From b42baa75820664aca91123e2c3528f81052f17c9 Mon Sep 17 00:00:00 2001 From: fjosw Date: Mon, 16 Jan 2023 14:58:20 +0000 Subject: [PATCH] Documentation updated --- docs/pyerrors/dirac.html | 254 +- docs/pyerrors/fits.html | 2032 ++++++++-------- docs/pyerrors/input/bdio.html | 2557 ++++++++++---------- docs/pyerrors/input/dobs.html | 2898 ++++++++++++----------- docs/pyerrors/input/hadrons.html | 1477 ++++++------ docs/pyerrors/input/json.html | 2058 +++++++++-------- docs/pyerrors/input/misc.html | 427 ++-- docs/pyerrors/input/openQCD.html | 3720 +++++++++++++++--------------- docs/pyerrors/input/pandas.html | 454 ++-- docs/pyerrors/input/sfcf.html | 1243 +++++----- docs/pyerrors/input/utils.html | 67 +- docs/pyerrors/misc.html | 421 ++-- docs/pyerrors/mpm.html | 157 +- docs/pyerrors/roots.html | 7 +- docs/search.js | 2 +- 15 files changed, 9322 insertions(+), 8452 deletions(-) diff --git a/docs/pyerrors/dirac.html b/docs/pyerrors/dirac.html index 206770ff..9ebb0312 100644 --- a/docs/pyerrors/dirac.html +++ b/docs/pyerrors/dirac.html @@ -110,63 +110,74 @@ 26 """Rank-3 epsilon tensor 27 28 Based on https://codegolf.stackexchange.com/a/160375 -29 """ -30 test_set = set((i, j, k)) -31 if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): -32 raise Exception("Unexpected input", i, j, k) -33 -34 return (i - j) * (j - k) * (k - i) / 2 -35 -36 -37def epsilon_tensor_rank4(i, j, k, o): -38 """Rank-4 epsilon tensor -39 -40 Extension of https://codegolf.stackexchange.com/a/160375 -41 """ -42 test_set = set((i, j, k, o)) -43 if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))): -44 raise Exception("Unexpected input", i, j, k, o) -45 -46 return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12 +29 +30 Returns +31 ------- +32 elem : int +33 Element (i,j,k) of the epsilon tensor of rank 3 +34 """ +35 test_set = set((i, j, k)) +36 if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): +37 raise Exception("Unexpected input", i, j, k) +38 +39 return (i - j) * (j - k) * (k - i) / 2 +40 +41 +42def epsilon_tensor_rank4(i, j, k, o): +43 """Rank-4 epsilon tensor +44 +45 Extension of https://codegolf.stackexchange.com/a/160375 +46 47 -48 -49def Grid_gamma(gamma_tag): -50 """Returns gamma matrix in Grid labeling.""" -51 if gamma_tag == 'Identity': -52 g = identity -53 elif gamma_tag == 'Gamma5': -54 g = gamma5 -55 elif gamma_tag == 'GammaX': -56 g = gamma[0] -57 elif gamma_tag == 'GammaY': -58 g = gamma[1] -59 elif gamma_tag == 'GammaZ': -60 g = gamma[2] -61 elif gamma_tag == 'GammaT': -62 g = gamma[3] -63 elif gamma_tag == 'GammaXGamma5': -64 g = gamma[0] @ gamma5 -65 elif gamma_tag == 'GammaYGamma5': -66 g = gamma[1] @ gamma5 -67 elif gamma_tag == 'GammaZGamma5': -68 g = gamma[2] @ gamma5 -69 elif gamma_tag == 'GammaTGamma5': -70 g = gamma[3] @ gamma5 -71 elif gamma_tag == 'SigmaXT': -72 g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0]) -73 elif gamma_tag == 'SigmaXY': -74 g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0]) -75 elif gamma_tag == 'SigmaXZ': -76 g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0]) -77 elif gamma_tag == 'SigmaYT': -78 g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1]) -79 elif gamma_tag == 'SigmaYZ': -80 g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1]) -81 elif gamma_tag == 'SigmaZT': -82 g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2]) -83 else: -84 raise Exception('Unkown gamma structure', gamma_tag) -85 return g +48 Returns +49 ------- +50 elem : int +51 Element (i,j,k,o) of the epsilon tensor of rank 4 +52 """ +53 test_set = set((i, j, k, o)) +54 if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))): +55 raise Exception("Unexpected input", i, j, k, o) +56 +57 return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12 +58 +59 +60def Grid_gamma(gamma_tag): +61 """Returns gamma matrix in Grid labeling.""" +62 if gamma_tag == 'Identity': +63 g = identity +64 elif gamma_tag == 'Gamma5': +65 g = gamma5 +66 elif gamma_tag == 'GammaX': +67 g = gamma[0] +68 elif gamma_tag == 'GammaY': +69 g = gamma[1] +70 elif gamma_tag == 'GammaZ': +71 g = gamma[2] +72 elif gamma_tag == 'GammaT': +73 g = gamma[3] +74 elif gamma_tag == 'GammaXGamma5': +75 g = gamma[0] @ gamma5 +76 elif gamma_tag == 'GammaYGamma5': +77 g = gamma[1] @ gamma5 +78 elif gamma_tag == 'GammaZGamma5': +79 g = gamma[2] @ gamma5 +80 elif gamma_tag == 'GammaTGamma5': +81 g = gamma[3] @ gamma5 +82 elif gamma_tag == 'SigmaXT': +83 g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0]) +84 elif gamma_tag == 'SigmaXY': +85 g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0]) +86 elif gamma_tag == 'SigmaXZ': +87 g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0]) +88 elif gamma_tag == 'SigmaYT': +89 g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1]) +90 elif gamma_tag == 'SigmaYZ': +91 g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1]) +92 elif gamma_tag == 'SigmaZT': +93 g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2]) +94 else: +95 raise Exception('Unkown gamma structure', gamma_tag) +96 return g @@ -186,18 +197,30 @@ 27 """Rank-3 epsilon tensor 28 29 Based on https://codegolf.stackexchange.com/a/160375 -30 """ -31 test_set = set((i, j, k)) -32 if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): -33 raise Exception("Unexpected input", i, j, k) -34 -35 return (i - j) * (j - k) * (k - i) / 2 +30 +31 Returns +32 ------- +33 elem : int +34 Element (i,j,k) of the epsilon tensor of rank 3 +35 """ +36 test_set = set((i, j, k)) +37 if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): +38 raise Exception("Unexpected input", i, j, k) +39 +40 return (i - j) * (j - k) * (k - i) / 2

Rank-3 epsilon tensor

Based on https://codegolf.stackexchange.com/a/160375

+ +
Returns
+ +
@@ -213,22 +236,35 @@ - 
38def epsilon_tensor_rank4(i, j, k, o):
-39    """Rank-4 epsilon tensor
-40
-41    Extension of https://codegolf.stackexchange.com/a/160375
-42    """
-43    test_set = set((i, j, k, o))
-44    if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
-45        raise Exception("Unexpected input", i, j, k, o)
-46
-47    return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
+            
43def epsilon_tensor_rank4(i, j, k, o):
+44    """Rank-4 epsilon tensor
+45
+46    Extension of https://codegolf.stackexchange.com/a/160375
+47
+48
+49    Returns
+50    -------
+51    elem : int
+52        Element (i,j,k,o) of the epsilon tensor of rank 4
+53    """
+54    test_set = set((i, j, k, o))
+55    if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
+56        raise Exception("Unexpected input", i, j, k, o)
+57
+58    return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
 

 
 
             
Rank-4 epsilon tensor

 
 
Extension of https://codegolf.stackexchange.com/a/160375

+
+
Returns

+
+
    
+
  • elem (int):
+Element (i,j,k,o) of the epsilon tensor of rank 4
    • 
+

 

 
 
@@ -244,43 +280,43 @@
- 
50def Grid_gamma(gamma_tag):
-51    """Returns gamma matrix in Grid labeling."""
-52    if gamma_tag == 'Identity':
-53        g = identity
-54    elif gamma_tag == 'Gamma5':
-55        g = gamma5
-56    elif gamma_tag == 'GammaX':
-57        g = gamma[0]
-58    elif gamma_tag == 'GammaY':
-59        g = gamma[1]
-60    elif gamma_tag == 'GammaZ':
-61        g = gamma[2]
-62    elif gamma_tag == 'GammaT':
-63        g = gamma[3]
-64    elif gamma_tag == 'GammaXGamma5':
-65        g = gamma[0] @ gamma5
-66    elif gamma_tag == 'GammaYGamma5':
-67        g = gamma[1] @ gamma5
-68    elif gamma_tag == 'GammaZGamma5':
-69        g = gamma[2] @ gamma5
-70    elif gamma_tag == 'GammaTGamma5':
-71        g = gamma[3] @ gamma5
-72    elif gamma_tag == 'SigmaXT':
-73        g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0])
-74    elif gamma_tag == 'SigmaXY':
-75        g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0])
-76    elif gamma_tag == 'SigmaXZ':
-77        g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0])
-78    elif gamma_tag == 'SigmaYT':
-79        g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1])
-80    elif gamma_tag == 'SigmaYZ':
-81        g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1])
-82    elif gamma_tag == 'SigmaZT':
-83        g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
-84    else:
-85        raise Exception('Unkown gamma structure', gamma_tag)
-86    return g
+            
61def Grid_gamma(gamma_tag):
+62    """Returns gamma matrix in Grid labeling."""
+63    if gamma_tag == 'Identity':
+64        g = identity
+65    elif gamma_tag == 'Gamma5':
+66        g = gamma5
+67    elif gamma_tag == 'GammaX':
+68        g = gamma[0]
+69    elif gamma_tag == 'GammaY':
+70        g = gamma[1]
+71    elif gamma_tag == 'GammaZ':
+72        g = gamma[2]
+73    elif gamma_tag == 'GammaT':
+74        g = gamma[3]
+75    elif gamma_tag == 'GammaXGamma5':
+76        g = gamma[0] @ gamma5
+77    elif gamma_tag == 'GammaYGamma5':
+78        g = gamma[1] @ gamma5
+79    elif gamma_tag == 'GammaZGamma5':
+80        g = gamma[2] @ gamma5
+81    elif gamma_tag == 'GammaTGamma5':
+82        g = gamma[3] @ gamma5
+83    elif gamma_tag == 'SigmaXT':
+84        g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0])
+85    elif gamma_tag == 'SigmaXY':
+86        g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0])
+87    elif gamma_tag == 'SigmaXZ':
+88        g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0])
+89    elif gamma_tag == 'SigmaYT':
+90        g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1])
+91    elif gamma_tag == 'SigmaYZ':
+92        g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1])
+93    elif gamma_tag == 'SigmaZT':
+94        g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
+95    else:
+96        raise Exception('Unkown gamma structure', gamma_tag)
+97    return g
 

 
 
