wassname2

import pandas as pd
import os
from anycache import anycache
from loguru import logger
import time


def chunking_by_time(startTime: pd.Timestamp, endTime: pd.Timestamp, freqs=['YE', 'ME', 'D']):
    """
    How do we cache timespans? We want to take year chunks, then for the remainder months, and so on.

wassname2

from pathlib import Path
import pandas as pd
from tqdm.auto import tqdm

experiments = {
  'exp1': dict(lr=1e-4),
 ]
cache_dir = Path('../data/30_processed/results_cache1')
cache_dir.mkdir(exists_ok=True)
  

wassname2

import pandas as pd
import numpy as np

def pandas_groupby_agg_mixed_dtype_df(df, groupby: list, agg='max', str_agg='first'):
    """
    resample a df with numeric and strings
    """
    numerics_cols = list(df.select_dtypes(include='number').columns)+groupby
    other_cols = list(df.select_dtypes(exclude='number').columns)+groupby

wassname2

import pandas as pd


def chunking_by_time(startTime: pd.Timestamp, endTime: pd.Timestamp, freqs=['Y', 'M', 'D']):
    """
    How do we cache timespans? We want to take year chunks, then for the remainder months, and so on.
    That way old data is cached in big chunks, and new data is rechunked as needed

    ```py
    startTime = pd.to_datetime('2023-01-02 01:01')