W15 Cap d'Agde stats & predictions

Understanding the Tournament Format

The Tennis W15 Cap d'Agde follows a standard tournament format with a mix of singles and doubles competitions. The singles draw typically features 32 players, while the doubles draw includes 16 teams. Matches are played on outdoor clay courts, which add an extra layer of strategy and skill to the games.

Key Features of the Tournament:

• Singles Competition: A field of 32 players compete in a knockout format.
• Doubles Competition: 16 teams battle it out on the clay courts.
• Surface: Outdoor clay courts, known for their slower pace and higher bounce.
• Daily Updates: Match results and updates are refreshed daily to keep fans informed.
• Betting Predictions: Expert analysis and predictions are available for those interested in betting.

The clay surface at Cap d'Agde is renowned for its unique playing conditions. Players must adapt their strategies to deal with the slower ball speed and higher bounce, making rallies longer and more physically demanding. This adds an exciting dynamic to the matches, as players who excel on clay often have an edge.

Daily Match Highlights

With matches updated daily, fans can never miss an action-packed day at the Tennis W15 Cap d'Agde. Each day brings new matchups, featuring both established players and rising stars. Here's what you can expect from a typical day at the tournament:

Morning Matches:

• Early rounds of singles and doubles competitions.
• Opportunities to watch emerging talents make their mark.
• Live updates on match progress and scores.

Afternoon Sessions:

• Key matches featuring top-seeded players.
• Dramatic comebacks and thrilling finishes.
• Expert commentary and analysis throughout.

Evening Showdowns:

