caniko

from typing import Iterable

from scipy.ndimage import label, find_objects


def volume_from_slices(*slices: Iterable[slice]):
    volume = 1
    for comp_slice in slices:
        volume *= (comp_slice.stop - comp_slice.start)
    return volume

caniko

Poetry docker integration

I landed on using archlinux as my base image. I use chaoti-aur to install the latest poetry from git, I can choose the exact Python version I want thanks to pyenv (made it conveniant by adding this as an env var, PYTHON_VERSION).

You can install your poetry package after copying it to your WORKDIR by poetry install --no-dev. Flush the poetry cache by poetry cache clear --all; no neat way to do this yet #887.

Advantages of archlinux + poetry in containarized production

• Installing poetry through pacman installs the poetry as root. Poetry installer is rootless by default, which is not production-friendly; it is now! Don't forget to switch to a user wihtout write-access to root! Otherwise, you won't benefit from this safety feature.
• Separation of Python used for production code, and poetry code. Poetry will run on the latest stable version of Python, while production code will run on whatever version it was designed for.
• Less maintanance of `dockerf

caniko

import 'package:df/df.dart';
import 'package:grizzly_io/io_loader.dart';   // to parse CSV string

DataFrame _convertHeaderRowDataToDf(List<List<String>> headerRowData) {
  List<String> headers = headerRowData[0];
  List<Map<String, Object?>> mappedData = [];
  for (int i = 1; i < headerRowData.length; i++) {
    Map<String, Object?> mappedRow = {};
    headerRowData[i].asMap().forEach((int rowIndex, value) {
      mappedRow[headers[rowIndex]] = value;

caniko

from typing import Union

import itk
import numpy as np
from yaspin import yaspin
from yaspin.spinners import Spinners

INTERPOLATION_TEXT = "Interpolating for the first time"

caniko

from logging import warning
from typing import Union, Iterable, Optional

from cassandra.cqlengine.models import Model
from cassandra.cluster import Session


def join_null_check_columns(column_keys):
    return " ".join(f"AND {key} IS NOT NULL" for key in column_keys)