danielhfrank

import asyncio
import trio

import sys


async def fib(n: int) -> int:
    if n < 0:
        raise ValueError('boom')
    elif n in {0, 1}:

danielhfrank

from typing import Generic, TypeVar

import numpy as np
from pydantic.fields import ModelField

JSON_ENCODERS = {
    np.ndarray: lambda arr: arr.tolist()
}

DType = TypeVar('DType')

danielhfrank

package com.stripe.danielhfrank

import com.twitter.algebird.Monoid

import scala.annotation.tailrec
import scala.collection.mutable

class OverTimeMonoid[V: Monoid] extends Monoid[ Seq[(Long, V)]] {
  override def zero:  Seq[(Long, V)] = Seq.empty

danielhfrank

#!/usr/bin/env ruby

PENDING=2
NO_STATUS=3
return_txt = ""

loop do
  return_txt = `hub ci-status -v`
  break unless [PENDING, NO_STATUS].include?($?.exitstatus)
  sleep(1)

danielhfrank

#!/usr/bin/env sh

while [ `hub ci-status` = "pending" ]; do
    sleep 5
done

terminal-notifier -message "Build finished"

danielhfrank

Keybase proof

I hereby claim:

• I am danielhfrank on github.
• I am df (https://keybase.io/df) on keybase.
• I have a public key whose fingerprint is 6627 401D 1986 3A54 3235 A733 5B08 4339 A82D F24E

To claim this, I am signing this object:

danielhfrank

def passthrough_errors(method):
    @functools.wraps(method)
    def wrapper(*args, **kwargs):
        callback = kwargs['callback']
        data = args[0]

        # Check if an error is being passed in, somehow
        error = None
        if isinstance(data, tornado.httpclient.HTTPResponse) and data.error:
            error = data.error

danielhfrank

def retry_with_backoff(method):
    """
    The idea was to use this to decorate a method that makes some kind of request and has a callback
    kwarg. The callback will be decorated to retry the calling function a few times,
    with backoff, if it receives an error.
    """
    @functools.wraps(method)
    def caller_wrapper(self, *args, **kwargs):
        callback = kwargs['callback']
        attempts = kwargs.pop('attempts', 0)

danielhfrank

class UnitIntervalScaler(object):

    def fit(self, xs):
        self._floor = np.min(xs)
        self._range = np.max(xs) - self._floor
        return self
        
    def transform(self, x):
        return (x - self._floor) / self._range

danielhfrank

class X(object):
    def __str__(self):
        return 'x'

    def my_print_arg(*args):
        print args[0]

def print_arg(*args):
    print args[0]