Yannis Katsaros yanniskatsaros

## settings.json
// This file was initially generated by Windows Terminal Preview 1.5.3242.0
// It should still be usable in newer versions, but newer versions might have additional
// settings, help text, or changes that you will not see unless you clear this file
// and let us generate a new one for you.

// To view the default settings, hold "alt" while clicking on the "Settings" button.
// For documentation on these settings, see: https://aka.ms/terminal-documentation
{
    "$schema": "https://aka.ms/terminal-profiles-schema",

## smoothing.py
from typing import Iterable

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from numpy import ndarray

def generate_random_walk(size: int) -> ndarray:
    draw = np.random.random(size)

## itersplit.py
from itertools import islice

def itersplit(iterable, n):
    i = len(iterable) % n
    start = 0; stop = i

    for _ in range(n):
        yield list(islice(iterable, start, stop))
        start += i; stop += i

## .gitlab-ci.yml
image: python:3.7.8-slim

default:
  tags:
    - common_runner
    - docker_spot_runner
    - group_runners

variables:
  USER: "{username}"

## scrape.py
import sys

import requests
from bs4 import BeautifulSoup

def download_html(url: str, filename: str):
    r = requests.get(url)
    r.raise_for_status()

    html = r.text

## autoregressive.py
"""
A pure auto-regressive model from scratch. (WIP)
--------------------------------------------------

The current implementation is incorrect. A proper
auto-regressive model uses $p$ "lags" of the variable
from $x_t$ to $x_{t-p}$ each with its own coefficient.

An auto-regressive model of order $p$ is written as:
$x_t = \sum_{i=1}^{p} \alpha_i x_{t-i} + \epsilon_t

## async_download_springer.py
"""
Download a selection or all of the 500+ freely available Springer textbooks.

For significantly better IO performance, prefer this asynchronous
implementation to the synchronous alternative also provided.

Orginally inspired by:
https://towardsdatascience.com/download-all-free-textbooks-from-springer-using-python-bd0b10e0ccc

"""

## fourier_transform.py
import numpy as np
import plotly.graph_objects as go

from scipy.fft import fft, ifft, fftfreq
from scipy.signal import find_peaks
from plotly.subplots import make_subplots

class FourierTransform:
    def __init__(self, x, time_step: int=1):
        """

## gamma_example.py
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns  # optional, just for aesthetics
from scipy.stats import gamma

# recreate the example shown in Wikipedia for reference
# param = (alpha, beta) == (k, 1/theta)
params = [
    (1, 1/2),
    (2, 1/2),

## environment.yml
# for more info see:
# https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually
name: core-env
channels:
  - conda-forge
dependencies:
  - python=3.7
  - matplotlib
  - jupyterlab
  - xlrd
	// This file was initially generated by Windows Terminal Preview 1.5.3242.0
	// It should still be usable in newer versions, but newer versions might have additional
	// settings, help text, or changes that you will not see unless you clear this file
	// and let us generate a new one for you.

	// To view the default settings, hold "alt" while clicking on the "Settings" button.
	// For documentation on these settings, see: https://aka.ms/terminal-documentation
	{
	"$schema": "https://aka.ms/terminal-profiles-schema",
	from typing import Iterable

	import numpy as np
	import matplotlib.pyplot as plt
	import seaborn as sns

	from numpy import ndarray

	def generate_random_walk(size: int) -> ndarray:
	draw = np.random.random(size)
	from itertools import islice

	def itersplit(iterable, n):
	i = len(iterable) % n
	start = 0; stop = i

	for _ in range(n):
	yield list(islice(iterable, start, stop))
	start += i; stop += i
	image: python:3.7.8-slim

	default:
	tags:
	- common_runner
	- docker_spot_runner
	- group_runners

	variables:
	USER: "{username}"
	import sys

	import requests
	from bs4 import BeautifulSoup

	def download_html(url: str, filename: str):
	r = requests.get(url)
	r.raise_for_status()

	html = r.text
	"""
	A pure auto-regressive model from scratch. (WIP)
	--------------------------------------------------

	The current implementation is incorrect. A proper
	auto-regressive model uses $p$ "lags" of the variable
	from $x_t$ to $x_{t-p}$ each with its own coefficient.

	An auto-regressive model of order $p$ is written as:
	$x_t = \sum_{i=1}^{p} \alpha_i x_{t-i} + \epsilon_t
	"""
	Download a selection or all of the 500+ freely available Springer textbooks.

	For significantly better IO performance, prefer this asynchronous
	implementation to the synchronous alternative also provided.

	Orginally inspired by:
	https://towardsdatascience.com/download-all-free-textbooks-from-springer-using-python-bd0b10e0ccc

	"""
	import numpy as np
	import plotly.graph_objects as go

	from scipy.fft import fft, ifft, fftfreq
	from scipy.signal import find_peaks
	from plotly.subplots import make_subplots

	class FourierTransform:
	def __init__(self, x, time_step: int=1):
	"""
	import numpy as np
	import matplotlib.pyplot as plt
	import seaborn as sns # optional, just for aesthetics
	from scipy.stats import gamma

	# recreate the example shown in Wikipedia for reference
	# param = (alpha, beta) == (k, 1/theta)
	params = [
	(1, 1/2),
	(2, 1/2),
	# for more info see:
	# https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually
	name: core-env
	channels:
	- conda-forge
	dependencies:
	- python=3.7
	- matplotlib
	- jupyterlab
	- xlrd