M15 Allershausen stats & predictions

Detailed Analysis of Key Matches

The Tennis M15 Allershausen Germany is not just about individual brilliance; it's about strategic battles where every point counts. Let's delve deeper into some key matches that could define this year's tournament:

Seminal Matchups

• Semifinal Clash: Player A vs. Player B: This matchup pits two contrasting styles against each other—Player A's power against Player B's agility. Expect long rallies and strategic serves as both players vie for dominance.
• Doubles Final: Team X vs. Team Y: Known for their impeccable coordination, Team X will face off against Team Y's relentless pressure game. This doubles final promises to be a tactical showcase of teamwork and precision.

Potential Dark Horses

• Newcomer Z: With minimal ranking points but impressive recent performances, Newcomer Z has shown potential to disrupt established hierarchies in this tournament.
• Rising Star W: Although relatively unknown, Rising Star W has been making headlines with his unorthodox playing style that leaves opponents scrambling for answers.sdowling/ARO-ANL<|file_sep|>/aromae/analyze.py #!/usr/bin/env python """ This module contains routines which can be used by ARGO users. """ from __future__ import print_function import numpy as np import matplotlib.pyplot as plt from aromae import tools def get_alpha_time(time, alpha): """ Return time at which given alpha (fraction) occurs. :param time: array containing times (should be increasing) :param alpha: array containing alphas (should be between -1 & +1) :return: array containing times corresponding to given alphas (same size as input alpha) If alpha is out of range then returns np.nan If alpha has multiple entries at same value then returns first occurence If time or alpha not monotonic then results undefined Example: >>> t = np.array([0.,1.,2.,3.,4.,5.,6.,7.,8.,9.,10]) >>> alpha = np.array([-0.9,-0.7,-0.5,-0.25,-0.1,+0.05,+0.1,+0.25,+0.5,+0.7,+0.9]) >>> get_alpha_time(t,alpha) array([-nan , -nan , -nan , -nan , -nan , -nan , -nan , -nan , -nan , -nan , -nan ]) >>> get_alpha_time(t,alpha[5:]) array([5., nan, nan, nan, nan, nan]) """ # print('time shape:',time.shape,'alpha shape:',alpha.shape) # if len(time.shape)>1: # raise ValueError('time must be one-dimensional') # if len(alpha.shape)>1: # raise ValueError('alpha must be one-dimensional') # if time.shape[0] != alpha.shape[0]: # raise ValueError('time & alpha must have same number of elements') # # First check that input arrays are monotonically increasing/decreasing # dt = np.diff(time) # if not np.all(dt > np.finfo(float).eps): # raise ValueError('time must be strictly increasing') # dalpha = np.diff(alpha) # if not np.all(np.abs(dalpha) > np.finfo(float).eps): # raise ValueError('alpha must be strictly monotonic') # # Check that alpha is between +/-1 # if not (np.all(alpha <= +1) & np.all(alpha >= -1)): # raise ValueError('alpha must be between +/-1') # # Find first index where alpha[i] >= requested value (or vice versa) # i = np.searchsorted(alpha,alpha,'right')-1 # # Linearly interpolate time values between adjacent indices # return time[i] + (time[i+1]-time[i]) * (alpha-alpha[i])/(alpha[i+1]-alpha[i]) def get_nu_time(time,nucycles): """ Given an array of times corresponding to successive crossings through zero potential energy (PE), return array containing times at which given fractions of oscillation occur. Fractional values are defined such that nucycles=0 corresponds to PE=PEmin, nucycles=+1 corresponds PE=PEmax, Example: t = np.array([0.,1.,2.,3.,4.,5.,6.,7.,8.,9.,10]) nucycles = np.array([0.,+1/6,+2/6,+3/6,+4/6,+5/6,+6/6, +7/6,+8/6,+9/6,+10/6]) get_nu_time(t,nucycles) # returns t t = np.array([0.,1.,2.,3.,4.,5.,6.,7.,8.,9.,10]) nucycles = np.array([+5/12,+11/12]) get_nu_time(t,nucycles) # returns [4.+2./3*1./12*(10.-4.),7.+1./12*(10.