• Climactic matches as players vie for a spot in the next round.
<|repo_name|>gitter-badger/echoprint<|file_sep|>/src/echoprint/core/merkletree.py # -*- coding: utf-8 -*- # # This file is part of echoprint-core. # # Copyright (C) 2015-2016 Spotify AB. # # echoprint-core is free software: you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation, either version 3 of the License, or (at your option) any later # version. # # echoprint-core is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # echoprint-core. If not, see http://www.gnu.org/licenses/. # from __future__ import unicode_literals import hashlib class MerkleTree(object): def __init__(self): self._leaves = [] self._hashes = [] self._tree = [] def add_leaf(self, leaf): self._leaves.append(leaf) def build(self): if len(self._leaves) == 0: raise ValueError("Cannot build merkle tree without leaves") # First level is just all leaves hashed self._hashes = [self.hash_leaf(leaf) for leaf in self._leaves] # Then we build up levels until we have only one hash left (the root) current_level = self._hashes while len(current_level) > 1: next_level = [] # If there's an odd number of hashes on this level, # duplicate last one to make it even if len(current_level) % 2 == 1: current_level.append(current_level[-1]) # For each pair of hashes on this level... for i in range(0, len(current_level), 2): left = current_level[i] right = current_level[i + 1] # ... combine them together into one hash on next level next_level.append(self.hash_node(left + right)) # Next level becomes current level current_level = next_level # The final hash remaining is our root hash self._tree = self._hashes + current_level return current_level[0] def get_tree(self): return self._tree @staticmethod def hash_leaf(leaf): return hashlib.sha256(leaf).hexdigest() @staticmethod def hash_node(node): return hashlib.sha256(node).hexdigest() <|repo_name|>gitter-badger/echoprint<|file_sep|>/src/echoprint/core/fingerprinter.py # -*- coding: utf-8 -*- # # This file is part of echoprint-core. # # Copyright (C) 2015-2016 Spotify AB. # # echoprint-core is free software: you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation, either version 3 of the License, or (at your option) any later # version. # # echoprint-core is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # echoprint-core. If not, see http://www.gnu.org/licenses/. # from __future__ import unicode_literals import logging import numpy as np from . import constants as const class Fingerprinter(object): def __init__(self): self.log = logging.getLogger('Fingerprinter') def fingerprint(self, buffer, sample_rate, **kwargs): """ Generate fingerprints from an audio buffer. :param buffer: An audio buffer containing samples from a song. :type buffer: numpy.ndarray :param sample_rate: Sample rate used when recording `buffer`. :type sample_rate: int :return: A list containing fingerprints generated from `buffer`. Each fingerprint contains metadata such as time offset, frequency index and peak amplitude. See :py:class:`Fingerprint` for details. :rtype: list(:py:class:`Fingerprint`) """ # Compute FFT spectrum over entire buffer... spectrum = np.fft.rfft(buffer) # ... then compute magnitude spectrum from complex-valued spectrum. magnitude_spectrum = np.absolute(spectrum) # Normalize magnitude spectrum so that it sums up to one... norm_magnitude_spectrum = magnitude_spectrum / sum(magnitude_spectrum) # ... then convert to dB scale... magnitude_spectrum_db = const.DB_OFFSET + const.BIN_TO_DB * np.log10(norm_magnitude_spectrum) # ... then roll off low frequencies (below `MIN_FREQ`)... magnitude_spectrum_db[:const.MIN_FREQ_INDEX] = -np.inf # ... then roll off high frequencies (above `MAX_FREQ`)... magnitude_spectrum_db[const.MAX_FREQ_INDEX:] = -np.inf # Calculate frequency histogram from dB-scaled magnitude spectrum... freq_histogram = np.copy(magnitude_spectrum_db) ''' print 'freq_histogram', freq_histogram.shape for i in range(freq_histogram.shape[0]): for j in range(freq_histogram.shape[1]): if freq_histogram[i][j] != -np.inf: print freq_histogram[i][j] ''' ''' for i in range(freq_histogram.shape[0]): for j in range(freq_histogram.shape[1]): if freq_histogram[i][j] == -np.inf: freq_histogram[i][j] = -1000000 ''' freq_histogram[np.isneginf(freq_histogram)] = -1000000 ''' for i in range(freq_histogram.shape[0]): for j in range(freq_histogram.shape[1]): if freq_histogram[i][j] != -np.inf: print freq_histogram[i][j] ''' <|repo_name|>gitter-badger/echoprint<|file_sep|>/src/echoprint/core/fingerprint.py # -*- coding: utf-8 -*- # # This file is part of echoprint-core. # # Copyright (C) 2015-2016 Spotify AB. # # echoprint-core is free software: you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation, either version 3 of the License, or (at your option) any later # version. # # echoprint-core is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # echoprint-core. If not, see http://www.gnu.org/licenses/. # from __future__ import unicode_literals class Fingerprint(object): """ A fingerprint generated from an audio signal. Attributes: offset (int): Time offset at which this fingerprint was generated. freq_index (int): Frequency index corresponding to this fingerprint. amp (float): Peak amplitude at frequency `freq_index` at time offset `offset`. hash (str): Hash corresponding to this fingerprint. Example: >>> fp = Fingerprint(offset=123456, freq_index=9876, amp=34.56, hash='c7f40f3a9d88bfb1e2799a9c01e80d7eead8e732') >>> fp.offset == 123456 True >>> fp.freq_index == 9876 True >>> fp.amp == 34.56 True >>> fp.hash == 'c7f40f3a9d88bfb1e2799a9c01e80d7eead8e732' True >>> str(fp) '123456@9876::c7f40f3a9d88bfb1e2799a9c01e80d7eead8e732' >>> str(fp.hash) 'c7f40f3a9d88bfb1e2799a9c01e80d7eead8e732' >>> str(fp.offset) '123456' >>> str(fp.freq_index) '9876' >>> str(fp.amp) '34.56' >>> fp == Fingerprint(offset=123456, freq_index=9876, amp=34.56, hash='c7f40f3a9d88bfb1e2799a9c01e80d7eead8e732') True def __repr__(self): return '{}@{}::{}'.format(str(self.offset), str(self.freq_index), str(self.hash)) def __str__(self): return '{}@{}::{}'.format(str(self.offset), str(self.freq_index), str(self.hash)) def __eq__(self, other): return self.offset == other.offset and self.freq_index == other.freq_index and self.hash == other.hash and type(other) == type(self) def __ne__(self, other): return not (self == other) def __init__(self, offset=None, freq_index=None, amp=None, hash=None): self.offset = offset self.freq_index = freq_index self.amp = amp self.hash = hash self.__repr__=__repr__ self.__str__=__str__ self.__eq__=__eq__ self.__ne__=__ne__ <|repo_name|>gitter-badger/echoprint<|file_sep|>/src/echoprint/core/constants.py #!/usr/bin/env python2 # # # # # # # # # # # # # # # # # # # # # # # # # # # # from __future__ import unicode_literals import numpy as np BIN_SIZE_SECONDS = .03 BIN_SIZE_SAMPLES = BIN_SIZE_SECONDS * SAMPLE_RATE MIN_HASH_TIME_DELTA = BIN_SIZE_SECONDS * MIN_HASH_TIME_DELTA_FRAC MAX_HASH_TIME_DELTA = BIN_SIZE_SECONDS * MAX_HASH_TIME_DELTA_FRAC MIN_HASH_TIME_DELTA_INDEX = int(np.floor(MIN_HASH_TIME_DELTA / BIN_SIZE_SECONDS)) MAX_HASH_TIME_DELTA_INDEX = int(np.floor(MAX_HASH_TIME_DELTA / BIN_SIZE_SECONDS)) MIN_FFT_SIZE_SAMPLES =