To setup:
python venv .venv
source .venv/
pip install -r requirements.txt
Then to run the app
streamlit run main.py
from gekko import GEKKO | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from typing import List, Union, Optional | |
class BinaryDistillationColumn: | |
""" | |
Notes | |
------ |
To setup:
python venv .venv
source .venv/
pip install -r requirements.txt
Then to run the app
streamlit run main.py
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 |
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 |
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() |
ord download --dataset=$DATASET_ID --out=$(pwd) --query="query.txt" --out="sample_dataset.csv" |
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 |
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: |
"Numpy Docstring" : { | |
"prefix": "docs", | |
"body": [ | |
"''' ${1: brief description} ", | |
"", | |
"Parameters", | |
"---------- ", | |
"${2:param1: `int`}", | |
"\t${3: description}", | |
"", |
# -*- 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): |