-7.)] t = np.array([0.,1.,2.,3.,4.,5.,6.,7.,8.,9.]) """ Define some standard functions which can be used by users. All functions should have signature: def func(...): ... return x,y,z where x,y,z are numpy arrays """ def simple_nu_time(time,nucycles): def nu_time(time,nucycles): def beta_ratio(beta,nucycles): def nu_beta_ratio(nu,beta,nucycles): def simple_nu_beta_ratio(nu,beta,nucycles): def dnu_dbeta(nu,beta,nucycles): def simple_dnu_dbeta(nu,beta,nucycles): def nu_beta_plot(nu,beta,nucycles,**kwargs): <|repo_name|>sdowling/ARO-ANL<|file_sep|>/aromae/__init__.py #!/usr/bin/env python """ ARO AE ToolKit This module contains classes & routines which can be used by ARGO users. Classes: ---------- FileData -- Reads data from files written by ARGO programs Routines: ---------- get_filename -- Generate standard filename based on variable inputs Tools: ------ Tools class -- provides common operations needed by analysis routines Examples: --------- >>> import aromae as ar >>> filename = ar.get_filename(rundir='run',prefix='calc',suffix='data', ... step=100) >>> fd = ar.FileData(filename) >>> fd.read() >>> fd.get_potential() >>> fd.get_velocities() """ from __future__ import print_function import os import sys import numpy as np __version__ = '2017-07-05' class FileData(object): """ Class which reads data from files written by ARGO programs To use class call constructor with name(s) of file(s) which contain data. For example: >>> fd = FileData('run/calc100.data','run/calc101.data') Then use read() method to read data from all files. >>> fd.read() Finally use various methods provided by class. >>> pot = fd.get_potential() >>> vel = fd.get_velocities() """ def __init__(self,*filenames,**kwargs): """ Constructor :param filenames: names of files containing data :param kwargs: optional arguments: 'verbose' (default=False): if True then print information about data """ self.verbose=kwargs.get('verbose',False) self._filenames=[] if type(filenames) == type([]) or type(filenames) == type(()): self._filenames.extend(filenames) elif type(filenames) == type(''): self._filenames.append(filenames) self._nfiles=len(self._filenames) self._step=None self._natoms=None self._nsteps=None self._step_index=None self._times=None self._positions=None self._velocities=None self._potentials=None def read(self,**kwargs): verbose=self.verbose filenames=self._filenames natoms=self.natoms step_index=self.step_index if verbose: print('') print('Reading %d files:'%(self.nfiles)) def get_filename(**kwargs): """ Generate standard filename based on variable inputs This routine generates standard filename based on variable inputs. It uses default values where appropriate but allows user specify values for each part. :param kwargs: keyword arguments specifying filename parts 'rundir' (default='run'): directory containing run 'prefix' (default='calc'): prefix 'suffix' (default='data'): suffix 'step' (default=100): step number 'extension' (default=''): file extension 'pathsep' (default='/'): path separator character :return: string containing generated filename Example: >>> filename=get_filename(rundir='run',prefix='calc',suffix='data', ... step=100) >>> print(filename) run/calc100.data *** Revision history *** 2017-07-05: First release 2017-07-06: Fixed bug in FileData.read() method when reading multiple files 2017-07-07: Added get_filename() routine 2017-07-08: Added check that filenames are non-empty strings when reading multiple files; added verbose flag; added test suite; fixed bug in get_filename() routine when step was set but extension was empty string 2017-07-09: Added check that filenames exist before reading; added method .get() which combines other methods; added _read_header() method; added support for reading velocities & potentials; added testsuite; modified _read_data() method so that it only reads data from given step(s); added check that file contents match header info before reading data; added check that each file contains correct number of atoms; modified FileData.__init__() method so that filenames may be passed as list or tuple instead of separate arguments; modified _read_data() method so that it reads velocities & potentials even if there are fewer steps than specified in header; modified _read_data() method so that it doesn't throw exception if there aren't enough