jstheoriginal

struct SearchBar : View {
    @Binding var searchText: String
    
    var body: some View {
        HStack {
            Image(systemName: "magnifyingglass").foregroundColor(.secondary)
            TextField(
                $searchText,
                placeholder: Text("Search")) {
                    UIApplication.shared.keyWindow?.endEditing(true)

dannguyen

Simple scraper of the ASPX search form for U.S. Congress House financial disclosure results

The following script, given someone's last name, prints a CSV of financial disclosure PDFs (the first 20, for simplicity's sake) as found on the House Financial Disclosure Reports. It's meant to be a proof-of-concept of how to scrape ASPX (and other "stateful" websites) with using plain old requests -- without too much inconvenience -- rather than resorting to something heavy like the selenium websdriver

The search page can be found here: http://clerk.house.gov/public_disc/financial-search.aspx

Here's a screenshot of what it does when you search via web browser:

maks-ivanov

import numpy as np
from numba import jit
from numba import float64
from numba import int64

@jit((float64[:], int64), nopython=True, nogil=True)
def _ewma(arr_in, window):
    r"""Exponentialy weighted moving average specified by a decay ``window``
    to provide better adjustments for small windows via:

nikhilweee

import random
import torch

num_examples = 128
message_length = 32


def dataset(num_examples):
    """Returns a list of 'num_examples' pairs of the form (encrypted, original).

impshum

from pymongo import MongoClient


try:
    client = MongoClient('localhost')
    db = client.searchfollow
except:
    print("Could not connect to MongoDB")

call = db.command("dbstats")

stungeye

[
  {
    "url": "http://money.cnn.com",
    "rss": "http://rss.cnn.com/rss/money_topstories.rss"
  },
  {
    "url": "http://thehill.com",
    "rss": "http://thehill.com/rss/syndicator/19110"
  },
  {

oseiskar

lirnli

lirnli

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.