juanluisrto

#!/usr/bin/env zsh
# chat – ask OpenAI using the last visible terminal lines (curl + jq)

chat() {
  local lines=30              # how many terminal lines to capture
  local model="gpt-4.1-nano"  # default model
  local verbose=false
  local -a query_parts=()

  # -------------------- parse options --------------------

juanluisrto

def calculadora_impuestos(ingresos):
    irpf_escalones = {
        12_450 : 0.19,
        20_200 : 0.24,
        35_200 : 0.30,
        60_000 : 0.37,
        300_000 : 0.45,
        1e100 : 0.47
    }
    

juanluisrto

javascript:(function(){
document.querySelectorAll(".fc-dialog-container, .fc-dialog-overlay").forEach(e => {e.style.setProperty("display", "none", "important");});
document.body.style.overflow = "visible";
})()
// Add this as a bookmark to your bookmark bar

juanluisrto

from math import atan2, degrees, radians, sin, cos
def fit_text_as_geometry_between_two_points(text, A, B):
    mid_coords = ((B[1] + A[1])/2,  (B[0] + A[0])/2)
    angle = atan2(B[1] - A[1],  B[0] - A[0])
    bbox = LineString([A,B]).bounds
    h = get_height(bbox)
    w = get_width(bbox)
    scale_factor = 100*(abs(h*sin(angle)) + abs(w*cos(angle)))
    ml = text_as_geometry(text, mid_coords, scale_factor, degrees(angle))
    return ml

juanluisrto

import numpy as np, pandas as pd
import freetype
from shapely.geometry import *
from shapely import affinity as aff
from cartoframes.viz import Layer

face = freetype.Face("Vera.ttf") # path to your font
face.set_char_size(3000, hres = 100)
# These parameters are ok for Vera, but should be adjusted for each font.
char_width = 1500

juanluisrto

deadly_one_liner = (
    lambda r, n : print("".join(
        ["00" 
         if any((i == p[0] and j == p[1] for p in 
                    (lambda r, n : [
                     [round((lambda x : 1 - (x**2)/2 + x**4/(4*3*2) - x**6/(6*5*4*3*2))(2*3.1415/n*x)*r + n/2),
                      round((lambda x : x - (x**3)/6 + x**5/(5*4*3*2) - x**7/(7*6*5*4*3*2))(2*3.1415/n*x)*r + n/2)] 
                      for x in range(-int(n/2),int(n/2))])(r,n)))
             or (i == round(n/2) and j < n) or j == round(n/2)
             or i == j or i == n - j

juanluisrto

import pandas as pd
import numpy as np
from itertools import cycle
def generate_fake_dataframe(size, cols, col_names = None, intervals = None, seed = None):
    
    categories_dict = {'animals': ['cow', 'rabbit', 'duck', 'shrimp', 'pig', 'goat', 'crab', 'deer', 'bee', 'sheep', 'fish', 'turkey', 'dove', 'chicken', 'horse'],
                       'names'  : ['James', 'Mary', 'Robert', 'Patricia', 'John', 'Jennifer', 'Michael', 'Linda', 'William', 'Elizabeth', 'Ahmed', 'Barbara', 'Richard', 'Susan', 'Salomon', 'Juan Luis'],
                       'cities' : ['Stockholm', 'Denver', 'Moscow', 'Marseille', 'Palermo', 'Tokyo', 'Lisbon', 'Oslo', 'Nairobi', 'Río de Janeiro', 'Berlin', 'Bogotá', 'Manila', 'Madrid', 'Milwaukee'],
                       'colors' : ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'purple', 'pink', 'silver', 'gold', 'beige', 'brown', 'grey', 'black', 'white']
                      }

juanluisrto

import math, numpy as np
from math import factorial as fact
import statistics as stats

def prob_of_sum_gaussian(s,n):
    mean = 3.5*n
    std = np.sqrt(n*105/36)
    normal_dist = stats.NormalDist(mean, std)
    return normal_dist.pdf(s)

juanluisrto

proxy = "IP:PORT"
http_proxy  = "http://" + proxy
https_proxy = "https://"+ proxy
ftp_proxy   = "ftp://"+ proxy

proxyDict = { 
              "http"  : http_proxy, 
              "https" : https_proxy, 
              "ftp"   : ftp_proxy
            }

juanluisrto

%load_ext autoreload
%autoreload 2


import sys, os
file_path = r"\path\to\file.py"
path = os.path.dirname(file_path)
sys.path.append(path)

from file import *