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from PIL import Image, ImageDraw, ImageFont
from pathlib import Path
from urllib.request import urlopen

def check_and_get_font_path(font_name="roboto_regular") -> Path:
    if font_name == "roboto_regular":
        path = Path("font/Roboto-Regular.ttf")
        if not path.exists():
            path.parent.mkdir(parents=True, exist_ok=True)
            url = "https://github.com/googlefonts/roboto/raw/main/src/hinted/Roboto-Regular.ttf"

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import bpy
import mathutils
import math

# blender rotate in the right hand direction

figher = bpy.context.scene.objects['airplane']

figher.location = (0, 0, 0)
figher.rotation_euler = (0, 0, 0)

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# Add this file into the root folder of your project
# Specify dependencies in requirements.txt and install dependencies with `pip install -e .`
# Then, you don't need to specify the `src` directory within PYTHONPATH when you run a python module in your project.
# This is an exeperimental/opinionated setting. So any comments are welcome.
# references:
# - https://stackoverflow.com/a/73600610/1874690
# - https://packaging.python.org/en/latest/guides/writing-pyproject-toml/

[project]
name = "my-project-name"

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sudo apt update
sudo apt install imagemagick
montage abc/* -tile 8x8 -geometry +1+1 grid_abc.png

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The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

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# grids = [create_grid(generated_batch[j]) for j in range(len(generated_batch))]
# save_as_gif_animation(grids, output_animation_path)

import math

import numpy as np
from PIL import Image

def create_grid(image_arrays: np.ndarray):
    batch_size = image_arrays.shape[0]

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class BaseClass:
    def __init__(self, _id, a, k=None):
        self.id = _id
        self.a = a
        self.k = k

    def updated(self, **kwargs):
        kwargs = {"k": self.k, **kwargs}
        return BaseClass(self.id, self.a, **kwargs)

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import pandas as pd
import numpy as np

input_csv = "./weather_history_weekly.csv"
output_csv_2w = "./weather_history_2w.csv"
output_csv_monthly = "./weather_history_monthly.csv"
df = pd.read_csv(input_csv)

N = len(df)
all_columns = df.columns

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import pandas as pd
import numpy as np

input_csv = "./weather_history_tiny.csv"
output_csv = "./weather_history_tiny_windowed.csv"
df = pd.read_csv(input_csv)

n_lags = 1
N = len(df)
all_columns = df.columns

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import signal
import threading
import traceback
import time
import sys


def func2():
    y = 2
    time.sleep(1)