marcosfelt

wedding = Expense(
  "Wedding", Uniform(Jan/1/2026, Oct/1/2026), Normal(3.3e4, 2e3)
)
house_dp = Expense(
  "House downpayment", Uniform(Jan/1/2027, Jan/1/2028), Uniform(8e4, 1.2e5)
)
sim = Simulation([house_dp, wedding], start=Oct/1/2024, end=Nov/1/2094, samples=10000)
sim.run()

marcosfelt

from dataclasses import dataclass
import pylaborchestrate as plo
from pylabrobot.resources import (
    TIP_CAR_480_A00,
    PLT_CAR_L5AC_A00,
    Cos_96_DW_1mL,
    HTF_L
)
from pylabrobot.resources import Deck, TipRack, Plate
from pylabrobot.liquid_handling import LiquidHandler

marcosfelt

marcosfelt

from typing import Dict, List, Literal
from scipy.stats import linregress, spearmanr
from sklearn import metrics

SCORE_NAMES = ["mae", "mse", "rmse", "mape", "r2", "maxe", "expl_var"]
def calculate_metrics(

    y_true, y_pred, scores: List[str] = SCORE_NAMES
) -> Dict[str, float]:
    """Calculate metrics on a given dataset."""

marcosfelt

import logging
import re
from typing import Tuple, Union

import pandas as pd
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm

wikipedia_base = "https://en.wikipedia.org"

marcosfelt

import numpy as np

def pareto_efficient(data, maximize=True):
    """
    Copied from Summit, which in turn was probably copied from Stackoverflow
    Find the pareto-efficient points
    Parameters
    ---------
    data: array-like
        An (n_points, n_data) array

marcosfelt

from typing import Dict, List, Optional
import matplotlib as mpl
from matplotlib.axes import Axes
import matplotlib.pyplot as plt
import numpy as np

def parity_plot(
    y,
    yhat,
    ax: Optional[Axes] = None,

marcosfelt

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm, ticker
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from scipy.interpolate import make_interp_spline
from typing import Callable, Dict, List, Optional, Union

marcosfelt

from rdkit import Chem

"""The code below is directly copied from the thermo group_contribution code (MIT license) 
https://github.com/CalebBell/thermo/blob/516dee4ceda8e100918c7645e393a42fdfdc4bef/thermo/group_contribution/

I made changes to the SMARTS strings to match the ones used in FeOs
"""
J_BIGGS_JOBACK_SMARTS = [
    ["Methyl", "CH3", "[CX4H3]"],
    ["Secondary acyclic", "CH2", "[!R;CX4H2]"],

marcosfelt

import numpy as np
x = np.arange(100)
N = 0
mean = 0
std = 0
for i in range(11):
    batch = x[10*i:10*(i+1)]
    k = len(batch)
    N += k
    old_mean = mean