Skip to main content

Is it possible to access the event object in a function without having to pass it in?

So i have this equalizer function here, which gives all elements the same minHeight. The function uses lodash's throttle function and executes everytime the window resizes and on load.

export const equalizer = throttle((elems, e, stopAt = 0) => {
  const elements = [...document.querySelectorAll(elems)];

  const setMinHeight = (el) => el.style.minHeight = '0px';

  if (e.type === 'resize') elements.forEach(setMinHeight);

  const minHeight = elements.reduce((acc, cur) => {
    if (cur.clientHeight >= acc) return cur.clientHeight;
    if (acc >= cur.clientHeight) return acc;
  }, 0);

  elements.forEach((el) => (el.style.minHeight = `${minHeight}px`));

}, 500);

So the "problem" now is that everytime I call this function I have to pass in the event object (because of e.type === "resize" ) and I want to know if there is a way to avoid that.

['load', 'resize'].forEach((event) => {
  window.addEventListener(event, function (e) {
    equalizer('.comment__body p', e);
    equalizer('.comment__header .comment__name', e);
  });
});

I thought that I don't even need to pass in the event object because of closures. And the way I understand closures is that a function has access to it's outer functions variables.

So is there a way to access the event object in this equalizer function without me having to everytime passing it in?

Via Active questions tagged javascript - Stack Overflow https://ift.tt/xcE6ZuR

Comments

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...