tejainece

filetype plugin indent on
syntax on

set autoread
set autowrite
set number

" Enable mouse
set mouse=a

tejainece

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

tejainece

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

tejainece

Postgres Cheat Sheet
Source: Postgresql Documentation

### shell commands
	
	creatuser <user>
	deletesuer <user>
	createdb -O <user> -E utf8 -T <template> <db_name>
	dropdb <db_name>

tejainece

curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add
sudo apt-add-repository "deb http://apt.kubernetes.io/ kubernetes-xenial main"
sudo apt install kubeadm

tejainece

class Endpoint {
    constructor(path: String) {
    }

    fun query(key: String, defaultValue: String = "", valueExample: String = "", description: String = "") {
    }
}

fun post(path: String, func: Endpoint.()->Unit): Endpoint {
    return Endpoint(path)

tejainece

/* Enable postgis */
CREATE EXTENSION  IF NOT EXISTS postgis;

CREATE TABLE ex1 (
  id VARCHAR(10) NOT NULL,
  loc geography(POINT, 4326)
);

CREATE INDEX  ex1_loc_gix ON  ex1  USING GIST (loc);

tejainece

import 'package:jaguar/jaguar.dart';
import 'package:jaguar_auth/jaguar_auth.dart';

import 'package:jaguar_example_session_models/jaguar_example_session_models.dart';

main() async {
  final server = Jaguar(port: 10000);
  // Register user fetcher
  server.userFetchers[User] = DummyUserFetcher(users);
  server.postJson(

tejainece

import 'dart:async';

Stream<int> stream() async* {
  int k = 0;
  while (k < 10) yield k++;
}

void main() async {
  await for(int i in stream()) {
    print('Starting $i ...');

tejainece

import numpy as np
from lightning.classification import SAGAClassifier
from scipy import sparse

from sklearn.datasets import load_iris, make_classification
from sklearn.linear_model.logistic import (
    LogisticRegression,
)