diff --git a/docs/pyerrors/fits.html b/docs/pyerrors/fits.html
index 0be3cb04..a8802145 100644
--- a/docs/pyerrors/fits.html
+++ b/docs/pyerrors/fits.html
@@ -240,673 +240,707 @@
 129        If True, a quantile-quantile plot of the fit result is generated (default False).
 130    num_grad : bool
 131        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
-132    '''
-133    if priors is not None:
-134        return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
-135    else:
-136        return _standard_fit(x, y, func, silent=silent, **kwargs)
-137
-138
-139def total_least_squares(x, y, func, silent=False, **kwargs):
-140    r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
-141
-142    Parameters
-143    ----------
-144    x : list
-145        list of Obs, or a tuple of lists of Obs
-146    y : list
-147        list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
-148    func : object
-149        func has to be of the form
-150
-151        ```python
-152        import autograd.numpy as anp
-153
-154        def func(a, x):
-155            return a[0] + a[1] * x + a[2] * anp.sinh(x)
-156        ```
-157
-158        For multiple x values func can be of the form
-159
-160        ```python
-161        def func(a, x):
-162            (x1, x2) = x
-163            return a[0] * x1 ** 2 + a[1] * x2
-164        ```
-165
-166        It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
-167        will not work.
-168    silent : bool, optional
-169        If true all output to the console is omitted (default False).
-170    initial_guess : list
-171        can provide an initial guess for the input parameters. Relevant for non-linear
-172        fits with many parameters.
-173    expected_chisquare : bool
-174        If true prints the expected chisquare which is
-175        corrected by effects caused by correlated input data.
-176        This can take a while as the full correlation matrix
-177        has to be calculated (default False).
-178    num_grad : bool
-179        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
-180
-181    Notes
-182    -----
-183    Based on the orthogonal distance regression module of scipy
-184    '''
+132
+133    Returns
+134    -------
+135    output : Fit_result
+136        Parameters and information on the fitted result.
+137    '''
+138    if priors is not None:
+139        return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
+140    else:
+141        return _standard_fit(x, y, func, silent=silent, **kwargs)
+142
+143
+144def total_least_squares(x, y, func, silent=False, **kwargs):
+145    r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
+146
+147    Parameters
+148    ----------
+149    x : list
+150        list of Obs, or a tuple of lists of Obs
+151    y : list
+152        list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
+153    func : object
+154        func has to be of the form
+155
+156        ```python
+157        import autograd.numpy as anp
+158
+159        def func(a, x):
+160            return a[0] + a[1] * x + a[2] * anp.sinh(x)
+161        ```
+162
+163        For multiple x values func can be of the form
+164
+165        ```python
+166        def func(a, x):
+167            (x1, x2) = x
+168            return a[0] * x1 ** 2 + a[1] * x2
+169        ```
+170
+171        It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
+172        will not work.
+173    silent : bool, optional
+174        If true all output to the console is omitted (default False).
+175    initial_guess : list
+176        can provide an initial guess for the input parameters. Relevant for non-linear
+177        fits with many parameters.
+178    expected_chisquare : bool
+179        If true prints the expected chisquare which is
+180        corrected by effects caused by correlated input data.
+181        This can take a while as the full correlation matrix
+182        has to be calculated (default False).
+183    num_grad : bool
+184        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
 185
-186    output = Fit_result()
-187
-188    output.fit_function = func
+186    Notes
+187    -----
+188    Based on the orthogonal distance regression module of scipy.
 189
-190    x = np.array(x)
-191
-192    x_shape = x.shape
-193
-194    if kwargs.get('num_grad') is True:
-195        jacobian = num_jacobian
-196        hessian = num_hessian
-197    else:
-198        jacobian = auto_jacobian
-199        hessian = auto_hessian
-200
-201    if not callable(func):
-202        raise TypeError('func has to be a function.')
+190    Returns
+191    -------
+192    output : Fit_result
+193        Parameters and information on the fitted result.
+194    '''
+195
+196    output = Fit_result()
+197
+198    output.fit_function = func
+199
+200    x = np.array(x)
+201
+202    x_shape = x.shape
 203
-204    for i in range(42):
-205        try:
-206            func(np.arange(i), x.T[0])
-207        except TypeError:
-208            continue
-209        except IndexError:
-210            continue
-211        else:
-212            break
-213    else:
-214        raise RuntimeError("Fit function is not valid.")
-215
-216    n_parms = i
-217    if not silent:
-218        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
-219
-220    x_f = np.vectorize(lambda o: o.value)(x)
-221    dx_f = np.vectorize(lambda o: o.dvalue)(x)
-222    y_f = np.array([o.value for o in y])
-223    dy_f = np.array([o.dvalue for o in y])
-224
-225    if np.any(np.asarray(dx_f) <= 0.0):
-226        raise Exception('No x errors available, run the gamma method first.')
-227
-228    if np.any(np.asarray(dy_f) <= 0.0):
-229        raise Exception('No y errors available, run the gamma method first.')
-230
-231    if 'initial_guess' in kwargs:
-232        x0 = kwargs.get('initial_guess')
-233        if len(x0) != n_parms:
-234            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
-235    else:
-236        x0 = [1] * n_parms
+204    if kwargs.get('num_grad') is True:
+205        jacobian = num_jacobian
+206        hessian = num_hessian
+207    else:
+208        jacobian = auto_jacobian
+209        hessian = auto_hessian
+210
+211    if not callable(func):
+212        raise TypeError('func has to be a function.')
+213
+214    for i in range(42):
+215        try:
+216            func(np.arange(i), x.T[0])
+217        except TypeError:
+218            continue
+219        except IndexError:
+220            continue
+221        else:
+222            break
+223    else:
+224        raise RuntimeError("Fit function is not valid.")
+225
+226    n_parms = i
+227    if not silent:
+228        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
+229
+230    x_f = np.vectorize(lambda o: o.value)(x)
+231    dx_f = np.vectorize(lambda o: o.dvalue)(x)
+232    y_f = np.array([o.value for o in y])
+233    dy_f = np.array([o.dvalue for o in y])
+234
+235    if np.any(np.asarray(dx_f) <= 0.0):
+236        raise Exception('No x errors available, run the gamma method first.')
 237
-238    data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
-239    model = Model(func)
-240    odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
-241    odr.set_job(fit_type=0, deriv=1)
-242    out = odr.run()
-243
-244    output.residual_variance = out.res_var
-245
-246    output.method = 'ODR'
+238    if np.any(np.asarray(dy_f) <= 0.0):
+239        raise Exception('No y errors available, run the gamma method first.')
+240
+241    if 'initial_guess' in kwargs:
+242        x0 = kwargs.get('initial_guess')
+243        if len(x0) != n_parms:
+244            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
+245    else:
+246        x0 = [1] * n_parms
 247
-248    output.message = out.stopreason
-249
-250    output.xplus = out.xplus
-251
-252    if not silent:
-253        print('Method: ODR')
-254        print(*out.stopreason)
-255        print('Residual variance:', output.residual_variance)
-256
-257    if out.info > 3:
-258        raise Exception('The minimization procedure did not converge.')
+248    data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
+249    model = Model(func)
+250    odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
+251    odr.set_job(fit_type=0, deriv=1)
+252    out = odr.run()
+253
+254    output.residual_variance = out.res_var
+255
+256    output.method = 'ODR'
+257
+258    output.message = out.stopreason
 259
-260    m = x_f.size
+260    output.xplus = out.xplus
 261
-262    def odr_chisquare(p):
-263        model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
-264        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
-265        return chisq
+262    if not silent:
+263        print('Method: ODR')
+264        print(*out.stopreason)
+265        print('Residual variance:', output.residual_variance)
 266
-267    if kwargs.get('expected_chisquare') is True:
-268        W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
+267    if out.info > 3:
+268        raise Exception('The minimization procedure did not converge.')
 269
-270        if kwargs.get('covariance') is not None:
-271            cov = kwargs.get('covariance')
-272        else:
-273            cov = covariance(np.concatenate((y, x.ravel())))
-274
-275        number_of_x_parameters = int(m / x_f.shape[-1])
+270    m = x_f.size
+271
+272    def odr_chisquare(p):
+273        model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
+274        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
+275        return chisq
 276
-277        old_jac = jacobian(func)(out.beta, out.xplus)
-278        fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
-279        fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
-280        new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
-281
-282        A = W @ new_jac
-283        P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
-284        expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
-285        if expected_chisquare <= 0.0:
-286            warnings.warn("Negative expected_chisquare.", RuntimeWarning)
-287            expected_chisquare = np.abs(expected_chisquare)
-288        output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
-289        if not silent:
-290            print('chisquare/expected_chisquare:',
-291                  output.chisquare_by_expected_chisquare)
-292
-293    fitp = out.beta
-294    try:
-295        hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
-296    except TypeError:
-297        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
-298
-299    def odr_chisquare_compact_x(d):
-300        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
-301        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
-302        return chisq
-303
-304    jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
-305
-306    # Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
-307    try:
-308        deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
-309    except np.linalg.LinAlgError:
-310        raise Exception("Cannot invert hessian matrix.")
-311
-312    def odr_chisquare_compact_y(d):
-313        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
-314        chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
-315        return chisq
-316
-317    jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
-318
-319    # Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
-320    try:
-321        deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
-322    except np.linalg.LinAlgError:
-323        raise Exception("Cannot invert hessian matrix.")
-324
-325    result = []
-326    for i in range(n_parms):
-327        result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
+277    if kwargs.get('expected_chisquare') is True:
+278        W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
+279
+280        if kwargs.get('covariance') is not None:
+281            cov = kwargs.get('covariance')
+282        else:
+283            cov = covariance(np.concatenate((y, x.ravel())))
+284
+285        number_of_x_parameters = int(m / x_f.shape[-1])
+286
+287        old_jac = jacobian(func)(out.beta, out.xplus)
+288        fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
+289        fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
+290        new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
+291
+292        A = W @ new_jac
+293        P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
+294        expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
+295        if expected_chisquare <= 0.0:
+296            warnings.warn("Negative expected_chisquare.", RuntimeWarning)
+297            expected_chisquare = np.abs(expected_chisquare)
+298        output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
+299        if not silent:
+300            print('chisquare/expected_chisquare:',
+301                  output.chisquare_by_expected_chisquare)
+302
+303    fitp = out.beta
+304    try:
+305        hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
+306    except TypeError:
+307        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+308
+309    def odr_chisquare_compact_x(d):
+310        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
+311        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
+312        return chisq
+313
+314    jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
+315
+316    # Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
+317    try:
+318        deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
+319    except np.linalg.LinAlgError:
+320        raise Exception("Cannot invert hessian matrix.")
+321
+322    def odr_chisquare_compact_y(d):
+323        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
+324        chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
+325        return chisq
+326
+327    jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
 328
-329    output.fit_parameters = result
-330
-331    output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
-332    output.dof = x.shape[-1] - n_parms
-333    output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
+329    # Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
+330    try:
+331        deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
+332    except np.linalg.LinAlgError:
+333        raise Exception("Cannot invert hessian matrix.")
 334
-335    return output
-336
-337
-338def _prior_fit(x, y, func, priors, silent=False, **kwargs):
-339    output = Fit_result()
+335    result = []
+336    for i in range(n_parms):
+337        result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
+338
+339    output.fit_parameters = result
 340
-341    output.fit_function = func
-342
-343    x = np.asarray(x)
+341    output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
+342    output.dof = x.shape[-1] - n_parms
+343    output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
 344
-345    if kwargs.get('num_grad') is True:
-346        hessian = num_hessian
-347    else:
-348        hessian = auto_hessian
-349
-350    if not callable(func):
-351        raise TypeError('func has to be a function.')
+345    return output
+346
+347
+348def _prior_fit(x, y, func, priors, silent=False, **kwargs):
+349    output = Fit_result()
+350
+351    output.fit_function = func
 352
-353    for i in range(100):
-354        try:
-355            func(np.arange(i), 0)
-356        except TypeError:
-357            continue
-358        except IndexError:
-359            continue
-360        else:
-361            break
-362    else:
-363        raise RuntimeError("Fit function is not valid.")
-364
-365    n_parms = i
-366
-367    if n_parms != len(priors):
-368        raise Exception('Priors does not have the correct length.')
-369
-370    def extract_val_and_dval(string):
-371        split_string = string.split('(')
-372        if '.' in split_string[0] and '.' not in split_string[1][:-1]:
-373            factor = 10 ** -len(split_string[0].partition('.')[2])
-374        else:
-375            factor = 1
-376        return float(split_string[0]), float(split_string[1][:-1]) * factor
-377
-378    loc_priors = []
-379    for i_n, i_prior in enumerate(priors):
-380        if isinstance(i_prior, Obs):
-381            loc_priors.append(i_prior)
-382        else:
-383            loc_val, loc_dval = extract_val_and_dval(i_prior)
-384            loc_priors.append(cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}"))
-385
-386    output.priors = loc_priors
+353    x = np.asarray(x)
+354
+355    if kwargs.get('num_grad') is True:
+356        hessian = num_hessian
+357    else:
+358        hessian = auto_hessian
+359
+360    if not callable(func):
+361        raise TypeError('func has to be a function.')
+362
+363    for i in range(100):
+364        try:
+365            func(np.arange(i), 0)
+366        except TypeError:
+367            continue
+368        except IndexError:
+369            continue
+370        else:
+371            break
+372    else:
+373        raise RuntimeError("Fit function is not valid.")
+374
+375    n_parms = i
+376
+377    if n_parms != len(priors):
+378        raise Exception('Priors does not have the correct length.')
+379
+380    def extract_val_and_dval(string):
+381        split_string = string.split('(')
+382        if '.' in split_string[0] and '.' not in split_string[1][:-1]:
+383            factor = 10 ** -len(split_string[0].partition('.')[2])
+384        else:
+385            factor = 1
+386        return float(split_string[0]), float(split_string[1][:-1]) * factor
 387
-388    if not silent:
-389        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
-390
-391    y_f = [o.value for o in y]
-392    dy_f = [o.dvalue for o in y]
-393
-394    if np.any(np.asarray(dy_f) <= 0.0):
-395        raise Exception('No y errors available, run the gamma method first.')
-396
-397    p_f = [o.value for o in loc_priors]
-398    dp_f = [o.dvalue for o in loc_priors]
-399
-400    if np.any(np.asarray(dp_f) <= 0.0):
-401        raise Exception('No prior errors available, run the gamma method first.')
-402
-403    if 'initial_guess' in kwargs:
-404        x0 = kwargs.get('initial_guess')
-405        if len(x0) != n_parms:
-406            raise Exception('Initial guess does not have the correct length.')
-407    else:
-408        x0 = p_f
+388    loc_priors = []
+389    for i_n, i_prior in enumerate(priors):
+390        if isinstance(i_prior, Obs):
+391            loc_priors.append(i_prior)
+392        else:
+393            loc_val, loc_dval = extract_val_and_dval(i_prior)
+394            loc_priors.append(cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}"))
+395
+396    output.priors = loc_priors
+397
+398    if not silent:
+399        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
+400
+401    y_f = [o.value for o in y]
+402    dy_f = [o.dvalue for o in y]
+403
+404    if np.any(np.asarray(dy_f) <= 0.0):
+405        raise Exception('No y errors available, run the gamma method first.')
+406
+407    p_f = [o.value for o in loc_priors]
+408    dp_f = [o.dvalue for o in loc_priors]
 409
-410    def chisqfunc(p):
-411        model = func(p, x)
-412        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((p_f - p) / dp_f) ** 2)
-413        return chisq
-414
-415    if not silent:
-416        print('Method: migrad')
-417
-418    m = iminuit.Minuit(chisqfunc, x0)
-419    m.errordef = 1
-420    m.print_level = 0
-421    if 'tol' in kwargs:
-422        m.tol = kwargs.get('tol')
-423    else:
-424        m.tol = 1e-4
-425    m.migrad()
-426    params = np.asarray(m.values)
+410    if np.any(np.asarray(dp_f) <= 0.0):
+411        raise Exception('No prior errors available, run the gamma method first.')
+412
+413    if 'initial_guess' in kwargs:
+414        x0 = kwargs.get('initial_guess')
+415        if len(x0) != n_parms:
+416            raise Exception('Initial guess does not have the correct length.')
+417    else:
+418        x0 = p_f
+419
+420    def chisqfunc(p):
+421        model = func(p, x)
+422        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((p_f - p) / dp_f) ** 2)
+423        return chisq
+424
+425    if not silent:
+426        print('Method: migrad')
 427
-428    output.chisquare_by_dof = m.fval / len(x)
-429
-430    output.method = 'migrad'
-431
-432    if not silent:
-433        print('chisquare/d.o.f.:', output.chisquare_by_dof)
-434
-435    if not m.fmin.is_valid:
-436        raise Exception('The minimization procedure did not converge.')
+428    m = iminuit.Minuit(chisqfunc, x0)
+429    m.errordef = 1
+430    m.print_level = 0
+431    if 'tol' in kwargs:
+432        m.tol = kwargs.get('tol')
+433    else:
+434        m.tol = 1e-4
+435    m.migrad()
+436    params = np.asarray(m.values)
 437
-438    hess = hessian(chisqfunc)(params)
-439    hess_inv = np.linalg.pinv(hess)
-440
-441    def chisqfunc_compact(d):
-442        model = func(d[:n_parms], x)
-443        chisq = anp.sum(((d[n_parms: n_parms + len(x)] - model) / dy_f) ** 2) + anp.sum(((d[n_parms + len(x):] - d[:n_parms]) / dp_f) ** 2)
-444        return chisq
-445
-446    jac_jac = hessian(chisqfunc_compact)(np.concatenate((params, y_f, p_f)))
+438    output.chisquare_by_dof = m.fval / len(x)
+439
+440    output.method = 'migrad'
+441
+442    if not silent:
+443        print('chisquare/d.o.f.:', output.chisquare_by_dof)
+444
+445    if not m.fmin.is_valid:
+446        raise Exception('The minimization procedure did not converge.')
 447
-448    deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
-449
-450    result = []
-451    for i in range(n_parms):
-452        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
-453
-454    output.fit_parameters = result
-455    output.chisquare = chisqfunc(np.asarray(params))
-456
-457    if kwargs.get('resplot') is True:
-458        residual_plot(x, y, func, result)
+448    hess = hessian(chisqfunc)(params)
+449    hess_inv = np.linalg.pinv(hess)
+450
+451    def chisqfunc_compact(d):
+452        model = func(d[:n_parms], x)
+453        chisq = anp.sum(((d[n_parms: n_parms + len(x)] - model) / dy_f) ** 2) + anp.sum(((d[n_parms + len(x):] - d[:n_parms]) / dp_f) ** 2)
+454        return chisq
+455
+456    jac_jac = hessian(chisqfunc_compact)(np.concatenate((params, y_f, p_f)))
+457
+458    deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
 459
-460    if kwargs.get('qqplot') is True:
-461        qqplot(x, y, func, result)
-462
-463    return output
-464
-465
-466def _standard_fit(x, y, func, silent=False, **kwargs):
-467
-468    output = Fit_result()
+460    result = []
+461    for i in range(n_parms):
+462        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
+463
+464    output.fit_parameters = result
+465    output.chisquare = chisqfunc(np.asarray(params))
+466
+467    if kwargs.get('resplot') is True:
+468        residual_plot(x, y, func, result)
 469
-470    output.fit_function = func
-471
-472    x = np.asarray(x)
-473
-474    if kwargs.get('num_grad') is True:
-475        jacobian = num_jacobian
-476        hessian = num_hessian
-477    else:
-478        jacobian = auto_jacobian
-479        hessian = auto_hessian
-480
-481    if x.shape[-1] != len(y):
-482        raise Exception('x and y input have to have the same length')
+470    if kwargs.get('qqplot') is True:
+471        qqplot(x, y, func, result)
+472
+473    return output
+474
+475
+476def _standard_fit(x, y, func, silent=False, **kwargs):
+477
+478    output = Fit_result()
+479
+480    output.fit_function = func
+481
+482    x = np.asarray(x)
 483
-484    if len(x.shape) > 2:
-485        raise Exception('Unknown format for x values')
-486
-487    if not callable(func):
-488        raise TypeError('func has to be a function.')
-489
-490    for i in range(42):
-491        try:
-492            func(np.arange(i), x.T[0])
-493        except TypeError:
-494            continue
-495        except IndexError:
-496            continue
-497        else:
-498            break
-499    else:
-500        raise RuntimeError("Fit function is not valid.")
-501
-502    n_parms = i
-503
-504    if not silent:
-505        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
-506
-507    y_f = [o.value for o in y]
-508    dy_f = [o.dvalue for o in y]
-509
-510    if np.any(np.asarray(dy_f) <= 0.0):
-511        raise Exception('No y errors available, run the gamma method first.')
-512
-513    if 'initial_guess' in kwargs:
-514        x0 = kwargs.get('initial_guess')
-515        if len(x0) != n_parms:
-516            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
-517    else:
-518        x0 = [0.1] * n_parms
+484    if kwargs.get('num_grad') is True:
+485        jacobian = num_jacobian
+486        hessian = num_hessian
+487    else:
+488        jacobian = auto_jacobian
+489        hessian = auto_hessian
+490
+491    if x.shape[-1] != len(y):
+492        raise Exception('x and y input have to have the same length')
+493
+494    if len(x.shape) > 2:
+495        raise Exception('Unknown format for x values')
+496
+497    if not callable(func):
+498        raise TypeError('func has to be a function.')
+499
+500    for i in range(42):
+501        try:
+502            func(np.arange(i), x.T[0])
+503        except TypeError:
+504            continue
+505        except IndexError:
+506            continue
+507        else:
+508            break
+509    else:
+510        raise RuntimeError("Fit function is not valid.")
+511
+512    n_parms = i
+513
+514    if not silent:
+515        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
+516
+517    y_f = [o.value for o in y]
+518    dy_f = [o.dvalue for o in y]
 519
-520    if kwargs.get('correlated_fit') is True:
-521        corr = covariance(y, correlation=True, **kwargs)
-522        covdiag = np.diag(1 / np.asarray(dy_f))
-523        condn = np.linalg.cond(corr)
-524        if condn > 0.1 / np.finfo(float).eps:
-525            raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
-526        if condn > 1e13:
-527            warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
-528        chol = np.linalg.cholesky(corr)
-529        chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
-530
-531        def chisqfunc_corr(p):
-532            model = func(p, x)
-533            chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
-534            return chisq
-535
-536    def chisqfunc(p):
-537        model = func(p, x)
-538        chisq = anp.sum(((y_f - model) / dy_f) ** 2)
-539        return chisq
+520    if np.any(np.asarray(dy_f) <= 0.0):
+521        raise Exception('No y errors available, run the gamma method first.')
+522
+523    if 'initial_guess' in kwargs:
+524        x0 = kwargs.get('initial_guess')
+525        if len(x0) != n_parms:
+526            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
+527    else:
+528        x0 = [0.1] * n_parms
+529
+530    if kwargs.get('correlated_fit') is True:
+531        corr = covariance(y, correlation=True, **kwargs)
+532        covdiag = np.diag(1 / np.asarray(dy_f))
+533        condn = np.linalg.cond(corr)
+534        if condn > 0.1 / np.finfo(float).eps:
+535            raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
+536        if condn > 1e13:
+537            warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
+538        chol = np.linalg.cholesky(corr)
+539        chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
 540
-541    output.method = kwargs.get('method', 'Levenberg-Marquardt')
-542    if not silent:
-543        print('Method:', output.method)
-544
-545    if output.method != 'Levenberg-Marquardt':
-546        if output.method == 'migrad':
-547            fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4)  # Stopping criterion 0.002 * tol * errordef
-548            if kwargs.get('correlated_fit') is True:
-549                fit_result = iminuit.minimize(chisqfunc_corr, fit_result.x, tol=1e-4)  # Stopping criterion 0.002 * tol * errordef
-550            output.iterations = fit_result.nfev
-551        else:
-552            fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
-553            if kwargs.get('correlated_fit') is True:
-554                fit_result = scipy.optimize.minimize(chisqfunc_corr, fit_result.x, method=kwargs.get('method'), tol=1e-12)
-555            output.iterations = fit_result.nit
-556
-557        chisquare = fit_result.fun
-558
-559    else:
-560        if kwargs.get('correlated_fit') is True:
-561            def chisqfunc_residuals_corr(p):
-562                model = func(p, x)
-563                chisq = anp.dot(chol_inv, (y_f - model))
-564                return chisq
-565
-566        def chisqfunc_residuals(p):
-567            model = func(p, x)
-568            chisq = ((y_f - model) / dy_f)
-569            return chisq
-570
-571        fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
-572        if kwargs.get('correlated_fit') is True:
-573            fit_result = scipy.optimize.least_squares(chisqfunc_residuals_corr, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
-574
-575        chisquare = np.sum(fit_result.fun ** 2)
-576        if kwargs.get('correlated_fit') is True:
-577            assert np.isclose(chisquare, chisqfunc_corr(fit_result.x), atol=1e-14)
-578        else:
-579            assert np.isclose(chisquare, chisqfunc(fit_result.x), atol=1e-14)
+541        def chisqfunc_corr(p):
+542            model = func(p, x)
+543            chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
+544            return chisq
+545
+546    def chisqfunc(p):
+547        model = func(p, x)
+548        chisq = anp.sum(((y_f - model) / dy_f) ** 2)
+549        return chisq
+550
+551    output.method = kwargs.get('method', 'Levenberg-Marquardt')
+552    if not silent:
+553        print('Method:', output.method)
+554
+555    if output.method != 'Levenberg-Marquardt':
+556        if output.method == 'migrad':
+557            fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4)  # Stopping criterion 0.002 * tol * errordef
+558            if kwargs.get('correlated_fit') is True:
+559                fit_result = iminuit.minimize(chisqfunc_corr, fit_result.x, tol=1e-4)  # Stopping criterion 0.002 * tol * errordef
+560            output.iterations = fit_result.nfev
+561        else:
+562            fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
+563            if kwargs.get('correlated_fit') is True:
+564                fit_result = scipy.optimize.minimize(chisqfunc_corr, fit_result.x, method=kwargs.get('method'), tol=1e-12)
+565            output.iterations = fit_result.nit
+566
+567        chisquare = fit_result.fun
+568
+569    else:
+570        if kwargs.get('correlated_fit') is True:
+571            def chisqfunc_residuals_corr(p):
+572                model = func(p, x)
+573                chisq = anp.dot(chol_inv, (y_f - model))
+574                return chisq
+575
+576        def chisqfunc_residuals(p):
+577            model = func(p, x)
+578            chisq = ((y_f - model) / dy_f)
+579            return chisq
 580
-581        output.iterations = fit_result.nfev
-582
-583    if not fit_result.success:
-584        raise Exception('The minimization procedure did not converge.')
-585
-586    if x.shape[-1] - n_parms > 0:
-587        output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
-588    else:
-589        output.chisquare_by_dof = float('nan')
+581        fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
+582        if kwargs.get('correlated_fit') is True:
