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# -*- coding: utf-8 -*-
"""
Tutorial 'Working with Spyder'
==============================
Generate 1D data that can be used in the tutorials to represent some results.
"""
from __future__ import annotations
import numpy as np
from sigima.client import SimpleRemoteProxy
def generate_1d_data(
amplitude: float,
peak_position: float,
peak_width: float,
relaxation_oscillations: bool = True,
noise: bool = True,
noise_std: float = 0.1,
x_min: float = -1,
x_max: float = 1,
num_points: int = 1000,
) -> tuple[np.ndarray, np.ndarray]:
"""Generate 1D data that can be used in the tutorials to represent some results.
Args:
amplitude: Amplitude of the peak.
peak_position: Position of the peak.
peak_width: Width of the peak.
relaxation_oscillations: Whether to add relaxation oscillations to the data,
by default True.
noise: Whether to add noise to the data, by default True.
noise_std: Standard deviation of the noise, by default 0.1.
x_min: Minimum value of the x axis, by default -1.
x_max: Maximum value of the x axis, by default 1.
num_points: Number of points in the generated data, by default 100.
Returns:
Generated data (x, y; where y is a 1D array and x is a 1D array).
"""
x = np.linspace(x_min, x_max, num_points)
y = amplitude * np.exp(-(((x - peak_position) / peak_width) ** 2))
if relaxation_oscillations:
y += 0.1 * np.sin(10 * x)
if noise:
y += np.random.normal(scale=noise_std, size=num_points)
return x, y
def generate_2d_data(
num_lines: int = 10,
noise_std: float = 0.1,
x_min: float = -1,
x_max: float = 1,
num_points: int = 100,
debug_with_datalab: bool = False,
) -> tuple[np.ndarray, np.ndarray]:
"""Generate 2D data that can be used in the tutorials to represent some results.
Args:
num_lines: Number of lines in the generated data, by default 10.
noise_std: Standard deviation of the noise, by default 0.1.
x_min: Minimum value of the x axis, by default -1.
x_max: Maximum value of the x axis, by default 1.
num_points: Number of points in the generated data, by default 100.
debug_with_datalab: Whether to use the DataLab to debug the function,
by default False.
Returns:
Generated data (x, y; where y is a 2D array and x is a 1D array).
"""
proxy = None
if debug_with_datalab:
proxy = SimpleRemoteProxy()
proxy.connect()
z = np.zeros((num_lines, num_points))
for i in range(num_lines):
amplitude = 0.1 * i**2
peak_position = 0.5 * i**2
peak_width = 0.1 * i**2
x, y = generate_1d_data(
amplitude,
peak_position,
peak_width,
relaxation_oscillations=True,
noise=True,
noise_std=noise_std,
x_min=x_min,
x_max=x_max,
num_points=num_points,
)
z[i] = y
if proxy is not None:
proxy.add_signal(f"Line {i}", x, y)
return x, z
def test_my_1d_algorithm() -> np.ndarray:
"""Generate 1D data that can be used in the tutorials to represent some results."""
# Generate 1D data using the function generate_1d_data:
data = generate_1d_data(1, 0, 0.1, relaxation_oscillations=True, noise=True)
return data
def test_my_2d_algorithm(debug_with_datalab: bool = False) -> np.ndarray:
"""Generate 2D data that can be used in the tutorials to represent some results."""
# Generate 2D data using the function generate_2d_data:
data = generate_2d_data(
num_lines=10, noise_std=0.1, debug_with_datalab=debug_with_datalab
)
return data
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