content stringlengths 7 1.05M |
|---|
#---
def plotUAVcontrolSignals(df,plt):
## - Plot the Control Signals
fig, axs = plt.subplots(2,2, sharex=True)
fig.suptitle("Control Signals (Executed by the Drone)")
# ---------- u_\theta
axs[0,0].plot(df['time'], df['A.pSCU[0]'])
axs[0,0].set(ylabel=r'$u_{\theta}~$ [rad]')
axs[0,0].grid(True)
axs[0,0].set(xlim=(min(df['time']), max(df['time'])),
ylim=(min(df['A.pSCU[0]'])-0.025, max(df['A.pSCU[0]'])+0.025))
# ---------- u_\phi
axs[0,1].plot(df['time'], df['U[1]'])
axs[0,1].set(ylabel=r'$u_{\phi}~$ [rad]')
axs[0,1].grid(True)
axs[0,1].set(xlim=(min(df['time']), max(df['time'])),
ylim=(min(df['U[1]'])-0.025, max(df['U[1]'])+0.025))
# ---------- u_{\Dot{\psi}}
axs[1,0].plot(df['time'], df['U[2]'])
axs[1,0].set(xlabel='time [$s$]', ylabel='$u_{\dot{\psi}}~$ [rad/s]')
axs[1,0].grid(True)
axs[1,0].set(xlim=(min(df['time']), max(df['time'])),
ylim=(min(df['U[2]'])-0.025, max(df['U[2]'])+0.025))
# ---------- u_{\Dot{z}}
axs[1,1].plot(df['time'], df['U[3]'])
axs[1,1].set(xlabel='time [$s$]', ylabel='$u_{z}~$ [m/s]')
axs[1,1].grid(True)
axs[1,1].set(xlim=(min(df['time']), max(df['time'])),
ylim=(min(df['U[3]'])-0.025, max(df['U[3]'])+0.025))
Fig = plt.gcf()
plt.show()
# the axes attributes need to be set before the call to subplot
plt.rcParams.update({
"grid.color": "0.5",
"grid.linestyle": "--",
"grid.linewidth": 0.25,
"lines.linewidth": 1,
"lines.color": "g"})
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica"]})
# set font of all elements to size 15
plt.rcParams['figure.figsize'] = (22,8)
plt.rc('font', size=26)
plt.rc('axes', titlesize=25)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
print('\n')
plt.show()
return fig
|
def arr_pair_sum(arr, k):
# edge case
if len(arr) < 2:
return
# set for tracking
seen = set()
output = set()
for num in arr:
target = k - num
if target not in seen:
seen.add(num)
else:
output.add((min(num, target), max(num, target)))
return len(output)
print(arr_pair_sum([1, 3, 2, 2], 4))
|
def sanitize(time_string):
if "-" in time_string:
splitter = "-"
elif ":" in time_string:
splitter = ":"
else:
return time_string
(mins, secs) = time_string.split(splitter)
return mins + "." + secs
with open("james.txt") as jaf:
data = jaf.readline()
james = sorted([sanitize(each_t) for each_t in data.strip().split(",")])
with open("julie.txt") as jaf:
data = jaf.readline()
julie = sorted([sanitize(each_t) for each_t in data.strip().split(",")])
with open("mikey.txt") as jaf:
data = jaf.readline()
mikey = sorted([sanitize(each_t) for each_t in data.strip().split(",")])
with open("sarah.txt") as jaf:
data = jaf.readline()
sarah = sorted([sanitize(each_t) for each_t in data.strip().split(",")])
clean_james = []
clean_julie = []
clean_mikey = []
clean_sarah = []
for each_t in james:
if each_t not in clean_james:
clean_james.append(sanitize(each_t))
for each_t in julie:
if each_t not in clean_julie:
clean_julie.append(sanitize(each_t))
for each_t in mikey:
if each_t not in clean_mikey:
clean_mikey.append(sanitize(each_t))
for each_t in sarah:
if each_t not in clean_sarah:
clean_sarah.append(sanitize(each_t))
print(clean_james[0:3])
print(clean_julie[0:3])
print(clean_mikey[0:3])
print(clean_sarah[0:3])
|
# Backport of str.removeprefix.
# TODO Remove when minimum python version is 3.9 or above
def removeprefix(string: str, prefix: str) -> str:
if string.startswith(prefix):
return string[len(prefix) :]
return string
|
# Some magic data values exist.
# They often represent missing data. More information can be found at the following link.
# https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-estimate-and-annotation-values.html
class Magic:
MISSING_VALUES = [
None,
-999999999,
-888888888,
-666666666,
-555555555,
-333333333,
-222222222,
]
|
"""
Author: Ioannis Paraskevakos
License: MIT
Copyright: 2018-2019
"""
# ------------------------------------------------------------------------------
# States
NEW = 0 # New campaign is submitted
PLANNING = 1 # Planning the exeuction of the campaign
EXECUTING = 2 # At least one workflow is executing
DONE = 3 # Campaign has finished successfully
FAILED = 4 # Campaign execution has failed
CANCELED = 5 # Campaign got canceled by the user.
CFINAL = [DONE, FAILED, CANCELED] # Final states for a campaign.
state_dict = {0: 'NEW',
1: 'PLANNING',
2: 'EXECUTING',
3: 'DONE',
4: 'FAILED',
5: 'CANCELED'
}
# ------------------------------------------------------------------------------
|
class Tile (object):
""" This is the abstract representation of Tiles, the building blocks of the world."""
def __init__ (self, stage, x, y):
self.stage = stage
self.x = x
self.y = y
"initializes the tile with a random type from the types list"
self.tile_type = self.stage.tile_type_initializer()
def get_tile_location (self):
"Returns an x,y tuple for the tile."
return (self.x, self.y)
def set_tile_type (self, target_type):
if target_type in self.stage.tile_types:
self.tile_type = target_type
else:
print("Not a valid tile type.")
|
def sample(task):
if task is not logistic:
raise NotImplementedError
# Parametric Generator for Logistic Regression Task (TODO: Generalize for Task - Parameter Specification)
theta = [np.random.uniform( 1, 10),
np.random.uniform( 1, 10),
np.random.uniform(-1, 1)]
return task(sample_space, theta), theta
def sample_points(task, batch_size):
# Sample Random Points from Sample Space
idx = np.random.choice(np.arange(len(sample_space)), batch_size, replace = False)
return sample_space[idx[:,None]], task[idx[:,None]]
def meta_sample(radius, count):
# Generate Sample Space of Specified Radius
sample_space = np.linspace(-radius, radius, count)
return sample_space |
def read_input():
joltages = []
with open('day10_input.txt') as input_file:
for line in input_file:
line = line.strip()
joltages.append( int(line) )
return joltages
# Find a chain that uses all of your adapters to connect the charging outlet
# to your device's built-in adapter and count the joltage differences
# between the charging outlet, the adapters, and your device. What is
# the number of 1-jolt differences multiplied by the number of 3-jolt
# differences?
def part1(joltages):
# 1 in position 3 as there's an implicit 3 jolt jump
# as the last step
diffs = [0, 0, 0, 1]
for i in range(1, len(joltages)):
diff = joltages[i] - joltages[i-1]
diffs[diff] += 1
return diffs
# Part 2: What is the total number of distinct ways you can arrange
# the adapters to connect the charging outlet to your device?
def part2(joltages, joltage_jumps):
# observation: 3-jolt jumps are mandatory and so don't count for anything
# How many possibilites does a run of n consecutive 1-jolt jumps allow for?
# Find all the runs of 1-jumps, then how many possibilites they provide,
# then multiply them all.
# Consider runs x, x+1, x+2, ..., x+N
# For N=2, the 2 possibilities are:
# 0, 1, 2 OR 0, 2
# For N=3, the 4 possibilities are:
# 0, 1, 2, 3 OR 0, 2, 3 OR 0, 1, 3 OR 0, 3
# For N=4, the 7 possibilities are:
# 0, 1, 2, 3, 4 OR 0, 2, 3, 4 OR 0, 1, 3, 4 OR 0, 1, 2, 4 OR
# 0, 1, 4 OR 0, 2, 4 OR 0, 3, 4
# There must be a more satisfying way to do this but N=4 is all that was
# needed to solve the problem
permutations = [1, 1, 2, 4, 7]
one_jump_runs = []
run_length = 0
for i in range(1, len(joltages)):
diff = joltages[i] - joltages[i - 1]
if diff == 1:
run_length += 1
else:
one_jump_runs.append(run_length)
run_length = 0
one_jump_runs.append(run_length)
one_jump_runs = list(filter(lambda x: x > 1, one_jump_runs))
total_possibilities = 1
for run_length in one_jump_runs:
total_possibilities *= permutations[run_length]
return total_possibilities
joltages = read_input()
joltages.append(0) # add implicit 0 that forms the start of the chain
joltages = sorted(joltages)
joltage_jumps = part1(joltages)
print("Part 1: product of 1-jolt jumps with 3-jolt jumps : {}"
.format(joltage_jumps[1] * joltage_jumps[3]))
print("Part 2: Total possible arrangements of adapters {}".format(part2(joltages, joltage_jumps)))
|
class Solution:
def compress(self, chars):
"""
:type chars: List[str]
:rtype: int
"""
last, n, y = chars[0], 1, 0
for x in range(1, len(chars)):
c = chars[x]
if c == last: n += 1
else:
for ch in last + str(n > 1 and n or ''):
chars[y] = ch
y += 1
last, n = c, 1
for ch in last + str(n > 1 and n or ''):
chars[y] = ch
y += 1
while len(chars) > y: chars.pop()
return y
|
def main(request, response):
response.headers.set(b"Access-Control-Allow-Origin", request.headers.get(b"origin"))
token = request.GET[b"token"]
request.server.stash.put(token, b"")
response.content = b"PASS"
|
#!/usr/bin/env python
# *-* coding: UTF-8 *-*
"""Tuxy scrie รฎn fiecare zi foarte multe formule matematice.
Pentru cฤ formulele sunt din ce รฎn ce mai complicate trebuie
sฤ foloseascฤ o serie de paranteze ศi a descoperit cฤ cea
mai frecventฤ problemฤ a lui este cฤ nu toate parantezele
sunt folosite cum trebuie.
Pentru acest lucru a apelat la ajutorul tฤu.
Cรขteva exemple:
- [] este bine
- []() este bine
- [()()] este bine
- ][ nu este bine
- (][][) nu este bine
- [)]()[(] nu este bine
"""
def este_corect(expresie):
"""Verificฤ dacฤ toate parantezele sunt folosite corespunzฤtor,
prin expresie.
"""
stiva = []
stiva.append(expresie[0])
for i in range(1, len(expresie)):
stiva.append(expresie[i])
if stiva[len(stiva) - 2] == '(' and stiva[len(stiva) - 1] == ')':
stiva.pop()
stiva.pop()
elif stiva[len(stiva) - 2] == '[' and stiva[len(stiva) - 1] == ']':
stiva.pop()
stiva.pop()
return not stiva
if __name__ == "__main__":
assert este_corect("[()[]]"), "Probleme la expresia 1"
assert este_corect("()()[][]"), "Probleme la expresia 2"
assert este_corect("([([])])"), "Probleme la expresia 3"
assert not este_corect("[)()()()"), "Probleme la expresia 4"
assert not este_corect("][[()][]"), "Probleme la expresia 5"
assert not este_corect("([()]))"), "Probleme la expresia 6"
assert not este_corect("([)]"), "Probleme la expresia 7"
|
def iterate_dictionary(d, path, squash_single=False):
"""
Takes a dict, and a path delimited with slashes like A/B/C/D, and returns a list of objects found at all leaf nodes at all trajectories `dict[A][B][C][D]`. It does this using BFS not DFS.
The word "leaf" hereby refers to an item at the search path level. That is, upon calling the function
iterate_dictionary(d_to_search, "A/B/C/D")
If `d_to_search` has five levels A/B/C/D/E, then D is the "leaf node level". Since `[E]` exists, then at least one object in the return list will be a dictionary.
Rules
===========================
Each node can be either
1) an arbitrary non-list, non-dictionary object
2) a dictionary
3) a list of arbitrary objects
All nodes of type 3 at each level are searched for nodes of type 1 and 2. Nodes of type 2 are the ones iterated in this tree search.
At the current time, nodes of type 1 are *not* inspected. They are returned in a list if they are at the search path and ignored otherwise.
Returns
===========================
1) If the path is an empty string, returns the original dict
2) *If* at least one object exists at the search path, it returns a list of all items at the search path. Using the above example terminology, a list of all objects at all trajectories `"A/B/C/D"`.
*Special Parameter*: If the optional Boolean parameter `squash_single` is True, and the return list contains only one object, the object is returned (*not* a list), else a list with that one object is returned. This optional flag is useful so that [0] does not have to be indexed on the return list in the case where only one item is expected.
3) None in the case that there are no objects at the search path.
"""
if path == "":
return d
path_parts = path.split("/")
return_list = []
sub_dicts = [d] # BFS, start with root node
for i, ival in enumerate(path_parts):
new_sub_dicts = []
for s in sub_dicts:
if ival in s: # this tree node is part of the search path
the_list = s[ival] if isinstance(s[ival], list) else [s[ival]]
for j in the_list:
if i < len(path_parts) - 1: # not a leaf node; check level
if isinstance(j, dict): # skip this non-leaf node if not a dict
new_sub_dicts.append(j) # BFS expansion
else: # leaf node at the desired path; add to final return list
return_list.append(j)
sub_dicts = new_sub_dicts
# return
return return_list[0] if squash_single and len(return_list) == 1 else return_list if len(return_list) >= 1 else None
def get_all_values(d):
"""
This function returns a list of all values in a nested dictionary.
By nested, this means each item X in d can either be a dict, a list, or a "value".
Other iterables other than dicts and lists are treated as values and are not iterated currently.
Returns a list, or None if there are no values found (for example a nested structure of all
empty dicts)
"""
if not isinstance(d, dict):
raise ValueError("input is not a dictionary")
return _get_all_values(d)
def _get_all_values(d):
vals = []
if isinstance(d, dict):
for _, v in d.items():
vals += _get_all_values(v)
elif isinstance(d, list):
for l in d:
vals += _get_all_values(l)
else:
vals.append(d)
return vals
|
class Address:
def __init__(self, street_address, city, country):
self.country = country
self.city = city
self.street_address = street_address
def __str__(self):
return f'{self.street_address}, {self.city}, {self.country}'
|
def extractBetwixtedtranslationsBlogspotCom(item):
'''
Parser for 'betwixtedtranslations.blogspot.com'
'''
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or "preview" in item['title'].lower():
return None
tagmap = [
('Transmigrated Senior Martial Brother', 'Transmigrated Senior Martial Brother', 'translated'),
('Part-Time Taoist Priest', 'Part-Time Taoist Priest', 'translated'),
('Criminal Psychology', 'Criminal Psychology', 'translated'),
('Death Progress Bar', 'Death Progress Bar', 'translated'),
('King of Classical Music', 'King of Classical Music', 'translated'),
('Everyday I Get up to See the Villain Stealing the Show', 'Everyday I Get up to See the Villain Stealing the Show', 'translated'),
('where is our agreement to be each other\'s arch rivals?', 'where is our agreement to be each other\'s arch rivals?', 'translated'),
('Gentle Beast', 'Gentle Beast', 'translated'),
('Epiphanies of Rebirth', 'Epiphanies of Rebirth', 'translated'),
]
for tagname, name, tl_type in tagmap:
if tagname in item['tags']:
return buildReleaseMessageWithType(item, name, vol, chp, frag=frag, postfix=postfix, tl_type=tl_type)
return False |
#
# This file contains the Python code from Program 16.20 of
# "Data Structures and Algorithms
# with Object-Oriented Design Patterns in Python"
# by Bruno R. Preiss.
#
# Copyright (c) 2003 by Bruno R. Preiss, P.Eng. All rights reserved.
#
# http://www.brpreiss.com/books/opus7/programs/pgm16_20.txt
#
class Algorithms(object):
class EarliestTimeVisitor(Visitor):
def __init__(self, earliestTime):
super(Algorithms.EarliestTimeVisitor,self).__init__()
self._earliestTime = earliestTime
def visit(self, w):
t = self._earliestTime[0]
for e in w.incidentEdges:
t = max(t,
self._earliestTime[e.v0.number] + e.weight)
self._earliestTime[w.number] = t
|
day_num = 15
file_load = open("input/day15.txt", "r")
file_in = file_load.read()
file_load.close()
file_in = list(map(int, file_in.split(",")))
def run():
def game(input_in, round_hunt):
num_last = {}
for temp_pos, temp_num in enumerate(input_in, 1):
num_last[temp_num] = temp_pos
game_round = len(input_in)
game_next = 0
for game_round in range(len(input_in) + 1, round_hunt):
if game_next not in num_last:
num_last[game_next] = game_round
game_next = 0
else:
temp_next = game_round - num_last[game_next]
num_last[game_next] = game_round
game_next = temp_next
return game_next
return game(file_in, 2020), game(file_in, 30000000)
if __name__ == "__main__":
print(run()) |
def findDuplicatesSequence(sequence):
stringySequence = str(sequence)
while stringySequence[0] == stringySequence[-1]:
stringySequence = stringySequence[-1] + stringySequence[0:-1]
iterableSequenceDuplicates = (re.finditer(r"(\d)\1+", str(stringySequence)))
duplicatesList = [iterable[0] for iterable in iterableSequenceDuplicates]
filteredDuplicatesList = []
for duplicates in duplicatesList:
duplicates = duplicates[:-1]
filteredDuplicatesList.append(duplicates)
counter = 0
for duplicates in filteredDuplicatesList:
int(duplicates[0])
counter += int(duplicates[0]) * len(duplicates)
print(counter)
return counter
|
"""
Given a list of words, find all pairs of unique indices such that the concatenation
of the two words is a palindrome.
For example, given the list ["code", "edoc", "da", "d"], return [(0, 1), (1, 0), (2, 3)].