+583            fit_result = scipy.optimize.least_squares(chisqfunc_residuals_corr, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
+584
+585        chisquare = np.sum(fit_result.fun ** 2)
+586        if kwargs.get('correlated_fit') is True:
+587            assert np.isclose(chisquare, chisqfunc_corr(fit_result.x), atol=1e-14)
+588        else:
+589            assert np.isclose(chisquare, chisqfunc(fit_result.x), atol=1e-14)
 590
-591    output.message = fit_result.message
-592    if not silent:
-593        print(fit_result.message)
-594        print('chisquare/d.o.f.:', output.chisquare_by_dof)
+591        output.iterations = fit_result.nfev
+592
+593    if not fit_result.success:
+594        raise Exception('The minimization procedure did not converge.')
 595
-596    if kwargs.get('expected_chisquare') is True:
-597        if kwargs.get('correlated_fit') is not True:
-598            W = np.diag(1 / np.asarray(dy_f))
-599            cov = covariance(y)
-600            A = W @ jacobian(func)(fit_result.x, x)
-601            P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
-602            expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
-603            output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
-604            if not silent:
-605                print('chisquare/expected_chisquare:',
-606                      output.chisquare_by_expected_chisquare)
-607
-608    fitp = fit_result.x
-609    try:
-610        if kwargs.get('correlated_fit') is True:
-611            hess = hessian(chisqfunc_corr)(fitp)
-612        else:
-613            hess = hessian(chisqfunc)(fitp)
-614    except TypeError:
-615        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
-616
-617    if kwargs.get('correlated_fit') is True:
-618        def chisqfunc_compact(d):
-619            model = func(d[:n_parms], x)
-620            chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
-621            return chisq
-622
-623    else:
-624        def chisqfunc_compact(d):
-625            model = func(d[:n_parms], x)
-626            chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
-627            return chisq
-628
-629    jac_jac = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))
-630
-631    # Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
-632    try:
-633        deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
-634    except np.linalg.LinAlgError:
-635        raise Exception("Cannot invert hessian matrix.")
-636
-637    result = []
-638    for i in range(n_parms):
-639        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
+596    if x.shape[-1] - n_parms > 0:
+597        output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
+598    else:
+599        output.chisquare_by_dof = float('nan')
+600
+601    output.message = fit_result.message
+602    if not silent:
+603        print(fit_result.message)
+604        print('chisquare/d.o.f.:', output.chisquare_by_dof)
+605
+606    if kwargs.get('expected_chisquare') is True:
+607        if kwargs.get('correlated_fit') is not True:
+608            W = np.diag(1 / np.asarray(dy_f))
+609            cov = covariance(y)
+610            A = W @ jacobian(func)(fit_result.x, x)
+611            P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
+612            expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
+613            output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
+614            if not silent:
+615                print('chisquare/expected_chisquare:',
+616                      output.chisquare_by_expected_chisquare)
+617
+618    fitp = fit_result.x
+619    try:
+620        if kwargs.get('correlated_fit') is True:
+621            hess = hessian(chisqfunc_corr)(fitp)
+622        else:
+623            hess = hessian(chisqfunc)(fitp)
+624    except TypeError:
+625        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+626
+627    if kwargs.get('correlated_fit') is True:
+628        def chisqfunc_compact(d):
+629            model = func(d[:n_parms], x)
+630            chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
+631            return chisq
+632
+633    else:
+634        def chisqfunc_compact(d):
+635            model = func(d[:n_parms], x)
+636            chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
+637            return chisq
+638
+639    jac_jac = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))
 640
-641    output.fit_parameters = result
-642
-643    output.chisquare = chisquare
-644    output.dof = x.shape[-1] - n_parms
-645    output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
-646    # Hotelling t-squared p-value for correlated fits.
-647    if kwargs.get('correlated_fit') is True:
-648        n_cov = np.min(np.vectorize(lambda x: x.N)(y))
-649        output.t2_p_value = 1 - scipy.stats.f.cdf((n_cov - output.dof) / (output.dof * (n_cov - 1)) * output.chisquare,
-650                                                  output.dof, n_cov - output.dof)
-651
-652    if kwargs.get('resplot') is True:
-653        residual_plot(x, y, func, result)
-654
-655    if kwargs.get('qqplot') is True:
-656        qqplot(x, y, func, result)
-657
-658    return output
-659
-660
-661def fit_lin(x, y, **kwargs):
-662    """Performs a linear fit to y = n + m * x and returns two Obs n, m.
-663
-664    Parameters
-665    ----------
-666    x : list
-667        Can either be a list of floats in which case no xerror is assumed, or
-668        a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
-669    y : list
-670        List of Obs, the dvalues of the Obs are used as yerror for the fit.
-671    """
-672
-673    def f(a, x):
-674        y = a[0] + a[1] * x
-675        return y
-676
-677    if all(isinstance(n, Obs) for n in x):
-678        out = total_least_squares(x, y, f, **kwargs)
-679        return out.fit_parameters
-680    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-681        out = least_squares(x, y, f, **kwargs)
-682        return out.fit_parameters
-683    else:
-684        raise Exception('Unsupported types for x')
-685
-686
-687def qqplot(x, o_y, func, p):
-688    """Generates a quantile-quantile plot of the fit result which can be used to
-689       check if the residuals of the fit are gaussian distributed.
-690    """
+641    # Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
+642    try:
+643        deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
+644    except np.linalg.LinAlgError:
+645        raise Exception("Cannot invert hessian matrix.")
+646
+647    result = []
+648    for i in range(n_parms):
+649        result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
+650
+651    output.fit_parameters = result
+652
+653    output.chisquare = chisquare
+654    output.dof = x.shape[-1] - n_parms
+655    output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
+656    # Hotelling t-squared p-value for correlated fits.
+657    if kwargs.get('correlated_fit') is True:
+658        n_cov = np.min(np.vectorize(lambda x: x.N)(y))
+659        output.t2_p_value = 1 - scipy.stats.f.cdf((n_cov - output.dof) / (output.dof * (n_cov - 1)) * output.chisquare,
+660                                                  output.dof, n_cov - output.dof)
+661
+662    if kwargs.get('resplot') is True:
+663        residual_plot(x, y, func, result)
+664
+665    if kwargs.get('qqplot') is True:
+666        qqplot(x, y, func, result)
+667
+668    return output
+669
+670
+671def fit_lin(x, y, **kwargs):
+672    """Performs a linear fit to y = n + m * x and returns two Obs n, m.
+673
+674    Parameters
+675    ----------
+676    x : list
+677        Can either be a list of floats in which case no xerror is assumed, or
+678        a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
+679    y : list
+680        List of Obs, the dvalues of the Obs are used as yerror for the fit.
+681
+682    Returns
+683    -------
+684    fit_parameters : list[Obs]
+685        LIist of fitted observables.
+686    """
+687
+688    def f(a, x):
+689        y = a[0] + a[1] * x
+690        return y
 691
-692    residuals = []
-693    for i_x, i_y in zip(x, o_y):
-694        residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
-695    residuals = sorted(residuals)
-696    my_y = [o.value for o in residuals]
-697    probplot = scipy.stats.probplot(my_y)
-698    my_x = probplot[0][0]
-699    plt.figure(figsize=(8, 8 / 1.618))
-700    plt.errorbar(my_x, my_y, fmt='o')
-701    fit_start = my_x[0]
-702    fit_stop = my_x[-1]
-703    samples = np.arange(fit_start, fit_stop, 0.01)
-704    plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
-705    plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
-706
-707    plt.xlabel('Theoretical quantiles')
-708    plt.ylabel('Ordered Values')
-709    plt.legend()
-710    plt.draw()
-711
-712
-713def residual_plot(x, y, func, fit_res):
-714    """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
-715    sorted_x = sorted(x)
-716    xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
-717    xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
-718    x_samples = np.arange(xstart, xstop + 0.01, 0.01)
-719
-720    plt.figure(figsize=(8, 8 / 1.618))
-721    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-722    ax0 = plt.subplot(gs[0])
-723    ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
-724    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
-725    ax0.set_xticklabels([])
-726    ax0.set_xlim([xstart, xstop])
-727    ax0.set_xticklabels([])
-728    ax0.legend()
-729
-730    residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
-731    ax1 = plt.subplot(gs[1])
-732    ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-733    ax1.tick_params(direction='out')
-734    ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-735    ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
-736    ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
-737    ax1.set_xlim([xstart, xstop])
-738    ax1.set_ylabel('Residuals')
-739    plt.subplots_adjust(wspace=None, hspace=None)
-740    plt.draw()
-741
-742
-743def error_band(x, func, beta):
-744    """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
-745    cov = covariance(beta)
-746    if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
-747        warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
-748
-749    deriv = []
-750    for i, item in enumerate(x):
-751        deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
-752
-753    err = []
-754    for i, item in enumerate(x):
-755        err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
-756    err = np.array(err)
-757
-758    return err
-759
-760
-761def ks_test(objects=None):
-762    """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
-763
-764    Parameters
-765    ----------
-766    objects : list
-767        List of fit results to include in the analysis (optional).
-768    """
+692    if all(isinstance(n, Obs) for n in x):
+693        out = total_least_squares(x, y, f, **kwargs)
+694        return out.fit_parameters
+695    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
+696        out = least_squares(x, y, f, **kwargs)
+697        return out.fit_parameters
+698    else:
+699        raise Exception('Unsupported types for x')
+700
+701
+702def qqplot(x, o_y, func, p):
+703    """Generates a quantile-quantile plot of the fit result which can be used to
+704       check if the residuals of the fit are gaussian distributed.
+705
+706    Returns
+707    -------
+708    None
+709    """
+710
+711    residuals = []
+712    for i_x, i_y in zip(x, o_y):
+713        residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
+714    residuals = sorted(residuals)
+715    my_y = [o.value for o in residuals]
+716    probplot = scipy.stats.probplot(my_y)
+717    my_x = probplot[0][0]
+718    plt.figure(figsize=(8, 8 / 1.618))
+719    plt.errorbar(my_x, my_y, fmt='o')
+720    fit_start = my_x[0]
+721    fit_stop = my_x[-1]
+722    samples = np.arange(fit_start, fit_stop, 0.01)
+723    plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
+724    plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
+725
+726    plt.xlabel('Theoretical quantiles')
+727    plt.ylabel('Ordered Values')
+728    plt.legend()
+729    plt.draw()
+730
+731
+732def residual_plot(x, y, func, fit_res):
+733    """Generates a plot which compares the fit to the data and displays the corresponding residuals
+734
+735    Returns
+736    -------
+737    None
+738    """
+739    sorted_x = sorted(x)
+740    xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
+741    xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
+742    x_samples = np.arange(xstart, xstop + 0.01, 0.01)
+743
+744    plt.figure(figsize=(8, 8 / 1.618))
+745    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+746    ax0 = plt.subplot(gs[0])
+747    ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
+748    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
+749    ax0.set_xticklabels([])
+750    ax0.set_xlim([xstart, xstop])
+751    ax0.set_xticklabels([])
+752    ax0.legend()
+753
+754    residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
+755    ax1 = plt.subplot(gs[1])
+756    ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+757    ax1.tick_params(direction='out')
+758    ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+759    ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
+760    ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
+761    ax1.set_xlim([xstart, xstop])
+762    ax1.set_ylabel('Residuals')
+763    plt.subplots_adjust(wspace=None, hspace=None)
+764    plt.draw()
+765
+766
+767def error_band(x, func, beta):
+768    """Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
 769
-770    if objects is None:
-771        obs_list = []
-772        for obj in gc.get_objects():
-773            if isinstance(obj, Fit_result):
-774                obs_list.append(obj)
-775    else:
-776        obs_list = objects
-777
-778    p_values = [o.p_value for o in obs_list]
-779
-780    bins = len(p_values)
-781    x = np.arange(0, 1.001, 0.001)
-782    plt.plot(x, x, 'k', zorder=1)
-783    plt.xlim(0, 1)
-784    plt.ylim(0, 1)
-785    plt.xlabel('p-value')
-786    plt.ylabel('Cumulative probability')
-787    plt.title(str(bins) + ' p-values')
-788
-789    n = np.arange(1, bins + 1) / np.float64(bins)
-790    Xs = np.sort(p_values)
-791    plt.step(Xs, n)
-792    diffs = n - Xs
-793    loc_max_diff = np.argmax(np.abs(diffs))
-794    loc = Xs[loc_max_diff]
-795    plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
-796    plt.draw()
-797
-798    print(scipy.stats.kstest(p_values, 'uniform'))
+770    Returns
+771    -------
+772    err : np.array(Obs)
+773        Error band for an array of sample values x
+774    """
+775    cov = covariance(beta)
+776    if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
+777        warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
+778
+779    deriv = []
+780    for i, item in enumerate(x):
+781        deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
+782
+783    err = []
+784    for i, item in enumerate(x):
+785        err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
+786    err = np.array(err)
+787
+788    return err
+789
+790
+791def ks_test(objects=None):
+792    """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
+793
+794    Parameters
+795    ----------
+796    objects : list
+797        List of fit results to include in the analysis (optional).
+798
+799    Returns
+800    -------
+801    None
+802    """
+803
+804    if objects is None:
+805        obs_list = []
+806        for obj in gc.get_objects():
+807            if isinstance(obj, Fit_result):
+808                obs_list.append(obj)
+809    else:
+810        obs_list = objects
+811
+812    p_values = [o.p_value for o in obs_list]
+813
+814    bins = len(p_values)
+815    x = np.arange(0, 1.001, 0.001)
+816    plt.plot(x, x, 'k', zorder=1)
+817    plt.xlim(0, 1)
+818    plt.ylim(0, 1)
+819    plt.xlabel('p-value')
+820    plt.ylabel('Cumulative probability')
+821    plt.title(str(bins) + ' p-values')
+822
+823    n = np.arange(1, bins + 1) / np.float64(bins)
+824    Xs = np.sort(p_values)
+825    plt.step(Xs, n)
+826    diffs = n - Xs
+827    loc_max_diff = np.argmax(np.abs(diffs))
+828    loc = Xs[loc_max_diff]
+829    plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
+830    plt.draw()
+831
+832    print(scipy.stats.kstest(p_values, 'uniform'))
@@ -1137,11 +1171,16 @@ Hotelling t-squared p-value for correlated fits. 130 If True, a quantile-quantile plot of the fit result is generated (default False). 131 num_grad : bool 132 Use numerical differentation instead of automatic differentiation to perform the error propagation (default False). -133 ''' -134 if priors is not None: -135 return _prior_fit(x, y, func, priors, silent=silent, **kwargs) -136 else: -137 return _standard_fit(x, y, func, silent=silent, **kwargs) +133 +134 Returns +135 ------- +136 output : Fit_result +137 Parameters and information on the fitted result. +138 ''' +139 if priors is not None: +140 return _prior_fit(x, y, func, priors, silent=silent, **kwargs) +141 else: +142 return _standard_fit(x, y, func, silent=silent, **kwargs) @@ -1207,6 +1246,13 @@ If True, a quantile-quantile plot of the fit result is generated (default False)
  • num_grad (bool): Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
    • + +
    Returns
    + + @@ -1222,203 +1268,208 @@ Use numerical differentation instead of automatic differentiation to perform the - 
    140def total_least_squares(x, y, func, silent=False, **kwargs):
-141    r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
-142
-143    Parameters
-144    ----------
-145    x : list
-146        list of Obs, or a tuple of lists of Obs
-147    y : list
-148        list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
-149    func : object
-150        func has to be of the form
-151
-152        ```python
-153        import autograd.numpy as anp
-154
-155        def func(a, x):
-156            return a[0] + a[1] * x + a[2] * anp.sinh(x)
-157        ```
-158
-159        For multiple x values func can be of the form
-160
-161        ```python
-162        def func(a, x):
-163            (x1, x2) = x
-164            return a[0] * x1 ** 2 + a[1] * x2
-165        ```
-166
-167        It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
-168        will not work.
-169    silent : bool, optional
-170        If true all output to the console is omitted (default False).
-171    initial_guess : list
-172        can provide an initial guess for the input parameters. Relevant for non-linear
-173        fits with many parameters.
-174    expected_chisquare : bool
-175        If true prints the expected chisquare which is
-176        corrected by effects caused by correlated input data.
-177        This can take a while as the full correlation matrix
-178        has to be calculated (default False).
-179    num_grad : bool
-180        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
-181
-182    Notes
-183    -----
-184    Based on the orthogonal distance regression module of scipy
-185    '''
+            
    145def total_least_squares(x, y, func, silent=False, **kwargs):
+146    r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
+147
+148    Parameters
+149    ----------
+150    x : list
+151        list of Obs, or a tuple of lists of Obs
+152    y : list
+153        list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
+154    func : object
+155        func has to be of the form
+156
+157        ```python
+158        import autograd.numpy as anp
+159
+160        def func(a, x):
+161            return a[0] + a[1] * x + a[2] * anp.sinh(x)
+162        ```
+163
+164        For multiple x values func can be of the form
+165
+166        ```python
+167        def func(a, x):
+168            (x1, x2) = x
+169            return a[0] * x1 ** 2 + a[1] * x2
+170        ```
+171
+172        It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
+173        will not work.
+174    silent : bool, optional
+175        If true all output to the console is omitted (default False).
+176    initial_guess : list
+177        can provide an initial guess for the input parameters. Relevant for non-linear
+178        fits with many parameters.
+179    expected_chisquare : bool
+180        If true prints the expected chisquare which is
+181        corrected by effects caused by correlated input data.
+182        This can take a while as the full correlation matrix
+183        has to be calculated (default False).
+184    num_grad : bool
+185        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
 186
-187    output = Fit_result()
-188
-189    output.fit_function = func
+187    Notes
+188    -----
+189    Based on the orthogonal distance regression module of scipy.
 190
-191    x = np.array(x)
-192
-193    x_shape = x.shape
-194
-195    if kwargs.get('num_grad') is True:
-196        jacobian = num_jacobian
-197        hessian = num_hessian
-198    else:
-199        jacobian = auto_jacobian
-200        hessian = auto_hessian
-201
-202    if not callable(func):
-203        raise TypeError('func has to be a function.')
+191    Returns
+192    -------
+193    output : Fit_result
+194        Parameters and information on the fitted result.
+195    '''
+196
+197    output = Fit_result()
+198
+199    output.fit_function = func
+200
+201    x = np.array(x)
+202
+203    x_shape = x.shape
 204
-205    for i in range(42):
-206        try:
-207            func(np.arange(i), x.T[0])
-208        except TypeError:
-209            continue
-210        except IndexError:
-211            continue
-212        else:
-213            break
-214    else:
-215        raise RuntimeError("Fit function is not valid.")
-216
-217    n_parms = i
-218    if not silent:
-219        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
-220
-221    x_f = np.vectorize(lambda o: o.value)(x)
-222    dx_f = np.vectorize(lambda o: o.dvalue)(x)
-223    y_f = np.array([o.value for o in y])
-224    dy_f = np.array([o.dvalue for o in y])
-225
-226    if np.any(np.asarray(dx_f) <= 0.0):
-227        raise Exception('No x errors available, run the gamma method first.')
-228
-229    if np.any(np.asarray(dy_f) <= 0.0):
-230        raise Exception('No y errors available, run the gamma method first.')
-231
-232    if 'initial_guess' in kwargs:
-233        x0 = kwargs.get('initial_guess')
-234        if len(x0) != n_parms:
-235            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
-236    else:
-237        x0 = [1] * n_parms
+205    if kwargs.get('num_grad') is True:
+206        jacobian = num_jacobian
+207        hessian = num_hessian
+208    else:
+209        jacobian = auto_jacobian
+210        hessian = auto_hessian
+211
+212    if not callable(func):
+213        raise TypeError('func has to be a function.')
+214
+215    for i in range(42):
+216        try:
+217            func(np.arange(i), x.T[0])
+218        except TypeError:
+219            continue
+220        except IndexError:
+221            continue
+222        else:
+223            break
+224    else:
+225        raise RuntimeError("Fit function is not valid.")
+226
+227    n_parms = i
+228    if not silent:
+229        print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
+230
+231    x_f = np.vectorize(lambda o: o.value)(x)
+232    dx_f = np.vectorize(lambda o: o.dvalue)(x)
+233    y_f = np.array([o.value for o in y])
+234    dy_f = np.array([o.dvalue for o in y])
+235
+236    if np.any(np.asarray(dx_f) <= 0.0):
+237        raise Exception('No x errors available, run the gamma method first.')
 238
-239    data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
-240    model = Model(func)
-241    odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
-242    odr.set_job(fit_type=0, deriv=1)
-243    out = odr.run()
-244
-245    output.residual_variance = out.res_var
-246
-247    output.method = 'ODR'
+239    if np.any(np.asarray(dy_f) <= 0.0):
+240        raise Exception('No y errors available, run the gamma method first.')
+241
+242    if 'initial_guess' in kwargs:
+243        x0 = kwargs.get('initial_guess')
+244        if len(x0) != n_parms:
+245            raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
+246    else:
+247        x0 = [1] * n_parms
 248
-249    output.message = out.stopreason
-250
-251    output.xplus = out.xplus
-252
-253    if not silent:
-254        print('Method: ODR')
-255        print(*out.stopreason)
-256        print('Residual variance:', output.residual_variance)
-257
-258    if out.info > 3:
-259        raise Exception('The minimization procedure did not converge.')
+249    data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
+250    model = Model(func)
+251    odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
+252    odr.set_job(fit_type=0, deriv=1)
+253    out = odr.run()
+254
+255    output.residual_variance = out.res_var
+256
+257    output.method = 'ODR'
+258
+259    output.message = out.stopreason
 260
-261    m = x_f.size
+261    output.xplus = out.xplus
 262
-263    def odr_chisquare(p):
-264        model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
-265        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
-266        return chisq
+263    if not silent:
+264        print('Method: ODR')
+265        print(*out.stopreason)
+266        print('Residual variance:', output.residual_variance)
 267
-268    if kwargs.get('expected_chisquare') is True:
-269        W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
+268    if out.info > 3:
+269        raise Exception('The minimization procedure did not converge.')
 270
-271        if kwargs.get('covariance') is not None:
-272            cov = kwargs.get('covariance')
-273        else:
-274            cov = covariance(np.concatenate((y, x.ravel())))
-275
-276        number_of_x_parameters = int(m / x_f.shape[-1])
+271    m = x_f.size
+272
+273    def odr_chisquare(p):
+274        model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
+275        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
+276        return chisq
 277
-278        old_jac = jacobian(func)(out.beta, out.xplus)
-279        fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
-280        fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
-281        new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
-282
-283        A = W @ new_jac
-284        P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
-285        expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
-286        if expected_chisquare <= 0.0:
-287            warnings.warn("Negative expected_chisquare.", RuntimeWarning)
-288            expected_chisquare = np.abs(expected_chisquare)
-289        output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
-290        if not silent:
-291            print('chisquare/expected_chisquare:',
-292                  output.chisquare_by_expected_chisquare)
-293
-294    fitp = out.beta
-295    try:
-296        hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
-297    except TypeError:
-298        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
-299
-300    def odr_chisquare_compact_x(d):
-301        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
-302        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
-303        return chisq
-304
-305    jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
-306
-307    # Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
-308    try:
-309        deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
-310    except np.linalg.LinAlgError:
-311        raise Exception("Cannot invert hessian matrix.")
-312
-313    def odr_chisquare_compact_y(d):
-314        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
-315        chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
-316        return chisq
-317
-318    jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
-319
-320    # Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
-321    try:
-322        deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
-323    except np.linalg.LinAlgError:
-324        raise Exception("Cannot invert hessian matrix.")
-325
-326    result = []
-327    for i in range(n_parms):
-328        result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
+278    if kwargs.get('expected_chisquare') is True:
+279        W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
+280
+281        if kwargs.get('covariance') is not None:
+282            cov = kwargs.get('covariance')
+283        else:
+284            cov = covariance(np.concatenate((y, x.ravel())))
+285
+286        number_of_x_parameters = int(m / x_f.shape[-1])
+287
+288        old_jac = jacobian(func)(out.beta, out.xplus)
+289        fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
+290        fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
+291        new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
+292
+293        A = W @ new_jac
+294        P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
+295        expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
+296        if expected_chisquare <= 0.0:
+297            warnings.warn("Negative expected_chisquare.", RuntimeWarning)
+298            expected_chisquare = np.abs(expected_chisquare)
+299        output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
+300        if not silent:
+301            print('chisquare/expected_chisquare:',
+302                  output.chisquare_by_expected_chisquare)
+303
+304    fitp = out.beta
+305    try:
+306        hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
+307    except TypeError:
+308        raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+309
+310    def odr_chisquare_compact_x(d):
+311        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
+312        chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
+313        return chisq
+314
+315    jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
+316
+317    # Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
+318    try:
+319        deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
+320    except np.linalg.LinAlgError:
+321        raise Exception("Cannot invert hessian matrix.")
+322
+323    def odr_chisquare_compact_y(d):
+324        model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
+325        chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
+326        return chisq
+327
+328    jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
 329
-330    output.fit_parameters = result
-331
-332    output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
-333    output.dof = x.shape[-1] - n_parms
-334    output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
+330    # Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
+331    try:
+332        deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
+333    except np.linalg.LinAlgError:
+334        raise Exception("Cannot invert hessian matrix.")
 335
-336    return output
+336    result = []
+337    for i in range(n_parms):
+338        result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
+339
+340    output.fit_parameters = result
+341
+342    output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
+343    output.dof = x.shape[-1] - n_parms
+344    output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
+345
+346    return output
 
