EURO Basket Preliminary Round Grp C stats & predictions

Fuentes Confiables para Información Actualizada

Mantente informado con estas fuentes confiables que ofrecen cobertura completa del EuroBasket:

• Eurobasket.com: Sitio oficial con noticias actualizadas, calendario completo e información detallada sobre cada equipo.
• FIBA Basketball Official Site: Fuente oficial para resultados oficiales, estadísticas y perfiles completos de jugadores internacionales.
• ESPN Deportes Basketbol: Cobertura exhaustiva con análisis experto e informes exclusivos sobre cada partido clave del torneo.

Influencers Deportivos a Seguir Durante el Torneo

Sigue a estos influencers deportivos para obtener opiniones expertas e interacciones directas durante cada jornada del EuroBasket:

yokogawa-sensing-systems/yss-pytorch<|file_sep|>/tests/test_spectral.py import unittest from pathlib import Path import numpy as np import pandas as pd from .utils import load_data class TestSpectral(unittest.TestCase): @classmethod def setUpClass(cls): cls.data_dir = Path(__file__).parent / 'data' cls.data = load_data(cls.data_dir) def test_load_spectra(self): data = self.data['spectra'] spectra = data['spectra'] self.assertEqual(spectra.shape[0], data['size']) self.assertEqual(spectra.shape[1], data['n_wavelengths']) def test_load_labels(self): data = self.data['spectra'] labels = data['labels'] self.assertEqual(labels.shape[0], data['size']) self.assertListEqual(labels.columns.tolist(), ['label', 'idx']) def test_load_raw_spectra(self): data = self.data['raw_spectra'] raw_spectra = data['raw_spectra'] self.assertEqual(raw_spectra.shape[0], data['size']) self.assertEqual(raw_spectra.shape[1], data['n_wavelengths']) def test_load_raw_labels(self): data = self.data['raw_spectra'] labels = data['labels'] self.assertEqual(labels.shape[0], data['size']) self.assertListEqual(labels.columns.tolist(), ['label', 'idx']) def test_load_wavelengths(self): data = self.data['spectra'] wavelengths = data['wavelengths'] n_wavelengths = len(wavelengths) wavelengths.sort() wavelengths = np.array(wavelengths) self.assertTrue(np.allclose(wavelengths, np.linspace(data['wavelength_start'], data['wavelength_end'], n_wavelengths))) def test_load_raw_wavelengths(self): data = self.data['raw_spectra'] wavelengths = data['wavelengths'] n_wavelengths = len(wavelengths) wavelengths.sort() wavelengths = np.array(wavelengths) self.assertTrue(np.allclose(wavelengths, np.linspace(data['wavelength_start'], data['wavelength_end'], n_wavelengths))) if __name__ == '__main__': unittest.main() <|repo_name|>yokogawa-sensing-systems/yss-pytorch<|file_sep|>/yss_pytorch/data/__init__.py from .base import BaseDataset from .spectral import SpectralDataset from .image import ImageDataset __all__ = [ 'BaseDataset', 'SpectralDataset', 'ImageDataset' ] <|file_sep|># -*- coding: utf-8 -*- """PyTorch module for loading and processing spectral dataset. The module supports loading spectral datasets in the form of CSV files. The CSV files must contain the following columns: * ``wavelength``: wavelength values. * ``intensity``: intensity values. See Also -------- * `PyTorch Dataset Class `_ """ import glob import logging import os from pathlib import Path import pandas as pd import torch from torch.utils.data import Dataset logger = logging.getLogger(__name__) def _read_csv(path): df = pd.read_csv(path) return df def _read_all_csv(path): path_list = glob.glob(os.path.join(path)) df_list = [_read_csv(p) for p in path_list] df_all = pd.concat(df_list) return df_all def _read_csv_and_process(path, label=None, label_col='label', wavelength_col='wavelength', intensity_col='intensity', **kwargs): """Read CSV file and process it to fit the input format for PyTorch. Parameters ---------- path : str or pathlib.Path Path to the CSV file. The CSV file must contain the following columns: ``wavelength``: wavelength values. ``intensity``: intensity values. label : str or int or float or None Label to be added to the dataframe. If None is given then no label is added. label_col : str Name of the column which will contain labels. wavelength_col : str Name of the column which will contain wavelength values. intensity_col : str Name of the column which will contain intensity values. **kwargs : dict Additional keyword arguments for ``pd.read_csv`` function. Returns ------- df : pandas.DataFrame DataFrame which contains processed information from the CSV file. The DataFrame has three columns: ``label``, ``wavelength``, and ``intensity``. * ``label``: Labels for each spectrum. * ``wavelength``: Wavelength values. * ``intensity``: Intensity values. """ if isinstance(path, Path): path = str(path) df = _read_csv(path=path, **kwargs) # Sort by wavelength value. df.sort_values(by=wavelength_col, inplace=True) # Rename wavelength column. df.rename(columns={wavelength_col: 'wavelength'}, inplace=True) # Rename intensity column. df.rename(columns={intensity_col: 'intensity'}, inplace=True) if label is not None: # Add label column if needed. df[label_col] = label return df def read_csv(path, label=None, label_col='label', wavelength_col='wavelength', intensity_col='intensity', **kwargs): """Read CSV file and process it to fit the input format for PyTorch. Parameters ---------- path : str or pathlib.Path Path to the CSV file. The CSV file must contain the following columns: ``wavelength``: wavelength values. ``intensity``: intensity values. label : str or int or float or None Label to be added to the dataframe. If None is given then no label is added. label_col : str Name of the column which will contain labels. wavelength_col : str Name of the column which will contain wavelength values. intensity_col : str Name of the column which will contain intensity values. **kwargs : dict Additional keyword arguments for ``pd.read_csv`` function. Returns ------- df : pandas.DataFrame DataFrame which contains processed information from the CSV file. The DataFrame has three columns: ``label``, ``wavelength``, and ``intensity``. * ``label``: Labels for each spectrum. * ``wavelength``: Wavelength values. * ``intensity``: Intensity values. """ class SpectralDataset(Dataset): class RawSpectralDataset(SpectralDataset): class NormalizedSpectralDataset(SpectralDataset): class SmoothedSpectralDataset(SpectralDataset): <|file_sep|># -*- coding: utf-8 -*- """Utility functions for converting image dataset from RGB to grayscale.""" import numpy as np