"""
test = ['code', 'edoc', 'da', 'd']
palindrome_index = []
for i in range(len(test)):
for j in range(len(test)):
if i !=j:
concat = test[i] + test[j]
if len(concat) % 2 == 0:
part1 = concat[0:(len(concat)//2)]
part2 = concat[(len(concat)//2):]
if part1 == part2[::-1]:
palindrome_index.append([i,j])
else:
part1 = concat[0:(len(concat)//2)+1]
part2 = concat[len(concat)//2:]
if part1 == part2[::-1]:
palindrome_index.append([i,j])
print(palindrome_index)
|
#
# PySNMP MIB module NNCBELLCOREGR820DS1STATISTICS-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/NNCBELLCOREGR820DS1STATISTICS-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:13:04 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueRangeConstraint, ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsUnion")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
nncExtensions, = mibBuilder.importSymbols("NNCGNI0001-SMI", "nncExtensions")
ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance")
MibIdentifier, NotificationType, IpAddress, ObjectIdentity, TimeTicks, Unsigned32, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, Gauge32, iso, Counter64, Integer32, Bits, Counter32 = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "NotificationType", "IpAddress", "ObjectIdentity", "TimeTicks", "Unsigned32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ModuleIdentity", "Gauge32", "iso", "Counter64", "Integer32", "Bits", "Counter32")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
nncBellcoreGR820Ds1Statistics = ModuleIdentity((1, 3, 6, 1, 4, 1, 123, 3, 70))
if mibBuilder.loadTexts: nncBellcoreGR820Ds1Statistics.setLastUpdated('9902151200Z')
if mibBuilder.loadTexts: nncBellcoreGR820Ds1Statistics.setOrganization('Newbridge Networks Corporation')
nncBellcoreGR820Ds1StatisticsObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 123, 3, 70, 1))
nncBellcoreGR820Ds1StatisticsGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 123, 3, 70, 2))
nncBellcoreGR820Ds1StatisticsCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 123, 3, 70, 3))
nncBellcoreGR820Ds1CurrStatsTable = MibTable((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1), )
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrStatsTable.setStatus('current')
nncBellcoreGR820Ds1CurrStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrStatsEntry.setStatus('current')
nncBellcoreGR820Ds1CurrLineCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 1), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrLineCV.setStatus('current')
nncBellcoreGR820Ds1CurrLineES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrLineES.setStatus('current')
nncBellcoreGR820Ds1CurrLineLOSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrLineLOSS.setStatus('current')
nncBellcoreGR820Ds1CurrPathCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathCV.setStatus('current')
nncBellcoreGR820Ds1CurrPathES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathES.setStatus('current')
nncBellcoreGR820Ds1CurrPathSES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathSES.setStatus('current')
nncBellcoreGR820Ds1CurrPathAISS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathAISS.setStatus('current')
nncBellcoreGR820Ds1CurrPathCSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathCSS.setStatus('current')
nncBellcoreGR820Ds1CurrPathUAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathUAS.setStatus('current')
nncBellcoreGR820Ds1CurrPathSAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 10), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathSAS.setStatus('current')
nncBellcoreGR820Ds1CurrPathFC = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 1, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1CurrPathFC.setStatus('current')
nncBellcoreGR820Ds1IntervalStatsTable = MibTable((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2), )
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalStatsTable.setStatus('current')
nncBellcoreGR820Ds1IntervalStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalIndex"))
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalStatsEntry.setStatus('current')
nncBellcoreGR820Ds1IntervalIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 96))).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalIndex.setStatus('current')
nncBellcoreGR820Ds1IntervalLineCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalLineCV.setStatus('current')
nncBellcoreGR820Ds1IntervalLineES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalLineES.setStatus('current')
nncBellcoreGR820Ds1IntervalLineLOSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalLineLOSS.setStatus('current')
nncBellcoreGR820Ds1IntervalPathCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathCV.setStatus('current')
nncBellcoreGR820Ds1IntervalPathES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathES.setStatus('current')
nncBellcoreGR820Ds1IntervalPathSES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathSES.setStatus('current')
nncBellcoreGR820Ds1IntervalPathAISS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathAISS.setStatus('current')
nncBellcoreGR820Ds1IntervalPathCSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathCSS.setStatus('current')
nncBellcoreGR820Ds1IntervalPathUAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 10), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathUAS.setStatus('current')
nncBellcoreGR820Ds1IntervalPathSAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathSAS.setStatus('current')
nncBellcoreGR820Ds1IntervalPathFC = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 2, 1, 12), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1IntervalPathFC.setStatus('current')
nncBellcoreGR820Ds1TotalStatsTable = MibTable((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3), )
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalStatsTable.setStatus('current')
nncBellcoreGR820Ds1TotalStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalStatsEntry.setStatus('current')
nncBellcoreGR820Ds1TotalLineCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 1), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalLineCV.setStatus('current')
nncBellcoreGR820Ds1TotalLineES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalLineES.setStatus('current')
nncBellcoreGR820Ds1TotalLineLOSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalLineLOSS.setStatus('current')
nncBellcoreGR820Ds1TotalPathCV = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathCV.setStatus('current')
nncBellcoreGR820Ds1TotalPathES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathES.setStatus('current')
nncBellcoreGR820Ds1TotalPathSES = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathSES.setStatus('current')
nncBellcoreGR820Ds1TotalPathAISS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathAISS.setStatus('current')
nncBellcoreGR820Ds1TotalPathCSS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathCSS.setStatus('current')
nncBellcoreGR820Ds1TotalPathUAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathUAS.setStatus('current')
nncBellcoreGR820Ds1TotalPathSAS = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 10), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathSAS.setStatus('current')
nncBellcoreGR820Ds1TotalPathFC = MibTableColumn((1, 3, 6, 1, 4, 1, 123, 3, 70, 1, 3, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: nncBellcoreGR820Ds1TotalPathFC.setStatus('current')
nncBellcoreGR820Ds1CurrStatsGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 123, 3, 70, 2, 1)).setObjects(("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrLineCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrLineES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrLineLOSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathSES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathAISS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathCSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathUAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathSAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrPathFC"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
nncBellcoreGR820Ds1CurrStatsGroup = nncBellcoreGR820Ds1CurrStatsGroup.setStatus('current')
nncBellcoreGR820Ds1IntervalStatsGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 123, 3, 70, 2, 2)).setObjects(("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalIndex"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalLineCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalLineES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalLineLOSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathSES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathAISS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathCSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathUAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathSAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalPathFC"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
nncBellcoreGR820Ds1IntervalStatsGroup = nncBellcoreGR820Ds1IntervalStatsGroup.setStatus('current')
nncBellcoreGR820Ds1TotalStatsGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 123, 3, 70, 2, 3)).setObjects(("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalLineCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalLineES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalLineLOSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathCV"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathSES"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathAISS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathCSS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathUAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathSAS"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalPathFC"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
nncBellcoreGR820Ds1TotalStatsGroup = nncBellcoreGR820Ds1TotalStatsGroup.setStatus('current')
nncBellcoreGR820Ds1StatisticsCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 123, 3, 70, 3, 1)).setObjects(("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1CurrStatsGroup"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1IntervalStatsGroup"), ("NNCBELLCOREGR820DS1STATISTICS-MIB", "nncBellcoreGR820Ds1TotalStatsGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
nncBellcoreGR820Ds1StatisticsCompliance = nncBellcoreGR820Ds1StatisticsCompliance.setStatus('current')
mibBuilder.exportSymbols("NNCBELLCOREGR820DS1STATISTICS-MIB", nncBellcoreGR820Ds1TotalPathSAS=nncBellcoreGR820Ds1TotalPathSAS, nncBellcoreGR820Ds1TotalStatsEntry=nncBellcoreGR820Ds1TotalStatsEntry, nncBellcoreGR820Ds1TotalStatsGroup=nncBellcoreGR820Ds1TotalStatsGroup, nncBellcoreGR820Ds1CurrLineLOSS=nncBellcoreGR820Ds1CurrLineLOSS, nncBellcoreGR820Ds1CurrStatsGroup=nncBellcoreGR820Ds1CurrStatsGroup, nncBellcoreGR820Ds1IntervalPathCSS=nncBellcoreGR820Ds1IntervalPathCSS, nncBellcoreGR820Ds1IntervalPathFC=nncBellcoreGR820Ds1IntervalPathFC, nncBellcoreGR820Ds1TotalLineES=nncBellcoreGR820Ds1TotalLineES, nncBellcoreGR820Ds1TotalPathSES=nncBellcoreGR820Ds1TotalPathSES, nncBellcoreGR820Ds1CurrPathES=nncBellcoreGR820Ds1CurrPathES, nncBellcoreGR820Ds1TotalPathES=nncBellcoreGR820Ds1TotalPathES, nncBellcoreGR820Ds1CurrLineES=nncBellcoreGR820Ds1CurrLineES, nncBellcoreGR820Ds1CurrStatsTable=nncBellcoreGR820Ds1CurrStatsTable, nncBellcoreGR820Ds1CurrStatsEntry=nncBellcoreGR820Ds1CurrStatsEntry, nncBellcoreGR820Ds1TotalPathAISS=nncBellcoreGR820Ds1TotalPathAISS, nncBellcoreGR820Ds1CurrPathSAS=nncBellcoreGR820Ds1CurrPathSAS, nncBellcoreGR820Ds1IntervalPathUAS=nncBellcoreGR820Ds1IntervalPathUAS, nncBellcoreGR820Ds1IntervalStatsGroup=nncBellcoreGR820Ds1IntervalStatsGroup, nncBellcoreGR820Ds1CurrPathSES=nncBellcoreGR820Ds1CurrPathSES, nncBellcoreGR820Ds1IntervalPathSAS=nncBellcoreGR820Ds1IntervalPathSAS, nncBellcoreGR820Ds1Statistics=nncBellcoreGR820Ds1Statistics, nncBellcoreGR820Ds1StatisticsObjects=nncBellcoreGR820Ds1StatisticsObjects, nncBellcoreGR820Ds1CurrPathCV=nncBellcoreGR820Ds1CurrPathCV, nncBellcoreGR820Ds1TotalLineLOSS=nncBellcoreGR820Ds1TotalLineLOSS, nncBellcoreGR820Ds1IntervalStatsEntry=nncBellcoreGR820Ds1IntervalStatsEntry, nncBellcoreGR820Ds1TotalLineCV=nncBellcoreGR820Ds1TotalLineCV, nncBellcoreGR820Ds1TotalStatsTable=nncBellcoreGR820Ds1TotalStatsTable, nncBellcoreGR820Ds1IntervalIndex=nncBellcoreGR820Ds1IntervalIndex, PYSNMP_MODULE_ID=nncBellcoreGR820Ds1Statistics, nncBellcoreGR820Ds1CurrLineCV=nncBellcoreGR820Ds1CurrLineCV, nncBellcoreGR820Ds1CurrPathAISS=nncBellcoreGR820Ds1CurrPathAISS, nncBellcoreGR820Ds1TotalPathUAS=nncBellcoreGR820Ds1TotalPathUAS, nncBellcoreGR820Ds1TotalPathCSS=nncBellcoreGR820Ds1TotalPathCSS, nncBellcoreGR820Ds1CurrPathFC=nncBellcoreGR820Ds1CurrPathFC, nncBellcoreGR820Ds1IntervalPathSES=nncBellcoreGR820Ds1IntervalPathSES, nncBellcoreGR820Ds1StatisticsGroups=nncBellcoreGR820Ds1StatisticsGroups, nncBellcoreGR820Ds1IntervalStatsTable=nncBellcoreGR820Ds1IntervalStatsTable, nncBellcoreGR820Ds1TotalPathFC=nncBellcoreGR820Ds1TotalPathFC, nncBellcoreGR820Ds1TotalPathCV=nncBellcoreGR820Ds1TotalPathCV, nncBellcoreGR820Ds1IntervalLineES=nncBellcoreGR820Ds1IntervalLineES, nncBellcoreGR820Ds1IntervalPathCV=nncBellcoreGR820Ds1IntervalPathCV, nncBellcoreGR820Ds1StatisticsCompliances=nncBellcoreGR820Ds1StatisticsCompliances, nncBellcoreGR820Ds1IntervalPathES=nncBellcoreGR820Ds1IntervalPathES, nncBellcoreGR820Ds1IntervalLineCV=nncBellcoreGR820Ds1IntervalLineCV, nncBellcoreGR820Ds1CurrPathCSS=nncBellcoreGR820Ds1CurrPathCSS, nncBellcoreGR820Ds1IntervalLineLOSS=nncBellcoreGR820Ds1IntervalLineLOSS, nncBellcoreGR820Ds1CurrPathUAS=nncBellcoreGR820Ds1CurrPathUAS, nncBellcoreGR820Ds1IntervalPathAISS=nncBellcoreGR820Ds1IntervalPathAISS, nncBellcoreGR820Ds1StatisticsCompliance=nncBellcoreGR820Ds1StatisticsCompliance)
|
cities = [
{
"city": "New York",
"growth_from_2000_to_2013": "4.8%",
"latitude": 40.7127837,
"longitude": -74.0059413,
"population": "8405837",
"rank": "1",
"state": "New York",
},
{
"city": "Los Angeles",
"growth_from_2000_to_2013": "4.8%",
"latitude": 34.0522342,
"longitude": -118.2436849,
"population": "3884307",
"rank": "2",
"state": "California",
},
{
"city": "Chicago",
"growth_from_2000_to_2013": "-6.1%",
"latitude": 41.8781136,
"longitude": -87.6297982,
"population": "2718782",
"rank": "3",
"state": "Illinois",
},
{
"city": "Houston",
"growth_from_2000_to_2013": "11.0%",
"latitude": 29.7604267,
"longitude": -95.3698028,
"population": "2195914",
"rank": "4",
"state": "Texas",
},
{
"city": "Philadelphia",
"growth_from_2000_to_2013": "2.6%",
"latitude": 39.9525839,
"longitude": -75.1652215,
"population": "1553165",
"rank": "5",
"state": "Pennsylvania",
},
{
"city": "Phoenix",
"growth_from_2000_to_2013": "14.0%",
"latitude": 33.4483771,
"longitude": -112.0740373,
"population": "1513367",
"rank": "6",
"state": "Arizona",
},
{
"city": "San Antonio",
"growth_from_2000_to_2013": "21.0%",
"latitude": 29.4241219,
"longitude": -98.49362819999999,
"population": "1409019",
"rank": "7",
"state": "Texas",
},
{
"city": "San Diego",
"growth_from_2000_to_2013": "10.5%",
"latitude": 32.715738,
"longitude": -117.1610838,
"population": "1355896",
"rank": "8",
"state": "California",
},
{
"city": "Dallas",
"growth_from_2000_to_2013": "5.6%",
"latitude": 32.7766642,
"longitude": -96.79698789999999,
"population": "1257676",
"rank": "9",
"state": "Texas",
},
{
"city": "San Jose",
"growth_from_2000_to_2013": "10.5%",
"latitude": 37.3382082,
"longitude": -121.8863286,
"population": "998537",
"rank": "10",
"state": "California",
},
{
"city": "Austin",
"growth_from_2000_to_2013": "31.7%",
"latitude": 30.267153,
"longitude": -97.7430608,
"population": "885400",
"rank": "11",
"state": "Texas",
},
{
"city": "Indianapolis",
"growth_from_2000_to_2013": "7.8%",
"latitude": 39.768403,
"longitude": -86.158068,
"population": "843393",
"rank": "12",
"state": "Indiana",
},
{
"city": "Jacksonville",
"growth_from_2000_to_2013": "14.3%",
"latitude": 30.3321838,
"longitude": -81.65565099999999,
"population": "842583",
"rank": "13",
"state": "Florida",
},
{
"city": "San Francisco",
"growth_from_2000_to_2013": "7.7%",
"latitude": 37.7749295,
"longitude": -122.4194155,
"population": "837442",
"rank": "14",
"state": "California",
},
{
"city": "Columbus",
"growth_from_2000_to_2013": "14.8%",
"latitude": 39.9611755,
"longitude": -82.99879419999999,
"population": "822553",
"rank": "15",
"state": "Ohio",
},
{
"city": "Charlotte",
"growth_from_2000_to_2013": "39.1%",
"latitude": 35.2270869,
"longitude": -80.8431267,
"population": "792862",
"rank": "16",
"state": "North Carolina",
},
{
"city": "Fort Worth",
"growth_from_2000_to_2013": "45.1%",
"latitude": 32.7554883,
"longitude": -97.3307658,
"population": "792727",
"rank": "17",
"state": "Texas",
},
{
"city": "Detroit",
"growth_from_2000_to_2013": "-27.1%",
"latitude": 42.331427,
"longitude": -83.0457538,
"population": "688701",
"rank": "18",
"state": "Michigan",
},
{
"city": "El Paso",
"growth_from_2000_to_2013": "19.4%",
"latitude": 31.7775757,
"longitude": -106.4424559,
"population": "674433",
"rank": "19",
"state": "Texas",
},
{
"city": "Memphis",
"growth_from_2000_to_2013": "-5.3%",
"latitude": 35.1495343,
"longitude": -90.0489801,
"population": "653450",
"rank": "20",
"state": "Tennessee",
},
{
"city": "Seattle",
"growth_from_2000_to_2013": "15.6%",
"latitude": 47.6062095,
"longitude": -122.3320708,
"population": "652405",
"rank": "21",
"state": "Washington",
},
{
"city": "Denver",
"growth_from_2000_to_2013": "16.7%",
"latitude": 39.7392358,
"longitude": -104.990251,
"population": "649495",
"rank": "22",
"state": "Colorado",
},
{
"city": "Washington",
"growth_from_2000_to_2013": "13.0%",
"latitude": 38.9071923,
"longitude": -77.0368707,
"population": "646449",
"rank": "23",
"state": "District of Columbia",
},
{
"city": "Boston",
"growth_from_2000_to_2013": "9.4%",
"latitude": 42.3600825,
"longitude": -71.0588801,
"population": "645966",
"rank": "24",
"state": "Massachusetts",
},
{
"city": "Nashville-Davidson",
"growth_from_2000_to_2013": "16.2%",
"latitude": 36.1626638,
"longitude": -86.7816016,
"population": "634464",
"rank": "25",
"state": "Tennessee",
},
{
"city": "Baltimore",
"growth_from_2000_to_2013": "-4.0%",
"latitude": 39.2903848,
"longitude": -76.6121893,
"population": "622104",
"rank": "26",
"state": "Maryland",
},
{
"city": "Oklahoma City",
"growth_from_2000_to_2013": "20.2%",
"latitude": 35.4675602,
"longitude": -97.5164276,
"population": "610613",
"rank": "27",
"state": "Oklahoma",
},
{
"city": "Louisville/Jefferson County",
"growth_from_2000_to_2013": "10.0%",
"latitude": 38.2526647,
"longitude": -85.7584557,
"population": "609893",
"rank": "28",
"state": "Kentucky",
},
{
"city": "Portland",
"growth_from_2000_to_2013": "15.0%",
"latitude": 45.5230622,
"longitude": -122.6764816,
"population": "609456",
"rank": "29",
"state": "Oregon",
},
{
"city": "Las Vegas",
"growth_from_2000_to_2013": "24.5%",
"latitude": 36.1699412,
"longitude": -115.1398296,
"population": "603488",
"rank": "30",
"state": "Nevada",
},
{
"city": "Milwaukee",
"growth_from_2000_to_2013": "0.3%",
"latitude": 43.0389025,
"longitude": -87.9064736,
"population": "599164",
"rank": "31",
"state": "Wisconsin",
},
{
"city": "Albuquerque",
"growth_from_2000_to_2013": "23.5%",
"latitude": 35.0853336,
"longitude": -106.6055534,
"population": "556495",
"rank": "32",
"state": "New Mexico",
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},
{
"city": "Northglenn",
"growth_from_2000_to_2013": "15.5%",
"latitude": 39.8961821,
"longitude": -104.9811468,
"population": "37499",
"rank": "982",
"state": "Colorado",
},
{
"city": "Phenix City",
"growth_from_2000_to_2013": "31.9%",
"latitude": 32.4709761,
"longitude": -85.0007653,
"population": "37498",
"rank": "983",
"state": "Alabama",
},
{
"city": "Grove City",
"growth_from_2000_to_2013": "35.6%",
"latitude": 39.88145189999999,
"longitude": -83.0929644,
"population": "37490",
"rank": "984",
"state": "Ohio",
},
{
"city": "Texarkana",
"growth_from_2000_to_2013": "7.4%",
"latitude": 33.425125,
"longitude": -94.04768820000001,
"population": "37442",
"rank": "985",
"state": "Texas",
},
{
"city": "Addison",
"growth_from_2000_to_2013": "2.6%",
"latitude": 41.931696,
"longitude": -87.9889556,
"population": "37385",
"rank": "986",
"state": "Illinois",
},
{
"city": "Dover",
"growth_from_2000_to_2013": "16.0%",
"latitude": 39.158168,
"longitude": -75.5243682,
"population": "37366",
"rank": "987",
"state": "Delaware",
},
{
"city": "Lincoln Park",
"growth_from_2000_to_2013": "-6.7%",
"latitude": 42.2505943,
"longitude": -83.1785361,
"population": "37313",
"rank": "988",
"state": "Michigan",
},
{
"city": "Calumet City",
"growth_from_2000_to_2013": "-4.5%",
"latitude": 41.6155909,
"longitude": -87.5294871,
"population": "37240",
"rank": "989",
"state": "Illinois",
},
{
"city": "Muskegon",
"growth_from_2000_to_2013": "-7.1%",
"latitude": 43.2341813,
"longitude": -86.24839209999999,
"population": "37213",
"rank": "990",
"state": "Michigan",
},
{
"city": "Aventura",
"growth_from_2000_to_2013": "47.2%",
"latitude": 25.9564812,
"longitude": -80.1392121,
"population": "37199",
"rank": "991",
"state": "Florida",
},
{
"city": "Martinez",
"growth_from_2000_to_2013": "3.4%",
"latitude": 38.0193657,
"longitude": -122.1341321,
"population": "37165",
"rank": "992",
"state": "California",
},
{
"city": "Greenfield",
"growth_from_2000_to_2013": "4.8%",
"latitude": 42.9614039,
"longitude": -88.0125865,
"population": "37159",
"rank": "993",
"state": "Wisconsin",
},
{
"city": "Apache Junction",
"growth_from_2000_to_2013": "15.7%",
"latitude": 33.4150485,
"longitude": -111.5495777,
"population": "37130",
"rank": "994",
"state": "Arizona",
},
{
"city": "Monrovia",
"growth_from_2000_to_2013": "0.2%",
"latitude": 34.1442616,
"longitude": -118.0019482,
"population": "37101",
"rank": "995",
"state": "California",
},
{
"city": "Weslaco",
"growth_from_2000_to_2013": "28.8%",
"latitude": 26.1595194,
"longitude": -97.9908366,
"population": "37093",
"rank": "996",
"state": "Texas",
},
{
"city": "Keizer",
"growth_from_2000_to_2013": "14.4%",
"latitude": 44.9901194,
"longitude": -123.0262077,
"population": "37064",
"rank": "997",
"state": "Oregon",
},
{
"city": "Spanish Fork",
"growth_from_2000_to_2013": "78.1%",
"latitude": 40.114955,
"longitude": -111.654923,
"population": "36956",
"rank": "998",
"state": "Utah",
},
{
"city": "Beloit",
"growth_from_2000_to_2013": "2.9%",
"latitude": 42.5083482,
"longitude": -89.03177649999999,
"population": "36888",
"rank": "999",
"state": "Wisconsin",
},
{
"city": "Panama City",
"growth_from_2000_to_2013": "0.1%",
"latitude": 30.1588129,
"longitude": -85.6602058,
"population": "36877",
"rank": "1000",
"state": "Florida",
},
]
|
class QualifierClassifier:
def __init__(self):
pass
@staticmethod
def get_qualifiers(act_state):
return {}
|
#def createUnitDict(filename):
# unit_info = dict()
# with open(filename, 'r') as unit_file:
# all_units = unit_file.readlines()
# for unit in all_units:
# unit = unit.split(",")
# unit_info[unit[1].split("\n")[0]] = int(unit[0])
# return unit_info
NUM_PROTOSS_ACTIONS = 70;
def getUnitData(units, unit_info):
unit_list = units.split("]")
# remove the empty character at the end of the list
unit_list = unit_list[: len(unit_list)-1]
unit_types = [0 for i in range(NUM_PROTOSS_ACTIONS)]
units_free = [0 for i in range(NUM_PROTOSS_ACTIONS)]
units_being_built = [0 for i in range(NUM_PROTOSS_ACTIONS)]
units_building = [0 for i in range(NUM_PROTOSS_ACTIONS)]
canChronoBoost = 0
for unit in unit_list:
unit = unit.split("[")[1]
csv_unit = unit.split(",")
unit_id = int(csv_unit[0])
frame_started = int(csv_unit[1])
frame_finished = int(csv_unit[2])
builder_id = int(csv_unit[3])
type_id = int(csv_unit[4])
addon_id = int(csv_unit[5])
buildtype_id = int(csv_unit[6])
build_id = int(csv_unit[7])
job_id = int(csv_unit[8])
time_until_built = int(csv_unit[9])
time_until_free = int(csv_unit[10])
time_chronoboost = int(csv_unit[11])
time_chronoboost_again = int(csv_unit[12])
max_energy = int(csv_unit[13])
energy = float(csv_unit[14])
# do stuff with the data here
unit_types[type_id] += 1
if max(time_until_built, time_until_free) == 0:
units_free[type_id] += 1
if time_until_built > 0:
units_being_built[type_id] += 1
if time_until_built == 0 and time_until_free > 0:
units_building[type_id] += 1
# Nexus can use chronoboost
if type_id == unit_info["Nexus"] and energy >= 50.0:
canChronoBoost = 1
return [unit_types, units_free, units_being_built, units_building], canChronoBoost
"""
output_type: 0 = file
1 = string
"""
def parseLine(line, unit_dict, mins_per_worker_per_sec, gas_per_worker_per_sec,
output_type, parsed_file=None):
units_finish = line.find("]]")
units = line[line.find("[[")+1:units_finish+1]
unit_info, canChronoBoost = getUnitData(units, unit_dict)
line = line[units_finish+3:]
being_built_finish = line.find("]")
being_built = line[line.find("[")+1:being_built_finish]
line = line[being_built_finish+2:]
finished_finish = line.find("]")
finished = line[line.find("[")+1:finished_finish]
line = line[finished_finish+2:]
chronoboosts_finish = line.find("]]")
if chronoboosts_finish != -1:
chronoboosts = line[line.find("[[")+1:chronoboosts_finish+1]
line = line[chronoboosts_finish+3:]
else:
line = line[3:]
single_values = line.split(",")
race = single_values[0]
minerals = single_values[1]
gas = single_values[2]
current_supply = single_values[3]
max_supply = single_values[4]
current_frame = single_values[5]
previous_frame = single_values[6]
frame_limit = single_values[7]
mineral_workers = single_values[8]
gas_workers = single_values[9]
building_workers = single_values[10]
num_refineries = single_values[11]
num_depots = single_values[12]
last_action = single_values[13]
last_ability_finish = line.find("]")
last_ability = line[line.find("[")+1:last_ability_finish]
y_value = line[last_ability_finish+2:]
if output_type == 0:
# unit info
for unit_list in unit_info:
for value in unit_list:
parsed_file.write(str(value) + ",")
parsed_file.write(str(canChronoBoost) + ",")
# state info
parsed_file.write(minerals + ",")
parsed_file.write(gas + ",")
parsed_file.write(current_supply + ",")
parsed_file.write(max_supply + ",")
parsed_file.write(current_frame + ",")
parsed_file.write(mineral_workers + ",")
parsed_file.write(gas_workers + ",")
parsed_file.write(frame_limit + ",")
parsed_file.write(str(mins_per_worker_per_sec) + ",")
if y_value == "":
parsed_file.write(str(gas_per_worker_per_sec))
else:
parsed_file.write(str(gas_per_worker_per_sec) + ",")
# y value
parsed_file.write(y_value)
elif output_type == 1:
output = ""
# unit info
for unit_list in unit_info:
for value in unit_list:
output += (str(value) + ",")
output += (str(canChronoBoost) + ",")
# state info
output += (minerals + ",")
output += (gas + ",")
output += (current_supply + ",")
output += (max_supply + ",")
output += (current_frame + ",")
output += (mineral_workers + ",")
output += (gas_workers + ",")
output += (frame_limit + ",")
output += (str(mins_per_worker_per_sec) + ",")
if y_value == "":
output += (str(gas_per_worker_per_sec))
else:
output += (str(gas_per_worker_per_sec) + ",")
# y value
output += (y_value)
return output |
# 2020.04.26: ไบๅๆ็ดขๅ้็ capacity๏ผ
class Solution:
def shipWithinDays(self, weights: List[int], D: int) -> int:
'''
ๆๆณๅฐฑๆฏๅป่ฏ๏ผ
ไฝๆฏcaipacity ไป 0 ๅผๅง ๅๅคช็บฏไบ๏ผ
้็จไบๅๆณๅป่ฏๅฐฑๅพๅฅฝ๏ผlow = max(weights), hight = sum(weights)
'''
def is_within(mid,D):
c = 0
idx = 1
for weight in weights:
c += weight
if c > mid:
c = weight
idx += 1
if idx > D:
return False
return True
low = max(weights)
hight = sum(weights)
while low < hight:
mid = low + (hight - low) // 2
if is_within(mid, D):
hight = mid
else:
low = mid + 1
return hight
|
a=int(input("enter the first number:"))
b=int(input("enter the second number:"))
s=(a&b) #sum
print(s)
u=(a/b) #or
print(u)
k=(~a) #not
print(k)
m=(a^b) #x-or
print(m)
o=(a<<b) #left shift
print(o)
t=(a>>b) #right shift
print(t)
|
"""
A person has X amount of money and wants to buy ice-creams. The cost of each ice-cream is Y.
