herberteuler

import java.net.InetSocketAddress;
import java.nio.ByteBuffer;

import io.netty.bootstrap.Bootstrap;
import io.netty.buffer.ByteBuf;
import io.netty.buffer.ByteBufUtil;
import io.netty.buffer.Unpooled;
import io.netty.channel.ChannelFuture;
import io.netty.channel.ChannelFutureListener;
import io.netty.channel.ChannelHandlerContext;

herberteuler

import java.net.InetSocketAddress;
import java.nio.ByteBuffer;
import java.util.List;

import io.netty.bootstrap.ServerBootstrap;
import io.netty.buffer.ByteBuf;
import io.netty.channel.ChannelFuture;
import io.netty.channel.ChannelFutureListener;
import io.netty.channel.ChannelHandler.Sharable;
import io.netty.channel.ChannelHandlerContext;

herberteuler

import java.net.InetSocketAddress;
import java.nio.ByteBuffer;
import java.util.List;

import io.netty.bootstrap.Bootstrap;
import io.netty.bootstrap.ServerBootstrap;
import io.netty.buffer.ByteBuf;
import io.netty.channel.ChannelFuture;
import io.netty.channel.ChannelFutureListener;
import io.netty.channel.ChannelHandler.Sharable;

herberteuler

data MyStateT s m a = MyStateT (s -> m (a, s))

runStateT :: MyStateT s m a -> s -> m (a, s)
runStateT = \ (MyStateT f) s -> f s

get :: Monad m => MyStateT s m s
get = MyStateT $ \ s -> return (s, s)

put :: Monad m => s -> MyStateT s m ()
put = \ s -> MyStateT $ \ s' -> return ((), s)

herberteuler

  case class Foo(s: String, n: Int)

  class MyClient(url: URL) extends featherbed.Client(url) {
    override def clientTransform(client: Http.Client) = {
      client.withSessionPool.maxSize(1)
    }
  }

  val client = new MyClient(new URL("http://localhost:8766/api/"))
  val req = client.post("foo/bar")

herberteuler

import numpy as np
import tensorflow as tf

def input_fn():
  def parse(tensor):
    return tensor[:3], tensor[-1:]
  ds = tf.data.Dataset.from_tensor_slices(np.random.random_sample([10,4]))
  ds = ds.map(parse).batch(10).repeat(5)
  return ds

herberteuler

import numpy as np
import tensorflow as tf

def input_fn():
  def parse(tensor):
    return tensor[:3], tensor[-1:]
  ds = tf.data.Dataset.from_tensor_slices(np.random.random_sample([10,4]))
  ds = ds.map(parse).batch(10).repeat(5)
  return ds

herberteuler

import dask.array as da
import numpy as np

arr = da.from_array(np.arange(9).reshape(3,3))
a = da.min(arr)
b = da.max(arr)
quantiles = da.linspace(a, b, 4)
print(np.array(quantiles))

# Traceback (most recent call last):

herberteuler

with import <nixpkgs> {};

(let

my-python =
  let packageOverrides = self: super: {
    dask = super.dask.overridePythonAttrs (old: rec {
      version = "2.15.0";
      src = super.fetchPypi {
        inherit (old) pname;

herberteuler

{ lib
, stdenv
, patchelf
, fetchurl
, coreutils
, python3
, writeTextFile
, makeWrapper
, requireFile
, alsa-lib