def solve(board, w, h):
distances = {}
q = PriorityQueue()
q.put((0, (1, 0)))
q.put((0, (0, 1)))
while not q.empty():
dist, coords = q.get()
x, y = coords
if x < 0 or x >= w or y < 0 or y >= h:
continue
dist += board[y][x]
if (x, y) in distances and dist >= distances[(x, y)]:
continue
distances[(x, y)] = dist
if (x, y) == (w-1, h-1):
break
q.put((dist, (x-1, y)))
q.put((dist, (x+1, y)))
q.put((dist, (x, y-1)))
q.put((dist, (x, y+1)))
import common
positions = common.to_int(common.read_file('2021/07/data.txt').split(','))
min_pos = min(positions)
max_pos = max(positions)
# part 1 and 2
fuel_cost = [0] * (max_pos - min_pos + 1)
fuel_cost2 = [0] * (max_pos - min_pos + 1)
for crab in positions:
for i in range(min_pos, max_pos + 1):
diff = abs(i - crab)
fuel_cost[i] += diff
fuel_cost2[i] += sum(range(diff + 1))
print(min(fuel_cost))
print(min(fuel_cost2))
import common
data = common.to_int(common.read_file('2019/01/data.txt').splitlines())
# part 1
acc = 0
for num in data:
acc += (num // 3) - 2
print('part 1: ' + str(acc))
# part 2
acc = 0
for num in data:
cost = max((num // 3) - 2, 0)
while cost > 0:
acc += cost
cost = max((cost // 3) - 2, 0)
print('part 2: ' + str(acc))
def traders_web_D(self):
# 33333本日の先物取引情報
table_name = "futures_op"
UURL = "https://www.traders.co.jp/domestic_stocks/invest_tool/futures/futures_op.asp"
# テーブル情報取得
dfs = pd.read_html(common.Chorme_get(UURL), header=1)
for ii in range(len(dfs)):
# テーブル番号検索
if dfs[ii].columns[0] == "証券会社名": # 証券会社名
num = ii
break
# Webから取得した情報のカラム名をリスト化
col_list = [i for i in dfs[num].columns]
# DB追加のカラム作成
col_tmp = []
H = ''
for i in dfs[num].columns:
if i.count("Unnamed"):
if 'Unnamed: 2' == i:
col_tmp.append('日付')
H = 'P'
else:
col_tmp.append('PUT_CALL')
H = 'C'
else:
col_tmp.append(H + i.replace(".1", ""))
# カラムのリネームcol_list→col_tmp
# col = dict(zip(col_list,col_tmp))
col = {}
col = {col_list[i]: col_tmp[i] for i in range(len(col_list))}
dfs[num] = dfs[num].rename(columns=col)
# DBからカラム情報取得。ない場合は追加する。
set_new = [i for i in dfs[num].columns]
res = common.column_check(DB_INFO, table_name, set_new)
# DBへのインポート
for idx, row in dfs[num].iterrows():
dict_w = {}
for ii in range(len(row)):
if str(row[ii]) != str(float("nan")):
try:
dict_w[dfs[num].columns[ii]] = int(row[ii])
except:
dict_w[dfs[num].columns[ii]] = row[ii]
else:
dict_w[dfs[num].columns[ii]] = 0
dict_w['日付'] = common.env_time()[0][:8]
common.insertDB3(DB_INFO, table_name, dict_w)
# 本日のカラム取得
sqls = "select * from futures_op where 日付 = %(key1)s" % {'key1': common.env_time()[0][:8]}
sql_pd = common.select_sql(DB_INFO, sqls)
set_new = [i for i in sql_pd.columns if i != 'now' and i != '証券会社名' and i != '日付' and i != 'PUT_CALL']
# 本日のカラムを対象に合計取得
sqls = "select SUM(" + "),SUM(".join(set_new) + ") from futures_op where 日付 = '%(key1)s'" % {'key1': common.env_time()[0][:8]}
sql_pd = common.select_sql(DB_INFO, sqls)
for i, row in sql_pd.iterrows():
set_val = []
for ii in range(len(row)):
if row[ii] is None:
set_val.append(0)
else:
set_val.append(row[ii])
set_val = common.to_int(set_val)
col = {}
col = {set_new[i]: set_val[i] for i in range(len(set_new))}
col['証券会社名'] = '合計'
col['日付'] = common.env_time()[0][:8]
col = common.to_int(col)
common.insertDB3(DB_INFO, table_name, col)
print(col)
# 2222222本日の先物取引情報
table_name = "futures"
UURL = "https://www.traders.co.jp/domestic_stocks/invest_tool/futures/futures_top.asp"
dfs = common.read_html2(common.Chorme_get(UURL), 1) # header=0,skiprows=0(省略可能)
for ii in range(len(dfs)):
if dfs[ii].columns[0] == "SELL":
num = ii
break
# カラムの入れ替え
CC = ['証券会社名', 'SELL_225', 'BUY_225', 'NET_225', '日付', 'SELL_TOPIX','BUY_TOPIX', 'NET_TOPIX', '更新日', 'SELL_225M', 'BUY_225M', 'NET_225M']
col_name = {}
col_name = {dfs[num].columns[c]: CC[c] for c in range(len(dfs[num].columns))}
dfs[num] = dfs[num].rename(columns=col_name)
# DBへのインポート
for idx, row in dfs[num].iterrows():
dict_w = {}
for ii in range(len(row)):
dict_w[dfs[num].columns[ii]] = row[ii]
dict_w['更新日'] = common.env_time()[1]
dict_w['日付'] = common.env_time()[0][:8]
common.insertDB3(DB_INFO, table_name, dict_w)