marcosfelt

# -*- coding: utf-8 -*-

"""Base Class for Temperature Controllers"""

from abc import ABC

class TemperatureControllers(ABC):
    '''Base Class for Temperature Controllers
    '''
    def get_current_temperature(self):

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"Numpy Docstring" : {
	"prefix": "docs",
	"body": [
		"''' ${1: brief description} ",
		"",
		"Parameters",
		"---------- ",
		"${2:param1: `int`}",
		"\t${3: description}",
		"",

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class Cache:
    def __init__(self, user_function):
        self.user_function = user_function
    
    def __call__(self, *args, **kwargs):
        sentinel = object()
        key = _make_key(args, kwargs, False)
        try:
            result = self.cache.get(key, sentinel)
        except NameError:

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import numpy as np
import GPy
import matplotlib.pyplot as plt
from plotting import plot_3d_model

#Teset data
seed = 947
rs = np.random.RandomState(seed)
X = rs.rand(1000, 2)*100
f = lambda x: np.atleast_2d(np.exp(-x[:, 0]) + 10*x[:, 1]**2).T

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ord download --dataset=$DATASET_ID --out=$(pwd) --query="query.txt" --out="sample_dataset.csv"

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from summit import *
import torch
from torch import Tensor

# Replace this with however you import your data
data = get_data()

# Replace this with however you specify your domain
domain = get_domain()

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from summit import *
import numpy as np
import pandas as pd
import GPy

def main():
    # Get the in-silico benchmark
    exp, catalyst_list, base_list = setup_benchmark()

    # Run optimization

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from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from tqdm import tqdm
import numpy as np
from sklearn.model_selection import GroupShuffleSplit
import logging


def scaffold_split(df, column_to_split:str, test_size=0.1):
  """" Split a dataframe by scaffolds

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To setup:

python venv .venv
source .venv/
pip install -r requirements.txt

Then to run the app

streamlit run main.py

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from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
from typing import List, Union, Optional


class BinaryDistillationColumn:
    """
    Notes
    ------