For every purchase of ice-creams he gets 1 unit of money which could be used to buy ice-creams.
Find the number of ice-creams he can buy.
"""
class Solution:
def approach2(self, money, cost_per_ice):
ans = 0
while money > 0:
if money < cost_per_ice:
break
ans += 1
money = money - cost_per_ice
# he gets one coupon
money += 1
return ans
def get_max_num_ice_creams(self, money, cost_per_ice, ans=0):
if money < cost_per_ice:
return ans
num_bought_ice = money // cost_per_ice
ans += num_bought_ice
money = money % cost_per_ice
coupon_money = num_bought_ice
money = money + coupon_money
return self.get_max_num_ice_creams(money, cost_per_ice, ans)
def get_num_ice_creams(self, money, cost_per_ice):
return self.get_max_num_ice_creams(money, cost_per_ice)
a = Solution()
assert 4 == a.get_num_ice_creams(5, 2)
assert 5 == a.get_num_ice_creams(6, 2)
assert 4 == a.approach2(5, 2)
assert 5 == a.approach2(6, 2)
|
# majicians=['alice','david','carolina']
# for majician in majicians:
# print(F"{majician.title()},that was a great trick!")
# print(f"I can't wait to see your next trick,{majician.title()}.\n")
# majicians = ['alice', 'david', 'carolina']
# for majician in majicians:
# print(f'{majician.title()}, that was a greate trick!')
# print(f"I can't wait to see your next trick, {majician.title()}")
# print()
# majicians = ['alice', 'david', 'carolina']
# for majician in majicians:
# print(f'{majician.title()}, that was a greate trick!')
# print(f"I can't wait to see your next trick, {majician.title()}\n")
# 4.2.1
# for majician in majicians:
# print(majician)
# print()
# SyntaxError ่ฏญๆณ้่ฏฏ
# IndentationError ็ผฉ่ฟ้่ฏฏ 1.ๅฐไบ็ผฉ่ฟ
# IndexError ็ดขๅผ่ถ็/่ถ
ๅบ่ๅด
# NameError ๅ้ๅจไฝฟ็จไนๅๆฒกๆๅฎไน๏ผ้ๅธธไนๅฏ่ฝๆฏๅ้ๆ้ๅญไบ๏ผ
# TypeError ็ฑปๅ้่ฏฏ
#nami = 12 #ๅฎไนไธไธชๆดๅๅ้ๅซname๏ผๅนถ่ตๅผไธบ12
# name = 12
# print(name)
# 4.2.2
# ไธๅคชๅฏ่ฝ็ฏ็้่ฏฏ๏ผๆณๆธ
ๆฅๅพช็ฏไฝๅ
ๅฎนๅฐฑๅฏไปฅไบ
# 4.2.3
# name = 12
# print(name)
# 4.2.4
# ่ท4.2.2็ธๅ๏ผๆณๆธ
ๆฅๅพช็ฏไฝๅ
ๅฎนๅฐฑๅฏไปฅไบ
# 4.2.5
# for m in majicians # SyntaxError
# print(m)
# / ๆๆ๏ผ้คๆณ 2/3
# \ ๅๆๆ ๏ผ้ๅธธ็จไบ่ฝฌไน n
# 4.3.3
# min max sum
#print(max(majicians))
# 4.3.2
# r = range(0, 1_000_000, 1000)
# a_big_list = list(r)
# print(min(a_big_list))
# 4.3.4
# ็จไธไธชๅ่กจ/่ๅดๆ้ ไธไธชๆฐๅ่กจ
# squares = []
# for i in range(1, 6):
# squares.append(i ** 2)
# print(squares)
# [1, 2, 3, 4, 5] -> [1, 4, 9, 16, 25]
squares = [i ** 2 for i in range(1, 6)] # [1, 2, 3, 4, 5]
print(squares)
# a = ['a', 'b', 'c']
# #b = ['aa', 'bb', 'cc']
# b = [i*2 for i in a]
# print(b)
# print('\n'.join([''.join([('GTYYX/'[(x-y)%len('GTYYX/')]if((x*0.05)**2+(y*0.1)**2-1)**3-(x*0.05)**2*(y*0.1)**3<=0ย else'ย ')forย xย inย range(-30,30)])forย yย inย range(15,-15,-1)])) |
class solve_day(object):
with open('inputs/day07.txt', 'r') as f:
data = f.readlines()
# method for NOT operation
def n(self, value):
return 65535 + ~value + 1
def part1(self):
wires = {}
for d in self.data:
d = d.strip()
# print(f'input: \t\t{d}')
try:
operator = [x for x in ['AND','OR','NOT','RSHIFT','LSHIFT'] if x in d.split()][0]
except:
operator = ''
# print(f'operator:\t{operator}')
target_wire = d[[x+1 for x,v in enumerate(d) if v == ' '][-1:][0]:].strip()
# print(f'target_wire:\t{target_wire}')
# if len == 3, wire/value to wire; need to check it first value is digit or string
if len(d.split()) == 3:
# print(f'input: \t\t{d.strip()}')
# print(f'operator:\t{operator}')
# print(f'target_wire:\t{target_wire}')
# print(d.split())
if d.split()[0].isdigit():
wires[target_wire] = int(d.split()[0])
else:
try:
wires[target_wire] = wires[d.split()[0]]
except:
pass
# print('wires: \t{}')
# print('\n')
# if len == 4, NOT
if len(d.split()) == 4:
# print(f'input: \t\t{d.strip()}')
# print(f'operator:\t{operator}')
# print(f'target_wire:\t{target_wire}')
try:
wires[target_wire] = self.n(wires[d.split()[0]])
except:
pass
# print('\n')
# if len == 5, AND OR LSHIFT RSHIFT
if len(d.split()) == 5:
# print(f'input: \t\t{d.strip()}')
# print(f'operator:\t{operator}')
# print(f'target_wire:\t{target_wire}')
# handle for LSHIFT AND RSHIFT
if operator == 'RSHIFT':
try:
wires[target_wire] = wires[d.split()[0]] >> int(d.split()[2])
except:
pass
if operator == 'LSHIFT':
try:
wires[target_wire] = wires[d.split()[0]] << int(d.split()[2])
except:
pass
# handle for digit digit
if d.split()[0].isdigit() and d.split()[2].isdigit():
if operator == 'AND':
wires[target_wire] = int(d.split()[0]) & int(d.split()[2])
if operator == 'OR':
wires[target_wire] = int(d.split()[0]) | int(d.split()[2])
# handle for digit wire
if d.split()[0].isdigit() and d.split()[2].isdigit()==False:
if operator == 'AND':
wires[target_wire] = wires[d.split()[0]] & int(d.split()[2])
if operator == 'OR':
wires[target_wire] = wires[d.split()[0]] | int(d.split()[2])
# handle for wire digit
if d.split()[0].isdigit()==False and d.split()[2].isdigit():
if operator == 'AND':
wires[target_wire] = int(d.split()[0]) & wires[d.split()[2]]
if operator == 'OR':
wires[target_wire] = int(d.split()[0]) | wires[d.split()[2]]
# handle for wire wire
if d.split()[0].isdigit() and d.split()[2].isdigit():
if operator == 'AND':
wires[target_wire] = wires[d.split()[0]] & wires[d.split()[2]]
if operator == 'OR':
wires[target_wire] = wires[d.split()[0]] | wires[d.split()[2]]
# print('\n')
# print('\n')
return wires
def part2(self):
pass
if __name__ == '__main__':
s = solve_day()
print(f'Part 1: {s.part1()}')
print(f'Part 2: {s.part2()}') |
# Function which adds two numbers
def compute(num1, num2):
return (num1 + num2)
|
def majuscule(chaine):
whitelist = "abcdefghijklmnopqrstuvwxyz"
chrs = [chr(ord(c) - 32) if c in whitelist else c for c in chaine]
return "".join(chrs)
print(majuscule("axel coezard"))
def val2ascii(entier):
ch = ""
for i in range(entier):
ch = chr(ord(ch) + 1)
print(ch)
val2ascii(355)
|
class Node(object):
def __init__(self,data):
self.data = data
self.prev = None
self.next = None
class DoubleLinkedList(object):
def __init__(self):
self.head = None
self.tail = None
def prepend(self,data):
node = Node(data)
if not self.head:
self.head = node
self.tail = node
else:
node.next = self.head
self.head.prev = node
self.head = node
def append(self,data):
node = Node(data)
if not self.head:
self.head = node
self.tail = node
else:
self.tail.next = node
node.prev = self.tail
self.tail = node
def search(self,data):
if not self.head:
return False
node = self.head
while node:
if node.data == data:
return True
node = node.next
return False
def remove(self,data):
if not self.head:
return False
if self.head.data == data:
if self.head == self.tail:
self.head = None
self.tail = None
else:
self.head = self.head.next
self.head.prev = None
return True
node = self.head.next
while node:
if node.data == data:
if node == self.tail:
self.tail = self.tail.prev
self.tail.next = None
else:
node.prev.next = node.next
node.next.prev = node.prev
return True
node = node.next
return False
def traverse(self):
node = self.head
while node:
print(node.data)
node = node.next
def reverse(self):
node = self.tail
while node:
print(node.data)
node = node.prev
def size(self):
node = self.head
count = 0
while node:
count +=1
node = node.next
return count
if __name__ == '__main__':
list = DoubleLinkedList()
list.append(2)
list.append(4)
list.append(6)
list.append(7)
list.append(9)
list.append(10)
print(list.remove(9))
list.traverse()
|
#!/usr/bin/python3
# -*-coding:utf-8-*-
# for ่ฟญไปฃ็ธๅ
ณ
# forๅพช็ฏๅฏไปฅ้ๅไปปไฝๅบๅ็้กน็ฎ๏ผๅฆไธไธชๅ่กจๆ่
ไธไธชๅญ็ฌฆไธฒ
# rangeๅฝๆฐ
print(range(10))
print(range(3, 10))
print(range(3, 10, 2))
for a in range(10):
print(a)
print("------------------------------")
for a in range(3, 10):
print(a)
print("------------------------------")
for a in range(3, 10, 2):
print(a)
print("------------------------------")
# ๆๅฑ๏ผ
for a in range(10, 0, -2):
print(a)
aa = ["huangbo", "xuzheng", "wangbaoqiang", 44]
print("------------------------------")
# for ้ๅๅ่กจ๏ผ็ฌฌไธ็ง๏ผไฝฟ็จin่ฟ่ก้ๅ
for a in aa:
print(a)
print("------------------------------")
# for ้ๅๅ่กจ๏ผ็ฌฌไบ็ง๏ผไฝฟ็จไธๆ ่ฟ่ก้ๅ
for i in range(len(aa)):
print(aa[i])
# ้ๅๅญ็ฌฆไธฒ
for letter in "huangbo":
print(letter)
"""
ๆ while โฆ else ่ฏญๅฅ๏ผๅฝ็ถไนๆ for โฆ else ่ฏญๅฅๅฆ๏ผfor ไธญ็่ฏญๅฅๅๆฎ้็ๆฒกๆๅบๅซ๏ผ
else ไธญ็่ฏญๅฅไผๅจๅพช็ฏๆญฃๅธธๆง่กๅฎ๏ผๅณ for ไธๆฏ้่ฟ break ่ทณๅบ่ไธญๆญ็๏ผ็ๆ
ๅตไธๆง่ก๏ผwhile โฆ else ไนๆฏไธๆ ทใ
"""
for num in range(10, 20): # ่ฟญไปฃ 10 ๅฐ 20 ไน้ด็ๆฐๅญ
for i in range(2, num): # ๆ นๆฎๅ ๅญ่ฟญไปฃ
if num % i == 0: # ็กฎๅฎๆฏๅฆ่ฝ้คๅฐฝ๏ผ่ฝ้คๅฐฝ๏ผๅ่กจ็คบๆฏๅๆฐ
print('%d ๆฏไธไธชๅๆฐ' %num)
break # ่ทณๅบๅฝๅๅพช็ฏ
else: # ๅพช็ฏ็ else ้จๅ
print('%d ๆฏไธไธช่ดจๆฐ' %num)
## ๆกไปถ่ฏญๅฅๅๅพช็ฏ่ฏญๅฅ็ปผๅๅฎไพ
# ๆๅฐ99ไนๆณ่กจ
for i in range(1, 10):
for j in range(1, i+1):
# ๆๅฐ่ฏญๅฅไธญ๏ผๅคงๆฌๅทๅๅ
ถ้้ข็ๅญ็ฌฆ (็งฐไฝๆ ผๅผๅๅญๆฎต) ๅฐไผ่ขซ .format() ไธญ็ๅๆฐๆฟๆข,ๆณจๆๆไธช็น็
print("{0}*{1}={2}\t".format(i, j, i*j), end="")
print()
## ไธๅฅ่ฏๆๅฐ99ไนๆณ่กจ
print ('\n'.join([' '.join(['%s*%s=%-3s' % (y, x, x*y) for y in range(1, x+1)]) for x in range(1, 10)]))
## ๅคๆญๆฏๅฆๆฏ ้ฐๅนด
year = int(input("่ฏท่พๅ
ฅไธไธชๅนดไปฝ: "))
if (year % 4) == 0 and (year % 100) != 0 or (year % 400) == 0:
print('{0} ๆฏ้ฐๅนด' .format(year))
else:
print('{0} ไธๆฏ้ฐๅนด' .format(year)) |
# ๆไปถๅ็งฐ
filename = '/home/yyh/Documents/VSCode_work/chapter10/guest_book.txt'
# ๅพช็ฏ
while True:
name = input("Please enter you name(to quit, please press quit): ")
if name == 'quit':
break
else:
print("Hello, " + name + "!")
