carloocchiena

# Augmented Dickey-Fuller Test (ADF)

from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

"""
Se p-value < 0.05 possiamo rifiutare l’ipotesi nulla e concludere che la serie è stazionaria.
Se p-value > 0.05, la serie non è stazionaria e necessita di trasformazioni.
"""

carloocchiena

import matplotlib.dates as mdates

#  Create a heatmap
heatmap_data = df.copy()
heatmap_data['Date'] = heatmap_data.index.date
heatmap_data['Hour'] = heatmap_data.index.hour

pivot = heatmap_data.pivot_table(index='Date', columns='Hour', values='Prezzo')

# 2. Plot della heatmap

carloocchiena

# Boxplot by hour of day

plt.figure(figsize=(12, 5))
sns.boxplot(data=df, x='Hour', y='Prezzo')
plt.title('Hourly Distribution of PUN Prices')
plt.xlabel('Hour of Day')
plt.ylabel('€/MWh')
plt.grid(True)
plt.tight_layout()
plt.show()

carloocchiena

# --- 1. Libraries ---
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import timedelta

# --- 2. Load the Excel file ---
# Replace with your actual filename
df = pd.read_excel('20240101_20241231_PUN.xlsx')

carloocchiena

"""
I'm starting to like the Variance-Gamma model quite a bit.
Here is a quick and dirty Octave (Matlab) code for generating the IV skew given VG parameters
I took the parameters from the Madan, Carr and Chang paper.
Instead of evaluating a double integral I use the mixing formula, which results in a single integral.
From Linkedin profile of Frido Rolloos
""


import numpy as np

carloocchiena

-- create our table
CREATE TABLE Videogiochi (
    ID INT PRIMARY KEY AUTO_INCREMENT,
    Nome VARCHAR(100) NOT NULL,
    AnnoUscita INT NOT NULL,
    Rating INT CHECK (Rating >= 1 AND Rating <= 5)
);

-- insert the data
INSERT INTO Videogiochi (Nome, AnnoUscita, Rating) VALUES

carloocchiena

Cargo CLI commands

Source: https://doc.rust-lang.org/cargo/commands/general-commands.html

bold = very useful

cargo --version (actual version)
cargon --list (list installed commands)
cargo --help (provide helps)
cargo --explain code (explain specific functions)

carloocchiena

app.location.recordings.each(function (recording) { 
  recording.tracks.each(function (track) { 
    track.downloadFromLocal(window.location.href); 
  }) 
})

carloocchiena

# copy and paste the script on a Jupyter Notebook to have the 
# table printed on screen

import numpy as np
import pandas as pd
from scipy.integrate import quad

def normal_probability_density(x):
    constant = 1.0 / np.sqrt(2 * np.pi)
    return(constant * np.exp((-x**2) / 2))

carloocchiena

# TSP with heuristic dynamic programming
# Solution is based on "always picking the shortest route available" on the matrix

from itertools import permutations


def travel_salesman_problem(graph: list, path_sum: int = 0) -> int:
    vertex = []
    min_path = []