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 |