Skip to main content

Can sysfs gpio descriptors stay open as long as wrapper class lives?

I'm trying to write a basic wrapper gpio class that will handle read and write to gpio pins in a nvidia jetson, using linux sysfs interface, with python. I want to keep /sys/class/gpio/gpioXXX/value open as a file to avoid repeating file open/close using with open(...) as f approach. The reason for my approach is that I will keep changing the value in my process (to toggle a LED on-off) regularly. I'm thinking of closing it once the wrapper class gets deleted. The thing is that I know that opening files without close counterparts can cause resources leaks and that makes me anxious.

class GPIOWrapper:
    def __init__(self):
        self.fd = open("/sys/class/gpio/gpioXXX/valu","w+") 

    def set(self,val):
        self.fd.write(str(val)): # 0  or 1
        self.fd.flush()

    def read(self):
        return self.fd.read()

    def __del__(self): 
        self.fd.close()

What I'm asking is if the above approach valid? Or is it risky and I should stick with:

class GPIOWrapper:

    def set(self,val):
        with open("/sys/class/gpio/gpioXXX/value","w")) as fd:
            fd.write(str(val)): # 0  or 1

Furthermore, can there be side effects of keeping a file descriptor open to gpio pins indefinitely? I can't think of any but I'm new to programming peripherals. My aim is to keep this class general for any device that could use gpio in future (not just LEDs).
Thank you for your attention and help.



source https://stackoverflow.com/questions/69397609/can-sysfs-gpio-descriptors-stay-open-as-long-as-wrapper-class-lives

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...