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import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import lfilter

# Step 1: Create a synthetic dataset
np.random.seed(42)  # For reproducibility
N = 1000  # Number of samples
x = np.random.randn(N)  # Input signal (random noise)
w_true = np.array([0.5, -0.3, 0.1])  # True filter coefficients
e_ = 0.01 * np.random.randn(N)  # Noise

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import numpy as np
import vtk
import pyvista as pv
from vtk.util import numpy_support
# from vtk.numpy_interface import dataset_adapter as dsa

data = np.zeros((50, 50, 50))
data[20:30, 20:30, 20:30] = 1

data_vtk = numpy_support.numpy_to_vtk(

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 20 19:45:49 2020

@author: zhanglingxi
"""

import os
import os.path as op

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from pathlib import Path
from nbconvert import PythonExporter
import textwrap

exporter = PythonExporter()

notebook = Path('docs/tutorials/decomposition/ajive_tutorial.ipynb')


def nb2py(notebook):

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"""
Benchmark of MultiTaskLasso
"""
import gc
from itertools import product
from time import time
import numpy as np
import pandas as pd

from sklearn.datasets import make_regression

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$ pip list
Package                           Version               Location
--------------------------------- --------------------- -------------------------------------------------------------------------
absl-py                           0.7.1
alabaster                         0.7.11
alembic                           1.0.11
alphacsc                          0.4.dev0              /Users/alex/work/src/alphacsc
alphawaves                        0.1                   /Users/alex/work/src/Alpha-Waves-Dataset
appdirs                           1.4.3
appnope                           0.1.0

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import numpy as np
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder

class CountOrdinalEncoder(OrdinalEncoder):
    """Encode categorical features as an integer array
    usint count information.
    """
    def __init__(self, categories='auto', dtype=np.float64):
        self.categories = categories