with open(filename, 'a') as file_object:
file_object.write(name + "\n")
|
# Copyright [2019] [Christopher Syben, Markus Michen]
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# training parameters
LEARNING_RATE = 1e-5
BATCH_SIZE_TRAIN = 50
NUM_TRAINING_SAMPLES = 800
MAX_TRAIN_STEPS = NUM_TRAINING_SAMPLES//BATCH_SIZE_TRAIN +1
BATCH_SIZE_VALIDATION = 50
NUM_VALIDATION_SAMPLES = 150
MAX_VALIDATION_STEPS = NUM_VALIDATION_SAMPLES//BATCH_SIZE_VALIDATION
NUM_TEST_SAMPLES = 1
MAX_TEST_STEPS = NUM_TEST_SAMPLES
MAX_EPOCHS = 700
#Path
LOG_DIR = 'logs/'
WEIGHTS_DIR = 'trained_models/' |
BOOLEAN_OPTION_TYPE = ['false', 'true']
# Extracted from the documentation of clang-format version 13
# https://releases.llvm.org/13.0.0/tools/clang/docs/ClangFormatStyleOptions.html
ALL_TUNEABLE_OPTIONS = {
'AccessModifierOffset': ['0', '-1', '-2', '-3', '-4'],
'AlignAfterOpenBracket': ['DontAlign', 'Align', 'AlwaysBreak'],
'AlignArrayOfStructures': ['None', 'Left', 'Right'],
'AlignConsecutiveAssignments': ['None', 'Consecutive', 'AcrossEmptyLines', 'AcrossComments', 'AcrossEmptyLinesAndComments'],
'AlignConsecutiveBitFields': ['None', 'Consecutive', 'AcrossEmptyLines', 'AcrossComments', 'AcrossEmptyLinesAndComments'],
'AlignConsecutiveDeclarations': ['None', 'Consecutive', 'AcrossEmptyLines', 'AcrossComments', 'AcrossEmptyLinesAndComments'],
'AlignConsecutiveMacros': ['None', 'Consecutive', 'AcrossEmptyLines', 'AcrossComments', 'AcrossEmptyLinesAndComments'],
'AlignEscapedNewlines': ['DontAlign', 'Left', 'Right'],
'AlignOperands': ['DontAlign', 'Align', 'AlignAfterOperator'],
'AlignTrailingComments': BOOLEAN_OPTION_TYPE,
'AllowAllArgumentsOnNextLine': BOOLEAN_OPTION_TYPE,
'AllowAllConstructorInitializersOnNextLine': BOOLEAN_OPTION_TYPE,
'AllowAllParametersOfDeclarationOnNextLine': BOOLEAN_OPTION_TYPE,
'AllowShortBlocksOnASingleLine': ['Never', 'Empty', 'Always'],
'AllowShortCaseLabelsOnASingleLine': BOOLEAN_OPTION_TYPE,
'AllowShortEnumsOnASingleLine': BOOLEAN_OPTION_TYPE,
'AllowShortFunctionsOnASingleLine': ['None', 'InlineOnly', 'Empty', 'Inline', 'All'],
'AllowShortIfStatementsOnASingleLine': ['Never', 'WithoutElse', 'OnlyFirstIf', 'AllIfsAndElse'],
'AllowShortLambdasOnASingleLine': ['None', 'Empty', 'Inline', 'All'],
'AllowShortLoopsOnASingleLine': BOOLEAN_OPTION_TYPE,
'AlwaysBreakAfterDefinitionReturnType': ['None', 'All', 'TopLevel'],
'AlwaysBreakAfterReturnType': ['None', 'All', 'TopLevel', 'AllDefinitions', 'TopLevelDefinitions'],
'AlwaysBreakBeforeMultilineStrings': BOOLEAN_OPTION_TYPE,
'AlwaysBreakTemplateDeclarations': ['No', 'MultiLine', 'Yes'],
'BinPackArguments': BOOLEAN_OPTION_TYPE,
'BinPackParameters': BOOLEAN_OPTION_TYPE,
'BitFieldColonSpacing': ['Both', 'None', 'Before', 'After'],
'BraceWrapping:AfterCaseLabel': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterClass': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterControlStatement': ['Never', 'MultiLine', 'Always'],
'BraceWrapping:AfterEnum': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterFunction': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterNamespace': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterStruct': BOOLEAN_OPTION_TYPE,
'BraceWrapping:AfterUnion': BOOLEAN_OPTION_TYPE,
'BraceWrapping:BeforeCatch': BOOLEAN_OPTION_TYPE,
'BraceWrapping:BeforeElse': BOOLEAN_OPTION_TYPE,
'BraceWrapping:BeforeLambdaBody': BOOLEAN_OPTION_TYPE,
'BraceWrapping:BeforeWhile': BOOLEAN_OPTION_TYPE,
'BraceWrapping:IndentBraces': BOOLEAN_OPTION_TYPE,
'BraceWrapping:SplitEmptyFunction': BOOLEAN_OPTION_TYPE,
'BraceWrapping:SplitEmptyRecord': BOOLEAN_OPTION_TYPE,
'BraceWrapping:SplitEmptyNamespace': BOOLEAN_OPTION_TYPE,
'BreakBeforeBinaryOperators': ['None', 'NonAssignment', 'All'],
'BreakBeforeConceptDeclarations': BOOLEAN_OPTION_TYPE,
'BreakBeforeInheritanceComma': BOOLEAN_OPTION_TYPE,
'BreakBeforeTernaryOperators': BOOLEAN_OPTION_TYPE,
'BreakConstructorInitializersBeforeComma': BOOLEAN_OPTION_TYPE,
'BreakConstructorInitializers': ['BeforeColon', 'BeforeComma', 'AfterColon'],
'BreakInheritanceList': ['BeforeColon', 'BeforeComma', 'AfterColon', 'AfterComma'],
'BreakStringLiterals': BOOLEAN_OPTION_TYPE,
'ColumnLimit': ['78', '80', '90', '100', '120', '0'],
'CompactNamespaces': BOOLEAN_OPTION_TYPE,
'ConstructorInitializerAllOnOneLineOrOnePerLine': BOOLEAN_OPTION_TYPE,
'ConstructorInitializerIndentWidth': ['0', '2', '3', '4', '6', '8'],
'ContinuationIndentWidth': ['0', '2', '3', '4', '6', '8'],
'Cpp11BracedListStyle': BOOLEAN_OPTION_TYPE,
'EmptyLineAfterAccessModifier': ['Leave', 'Never', 'Always'],
'EmptyLineBeforeAccessModifier': ['Leave', 'Never', 'LogicalBlock', 'Always'],
'FixNamespaceComments': BOOLEAN_OPTION_TYPE,
'IncludeBlocks': ['Preserve', 'Merge', 'Regroup'],
'IndentAccessModifiers': BOOLEAN_OPTION_TYPE,
'IndentCaseBlocks': BOOLEAN_OPTION_TYPE,
'IndentCaseLabels': BOOLEAN_OPTION_TYPE,
'IndentExternBlock': ['NoIndent', 'Indent'],
'IndentGotoLabels': BOOLEAN_OPTION_TYPE,
'IndentPPDirectives': ['None', 'AfterHash', 'BeforeHash'],
'IndentRequires': BOOLEAN_OPTION_TYPE,
'IndentWidth': ['2', '3', '4', '8'],
'IndentWrappedFunctionNames': BOOLEAN_OPTION_TYPE,
'InsertTrailingCommas': ['None', 'Wrapped'],
'KeepEmptyLinesAtTheStartOfBlocks': BOOLEAN_OPTION_TYPE,
'LambdaBodyIndentation': ['Signature', 'OuterScope'],
'MaxEmptyLinesToKeep': ['0', '1', '2', '3'],
'NamespaceIndentation': ['None', 'Inner', 'All'],
'PenaltyBreakAssignment': ['2', '100', '1000'],
'PenaltyBreakBeforeFirstCallParameter': ['1', '19', '100'],
'PenaltyBreakComment': ['300'],
'PenaltyBreakFirstLessLess': ['120'],
'PenaltyBreakString': ['1000'],
'PenaltyBreakTemplateDeclaration': ['10'],
'PenaltyExcessCharacter': ['100', '1000000'],
'PenaltyReturnTypeOnItsOwnLine': ['60', '200', '1000'],
'PenaltyIndentedWhitespace': ['0', '1'],
'PointerAlignment': ['Left', 'Right', 'Middle'],
'ReferenceAlignment': ['Pointer', 'Left', 'Right', 'Middle'],
'ReflowComments': BOOLEAN_OPTION_TYPE,
'ShortNamespaceLines': ['0', '1'],
'SortIncludes': ['Never', 'CaseSensitive', 'CaseInsensitive'],
'SortUsingDeclarations': BOOLEAN_OPTION_TYPE,
'SpaceAfterCStyleCast': BOOLEAN_OPTION_TYPE,
'SpaceAfterLogicalNot': BOOLEAN_OPTION_TYPE,
'SpaceAfterTemplateKeyword': BOOLEAN_OPTION_TYPE,
'SpaceAroundPointerQualifiers': ['Default', 'Before', 'After', 'Both'],
'SpaceBeforeAssignmentOperators': BOOLEAN_OPTION_TYPE,
'SpaceBeforeCaseColon': BOOLEAN_OPTION_TYPE,
'SpaceBeforeCpp11BracedList': BOOLEAN_OPTION_TYPE,
'SpaceBeforeCtorInitializerColon': BOOLEAN_OPTION_TYPE,
'SpaceBeforeInheritanceColon': BOOLEAN_OPTION_TYPE,
'SpaceBeforeParens': ['Never', 'ControlStatements', 'ControlStatementsExceptControlMacros', 'NonEmptyParentheses', 'Always'],
'SpaceBeforeRangeBasedForLoopColon': BOOLEAN_OPTION_TYPE,
'SpaceInEmptyBlock': BOOLEAN_OPTION_TYPE,
'SpaceInEmptyParentheses': BOOLEAN_OPTION_TYPE,
'SpacesBeforeTrailingComments': ['0', '1'],
'SpacesInAngles': ['Leave', 'Never', 'Always'],
'SpacesInCStyleCastParentheses': BOOLEAN_OPTION_TYPE,
'SpacesInConditionalStatement': BOOLEAN_OPTION_TYPE,
'SpacesInContainerLiterals': BOOLEAN_OPTION_TYPE,
'SpacesInLineCommentPrefix:Minimum': ['0', '1'],
'SpacesInLineCommentPrefix:Maximum': ['0', '1', '-1'],
'SpacesInParentheses': BOOLEAN_OPTION_TYPE,
'SpacesInSquareBrackets': BOOLEAN_OPTION_TYPE,
'SpaceBeforeSquareBrackets': BOOLEAN_OPTION_TYPE,
'Standard': ['c++03', 'c++11', 'c++14', 'c++17', 'c++20', 'Latest'],
'UseTab': ['Never', 'ForIndentation', 'ForContinuationAndIndentation', 'AlignWithSpaces', 'Always'],
}
PRIORITY_OPTIONS = ['IndentWidth', 'UseTab', 'SortIncludes', 'IncludeBlocks']
|
{
'variables': {
'node_shared_openssl%': 'true'
},
'targets': [
{
'target_name': 'ed25519',
'sources': [
'src/ed25519/keypair.c',
'src/ed25519/sign.c',
'src/ed25519/open.c',
'src/ed25519/crypto_verify_32.c',
'src/ed25519/ge_double_scalarmult.c',
'src/ed25519/ge_frombytes.c',
'src/ed25519/ge_scalarmult_base.c',
'src/ed25519/ge_precomp_0.c',
'src/ed25519/ge_p2_0.c',
'src/ed25519/ge_p2_dbl.c',
'src/ed25519/ge_p3_0.c',
'src/ed25519/ge_p3_dbl.c',
'src/ed25519/ge_p3_to_p2.c',
'src/ed25519/ge_p3_to_cached.c',
'src/ed25519/ge_p3_tobytes.c',
'src/ed25519/ge_madd.c',
'src/ed25519/ge_add.c',
'src/ed25519/ge_msub.c',
'src/ed25519/ge_sub.c',
'src/ed25519/ge_p1p1_to_p3.c',
'src/ed25519/ge_p1p1_to_p2.c',
'src/ed25519/ge_tobytes.c',
'src/ed25519/fe_0.c',
'src/ed25519/fe_1.c',
'src/ed25519/fe_cmov.c',
'src/ed25519/fe_copy.c',
'src/ed25519/fe_neg.c',
'src/ed25519/fe_add.c',
'src/ed25519/fe_sub.c',
'src/ed25519/fe_mul.c',
'src/ed25519/fe_sq.c',
'src/ed25519/fe_sq2.c',
'src/ed25519/fe_invert.c',
'src/ed25519/fe_tobytes.c',
'src/ed25519/fe_isnegative.c',
'src/ed25519/fe_isnonzero.c',
'src/ed25519/fe_frombytes.c',
'src/ed25519/fe_pow22523.c',
'src/ed25519/sc_reduce.c',
'src/ed25519/sc_muladd.c',
'src/ed25519.cc'
],
'conditions': [
['node_shared_openssl=="false"', {
# so when "node_shared_openssl" is "false", then OpenSSL has been
# bundled into the node executable. So we need to include the same
# header files that were used when building node.
'include_dirs': [
'<(node_root_dir)/deps/openssl/openssl/include'
],
"conditions": [
["target_arch=='ia32'", {
"include_dirs": ["<(node_root_dir)/deps/openssl/config/piii"]
}],
["target_arch=='x64'", {
"include_dirs": ["<(node_root_dir)/deps/openssl/config/k8"]
}],
["target_arch=='arm'", {
"include_dirs": ["<(node_root_dir)/deps/openssl/config/arm"]
}]
]
}],
# https://github.com/TooTallNate/node-gyp/wiki/Linking-to-OpenSSL
['OS=="win"', {
'conditions': [
# "openssl_root" is the directory on Windows of the OpenSSL files.
# Check the "target_arch" variable to set good default values for
# both 64-bit and 32-bit builds of the module.
['target_arch=="x64"', {
'variables': {
'openssl_root%': 'C:/Program Files/OpenSSL-Win64'
},
}, {
'variables': {
'openssl_root%': 'C:/Program Files (x86)/OpenSSL-Win32'
},
}],
],
'libraries': [
'-l<(openssl_root)/lib/libssl.lib',
],
'include_dirs': [
'<(openssl_root)/include',
],
}]
],
'include_dirs': [
"<!(node -e \"require('nan')\")"
]
}
]
}
|
def issorted(str1, str2):
if len(str1) != len(str2):
return False
bool_b1 = [0] * 256
bool_b2 = [0] * 256
for i in range(len(str1)):
bool_b1[ord(str1[i])] += 1
bool_b2[ord(str2[i])] += 1
if bool_b1 == bool_b2:
return True
else:
return False
#False
print(issorted("aaa", "aaaa"))
print(issorted("aaa", "bbb"))
#True
print(issorted("hell", "lleh"))
print(issorted("mahiru", "urihma")) |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 28 13:30:29 2021
@author: jr3
"""
"""
Write a Python program that takes as input a file containing DNA sequences in multi-FASTA format,
and computes the answers to the following questions.
You can choose to write one program with multiple functions to answer these questions,
or you can write several programs to address them.
We will provide a multi-FASTA file for you, and you will run your program to answer the exam questions.
While developing your program(s), please use the following example file to test your work:
dna.example.fasta
You'll be given a different input file to launch the exam itself.
Here are the questions your program needs to answer.
The quiz itself contains the specific multiple-choice questions you need to answer for the file you will be provided.
"""
"""
(1)
How many records are in the file? A record in a FASTA file is defined as a single-line header,followed by lines of sequence data.
The header line is distinguished from the sequence data by a greater-than (">") symbol in the first column.
The word following the ">" symbol is the identifier of the sequence, and the rest of the line is an optional description of the entry.
There should be no space between the ">" and the first letter of the identifier.
"""
# Solution to Part 1 begins here -----------------------------------------------------------------------------------------
try:
f = open('dna.example.fasta','r') # opens fasta file
except IOError:
print('the example fasta file selected does not exist!!')
f.seek(0) # initializes position of the "invisible cursor"
seqs = {}
seqs_lengths = {}
count = 0;
for line in f:
# discard the new line at the end (if any)
line = line.rstrip() # rstrip([chars]) returns a copy of the string with trailing characters chars removed
#distinguish header from sequence
if line[0] =='>':
words = line.split()
name = words[0][1:]
seqs[name]=''
count = count + 1;
print(count)
else:
seqs[name] = seqs[name] + line
seqs_lengths[name] = seqs[name] + line
f.close()
# Solution to Part 1 ends here ------------------------------------------------------------------------------------------
print("\n \n")
"""
(2)
What are the lengths of the sequences in the file?
What is the longest sequence and what is the shortest sequence?
Is there more than one longest or shortest sequence?
What are their identifiers?
"""
# Solution to Part 2 begins here -----------------------------------------------------------------------------------------
# prints sequence names and associated lengths
for name, seq in seqs.items():
print("Sequence: ", name,"\nLength: ", len(seq),'\n')
print('------------------------------------------------------------------------- \n')
for name, seq in seqs.items():
length = len(seq)
seqs_lengths[name] = [length]
#displays values of sequence lengths in a dictionary 'seqs_lengths'
#print(seqs_lengths.values(),'\n')
longest_sequence = max(seqs_lengths.values());
scnd_longest_sequence = sorted(seqs_lengths.values())[1];
shortest_seqeunce = min(seqs_lengths.values());
scnd_shortest_sequence = sorted(seqs_lengths.values())[-2];
# prints longest sequence
print("Length of longest sequence:", longest_sequence,'\n')
# prints shortest sequence
print("Length of shortest sequence:", shortest_seqeunce,'\n')
# prints second element of sorted sequence length (second shortest)
print("Length of 2nd longest sequence:", scnd_longest_sequence, '\n')
# prints second-to-last element of sorted sequence length (second longest)
print("Length of 2nd shortest sequence:", scnd_shortest_sequence, '\n')
# # prints longest sequence
# print("Length of longest sequence:", max(seqs_lengths.values()),'\n')
# # prints shortest sequence
# print("Length of shortest sequence:", min(seqs_lengths.values()),'\n')
# # prints second element of sorted sequence length (second shortest)
# print("Length of 2nd longest sequence:", sorted(seqs_lengths.values())[1])
# # prints second-to-last element of sorted sequence length (second longest)
# print("Length of 2nd shortest sequence:", sorted(seqs_lengths.values())[-2])
# Solution to Part 2 ends here ------------------------------------------------------------------------------------------
"""
(3) In molecular biology, a reading frame is a way of dividing the DNA sequence of nucleotides into a set of consecutive,
non-overlapping triplets (or codons).
Depending on where we start, there are six possible reading frames:
three in the forward (5' to 3') direction and
three in the reverse (3' to 5').
For instance, the three possible forward reading frames for the sequence AGGTGACACCGCAAGCCTTATATTAGC are:
AGG TGA CAC CGC AAG CCT TAT ATT AGC
A GGT GAC ACC GCA AGC CTT ATA TTA GC
AG GTG ACA CCG CAA GCC TTA TAT TAG C
These are called reading frames 1, 2, and 3 respectively.
An open reading frame (ORF) is the part of a reading frame that has the potential to encode a protein.
It starts with a start codon (ATG), and ends with a stop codon (TAA, TAG or TGA).
For instance, ATGAAATAG is an ORF of length 9.
Given an input reading frame on the forward strand (1, 2, or 3) your program should be able to
identify all ORFs present in each sequence of the FASTA file, and answer the following questions:
what is the length of the longest ORF in the file?
# the ORF should include the starting and ending codons
What is the identifier of the sequence containing the longest ORF?
For a given sequence identifier, what is the longest ORF contained in the sequence represented by that identifier?
What is the starting position of the longest ORF in the sequence that contains it?
The position should indicate the character number in the sequence.
For instance, the following ORF in reading frame 1:
>sequence1
ATGCCCTAG
starts at position 1.
Note that because the following sequence:
>sequence2
ATGAAAAAA
does not have any stop codon in reading frame 1, we do not consider it to be an ORF in reading frame 1.
"""
# Solution to Part 3 begins here
# for name, seq in seqs.items():
# print(seqs.values())
stop_codons = ['TAA','TAG','TGA']
example = 'ATGGATTAGCCGTAGAATT'
for i in stop_codons:
example = example.split(i)
example = 'end'.join(example)
if i == len(stop_codons):
break
print(example)
example = example.split('ATG')
example = 'start'.join(example)
print(example)
rf = example.split('start','end')
rf = ''.join(rf)
print(rf)
# example = 'ATGGATTAGCCGTAGAATT'
# after_stop = example.split('TAG') # notice the first and last elements of the resultant list
# after_stop = 'stop'.join(after_stop)
# print(after_stop)
# # are not preceded (first element) and followed (last element)
# # by the arguemnt passed into the .split method ('TAG')
# # if an arugment that is not present in the sequence is
# # passed to the .split method then the method will return the
# # entire string / integers that were supposed to be split
# after_stop = after_stop.join()
# print(after_stop)
# for i in after_stop:
# after_start = split(after_stop, 'TAG','TAA', 'TGA')
# print(after_start)
# find indices of all potential stop codons in sequences
# split the sequences by the stop codons
# search the resultant lists for start codons
# if the lists contents have stop codons, take the element and feed the latter part (after 'ATG' start codon
# into another 'list inside a list' associated with the sequence considered) - these should be the
# find indices of all start codons in sequences
#
# set the open reading frames in a dictionary for each sequence in "seqs"
# create structure defining the reading frames as the distances between the nearest start codon and the stop codon,
# using the stop codon as the reference (ensuring the reading frames being considered are open reading frames)
# create RF dictionary with the identifiers and locations of the reading frames
# calculate the lengths of the reading frames
# calculate the length of the longest reading frame
# create RF dictionary with the identifiers and the lengths of the reading frames
# Solution to Part 3 ends here
"""
(4) A repeat is a substring of a DNA sequence that occurs in multiple copies (more than one) somewhere in the sequence.
Although repeats can occur on both the forward and reverse strands of the DNA sequence,
we will only consider repeats on the forward strand here.
Also we will allow repeats to overlap themselves.
For example, the sequence ACACA contains two copies of the sequence ACA - once at position 1 (index 0 in Python), and once at position 3.
Given a length n, your program should be able to identify all repeats of length n in all sequences in the FASTA file.
Your program should also determine how many times each repeat occurs in the file, and which is the most frequent repeat of a given length.
"""
# Solution to Part 4 begins here
# Solution to Part 4 ends here
|
class Event:
def __init__(self, name, payload):
self.name = name
self.payload = payload
|
"""
Em uma competiรงรฃo de salto em distรขncia cada atleta tem direito a cinco saltos. O resultado do atleta serรก
determinado pela mรฉdia dos cinco saltos. Vocรช deve fazer um programa que receba o nome e as cinco distรขncias
alcanรงadas pelo atleta em seus saltos e depois informe o nome, os saltos e a mรฉdia dos saltos. O programa
deve ser encerrado quando nรฃo for informado o nome do atleta. A saรญda do programa deve ser conforme o exemplo abaixo:
Atleta: Rodrigo Curvรชllo
Primeiro Salto: 6.5 m
Segundo Salto: 6.1 m
Terceiro Salto: 6.2 m
Quarto Salto: 5.4 m
Quinto Salto: 5.3 m
Resultado final:
Atleta: Rodrigo Curvรชllo
Saltos: 6.5 - 6.1 - 6.2 - 5.4 - 5.3
Mรฉdia dos saltos: 5.9
"""
saltos = []
registro = list()
while True:
nome = str(input('Nome: ')).title()
for s in range(1, 3):
saltos.append(float(input(f'{s}ยบ salto: ')))
registro.append([nome, saltos[:], sum(saltos) / len(saltos)])
saltos.clear()
while True:
resposta = str(input('\nInserir um novo atleta? [S | N]: ')).upper().strip()
if resposta not in 'NS' or resposta == '':
print('Resposta invรกlida!')
elif resposta == 'N':
break
else:
print()
break
if resposta == 'N':
break
# --------------------- imprimindo -------------------------
# registro [ [nome, [5saltos], mรฉdia] , [nome, [5saltos], mรฉdia] ]
#for c, a in enumerate(registro):
# print(f'{c + 1} >>>> {a}')
linha = '-' * 30
print()
for atleta in registro:
print(f'Nome: {atleta[0]:>5}\nSaltos: {atleta[1]}\nMรฉdia: {atleta[2]:.2f}\n{linha}\n')
|
alphabet = "abcdefghijklmnopqrstuvwxyz"
class Scrambler():
"""
Class to represent a rotor in the machine.
