suessspeise

from PIL import Image, ImageEnhance
import warnings
try:    import matplotlib
except: warnings.warn('matplotlib not available.')
    
__all__ = ['PixelfyOps', 'Pixelfy']

class PixelfyOps:
    """
    Stateless toolkit of image processing operations for the pixel art effect.

suessspeise

import cv2
import numpy as np
import sklearn.cluster
import matplotlib.pyplot as plt

# https://pypi.org/project/MemePy/
# https://kitchencrafthubs.com/how-do-you-make-deep-fried-pictures/

class Deepfrier:
    def __init__(self, path):

suessspeise

dictionary_path = "/usr/share/dict/words"
# On Unix systems you may have /usr/share/dict/words installed
# but this can really be whatever you provide
with open(dictionary_path) as f:
    WORDS = {w.strip().lower() for w in f}

def in_wordlist(word):
    return word.lower() in WORDS

def permutations(letters):

suessspeise

import os
import re
from PIL import Image
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from datetime import datetime

def get_scan_dirs(dirname):
    filelist = os.listdir(dirname)
    pattern = re.compile(r'^\d{3}_\d{10}$')

suessspeise

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats

def ax_example_correlation(ax, correlation):
    """
    Generates a plot of random data with a specified Pearson correlation coefficient.
    
    Parameters:
    correlation (float): A number between 0 and 1 representing the Pearson correlation coefficient.

suessspeise

from abc import ABC, abstractmethod
import json

class RPSCompetitor(ABC):
    @abstractmethod
    def play(self):
        """
        Returns the competitor's move.

        Returns:

suessspeise

import numpy as np
import struct

class MonoAudioBuffer:
    """
    A class to generate audio data and store it for playback or further processing.

    Attributes:
        sample_rate (int): The sample rate of the audio data.
        audio_buffer (bytearray): The buffer to store the generated audio data.

suessspeise

import numpy as np
import matplotlib.pyplot as plt

def fig_time_series(t, y, focus=(0,1)):
    fig, axs = plt.subplots(1, 2, figsize=(20,2))
    ax = axs[0]
    ax.set_title('mixed signal')
    ax.plot(t,y)
    ax.axvspan(*focus, color='tab:blue', alpha=0.2)
    ax = axs[1]

suessspeise

import sympy
import numpy as np
import matplotlib.pyplot as plt

# use sympy to solve for y
x, y = sympy.symbols('x y')
equation = x**2 + (y - sympy.root(x**2, 3))**2 - 1
solutions = sympy.solve(equation, y)

# nice things up

suessspeise

import os
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

def smooth_timeseries(timeseries: xr.DataArray, window_size: int = 3, method: str = 'running_mean') -> xr.DataArray:
    """
    Smooths a timeseries using a specified method.

    Parameters: