489 def deriv(self, variant="symmetric"):
-490 """Return the first derivative of the correlator with respect to x0.
-491
-492 Parameters
-493 ----------
-494 variant : str
-495 decides which definition of the finite differences derivative is used.
-496 Available choice: symmetric, forward, backward, improved, default: symmetric
-497 """
-498 if variant == "symmetric":
-499 newcontent = []
-500 for t in range(1, self.T - 1):
-501 if (self.content[t - 1] is None) or (self.content[t + 1] is None):
-502 newcontent.append(None)
-503 else:
-504 newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
-505 if(all([x is None for x in newcontent])):
-506 raise Exception('Derivative is undefined at all timeslices')
-507 return Corr(newcontent, padding=[1, 1])
-508 elif variant == "forward":
-509 newcontent = []
-510 for t in range(self.T - 1):
-511 if (self.content[t] is None) or (self.content[t + 1] is None):
-512 newcontent.append(None)
-513 else:
-514 newcontent.append(self.content[t + 1] - self.content[t])
-515 if(all([x is None for x in newcontent])):
-516 raise Exception("Derivative is undefined at all timeslices")
-517 return Corr(newcontent, padding=[0, 1])
-518 elif variant == "backward":
-519 newcontent = []
-520 for t in range(1, self.T):
-521 if (self.content[t - 1] is None) or (self.content[t] is None):
-522 newcontent.append(None)
-523 else:
-524 newcontent.append(self.content[t] - self.content[t - 1])
-525 if(all([x is None for x in newcontent])):
-526 raise Exception("Derivative is undefined at all timeslices")
-527 return Corr(newcontent, padding=[1, 0])
-528 elif variant == "improved":
-529 newcontent = []
-530 for t in range(2, self.T - 2):
-531 if (self.content[t - 2] is None) or (self.content[t - 1] is None) or (self.content[t + 1] is None) or (self.content[t + 2] is None):
-532 newcontent.append(None)
-533 else:
-534 newcontent.append((1 / 12) * (self.content[t - 2] - 8 * self.content[t - 1] + 8 * self.content[t + 1] - self.content[t + 2]))
-535 if(all([x is None for x in newcontent])):
-536 raise Exception('Derivative is undefined at all timeslices')
-537 return Corr(newcontent, padding=[2, 2])
-538 else:
-539 raise Exception("Unknown variant.")
+ 499 def deriv(self, variant="symmetric"):
+500 """Return the first derivative of the correlator with respect to x0.
+501
+502 Parameters
+503 ----------
+504 variant : str
+505 decides which definition of the finite differences derivative is used.
+506 Available choice: symmetric, forward, backward, improved, default: symmetric
+507 """
+508 if self.N != 1:
+509 raise Exception("deriv only implemented for one-dimensional correlators.")
+510 if variant == "symmetric":
+511 newcontent = []
+512 for t in range(1, self.T - 1):
+513 if (self.content[t - 1] is None) or (self.content[t + 1] is None):
+514 newcontent.append(None)
+515 else:
+516 newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
+517 if(all([x is None for x in newcontent])):
+518 raise Exception('Derivative is undefined at all timeslices')
+519 return Corr(newcontent, padding=[1, 1])
+520 elif variant == "forward":
+521 newcontent = []
+522 for t in range(self.T - 1):
+523 if (self.content[t] is None) or (self.content[t + 1] is None):
+524 newcontent.append(None)
+525 else:
+526 newcontent.append(self.content[t + 1] - self.content[t])
+527 if(all([x is None for x in newcontent])):
+528 raise Exception("Derivative is undefined at all timeslices")
+529 return Corr(newcontent, padding=[0, 1])
+530 elif variant == "backward":
+531 newcontent = []
+532 for t in range(1, self.T):
+533 if (self.content[t - 1] is None) or (self.content[t] is None):
+534 newcontent.append(None)
+535 else:
+536 newcontent.append(self.content[t] - self.content[t - 1])
+537 if(all([x is None for x in newcontent])):
+538 raise Exception("Derivative is undefined at all timeslices")
+539 return Corr(newcontent, padding=[1, 0])
+540 elif variant == "improved":
+541 newcontent = []
+542 for t in range(2, self.T - 2):
+543 if (self.content[t - 2] is None) or (self.content[t - 1] is None) or (self.content[t + 1] is None) or (self.content[t + 2] is None):
+544 newcontent.append(None)
+545 else:
+546 newcontent.append((1 / 12) * (self.content[t - 2] - 8 * self.content[t - 1] + 8 * self.content[t + 1] - self.content[t + 2]))
+547 if(all([x is None for x in newcontent])):
+548 raise Exception('Derivative is undefined at all timeslices')
+549 return Corr(newcontent, padding=[2, 2])
+550 else:
+551 raise Exception("Unknown variant.")
541 def second_deriv(self, variant="symmetric"):
-542 """Return the second derivative of the correlator with respect to x0.
-543
-544 Parameters
-545 ----------
-546 variant : str
-547 decides which definition of the finite differences derivative is used.
-548 Available choice: symmetric, improved, default: symmetric
-549 """
-550 if variant == "symmetric":
-551 newcontent = []
-552 for t in range(1, self.T - 1):
-553 if (self.content[t - 1] is None) or (self.content[t + 1] is None):
-554 newcontent.append(None)
-555 else:
-556 newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
-557 if(all([x is None for x in newcontent])):
-558 raise Exception("Derivative is undefined at all timeslices")
-559 return Corr(newcontent, padding=[1, 1])
-560 elif variant == "improved":
-561 newcontent = []
-562 for t in range(2, self.T - 2):
-563 if (self.content[t - 2] is None) or (self.content[t - 1] is None) or (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t + 2] is None):
-564 newcontent.append(None)
-565 else:
-566 newcontent.append((1 / 12) * (-self.content[t + 2] + 16 * self.content[t + 1] - 30 * self.content[t] + 16 * self.content[t - 1] - self.content[t - 2]))
-567 if(all([x is None for x in newcontent])):
-568 raise Exception("Derivative is undefined at all timeslices")
-569 return Corr(newcontent, padding=[2, 2])
-570 else:
-571 raise Exception("Unknown variant.")
+ 553 def second_deriv(self, variant="symmetric"):
+554 """Return the second derivative of the correlator with respect to x0.
+555
+556 Parameters
+557 ----------
+558 variant : str
+559 decides which definition of the finite differences derivative is used.
+560 Available choice: symmetric, improved, default: symmetric
+561 """
+562 if self.N != 1:
+563 raise Exception("second_deriv only implemented for one-dimensional correlators.")
+564 if variant == "symmetric":
+565 newcontent = []
+566 for t in range(1, self.T - 1):
+567 if (self.content[t - 1] is None) or (self.content[t + 1] is None):
+568 newcontent.append(None)
+569 else:
+570 newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
+571 if(all([x is None for x in newcontent])):
+572 raise Exception("Derivative is undefined at all timeslices")
+573 return Corr(newcontent, padding=[1, 1])
+574 elif variant == "improved":
+575 newcontent = []
+576 for t in range(2, self.T - 2):
+577 if (self.content[t - 2] is None) or (self.content[t - 1] is None) or (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t + 2] is None):
+578 newcontent.append(None)
+579 else:
+580 newcontent.append((1 / 12) * (-self.content[t + 2] + 16 * self.content[t + 1] - 30 * self.content[t] + 16 * self.content[t - 1] - self.content[t - 2]))
+581 if(all([x is None for x in newcontent])):
+582 raise Exception("Derivative is undefined at all timeslices")
+583 return Corr(newcontent, padding=[2, 2])
+584 else:
+585 raise Exception("Unknown variant.")
573 def m_eff(self, variant='log', guess=1.0):
-574 """Returns the effective mass of the correlator as correlator object
-575
-576 Parameters
-577 ----------
-578 variant : str
-579 log : uses the standard effective mass log(C(t) / C(t+1))
-580 cosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.
-581 sinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.
-582 See, e.g., arXiv:1205.5380
-583 arccosh : Uses the explicit form of the symmetrized correlator (not recommended)
-584 guess : float
-585 guess for the root finder, only relevant for the root variant
-586 """
-587 if self.N != 1:
-588 raise Exception('Correlator must be projected before getting m_eff')
-589 if variant == 'log':
-590 newcontent = []
-591 for t in range(self.T - 1):
-592 if (self.content[t] is None) or (self.content[t + 1] is None):
-593 newcontent.append(None)
-594 else:
-595 newcontent.append(self.content[t] / self.content[t + 1])
-596 if(all([x is None for x in newcontent])):
-597 raise Exception('m_eff is undefined at all timeslices')
-598
-599 return np.log(Corr(newcontent, padding=[0, 1]))
-600
-601 elif variant in ['periodic', 'cosh', 'sinh']:
-602 if variant in ['periodic', 'cosh']:
-603 func = anp.cosh
-604 else:
-605 func = anp.sinh
-606
-607 def root_function(x, d):
-608 return func(x * (t - self.T / 2)) / func(x * (t + 1 - self.T / 2)) - d
-609
-610 newcontent = []
-611 for t in range(self.T - 1):
-612 if (self.content[t] is None) or (self.content[t + 1] is None):
-613 newcontent.append(None)
-614 # Fill the two timeslices in the middle of the lattice with their predecessors
-615 elif variant == 'sinh' and t in [self.T / 2, self.T / 2 - 1]:
-616 newcontent.append(newcontent[-1])
-617 else:
-618 newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess)))
-619 if(all([x is None for x in newcontent])):
-620 raise Exception('m_eff is undefined at all timeslices')
-621
-622 return Corr(newcontent, padding=[0, 1])
+ 587 def m_eff(self, variant='log', guess=1.0):
+588 """Returns the effective mass of the correlator as correlator object
+589
+590 Parameters
+591 ----------
+592 variant : str
+593 log : uses the standard effective mass log(C(t) / C(t+1))
+594 cosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.
+595 sinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.
+596 See, e.g., arXiv:1205.5380
+597 arccosh : Uses the explicit form of the symmetrized correlator (not recommended)
+598 guess : float
+599 guess for the root finder, only relevant for the root variant
+600 """
+601 if self.N != 1:
+602 raise Exception('Correlator must be projected before getting m_eff')
+603 if variant == 'log':
+604 newcontent = []
+605 for t in range(self.T - 1):
+606 if (self.content[t] is None) or (self.content[t + 1] is None):
+607 newcontent.append(None)
+608 else:
+609 newcontent.append(self.content[t] / self.content[t + 1])
+610 if(all([x is None for x in newcontent])):
+611 raise Exception('m_eff is undefined at all timeslices')
+612
+613 return np.log(Corr(newcontent, padding=[0, 1]))
+614
+615 elif variant in ['periodic', 'cosh', 'sinh']:
+616 if variant in ['periodic', 'cosh']:
+617 func = anp.cosh
+618 else:
+619 func = anp.sinh
+620
+621 def root_function(x, d):
+622 return func(x * (t - self.T / 2)) / func(x * (t + 1 - self.T / 2)) - d
623
-624 elif variant == 'arccosh':
-625 newcontent = []
-626 for t in range(1, self.T - 1):
-627 if (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t - 1] is None):
-628 newcontent.append(None)
-629 else:
-630 newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
-631 if(all([x is None for x in newcontent])):
-632 raise Exception("m_eff is undefined at all timeslices")
-633 return np.arccosh(Corr(newcontent, padding=[1, 1]))
-634
-635 else:
-636 raise Exception('Unknown variant.')
+624 newcontent = []
+625 for t in range(self.T - 1):
+626 if (self.content[t] is None) or (self.content[t + 1] is None):
+627 newcontent.append(None)
+628 # Fill the two timeslices in the middle of the lattice with their predecessors
+629 elif variant == 'sinh' and t in [self.T / 2, self.T / 2 - 1]:
+630 newcontent.append(newcontent[-1])
+631 else:
+632 newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess)))
+633 if(all([x is None for x in newcontent])):
+634 raise Exception('m_eff is undefined at all timeslices')
+635
+636 return Corr(newcontent, padding=[0, 1])
+637
+638 elif variant == 'arccosh':
+639 newcontent = []
+640 for t in range(1, self.T - 1):
+641 if (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t - 1] is None):
+642 newcontent.append(None)
+643 else:
+644 newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
+645 if(all([x is None for x in newcontent])):
+646 raise Exception("m_eff is undefined at all timeslices")
+647 return np.arccosh(Corr(newcontent, padding=[1, 1]))
+648
+649 else:
+650 raise Exception('Unknown variant.')
638 def fit(self, function, fitrange=None, silent=False, **kwargs):
-639 r'''Fits function to the data
-640
-641 Parameters
-642 ----------
-643 function : obj
-644 function to fit to the data. See fits.least_squares for details.
-645 fitrange : list
-646 Two element list containing the timeslices on which the fit is supposed to start and stop.
-647 Caution: This range is inclusive as opposed to standard python indexing.
-648 `fitrange=[4, 6]` corresponds to the three entries 4, 5 and 6.
-649 If not specified, self.prange or all timeslices are used.
-650 silent : bool
-651 Decides whether output is printed to the standard output.
-652 '''
-653 if self.N != 1:
-654 raise Exception("Correlator must be projected before fitting")
-655
-656 if fitrange is None:
-657 if self.prange:
-658 fitrange = self.prange
-659 else:
-660 fitrange = [0, self.T - 1]
-661 else:
-662 if not isinstance(fitrange, list):
-663 raise Exception("fitrange has to be a list with two elements")
-664 if len(fitrange) != 2:
-665 raise Exception("fitrange has to have exactly two elements [fit_start, fit_stop]")
-666
-667 xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
-668 ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
-669 result = least_squares(xs, ys, function, silent=silent, **kwargs)
-670 return result
+ 652 def fit(self, function, fitrange=None, silent=False, **kwargs):
+653 r'''Fits function to the data
+654
+655 Parameters
+656 ----------
+657 function : obj
+658 function to fit to the data. See fits.least_squares for details.
+659 fitrange : list
+660 Two element list containing the timeslices on which the fit is supposed to start and stop.
+661 Caution: This range is inclusive as opposed to standard python indexing.
+662 `fitrange=[4, 6]` corresponds to the three entries 4, 5 and 6.
+663 If not specified, self.prange or all timeslices are used.
+664 silent : bool
+665 Decides whether output is printed to the standard output.
+666 '''
+667 if self.N != 1:
+668 raise Exception("Correlator must be projected before fitting")
+669
+670 if fitrange is None:
+671 if self.prange:
+672 fitrange = self.prange
+673 else:
+674 fitrange = [0, self.T - 1]
+675 else:
+676 if not isinstance(fitrange, list):
+677 raise Exception("fitrange has to be a list with two elements")
+678 if len(fitrange) != 2:
+679 raise Exception("fitrange has to have exactly two elements [fit_start, fit_stop]")
+680
+681 xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
+682 ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
+683 result = least_squares(xs, ys, function, silent=silent, **kwargs)
+684 return result
672 def plateau(self, plateau_range=None, method="fit", auto_gamma=False):
-673 """ Extract a plateau value from a Corr object
-674
-675 Parameters
-676 ----------
-677 plateau_range : list
-678 list with two entries, indicating the first and the last timeslice
-679 of the plateau region.
-680 method : str
-681 method to extract the plateau.
-682 'fit' fits a constant to the plateau region
-683 'avg', 'average' or 'mean' just average over the given timeslices.
-684 auto_gamma : bool
-685 apply gamma_method with default parameters to the Corr. Defaults to None
-686 """
-687 if not plateau_range:
-688 if self.prange:
-689 plateau_range = self.prange
-690 else:
-691 raise Exception("no plateau range provided")
-692 if self.N != 1:
-693 raise Exception("Correlator must be projected before getting a plateau.")
-694 if(all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1] + 1)])):
-695 raise Exception("plateau is undefined at all timeslices in plateaurange.")
-696 if auto_gamma:
-697 self.gamma_method()
-698 if method == "fit":
-699 def const_func(a, t):
-700 return a[0]
-701 return self.fit(const_func, plateau_range)[0]
-702 elif method in ["avg", "average", "mean"]:
-703 returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None])
-704 return returnvalue
-705
-706 else:
-707 raise Exception("Unsupported plateau method: " + method)
+ 686 def plateau(self, plateau_range=None, method="fit", auto_gamma=False):
+687 """ Extract a plateau value from a Corr object
+688
+689 Parameters
+690 ----------
+691 plateau_range : list
+692 list with two entries, indicating the first and the last timeslice
+693 of the plateau region.
+694 method : str
+695 method to extract the plateau.
+696 'fit' fits a constant to the plateau region
+697 'avg', 'average' or 'mean' just average over the given timeslices.
+698 auto_gamma : bool
+699 apply gamma_method with default parameters to the Corr. Defaults to None
+700 """
+701 if not plateau_range:
+702 if self.prange:
+703 plateau_range = self.prange
+704 else:
+705 raise Exception("no plateau range provided")
+706 if self.N != 1:
+707 raise Exception("Correlator must be projected before getting a plateau.")
+708 if(all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1] + 1)])):
+709 raise Exception("plateau is undefined at all timeslices in plateaurange.")
+710 if auto_gamma:
+711 self.gamma_method()
+712 if method == "fit":
+713 def const_func(a, t):
+714 return a[0]
+715 return self.fit(const_func, plateau_range)[0]
+716 elif method in ["avg", "average", "mean"]:
+717 returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None])
+718 return returnvalue
+719
+720 else:
+721 raise Exception("Unsupported plateau method: " + method)
709 def set_prange(self, prange):
-710 """Sets the attribute prange of the Corr object."""
-711 if not len(prange) == 2:
-712 raise Exception("prange must be a list or array with two values")
-713 if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
-714 raise Exception("Start and end point must be integers")
-715 if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] < prange[1]):
-716 raise Exception("Start and end point must define a range in the interval 0,T")
-717
-718 self.prange = prange
-719 return
+ 723 def set_prange(self, prange):
+724 """Sets the attribute prange of the Corr object."""
+725 if not len(prange) == 2:
+726 raise Exception("prange must be a list or array with two values")
+727 if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
+728 raise Exception("Start and end point must be integers")
+729 if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] < prange[1]):
+730 raise Exception("Start and end point must define a range in the interval 0,T")
+731
+732 self.prange = prange
+733 return
721 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False, hide_sigma=None, references=None):
-722 """Plots the correlator using the tag of the correlator as label if available.
-723
-724 Parameters
-725 ----------
-726 x_range : list
-727 list of two values, determining the range of the x-axis e.g. [4, 8]
-728 comp : Corr or list of Corr
-729 Correlator or list of correlators which are plotted for comparison.
-730 The tags of these correlators are used as labels if available.
-731 logscale : bool
-732 Sets y-axis to logscale
-733 plateau : Obs
-734 Plateau value to be visualized in the figure
-735 fit_res : Fit_result
-736 Fit_result object to be visualized
-737 ylabel : str
-738 Label for the y-axis
-739 save : str
-740 path to file in which the figure should be saved
-741 auto_gamma : bool
-742 Apply the gamma method with standard parameters to all correlators and plateau values before plotting.
-743 hide_sigma : float
-744 Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
-745 references : list
-746 List of floating point values that are displayed as horizontal lines for reference.
-747 """
-748 if self.N != 1:
-749 raise Exception("Correlator must be projected before plotting")
-750
-751 if auto_gamma:
-752 self.gamma_method()
-753
-754 if x_range is None:
-755 x_range = [0, self.T - 1]
-756
-757 fig = plt.figure()
-758 ax1 = fig.add_subplot(111)
-759
-760 x, y, y_err = self.plottable()
-761 if hide_sigma:
-762 hide_from = np.argmax((hide_sigma * np.array(y_err[1:])) > np.abs(y[1:])) - 1
-763 else:
-764 hide_from = None
-765 ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=self.tag)
-766 if logscale:
-767 ax1.set_yscale('log')
-768 else:
-769 if y_range is None:
-770 try:
-771 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-772 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-773 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
-774 except Exception:
-775 pass
-776 else:
-777 ax1.set_ylim(y_range)
-778 if comp:
-779 if isinstance(comp, (Corr, list)):
-780 for corr in comp if isinstance(comp, list) else [comp]:
-781 if auto_gamma:
-782 corr.gamma_method()
-783 x, y, y_err = corr.plottable()
-784 if hide_sigma:
-785 hide_from = np.argmax((hide_sigma * np.array(y_err[1:])) > np.abs(y[1:])) - 1
-786 else:
-787 hide_from = None
-788 plt.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
-789 else:
-790 raise Exception("'comp' must be a correlator or a list of correlators.")
-791
-792 if plateau:
-793 if isinstance(plateau, Obs):
-794 if auto_gamma:
-795 plateau.gamma_method()
-796 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
-797 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
-798 else:
-799 raise Exception("'plateau' must be an Obs")
-800
-801 if references:
-802 if isinstance(references, list):
-803 for ref in references:
-804 ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--')
-805 else:
-806 raise Exception("'references' must be a list of floating pint values.")
-807
-808 if self.prange:
-809 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',')
-810 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',')
-811
-812 if fit_res:
-813 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
-814 ax1.plot(x_samples,
-815 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples),
-816 ls='-', marker=',', lw=2)
-817
-818 ax1.set_xlabel(r'$x_0 / a$')
-819 if ylabel:
-820 ax1.set_ylabel(ylabel)
-821 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])
-822
-823 handles, labels = ax1.get_legend_handles_labels()
-824 if labels:
-825 ax1.legend()
-826 plt.draw()
-827
-828 if save:
-829 if isinstance(save, str):
-830 fig.savefig(save)
-831 else:
-832 raise Exception("'save' has to be a string.")
+ 735 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False, hide_sigma=None, references=None):
+736 """Plots the correlator using the tag of the correlator as label if available.
+737
+738 Parameters
+739 ----------
+740 x_range : list
+741 list of two values, determining the range of the x-axis e.g. [4, 8]
+742 comp : Corr or list of Corr
+743 Correlator or list of correlators which are plotted for comparison.
+744 The tags of these correlators are used as labels if available.
+745 logscale : bool
+746 Sets y-axis to logscale
+747 plateau : Obs
+748 Plateau value to be visualized in the figure
+749 fit_res : Fit_result
+750 Fit_result object to be visualized
+751 ylabel : str
+752 Label for the y-axis
+753 save : str
+754 path to file in which the figure should be saved
+755 auto_gamma : bool
+756 Apply the gamma method with standard parameters to all correlators and plateau values before plotting.
+757 hide_sigma : float
+758 Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
+759 references : list
+760 List of floating point values that are displayed as horizontal lines for reference.
+761 """
+762 if self.N != 1:
+763 raise Exception("Correlator must be projected before plotting")
+764
+765 if auto_gamma:
+766 self.gamma_method()
+767
+768 if x_range is None:
+769 x_range = [0, self.T - 1]
+770
+771 fig = plt.figure()
+772 ax1 = fig.add_subplot(111)
+773
+774 x, y, y_err = self.plottable()
+775 if hide_sigma:
+776 hide_from = np.argmax((hide_sigma * np.array(y_err[1:])) > np.abs(y[1:])) - 1
+777 else:
+778 hide_from = None
+779 ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=self.tag)
+780 if logscale:
+781 ax1.set_yscale('log')
+782 else:
+783 if y_range is None:
+784 try:
+785 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+786 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+787 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
+788 except Exception:
+789 pass
+790 else:
+791 ax1.set_ylim(y_range)
+792 if comp:
+793 if isinstance(comp, (Corr, list)):
+794 for corr in comp if isinstance(comp, list) else [comp]:
+795 if auto_gamma:
+796 corr.gamma_method()
+797 x, y, y_err = corr.plottable()
+798 if hide_sigma:
+799 hide_from = np.argmax((hide_sigma * np.array(y_err[1:])) > np.abs(y[1:])) - 1
+800 else:
+801 hide_from = None
+802 plt.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
+803 else:
+804 raise Exception("'comp' must be a correlator or a list of correlators.")
+805
+806 if plateau:
+807 if isinstance(plateau, Obs):
+808 if auto_gamma:
+809 plateau.gamma_method()
+810 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
+811 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
+812 else:
+813 raise Exception("'plateau' must be an Obs")
+814
+815 if references:
+816 if isinstance(references, list):
+817 for ref in references:
+818 ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--')
+819 else:
+820 raise Exception("'references' must be a list of floating pint values.")
+821
+822 if self.prange:
+823 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',')
+824 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',')
+825
+826 if fit_res:
+827 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
+828 ax1.plot(x_samples,
+829 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples),
+830 ls='-', marker=',', lw=2)
+831
+832 ax1.set_xlabel(r'$x_0 / a$')
+833 if ylabel:
+834 ax1.set_ylabel(ylabel)
+835 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])
+836
+837 handles, labels = ax1.get_legend_handles_labels()
+838 if labels:
+839 ax1.legend()
+840 plt.draw()
+841
+842 if save:
+843 if isinstance(save, str):
+844 fig.savefig(save)
+845 else:
+846 raise Exception("'save' has to be a string.")
834 def spaghetti_plot(self, logscale=True):
-835 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
-836
-837 Parameters
-838 ----------
-839 logscale : bool
-840 Determines whether the scale of the y-axis is logarithmic or standard.
-841 """
-842 if self.N != 1:
-843 raise Exception("Correlator needs to be projected first.")
-844
-845 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
-846 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
-847
-848 for name in mc_names:
-849 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
+ 848 def spaghetti_plot(self, logscale=True):
+849 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
850
-851 fig = plt.figure()
-852 ax = fig.add_subplot(111)
-853 for dat in data:
-854 ax.plot(x0_vals, dat, ls='-', marker='')
-855
-856 if logscale is True:
-857 ax.set_yscale('log')
+851 Parameters
+852 ----------
+853 logscale : bool
+854 Determines whether the scale of the y-axis is logarithmic or standard.
+855 """
+856 if self.N != 1:
+857 raise Exception("Correlator needs to be projected first.")
858
-859 ax.set_xlabel(r'$x_0 / a$')
-860 plt.title(name)
-861 plt.draw()
+859 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
+860 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
+861
+862 for name in mc_names:
+863 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
+864
+865 fig = plt.figure()
+866 ax = fig.add_subplot(111)
+867 for dat in data:
+868 ax.plot(x0_vals, dat, ls='-', marker='')
+869
+870 if logscale is True:
+871 ax.set_yscale('log')
+872
+873 ax.set_xlabel(r'$x_0 / a$')
+874 plt.title(name)
+875 plt.draw()
863 def dump(self, filename, datatype="json.gz", **kwargs):
-864 """Dumps the Corr into a file of chosen type
-865 Parameters
-866 ----------
-867 filename : str
-868 Name of the file to be saved.
-869 datatype : str
-870 Format of the exported file. Supported formats include
-871 "json.gz" and "pickle"
-872 path : str
-873 specifies a custom path for the file (default '.')
-874 """
-875 if datatype == "json.gz":
-876 from .input.json import dump_to_json
-877 if 'path' in kwargs:
-878 file_name = kwargs.get('path') + '/' + filename
-879 else:
-880 file_name = filename
-881 dump_to_json(self, file_name)
-882 elif datatype == "pickle":
-883 dump_object(self, filename, **kwargs)
-884 else:
-885 raise Exception("Unknown datatype " + str(datatype))
+ 877 def dump(self, filename, datatype="json.gz", **kwargs):
+878 """Dumps the Corr into a file of chosen type
+879 Parameters
+880 ----------
+881 filename : str
+882 Name of the file to be saved.
+883 datatype : str
+884 Format of the exported file. Supported formats include
+885 "json.gz" and "pickle"
+886 path : str
+887 specifies a custom path for the file (default '.')
+888 """
+889 if datatype == "json.gz":
+890 from .input.json import dump_to_json
+891 if 'path' in kwargs:
+892 file_name = kwargs.get('path') + '/' + filename
+893 else:
+894 file_name = filename
+895 dump_to_json(self, file_name)
+896 elif datatype == "pickle":
+897 dump_object(self, filename, **kwargs)
+898 else:
+899 raise Exception("Unknown datatype " + str(datatype))
887 def print(self, range=[0, None]):
-888 print(self.__repr__(range))
+ 901 def print(self, range=[0, None]):
+902 print(self.__repr__(range))
1050 def sqrt(self):
-1051 return self**0.5
+ 1064 def sqrt(self):
+1065 return self ** 0.5
1053 def log(self):
-1054 newcontent = [None if (item is None) else np.log(item) for item in self.content]
-1055 return Corr(newcontent, prange=self.prange)
+ 1067 def log(self):
+1068 newcontent = [None if _check_for_none(self, item) else np.log(item) for item in self.content]
+1069 return Corr(newcontent, prange=self.prange)
1057 def exp(self):
-1058 newcontent = [None if (item is None) else np.exp(item) for item in self.content]
-1059 return Corr(newcontent, prange=self.prange)
+ 1071 def exp(self):
+1072 newcontent = [None if _check_for_none(self, item) else np.exp(item) for item in self.content]
+1073 return Corr(newcontent, prange=self.prange)
1072 def sin(self):
-1073 return self._apply_func_to_corr(np.sin)
+ 1086 def sin(self):
+1087 return self._apply_func_to_corr(np.sin)
1075 def cos(self):
-1076 return self._apply_func_to_corr(np.cos)
+ 1089 def cos(self):
+1090 return self._apply_func_to_corr(np.cos)
1078 def tan(self):
-1079 return self._apply_func_to_corr(np.tan)
+ 1092 def tan(self):
+1093 return self._apply_func_to_corr(np.tan)
1081 def sinh(self):
-1082 return self._apply_func_to_corr(np.sinh)
+ 1095 def sinh(self):
+1096 return self._apply_func_to_corr(np.sinh)
1084 def cosh(self):
-1085 return self._apply_func_to_corr(np.cosh)
+ 1098 def cosh(self):
+1099 return self._apply_func_to_corr(np.cosh)
1087 def tanh(self):
-1088 return self._apply_func_to_corr(np.tanh)
+ 1101 def tanh(self):
+1102 return self._apply_func_to_corr(np.tanh)
1090 def arcsin(self):
-1091 return self._apply_func_to_corr(np.arcsin)
+ 1104 def arcsin(self):
+1105 return self._apply_func_to_corr(np.arcsin)
1093 def arccos(self):
-1094 return self._apply_func_to_corr(np.arccos)
+ 1107 def arccos(self):
+1108 return self._apply_func_to_corr(np.arccos)
1096 def arctan(self):
-1097 return self._apply_func_to_corr(np.arctan)
+ 1110 def arctan(self):
+1111 return self._apply_func_to_corr(np.arctan)
1099 def arcsinh(self):
-1100 return self._apply_func_to_corr(np.arcsinh)
+ 1113 def arcsinh(self):
+1114 return self._apply_func_to_corr(np.arcsinh)
1102 def arccosh(self):
-1103 return self._apply_func_to_corr(np.arccosh)
+ 1116 def arccosh(self):
+1117 return self._apply_func_to_corr(np.arccosh)
1105 def arctanh(self):
-1106 return self._apply_func_to_corr(np.arctanh)
+ 1119 def arctanh(self):
+1120 return self._apply_func_to_corr(np.arctanh)
1141 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):
-1142 r''' Project large correlation matrix to lowest states
-1143
-1144 This method can be used to reduce the size of an (N x N) correlation matrix
-1145 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise
-1146 is still small.
-1147
-1148 Parameters
-1149 ----------
-1150 Ntrunc: int
-1151 Rank of the target matrix.
-1152 tproj: int
-1153 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method.
-1154 The default value is 3.
-1155 t0proj: int
-1156 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly
-1157 discouraged for O(a) improved theories, since the correctness of the procedure
-1158 cannot be granted in this case. The default value is 2.
-1159 basematrix : Corr
-1160 Correlation matrix that is used to determine the eigenvectors of the
-1161 lowest states based on a GEVP. basematrix is taken to be the Corr itself if
-1162 is is not specified.
-1163
-1164 Notes
-1165 -----
-1166 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving
-1167 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$
-1168 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the
-1169 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via
-1170 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large
-1171 correlation matrix and to remove some noise that is added by irrelevant operators.
-1172 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated
-1173 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
-1174 '''
-1175
-1176 if self.N == 1:
-1177 raise Exception('Method cannot be applied to one-dimensional correlators.')
-1178 if basematrix is None:
-1179 basematrix = self
-1180 if Ntrunc >= basematrix.N:
-1181 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
-1182 if basematrix.N != self.N:
-1183 raise Exception('basematrix and targetmatrix have to be of the same size.')
-1184
-1185 evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc]
-1186
-1187 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object)
-1188 rmat = []
-1189 for t in range(basematrix.T):
-1190 for i in range(Ntrunc):
-1191 for j in range(Ntrunc):
-1192 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
-1193 rmat.append(np.copy(tmpmat))
-1194
-1195 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)]
-1196 return Corr(newcontent)
+ 1155 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):
+1156 r''' Project large correlation matrix to lowest states
+1157
+1158 This method can be used to reduce the size of an (N x N) correlation matrix
+1159 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise
+1160 is still small.
+1161
+1162 Parameters
+1163 ----------
+1164 Ntrunc: int
+1165 Rank of the target matrix.
+1166 tproj: int
+1167 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method.
+1168 The default value is 3.
+1169 t0proj: int
+1170 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly
+1171 discouraged for O(a) improved theories, since the correctness of the procedure
+1172 cannot be granted in this case. The default value is 2.
+1173 basematrix : Corr
+1174 Correlation matrix that is used to determine the eigenvectors of the
+1175 lowest states based on a GEVP. basematrix is taken to be the Corr itself if
+1176 is is not specified.
+1177
+1178 Notes
+1179 -----
+1180 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving
+1181 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$
+1182 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the
+1183 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via
+1184 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large
+1185 correlation matrix and to remove some noise that is added by irrelevant operators.
+1186 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated
+1187 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
+1188 '''
+1189
+1190 if self.N == 1:
+1191 raise Exception('Method cannot be applied to one-dimensional correlators.')
+1192 if basematrix is None:
+1193 basematrix = self
+1194 if Ntrunc >= basematrix.N:
+1195 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
+1196 if basematrix.N != self.N:
+1197 raise Exception('basematrix and targetmatrix have to be of the same size.')
+1198
+1199 evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc]
+1200
+1201 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object)
+1202 rmat = []
+1203 for t in range(basematrix.T):
+1204 for i in range(Ntrunc):
+1205 for j in range(Ntrunc):
+1206 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
+1207 rmat.append(np.copy(tmpmat))
+1208
+1209 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)]
+1210 return Corr(newcontent)