dunossauro

from flask import Flask, request
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.future import select
from sqlalchemy.orm import declarative_base, sessionmaker

engine = create_async_engine('sqlite+aiosqlite:///./db.db')
async_session = sessionmaker(
    engine, expire_on_commit=False, class_=AsyncSession
)

nhamilakis

class Corpus {
  name: string;
}


@TrucAngularAUZEF
class MyCorporaComponent extends OnInit {
  listCorpora : Array<Corpus> = [];
    
  constructor(private api: MyAPIServiceDeLEspace){}

bre7

Solution 1

convert original.pdf -colorspace gray +clone -blur 0x1 +swap -compose divide -composite -linear-stretch 5%x0% -rotate 1.5 fake-scanned.pdf

Colorscan variation:

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

rroohhh

#!/bin/bash                                                                                                                                                                       
gst-launch-1.0 -v udpsrc uri=udp://239.1.1.1:5000 caps="application/x-rtp,channels=(int)2,format=(string)S16LE,media=(string)audio,payload=(int)96,clock-rate=(int)44100,encoding-name=(string)L24" ! rtpL24depay ! audioconvert ! autoaudiosink sync=false

tuxedocat

import numpy as np
from numba import jit


def levenshtein(x, y):
    """ levenshtein distance for iterable sequences
    """
    # check type
    if (np.all(map(type, x)) is str) and (np.all(map(type, y)) is str):
        _x = np.array(x, dtype=np.str)

hosackm

from flask import Flask, Response

app = Flask(__name__)


@app.route("/wav")
def streamwav():
    def generate():
        with open("signals/song.wav", "rb") as fwav:
            data = fwav.read(1024)

rogererens

# As the Kivy docs ( http://kivy.org/docs/guide/other-frameworks.html ) state:
# install_twisted_rector must be called before importing and using the reactor.
from kivy.support import install_twisted_reactor
install_twisted_reactor()

from autobahn.twisted.websocket import WebSocketClientProtocol, \
                                       WebSocketClientFactory


class MyKivyClientProtocol(WebSocketClientProtocol):

rriemann

convert Image-0001.png Image-0002.png -depth 2 -colorspace Gray output.tiff