    
 
 
@@ -1469,7 +1520,14 @@ Use numerical differentation instead of automatic differentiation to perform the
 
 
    Notes
    
 
-
    Based on the orthogonal distance regression module of scipy
    
+
    Based on the orthogonal distance regression module of scipy.
    
+
+
    Returns
    
+
+
      
+
    • output (Fit_result):
+Parameters and information on the fitted result.
      • 
+
    @@ -1485,30 +1543,35 @@ Use numerical differentation instead of automatic differentiation to perform the - 
    662def fit_lin(x, y, **kwargs):
-663    """Performs a linear fit to y = n + m * x and returns two Obs n, m.
-664
-665    Parameters
-666    ----------
-667    x : list
-668        Can either be a list of floats in which case no xerror is assumed, or
-669        a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
-670    y : list
-671        List of Obs, the dvalues of the Obs are used as yerror for the fit.
-672    """
-673
-674    def f(a, x):
-675        y = a[0] + a[1] * x
-676        return y
-677
-678    if all(isinstance(n, Obs) for n in x):
-679        out = total_least_squares(x, y, f, **kwargs)
-680        return out.fit_parameters
-681    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-682        out = least_squares(x, y, f, **kwargs)
-683        return out.fit_parameters
-684    else:
-685        raise Exception('Unsupported types for x')
+            
    672def fit_lin(x, y, **kwargs):
+673    """Performs a linear fit to y = n + m * x and returns two Obs n, m.
+674
+675    Parameters
+676    ----------
+677    x : list
+678        Can either be a list of floats in which case no xerror is assumed, or
+679        a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
+680    y : list
+681        List of Obs, the dvalues of the Obs are used as yerror for the fit.
+682
+683    Returns
+684    -------
+685    fit_parameters : list[Obs]
+686        LIist of fitted observables.
+687    """
+688
+689    def f(a, x):
+690        y = a[0] + a[1] * x
+691        return y
+692
+693    if all(isinstance(n, Obs) for n in x):
+694        out = total_least_squares(x, y, f, **kwargs)
+695        return out.fit_parameters
+696    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
+697        out = least_squares(x, y, f, **kwargs)
+698        return out.fit_parameters
+699    else:
+700        raise Exception('Unsupported types for x')
 
    
 
 
@@ -1523,6 +1586,13 @@ a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
 
  • y (list):
 List of Obs, the dvalues of the Obs are used as yerror for the fit.
    • 
 
+
+
    Returns
    
+
+
      
+
    • fit_parameters (list[Obs]):
+LIist of fitted observables.
      • 
+
    @@ -1538,35 +1608,45 @@ List of Obs, the dvalues of the Obs are used as yerror for the fit. - 
    688def qqplot(x, o_y, func, p):
-689    """Generates a quantile-quantile plot of the fit result which can be used to
-690       check if the residuals of the fit are gaussian distributed.
-691    """
-692
-693    residuals = []
-694    for i_x, i_y in zip(x, o_y):
-695        residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
-696    residuals = sorted(residuals)
-697    my_y = [o.value for o in residuals]
-698    probplot = scipy.stats.probplot(my_y)
-699    my_x = probplot[0][0]
-700    plt.figure(figsize=(8, 8 / 1.618))
-701    plt.errorbar(my_x, my_y, fmt='o')
-702    fit_start = my_x[0]
-703    fit_stop = my_x[-1]
-704    samples = np.arange(fit_start, fit_stop, 0.01)
-705    plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
-706    plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
-707
-708    plt.xlabel('Theoretical quantiles')
-709    plt.ylabel('Ordered Values')
-710    plt.legend()
-711    plt.draw()
+            
    703def qqplot(x, o_y, func, p):
+704    """Generates a quantile-quantile plot of the fit result which can be used to
+705       check if the residuals of the fit are gaussian distributed.
+706
+707    Returns
+708    -------
+709    None
+710    """
+711
+712    residuals = []
+713    for i_x, i_y in zip(x, o_y):
+714        residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
+715    residuals = sorted(residuals)
+716    my_y = [o.value for o in residuals]
+717    probplot = scipy.stats.probplot(my_y)
+718    my_x = probplot[0][0]
+719    plt.figure(figsize=(8, 8 / 1.618))
+720    plt.errorbar(my_x, my_y, fmt='o')
+721    fit_start = my_x[0]
+722    fit_stop = my_x[-1]
+723    samples = np.arange(fit_start, fit_stop, 0.01)
+724    plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
+725    plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
+726
+727    plt.xlabel('Theoretical quantiles')
+728    plt.ylabel('Ordered Values')
+729    plt.legend()
+730    plt.draw()
 
    
 
 
             
    Generates a quantile-quantile plot of the fit result which can be used to
-check if the residuals of the fit are gaussian distributed.
    
+   check if the residuals of the fit are gaussian distributed.
    
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    
 
    
 
 
@@ -1582,38 +1662,49 @@ check if the residuals of the fit are gaussian distributed.
    - 
    714def residual_plot(x, y, func, fit_res):
-715    """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
-716    sorted_x = sorted(x)
-717    xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
-718    xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
-719    x_samples = np.arange(xstart, xstop + 0.01, 0.01)
-720
-721    plt.figure(figsize=(8, 8 / 1.618))
-722    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-723    ax0 = plt.subplot(gs[0])
-724    ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
-725    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
-726    ax0.set_xticklabels([])
-727    ax0.set_xlim([xstart, xstop])
-728    ax0.set_xticklabels([])
-729    ax0.legend()
-730
-731    residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
-732    ax1 = plt.subplot(gs[1])
-733    ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-734    ax1.tick_params(direction='out')
-735    ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-736    ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
-737    ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
-738    ax1.set_xlim([xstart, xstop])
-739    ax1.set_ylabel('Residuals')
-740    plt.subplots_adjust(wspace=None, hspace=None)
-741    plt.draw()
+            
    733def residual_plot(x, y, func, fit_res):
+734    """Generates a plot which compares the fit to the data and displays the corresponding residuals
+735
+736    Returns
+737    -------
+738    None
+739    """
+740    sorted_x = sorted(x)
+741    xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
+742    xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
+743    x_samples = np.arange(xstart, xstop + 0.01, 0.01)
+744
+745    plt.figure(figsize=(8, 8 / 1.618))
+746    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+747    ax0 = plt.subplot(gs[0])
+748    ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
+749    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
+750    ax0.set_xticklabels([])
+751    ax0.set_xlim([xstart, xstop])
+752    ax0.set_xticklabels([])
+753    ax0.legend()
+754
+755    residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
+756    ax1 = plt.subplot(gs[1])
+757    ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+758    ax1.tick_params(direction='out')
+759    ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+760    ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
+761    ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
+762    ax1.set_xlim([xstart, xstop])
+763    ax1.set_ylabel('Residuals')
+764    plt.subplots_adjust(wspace=None, hspace=None)
+765    plt.draw()
 
    
 
 
             
    Generates a plot which compares the fit to the data and displays the corresponding residuals
    
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    
 
    
 
 
@@ -1629,26 +1720,39 @@ check if the residuals of the fit are gaussian distributed.
    - 
    744def error_band(x, func, beta):
-745    """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
-746    cov = covariance(beta)
-747    if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
-748        warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
-749
-750    deriv = []
-751    for i, item in enumerate(x):
-752        deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
-753
-754    err = []
-755    for i, item in enumerate(x):
-756        err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
-757    err = np.array(err)
-758
-759    return err
+            
    768def error_band(x, func, beta):
+769    """Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
+770
+771    Returns
+772    -------
+773    err : np.array(Obs)
+774        Error band for an array of sample values x
+775    """
+776    cov = covariance(beta)
+777    if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
+778        warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
+779
+780    deriv = []
+781    for i, item in enumerate(x):
+782        deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
+783
+784    err = []
+785    for i, item in enumerate(x):
+786        err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
+787    err = np.array(err)
+788
+789    return err
 
    
 
 
-            
    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.
    
+            
    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
    
+
+
    Returns
    
+
+
      
+
    • err (np.array(Obs)):
+Error band for an array of sample values x
      • 
+
    
 
    
 
 
@@ -1664,44 +1768,48 @@ check if the residuals of the fit are gaussian distributed.
    
 
     
    
     
-            
    762def ks_test(objects=None):
-763    """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
-764
-765    Parameters
-766    ----------
-767    objects : list
-768        List of fit results to include in the analysis (optional).
-769    """
-770
-771    if objects is None:
-772        obs_list = []
-773        for obj in gc.get_objects():
-774            if isinstance(obj, Fit_result):
-775                obs_list.append(obj)
-776    else:
-777        obs_list = objects
-778
-779    p_values = [o.p_value for o in obs_list]
-780
-781    bins = len(p_values)
-782    x = np.arange(0, 1.001, 0.001)
-783    plt.plot(x, x, 'k', zorder=1)
-784    plt.xlim(0, 1)
-785    plt.ylim(0, 1)
-786    plt.xlabel('p-value')
-787    plt.ylabel('Cumulative probability')
-788    plt.title(str(bins) + ' p-values')
-789
-790    n = np.arange(1, bins + 1) / np.float64(bins)
-791    Xs = np.sort(p_values)
-792    plt.step(Xs, n)
-793    diffs = n - Xs
-794    loc_max_diff = np.argmax(np.abs(diffs))
-795    loc = Xs[loc_max_diff]
-796    plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
-797    plt.draw()
-798
-799    print(scipy.stats.kstest(p_values, 'uniform'))
+            
    792def ks_test(objects=None):
+793    """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
+794
+795    Parameters
+796    ----------
+797    objects : list
+798        List of fit results to include in the analysis (optional).
+799
+800    Returns
+801    -------
+802    None
+803    """
+804
+805    if objects is None:
+806        obs_list = []
+807        for obj in gc.get_objects():
+808            if isinstance(obj, Fit_result):
+809                obs_list.append(obj)
+810    else:
+811        obs_list = objects
+812
+813    p_values = [o.p_value for o in obs_list]
+814
+815    bins = len(p_values)
+816    x = np.arange(0, 1.001, 0.001)
+817    plt.plot(x, x, 'k', zorder=1)
+818    plt.xlim(0, 1)
+819    plt.ylim(0, 1)
+820    plt.xlabel('p-value')
+821    plt.ylabel('Cumulative probability')
+822    plt.title(str(bins) + ' p-values')
+823
+824    n = np.arange(1, bins + 1) / np.float64(bins)
+825    Xs = np.sort(p_values)
+826    plt.step(Xs, n)
+827    diffs = n - Xs
+828    loc_max_diff = np.argmax(np.abs(diffs))
+829    loc = Xs[loc_max_diff]
+830    plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
+831    plt.draw()
+832
+833    print(scipy.stats.kstest(p_values, 'uniform'))
 
    
 
 
@@ -1713,6 +1821,12 @@ check if the residuals of the fit are gaussian distributed.
    
 
  • objects (list):
 List of fit results to include in the analysis (optional).
    • 
 
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    
 
    
 
