start new:
tmux
start new with session name:
tmux new -s myname
import org.ajoberstar.grgit.Grgit | |
def getVersionName = { -> | |
def stdout = new ByteArrayOutputStream() | |
exec { | |
commandLine 'git', 'describe', '--tags' | |
standardOutput = stdout | |
} | |
return stdout.toString().trim() | |
} |
Latency Comparison Numbers | |
-------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns | |
Send 1K bytes over 1 Gbps network 10,000 ns 0.01 ms | |
Read 4K randomly from SSD* 150,000 ns 0.15 ms |
$ fastlane run [action] key1:value1 key2:value2 |
public struct ProvisioningProfile { | |
public static let sharedInstance = ProvisioningProfile() | |
#if (arch(i386) || arch(x86_64)) && os(iOS) | |
public let isDevelopment = true | |
#else | |
public let isDevelopment: Bool | |
private init() { | |
guard let provision = NSBundle.mainBundle().pathForResource("embedded", ofType: "mobileprovision"), | |
let data = NSData(contentsOfFile: provision) else { | |
isDevelopment = false |
import Nimble | |
func notBeNilAnd<T>(closure: (actual: T) -> Void) -> MatcherFunc<T> { | |
return MatcherFunc { actualExpression, failureMessage in | |
failureMessage.postfixMessage = "be nil" | |
guard let actual = try actualExpression.evaluate() else { | |
return false | |
} | |
closure(actual: actual) | |
return true |
$ cd /usr/local/Library/Formula/ | |
$ git checkout master carthage.rb |
extension SequenceType { | |
func toDictionary<K, V>() -> [K: V] { | |
var dictionary: [K: V] = [:] | |
self.forEach { e in | |
if let kv = e as? (K, V) { | |
dictionary[kv.0] = kv.1 | |
} | |
} | |
return dictionary | |
} |
import numpy as np | |
import tensorflow as tf | |
def unpool(input_images, argmax, output_shape, name='unpooling'): | |
os = output_shape.as_list() | |
output_sz = np.prod(os) | |
b = os[0] | |
output_hwc = np.prod(os[1:]) | |
input_hwc = np.prod(argmax.get_shape().as_list()[1:]) |
0 |