Scratch

This is an example of using org babel as a jupyter replacement!

Notebook

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset("iris")
print("Here's some data!")
print(df)
sns.scatterplot(data=df, x="sepal_length", y="sepal_width", hue="species")
plt.show()

Here’s some data!

idx	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
5	5.4	3.9	1.7	0.4	setosa
6	4.6	3.4	1.4	0.3	setosa
7	5.0	3.4	1.5	0.2	setosa
8	4.4	2.9	1.4	0.2	setosa
9	4.9	3.1	1.5	0.1	setosa
10	5.4	3.7	1.5	0.2	setosa
11	4.8	3.4	1.6	0.2	setosa
12	4.8	3.0	1.4	0.1	setosa
13	4.3	3.0	1.1	0.1	setosa
14	5.8	4.0	1.2	0.2	setosa
15	5.7	4.4	1.5	0.4	setosa
16	5.4	3.9	1.3	0.4	setosa
17	5.1	3.5	1.4	0.3	setosa
18	5.7	3.8	1.7	0.3	setosa
19	5.1	3.8	1.5	0.3	setosa

Cell Timer: 0:00:01

import numpy as np

def iterate_values(values, iterations=10):
    # Convert the input list to a NumPy array
    values = np.array(values)

    # Initialize the sum of squares for the first iteration
    previous_sum_of_squares = np.sum(values ** 2)

    # Perform the iteration process
    for i in range(iterations):
        # Compute the difference between consecutive elements using np.roll
        values = values - np.roll(values, -1)

        # Calculate the current sum of squares
        current_sum_of_squares = np.sum(values ** 2)

        # Print the current values, sum of squares, and ratio to previous sum
        if i == 0:
            print(f"Iteration {i+1}: {values}, Sum of squares: {current_sum_of_squares}")
        else:
            ratio = current_sum_of_squares / previous_sum_of_squares
            print(f"Iteration {i+1}: {values}, Sum of squares: {current_sum_of_squares}, Ratio to previous: {ratio}")

        # Update previous_sum_of_squares for the next iteration
        previous_sum_of_squares = current_sum_of_squares

    return values

# Example usage for k=4
initial_values = [0, 0, 0,5]  # Replace with any integers
final_values = iterate_values(initial_values, iterations=20)
print("Final values after iterations:", final_values)

Iteration 1: [ 0 0 -5 5], Sum of squares: 50
Iteration 2: [ 0 5 -10 5], Sum of squares: 150, Ratio to previous: 3.0
Iteration 3: [ -5 15 -15 5], Sum of squares: 500, Ratio to previous: 3.3333333333333335
Iteration 4: [-20 30 -20 10], Sum of squares: 1800, Ratio to previous: 3.6
Iteration 5: [-50 50 -30 30], Sum of squares: 6800, Ratio to previous: 3.7777777777777777
Iteration 6: [-100 80 -60 80], Sum of squares: 26400, Ratio to previous: 3.8823529411764706
Iteration 7: [-180 140 -140 180], Sum of squares: 104000, Ratio to previous: 3.9393939393939394
Iteration 8: [-320 280 -320 360], Sum of squares: 412800, Ratio to previous: 3.9692307692307693
Iteration 9: [-600 600 -680 680], Sum of squares: 1644800, Ratio to previous: 3.9844961240310077
Iteration 10: [-1200 1280 -1360 1280], Sum of squares: 6566400, Ratio to previous: 3.992217898832685
Iteration 11: [-2480 2640 -2640 2480], Sum of squares: 26240000, Ratio to previous: 3.996101364522417
Iteration 12: [-5120 5280 -5120 4960], Sum of squares: 104908800, Ratio to previous: 3.998048780487805
Iteration 13: [-10400 10400 -10080 10080], Sum of squares: 419532800, Ratio to previous: 3.9990239141044412
Iteration 14: [-20800 20480 -20160 20480], Sum of squares: 1677926400, Ratio to previous: 3.999511837930193
Iteration 15: [-41280 40640 -40640 41280], Sum of squares: 6711296000, Ratio to previous: 3.999755889173685
Iteration 16: [-81920 81280 -81920 82560], Sum of squares: 26844364800, Ratio to previous: 3.999877937137626
Iteration 17: [-163200 163200 -164480 164480], Sum of squares: 107375820800, Ratio to previous: 3.9999389667063383
Iteration 18: [-326400 327680 -328960 327680], Sum of squares: 429500006400, Ratio to previous: 3.999969482887529
Iteration 19: [-654080 656640 -656640 654080], Sum of squares: 1717993472000, Ratio to previous: 3.999984741327352
Iteration 20: [-1310720 1313280 -1310720 1308160], Sum of squares: 6871960780800, Ratio to previous: 3.9999923706345726
Final values after iterations:
[-1310720 1313280 -1310720 1308160]
Cell Timer: 0:00:00