 
diff --git a/docs/pyerrors/input/bdio.html b/docs/pyerrors/input/bdio.html
index ebce901d..4de802e0 100644
--- a/docs/pyerrors/input/bdio.html
+++ b/docs/pyerrors/input/bdio.html
@@ -105,665 +105,680 @@
      18    ----------
  19    file_path -- path to the bdio file
  20    bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
- 21    """
- 22    bdio = ctypes.cdll.LoadLibrary(bdio_path)
- 23
- 24    bdio_open = bdio.bdio_open
- 25    bdio_open.restype = ctypes.c_void_p
- 26
- 27    bdio_close = bdio.bdio_close
- 28    bdio_close.restype = ctypes.c_int
- 29    bdio_close.argtypes = [ctypes.c_void_p]
- 30
- 31    bdio_seek_record = bdio.bdio_seek_record
- 32    bdio_seek_record.restype = ctypes.c_int
- 33    bdio_seek_record.argtypes = [ctypes.c_void_p]
- 34
- 35    bdio_get_rlen = bdio.bdio_get_rlen
- 36    bdio_get_rlen.restype = ctypes.c_int
- 37    bdio_get_rlen.argtypes = [ctypes.c_void_p]
- 38
- 39    bdio_get_ruinfo = bdio.bdio_get_ruinfo
- 40    bdio_get_ruinfo.restype = ctypes.c_int
- 41    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
- 42
- 43    bdio_read = bdio.bdio_read
- 44    bdio_read.restype = ctypes.c_size_t
- 45    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
- 46
- 47    bdio_read_f64 = bdio.bdio_read_f64
- 48    bdio_read_f64.restype = ctypes.c_size_t
- 49    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
- 50
- 51    bdio_read_int32 = bdio.bdio_read_int32
- 52    bdio_read_int32.restype = ctypes.c_size_t
- 53    bdio_read_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
- 54
- 55    b_path = file_path.encode('utf-8')
- 56    read = 'r'
- 57    b_read = read.encode('utf-8')
- 58
- 59    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), None)
- 60
- 61    return_list = []
- 62
- 63    print('Reading of bdio file started')
- 64    while True:
- 65        bdio_seek_record(fbdio)
- 66        ruinfo = bdio_get_ruinfo(fbdio)
+ 21
+ 22    Returns
+ 23    -------
+ 24    data : List[Obs]
+ 25        Extracted data
+ 26    """
+ 27    bdio = ctypes.cdll.LoadLibrary(bdio_path)
+ 28
+ 29    bdio_open = bdio.bdio_open
+ 30    bdio_open.restype = ctypes.c_void_p
+ 31
+ 32    bdio_close = bdio.bdio_close
+ 33    bdio_close.restype = ctypes.c_int
+ 34    bdio_close.argtypes = [ctypes.c_void_p]
+ 35
+ 36    bdio_seek_record = bdio.bdio_seek_record
+ 37    bdio_seek_record.restype = ctypes.c_int
+ 38    bdio_seek_record.argtypes = [ctypes.c_void_p]
+ 39
+ 40    bdio_get_rlen = bdio.bdio_get_rlen
+ 41    bdio_get_rlen.restype = ctypes.c_int
+ 42    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+ 43
+ 44    bdio_get_ruinfo = bdio.bdio_get_ruinfo
+ 45    bdio_get_ruinfo.restype = ctypes.c_int
+ 46    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
+ 47
+ 48    bdio_read = bdio.bdio_read
+ 49    bdio_read.restype = ctypes.c_size_t
+ 50    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
+ 51
+ 52    bdio_read_f64 = bdio.bdio_read_f64
+ 53    bdio_read_f64.restype = ctypes.c_size_t
+ 54    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+ 55
+ 56    bdio_read_int32 = bdio.bdio_read_int32
+ 57    bdio_read_int32.restype = ctypes.c_size_t
+ 58    bdio_read_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+ 59
+ 60    b_path = file_path.encode('utf-8')
+ 61    read = 'r'
+ 62    b_read = read.encode('utf-8')
+ 63
+ 64    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), None)
+ 65
+ 66    return_list = []
  67
- 68        if ruinfo == 7:
- 69            print('MD5sum found')  # For now we just ignore these entries and do not perform any checks on them
- 70            continue
- 71
- 72        if ruinfo < 0:
- 73            # EOF reached
- 74            break
- 75        bdio_get_rlen(fbdio)
+ 68    print('Reading of bdio file started')
+ 69    while True:
+ 70        bdio_seek_record(fbdio)
+ 71        ruinfo = bdio_get_ruinfo(fbdio)
+ 72
+ 73        if ruinfo == 7:
+ 74            print('MD5sum found')  # For now we just ignore these entries and do not perform any checks on them
+ 75            continue
  76
- 77        def read_c_double():
- 78            d_buf = ctypes.c_double
- 79            pd_buf = d_buf()
- 80            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
- 81            bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
- 82            return pd_buf.value
- 83
- 84        mean = read_c_double()
- 85        print('mean', mean)
- 86
- 87        def read_c_size_t():
- 88            d_buf = ctypes.c_size_t
- 89            pd_buf = d_buf()
- 90            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
- 91            bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
- 92            return pd_buf.value
- 93
- 94        neid = read_c_size_t()
- 95        print('neid', neid)
- 96
- 97        ndata = []
- 98        for index in range(neid):
- 99            ndata.append(read_c_size_t())
-100        print('ndata', ndata)
+ 77        if ruinfo < 0:
+ 78            # EOF reached
+ 79            break
+ 80        bdio_get_rlen(fbdio)
+ 81
+ 82        def read_c_double():
+ 83            d_buf = ctypes.c_double
+ 84            pd_buf = d_buf()
+ 85            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+ 86            bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
+ 87            return pd_buf.value
+ 88
+ 89        mean = read_c_double()
+ 90        print('mean', mean)
+ 91
+ 92        def read_c_size_t():
+ 93            d_buf = ctypes.c_size_t
+ 94            pd_buf = d_buf()
+ 95            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+ 96            bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+ 97            return pd_buf.value
+ 98
+ 99        neid = read_c_size_t()
+100        print('neid', neid)
 101
-102        nrep = []
+102        ndata = []
 103        for index in range(neid):
-104            nrep.append(read_c_size_t())
-105        print('nrep', nrep)
+104            ndata.append(read_c_size_t())
+105        print('ndata', ndata)
 106
-107        vrep = []
+107        nrep = []
 108        for index in range(neid):
-109            vrep.append([])
-110            for jndex in range(nrep[index]):
-111                vrep[-1].append(read_c_size_t())
-112        print('vrep', vrep)
-113
-114        ids = []
-115        for index in range(neid):
-116            ids.append(read_c_size_t())
-117        print('ids', ids)
+109            nrep.append(read_c_size_t())
+110        print('nrep', nrep)
+111
+112        vrep = []
+113        for index in range(neid):
+114            vrep.append([])
+115            for jndex in range(nrep[index]):
+116                vrep[-1].append(read_c_size_t())
+117        print('vrep', vrep)
 118
-119        nt = []
+119        ids = []
 120        for index in range(neid):
-121            nt.append(read_c_size_t())
-122        print('nt', nt)
+121            ids.append(read_c_size_t())
+122        print('ids', ids)
 123
-124        zero = []
+124        nt = []
 125        for index in range(neid):
-126            zero.append(read_c_double())
-127        print('zero', zero)
+126            nt.append(read_c_size_t())
+127        print('nt', nt)
 128
-129        four = []
+129        zero = []
 130        for index in range(neid):
-131            four.append(read_c_double())
-132        print('four', four)
+131            zero.append(read_c_double())
+132        print('zero', zero)
 133
-134        d_buf = ctypes.c_double * np.sum(ndata)
-135        pd_buf = d_buf()
-136        ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-137        bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio))
-138        delta = pd_buf[:]
-139
-140        samples = np.split(np.asarray(delta) + mean, np.cumsum([a for su in vrep for a in su])[:-1])
-141        no_reps = [len(o) for o in vrep]
-142        assert len(ids) == len(no_reps)
-143        tmp_names = []
-144        ens_length = max([len(str(o)) for o in ids])
-145        for loc_id, reps in zip(ids, no_reps):
-146            for index in range(reps):
-147                missing_chars = ens_length - len(str(loc_id))
-148                tmp_names.append(str(loc_id) + ' ' * missing_chars + '|r' + '{0:03d}'.format(index))
-149
-150        return_list.append(Obs(samples, tmp_names))
-151
-152    bdio_close(fbdio)
-153    print()
-154    print(len(return_list), 'observable(s) extracted.')
-155    return return_list
+134        four = []
+135        for index in range(neid):
+136            four.append(read_c_double())
+137        print('four', four)
+138
+139        d_buf = ctypes.c_double * np.sum(ndata)
+140        pd_buf = d_buf()
+141        ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+142        bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio))
+143        delta = pd_buf[:]
+144
+145        samples = np.split(np.asarray(delta) + mean, np.cumsum([a for su in vrep for a in su])[:-1])
+146        no_reps = [len(o) for o in vrep]
+147        assert len(ids) == len(no_reps)
+148        tmp_names = []
+149        ens_length = max([len(str(o)) for o in ids])
+150        for loc_id, reps in zip(ids, no_reps):
+151            for index in range(reps):
+152                missing_chars = ens_length - len(str(loc_id))
+153                tmp_names.append(str(loc_id) + ' ' * missing_chars + '|r' + '{0:03d}'.format(index))
+154
+155        return_list.append(Obs(samples, tmp_names))
 156
-157
-158def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
-159    """ Write Obs to a bdio file according to ADerrors conventions
-160
-161    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
-162    adding the flag -fPIC to CC and changing the all target to
-163
-164    all:		bdio.o $(LIBDIR)
-165                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-166                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
-167
-168    Parameters
-169    ----------
-170    file_path -- path to the bdio file
-171    bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
-172    """
-173
-174    for obs in obs_list:
-175        if not hasattr(obs, 'e_names'):
-176            raise Exception('Run the gamma method first for all obs.')
+157    bdio_close(fbdio)
+158    print()
+159    print(len(return_list), 'observable(s) extracted.')
+160    return return_list
+161
+162
+163def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
+164    """ Write Obs to a bdio file according to ADerrors conventions
+165
+166    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
+167    adding the flag -fPIC to CC and changing the all target to
+168
+169    all:		bdio.o $(LIBDIR)
+170                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+171                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+172
+173    Parameters
+174    ----------
+175    file_path -- path to the bdio file
+176    bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
 177
-178    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-179
-180    bdio_open = bdio.bdio_open
-181    bdio_open.restype = ctypes.c_void_p
-182
-183    bdio_close = bdio.bdio_close
-184    bdio_close.restype = ctypes.c_int
-185    bdio_close.argtypes = [ctypes.c_void_p]
-186
-187    bdio_start_record = bdio.bdio_start_record
-188    bdio_start_record.restype = ctypes.c_int
-189    bdio_start_record.argtypes = [ctypes.c_size_t, ctypes.c_size_t, ctypes.c_void_p]
-190
-191    bdio_flush_record = bdio.bdio_flush_record
-192    bdio_flush_record.restype = ctypes.c_int
-193    bdio_flush_record.argytpes = [ctypes.c_void_p]
-194
-195    bdio_write_f64 = bdio.bdio_write_f64
-196    bdio_write_f64.restype = ctypes.c_size_t
-197    bdio_write_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-198
-199    bdio_write_int32 = bdio.bdio_write_int32
-200    bdio_write_int32.restype = ctypes.c_size_t
-201    bdio_write_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-202
-203    b_path = file_path.encode('utf-8')
-204    write = 'w'
-205    b_write = write.encode('utf-8')
-206    form = 'pyerrors ADerror export'
-207    b_form = form.encode('utf-8')
+178    Returns
+179    -------
+180    success : int
+181        returns 0 is successful
+182    """
+183
+184    for obs in obs_list:
+185        if not hasattr(obs, 'e_names'):
+186            raise Exception('Run the gamma method first for all obs.')
+187
+188    bdio = ctypes.cdll.LoadLibrary(bdio_path)
+189
+190    bdio_open = bdio.bdio_open
+191    bdio_open.restype = ctypes.c_void_p
+192
+193    bdio_close = bdio.bdio_close
+194    bdio_close.restype = ctypes.c_int
+195    bdio_close.argtypes = [ctypes.c_void_p]
+196
+197    bdio_start_record = bdio.bdio_start_record
+198    bdio_start_record.restype = ctypes.c_int
+199    bdio_start_record.argtypes = [ctypes.c_size_t, ctypes.c_size_t, ctypes.c_void_p]
+200
+201    bdio_flush_record = bdio.bdio_flush_record
+202    bdio_flush_record.restype = ctypes.c_int
+203    bdio_flush_record.argytpes = [ctypes.c_void_p]
+204
+205    bdio_write_f64 = bdio.bdio_write_f64
+206    bdio_write_f64.restype = ctypes.c_size_t
+207    bdio_write_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
 208
-209    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_write), b_form)
-210
-211    for obs in obs_list:
-212        # mean = obs.value
-213        neid = len(obs.e_names)
-214        vrep = [[obs.shape[o] for o in sl] for sl in list(obs.e_content.values())]
-215        vrep_write = [item for sublist in vrep for item in sublist]
-216        ndata = [np.sum(o) for o in vrep]
-217        nrep = [len(o) for o in vrep]
-218        print('ndata', ndata)
-219        print('nrep', nrep)
-220        print('vrep', vrep)
-221        keys = list(obs.e_content.keys())
-222        ids = []
-223        for key in keys:
-224            try:  # Try to convert key to integer
-225                ids.append(int(key))
-226            except Exception:  # If not possible construct a hash
-227                ids.append(int(hashlib.sha256(key.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
-228        print('ids', ids)
-229        nt = []
-230        for e, e_name in enumerate(obs.e_names):
-231
-232            r_length = []
-233            for r_name in obs.e_content[e_name]:
-234                r_length.append(len(obs.deltas[r_name]))
-235
-236            # e_N = np.sum(r_length)
-237            nt.append(max(r_length) // 2)
-238        print('nt', nt)
-239        zero = neid * [0.0]
-240        four = neid * [4.0]
-241        print('zero', zero)
-242        print('four', four)
-243        delta = np.concatenate([item for sublist in [[obs.deltas[o] for o in sl] for sl in list(obs.e_content.values())] for item in sublist])
-244
-245        bdio_start_record(0x00, 8, fbdio)
-246
-247        def write_c_double(double):
-248            pd_buf = ctypes.c_double(double)
-249            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-250            bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
-251
-252        def write_c_size_t(int32):
-253            pd_buf = ctypes.c_size_t(int32)
-254            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-255            bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+209    bdio_write_int32 = bdio.bdio_write_int32
+210    bdio_write_int32.restype = ctypes.c_size_t
+211    bdio_write_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+212
+213    b_path = file_path.encode('utf-8')
+214    write = 'w'
+215    b_write = write.encode('utf-8')
+216    form = 'pyerrors ADerror export'
+217    b_form = form.encode('utf-8')
+218
+219    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_write), b_form)
+220
+221    for obs in obs_list:
+222        # mean = obs.value
+223        neid = len(obs.e_names)
+224        vrep = [[obs.shape[o] for o in sl] for sl in list(obs.e_content.values())]
+225        vrep_write = [item for sublist in vrep for item in sublist]
+226        ndata = [np.sum(o) for o in vrep]
+227        nrep = [len(o) for o in vrep]
+228        print('ndata', ndata)
+229        print('nrep', nrep)
+230        print('vrep', vrep)
+231        keys = list(obs.e_content.keys())
+232        ids = []
+233        for key in keys:
+234            try:  # Try to convert key to integer
+235                ids.append(int(key))
+236            except Exception:  # If not possible construct a hash
+237                ids.append(int(hashlib.sha256(key.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
+238        print('ids', ids)
+239        nt = []
+240        for e, e_name in enumerate(obs.e_names):
+241
+242            r_length = []
+243            for r_name in obs.e_content[e_name]:
+244                r_length.append(len(obs.deltas[r_name]))
+245
+246            # e_N = np.sum(r_length)
+247            nt.append(max(r_length) // 2)
+248        print('nt', nt)
+249        zero = neid * [0.0]
+250        four = neid * [4.0]
+251        print('zero', zero)
+252        print('four', four)
+253        delta = np.concatenate([item for sublist in [[obs.deltas[o] for o in sl] for sl in list(obs.e_content.values())] for item in sublist])
+254
+255        bdio_start_record(0x00, 8, fbdio)
 256
-257        write_c_double(obs.value)
-258        write_c_size_t(neid)
-259
-260        for element in ndata:
-261            write_c_size_t(element)
-262        for element in nrep:
-263            write_c_size_t(element)
-264        for element in vrep_write:
-265            write_c_size_t(element)
-266        for element in ids:
-267            write_c_size_t(element)
-268        for element in nt:
-269            write_c_size_t(element)
-270
-271        for element in zero:
-272            write_c_double(element)
-273        for element in four:
-274            write_c_double(element)
-275
-276        for element in delta:
-277            write_c_double(element)
-278
-279    bdio_close(fbdio)
-280    return 0
-281
-282
-283def _get_kwd(string, key):
-284    return (string.split(key, 1)[1]).split(" ", 1)[0]
+257        def write_c_double(double):
+258            pd_buf = ctypes.c_double(double)
+259            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+260            bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
+261
+262        def write_c_size_t(int32):
+263            pd_buf = ctypes.c_size_t(int32)
+264            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+265            bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+266
+267        write_c_double(obs.value)
+268        write_c_size_t(neid)
+269
+270        for element in ndata:
+271            write_c_size_t(element)
+272        for element in nrep:
+273            write_c_size_t(element)
+274        for element in vrep_write:
+275            write_c_size_t(element)
+276        for element in ids:
+277            write_c_size_t(element)
+278        for element in nt:
+279            write_c_size_t(element)
+280
+281        for element in zero:
+282            write_c_double(element)
+283        for element in four:
+284            write_c_double(element)
 285
-286
-287def _get_corr_name(string, key):
-288    return (string.split(key, 1)[1]).split(' NDIM=', 1)[0]
-289
-290
-291def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
-292    """ Extract mesons data from a bdio file and return it as a dictionary
-293
-294    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
+286        for element in delta:
+287            write_c_double(element)
+288
+289    bdio_close(fbdio)
+290    return 0
+291
+292
+293def _get_kwd(string, key):
+294    return (string.split(key, 1)[1]).split(" ", 1)[0]
 295
-296    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
-297    adding the flag -fPIC to CC and changing the all target to
-298
-299    all:		bdio.o $(LIBDIR)
-300                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-301                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
-302
-303    Parameters
-304    ----------
-305    file_path : str
-306        path to the bdio file
-307    bdio_path : str
-308        path to the shared bdio library libbdio.so (default ./libbdio.so)
-309    start : int
-310        The first configuration to be read (default 1)
-311    stop : int
-312        The last configuration to be read (default None)
-313    step : int
-314        Fixed step size between two measurements (default 1)
-315    alternative_ensemble_name : str
-316        Manually overwrite ensemble name
-317    """
-318
-319    start = kwargs.get('start', 1)
-320    stop = kwargs.get('stop', None)
-321    step = kwargs.get('step', 1)
-322
-323    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-324
-325    bdio_open = bdio.bdio_open
-326    bdio_open.restype = ctypes.c_void_p
+296
+297def _get_corr_name(string, key):
+298    return (string.split(key, 1)[1]).split(' NDIM=', 1)[0]
+299
+300
+301def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
+302    """ Extract mesons data from a bdio file and return it as a dictionary
+303
+304    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
+305
+306    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
+307    adding the flag -fPIC to CC and changing the all target to
+308
+309    all:		bdio.o $(LIBDIR)
+310                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+311                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+312
+313    Parameters
+314    ----------
+315    file_path : str
+316        path to the bdio file
+317    bdio_path : str
+318        path to the shared bdio library libbdio.so (default ./libbdio.so)
+319    start : int
+320        The first configuration to be read (default 1)
+321    stop : int
+322        The last configuration to be read (default None)
+323    step : int
+324        Fixed step size between two measurements (default 1)
+325    alternative_ensemble_name : str
+326        Manually overwrite ensemble name
 327
-328    bdio_close = bdio.bdio_close
-329    bdio_close.restype = ctypes.c_int
-330    bdio_close.argtypes = [ctypes.c_void_p]
-331
-332    bdio_seek_record = bdio.bdio_seek_record
-333    bdio_seek_record.restype = ctypes.c_int
-334    bdio_seek_record.argtypes = [ctypes.c_void_p]
-335
-336    bdio_get_rlen = bdio.bdio_get_rlen
-337    bdio_get_rlen.restype = ctypes.c_int
-338    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+328    Returns
+329    -------
+330    data : dict
+331        Extracted meson data
+332    """
+333
+334    start = kwargs.get('start', 1)
+335    stop = kwargs.get('stop', None)
+336    step = kwargs.get('step', 1)
+337
+338    bdio = ctypes.cdll.LoadLibrary(bdio_path)
 339
-340    bdio_get_ruinfo = bdio.bdio_get_ruinfo
-341    bdio_get_ruinfo.restype = ctypes.c_int
-342    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
-343
-344    bdio_read = bdio.bdio_read
-345    bdio_read.restype = ctypes.c_size_t
-346    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
-347
-348    bdio_read_f64 = bdio.bdio_read_f64
-349    bdio_read_f64.restype = ctypes.c_size_t
-350    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-351
-352    b_path = file_path.encode('utf-8')
-353    read = 'r'
-354    b_read = read.encode('utf-8')
-355    form = 'Generic Correlator Format 1.0'
-356    b_form = form.encode('utf-8')
-357
-358    ensemble_name = ''
-359    volume = []  # lattice volume
-360    boundary_conditions = []
-361    corr_name = []  # Contains correlator names
-362    corr_type = []  # Contains correlator data type (important for reading out numerical data)
-363    corr_props = []  # Contanis propagator types (Component of corr_kappa)
-364    d0 = 0  # tvals
-365    d1 = 0  # nnoise
-366    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
-367    prop_source = []  # Contains propagator source positions
-368    # Check noise type for multiple replica?
-369    corr_no = -1
-370    data = []
-371    idl = []
+340    bdio_open = bdio.bdio_open
+341    bdio_open.restype = ctypes.c_void_p
+342
+343    bdio_close = bdio.bdio_close
+344    bdio_close.restype = ctypes.c_int
+345    bdio_close.argtypes = [ctypes.c_void_p]
+346
+347    bdio_seek_record = bdio.bdio_seek_record
+348    bdio_seek_record.restype = ctypes.c_int
+349    bdio_seek_record.argtypes = [ctypes.c_void_p]
+350
+351    bdio_get_rlen = bdio.bdio_get_rlen
+352    bdio_get_rlen.restype = ctypes.c_int
+353    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+354
+355    bdio_get_ruinfo = bdio.bdio_get_ruinfo
+356    bdio_get_ruinfo.restype = ctypes.c_int
+357    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
+358
+359    bdio_read = bdio.bdio_read
+360    bdio_read.restype = ctypes.c_size_t
+361    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
+362
+363    bdio_read_f64 = bdio.bdio_read_f64
+364    bdio_read_f64.restype = ctypes.c_size_t
+365    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+366
+367    b_path = file_path.encode('utf-8')
+368    read = 'r'
+369    b_read = read.encode('utf-8')
+370    form = 'Generic Correlator Format 1.0'
+371    b_form = form.encode('utf-8')
 372
-373    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
-374
-375    print('Reading of bdio file started')
-376    while True:
-377        bdio_seek_record(fbdio)
-378        ruinfo = bdio_get_ruinfo(fbdio)
-379        if ruinfo < 0:
-380            # EOF reached
-381            break
-382        rlen = bdio_get_rlen(fbdio)
-383        if ruinfo == 5:
-384            d_buf = ctypes.c_double * (2 + d0 * d1 * 2)
-385            pd_buf = d_buf()
-386            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-387            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-388            if corr_type[corr_no] == 'complex':
-389                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + 2 * d1:-2 * d1:2]), d0 - 2)), axis=1)
-390            else:
-391                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + d1:-d0 * d1 - d1]), d0 - 2)), axis=1)
-392
-393            data[corr_no].append(tmp_mean)
-394            corr_no += 1
-395        else:
-396            alt_buf = ctypes.create_string_buffer(1024)
-397            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
-398            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-399            if rlen != iread:
-400                print('Error')
-401            for i, item in enumerate(alt_buf):
-402                if item == b'\x00':
-403                    alt_buf[i] = b' '
-404            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
-405            if ruinfo == 0:
-406                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
-407                volume.append(int(_get_kwd(tmp_string, 'L0=')))
-408                volume.append(int(_get_kwd(tmp_string, 'L1=')))
-409                volume.append(int(_get_kwd(tmp_string, 'L2=')))
-410                volume.append(int(_get_kwd(tmp_string, 'L3=')))
-411                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
-412                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
-413                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
-414                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
-415
-416            if ruinfo == 1:
-417                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
-418                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
-419                corr_props.append([_get_kwd(tmp_string, 'PROP0='), _get_kwd(tmp_string, 'PROP1=')])
-420                if d0 == 0:
-421                    d0 = int(_get_kwd(tmp_string, 'D0='))
-422                else:
-423                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
-424                        print('Error: Varying number of time values')
-425                if d1 == 0:
-426                    d1 = int(_get_kwd(tmp_string, 'D1='))
-427                else:
-428                    if d1 != int(_get_kwd(tmp_string, 'D1=')):
-429                        print('Error: Varying number of random sources')
-430            if ruinfo == 2:
-431                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
-432                prop_source.append(_get_kwd(tmp_string, 'x0='))
-433            if ruinfo == 4:
-434                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
-435                if stop:
-436                    if cnfg_no > kwargs.get('stop'):
-437                        break
-438                idl.append(cnfg_no)
-439                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
-440                if len(idl) == 1:
-441                    no_corrs = len(corr_name)
-442                    data = []
-443                    for c in range(no_corrs):
-444                        data.append([])
-445
-446                corr_no = 0
-447
-448    bdio_close(fbdio)
-449
-450    print('\nEnsemble: ', ensemble_name)
-451    if 'alternative_ensemble_name' in kwargs:
-452        ensemble_name = kwargs.get('alternative_ensemble_name')
-453        print('Ensemble name overwritten to', ensemble_name)
-454    print('Lattice volume: ', volume)
-455    print('Boundary conditions: ', boundary_conditions)
-456    print('Number of time values: ', d0)
-457    print('Number of random sources: ', d1)
-458    print('Number of corrs: ', len(corr_name))
-459    print('Number of configurations: ', len(idl))
+373    ensemble_name = ''
+374    volume = []  # lattice volume
+375    boundary_conditions = []
+376    corr_name = []  # Contains correlator names
+377    corr_type = []  # Contains correlator data type (important for reading out numerical data)
+378    corr_props = []  # Contanis propagator types (Component of corr_kappa)
+379    d0 = 0  # tvals
+380    d1 = 0  # nnoise
+381    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
+382    prop_source = []  # Contains propagator source positions
+383    # Check noise type for multiple replica?
+384    corr_no = -1
+385    data = []
+386    idl = []
+387
+388    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
+389
+390    print('Reading of bdio file started')
+391    while True:
+392        bdio_seek_record(fbdio)
+393        ruinfo = bdio_get_ruinfo(fbdio)
+394        if ruinfo < 0:
+395            # EOF reached
+396            break
+397        rlen = bdio_get_rlen(fbdio)
+398        if ruinfo == 5:
+399            d_buf = ctypes.c_double * (2 + d0 * d1 * 2)
+400            pd_buf = d_buf()
+401            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+402            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+403            if corr_type[corr_no] == 'complex':
+404                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + 2 * d1:-2 * d1:2]), d0 - 2)), axis=1)
+405            else:
+406                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + d1:-d0 * d1 - d1]), d0 - 2)), axis=1)
+407
+408            data[corr_no].append(tmp_mean)
+409            corr_no += 1
+410        else:
+411            alt_buf = ctypes.create_string_buffer(1024)
+412            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
+413            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+414            if rlen != iread:
+415                print('Error')
+416            for i, item in enumerate(alt_buf):
+417                if item == b'\x00':
+418                    alt_buf[i] = b' '
+419            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
+420            if ruinfo == 0:
+421                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
+422                volume.append(int(_get_kwd(tmp_string, 'L0=')))
+423                volume.append(int(_get_kwd(tmp_string, 'L1=')))
+424                volume.append(int(_get_kwd(tmp_string, 'L2=')))
+425                volume.append(int(_get_kwd(tmp_string, 'L3=')))
+426                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
+427                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
+428                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
+429                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
+430
+431            if ruinfo == 1:
+432                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
+433                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
+434                corr_props.append([_get_kwd(tmp_string, 'PROP0='), _get_kwd(tmp_string, 'PROP1=')])
+435                if d0 == 0:
+436                    d0 = int(_get_kwd(tmp_string, 'D0='))
+437                else:
+438                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
+439                        print('Error: Varying number of time values')
+440                if d1 == 0:
+441                    d1 = int(_get_kwd(tmp_string, 'D1='))
+442                else:
+443                    if d1 != int(_get_kwd(tmp_string, 'D1=')):
+444                        print('Error: Varying number of random sources')
+445            if ruinfo == 2:
+446                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
+447                prop_source.append(_get_kwd(tmp_string, 'x0='))
+448            if ruinfo == 4:
+449                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
+450                if stop:
+451                    if cnfg_no > kwargs.get('stop'):
+452                        break
+453                idl.append(cnfg_no)
+454                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
+455                if len(idl) == 1:
+456                    no_corrs = len(corr_name)
+457                    data = []
+458                    for c in range(no_corrs):
+459                        data.append([])
 460
-461    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
-462    corr_source = []
-463    for item in corr_props:
-464        corr_kappa.append([float(prop_kappa[int(item[0])]), float(prop_kappa[int(item[1])])])
-465        if prop_source[int(item[0])] != prop_source[int(item[1])]:
-466            raise Exception('Source position do not match for correlator' + str(item))
-467        else:
-468            corr_source.append(int(prop_source[int(item[0])]))
-469
-470    if stop is None:
-471        stop = idl[-1]
-472    idl_target = range(start, stop + 1, step)
-473
-474    if set(idl) != set(idl_target):
-475        try:
-476            indices = [idl.index(i) for i in idl_target]
-477        except ValueError as err:
-478            raise Exception('Configurations in file do no match target list!', err)
-479    else:
-480        indices = None
-481
-482    result = {}
-483    for c in range(no_corrs):
-484        tmp_corr = []
-485        tmp_data = np.asarray(data[c])
-486        for t in range(d0 - 2):
-487            if indices:
-488                deltas = [tmp_data[:, t][index] for index in indices]
-489            else:
-490                deltas = tmp_data[:, t]
-491            tmp_corr.append(Obs([deltas], [ensemble_name], idl=[idl_target]))
-492        result[(corr_name[c], corr_source[c]) + tuple(corr_kappa[c])] = tmp_corr
-493
-494    # Check that all data entries have the same number of configurations
-495    if len(set([o[0].N for o in list(result.values())])) != 1:
-496        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
-497
-498    return result
-499
-500
-501def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
-502    """ Extract dSdm data from a bdio file and return it as a dictionary
-503
-504    The dictionary can be accessed with a tuple consisting of (type, kappa)
-505
-506    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by
-507    adding the flag -fPIC to CC and changing the all target to
+461                corr_no = 0
+462
+463    bdio_close(fbdio)
+464
+465    print('\nEnsemble: ', ensemble_name)
+466    if 'alternative_ensemble_name' in kwargs:
+467        ensemble_name = kwargs.get('alternative_ensemble_name')
+468        print('Ensemble name overwritten to', ensemble_name)
+469    print('Lattice volume: ', volume)
+470    print('Boundary conditions: ', boundary_conditions)
+471    print('Number of time values: ', d0)
+472    print('Number of random sources: ', d1)
+473    print('Number of corrs: ', len(corr_name))
+474    print('Number of configurations: ', len(idl))
+475
+476    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
+477    corr_source = []
+478    for item in corr_props:
+479        corr_kappa.append([float(prop_kappa[int(item[0])]), float(prop_kappa[int(item[1])])])
+480        if prop_source[int(item[0])] != prop_source[int(item[1])]:
+481            raise Exception('Source position do not match for correlator' + str(item))
+482        else:
+483            corr_source.append(int(prop_source[int(item[0])]))
+484
+485    if stop is None:
+486        stop = idl[-1]
+487    idl_target = range(start, stop + 1, step)
+488
+489    if set(idl) != set(idl_target):
+490        try:
+491            indices = [idl.index(i) for i in idl_target]
+492        except ValueError as err:
+493            raise Exception('Configurations in file do no match target list!', err)
+494    else:
+495        indices = None
+496
+497    result = {}
+498    for c in range(no_corrs):
+499        tmp_corr = []
+500        tmp_data = np.asarray(data[c])
+501        for t in range(d0 - 2):
+502            if indices:
+503                deltas = [tmp_data[:, t][index] for index in indices]
+504            else:
+505                deltas = tmp_data[:, t]
+506            tmp_corr.append(Obs([deltas], [ensemble_name], idl=[idl_target]))
+507        result[(corr_name[c], corr_source[c]) + tuple(corr_kappa[c])] = tmp_corr
 508
-509    all:		bdio.o $(LIBDIR)
-510                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-511                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+509    # Check that all data entries have the same number of configurations
+510    if len(set([o[0].N for o in list(result.values())])) != 1:
+511        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
 512
-513    Parameters
-514    ----------
-515    file_path : str
-516        path to the bdio file
-517    bdio_path : str
-518        path to the shared bdio library libbdio.so (default ./libbdio.so)
-519    start : int
-520        The first configuration to be read (default 1)
-521    stop : int
-522        The last configuration to be read (default None)
-523    step : int
-524        Fixed step size between two measurements (default 1)
-525    alternative_ensemble_name : str
-526        Manually overwrite ensemble name
-527    """
-528
-529    start = kwargs.get('start', 1)
-530    stop = kwargs.get('stop', None)
-531    step = kwargs.get('step', 1)
-532
-533    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-534
-535    bdio_open = bdio.bdio_open
-536    bdio_open.restype = ctypes.c_void_p
-537
-538    bdio_close = bdio.bdio_close
-539    bdio_close.restype = ctypes.c_int
-540    bdio_close.argtypes = [ctypes.c_void_p]
-541
-542    bdio_seek_record = bdio.bdio_seek_record
-543    bdio_seek_record.restype = ctypes.c_int
-544    bdio_seek_record.argtypes = [ctypes.c_void_p]
-545
-546    bdio_get_rlen = bdio.bdio_get_rlen
-547    bdio_get_rlen.restype = ctypes.c_int
-548    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+513    return result
+514
+515
+516def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
+517    """ Extract dSdm data from a bdio file and return it as a dictionary
+518
+519    The dictionary can be accessed with a tuple consisting of (type, kappa)
+520
+521    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by
+522    adding the flag -fPIC to CC and changing the all target to
+523
+524    all:		bdio.o $(LIBDIR)
+525                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+526                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+527
+528    Parameters
+529    ----------
+530    file_path : str
+531        path to the bdio file
+532    bdio_path : str
+533        path to the shared bdio library libbdio.so (default ./libbdio.so)
+534    start : int
+535        The first configuration to be read (default 1)
+536    stop : int
+537        The last configuration to be read (default None)
+538    step : int
+539        Fixed step size between two measurements (default 1)
+540    alternative_ensemble_name : str
+541        Manually overwrite ensemble name
+542    """
+543
+544    start = kwargs.get('start', 1)
+545    stop = kwargs.get('stop', None)
+546    step = kwargs.get('step', 1)
+547
+548    bdio = ctypes.cdll.LoadLibrary(bdio_path)
 549
-550    bdio_get_ruinfo = bdio.bdio_get_ruinfo
-551    bdio_get_ruinfo.restype = ctypes.c_int
-552    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
-553
-554    bdio_read = bdio.bdio_read
-555    bdio_read.restype = ctypes.c_size_t
-556    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
-557
-558    bdio_read_f64 = bdio.bdio_read_f64
-559    bdio_read_f64.restype = ctypes.c_size_t
-560    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-561
-562    b_path = file_path.encode('utf-8')
-563    read = 'r'
-564    b_read = read.encode('utf-8')
-565    form = 'Generic Correlator Format 1.0'
-566    b_form = form.encode('utf-8')
-567
-568    ensemble_name = ''
-569    volume = []  # lattice volume
-570    boundary_conditions = []
-571    corr_name = []  # Contains correlator names
-572    corr_type = []  # Contains correlator data type (important for reading out numerical data)
-573    corr_props = []  # Contains propagator types (Component of corr_kappa)
-574    d0 = 0  # tvals
-575    # d1 = 0  # nnoise
-576    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
-577    # Check noise type for multiple replica?
-578    corr_no = -1
-579    data = []
-580    idl = []
-581
-582    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
-583
-584    print('Reading of bdio file started')
-585    while True:
-586        bdio_seek_record(fbdio)
-587        ruinfo = bdio_get_ruinfo(fbdio)
-588        if ruinfo < 0:
-589            # EOF reached
-590            break
-591        rlen = bdio_get_rlen(fbdio)
-592        if ruinfo == 5:
-593            d_buf = ctypes.c_double * (2 + d0)
-594            pd_buf = d_buf()
-595            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-596            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-597            tmp_mean = np.mean(np.asarray(pd_buf[2:]))
+550    bdio_open = bdio.bdio_open
+551    bdio_open.restype = ctypes.c_void_p
+552
+553    bdio_close = bdio.bdio_close
+554    bdio_close.restype = ctypes.c_int
+555    bdio_close.argtypes = [ctypes.c_void_p]
+556
+557    bdio_seek_record = bdio.bdio_seek_record
+558    bdio_seek_record.restype = ctypes.c_int
+559    bdio_seek_record.argtypes = [ctypes.c_void_p]
+560
+561    bdio_get_rlen = bdio.bdio_get_rlen
+562    bdio_get_rlen.restype = ctypes.c_int
+563    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+564
+565    bdio_get_ruinfo = bdio.bdio_get_ruinfo
+566    bdio_get_ruinfo.restype = ctypes.c_int
+567    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
+568
+569    bdio_read = bdio.bdio_read
+570    bdio_read.restype = ctypes.c_size_t
+571    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
+572
+573    bdio_read_f64 = bdio.bdio_read_f64
+574    bdio_read_f64.restype = ctypes.c_size_t
+575    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+576
+577    b_path = file_path.encode('utf-8')
+578    read = 'r'
+579    b_read = read.encode('utf-8')
+580    form = 'Generic Correlator Format 1.0'
+581    b_form = form.encode('utf-8')
+582
+583    ensemble_name = ''
+584    volume = []  # lattice volume
+585    boundary_conditions = []
+586    corr_name = []  # Contains correlator names
+587    corr_type = []  # Contains correlator data type (important for reading out numerical data)
+588    corr_props = []  # Contains propagator types (Component of corr_kappa)
+589    d0 = 0  # tvals
+590    # d1 = 0  # nnoise
+591    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
+592    # Check noise type for multiple replica?
+593    corr_no = -1
+594    data = []
+595    idl = []
+596
+597    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
 598
-599            data[corr_no].append(tmp_mean)
-600            corr_no += 1
-601        else:
-602            alt_buf = ctypes.create_string_buffer(1024)
-603            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
-604            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-605            if rlen != iread:
-606                print('Error')
-607            for i, item in enumerate(alt_buf):
-608                if item == b'\x00':
-609                    alt_buf[i] = b' '
-610            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
-611            if ruinfo == 0:
-612                creator = _get_kwd(tmp_string, 'CREATOR=')
-613                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
-614                volume.append(int(_get_kwd(tmp_string, 'L0=')))
-615                volume.append(int(_get_kwd(tmp_string, 'L1=')))
-616                volume.append(int(_get_kwd(tmp_string, 'L2=')))
-617                volume.append(int(_get_kwd(tmp_string, 'L3=')))
-618                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
-619                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
-620                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
-621                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
-622
-623            if ruinfo == 1:
-624                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
-625                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
-626                corr_props.append(_get_kwd(tmp_string, 'PROP0='))
-627                if d0 == 0:
-628                    d0 = int(_get_kwd(tmp_string, 'D0='))
-629                else:
-630                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
-631                        print('Error: Varying number of time values')
-632            if ruinfo == 2:
-633                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
-634            if ruinfo == 4:
-635                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
-636                if stop:
-637                    if cnfg_no > kwargs.get('stop'):
-638                        break
-639                idl.append(cnfg_no)
-640                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
-641                if len(idl) == 1:
-642                    no_corrs = len(corr_name)
-643                    data = []
-644                    for c in range(no_corrs):
-645                        data.append([])
-646
-647                corr_no = 0
-648    bdio_close(fbdio)
-649
-650    print('\nCreator: ', creator)
-651    print('Ensemble: ', ensemble_name)
-652    print('Lattice volume: ', volume)
-653    print('Boundary conditions: ', boundary_conditions)
-654    print('Number of random sources: ', d0)
-655    print('Number of corrs: ', len(corr_name))
-656    print('Number of configurations: ', cnfg_no + 1)
-657
-658    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
-659    for item in corr_props:
-660        corr_kappa.append(float(prop_kappa[int(item)]))
+599    print('Reading of bdio file started')
+600    while True:
+601        bdio_seek_record(fbdio)
+602        ruinfo = bdio_get_ruinfo(fbdio)
+603        if ruinfo < 0:
+604            # EOF reached
+605            break
+606        rlen = bdio_get_rlen(fbdio)
+607        if ruinfo == 5:
+608            d_buf = ctypes.c_double * (2 + d0)
+609            pd_buf = d_buf()
+610            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+611            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+612            tmp_mean = np.mean(np.asarray(pd_buf[2:]))
+613
+614            data[corr_no].append(tmp_mean)
+615            corr_no += 1
+616        else:
+617            alt_buf = ctypes.create_string_buffer(1024)
+618            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
+619            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+620            if rlen != iread:
+621                print('Error')
+622            for i, item in enumerate(alt_buf):
+623                if item == b'\x00':
+624                    alt_buf[i] = b' '
+625            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
+626            if ruinfo == 0:
+627                creator = _get_kwd(tmp_string, 'CREATOR=')
+628                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
+629                volume.append(int(_get_kwd(tmp_string, 'L0=')))
+630                volume.append(int(_get_kwd(tmp_string, 'L1=')))
+631                volume.append(int(_get_kwd(tmp_string, 'L2=')))
+632                volume.append(int(_get_kwd(tmp_string, 'L3=')))
+633                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
+634                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
+635                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
+636                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
+637
+638            if ruinfo == 1:
+639                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
+640                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
+641                corr_props.append(_get_kwd(tmp_string, 'PROP0='))
+642                if d0 == 0:
+643                    d0 = int(_get_kwd(tmp_string, 'D0='))
+644                else:
+645                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
+646                        print('Error: Varying number of time values')
+647            if ruinfo == 2:
+648                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
+649            if ruinfo == 4:
+650                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
+651                if stop:
+652                    if cnfg_no > kwargs.get('stop'):
+653                        break
+654                idl.append(cnfg_no)
+655                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
+656                if len(idl) == 1:
+657                    no_corrs = len(corr_name)
+658                    data = []
+659                    for c in range(no_corrs):
+660                        data.append([])
 661
-662    if stop is None:
-663        stop = idl[-1]
-664    idl_target = range(start, stop + 1, step)
-665    try:
-666        indices = [idl.index(i) for i in idl_target]
-667    except ValueError as err:
-668        raise Exception('Configurations in file do no match target list!', err)
-669
-670    result = {}
-671    for c in range(no_corrs):
-672        deltas = [np.asarray(data[c])[index] for index in indices]
-673        result[(corr_name[c], str(corr_kappa[c]))] = Obs([deltas], [ensemble_name], idl=[idl_target])
-674
-675    # Check that all data entries have the same number of configurations
-676    if len(set([o.N for o in list(result.values())])) != 1:
-677        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
-678
-679    return result
+662                corr_no = 0
+663    bdio_close(fbdio)
+664
+665    print('\nCreator: ', creator)
+666    print('Ensemble: ', ensemble_name)
+667    print('Lattice volume: ', volume)
+668    print('Boundary conditions: ', boundary_conditions)
+669    print('Number of random sources: ', d0)
+670    print('Number of corrs: ', len(corr_name))
+671    print('Number of configurations: ', cnfg_no + 1)
+672
+673    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
+674    for item in corr_props:
+675        corr_kappa.append(float(prop_kappa[int(item)]))
+676
+677    if stop is None:
+678        stop = idl[-1]
+679    idl_target = range(start, stop + 1, step)
+680    try:
+681        indices = [idl.index(i) for i in idl_target]
+682    except ValueError as err:
+683        raise Exception('Configurations in file do no match target list!', err)
+684
+685    result = {}
+686    for c in range(no_corrs):
+687        deltas = [np.asarray(data[c])[index] for index in indices]
+688        result[(corr_name[c], str(corr_kappa[c]))] = Obs([deltas], [ensemble_name], idl=[idl_target])
+689
+690    # Check that all data entries have the same number of configurations
+691    if len(set([o.N for o in list(result.values())])) != 1:
+692        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
+693
+694    return result
    @@ -793,141 +808,146 @@ 19 ---------- 20 file_path -- path to the bdio file 21 bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so) - 22 """ - 23 bdio = ctypes.cdll.LoadLibrary(bdio_path) - 24 - 25 bdio_open = bdio.bdio_open - 26 bdio_open.restype = ctypes.c_void_p - 27 - 28 bdio_close = bdio.bdio_close - 29 bdio_close.restype = ctypes.c_int - 30 bdio_close.argtypes = [ctypes.c_void_p] - 31 - 32 bdio_seek_record = bdio.bdio_seek_record - 33 bdio_seek_record.restype = ctypes.c_int - 34 bdio_seek_record.argtypes = [ctypes.c_void_p] - 35 - 36 bdio_get_rlen = bdio.bdio_get_rlen - 37 bdio_get_rlen.restype = ctypes.c_int - 38 bdio_get_rlen.argtypes = [ctypes.c_void_p] - 39 - 40 bdio_get_ruinfo = bdio.bdio_get_ruinfo - 41 bdio_get_ruinfo.restype = ctypes.c_int - 42 bdio_get_ruinfo.argtypes = [ctypes.c_void_p] - 43 - 44 bdio_read = bdio.bdio_read - 45 bdio_read.restype = ctypes.c_size_t - 46 bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p] - 47 - 48 bdio_read_f64 = bdio.bdio_read_f64 - 49 bdio_read_f64.restype = ctypes.c_size_t - 50 bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p] - 51 - 52 bdio_read_int32 = bdio.bdio_read_int32 - 53 bdio_read_int32.restype = ctypes.c_size_t - 54 bdio_read_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p] - 55 - 56 b_path = file_path.encode('utf-8') - 57 read = 'r' - 58 b_read = read.encode('utf-8') - 59 - 60 fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), None) - 61 - 62 return_list = [] - 63 - 64 print('Reading of bdio file started') - 65 while True: - 66 bdio_seek_record(fbdio) - 67 ruinfo = bdio_get_ruinfo(fbdio) + 22 + 23 Returns + 24 ------- + 25 data : List[Obs] + 26 Extracted data + 27 """ + 28 bdio = ctypes.cdll.LoadLibrary(bdio_path) + 29 + 30 bdio_open = bdio.bdio_open + 31 bdio_open.restype = ctypes.c_void_p + 32 + 33 bdio_close = bdio.bdio_close + 34 bdio_close.restype = ctypes.c_int + 35 bdio_close.argtypes = [ctypes.c_void_p] + 36 + 37 bdio_seek_record = bdio.bdio_seek_record + 38 bdio_seek_record.restype = ctypes.c_int + 39 bdio_seek_record.argtypes = [ctypes.c_void_p] + 40 + 41 bdio_get_rlen = bdio.bdio_get_rlen + 42 bdio_get_rlen.restype = ctypes.c_int + 43 bdio_get_rlen.argtypes = [ctypes.c_void_p] + 44 + 45 bdio_get_ruinfo = bdio.bdio_get_ruinfo + 46 bdio_get_ruinfo.restype = ctypes.c_int + 47 bdio_get_ruinfo.argtypes = [ctypes.c_void_p] + 48 + 49 bdio_read = bdio.bdio_read + 50 bdio_read.restype = ctypes.c_size_t + 51 bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p] + 52 + 53 bdio_read_f64 = bdio.bdio_read_f64 + 54 bdio_read_f64.restype = ctypes.c_size_t + 55 bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p] + 56 + 57 bdio_read_int32 = bdio.bdio_read_int32 + 58 bdio_read_int32.restype = ctypes.c_size_t + 59 bdio_read_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p] + 60 + 61 b_path = file_path.encode('utf-8') + 62 read = 'r' + 63 b_read = read.encode('utf-8') + 64 + 65 fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), None) + 66 + 67 return_list = [] 68 - 69 if ruinfo == 7: - 70 print('MD5sum found') # For now we just ignore these entries and do not perform any checks on them - 71 continue - 72 - 73 if ruinfo < 0: - 74 # EOF reached - 75 break - 76 bdio_get_rlen(fbdio) + 69 print('Reading of bdio file started') + 70 while True: + 71 bdio_seek_record(fbdio) + 72 ruinfo = bdio_get_ruinfo(fbdio) + 73 + 74 if ruinfo == 7: + 75 print('MD5sum found') # For now we just ignore these entries and do not perform any checks on them + 76 continue 77 - 78 def read_c_double(): - 79 d_buf = ctypes.c_double - 80 pd_buf = d_buf() - 81 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) - 82 bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio)) - 83 return pd_buf.value - 84 - 85 mean = read_c_double() - 86 print('mean', mean) - 87 - 88 def read_c_size_t(): - 89 d_buf = ctypes.c_size_t - 90 pd_buf = d_buf() - 91 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) - 92 bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio)) - 93 return pd_buf.value - 94 - 95 neid = read_c_size_t() - 96 print('neid', neid) - 97 - 98 ndata = [] - 99 for index in range(neid): -100 ndata.append(read_c_size_t()) -101 print('ndata', ndata) + 78 if ruinfo < 0: + 79 # EOF reached + 80 break + 81 bdio_get_rlen(fbdio) + 82 + 83 def read_c_double(): + 84 d_buf = ctypes.c_double + 85 pd_buf = d_buf() + 86 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) + 87 bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio)) + 88 return pd_buf.value + 89 + 90 mean = read_c_double() + 91 print('mean', mean) + 92 + 93 def read_c_size_t(): + 94 d_buf = ctypes.c_size_t + 95 pd_buf = d_buf() + 96 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) + 97 bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio)) + 98 return pd_buf.value + 99 +100 neid = read_c_size_t() +101 print('neid', neid) 102 -103 nrep = [] +103 ndata = [] 104 for index in range(neid): -105 nrep.append(read_c_size_t()) -106 print('nrep', nrep) +105 ndata.append(read_c_size_t()) +106 print('ndata', ndata) 107 -108 vrep = [] +108 nrep = [] 109 for index in range(neid): -110 vrep.append([]) -111 for jndex in range(nrep[index]): -112 vrep[-1].append(read_c_size_t()) -113 print('vrep', vrep) -114 -115 ids = [] -116 for index in range(neid): -117 ids.append(read_c_size_t()) -118 print('ids', ids) +110 nrep.append(read_c_size_t()) +111 print('nrep', nrep) +112 +113 vrep = [] +114 for index in range(neid): +115 vrep.append([]) +116 for jndex in range(nrep[index]): +117 vrep[-1].append(read_c_size_t()) +118 print('vrep', vrep) 119 -120 nt = [] +120 ids = [] 121 for index in range(neid): -122 nt.append(read_c_size_t()) -123 print('nt', nt) +122 ids.append(read_c_size_t()) +123 print('ids', ids) 124 -125 zero = [] +125 nt = [] 126 for index in range(neid): -127 zero.append(read_c_double()) -128 print('zero', zero) +127 nt.append(read_c_size_t()) +128 print('nt', nt) 129 -130 four = [] +130 zero = [] 131 for index in range(neid): -132 four.append(read_c_double()) -133 print('four', four) +132 zero.append(read_c_double()) +133 print('zero', zero) 134 -135 d_buf = ctypes.c_double * np.sum(ndata) -136 pd_buf = d_buf() -137 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) -138 bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio)) -139 delta = pd_buf[:] -140 -141 samples = np.split(np.asarray(delta) + mean, np.cumsum([a for su in vrep for a in su])[:-1]) -142 no_reps = [len(o) for o in vrep] -143 assert len(ids) == len(no_reps) -144 tmp_names = [] -145 ens_length = max([len(str(o)) for o in ids]) -146 for loc_id, reps in zip(ids, no_reps): -147 for index in range(reps): -148 missing_chars = ens_length - len(str(loc_id)) -149 tmp_names.append(str(loc_id) + ' ' * missing_chars + '|r' + '{0:03d}'.format(index)) -150 -151 return_list.append(Obs(samples, tmp_names)) -152 -153 bdio_close(fbdio) -154 print() -155 print(len(return_list), 'observable(s) extracted.') -156 return return_list +135 four = [] +136 for index in range(neid): +137 four.append(read_c_double()) +138 print('four', four) +139 +140 d_buf = ctypes.c_double * np.sum(ndata) +141 pd_buf = d_buf() +142 ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf)) +143 bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio)) +144 delta = pd_buf[:] +145 +146 samples = np.split(np.asarray(delta) + mean, np.cumsum([a for su in vrep for a in su])[:-1]) +147 no_reps = [len(o) for o in vrep] +148 assert len(ids) == len(no_reps) +149 tmp_names = [] +150 ens_length = max([len(str(o)) for o in ids]) +151 for loc_id, reps in zip(ids, no_reps): +152 for index in range(reps): +153 missing_chars = ens_length - len(str(loc_id)) +154 tmp_names.append(str(loc_id) + ' ' * missing_chars + '|r' + '{0:03d}'.format(index)) +155 +156 return_list.append(Obs(samples, tmp_names)) +157 +158 bdio_close(fbdio) +159 print() +160 print(len(return_list), 'observable(s) extracted.') +161 return return_list @@ -946,6 +966,13 @@ adding the flag -fPIC to CC and changing the all target to

  • file_path -- path to the bdio file
  • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • + +
    Returns
    + + @@ -961,129 +988,134 @@ adding the flag -fPIC to CC and changing the all target to

    - 
    159def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
-160    """ Write Obs to a bdio file according to ADerrors conventions
-161
-162    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
-163    adding the flag -fPIC to CC and changing the all target to
-164
-165    all:		bdio.o $(LIBDIR)
-166                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-167                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
-168
-169    Parameters
-170    ----------
-171    file_path -- path to the bdio file
-172    bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
-173    """
-174
-175    for obs in obs_list:
-176        if not hasattr(obs, 'e_names'):
-177            raise Exception('Run the gamma method first for all obs.')
+            
    164def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
+165    """ Write Obs to a bdio file according to ADerrors conventions
+166
+167    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
+168    adding the flag -fPIC to CC and changing the all target to
+169
+170    all:		bdio.o $(LIBDIR)
+171                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+172                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+173
+174    Parameters
+175    ----------
+176    file_path -- path to the bdio file
+177    bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
 178
-179    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-180
-181    bdio_open = bdio.bdio_open
-182    bdio_open.restype = ctypes.c_void_p
-183
-184    bdio_close = bdio.bdio_close
-185    bdio_close.restype = ctypes.c_int
-186    bdio_close.argtypes = [ctypes.c_void_p]
-187
-188    bdio_start_record = bdio.bdio_start_record
-189    bdio_start_record.restype = ctypes.c_int
-190    bdio_start_record.argtypes = [ctypes.c_size_t, ctypes.c_size_t, ctypes.c_void_p]
-191
-192    bdio_flush_record = bdio.bdio_flush_record
-193    bdio_flush_record.restype = ctypes.c_int
-194    bdio_flush_record.argytpes = [ctypes.c_void_p]
-195
-196    bdio_write_f64 = bdio.bdio_write_f64
-197    bdio_write_f64.restype = ctypes.c_size_t
-198    bdio_write_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-199
-200    bdio_write_int32 = bdio.bdio_write_int32
-201    bdio_write_int32.restype = ctypes.c_size_t
-202    bdio_write_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-203
-204    b_path = file_path.encode('utf-8')
-205    write = 'w'
-206    b_write = write.encode('utf-8')
-207    form = 'pyerrors ADerror export'
-208    b_form = form.encode('utf-8')
+179    Returns
+180    -------
+181    success : int
+182        returns 0 is successful
+183    """
+184
+185    for obs in obs_list:
+186        if not hasattr(obs, 'e_names'):
+187            raise Exception('Run the gamma method first for all obs.')
+188
+189    bdio = ctypes.cdll.LoadLibrary(bdio_path)
+190
+191    bdio_open = bdio.bdio_open
+192    bdio_open.restype = ctypes.c_void_p
+193
+194    bdio_close = bdio.bdio_close
+195    bdio_close.restype = ctypes.c_int
+196    bdio_close.argtypes = [ctypes.c_void_p]
+197
+198    bdio_start_record = bdio.bdio_start_record
+199    bdio_start_record.restype = ctypes.c_int
+200    bdio_start_record.argtypes = [ctypes.c_size_t, ctypes.c_size_t, ctypes.c_void_p]
+201
+202    bdio_flush_record = bdio.bdio_flush_record
+203    bdio_flush_record.restype = ctypes.c_int
+204    bdio_flush_record.argytpes = [ctypes.c_void_p]
+205
+206    bdio_write_f64 = bdio.bdio_write_f64
+207    bdio_write_f64.restype = ctypes.c_size_t
+208    bdio_write_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
 209
-210    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_write), b_form)
-211
-212    for obs in obs_list:
-213        # mean = obs.value
-214        neid = len(obs.e_names)
-215        vrep = [[obs.shape[o] for o in sl] for sl in list(obs.e_content.values())]
-216        vrep_write = [item for sublist in vrep for item in sublist]
-217        ndata = [np.sum(o) for o in vrep]
-218        nrep = [len(o) for o in vrep]
-219        print('ndata', ndata)
-220        print('nrep', nrep)
-221        print('vrep', vrep)
-222        keys = list(obs.e_content.keys())
-223        ids = []
-224        for key in keys:
-225            try:  # Try to convert key to integer
-226                ids.append(int(key))
-227            except Exception:  # If not possible construct a hash
-228                ids.append(int(hashlib.sha256(key.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
-229        print('ids', ids)
-230        nt = []
-231        for e, e_name in enumerate(obs.e_names):
-232
-233            r_length = []
-234            for r_name in obs.e_content[e_name]:
-235                r_length.append(len(obs.deltas[r_name]))
-236
-237            # e_N = np.sum(r_length)
-238            nt.append(max(r_length) // 2)
-239        print('nt', nt)
-240        zero = neid * [0.0]
-241        four = neid * [4.0]
-242        print('zero', zero)
-243        print('four', four)
-244        delta = np.concatenate([item for sublist in [[obs.deltas[o] for o in sl] for sl in list(obs.e_content.values())] for item in sublist])
-245
-246        bdio_start_record(0x00, 8, fbdio)
-247
-248        def write_c_double(double):
-249            pd_buf = ctypes.c_double(double)
-250            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-251            bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
-252
-253        def write_c_size_t(int32):
-254            pd_buf = ctypes.c_size_t(int32)
-255            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-256            bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+210    bdio_write_int32 = bdio.bdio_write_int32
+211    bdio_write_int32.restype = ctypes.c_size_t
+212    bdio_write_int32.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+213
+214    b_path = file_path.encode('utf-8')
+215    write = 'w'
+216    b_write = write.encode('utf-8')
+217    form = 'pyerrors ADerror export'
+218    b_form = form.encode('utf-8')
+219
+220    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_write), b_form)
+221
+222    for obs in obs_list:
+223        # mean = obs.value
+224        neid = len(obs.e_names)
+225        vrep = [[obs.shape[o] for o in sl] for sl in list(obs.e_content.values())]
+226        vrep_write = [item for sublist in vrep for item in sublist]
+227        ndata = [np.sum(o) for o in vrep]
+228        nrep = [len(o) for o in vrep]
+229        print('ndata', ndata)
+230        print('nrep', nrep)
+231        print('vrep', vrep)
+232        keys = list(obs.e_content.keys())
+233        ids = []
+234        for key in keys:
+235            try:  # Try to convert key to integer
+236                ids.append(int(key))
+237            except Exception:  # If not possible construct a hash
+238                ids.append(int(hashlib.sha256(key.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
+239        print('ids', ids)
+240        nt = []
+241        for e, e_name in enumerate(obs.e_names):
+242
+243            r_length = []
+244            for r_name in obs.e_content[e_name]:
+245                r_length.append(len(obs.deltas[r_name]))
+246
+247            # e_N = np.sum(r_length)
+248            nt.append(max(r_length) // 2)
+249        print('nt', nt)
+250        zero = neid * [0.0]
+251        four = neid * [4.0]
+252        print('zero', zero)
+253        print('four', four)
+254        delta = np.concatenate([item for sublist in [[obs.deltas[o] for o in sl] for sl in list(obs.e_content.values())] for item in sublist])
+255
+256        bdio_start_record(0x00, 8, fbdio)
 257
-258        write_c_double(obs.value)
-259        write_c_size_t(neid)
-260
-261        for element in ndata:
-262            write_c_size_t(element)
-263        for element in nrep:
-264            write_c_size_t(element)
-265        for element in vrep_write:
-266            write_c_size_t(element)
-267        for element in ids:
-268            write_c_size_t(element)
-269        for element in nt:
-270            write_c_size_t(element)
-271
-272        for element in zero:
-273            write_c_double(element)
-274        for element in four:
-275            write_c_double(element)
-276
-277        for element in delta:
-278            write_c_double(element)
-279
-280    bdio_close(fbdio)
-281    return 0
+258        def write_c_double(double):
+259            pd_buf = ctypes.c_double(double)
+260            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+261            bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
+262
+263        def write_c_size_t(int32):
+264            pd_buf = ctypes.c_size_t(int32)
+265            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+266            bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+267
+268        write_c_double(obs.value)
+269        write_c_size_t(neid)
+270
+271        for element in ndata:
+272            write_c_size_t(element)
+273        for element in nrep:
+274            write_c_size_t(element)
+275        for element in vrep_write:
+276            write_c_size_t(element)
+277        for element in ids:
+278            write_c_size_t(element)
+279        for element in nt:
+280            write_c_size_t(element)
+281
+282        for element in zero:
+283            write_c_double(element)
+284        for element in four:
+285            write_c_double(element)
+286
+287        for element in delta:
+288            write_c_double(element)
+289
+290    bdio_close(fbdio)
+291    return 0
 
    
 
 
@@ -1102,6 +1134,13 @@ adding the flag -fPIC to CC and changing the all target to
    
 
  • file_path -- path to the bdio file
    • 
 
  • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • 
 
+
+
    Returns
    
+
+
      
+
    • success (int):
+returns 0 is successful
      • 
+
    @@ -1117,214 +1156,219 @@ adding the flag -fPIC to CC and changing the all target to

    - 
    292def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
-293    """ Extract mesons data from a bdio file and return it as a dictionary
-294
-295    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
-296
-297    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
-298    adding the flag -fPIC to CC and changing the all target to
-299
-300    all:		bdio.o $(LIBDIR)
-301                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-302                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
-303
-304    Parameters
-305    ----------
-306    file_path : str
-307        path to the bdio file
-308    bdio_path : str
-309        path to the shared bdio library libbdio.so (default ./libbdio.so)
-310    start : int
-311        The first configuration to be read (default 1)
-312    stop : int
-313        The last configuration to be read (default None)
-314    step : int
-315        Fixed step size between two measurements (default 1)
-316    alternative_ensemble_name : str
-317        Manually overwrite ensemble name
-318    """
-319
-320    start = kwargs.get('start', 1)
-321    stop = kwargs.get('stop', None)
-322    step = kwargs.get('step', 1)
-323
-324    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-325
-326    bdio_open = bdio.bdio_open
-327    bdio_open.restype = ctypes.c_void_p
+            
    302def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
+303    """ Extract mesons data from a bdio file and return it as a dictionary
+304
+305    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
+306
+307    read_mesons requires bdio to be compiled into a shared library. This can be achieved by
+308    adding the flag -fPIC to CC and changing the all target to
+309
+310    all:		bdio.o $(LIBDIR)
+311                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+312                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+313
+314    Parameters
+315    ----------
+316    file_path : str
+317        path to the bdio file
+318    bdio_path : str
+319        path to the shared bdio library libbdio.so (default ./libbdio.so)
+320    start : int
+321        The first configuration to be read (default 1)
+322    stop : int
+323        The last configuration to be read (default None)
+324    step : int
+325        Fixed step size between two measurements (default 1)
+326    alternative_ensemble_name : str
+327        Manually overwrite ensemble name
 328
-329    bdio_close = bdio.bdio_close
-330    bdio_close.restype = ctypes.c_int
-331    bdio_close.argtypes = [ctypes.c_void_p]
-332
-333    bdio_seek_record = bdio.bdio_seek_record
-334    bdio_seek_record.restype = ctypes.c_int
-335    bdio_seek_record.argtypes = [ctypes.c_void_p]
-336
-337    bdio_get_rlen = bdio.bdio_get_rlen
-338    bdio_get_rlen.restype = ctypes.c_int
-339    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+329    Returns
+330    -------
+331    data : dict
+332        Extracted meson data
+333    """
+334
+335    start = kwargs.get('start', 1)
+336    stop = kwargs.get('stop', None)
+337    step = kwargs.get('step', 1)
+338
+339    bdio = ctypes.cdll.LoadLibrary(bdio_path)
 340
-341    bdio_get_ruinfo = bdio.bdio_get_ruinfo
-342    bdio_get_ruinfo.restype = ctypes.c_int
-343    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
-344
-345    bdio_read = bdio.bdio_read
-346    bdio_read.restype = ctypes.c_size_t
-347    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
-348
-349    bdio_read_f64 = bdio.bdio_read_f64
-350    bdio_read_f64.restype = ctypes.c_size_t
-351    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-352
-353    b_path = file_path.encode('utf-8')
-354    read = 'r'
-355    b_read = read.encode('utf-8')
-356    form = 'Generic Correlator Format 1.0'
-357    b_form = form.encode('utf-8')
-358
-359    ensemble_name = ''
-360    volume = []  # lattice volume
-361    boundary_conditions = []
-362    corr_name = []  # Contains correlator names
-363    corr_type = []  # Contains correlator data type (important for reading out numerical data)
-364    corr_props = []  # Contanis propagator types (Component of corr_kappa)
-365    d0 = 0  # tvals
-366    d1 = 0  # nnoise
-367    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
-368    prop_source = []  # Contains propagator source positions
-369    # Check noise type for multiple replica?
-370    corr_no = -1
-371    data = []
-372    idl = []
+341    bdio_open = bdio.bdio_open
+342    bdio_open.restype = ctypes.c_void_p
+343
+344    bdio_close = bdio.bdio_close
+345    bdio_close.restype = ctypes.c_int
+346    bdio_close.argtypes = [ctypes.c_void_p]
+347
+348    bdio_seek_record = bdio.bdio_seek_record
+349    bdio_seek_record.restype = ctypes.c_int
+350    bdio_seek_record.argtypes = [ctypes.c_void_p]
+351
+352    bdio_get_rlen = bdio.bdio_get_rlen
+353    bdio_get_rlen.restype = ctypes.c_int
+354    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+355
+356    bdio_get_ruinfo = bdio.bdio_get_ruinfo
+357    bdio_get_ruinfo.restype = ctypes.c_int
+358    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
+359
+360    bdio_read = bdio.bdio_read
+361    bdio_read.restype = ctypes.c_size_t
+362    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
+363
+364    bdio_read_f64 = bdio.bdio_read_f64
+365    bdio_read_f64.restype = ctypes.c_size_t
+366    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+367
+368    b_path = file_path.encode('utf-8')
+369    read = 'r'
+370    b_read = read.encode('utf-8')
+371    form = 'Generic Correlator Format 1.0'
+372    b_form = form.encode('utf-8')
 373
-374    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
-375
-376    print('Reading of bdio file started')
-377    while True:
-378        bdio_seek_record(fbdio)
-379        ruinfo = bdio_get_ruinfo(fbdio)
-380        if ruinfo < 0:
-381            # EOF reached
-382            break
-383        rlen = bdio_get_rlen(fbdio)
-384        if ruinfo == 5:
-385            d_buf = ctypes.c_double * (2 + d0 * d1 * 2)
-386            pd_buf = d_buf()
-387            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-388            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-389            if corr_type[corr_no] == 'complex':
-390                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + 2 * d1:-2 * d1:2]), d0 - 2)), axis=1)
-391            else:
-392                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + d1:-d0 * d1 - d1]), d0 - 2)), axis=1)
-393
-394            data[corr_no].append(tmp_mean)
-395            corr_no += 1
-396        else:
-397            alt_buf = ctypes.create_string_buffer(1024)
-398            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
-399            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-400            if rlen != iread:
-401                print('Error')
-402            for i, item in enumerate(alt_buf):
-403                if item == b'\x00':
-404                    alt_buf[i] = b' '
-405            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
-406            if ruinfo == 0:
-407                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
-408                volume.append(int(_get_kwd(tmp_string, 'L0=')))
-409                volume.append(int(_get_kwd(tmp_string, 'L1=')))
-410                volume.append(int(_get_kwd(tmp_string, 'L2=')))
-411                volume.append(int(_get_kwd(tmp_string, 'L3=')))
-412                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
-413                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
-414                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
-415                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
-416
-417            if ruinfo == 1:
-418                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
-419                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
-420                corr_props.append([_get_kwd(tmp_string, 'PROP0='), _get_kwd(tmp_string, 'PROP1=')])
-421                if d0 == 0:
-422                    d0 = int(_get_kwd(tmp_string, 'D0='))
-423                else:
-424                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
-425                        print('Error: Varying number of time values')
-426                if d1 == 0:
-427                    d1 = int(_get_kwd(tmp_string, 'D1='))
-428                else:
-429                    if d1 != int(_get_kwd(tmp_string, 'D1=')):
-430                        print('Error: Varying number of random sources')
-431            if ruinfo == 2:
-432                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
-433                prop_source.append(_get_kwd(tmp_string, 'x0='))
-434            if ruinfo == 4:
-435                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
-436                if stop:
-437                    if cnfg_no > kwargs.get('stop'):
-438                        break
-439                idl.append(cnfg_no)
-440                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
-441                if len(idl) == 1:
-442                    no_corrs = len(corr_name)
-443                    data = []
-444                    for c in range(no_corrs):
-445                        data.append([])
-446
-447                corr_no = 0
-448
-449    bdio_close(fbdio)
-450
-451    print('\nEnsemble: ', ensemble_name)
-452    if 'alternative_ensemble_name' in kwargs:
-453        ensemble_name = kwargs.get('alternative_ensemble_name')
-454        print('Ensemble name overwritten to', ensemble_name)
-455    print('Lattice volume: ', volume)
-456    print('Boundary conditions: ', boundary_conditions)
-457    print('Number of time values: ', d0)
-458    print('Number of random sources: ', d1)
-459    print('Number of corrs: ', len(corr_name))
-460    print('Number of configurations: ', len(idl))
+374    ensemble_name = ''
+375    volume = []  # lattice volume
+376    boundary_conditions = []
+377    corr_name = []  # Contains correlator names
+378    corr_type = []  # Contains correlator data type (important for reading out numerical data)
+379    corr_props = []  # Contanis propagator types (Component of corr_kappa)
+380    d0 = 0  # tvals
+381    d1 = 0  # nnoise
+382    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
+383    prop_source = []  # Contains propagator source positions
+384    # Check noise type for multiple replica?
+385    corr_no = -1
+386    data = []
+387    idl = []
+388
+389    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
+390
+391    print('Reading of bdio file started')
+392    while True:
+393        bdio_seek_record(fbdio)
+394        ruinfo = bdio_get_ruinfo(fbdio)
+395        if ruinfo < 0:
+396            # EOF reached
+397            break
+398        rlen = bdio_get_rlen(fbdio)
+399        if ruinfo == 5:
+400            d_buf = ctypes.c_double * (2 + d0 * d1 * 2)
+401            pd_buf = d_buf()
+402            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+403            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+404            if corr_type[corr_no] == 'complex':
+405                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + 2 * d1:-2 * d1:2]), d0 - 2)), axis=1)
+406            else:
+407                tmp_mean = np.mean(np.asarray(np.split(np.asarray(pd_buf[2 + d1:-d0 * d1 - d1]), d0 - 2)), axis=1)
+408
+409            data[corr_no].append(tmp_mean)
+410            corr_no += 1
+411        else:
+412            alt_buf = ctypes.create_string_buffer(1024)
+413            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
+414            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+415            if rlen != iread:
+416                print('Error')
+417            for i, item in enumerate(alt_buf):
+418                if item == b'\x00':
+419                    alt_buf[i] = b' '
+420            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
+421            if ruinfo == 0:
+422                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
+423                volume.append(int(_get_kwd(tmp_string, 'L0=')))
+424                volume.append(int(_get_kwd(tmp_string, 'L1=')))
+425                volume.append(int(_get_kwd(tmp_string, 'L2=')))
+426                volume.append(int(_get_kwd(tmp_string, 'L3=')))
+427                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
+428                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
+429                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
+430                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
+431
+432            if ruinfo == 1:
+433                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
+434                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
+435                corr_props.append([_get_kwd(tmp_string, 'PROP0='), _get_kwd(tmp_string, 'PROP1=')])
+436                if d0 == 0:
+437                    d0 = int(_get_kwd(tmp_string, 'D0='))
+438                else:
+439                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
+440                        print('Error: Varying number of time values')
+441                if d1 == 0:
+442                    d1 = int(_get_kwd(tmp_string, 'D1='))
+443                else:
+444                    if d1 != int(_get_kwd(tmp_string, 'D1=')):
+445                        print('Error: Varying number of random sources')
+446            if ruinfo == 2:
+447                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
+448                prop_source.append(_get_kwd(tmp_string, 'x0='))
+449            if ruinfo == 4:
+450                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
+451                if stop:
+452                    if cnfg_no > kwargs.get('stop'):
+453                        break
+454                idl.append(cnfg_no)
+455                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
+456                if len(idl) == 1:
+457                    no_corrs = len(corr_name)
+458                    data = []
+459                    for c in range(no_corrs):
+460                        data.append([])
 461
-462    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
-463    corr_source = []
-464    for item in corr_props:
-465        corr_kappa.append([float(prop_kappa[int(item[0])]), float(prop_kappa[int(item[1])])])
-466        if prop_source[int(item[0])] != prop_source[int(item[1])]:
-467            raise Exception('Source position do not match for correlator' + str(item))
-468        else:
-469            corr_source.append(int(prop_source[int(item[0])]))
-470
-471    if stop is None:
-472        stop = idl[-1]
-473    idl_target = range(start, stop + 1, step)
-474
-475    if set(idl) != set(idl_target):
-476        try:
-477            indices = [idl.index(i) for i in idl_target]
-478        except ValueError as err:
-479            raise Exception('Configurations in file do no match target list!', err)
-480    else:
-481        indices = None
-482
-483    result = {}
-484    for c in range(no_corrs):
-485        tmp_corr = []
-486        tmp_data = np.asarray(data[c])
-487        for t in range(d0 - 2):
-488            if indices:
-489                deltas = [tmp_data[:, t][index] for index in indices]
-490            else:
-491                deltas = tmp_data[:, t]
-492            tmp_corr.append(Obs([deltas], [ensemble_name], idl=[idl_target]))
-493        result[(corr_name[c], corr_source[c]) + tuple(corr_kappa[c])] = tmp_corr
-494
-495    # Check that all data entries have the same number of configurations
-496    if len(set([o[0].N for o in list(result.values())])) != 1:
-497        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
-498
-499    return result
+462                corr_no = 0
+463
+464    bdio_close(fbdio)
+465
+466    print('\nEnsemble: ', ensemble_name)
+467    if 'alternative_ensemble_name' in kwargs:
+468        ensemble_name = kwargs.get('alternative_ensemble_name')
+469        print('Ensemble name overwritten to', ensemble_name)
+470    print('Lattice volume: ', volume)
+471    print('Boundary conditions: ', boundary_conditions)
+472    print('Number of time values: ', d0)
+473    print('Number of random sources: ', d1)
+474    print('Number of corrs: ', len(corr_name))
+475    print('Number of configurations: ', len(idl))
+476
+477    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
+478    corr_source = []
+479    for item in corr_props:
+480        corr_kappa.append([float(prop_kappa[int(item[0])]), float(prop_kappa[int(item[1])])])
+481        if prop_source[int(item[0])] != prop_source[int(item[1])]:
+482            raise Exception('Source position do not match for correlator' + str(item))
+483        else:
+484            corr_source.append(int(prop_source[int(item[0])]))
+485
+486    if stop is None:
+487        stop = idl[-1]
+488    idl_target = range(start, stop + 1, step)
+489
+490    if set(idl) != set(idl_target):
+491        try:
+492            indices = [idl.index(i) for i in idl_target]
+493        except ValueError as err:
+494            raise Exception('Configurations in file do no match target list!', err)
+495    else:
+496        indices = None
+497
+498    result = {}
+499    for c in range(no_corrs):
+500        tmp_corr = []
+501        tmp_data = np.asarray(data[c])
+502        for t in range(d0 - 2):
+503            if indices:
+504                deltas = [tmp_data[:, t][index] for index in indices]
+505            else:
+506                deltas = tmp_data[:, t]
+507            tmp_corr.append(Obs([deltas], [ensemble_name], idl=[idl_target]))
+508        result[(corr_name[c], corr_source[c]) + tuple(corr_kappa[c])] = tmp_corr
+509
+510    # Check that all data entries have the same number of configurations
+511    if len(set([o[0].N for o in list(result.values())])) != 1:
+512        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
+513
+514    return result
 
    
 
 
@@ -1355,6 +1399,13 @@ Fixed step size between two measurements (default 1)
 