"""
def __init__(self, mapping, step_orientation=[0], orientation=0):
self.orientation = orientation # integer representing current orientation of scrambler
self.m = mapping # list of numbers specifying how to map from input to output characters (by addition)
self.inverse_m = self.calculate_inverse_m() # list representing base mapping of scrambler in reverse direction
self.step_orientation = step_orientation # tuple of orientations AFTER has caused next rotor to step on
def calculate_inverse_m(self):
"""
From the original mapping, work out what the mapping is when you pass a character through in the other direction
ie on the way back from the reflector
"""
inverse_m = [0]*len(alphabet) # initialise it at a list of zeros
for i in range(len(alphabet)):
result = self.m[i]
new_map = len(alphabet) - result
inverse_m[(result+i) % len(alphabet)] = new_map
return inverse_m
def encrypt_char_forward(self, integer):
"""
Calculate what happens when you pass a single character, represented as integer, through the scrambler
"""
current_wiring_f = self.m[self.orientation:] + self.m[:self.orientation]
new_int = (integer + current_wiring_f[integer]) % len(alphabet)
return new_int
def encrypt_char_backward(self, integer):
"""
Calculate what happens when you pass a single character, represented as integer, through the scrambler backwards
"""
current_wiring_b = self.inverse_m[self.orientation:] + self.inverse_m[:self.orientation]
new_int = (integer + current_wiring_b[integer]) % len(alphabet)
return new_int
def display_mapping(self): # prints out the alphabetic mapping (for debugging)
cipher = ""
for i in range(len(alphabet)):
cipher += alphabet[self.encrypt_char_forward(i)]
print(alphabet)
print(cipher)
class PairMap:
"""
Superclass for things that swap pairs of characters (the reflector and the plugboard)
"""
def __init__(self, mapping):
self.m = mapping
def encrypt_char(self, integer):
"""
Calculate what happens when you pass a single character, represented as integer, through the reflector/plugboard
"""
new_int = (integer + self.m[integer]) % len(alphabet)
return new_int
def display_mapping(self): # prints out the alphabetic mapping (for debugging)
cipher = ""
for i in range(len(alphabet)):
cipher += alphabet[self.encrypt_char(i)]
print(alphabet)
print(cipher)
class Reflector(PairMap):
def __init__(self, mapping):
PairMap.__init__(self, mapping)
self.check = self.check_mapping()
def check_mapping(self):
"""
Check that the mapping is a valid reflector, ie swaps pairs of characters
"""
check = True
for i in range(len(self.m)):
maps_to = (i + self.m[i]) % len(self.m)
if (maps_to + self.m[maps_to]) % len(self.m) != i:
check = False
return check
class Plugboard(PairMap): # note directionality is irrelevant for plugboard!
def __init__(self, mapping):
PairMap.__init__(self, mapping)
self.check = self.check_mapping()
def check_mapping(self):
"""
Check that the plugboard is valid, ie swaps pairs of characters
"""
check = True
for i in range(len(self.m)):
if self.m[i] != 0:
maps_to = (i + self.m[i]) % len(self.m)
if (maps_to + self.m[maps_to]) % len(self.m) != i:
check = False
return check
class Machine:
"""
Class to represent a whole enigma machine, including what scramblers it contains, reflector and plugboard settings
"""
def __init__(self, scrambler_list, reflector=Reflector([0]*26), plugboard=Plugboard([0]*26)):
self.s = scrambler_list
self.ref = reflector
self.plug = plugboard
def increment_scramblers(self):
"""
Step on the scramblers. Always step the first, step the others if pushed on by the previous
"""
for i in range(len(self.s)):
self.s[i].orientation = (self.s[i].orientation + 1) % len(alphabet) # increment orientation of s[i]
if self.s[i].orientation not in self.s[i].step_orientation: # unless this means moving past push point
break
def loop_scramblers_f(self, num):
"""
Pass a character forward through however many scramblers there are in the machine
"""
for i in range(len(self.s)):
num = self.s[i].encrypt_char_forward(num)
return num
def loops_scramblers_b(self, num):
"""
Pass a character back through however many scramblers there are in the machine
"""
for i in range(len(self.s)):
num = self.s[len(self.s)-1 - i].encrypt_char_backward(num)
return num
def encrypt(self, plaintext):
"""
Do encryption!
:param plaintext: plaintext message, String
:return: ciphertext message, String
"""
ciphertext = ""
for c in plaintext:
self.increment_scramblers()
initial_no = alphabet.find(c) # map the letter to its equivalent integer in the alphabet
if c == " ":
ciphertext += " "
elif initial_no == -1:
ciphertext += "*"
else:
num = self.plug.encrypt_char(initial_no) # plugboard forward
num = self.loop_scramblers_f(num) # scrambler(s) forward
if self.ref.m != [0]*len(alphabet): # not null
num = self.ref.encrypt_char(num) # reflector
num = self.loops_scramblers_b(num) # scramblers backward
num = self.plug.encrypt_char(num) # plugboard backward
ciphertext += alphabet[num] # map from integer back to letter using alphabet, add to ciphertext str
else:
ciphertext += alphabet[num] # map from integer back to letter using alphabet, add to ciphertext str
return ciphertext
# saved examples of real wartime scramblers, reflectors
si = Scrambler([4, 9, 10, 2, 7, 1, 23, 9, 13, 16, 3, 8, 2, 9, 10, 18, 7, 3, 0, 22, 6, 13, 5, 20, 4, 10], [17])
sii = Scrambler([0, 8, 1, 7, 14, 3, 11, 13, 15, 18, 1, 22, 10, 6, 24, 13, 0, 15, 7, 20, 21, 3, 9, 24, 16, 5], [6])
siii = Scrambler([1, 2, 3, 4, 5, 6, 22, 8, 9, 10, 13, 10, 13, 0, 10, 15, 18, 5, 14, 7, 16, 17, 24, 21, 18, 15], [23])
siv = Scrambler([4, 17, 12, 18, 11, 20, 3, 19, 16, 7, 10, 23, 5, 20, 9, 22, 23, 14, 1, 13, 16, 8, 6, 15, 24, 2], [10])
sv = Scrambler([21, 24, 25, 14, 2, 3, 13, 17, 12, 6, 8, 18, 1, 20, 23, 8, 10, 5, 20, 16, 22, 19, 9, 7, 4, 11], [0])
svi = Scrambler([9, 14, 4, 18, 10, 15, 6, 24, 16, 7, 17, 19, 1, 20, 11, 2, 13, 19, 8, 25, 3, 16, 12, 5, 21, 23],
[0, 14])
svii = Scrambler([13, 24, 7, 4, 2, 12, 22, 16, 4, 15, 8, 11, 15, 1, 6, 16, 10, 17, 3, 18, 21, 9, 14, 19, 5, 20],
[0, 14])
sviii = Scrambler([5, 9, 14, 4, 15, 6, 17, 7, 20, 18, 25, 7, 3, 16, 11, 2, 10, 21, 12, 3, 19, 13, 24, 1, 8, 22],
[0, 14])
ra = Reflector([4, 8, 10, 22, 22, 6, 18, 16, 13, 18, 12, 20, 16, 4, 2, 5, 24, 22, 1, 25, 21, 13, 14, 10, 8, 4])
rb = Reflector([24, 16, 18, 4, 12, 13, 5, 22, 7, 14, 3, 21, 2, 23, 24, 19, 14, 10, 13, 6, 8, 1, 25, 12, 2, 20])
rc = Reflector([5, 20, 13, 6, 4, 21, 8, 17, 22, 20, 7, 14, 11, 9, 18, 13, 3, 19, 2, 23, 24, 6, 17, 15, 9, 12])
rbt = Reflector([4, 12, 8, 13, 22, 15, 18, 15, 1, 25, 18, 3, 3, 14, 23, 23, 13, 6, 7, 2, 11, 24, 11, 20, 8, 19])
rct = Reflector([17, 2, 12, 24, 5, 8, 13, 3, 13, 21, 23, 1, 25, 18, 14, 7, 9, 9, 5, 13, 4, 13, 19, 21, 22, 17])
sx = Scrambler([0] * 26) # some useful blank/minimal ones for testing
rx = Reflector([0] * 26)
rt = Reflector([13] * 26)
px = Plugboard([0] * 26)
possible_scramblers = [si, sii, siii, siv, sv, svi, svii, sviii, sx]
possible_reflectors = [ra, rb, rc, rbt, rct, rx, rt]
# stuff needed for enigmaGUIbasic
main_scrambler_list = [si, sii, siii]
default_machine = Machine(main_scrambler_list, ra, px) |
class Solution:
def numDecodings(self, s):
if not s:
return 0
second_last, last = 0, 1
for i, ch in enumerate(s):
ways = 0
curr = int(ch)
# current number is not 0
if curr:
# there is at least one way to decode
ways = last
# we are at the second number of further
if i:
prev = int(s[i-1])
# 10 <= s[prev:curr+1] <=26
if 1 <= prev < 2 or (prev == 2 and curr <= 6):
ways += second_last
# current number is 0
else:
# string starts with zero
if not i:
return 0
prev = int(s[i-1])
# string contains 00 or 30, 40...
if not prev or prev > 2:
return 0
ways = second_last
second_last, last = last, ways
return last
|
def Divide(a,b):
try:
return (a/b)
except ZeroDivisionError:
print("\nHey!Dont be insane!\n")
def Rem(a,b):
try:
return (a%b)
except ZeroDivisionError:
print("\nHey!Dont be insane!\n")
print("Enter Operand1:\n")
oper1=float(input())
print("Enter Operand2:\n")
oper2=float(input())
while(True):
print("\n")
print("********MENU*********\n")
print("1.Addition\n")
print("2.Operand1-Operand2\n")
print("3.Operand2-Operand1\n")
print("4.Operand1 Multi Operand2\n")
print("5.Operand1 / Operand2\n")
print("6.opernad1 % Operand2\n")
print("7.Operand2 / Operand1\n")
print("8.Operand2 % Operand1\n")
print("9.Operand1 ^ Operand2\n")
print("10.Operand2 ^ Operand1\n")
print("11.EXIT\n")
print("\n\nEnter your Option:\n")
ch=int(float(input()))
if(ch>11 and ch<1):
print("\nWrong Entery!!!\nRetry...")
continue
if(ch==1):
print(oper1+oper2)
continue
if(ch==2):
print(oper1-oper2)
continue
if(ch==3):
print(oper2-oper1)
continue
if(ch==4):
print(oper1*oper2)
continue
if(ch==5):
x=Divide(oper1,oper2)
if(x!=None):
print(x)
continue
if(ch==6):
x=Rem(oper1,oper2)
if(x!=None):
print(x)
continue
if(ch==7):
x=Divide(oper2,oper1)
if(x!=None):
print(x)
continue
if(ch==8):
x=Rem(oper2,oper1)
if(x!=None):
print(x)
continue
if(ch==9):
print(oper1**oper2)
continue
if(ch==10):
print(oper2**oper1)
continue
if(ch==11):
print("\n****BYE***")
exit()
|
#global Variable
x = 100
def fun(x):
#local scope of variable
x = 3 + 2
return x
local = fun(x)
print(local)
print(x) |
# N๊ฐ์ ์์ฐ์๊ฐ ์ฃผ์ด์ง ๋, ๊ฐ ์ M์ ๋ํ์ฌ M๋ฒ์งธ ์์๋ฅผ ๊ตฌํ์์ค
# Given N integers, for each integer M, find the Mth prime
limit = 200000
primes = list(range(limit))
primes[0], primes[1] = 0, 0
for i in range(2, limit):
if primes[i]:
for j in range(i+i, limit, i):
primes[j] = 0
primes = tuple(prime for prime in primes if prime)
for _ in range(int(input())):
print(primes[int(input())-1]) |
sqrt = lambda n: n ** .5
sqrt_5 = sqrt(5)
phi = (1 + sqrt_5) / 2
psi = (1 - sqrt_5) / 2
fibonacci = lambda n: int(round((phi ** n - psi ** n) / sqrt_5))
for n in range(-12, 12):
print(fibonacci(n))
|
class Solution(object):
def calcEquation(self, equations, values, queries):
"""
:type equations: List[List[str]]
:type values: List[float]
:type queries: List[List[str]]
:rtype: List[float]
"""
key_pairs = {}
value_pairs = {}
for i in xrange(len(equations)):
equation = equations[i]
if equation[0] not in key_pairs:
key_pairs[equation[0]] = []
value_pairs[equation[0]] = []
if equation[1] not in value_pairs:
key_pairs[equation[1]] = []
value_pairs[equation[1]] = []
key_pairs[equation[0]].append(equation[1])
key_pairs[equation[1]].append(equation[0])
value_pairs[equation[0]].append(values[i])
value_pairs[equation[1]].append(1.0 / values[i])
res = []
for query in queries:
r = self.dfs(query[0], query[1], key_pairs, value_pairs, set(), 1.0)
if r == 0.0:
res.append(-1.0)
else:
res.append(r)
return res
def dfs(self, start, end, key_pairs, value_pairs, mid_set, value):
if start in mid_set:
return 0.0
if start not in key_pairs:
return 0.0
if start == end:
return value
mid_set.add(start)
str_list = key_pairs[start]
value_list = value_pairs[start]
temp = 0.0
for i in xrange(len(str_list)):
temp = self.dfs(str_list[i], end, key_pairs, value_pairs,
mid_set, value * value_list[i])
if temp != 0.0:
break
mid_set.remove(start)
return temp
|
def originalSystemPrettyPrint():
var = "Original System \n\ny1' = 7 y3 + 4.5 y5 + -19.5 y1^2 -9.5 y1 y2 + -10 y1 y5 + -9.75 y1 y3 -9.75 y1 y4\n" + \
"y2' = 9 y4 + 4.5 y5 + -6 y2^2 -9.5 y1 y2 + -10 y3 y2 + -4 y5 y2 -1.75 y2 y4\n" + \
"y3' = 9.75 y1^2 -3.5 y3 + -9.75 y1 y3 + -19.5 y3^2 -10 y3 y2\n" + \
"y4' = 8 y2^2 -4.5 y4 + -9.75 y1 y4 + -1.75 y4 y2\n" + \
"y5' = 9.5 y1 y2 + -4.5 y5 -10 y5 y1 - 4 y5 y2"
return var
|
# Using names.txt (right click and 'Save Link/Target As...'), a 46K text file containing over five-thousand first names, begin by sorting it into alphabetical order. Then working out the alphabetical value for each name, multiply this value by its alphabetical position in the list to obtain a name score.
# For example, when the list is sorted into alphabetical order, COLIN, which is worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would obtain a score of 938 ร 53 = 49714.
# What is the total of all the name scores in the file?
alphabet = {
'a':1,
'b':2,
'c':3,
'd':4,
'e':5,
'f':6,
'g':7,
'h':8,
'i':9,
'j':10,
'k':11,
'l':12,
'm':13,
'n':14,
'o':15,
'p':16,
'q':17,
'r':18,
's':19,
't':20,
'u':21,
'v':22,
'w':23,
'x':24,
'y':25,
'z':26,
}
def name_val(n):
t = 0
for i in n.lower():
t += alphabet[i]
return t
with open("prob_extras/p022_names.txt") as f:
raw_names = f.readline()
names = sorted([name for name in raw_names.replace('"',"").split(",")])
scores = 0
for i, name in enumerate(names):
scores += (i + 1) * name_val(name)
# print(i+1, name, name_val(name))
print(scores)
|
class DataQueue:
def __init__(self):
self.data = []
def Add(self, unitData):
self.Enqueue(unitData)
self.Dequeue()
def Enqueue(self, unitData):
self.data.append(unitData)
def Dequeue(self):
self.data.pop(0) |
QUIT_MSG = "go home, you're drunk"
class CreativeQuitPlugin(object):
name = "Creative quit plugin"
def __init__(self, bot_instance):
self.bot_instance = bot_instance
def leave(self, user, channel):
"""
Wrapper around bot quit, since we have extra args per
the plugin interface.
"""
self.bot_instance.leave(channel, reason="Hasta la vista!")
@classmethod
def install(cls, bot_instance):
plugin = cls(bot_instance)
return {QUIT_MSG: plugin.leave}
|
#!/usr/bin/python3
def print_matrix_integer(matrix=[[]]):
if not matrix:
print()
else:
for row in range(len(matrix)):
for item in range(len(matrix[row])):
if item != len(matrix[row]) - 1:
endspace = ' '
else:
endspace = ''
print("{:d}".format(matrix[row][item]), end=endspace)
print()
|
value = int(input())
def dumb_fib(n):
if n < 3:
return 1
else:
return dumb_fib(n - 1) + dumb_fib(n - 2)
print(dumb_fib(value + 1))
|
lst = [8, 3, 9, 6, 4, 7, 5, 2, 1]
def main():
test(lst)
def test(lst):
line_one = []
reverse_line_two = lst
shifted_num = []
reverse_line_two = reverse_line_two[::-1]
limit = len(reverse_line_two)
for i in range(limit):
line_one.append(i + 1)
line_one.pop(0)
left = reverse_line_two[:i]
right = reverse_line_two[i:]
reverse_line_two1 = reverse_line_two[1:]
for i in range(len(reverse_line_two1)):
count = 0
if i == 0:
for j in left:
if j < reverse_line_two1[i]:
count+=1
shifted_num.append(count)
if i > 0:
if line_one[i] % 2 == 1:
if shifted_num[i-1]%2 ==0:
for j in reverse_line_two[:reverse_line_two.index(line_one[i])]:
if line_one[i] > j:
count+=1
shifted_num.append(count)
elif shifted_num[i-1]%2 ==1:
for j in reverse_line_two[reverse_line_two.index(line_one[i]):]:
if line_one[i] > j:
count+=1
shifted_num.append(count)
elif line_one[i] % 2 == 0:
if (shifted_num[i-1]+shifted_num[i-2])%2 ==0:
for j in reverse_line_two[:reverse_line_two.index(line_one[i])]:
if line_one[i] > j:
count+=1
shifted_num.append(count)
if (shifted_num[i-1]+shifted_num[i-2])%2 ==1:
for j in reverse_line_two[reverse_line_two.index(line_one[i]):]:
if line_one[i] > j:
count+=1
shifted_num.append(count)
print(shifted_num)
return shifted_num
#def order(lst):
if __name__ == '__main__':
main()
|
n1 = float(input('Primeira nota: '))
n2 = float(input('Segunda nota: '))
media = (n1 + n2) / 2
print(f'Tirando {n1:.1f} e {n2:.1f}, a mรฉdia do aluno รฉ {media:.1f}')
if media <= 5:
print('O aluno estรก \033[31mREPROVADO\033[m!')
elif media <= 6.9:
print('O aluno estรก de \033[33mRECUPERAรรO\033[m!')
elif media >= 7:
print('O aluno estรก \033[32mAPROVADO\033[m!')
|
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 3 23:45:39 2017
@author: ASUS
This code snippet takes the complement and reverse of a DNA string
"""
string = 'GTGGTTACGCTCCCGGGGGGGTTCTACGTGCAGATACTGTTCGGGAAGAAGGAGGCACGTCAGGGAGCGCACCCCCCGGTCTACTTGACGGTGGTGAACGTTATCGGCTGGGCCATGTCGTGGAGATAGGAAGCAGGGGAAGTGTGTAAGACAAGTGTACGTGTGTGCATATCTTTTCGTTTGTATAATGTATCGCCAGTGTTATGTCAACCCTAATCCCGGGTATTAAGGAGACAGTCGTGGATTCAGAGGGGTGTCGTCGTGAGCTAGGGGACATTCTTGTAGCGGAGATATGACAACGAGACCGTTGAACATCTTAGTTTTAAGTCTCGCCTCACCTTCCTGGCAAGCTAAACCGGTGGTTCAGCGGTGTTTTGCCCACTTAACCGCCCACCGATTAAGCCATCGACCTACCGAGGTCGGATATAGTGGACCATAGTTACCAAGAATCACCTCAGGTCTGGGCTCATGAAAGGATTAAGGGGTATACAAAAGGCACACAGCCCATCTTACGTTGATCGAGGCGGATGGTTTAAAGGTCCCAGCTTACCTTTTTCCCTTAGACGTAGACTACCGTCGAGGGAGCTGAGAGATTTCAGCCGTATTAAACAACTGAAACCGTCCAACGATACTCATATTCTACGGTGCTCAAGGTGACCGCTGACGGTGATTCTTGACCCCCCTCACTATAGAACACCTCCTATCGCACGACACATTGTCGACTTTTGACCACCCCCGGTTCCCGGGTTTACCTAGAGATCTAGGAATCACTCAAATCGTCTCTCGTGGCGGTGTGCAATGCCAAGAAGAGAAACACTACGGTCGACGAGGGGGCCTGTTATTCTAGGGACGGTCGTGCAACTGCTCCGC'
#Take the reverse
reversed_string = string[::-1]
#Create a dictionary
complement_dict = {'A':'T','T':'A','G':'C','C':'G'}
#Take the compelement of the reverse
complement_reversed_string = ""
for base in reversed_string:
complement_reversed_string += complement_dict[base]
print(complement_reversed_string)
|
def age_predict():
name = input("What is your name?\n")
print("Hello " + name + " it is nice to meet you!")
x = True
while x:
age = input("How old are you " + name + "?\n")
if age.isnumeric():
x = False
else:
print("Please try again.")
age_date = ((100 - (int(age))) + 2016)
print(name + " will turn 100 in the year " + str(age_date) + "!")
if ((int(age) % 2) == 0):
print(age + " is an even number!")
else:
print(age + " is an odd number :(")
if ((age_date % 3) == 0):
print("The year " + str(age_date) + " is divisible by three!")
else:
print("The year " + str(age_date) + " is not divisible by three :(")
print("The first letter of your name repeated " + age + " times is " + (
name[0] * int(age)))
age_predict()
|
"""
CONVERSรO DE MEDIDAS
"""
medida = float(input('Digite a quantidade de Metros: '))
print('Conversรฃo')
print('\nCentimetros: {}cm'.format(medida*100))
print('\nMilรญmetros: {}mm'.format(medida*1000)) |
def get_eben(n: list):
if sum(n) % 2 == 0:
return int("".join(map(str, n)))
num = n
s = sum(num)
while s % 2 != 0:
s
def main():
t = int(input())
for _ in range(t):
length = int(input())
n = map(int, input().split())
print(get_eben(n))
if __name__=='__main__':
main()
|
# encoding: utf-8
class Enum(object):
@classmethod
def get_keys(cls):
return filter(lambda x: not x.startswith('_'), cls.__dict__.keys())
@classmethod
def items(cls):
return map(lambda x: (x, getattr(cls, x)), cls.get_keys())
@classmethod
def get_values(cls):
return map(lambda x: getattr(cls, x), cls.get_keys())
@classmethod
def as_choices(cls):
_choices = cls.get_values()
choices = []
for choice in _choices:
choices.append((choice, cls.get_key_from_value(choice)))
return tuple(choices)
@classmethod
def inverted_choices(cls):
_choices = cls.get_keys()
choices = []
for choice in _choices:
choices.append((choice, getattr(cls, choice)))
return tuple(choices)
@classmethod
def get_key_from_value(cls, value):
for key, v in cls.__dict__.items():
if value == v:
return key
@classmethod
def get_value(cls, key):
return getattr(cls, key)
|
def euro(b, m, c):
czas = dict()
mecze = dict()
baza = dict()
wynik = ['', float('inf')]
for baz in b:
baza[baz[0]] = baz[1]
for mecz in m:
if mecz[0] not in mecze.keys():
mecze[mecz[0]] = [mecz[2]]
else:
mecze[mecz[0]].append(mecz[2])
if mecz[1] not in mecze.keys():
mecze[mecz[1]] = [mecz[2]]
else:
mecze[mecz[1]].append(mecz[2])
for polaczenie in c:
czas[repr(polaczenie[:2])] = int(polaczenie[2])
czas[repr(polaczenie[:2][::-1])] = int(polaczenie[2])
for druzyna, mlist in mecze.items():
podroze = 0
kwatera = baza[druzyna]
for me in mlist:
if me == kwatera:
continue
podroze += czas[repr([kwatera, me])]
if podroze < wynik[1]:
wynik[0] = druzyna
wynik[1] = podroze
return wynik[0]
#euro([['A', 'M1'],['B', 'M2'],['C', 'M1'],['D', 'M3']], [['A', 'B', 'M1'],['A', 'C', 'M1'],['A', 'D', 'M3'],['B', 'C', 'M1'],['B', 'D', 'M3'],['C', 'D', 'M3']], [['M1', 'M2', '1'],['M1', 'M3', '5'], ['M2', 'M3', '6']])
#euro([['D1', 'M1'],['D2', 'M2'],['D3', 'M3'],['D4', 'M4']], [['D1', 'D3', 'M5'],['D2', 'D4', 'M6'],['D1', 'D2', 'M5'],['D3', 'D4', 'M6'],['D1', 'D4', 'M5'],['D2', 'D3', 'M6']], [['M1', 'M5', '20'],['M1', 'M6', '5'],['M2', 'M5', '15'],['M2', 'M6', '10'],['M3', 'M5', '25'], ['M3', 'M6', '20'], ['M4', 'M5', '20'],['M4', 'M6', '20']])
|
dataset_type = 'CocoDataset'
data_root = '/work/u5216579/ctr/data/PCB_v3/'#'/home/u5216579/vf/data/coco/' #'data/coco/'
img_norm_cfg = dict(
#mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
mean=[38.720, 51.155, 40.22], std=[53.275, 52.273, 46.819], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
dict(type='Sharpness', prob=0.0, level=8),
dict(type='Rotate', prob=0.75, level=10, max_rotate_angle=360),
dict(type='Color', prob=0.6, level=6),
dict(type='ColorTransform', level=4.0, prob=0.5),
dict(type='BrightnessTransform', level=4.0, prob=0.5),
dict(type='ContrastTransform', level=4.0, prob=0.5),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(512, 512),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
#train=dict(
# type=dataset_type,
# ann_file=data_root + 'annotations/instances_train2017.json',
# img_prefix=data_root + 'train2017/',
# pipeline=train_pipeline),
#val=dict(
# type=dataset_type,
# ann_file=data_root + 'annotations/instances_val2017.json',
# img_prefix=data_root + 'val2017/',
# pipeline=test_pipeline),
#test=dict(
# type=dataset_type,
# ann_file=data_root + 'annotations/instances_val2017.json',
# img_prefix=data_root + 'val2017/',
# pipeline=test_pipeline))
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/train.json',
img_prefix=data_root + 'train/',
pipeline=train_pipeline,
filter_empty_gt=False),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/val.json',
img_prefix=data_root + 'val/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/val.json',
img_prefix=data_root + 'val/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
|
def get_nd_par(ref_len, read_type, basecaller):
if read_type:
if read_type == "dRNA":
return drna_nd_par(ref_len, basecaller)
else:
return cdna_nd_par(ref_len, read_type)
else:
return dna_nd_par(ref_len, basecaller)
def seg_par(ref_len, changepoint):
if ref_len > changepoint:
xe2 = 0
xe3 = ref_len - changepoint
else:
xe2 = ref_len - changepoint
xe3 = 0
xe1 = ref_len - changepoint
return xe1, xe2, xe3
def dna_nd_par(ref_len, basecaller):
if basecaller == "albacore":
at_xe1, at_xe2, at_xe3 = seg_par(ref_len, 12)
at_mu = 9.32968110 + 0.75215056 * at_xe1 + 0.01234263 * at_xe2 ** 2 - 0.02699184 * at_xe3 ** 2
at_sigma = 0.2507 * ref_len - 0.1510
cg_xe1, cg_xe2, cg_xe3 = seg_par(ref_len, 7)
cg_mu = 4.76783156 + 0.21751512 * cg_xe1 - 0.10414613 * cg_xe2 ** 2 - 0.01647626 * cg_xe3 ** 2
cg_sigma = 0.1109 * ref_len + 0.8959
elif basecaller == "guppy":
at_xe1, at_xe2, at_xe3 = seg_par(ref_len, 16)
at_mu = 12.13283713 + 0.71843395 * at_xe1 + 0.00127124 * at_xe2 ** 2 - 0.01429113 * at_xe3 ** 2
at_sigma = 0.2138 * ref_len + 0.4799
cg_xe1, cg_xe2, cg_xe3 = seg_par(ref_len, 12)
cg_mu = 6.13526513 + 0.21219760 * cg_xe1 - 0.01249273 * cg_xe2 ** 2 - 0.04870821 * cg_xe3 ** 2
cg_sigma = 0.3021 * ref_len - 0.06803
else: # guppy-flipflop
at_xe1, at_xe2, at_xe3 = seg_par(ref_len, 12)
at_mu = 9.65577227 + 0.92524095 * at_xe1 + 0.02814258 * at_xe2 ** 2 - 0.01666699 * at_xe3 ** 2
at_sigma = 0.2013 * ref_len + 0.2159
cg_xe1, cg_xe2, cg_xe3 = seg_par(ref_len, 14)
cg_mu = 5.5402163422 - 0.0163232962 * cg_xe1 - 0.0205230566 * cg_xe2 ** 2 + 0.0009633945 * cg_xe3 ** 2
cg_sigma = 0.1514 * ref_len + 0.5611
return at_mu, at_sigma, at_mu, at_sigma, cg_mu, cg_sigma, cg_mu, cg_sigma
def drna_nd_par(ref_len, basecaller):
if basecaller == "albacore":
a_xe1, a_xe2, a_xe3 = seg_par(ref_len, 15)
a_mu = 7.920954923 + 0.060905864 * a_xe1 - 0.031587949 * a_xe2 ** 2 + 0.008346099 * a_xe3 ** 2
a_sigma = 0.21924 * ref_len + 0.08617
t_xe1, t_xe2, t_xe3 = seg_par(ref_len, 10)
t_mu = 5.57952387 + 0.11981070 * t_xe1 - 0.05381813 * t_xe2 ** 2 - 0.01851555 * t_xe3 ** 2
t_sigma = 0.1438 * ref_len + 0.9004
c_xe1, c_xe2, c_xe3 = seg_par(ref_len, 6)
c_mu = 4.34230221 + 0.23685824 * c_xe1 - 0.08072337 * c_xe2 ** 2 - 0.01720295 * c_xe3 ** 2
c_sigma = 0.06822 * ref_len + 0.87553
g_xe1, g_xe2, g_xe3 = seg_par(ref_len, 10)
g_mu = 4.5110033642 + 0.1757467623 * g_xe1 - 0.0009943928 * g_xe2 ** 2 - 0.0915431864 * g_xe3 ** 2
g_sigma = 0.1066 * ref_len + 0.8223
else: # guppy
a_xe1, a_xe2, a_xe3 = seg_par(ref_len, 16)
a_mu = 10.619825093 + 0.367474710 * a_xe1 - 0.018351943 * a_xe2 ** 2 - 0.001138652 * a_xe3 ** 2
a_sigma = 0.3128 * ref_len - 0.6731
t_xe1, t_xe2, t_xe3 = seg_par(ref_len, 18)
t_mu = 9.819245514 + 0.212176633 * t_xe1 - 0.016791683 * t_xe2 ** 2 - 0.001310379 * t_xe3 ** 2
t_sigma = 0.23963 * ref_len + 0.02851
c_xe1, c_xe2, c_xe3 = seg_par(ref_len, 9)
c_mu = 4.873485405 + 0.006877753 * c_xe1 - 0.043582044 * c_xe2 ** 2 + 0.018552245 * c_xe3 ** 2
c_sigma = 0.07976 * ref_len + 0.67479
g_xe1, g_xe2, g_xe3 = seg_par(ref_len, 7)
g_mu = 4.431945507 + 0.162662708 * g_xe1 - 0.070326449 * g_xe2 ** 2 + 0.004705819 * g_xe3 ** 2
g_sigma = 0.08815 * ref_len + 0.81112
return a_mu, a_sigma, t_mu, t_sigma, c_mu, c_sigma, g_mu, g_sigma
def cdna_nd_par(ref_len, read_type):
if read_type == "cDNA_1D":
a_xe1, a_xe2, a_xe3 = seg_par(ref_len, 17)
a_mu = 12.242619571 + 0.668226076 * a_xe1 + 0.001965846 * a_xe2 ** 2 - 0.026708831 * a_xe3 ** 2
a_sigma = 0.3703 * ref_len - 0.8779
t_xe1, t_xe2, t_xe3 = seg_par(ref_len, 14)
t_mu = 11.979887272 + 1.005918453 * t_xe1 + 0.018442004 * t_xe2 ** 2 - 0.001733806 * t_xe3 ** 2
t_sigma = 0.2374 * ref_len + 0.0647
c_xe1, c_xe2, c_xe3 = seg_par(ref_len, 7)
c_mu = 4.63015250 + 0.02288890 * c_xe1 - 0.13258183 * c_xe2 ** 2 + 0.01859354 * c_xe3 ** 2
c_sigma = 0.1805 * ref_len + 0.3770
g_xe1, g_xe2, g_xe3 = seg_par(ref_len, 6)
g_mu = 4.26732085 + 0.18475982 * g_xe1 - 0.14151564 * g_xe2 ** 2 - 0.03719026 * g_xe3 ** 2
g_sigma = 0.1065 * ref_len + 0.8318
else: # cDNA 1D2
a_xe1, a_xe2, a_xe3 = seg_par(ref_len, 16)
a_mu = 14.807216375 + 0.957883839 * a_xe1 + 0.003869761 * a_xe2 ** 2 - 0.027664685 * a_xe3 ** 2
a_sigma = 0.1842 * ref_len + 0.5642
t_xe1, t_xe2, at_xe3 = seg_par(ref_len, 12)
t_mu = 11.281353708 + 1.039742281 * t_xe1 + 0.015074972 * t_xe2 ** 2 - 0.004161978 * at_xe3 ** 2
t_sigma = 0.1842 * ref_len + 0.5642
c_xe1, c_xe2, c_xe3 = seg_par(ref_len, 11)
c_mu = 6.925880017 + 0.383403249 * c_xe1 - 0.003611025 * c_xe2 ** 2 - 0.038495472 * c_xe3 ** 2
c_sigma = 0.32113 * ref_len - 0.04017
g_xe1, g_xe2, g_xe3 = seg_par(ref_len, 14)
g_mu = 9.10846385 + 0.68454921 * g_xe1 + 0.01769293 * g_xe2 ** 2 - 0.07252329 * g_xe3 ** 2
g_sigma = 0.32113 * ref_len - 0.04017
return a_mu, a_sigma, t_mu, t_sigma, c_mu, c_sigma, g_mu, g_sigma
def get_hpmis_rate(read_type, basecaller):
if read_type:
if read_type == "dRNA":
if basecaller == "albacore":
return 0.041483
elif basecaller == "guppy":
return 0.027234
elif read_type == "cDNA_1D":
return 0.036122
else: # cDNA 1D2
return 0.040993
else: #DNA
if basecaller == "albacore":
return 0.02204
elif basecaller == "guppy":
return 0.02166
elif basecaller == "guppy-flipflop":
return 0.02215
|
if __name__ == "__main__":
T = int(input().strip())
correct = 1 << 32
for _ in range(T):
num = int(input().strip())
print(~num + correct)
|
def format_user(userdata, format):
result = ""
u = userdata["name"]
if format == "greeting":
result = "{}, {} {}".format(u["title"], u["first"], u["last"])
elif format == "short":
result = "{}{}".format(u["title"], u["last"])
elif format == "country":
result = userdata["nat"]
elif format == "table":
result = "{} | {} | {} | {}".format(u["first"], u["last"], u["title"], userdata["nat"])
else:
result = "{} {}".format(u["first"], u["last"])
return result
|
"""
DEVA AI Oversight Tool.
Copyright 2021-2022 Gradient Institute Ltd. <info@gradientinstitute.org>
"""
|
t = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
print(t[3])
print(t[-99:-7])
print(t[-99:-5])
print(t[::])
|
SCHEMA = {
"$schema": "http://json-schema.org/draft-04/schema#",
"definitions": {
"DEB_REPO_SCHEMA": {
"type": "object",
"required": [
"name",
"uri",
"suite",
"section"
],
"properties": {
"name": {
"type": "string"
},
"type": {
"type": "string",
"enum": ["deb"]
},
"uri": {
"type": "string"
},
"priority": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
]
},
"suite": {
"type": "string"
},
"section": {
"type": "array",
"items": {"type": "string"}
},
}
},
"RPM_REPO_SCHEMA": {
"type": "object",
"required": [
"name",
"uri",
],
"properties": {
"name": {
"type": "string"
},
"type": {
"type": "string",
"enum": ["rpm"]
},
"uri": {
"type": "string"
},
"priority": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
]
},
}
},
"REPO_SCHEMA": {
"anyOf":
[
{"$ref": "#/definitions/DEB_REPO_SCHEMA"},
{"$ref": "#/definitions/RPM_REPO_SCHEMA"}
]
},
"REPOS_SCHEMA": {
"type": "array", "items": {"$ref": "#/definitions/REPO_SCHEMA"}
}
},
"type": "object",
"required": [
"groups",
],
"properties": {
"fuel_release_match": {
"type": "object",
"properties": {
"operating_system": {
"type": "string"
}
},
"required": [
"operating_system"
]
},
"requirements": {
"type": "object",
"patternProperties": {
"^[0-9a-z_-]+$": {
"type": "object",
"anyOf": [
{"required": ["packages"]},
{"required": ["repositories"]},
{"required": ["mandatory"]}
],
"properties": {
"repositories": {
"type": "array",
"items": {
"type": "object",
"required": ["name"],
"properties": {
"name": {
"type": "string"
},
"excludes": {
"type": "array",
"items": {
"type": "object",
"patternProperties": {
r"[a-z][\w_]*": {
"type": "string"
}
}
}
}
}
}
},
"packages": {
"type": "array",
"items": {
"type": "object",
"required": ["name"],
"properties": {
"name": {
"type": "string"
},
"versions": {
"type": "array",
"items": {
"type": "string",
"pattern": "^([<>]=?|=)\s+.+$"
}
}
}
}
},
"mandatory": {
"enum": ["exact", "newest"]
}
}
}
},
"additionalProperties": False,
},
"groups": {
"type": "object",
"patternProperties": {
"^[0-9a-z_-]+$": {"$ref": "#/definitions/REPOS_SCHEMA"}
},
"additionalProperties": False,
},
"inheritance": {
"type": "object",
"patternProperties": {
"^[0-9a-z_-]+$": {"type": "string"}
},
"additionalProperties": False,
}
}
}
|
# -*- coding: utf-8 -*-
# Author: Rodrigo E. Principe
# Email: fitoprincipe82 at gmail
# Make a prediction with weights
# one vector has n elements
# p = (element1*weight1) + (element2*weight2) + (elementn*weightn) + bias
# if p >= 0; 1; 0
def predict(vector, bias, weights):
pairs = zip(vector, weights) # [[element1, weight1], [..]]
for (element, weight) in pairs:
bias += element * weight
return 1.0 if bias >= 0.0 else 0.0
# Estimate Perceptron weights using stochastic gradient descent
def train_weights(train, l_rate, n_epoch, bias=0):
first_vector = train[0][0]
# weights = [0.0 for i in range(len(first_vector))]
weights = [random() for i in range(len(first_vector))]
# iterate over the epochs
for epoch in range(n_epoch):
# each epoch has a sum_error, starting at 0
sum_error = 0.0
for row in train:
vector = row[0]
expected = row[1]
prediction = predict(vector, bias, weights)
error = expected - prediction
sum_error += error**2
# update activation (weights[0]))
bias = bias + (l_rate * error)
# update weights
for i in range(len(vector)):
# for each element of the vector
weights[i] = weights[i] + (l_rate * error * vector[i])
# print('>epoch={}, lrate={}, error={}'.format(epoch, l_rate, sum_error))
return bias, weights
# Perceptron Algorithm With Stochastic Gradient Descent
def perceptron(train, test, l_rate, n_epoch):
predictions = list()
weights = train_weights(train, l_rate, n_epoch)
for row in test:
prediction = predict(row, weights)
predictions.append(prediction)
return(predictions)
|
# -*- coding: utf-8 -*-
class GPSException(Exception):
def __init__(self,*args):
super(GPSException, self).__init__(*args)
# From K6WRU via stackexchange : see https://ham.stackexchange.com/questions/221/how-can-one-convert-from-lat-long-to-grid-square/244#244
# Convert latitude and longitude to Maidenhead grid locators.
#
# Arguments are in signed decimal latitude and longitude. For example,
# the location of my QTH Palo Alto, CA is: 37.429167, -122.138056 or
# in degrees, minutes, and seconds: 37ยฐ 24' 49" N 122ยฐ 6' 26" W
class LatLongToGridSquare(object):
upper = 'ABCDEFGHIJKLMNOPQRSTUVWX'
lower = 'abcdefghijklmnopqrstuvwx'
@classmethod
def to_grid(cls,dec_lat, dec_lon):
if not (-180<=dec_lon<180):
raise GPSException('longitude must be -180<=lon<180, given %f\n'%dec_lon)
if not (-90<=dec_lat<90):
raise GPSException('latitude must be -90<=lat<90, given %f\n'%dec_lat)
adj_lat = dec_lat + 90.0
adj_lon = dec_lon + 180.0
grid_lat_sq = LatLongToGridSquare.upper[int(adj_lat/10)]
grid_lon_sq = LatLongToGridSquare.upper[int(adj_lon/20)]
grid_lat_field = str(int(adj_lat%10))
grid_lon_field = str(int((adj_lon/2)%10))
adj_lat_remainder = (adj_lat - int(adj_lat)) * 60
adj_lon_remainder = ((adj_lon) - int(adj_lon/2)*2) * 60
grid_lat_subsq = LatLongToGridSquare.lower[int(adj_lat_remainder/2.5)]
grid_lon_subsq = LatLongToGridSquare.lower[int(adj_lon_remainder/5)]
return grid_lon_sq + grid_lat_sq + grid_lon_field + grid_lat_field + grid_lon_subsq + grid_lat_subsq
# GPS sentences are encoded
@classmethod
def convert_to_degrees(cls, gps_value, direction):
if direction not in ['N','S','E','W']:
raise GPSException("Invalid direction specifier for lat/long: {}".format(direction))
dir_mult = 1
if direction in ['S','W']:
dir_mult = -1
if len(gps_value) < 3:
raise GPSException("Invalid Value for lat/long: {}".format(gps_value))
dot_posn = gps_value.index('.')
if dot_posn < 0:
raise GPSException("Invalid Format for lat/long: {}".format(gps_value))
degrees = gps_value[0:dot_posn-2]
mins = gps_value[dot_posn-2:]
f_degrees = dir_mult * (float(degrees) + (float(mins) / 60.0))
return f_degrees
@classmethod
def GPGLL_to_grid(cls, GPSLLText):
# example: $GPGLL,4740.99254,N,12212.31179,W,223311.00,A,A*70\r\n
try:
components = GPSLLText.split(",")
if components[0]=='$GPGLL':
del components[0]
if components[5] != 'A':
raise GPSException("Not a valid GPS fix")
lat = LatLongToGridSquare.convert_to_degrees(components[0], components[1])
long = LatLongToGridSquare.convert_to_degrees(components[2], components [3])
grid = LatLongToGridSquare.to_grid(lat, long)
except GPSException:
grid = ""
return grid
|
{
"variables": {
"buffer_impl" : "<!(node -pe 'v=process.versions.node.split(\".\");v[0] > 0 || v[0] == 0 && v[1] >= 11 ? \"POS_0_11\" : \"PRE_0_11\"')",
"callback_style" : "<!(node -pe 'v=process.versions.v8.split(\".\");v[0] > 3 || v[0] == 3 && v[1] >= 20 ? \"POS_3_20\" : \"PRE_3_20\"')"
},
"targets": [
{
"target_name": "openvg",
"sources": [
"src/openvg.cc",
"src/egl.cc"
],
"defines": [
"NODE_BUFFER_TYPE_<(buffer_impl)",
"TYPED_ARRAY_TYPE_<(buffer_impl)",
"V8_CALLBACK_STYLE_<(callback_style)"
],
"ldflags": [
"-lGLESv2 -lEGL -lOpenVG -lSDL2",
],
"cflags": [
"-DENABLE_GDB_JIT_INTERFACE",
"-Wall",
"-I/usr/include/SDL2 -D_REENTRANT"
],
},
{
"target_name": "init-egl",
"sources": [
"src/init-egl.cc"
],
"ldflags": [
"-lGLESv2 -lEGL -lOpenVG -lSDL2",
],
"cflags": [
"-DENABLE_GDB_JIT_INTERFACE",
"-Wall",
"-I/usr/include/SDL2 -D_REENTRANT"
],
},
]
}
|
#method 'find' finds index positon of specified string
#index position starts with 0
my_fruit = "My favorite fruit is apple".find('apple')
print(my_fruit) |
"""Constants for Seat Connect library."""
