It turned out to be incredibly useful. With git worktree, you can maintain multiple working directories linked to the same Git repository with almost no friction.

Why use worktrees?

Imagine you're working on a long-running feature — say, an optimization — and suddenly you’re assigned an urgent production bug. Typically, you would stash your changes or make a temporary commit, switch branches, fix the bug, then restore everything. It's annoying and error-prone.

With worktrees, you can directly spin up a parallel working directory:

git worktree add <path>
# Example:
git worktree add ../hotfix

This creates a new linked worktree, associated with your current repository, with its own metadata and branch checkout. Your original work remains untouched.

Removing a worktree

Once you're done with the hotfix (or any task), removing the worktree is just as simple:

git worktree remove <path>

If you delete the directory manually, Git will eventually clean up its administrative files automatically (based on gc.worktreePruneExpire in git-config). You can also remove stale entries explicitly:

git worktree prune

Other useful worktree commands

1. Create a throwaway worktree (detached HEAD)

Perfect for quick experiments:

git worktree add -d <path>

2. Create a worktree for an existing branch

git worktree add <path> <branch>

This checks out the given branch into a new, isolated working directory.

Further reading

To dive deeper into git worktree:

• Official docs: https://git-scm.com/docs/git-worktree
• Or simply run:

  git help worktree

Cheers!

#Git #TIL #Worktree

Later in the evening, I searched the web to find an alternate way to extract the data and discovered that HTML tables can be converted to CSV files. All I had to do was save the code in CSV format. I opened the Chrome browser's inspect code feature, copied the table, saved it with the CSV extension, and then opened the file with LibreOffice. Voila! I had the spreadsheet with all my transactions.

Cheers!

#TIL #CSV #HTML Table

What is EXIF Exchange image file format is a protocol whose initial definition was produced by Japan Electronic Industries Development Association(JEIDA). It stores the various meta information of the images taken by a digital camera, which is stored as tag and value. There are many tags but for my problem Orientation (rotation) tag is of interest. The orientation tag value can be from 1 to 8 which signifies different meanings according to the position of the camera while taking the image.

Image metadata from GIMP tool

Different Rotation EXIF rotation helps the image viewer application to show the image in the right orientation if it's compatible with the EXIF metadata. Window users might have noticed that before Window 8 image shown is without rotation, but after Window 8 all images are shown in their right orientation because of the compatibility with EXIF.

Above image is taken from this blog

What Problem I have and how EXIF meta helpful So, the issue I am trying to solve is that we need to show the user's uploaded images that can have a different orientation, to fix this we need to rotate images which can be achieved at the server or the browser side. Working with Python makes it easy to handle the image with the help of Pillow library.

Image with different rotation

from PIL import Image

rotation_dict = {3: 180, 6: 270, 8: 90}
EXIF_ORIENTATION_TAG = 274
image = Image.open(image)
exif_data = image._getexif()
if exif_data:
    rotation_degree = rotation_dict.get(exif_data.get(EXIF_ORIENTATION_TAG))
    if rotation_degree:
        image_file = image_file.rotate(rotation_degree)

This also can be achieved with CSS image-orientation which can be used with mostly all browser

/* keyword values */
image-orientation: none;
image-orientation: from-image; /* Use EXIF data from the image */

/* Global values */
image-orientation: inherit;
image-orientation: initial;
image-orientation: revert;
image-orientation: unset;

At last, I go with the CSS solution, which solves our use case with the least effort/code changes.

Cheers!

#100DaysToOffload #TIL

Let see how to define your data class with the decorator @dataclass

from dataclasses import dataclass

@dataclass
class Batch:
    sku: int
    name: str
    qty: int

>>> Batch(1, 'desk', 100)
Batch(sku=1, name='desk', qty=100)

We can also add the default value to the data class, which works exactly as if we do in the __init__ method of regular class. As you have noticed in the above we have defined the fields with type hints, which is kind of mandatory thing in the data class, if you do not do it will not take the field in your data class.

@dataclass
class Batch:
    sku: int = 1
    name: str = 'desk'
    qty: int = 100

# if you don't want to explicity type the fields you can use any
from typing import Any
class AnyBatch:
    sku: Any
    name: Any = 'desk'
    qty: Any = 100

If you want to define mutable default value in data class, it can be done with the help of default_factory and field. Field() is used to customize each field in data class, different parameter that can be passed to field are default_factory, compare, hash, init, you can check about them over here

from dataclasses import dataclass, field
from typing import List

@dataclass()
class Batch:
    sku: int
    name: str
    qty: int = 0
    creator: List[str] = field(default_factory=<function/mutable value>)

Immutable Data Class we can also define our data class as immutable by setting frozen=True, which basically means we cannot assign value to the fields after creation

@dataclass(frozen=True)
class Batch:
    sku: int
    name: str
    qty: int = 0

>>> b = Batch(12, 'desk', 100)
>>> b.qty 
100
>>> b.qty = 90
dataclasses.FrozenInstanceError: cannot assign to field 'qty'

Data class saves us from writing boilerplate code, help us to focus on logic, this new feature of Python3.7 is great, so what waiting for go and right some data classes.

Cheers!

#100DaysToOffload #Python #DataClass #TIL #DGPLUG