BASE_SESSION = 'https://msg.volkswagen.de'
BASE_AUTH = 'https://identity.vwgroup.io'
CLIENT_ID = '7f045eee-7003-4379-9968-9355ed2adb06%40apps_vw-dilab_com'
XCLIENT_ID = '28cd30c6-dee7-4529-a0e6-b1e07ff90b79'
XAPPVERSION = '3.2.6'
XAPPNAME = 'es.seatauto.connect'
USER_AGENT = 'okhttp/3.7.0'
APP_URI = 'seatconnect://oidc.login/'
HEADERS_SESSION = {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept-charset': 'UTF-8',
'Accept': 'application/json',
'X-Client-Id': XCLIENT_ID,
'X-App-Version': XAPPVERSION,
'X-App-Name': XAPPNAME,
'User-Agent': USER_AGENT
}
HEADERS_AUTH = {
'Connection': 'keep-alive',
'Accept': 'application/json,text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,\
image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'Content-Type': 'application/x-www-form-urlencoded',
'User-Agent': USER_AGENT
}
|
# File: Slicing_in_Python.py
# Description: How to slice strings in Python
# Environment: PyCharm and Anaconda environment
#
# MIT License
# Copyright (c) 2018 Valentyn N Sichkar
# github.com/sichkar-valentyn
#
# Reference to:
# [1] Valentyn N Sichkar. Slicing in Python // GitHub platform [Electronic resource]. URL: https://github.com/sichkar-valentyn/Slicing_in_Python (date of access: XX.XX.XXXX)
# Initial string
dna = 'ATTCGGAGCT'
# = '0123456789'
# = 'A T T C G G A G C T'
# = '(-10)(-9)(-8)(-7)(-6)(-5)(-4)(-3)(-2)(-1)'
print(dna[1]) # Showing the 1 element - T
print(dna[1:4]) # Showing the elements from 1 to 4 - TTC
print(dna[:4]) # Showing the elements from 0 (beginning) to 4 - ATTC
print(dna[4:]) # Showing the elements from 4th to the end - GGAGCT
print(dna[-4:]) # Showing the elements from -4th counting from the end to the end - AGCT
print(dna[1:-1]) # Showing the elements from 1 to -1th - TTCGGAGC
print(dna[1:-1:2]) # Showing the elements from 1 to -1th with step 2 - TCGAG
print(dna[::-1]) # Showing the elements from the beginning to the end in revers direction with step 1 - TCGAGGCTTA
|
# Escreva um programa que leia a velocidade de um carro. Se ele ultrapassar
# 80km/h, mostre uma mensagem dizendo que ele foi multado. A multa vai custar
# R$ 7,00 por cada Km acima do limite.
velocidade = float(input('Velocidade: '))
if velocidade > 80:
multa = (velocidade - 80) * 7
print('Limite excedido! Sua multa รฉ de R$ {:.2f}'.format(multa))
else:
print('Vocรช estรก dentro do limite permitido!') |
class Hund:
def __init__(self, alder, vekt):
self._alder = alder
self._vekt = vekt
self._metthet = 10
def hentAlder(self):
return self._alder
def hentVekt(self):
return self._vekt
def spring(self):
self._metthet -= 1
if self._metthet < 5:
self._vekt -= 1
def spis(self):
self._metthet += 1
if self._metthet > 7:
self._vekt += 1
|
#
# PySNMP MIB module LLDP-EXT-DOT1-PE-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/LLDP-EXT-DOT1-PE-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 19:57:50 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ValueRangeConstraint, ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueRangeConstraint", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint")
ifGeneralInformationGroup, = mibBuilder.importSymbols("IF-MIB", "ifGeneralInformationGroup")
lldpXdot1StandAloneExtensions, = mibBuilder.importSymbols("LLDP-EXT-DOT1-EVB-EXTENSIONS-MIB", "lldpXdot1StandAloneExtensions")
lldpV2RemLocalDestMACAddress, lldpV2RemLocalIfIndex, lldpV2RemTimeMark, lldpV2RemIndex, lldpV2LocPortIfIndex, lldpV2Extensions, lldpV2PortConfigEntry = mibBuilder.importSymbols("LLDP-V2-MIB", "lldpV2RemLocalDestMACAddress", "lldpV2RemLocalIfIndex", "lldpV2RemTimeMark", "lldpV2RemIndex", "lldpV2LocPortIfIndex", "lldpV2Extensions", "lldpV2PortConfigEntry")
ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance")
iso, NotificationType, Unsigned32, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity, MibIdentifier, ModuleIdentity, Integer32, Counter64, Counter32, IpAddress, Bits, TimeTicks, Gauge32 = mibBuilder.importSymbols("SNMPv2-SMI", "iso", "NotificationType", "Unsigned32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity", "MibIdentifier", "ModuleIdentity", "Integer32", "Counter64", "Counter32", "IpAddress", "Bits", "TimeTicks", "Gauge32")
DisplayString, MacAddress, TextualConvention, TruthValue = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "MacAddress", "TextualConvention", "TruthValue")
lldpXDot1PEExtensions = ModuleIdentity((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2))
lldpXDot1PEExtensions.setRevisions(('2012-01-23 00:00',))
if mibBuilder.loadTexts: lldpXDot1PEExtensions.setLastUpdated('201201230000Z')
if mibBuilder.loadTexts: lldpXDot1PEExtensions.setOrganization('IEEE 802.1 Working Group')
lldpXdot1PeMIB = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1))
lldpXdot1PeObjects = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1))
lldpXdot1PeConfig = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 1))
lldpXdot1PeLocalData = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2))
lldpXdot1PeRemoteData = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3))
lldpXdot1PeConfigPortExtensionTable = MibTable((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 1, 1), )
if mibBuilder.loadTexts: lldpXdot1PeConfigPortExtensionTable.setStatus('current')
lldpXdot1PeConfigPortExtensionEntry = MibTableRow((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 1, 1, 1), )
lldpV2PortConfigEntry.registerAugmentions(("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeConfigPortExtensionEntry"))
lldpXdot1PeConfigPortExtensionEntry.setIndexNames(*lldpV2PortConfigEntry.getIndexNames())
if mibBuilder.loadTexts: lldpXdot1PeConfigPortExtensionEntry.setStatus('current')
lldpXdot1PeConfigPortExtensionTxEnable = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 1, 1, 1, 1), TruthValue().clone('true')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: lldpXdot1PeConfigPortExtensionTxEnable.setStatus('current')
lldpXdot1PeLocPortExtensionTable = MibTable((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2, 1), )
if mibBuilder.loadTexts: lldpXdot1PeLocPortExtensionTable.setStatus('current')
lldpXdot1PeLocPortExtensionEntry = MibTableRow((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2, 1, 1), ).setIndexNames((0, "LLDP-V2-MIB", "lldpV2LocPortIfIndex"))
if mibBuilder.loadTexts: lldpXdot1PeLocPortExtensionEntry.setStatus('current')
lldpXdot1PeLocPECascadePortPriority = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2, 1, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: lldpXdot1PeLocPECascadePortPriority.setStatus('current')
lldpXdot1PeLocPEAddress = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2, 1, 1, 2), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: lldpXdot1PeLocPEAddress.setStatus('current')
lldpXdot1PeLocPECSPAddress = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 2, 1, 1, 3), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: lldpXdot1PeLocPECSPAddress.setStatus('current')
lldpXdot1PeRemPortExtensionTable = MibTable((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3, 1), )
if mibBuilder.loadTexts: lldpXdot1PeRemPortExtensionTable.setStatus('current')
lldpXdot1PeRemPortExtensionEntry = MibTableRow((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3, 1, 1), ).setIndexNames((0, "LLDP-V2-MIB", "lldpV2RemTimeMark"), (0, "LLDP-V2-MIB", "lldpV2RemLocalIfIndex"), (0, "LLDP-V2-MIB", "lldpV2RemLocalDestMACAddress"), (0, "LLDP-V2-MIB", "lldpV2RemIndex"))
if mibBuilder.loadTexts: lldpXdot1PeRemPortExtensionEntry.setStatus('current')
lldpXdot1PeRemPECascadePortPriority = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3, 1, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: lldpXdot1PeRemPECascadePortPriority.setStatus('current')
lldpXdot1PeRemPEAddress = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3, 1, 1, 2), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: lldpXdot1PeRemPEAddress.setStatus('current')
lldpXdot1PeRemPECSPAddress = MibTableColumn((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 1, 1, 3, 1, 1, 3), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: lldpXdot1PeRemPECSPAddress.setStatus('current')
lldpXdot1PeConformance = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 2))
lldpXdot1PeCompliances = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 2, 1))
lldpXdot1PeGroups = MibIdentifier((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 2, 2))
lldpXdot1PeCompliance = ModuleCompliance((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 2, 1, 1)).setObjects(("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeGroup"), ("LLDP-EXT-DOT1-PE-MIB", "ifGeneralInformationGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
lldpXdot1PeCompliance = lldpXdot1PeCompliance.setStatus('current')
lldpXdot1PeGroup = ObjectGroup((1, 3, 111, 2, 802, 1, 1, 13, 1, 5, 32962, 7, 2, 2, 2, 1)).setObjects(("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeConfigPortExtensionTxEnable"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeLocPECascadePortPriority"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeLocPEAddress"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeLocPECSPAddress"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeRemPECascadePortPriority"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeRemPEAddress"), ("LLDP-EXT-DOT1-PE-MIB", "lldpXdot1PeRemPECSPAddress"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
lldpXdot1PeGroup = lldpXdot1PeGroup.setStatus('current')
mibBuilder.exportSymbols("LLDP-EXT-DOT1-PE-MIB", lldpXdot1PeGroup=lldpXdot1PeGroup, lldpXdot1PeLocalData=lldpXdot1PeLocalData, lldpXdot1PeLocPECSPAddress=lldpXdot1PeLocPECSPAddress, lldpXdot1PeLocPortExtensionTable=lldpXdot1PeLocPortExtensionTable, lldpXdot1PeLocPEAddress=lldpXdot1PeLocPEAddress, lldpXdot1PeGroups=lldpXdot1PeGroups, lldpXdot1PeRemPEAddress=lldpXdot1PeRemPEAddress, lldpXdot1PeCompliance=lldpXdot1PeCompliance, lldpXDot1PEExtensions=lldpXDot1PEExtensions, lldpXdot1PeRemPortExtensionTable=lldpXdot1PeRemPortExtensionTable, lldpXdot1PeObjects=lldpXdot1PeObjects, lldpXdot1PeRemPECascadePortPriority=lldpXdot1PeRemPECascadePortPriority, lldpXdot1PeRemPortExtensionEntry=lldpXdot1PeRemPortExtensionEntry, lldpXdot1PeConfigPortExtensionTxEnable=lldpXdot1PeConfigPortExtensionTxEnable, PYSNMP_MODULE_ID=lldpXDot1PEExtensions, lldpXdot1PeConfigPortExtensionEntry=lldpXdot1PeConfigPortExtensionEntry, lldpXdot1PeMIB=lldpXdot1PeMIB, lldpXdot1PeCompliances=lldpXdot1PeCompliances, lldpXdot1PeRemoteData=lldpXdot1PeRemoteData, lldpXdot1PeConfigPortExtensionTable=lldpXdot1PeConfigPortExtensionTable, lldpXdot1PeLocPECascadePortPriority=lldpXdot1PeLocPECascadePortPriority, lldpXdot1PeLocPortExtensionEntry=lldpXdot1PeLocPortExtensionEntry, lldpXdot1PeRemPECSPAddress=lldpXdot1PeRemPECSPAddress, lldpXdot1PeConformance=lldpXdot1PeConformance, lldpXdot1PeConfig=lldpXdot1PeConfig)
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:
output = ListNode(None)
outputPointer = output
carry = 0
pointer1, pointer2 = l1, l2
while pointer1 or pointer2:
if pointer1 and pointer2:
new = pointer1.val + pointer2.val
elif pointer1:
new = pointer1.val
elif pointer2:
new = pointer2.val
new += carry
if new >= 10:
carry = int(new / 10)
new -= 10
else:
carry = 0
outputPointer.next = ListNode(new)
outputPointer = outputPointer.next
pointer1 = pointer1.next if pointer1 else None
pointer2 = pointer2.next if pointer2 else None
if carry > 0:
outputPointer.next = ListNode(carry)
return output.next |
"""Messages used in the code that can or can't be used repeatedly."""
def welcome():
"""Welcoming message of the game."""
print("BLACKJACK IN PYTHON!")
print()
print("โโโโโโโโโโโ โโโโโโโโโโโ")
print("โ A โ โ J โ")
print("โ โ โ โ")
print("โ โ โ โ")
print("โ โ โ โ โ โ")
print("โ โ โ โ")
print("โ โ โ โ")
print("โ A โ โ J โ")
print("โโโโโโโโโโโ โโโโโโโโโโโ")
def rules():
"""Rules of the game."""
# title
print("RULES OF THE GAME")
print()
# brief description
print(
"In this simplified version of a game of blackjack you will play against "
"a computer Dealer."
)
print()
# overview
print(
"When you start the game, you will begin with $200 and you'll need to place a "
"bet so the computer can start dealing the cards. After that you can choose "
"at each round to either 'Hit' to receive another card or 'Stay' to stop "
"receiving more cards."
)
print()
# quick note
print(
"NOTE: If you choose to 'Stay', the computer will only deal cards to itself "
"as long as it's total value in hand is lower than 18."
)
print()
# win/lose/tie conditions
print(
"You win a round by having the closest value in cards to the number 21, "
"receiving double the betted amount."
)
print(
"You lose if the dealer ends up with a higher value in cards than you, "
"losing the betted amount."
)
print(
"A tie can also happen if both player and dealer have the same value in "
"cards, returning your original betted amount to you."
)
print()
# value of cards
print("The value of the cards are as follows, from lower to higher:")
print()
print("Two = 2")
print("Three = 3")
print("Four = 4")
print("Five = 5")
print("Six = 6")
print("Seven = 7")
print("Eight = 8")
print("Nine = 9")
print("Ten = 10")
print("Jack = 10")
print("Queen = 10")
print("King = 10")
print("Ace = 1 or 11 (depending on what is best for the player to hit 21)")
print()
# ask the player for input to continue the game
input("Please, press Enter to continue! ")
def win():
"""Win message."""
print("โโโ โโโ โโโโโโโโโ โโโ โโโ โโโ โโโ โโโ โโโ โโโโโโโโโ โโโ โโโ โโโ")
print("โ โโโโโ โ โ โโโโโ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โโโโโ โ โ โ โ โ โ โ")
print("โโโโ โโโโ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ")
print(" โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โโโ โโโ โโโ")
print(" โ โ โ โโโโโ โ โ โโโโโ โ โ โโโ โโโ โ โ โ โ โ โ โ โโโ โโโ โโโ")
print(" โโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโโโ โโโ โโโ โโโ โโโ โโโ โโโ")
def lose():
"""Lose message."""
print("โโโ โโโ โโโโโโโโโ โโโ โโโ โโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ")
print("โ โโโโโ โ โ โโโโโ โ โ โ โ โ โ โ โ โโโโโ โ โ โโโโโโโ โ โโโโโโโ")
print("โโโโ โโโโ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โโโโโโโ โ โโโโโโโ")
print(" โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โโโโโโโ โ โ โโโโโโโ")
print(" โ โ โ โโโโโ โ โ โโโโโ โ โ โโโโโโโ โ โโโโโ โ โโโโโโโ โ โ โโโโโโโ")
print(" โโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ")
|
class Trackable(object):
def __init__(self, name):
self.name = name
print('CREATE {}'.format(name))
def __del__(self):
print('DELETE {}'.format(self.name))
|
CURRENCY_ALGO = "ALGO"
EXCHANGE_ALGORAND_BLOCKCHAIN = "algorand_blockchain"
TRANSACTION_TYPE_PAYMENT = "pay"
TRANSACTION_TYPE_ASSET_TRANSFER = "axfer"
TRANSACTION_TYPE_APP_CALL = "appl"
APPLICATION_ID_TINYMAN_v10 = 350338509
APPLICATION_ID_TINYMAN_v11 = 552635992
APPLICATION_ID_YIELDLY = 233725848
APPLICATION_ID_YIELDLY_NLL = 233725844
APPLICATION_ID_YIELDLY_YLDY_ALGO_POOL = 233725850
APPLICATION_ID_YIELDLY_YLDY_OPUL_POOL = 348079765
APPLICATION_ID_YIELDLY_OPUL_OPUL_POOL = 367431051
APPLICATION_ID_YIELDLY_YLDY_SMILE_POOL = 352116819
APPLICATION_ID_YIELDLY_SMILE_SMILE_POOL = 373819681
APPLICATION_ID_YIELDLY_YLDY_ARCC_POOL = 385089192
APPLICATION_ID_YIELDLY_ARRC_ARCC_POOL = 498747685
APPLICATION_ID_YIELDLY_YLDY_GEMS_POOL = 393388133
APPLICATION_ID_YIELDLY_GEMS_GEMS_POOL = 419301793
APPLICATION_ID_YIELDLY_YLDY_XET_POOL = 424101057
APPLICATION_ID_YIELDLY_XET_XET_POOL = 470390215
APPLICATION_ID_YIELDLY_YLDY_CHOICE_POOL = 447336112
APPLICATION_ID_YIELDLY_CHOICE_CHOICE_POOL = 464365150
APPLICATION_ID_YIELDLY_YLDY_AKITA_POOL = 511597182
APPLICATION_ID_YIELDLY_AKITA_LP_POOL = 511593477
YIELDLY_APPLICATIONS = [
APPLICATION_ID_YIELDLY,
APPLICATION_ID_YIELDLY_NLL,
APPLICATION_ID_YIELDLY_YLDY_ALGO_POOL,
APPLICATION_ID_YIELDLY_YLDY_OPUL_POOL,
APPLICATION_ID_YIELDLY_OPUL_OPUL_POOL,
APPLICATION_ID_YIELDLY_YLDY_SMILE_POOL,
APPLICATION_ID_YIELDLY_SMILE_SMILE_POOL,
APPLICATION_ID_YIELDLY_YLDY_ARCC_POOL,
APPLICATION_ID_YIELDLY_ARRC_ARCC_POOL,
APPLICATION_ID_YIELDLY_YLDY_GEMS_POOL,
APPLICATION_ID_YIELDLY_GEMS_GEMS_POOL,
APPLICATION_ID_YIELDLY_YLDY_XET_POOL,
APPLICATION_ID_YIELDLY_XET_XET_POOL,
APPLICATION_ID_YIELDLY_YLDY_CHOICE_POOL,
APPLICATION_ID_YIELDLY_CHOICE_CHOICE_POOL,
APPLICATION_ID_YIELDLY_YLDY_AKITA_POOL,
APPLICATION_ID_YIELDLY_AKITA_LP_POOL,
]
TINYMAN_TRANSACTION_SWAP = "c3dhcA=="
TINYMAN_TRANSACTION_LP_ADD = "bWludA=="
TINYMAN_TRANSACTION_LP_REMOVE = "YnVybg=="
YIELDLY_TRANSACTION_POOL_CLAIM = "Q0E="
YIELDLY_TRANSACTION_POOL_CLOSE = "Q0FX"
|
#--- Exercicio 2 - Impressรฃo de dados com a funรงรฃo Input
#--- Imprima o menu de uma aplicaรงรฃo de cadastro de pessoas
#--- O menu deve conter as opรงรตes de Cadastrar, Alterar, listar pessoas, alem da opรงรฃo sair
print ('=='*50)
print (' ', 'Bem vindo' ' ')
print ('Para cadastrar digite 1')
print ('Para alterar algo no cadastro digite 2')
print ('Para ver os ja cadastrados digite 3 ')
print ('Para sair digite 4')
print ('=='*50) |
XK_emspace = 0xaa1
XK_enspace = 0xaa2
XK_em3space = 0xaa3
XK_em4space = 0xaa4
XK_digitspace = 0xaa5
XK_punctspace = 0xaa6
XK_thinspace = 0xaa7
XK_hairspace = 0xaa8
XK_emdash = 0xaa9
XK_endash = 0xaaa
XK_signifblank = 0xaac
XK_ellipsis = 0xaae
XK_doubbaselinedot = 0xaaf
XK_onethird = 0xab0
XK_twothirds = 0xab1
XK_onefifth = 0xab2
XK_twofifths = 0xab3
XK_threefifths = 0xab4
XK_fourfifths = 0xab5
XK_onesixth = 0xab6
XK_fivesixths = 0xab7
XK_careof = 0xab8
XK_figdash = 0xabb
XK_leftanglebracket = 0xabc
XK_decimalpoint = 0xabd
XK_rightanglebracket = 0xabe
XK_marker = 0xabf
XK_oneeighth = 0xac3
XK_threeeighths = 0xac4
XK_fiveeighths = 0xac5
XK_seveneighths = 0xac6
XK_trademark = 0xac9
XK_signaturemark = 0xaca
XK_trademarkincircle = 0xacb
XK_leftopentriangle = 0xacc
XK_rightopentriangle = 0xacd
XK_emopencircle = 0xace
XK_emopenrectangle = 0xacf
XK_leftsinglequotemark = 0xad0
XK_rightsinglequotemark = 0xad1
XK_leftdoublequotemark = 0xad2
XK_rightdoublequotemark = 0xad3
XK_prescription = 0xad4
XK_minutes = 0xad6
XK_seconds = 0xad7
XK_latincross = 0xad9
XK_hexagram = 0xada
XK_filledrectbullet = 0xadb
XK_filledlefttribullet = 0xadc
XK_filledrighttribullet = 0xadd
XK_emfilledcircle = 0xade
XK_emfilledrect = 0xadf
XK_enopencircbullet = 0xae0
XK_enopensquarebullet = 0xae1
XK_openrectbullet = 0xae2
XK_opentribulletup = 0xae3
XK_opentribulletdown = 0xae4
XK_openstar = 0xae5
XK_enfilledcircbullet = 0xae6
XK_enfilledsqbullet = 0xae7
XK_filledtribulletup = 0xae8
XK_filledtribulletdown = 0xae9
XK_leftpointer = 0xaea
XK_rightpointer = 0xaeb
XK_club = 0xaec
XK_diamond = 0xaed
XK_heart = 0xaee
XK_maltesecross = 0xaf0
XK_dagger = 0xaf1
XK_doubledagger = 0xaf2
XK_checkmark = 0xaf3
XK_ballotcross = 0xaf4
XK_musicalsharp = 0xaf5
XK_musicalflat = 0xaf6
XK_malesymbol = 0xaf7
XK_femalesymbol = 0xaf8
XK_telephone = 0xaf9
XK_telephonerecorder = 0xafa
XK_phonographcopyright = 0xafb
XK_caret = 0xafc
XK_singlelowquotemark = 0xafd
XK_doublelowquotemark = 0xafe
XK_cursor = 0xaff
|
firstname = str(input("Enter your first name: "))
lastname = str(input("Enter your last name: "))
print("Initials: " + firstname[0] + "." + lastname[0] + ".")
|
"""
The module for training the SVM classifer.
"""
def train(database_num=3):
"""
use SVM provided by sklearn with databases to train the classifier and
dump it into a pickle.
:param database_num: 3 means NUAA, CASIA, REPLAY-ATTACK;
2 means CASIA, REPLAY-ATTACK.
"""
|
POST_JSON_RESPONSES = {
'/auth/realms/test/protocol/openid-connect/token': {
'access_token': '54604e3b-4d6a-419d-9173-4b1af0530bfb',
'token_type': 'bearer',
'expires_in': 42695,
'scope': 'read write'},
'/v2/observations': {
'dimensionDeclarations': [
{
'name': 'study',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{
'name': 'name',
'type': 'String'
}
]
}, {
'name': 'concept',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{
'name': 'conceptPath',
'type': 'String'
},
{
'name': 'conceptCode',
'type': 'String'
},
{
'name': 'name',
'type': 'String'
}
]
}, {
'name': 'patient',
'dimensionType': 'subject',
'sortIndex': 1,
'valueType': 'Object',
'fields': [
{
'name': 'id',
'type': 'Int'
},
{
'name': 'trial',
'type': 'String'
},
{
'name': 'inTrialId',
'type': 'String'
},
{
'name': 'subjectIds',
'type': 'Object'
},
{
'name': 'birthDate',
'type': 'Timestamp'
},
{
'name': 'deathDate',
'type': 'Timestamp'
},
{
'name': 'age',
'type': 'Int'
},
{
'name': 'race',
'type': 'String'
},
{
'name': 'maritalStatus',
'type': 'String'
},
{
'name': 'religion',
'type': 'String'
},
{
'name': 'sexCd',
'type': 'String'
},
{
'name': 'sex',
'type': 'String'
}
]
}, {
'name': 'visit',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{
'name': 'id',
'type': 'Int'
},
{
'name': 'activeStatusCd',
'type': 'String'
},
{
'name': 'startDate',
'type': 'Timestamp'
},
{
'name': 'endDate',
'type': 'Timestamp'
},
{
'name': 'inoutCd',
'type': 'String'
},
{
'name': 'locationCd',
'type': 'String'
},
{
'name': 'encounterIds',
'type': 'Object'
}
]
}, {
'name': 'start time',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Timestamp',
'inline': 'true'
}, {
'name': 'end time',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Timestamp',
'inline': 'true'
}, {
'name': 'location',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'String',
'inline': 'true'
}, {
'name': 'trial visit',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{
'name': 'id',
'type': 'Int'
},
{
'name': 'studyId',
'type': 'String'
},
{
'name': 'relTimeLabel',
'type': 'String'
},
{
'name': 'relTimeUnit',
'type': 'String'
},
{
'name': 'relTime',
'type': 'Int'
}
]
}, {
'name': 'provider',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'String'
}, {
'name': 'sample_type',
'dimensionType': None,
'sortIndex': None,
'valueType': 'String'
}, {
'name': 'missing_value',
'dimensionType': None,
'sortIndex': None,
'valueType': 'String'
},
{
'name': 'Diagnosis ID',
'dimensionType': 'subject',
'sortIndex': 2,
'valueType': 'String',
'modifierCode': 'CSR_DIAGNOSIS_MOD'
}],
'sort': [{
'dimension': 'concept',
'sortOrder': 'asc'
}, {
'dimension': 'provider',
'sortOrder': 'asc'
}, {
'dimension': 'patient',
'sortOrder': 'asc'
}, {
'dimension': 'visit',
'sortOrder': 'asc'
}, {
'dimension': 'start time',
'sortOrder': 'asc'
}],
'cells': [{
'inlineDimensions': [
'2019-05-05 11:11:11',
'2019-07-05 11:11:11',
'@'
],
'dimensionIndexes': [
0,
0,
1,
0,
0,
None,
None,
None,
0
],
'numericValue': 20
},
{
'inlineDimensions': [
'2019-07-22 12:00:00',
None,
'@'
],
'dimensionIndexes': [
0,
1,
0,
None,
0,
None,
None,
None,
None
],
'stringValue': 'Caucasian'
},
{
'inlineDimensions': [
None,
None,
'@'
],
'dimensionIndexes': [
0,
2,
0,
None,
0,
None,
None,
None,
None
],
'stringValue': 'Female'
}
],
'dimensionElements': {
'study': [
{
'name': 'CATEGORICAL_VALUES'
}
],
'concept': [
{
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Age\\',
'conceptCode': 'CV:DEM:AGE',
'name': 'Age'
},
{
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Race\\',
'conceptCode': 'CV:DEM:RACE',
'name': 'Race'
},
{
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\Female\\',
'conceptCode': 'CV:DEM:SEX:F',
'name': 'Female'
}
],
'patient': [
{
'id': 1,
'trial': 'CATEGORICAL_VALUES',
'inTrialId': '3',
'subjectIds': {
'SUBJ_ID': 'CV:40'
},
'birthDate': None,
'deathDate': None,
'age': 20,
'race': 'Caucasian',
'maritalStatus': None,
'religion': None,
'sexCd': 'Female',
'sex': 'female'
},
{
'id': 2,
'trial': 'CATEGORICAL_VALUES',
'inTrialId': '3',
'subjectIds': {
'SUBJ_ID': 'CV:12'
},
'birthDate': None,
'deathDate': None,
'age': 28,
'race': 'Caucasian',
'maritalStatus': None,
'religion': None,
'sexCd': 'Female',
'sex': 'male'
}
],
'visit': [
{
'id': 1,
'patientId': 1,
'activeStatusCd': None,
'startDate': '2016-03-29T09:00:00Z',
'endDate': '2016-03-29T11:00:00Z',
'inoutCd': None,
'locationCd': None,
'lengthOfStay': None,
'encounterIds': {
'VISIT_ID': 'EHR:62:1'
}
}
],
'trial visit': [
{
'id': 1,
'studyId': 'CATEGORICAL_VALUES',
'relTimeLabel': '1',
'relTimeUnit': None,
'relTime': None,
}
],
'provider': [],
'sample_type': [],
'missing_value': [],
'Diagnosis ID': [
'D1'
]
}
}
}
GET_JSON_RESPONSES = {
'/v2/tree_nodes?depth=0&tags=True&counts=False&constraints=False': {
'tree_nodes': [
{
'name': 'CATEGORICAL_VALUES',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\',
'studyId': 'CATEGORICAL_VALUES',
'type': 'STUDY',
'visualAttributes': [
'FOLDER',
'ACTIVE',
'STUDY'
],
'constraint': {
'type': 'study_name',
'studyId': 'CATEGORICAL_VALUES'
},
'metadata': {
'upload date': '2019-07-31'
},
'children': [
{
'name': 'Demography',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\',
'studyId': 'CATEGORICAL_VALUES',
'type': 'UNKNOWN',
'visualAttributes': [
'FOLDER',
'ACTIVE'
],
'children': [
{
'name': 'Age',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Age\\',
'studyId': 'CATEGORICAL_VALUES',
'conceptCode': 'CV:DEM:AGE',
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Age\\',
'type': 'NUMERIC',
'visualAttributes': [
'LEAF',
'ACTIVE',
'NUMERICAL'
],
'constraint': {
'type': 'and',
'args': [
{
'type': 'concept',
'conceptCode': 'CV:DEM:AGE'
},
{
'type': 'study_name',
'studyId': 'CATEGORICAL_VALUES'
}
]
}
},
{
'name': 'Gender',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\',
'studyId': 'CATEGORICAL_VALUES',
'type': 'CATEGORICAL',
'visualAttributes': [
'FOLDER',
'ACTIVE',
'CATEGORICAL'
],
'children': [
{
'name': 'Female',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\Female\\',
'studyId': 'CATEGORICAL_VALUES',
'conceptCode': 'CV:DEM:SEX:F',
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\Female\\',
'type': 'CATEGORICAL_OPTION',
'visualAttributes': [
'LEAF',
'ACTIVE',
'CATEGORICAL_OPTION'
],
'constraint': {
'type': 'and',
'args': [
{
'type': 'concept',
'conceptCode': 'CV:DEM:SEX:F'
},
{
'type': 'study_name',
'studyId': 'CATEGORICAL_VALUES'
}
]
}
},
{
'name': 'Male',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\Male\\',
'studyId': 'CATEGORICAL_VALUES',
'conceptCode': 'CV:DEM:SEX:M',
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Gender\\Male\\',
'type': 'CATEGORICAL_OPTION',
'visualAttributes': [
'LEAF',
'ACTIVE',
'CATEGORICAL_OPTION'
],
'constraint': {
'type': 'and',
'args': [
{
'type': 'concept',
'conceptCode': 'CV:DEM:SEX:M'
},
{
'type': 'study_name',
'studyId': 'CATEGORICAL_VALUES'
}
]
}
}
]
},
{
'name': 'Race',
'fullName': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Race\\',
'studyId': 'CATEGORICAL_VALUES',
'conceptCode': 'CV:DEM:RACE',
'conceptPath': '\\Public Studies\\CATEGORICAL_VALUES\\Demography\\Race\\',
'type': 'CATEGORICAL',
'visualAttributes': [
'LEAF',
'ACTIVE',
'CATEGORICAL'
],
'constraint': {
'type': 'and',
'args': [
{
'type': 'concept',
'conceptCode': 'CV:DEM:RACE'
},
{
'type': 'study_name',
'studyId': 'CATEGORICAL_VALUES'
}
]
}
}
]
}
]
}
]
},
'/v2/dimensions': {
'dimensions': [
{
'name': 'study',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [{'name': 'name', 'type': 'String'}],
'inline': False
},
{
'name': 'patient',
'dimensionType': 'subject',
'sortIndex': 1,
'valueType': 'Object',
'fields': [
{'name': 'id', 'type': 'Int'},
{'name': 'subjectIds', 'type': 'Object'},
{'name': 'age', 'type': 'Int'},
{'name': 'sex', 'type': 'String'}
],
'inline': False
},
{
'name': 'concept',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{'name': 'conceptPath', 'type': 'String'},
{'name': 'conceptCode', 'type': 'String'},
{'name': 'name', 'type': 'String'}
],
'inline': False
},
{
'name': 'trial visit',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{'name': 'id', 'type': 'Int'},
{'name': 'relTimeLabel', 'type': 'String'},
{'name': 'relTimeUnit', 'type': 'String'},
{'name': 'relTime', 'type': 'Int'}
],
'inline': False
},
{
'name': 'start time',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Timestamp',
'inline': True
},
{
'name': 'visit',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Object',
'fields': [
{'name': 'id', 'type': 'Int'},
{'name': 'activeStatusCd', 'type': 'String'},
{'name': 'startDate', 'type': 'Timestamp'},
{'name': 'endDate', 'type': 'Timestamp'},
{'name': 'inoutCd', 'type': 'String'},
{'name': 'locationCd', 'type': 'String'},
{'name': 'encounterIds', 'type': 'Object'}
],
'inline': False
},
{
'name': 'end time',
'dimensionType': 'attribute',
'sortIndex': None,
'valueType': 'Timestamp',
'inline': True
},
{
'name': 'Diagnosis ID',
'modifierCode': 'CSR_DIAGNOSIS_MOD',
'dimensionType': 'subject',
'sortIndex': 2,
'valueType': 'String',
'inline': False
},
{
'name': 'Biomaterial ID',
'modifierCode': 'CSR_BIOMATERIAL_MOD',
'dimensionType': 'subject',
'sortIndex': 4,
'valueType': 'String',
'inline': False
},
{
'name': 'Biosource ID',
'modifierCode': 'CSR_BIOSOURCE_MOD',
'dimensionType': 'subject',
'sortIndex': 3,
'valueType': 'String',
'inline': False
}
]
},
'/v2/studies': {
'studies': [
{
'id': 1,
'studyId': 'CATEGORICAL_VALUES',
'bioExperimentId': None,
'secureObjectToken': 'PUBLIC',
'dimensions': [
'study',
'concept',
'patient'
],
'metadata': {
'conceptCodeToVariableMetadata': {
'gender': {
'columns': 14,
'decimals': None,
'description': 'Gender',
'measure': 'NOMINAL',
'missingValues': {
'lower': None,
'upper': None,
'values': [
-2
]
},
'name': 'gender1',
'type': 'NUMERIC',
'valueLabels': {
'1': 'Female',
'2': 'Male',
'-2': 'Not Specified'
},
'width': 12
},
'birthdate': {
'columns': 22,
'decimals': None,
'description': 'Birth Date',
'measure': 'SCALE',
'missingValues': None,
'name': 'birthdate1',
'type': 'DATE',
'valueLabels': {},
'width': 22
}
}
}
}
]
},
'/v2/pedigree/relation_types': {
'relationTypes': [
{
'id': 1, 'biological': False, 'description': 'Parent', 'label': 'PAR', 'symmetrical': False
},
{
'id': 2, 'biological': False, 'description': 'Spouse', 'label': 'SPO', 'symmetrical': True
},
{
'id': 3, 'biological': False, 'description': 'Sibling', 'label': 'SIB', 'symmetrical': True
},
{
'id': 4, 'biological': True, 'description': 'Monozygotic twin', 'label': 'MZ', 'symmetrical': True
},
{
'id': 5, 'biological': True, 'description': 'Dizygotic twin', 'label': 'DZ', 'symmetrical': True
},
{
'id': 6, 'biological': True, 'description': 'Twin with unknown zygosity', 'label': 'COT', 'symmetrical': True
},
{
'id': 7, 'biological': False, 'description': 'Child', 'label': 'CHI', 'symmetrical': False
}
]
},
'/v2/pedigree/relations': {
'relations': [
{
'leftSubjectId': 1,
'relationTypeLabel': 'SPO',
'rightSubjectId': 2,
'biological': False,
'shareHousehold': False
}
]
}
}
|
def ends_with_punctuation(string):
if string is None:
return False
for punctuation in ['.', '!', '?']:
if string.rstrip().endswith(punctuation):
return True
return False
|
# PREDSTORM L1 input parameters file
# ----------------------------------
# If True, show interpolated data points on the DSCOVR input plot
showinterpolated = True
# Time interval for both the observed and predicted windDelta T (hours), start with 24 hours here (covers 1 night of aurora)
deltat = 24
# Time range of training data (in example 4 solar minimum years as training data for 2018)
trainstart = '2006-01-01 00:00'
trainend = '2010-01-01 00:00'
|
size = int(input())
matrix = []
for _ in range(size):
matrix.append([int(x) for x in input().split()])
primary_diagonal_sum = 0
secondary_diagonal_sum = 0
for i in range(len(matrix)):
primary_diagonal_sum += matrix[i][i]
secondary_diagonal_sum += matrix[i][size - i - 1]
total = abs(primary_diagonal_sum - secondary_diagonal_sum)
print(total)
|
class BlockLinkedList:
def __init__(self):
self.size = 0
self.header = None
self.trailer = None
def addbh(self, block):
if self.size == 0:
self.trailer = block
else:
block.set_next(self.header)
self.header.set_previous(block)
self.header = block
self.size += 1
def addbt(self, block):
if self.size == 0:
self.header = block
else:
block.set_previous(self.trailer)
self.trailer.set_next(block)
self.trailer = block
self.size += 1
def rembh(self):
if self.size == 0:
return None
else:
self.size -= 1
b = self.header
self.header = self.header.get_next()
def rembt(self):
if self.size == 0:
return None
else:
self.size -= 1
b = self.trailer
self.trailer = self.trailer.get_previous() |
# -*- coding:utf-8 -*-
# package information.
INFO = dict(
name = "exputils",
description = "Utilities for experiment analysis",
author = "Yohsuke T. Fukai",
author_email = "ysk@yfukai.net",
license = "MIT License",
url = "",
classifiers = [
"Programming Language :: Python :: 3.6",
"License :: OSI Approved :: MIT License"
]
)
|
"""
from rest_framework.serializers import ModelSerializer
from netbox_newplugin.models import MyModel1
class MyModel1Serializer(ModelSerializer):
class Meta:
model = MyModel1
fields = '__all__'
"""
|
# Given a linked list, determine if it has a cycle in it.
#
# To represent a cycle in the given linked list, we use an integer pos which
# represents the position (0-indexed) in the linked list where tail connects to.
# If pos is -1, then there is no cycle in the linked list.
#
# Input: head = [3,2,0,-4], pos = 1
# Output: true
# Explanation: There is a cycle in the linked list, where tail connects to the second node.
#
# Input: head = [1], pos = -1
# Output: false
# Explanation: There is no cycle in the linked list.
class ListNode:
def __init__(self, val):
self.val = val
self.next = None
class Solution:
def isCycle(self, head):
pointer1 = head
pointer2 = head.next
while pointer1 != pointer2:
if pointer2 is None or pointer2.next is None:
return False
pointer1 = pointer1.next
pointer2 = pointer2.next.next
return True
if __name__ == "__main__":
arr = [3, 2, 0, -4]
node = ListNode(arr[0])
n = node
for i in arr[1:]:
n.next = ListNode(i)
n = n.next
ans = Solution().isCycle(node)
print(ans)
|
def print_table(n):
""" (int) -> NoneType
Print the multiplication table for numbers 1 through n inclusive.
>>> print_table(5)
1 2 3 4 5
1 1 2 3 4 5
2 2 4 6 8 10
3 3 6 9 12 15
4 4 8 12 16 20
5 5 10 15 20 25
"""
# The numbers to include in the table.
numbers = list(range(1, n + 1))
# Print the header row.
for i in numbers:
print('\t' + str(i), end='')
# End the header row.
print()
# Print each row number and the contents of each row.
for i in numbers:
print (i, end='')
for j in numbers:
print('\t' + str(i * j), end='')
# End the current row.
print()
|
# %% [492. Construct the Rectangle](https://leetcode.com/problems/construct-the-rectangle/)
class Solution:
def constructRectangle(self, area: int) -> List[int]:
w = int(area ** 0.5)
while area % w:
w -= 1
return area // w, w
|
# EXERCICIO 053 - DETECTOR DE PALINDROMO
frase = str(input('Digite uma frase: ')).strip().upper()
palavras = frase.split()
junto = ''.join(palavras)
inverso = junto[::-1]
if inverso == junto:
print('A frase digitada รฉ um palรญndromo!')
else:
print('A frase digitada nรฃo รฉ um palรญndromo!')
|
"""Empty test.
Empty so that tox can be used for CI in Github actions
"""
# def test_sum():
# assert sum([1, 2, 3]) == 6, "Should be 6"
def test():
assert True is True |
def generateMatrix(n):
"""
:type n: int
:rtype: List[List[int]]
"""
ans = [[0 for i in range(n)] for j in range(n)]
# i = j = 0
# blow = 0
# bhigh = n-1
# for num in range(1, n ** 2 +1):
# ans[i][j] = num
# if j < bhigh and i == blow:
# j += 1
# continue
# if i < bhigh and j == bhigh:
# i += 1
# continue
# if j > blow and i == bhigh:
# j -= 1
# if j == blow:
# blow += 1
# continue
# if i > blow and j == blow-1:
# i -= 1
# if i == blow:
# bhigh -= 1
# continue
row = list(range(n))
col = list(range(n))
num = 1
while row or col:
for j in col:
ans[row[0]][j] = num
num += 1
row.pop(0)
for i in row:
ans[i][col[-1]] = num
num += 1
col.pop(-1)
col.reverse()
row.reverse()
return ans
print(generateMatrix(10))
print() |
# config.py
class Config(object):
embed_size = 300
in_channels = 1
num_channels = 100
kernel_size = [3,4,5]
output_size = 4
max_epochs = 10
lr = 0.25
batch_size = 64
max_sen_len = 20
dropout_keep = 0.6 |
# Byte code returned from flask-http in the case of auth failure.
NOT_AUTHORIZED_BYTE_STRING = b'Unauthorized Access'
def check_for_unauthorized_response(res):
"""
Raise an Unauthorized exception only if the response object contains the Not Authorized
byte string.
:param res: Response object to check.
:raise Unauthorized: If the response object contains the Not Authorized byte string.
"""
if not isinstance(res, dict):
if res.response and len(res.response) > 0 and isinstance(res.response[0], bytes):
if res.response[0] == NOT_AUTHORIZED_BYTE_STRING:
raise Unauthorized("Not Authorized")
|
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