  • alternative_ensemble_name (str):
 Manually overwrite ensemble name
    • 
 
+
+
    Returns
    
+
+
      
+
    • data (dict):
+Extracted meson data
      • 
+
    @@ -1370,185 +1421,185 @@ Manually overwrite ensemble name - 
    502def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
-503    """ Extract dSdm data from a bdio file and return it as a dictionary
-504
-505    The dictionary can be accessed with a tuple consisting of (type, kappa)
-506
-507    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by
-508    adding the flag -fPIC to CC and changing the all target to
-509
-510    all:		bdio.o $(LIBDIR)
-511                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
-512                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
-513
-514    Parameters
-515    ----------
-516    file_path : str
-517        path to the bdio file
-518    bdio_path : str
-519        path to the shared bdio library libbdio.so (default ./libbdio.so)
-520    start : int
-521        The first configuration to be read (default 1)
-522    stop : int
-523        The last configuration to be read (default None)
-524    step : int
-525        Fixed step size between two measurements (default 1)
-526    alternative_ensemble_name : str
-527        Manually overwrite ensemble name
-528    """
-529
-530    start = kwargs.get('start', 1)
-531    stop = kwargs.get('stop', None)
-532    step = kwargs.get('step', 1)
-533
-534    bdio = ctypes.cdll.LoadLibrary(bdio_path)
-535
-536    bdio_open = bdio.bdio_open
-537    bdio_open.restype = ctypes.c_void_p
-538
-539    bdio_close = bdio.bdio_close
-540    bdio_close.restype = ctypes.c_int
-541    bdio_close.argtypes = [ctypes.c_void_p]
-542
-543    bdio_seek_record = bdio.bdio_seek_record
-544    bdio_seek_record.restype = ctypes.c_int
-545    bdio_seek_record.argtypes = [ctypes.c_void_p]
-546
-547    bdio_get_rlen = bdio.bdio_get_rlen
-548    bdio_get_rlen.restype = ctypes.c_int
-549    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+            
    517def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
+518    """ Extract dSdm data from a bdio file and return it as a dictionary
+519
+520    The dictionary can be accessed with a tuple consisting of (type, kappa)
+521
+522    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by
+523    adding the flag -fPIC to CC and changing the all target to
+524
+525    all:		bdio.o $(LIBDIR)
+526                gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o
+527                cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
+528
+529    Parameters
+530    ----------
+531    file_path : str
+532        path to the bdio file
+533    bdio_path : str
+534        path to the shared bdio library libbdio.so (default ./libbdio.so)
+535    start : int
+536        The first configuration to be read (default 1)
+537    stop : int
+538        The last configuration to be read (default None)
+539    step : int
+540        Fixed step size between two measurements (default 1)
+541    alternative_ensemble_name : str
+542        Manually overwrite ensemble name
+543    """
+544
+545    start = kwargs.get('start', 1)
+546    stop = kwargs.get('stop', None)
+547    step = kwargs.get('step', 1)
+548
+549    bdio = ctypes.cdll.LoadLibrary(bdio_path)
 550
-551    bdio_get_ruinfo = bdio.bdio_get_ruinfo
-552    bdio_get_ruinfo.restype = ctypes.c_int
-553    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
-554
-555    bdio_read = bdio.bdio_read
-556    bdio_read.restype = ctypes.c_size_t
-557    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
-558
-559    bdio_read_f64 = bdio.bdio_read_f64
-560    bdio_read_f64.restype = ctypes.c_size_t
-561    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
-562
-563    b_path = file_path.encode('utf-8')
-564    read = 'r'
-565    b_read = read.encode('utf-8')
-566    form = 'Generic Correlator Format 1.0'
-567    b_form = form.encode('utf-8')
-568
-569    ensemble_name = ''
-570    volume = []  # lattice volume
-571    boundary_conditions = []
-572    corr_name = []  # Contains correlator names
-573    corr_type = []  # Contains correlator data type (important for reading out numerical data)
-574    corr_props = []  # Contains propagator types (Component of corr_kappa)
-575    d0 = 0  # tvals
-576    # d1 = 0  # nnoise
-577    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
-578    # Check noise type for multiple replica?
-579    corr_no = -1
-580    data = []
-581    idl = []
-582
-583    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
-584
-585    print('Reading of bdio file started')
-586    while True:
-587        bdio_seek_record(fbdio)
-588        ruinfo = bdio_get_ruinfo(fbdio)
-589        if ruinfo < 0:
-590            # EOF reached
-591            break
-592        rlen = bdio_get_rlen(fbdio)
-593        if ruinfo == 5:
-594            d_buf = ctypes.c_double * (2 + d0)
-595            pd_buf = d_buf()
-596            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-597            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-598            tmp_mean = np.mean(np.asarray(pd_buf[2:]))
+551    bdio_open = bdio.bdio_open
+552    bdio_open.restype = ctypes.c_void_p
+553
+554    bdio_close = bdio.bdio_close
+555    bdio_close.restype = ctypes.c_int
+556    bdio_close.argtypes = [ctypes.c_void_p]
+557
+558    bdio_seek_record = bdio.bdio_seek_record
+559    bdio_seek_record.restype = ctypes.c_int
+560    bdio_seek_record.argtypes = [ctypes.c_void_p]
+561
+562    bdio_get_rlen = bdio.bdio_get_rlen
+563    bdio_get_rlen.restype = ctypes.c_int
+564    bdio_get_rlen.argtypes = [ctypes.c_void_p]
+565
+566    bdio_get_ruinfo = bdio.bdio_get_ruinfo
+567    bdio_get_ruinfo.restype = ctypes.c_int
+568    bdio_get_ruinfo.argtypes = [ctypes.c_void_p]
+569
+570    bdio_read = bdio.bdio_read
+571    bdio_read.restype = ctypes.c_size_t
+572    bdio_read.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_void_p]
+573
+574    bdio_read_f64 = bdio.bdio_read_f64
+575    bdio_read_f64.restype = ctypes.c_size_t
+576    bdio_read_f64.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_void_p]
+577
+578    b_path = file_path.encode('utf-8')
+579    read = 'r'
+580    b_read = read.encode('utf-8')
+581    form = 'Generic Correlator Format 1.0'
+582    b_form = form.encode('utf-8')
+583
+584    ensemble_name = ''
+585    volume = []  # lattice volume
+586    boundary_conditions = []
+587    corr_name = []  # Contains correlator names
+588    corr_type = []  # Contains correlator data type (important for reading out numerical data)
+589    corr_props = []  # Contains propagator types (Component of corr_kappa)
+590    d0 = 0  # tvals
+591    # d1 = 0  # nnoise
+592    prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
+593    # Check noise type for multiple replica?
+594    corr_no = -1
+595    data = []
+596    idl = []
+597
+598    fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_read), ctypes.c_char_p(b_form))
 599
-600            data[corr_no].append(tmp_mean)
-601            corr_no += 1
-602        else:
-603            alt_buf = ctypes.create_string_buffer(1024)
-604            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
-605            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
-606            if rlen != iread:
-607                print('Error')
-608            for i, item in enumerate(alt_buf):
-609                if item == b'\x00':
-610                    alt_buf[i] = b' '
-611            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
-612            if ruinfo == 0:
-613                creator = _get_kwd(tmp_string, 'CREATOR=')
-614                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
-615                volume.append(int(_get_kwd(tmp_string, 'L0=')))
-616                volume.append(int(_get_kwd(tmp_string, 'L1=')))
-617                volume.append(int(_get_kwd(tmp_string, 'L2=')))
-618                volume.append(int(_get_kwd(tmp_string, 'L3=')))
-619                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
-620                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
-621                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
-622                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
-623
-624            if ruinfo == 1:
-625                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
-626                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
-627                corr_props.append(_get_kwd(tmp_string, 'PROP0='))
-628                if d0 == 0:
-629                    d0 = int(_get_kwd(tmp_string, 'D0='))
-630                else:
-631                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
-632                        print('Error: Varying number of time values')
-633            if ruinfo == 2:
-634                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
-635            if ruinfo == 4:
-636                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
-637                if stop:
-638                    if cnfg_no > kwargs.get('stop'):
-639                        break
-640                idl.append(cnfg_no)
-641                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
-642                if len(idl) == 1:
-643                    no_corrs = len(corr_name)
-644                    data = []
-645                    for c in range(no_corrs):
-646                        data.append([])
-647
-648                corr_no = 0
-649    bdio_close(fbdio)
-650
-651    print('\nCreator: ', creator)
-652    print('Ensemble: ', ensemble_name)
-653    print('Lattice volume: ', volume)
-654    print('Boundary conditions: ', boundary_conditions)
-655    print('Number of random sources: ', d0)
-656    print('Number of corrs: ', len(corr_name))
-657    print('Number of configurations: ', cnfg_no + 1)
-658
-659    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
-660    for item in corr_props:
-661        corr_kappa.append(float(prop_kappa[int(item)]))
+600    print('Reading of bdio file started')
+601    while True:
+602        bdio_seek_record(fbdio)
+603        ruinfo = bdio_get_ruinfo(fbdio)
+604        if ruinfo < 0:
+605            # EOF reached
+606            break
+607        rlen = bdio_get_rlen(fbdio)
+608        if ruinfo == 5:
+609            d_buf = ctypes.c_double * (2 + d0)
+610            pd_buf = d_buf()
+611            ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
+612            bdio_read_f64(ppd_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+613            tmp_mean = np.mean(np.asarray(pd_buf[2:]))
+614
+615            data[corr_no].append(tmp_mean)
+616            corr_no += 1
+617        else:
+618            alt_buf = ctypes.create_string_buffer(1024)
+619            palt_buf = ctypes.c_char_p(ctypes.addressof(alt_buf))
+620            iread = bdio_read(palt_buf, ctypes.c_size_t(rlen), ctypes.c_void_p(fbdio))
+621            if rlen != iread:
+622                print('Error')
+623            for i, item in enumerate(alt_buf):
+624                if item == b'\x00':
+625                    alt_buf[i] = b' '
+626            tmp_string = (alt_buf[:].decode("utf-8")).rstrip()
+627            if ruinfo == 0:
+628                creator = _get_kwd(tmp_string, 'CREATOR=')
+629                ensemble_name = _get_kwd(tmp_string, 'ENSEMBLE=')
+630                volume.append(int(_get_kwd(tmp_string, 'L0=')))
+631                volume.append(int(_get_kwd(tmp_string, 'L1=')))
+632                volume.append(int(_get_kwd(tmp_string, 'L2=')))
+633                volume.append(int(_get_kwd(tmp_string, 'L3=')))
+634                boundary_conditions.append(_get_kwd(tmp_string, 'BC0='))
+635                boundary_conditions.append(_get_kwd(tmp_string, 'BC1='))
+636                boundary_conditions.append(_get_kwd(tmp_string, 'BC2='))
+637                boundary_conditions.append(_get_kwd(tmp_string, 'BC3='))
+638
+639            if ruinfo == 1:
+640                corr_name.append(_get_corr_name(tmp_string, 'CORR_NAME='))
+641                corr_type.append(_get_kwd(tmp_string, 'DATATYPE='))
+642                corr_props.append(_get_kwd(tmp_string, 'PROP0='))
+643                if d0 == 0:
+644                    d0 = int(_get_kwd(tmp_string, 'D0='))
+645                else:
+646                    if d0 != int(_get_kwd(tmp_string, 'D0=')):
+647                        print('Error: Varying number of time values')
+648            if ruinfo == 2:
+649                prop_kappa.append(_get_kwd(tmp_string, 'KAPPA='))
+650            if ruinfo == 4:
+651                cnfg_no = int(_get_kwd(tmp_string, 'CNFG_ID='))
+652                if stop:
+653                    if cnfg_no > kwargs.get('stop'):
+654                        break
+655                idl.append(cnfg_no)
+656                print('\r%s %i' % ('Reading configuration', cnfg_no), end='\r')
+657                if len(idl) == 1:
+658                    no_corrs = len(corr_name)
+659                    data = []
+660                    for c in range(no_corrs):
+661                        data.append([])
 662
-663    if stop is None:
-664        stop = idl[-1]
-665    idl_target = range(start, stop + 1, step)
-666    try:
-667        indices = [idl.index(i) for i in idl_target]
-668    except ValueError as err:
-669        raise Exception('Configurations in file do no match target list!', err)
-670
-671    result = {}
-672    for c in range(no_corrs):
-673        deltas = [np.asarray(data[c])[index] for index in indices]
-674        result[(corr_name[c], str(corr_kappa[c]))] = Obs([deltas], [ensemble_name], idl=[idl_target])
-675
-676    # Check that all data entries have the same number of configurations
-677    if len(set([o.N for o in list(result.values())])) != 1:
-678        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
-679
-680    return result
+663                corr_no = 0
+664    bdio_close(fbdio)
+665
+666    print('\nCreator: ', creator)
+667    print('Ensemble: ', ensemble_name)
+668    print('Lattice volume: ', volume)
+669    print('Boundary conditions: ', boundary_conditions)
+670    print('Number of random sources: ', d0)
+671    print('Number of corrs: ', len(corr_name))
+672    print('Number of configurations: ', cnfg_no + 1)
+673
+674    corr_kappa = []  # Contains kappa values for both propagators of given correlation function
+675    for item in corr_props:
+676        corr_kappa.append(float(prop_kappa[int(item)]))
+677
+678    if stop is None:
+679        stop = idl[-1]
+680    idl_target = range(start, stop + 1, step)
+681    try:
+682        indices = [idl.index(i) for i in idl_target]
+683    except ValueError as err:
+684        raise Exception('Configurations in file do no match target list!', err)
+685
+686    result = {}
+687    for c in range(no_corrs):
+688        deltas = [np.asarray(data[c])[index] for index in indices]
+689        result[(corr_name[c], str(corr_kappa[c]))] = Obs([deltas], [ensemble_name], idl=[idl_target])
+690
+691    # Check that all data entries have the same number of configurations
+692    if len(set([o.N for o in list(result.values())])) != 1:
+693        raise Exception('Error: Not all correlators have the same number of configurations. bdio file is possibly corrupted.')
+694
+695    return result
 
    
 
 
diff --git a/docs/pyerrors/input/dobs.html b/docs/pyerrors/input/dobs.html
index 0531c98c..3c37ca33 100644
--- a/docs/pyerrors/input/dobs.html
+++ b/docs/pyerrors/input/dobs.html
@@ -202,776 +202,818 @@
     106        A list of symbols that describe the observables to be written. May be empty.
 107    enstag : str
 108        Enstag that is written to pobs. If None, the ensemble name is used.
-109    """
-110
-111    od = {}
-112    ename = obsl[0].e_names[0]
-113    names = list(obsl[0].deltas.keys())
-114    nr = len(names)
-115    onames = [name.replace('|', '') for name in names]
-116    for o in obsl:
-117        if len(o.e_names) != 1:
-118            raise Exception('You try to export dobs to obs!')
-119        if o.e_names[0] != ename:
-120            raise Exception('You try to export dobs to obs!')
-121        if len(o.deltas.keys()) != nr:
-122            raise Exception('Incompatible obses in list')
-123    od['observables'] = {}
-124    od['observables']['schema'] = {'name': 'lattobs', 'version': '1.0'}
-125    od['observables']['origin'] = {
-126        'who': getpass.getuser(),
-127        'date': str(datetime.datetime.now())[:-7],
-128        'host': socket.gethostname(),
-129        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
-130    od['observables']['pobs'] = {}
-131    pd = od['observables']['pobs']
-132    pd['spec'] = spec
-133    pd['origin'] = origin
-134    pd['name'] = name
-135    if enstag:
-136        if not isinstance(enstag, str):
-137            raise Exception('enstag has to be a string!')
-138        pd['enstag'] = enstag
-139    else:
-140        pd['enstag'] = ename
-141    pd['nr'] = '%d' % (nr)
-142    pd['array'] = []
-143    osymbol = 'cfg'
-144    if not isinstance(symbol, list):
-145        raise Exception('Symbol has to be a list!')
-146    if not (len(symbol) == 0 or len(symbol) == len(obsl)):
-147        raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
-148    for s in symbol:
-149        osymbol += ' %s' % s
-150    for r in range(nr):
-151        ad = {}
-152        ad['id'] = onames[r]
-153        Nconf = len(obsl[0].deltas[names[r]])
-154        layout = '%d i f%d' % (Nconf, len(obsl))
-155        ad['layout'] = layout
-156        ad['symbol'] = osymbol
-157        data = ''
-158        for c in range(Nconf):
-159            data += '%d ' % obsl[0].idl[names[r]][c]
-160            for o in obsl:
-161                num = o.deltas[names[r]][c] + o.r_values[names[r]]
-162                if num == 0:
-163                    data += '0 '
-164                else:
-165                    data += '%1.16e ' % (num)
-166            data += '\n'
-167        ad['#data'] = data
-168        pd['array'].append(ad)
-169
-170    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dict_to_xmlstring_spaces(od)
-171    return rs
-172
-173
-174def write_pobs(obsl, fname, name, spec='', origin='', symbol=[], enstag=None, gz=True):
-175    """Export a list of Obs or structures containing Obs to a .xml.gz file
-176    according to the Zeuthen pobs format.
+109
+110    Returns
+111    -------
+112    xml_str : str
+113        XML formatted string of the input data
+114    """
+115
+116    od = {}
+117    ename = obsl[0].e_names[0]
+118    names = list(obsl[0].deltas.keys())
+119    nr = len(names)
+120    onames = [name.replace('|', '') for name in names]
+121    for o in obsl:
+122        if len(o.e_names) != 1:
+123            raise Exception('You try to export dobs to obs!')
+124        if o.e_names[0] != ename:
+125            raise Exception('You try to export dobs to obs!')
+126        if len(o.deltas.keys()) != nr:
+127            raise Exception('Incompatible obses in list')
+128    od['observables'] = {}
+129    od['observables']['schema'] = {'name': 'lattobs', 'version': '1.0'}
+130    od['observables']['origin'] = {
+131        'who': getpass.getuser(),
+132        'date': str(datetime.datetime.now())[:-7],
+133        'host': socket.gethostname(),
+134        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
+135    od['observables']['pobs'] = {}
+136    pd = od['observables']['pobs']
+137    pd['spec'] = spec
+138    pd['origin'] = origin
+139    pd['name'] = name
+140    if enstag:
+141        if not isinstance(enstag, str):
+142            raise Exception('enstag has to be a string!')
+143        pd['enstag'] = enstag
+144    else:
+145        pd['enstag'] = ename
+146    pd['nr'] = '%d' % (nr)
+147    pd['array'] = []
+148    osymbol = 'cfg'
+149    if not isinstance(symbol, list):
+150        raise Exception('Symbol has to be a list!')
+151    if not (len(symbol) == 0 or len(symbol) == len(obsl)):
+152        raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
+153    for s in symbol:
+154        osymbol += ' %s' % s
+155    for r in range(nr):
+156        ad = {}
+157        ad['id'] = onames[r]
+158        Nconf = len(obsl[0].deltas[names[r]])
+159        layout = '%d i f%d' % (Nconf, len(obsl))
+160        ad['layout'] = layout
+161        ad['symbol'] = osymbol
+162        data = ''
+163        for c in range(Nconf):
+164            data += '%d ' % obsl[0].idl[names[r]][c]
+165            for o in obsl:
+166                num = o.deltas[names[r]][c] + o.r_values[names[r]]
+167                if num == 0:
+168                    data += '0 '
+169                else:
+170                    data += '%1.16e ' % (num)
+171            data += '\n'
+172        ad['#data'] = data
+173        pd['array'].append(ad)
+174
+175    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dict_to_xmlstring_spaces(od)
+176    return rs
 177
-178    Tags are not written or recovered automatically. The separator | is removed from the replica names.
-179
-180    Parameters
-181    ----------
-182    obsl : list
-183        List of Obs that will be exported.
-184        The Obs inside a structure have to be defined on the same ensemble.
-185    fname : str
-186        Filename of the output file.
-187    name : str
-188        The name of the observable.
-189    spec : str
-190        Optional string that describes the contents of the file.
-191    origin : str
-192        Specify where the data has its origin.
-193    symbol : list
-194        A list of symbols that describe the observables to be written. May be empty.
-195    enstag : str
-196        Enstag that is written to pobs. If None, the ensemble name is used.
-197    gz : bool
-198        If True, the output is a gzipped xml. If False, the output is an xml file.
-199    """
-200    pobsstring = create_pobs_string(obsl, name, spec, origin, symbol, enstag)
-201
-202    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-203        fname += '.xml'
+178
+179def write_pobs(obsl, fname, name, spec='', origin='', symbol=[], enstag=None, gz=True):
+180    """Export a list of Obs or structures containing Obs to a .xml.gz file
+181    according to the Zeuthen pobs format.
+182
+183    Tags are not written or recovered automatically. The separator | is removed from the replica names.
+184
+185    Parameters
+186    ----------
+187    obsl : list
+188        List of Obs that will be exported.
+189        The Obs inside a structure have to be defined on the same ensemble.
+190    fname : str
+191        Filename of the output file.
+192    name : str
+193        The name of the observable.
+194    spec : str
+195        Optional string that describes the contents of the file.
+196    origin : str
+197        Specify where the data has its origin.
+198    symbol : list
+199        A list of symbols that describe the observables to be written. May be empty.
+200    enstag : str
+201        Enstag that is written to pobs. If None, the ensemble name is used.
+202    gz : bool
+203        If True, the output is a gzipped xml. If False, the output is an xml file.
 204
-205    if gz:
-206        if not fname.endswith('.gz'):
-207            fname += '.gz'
-208
-209        fp = gzip.open(fname, 'wb')
-210        fp.write(pobsstring.encode('utf-8'))
-211    else:
-212        fp = open(fname, 'w', encoding='utf-8')
-213        fp.write(pobsstring)
-214    fp.close()
-215
-216
-217def _import_data(string):
-218    return json.loads("[" + ",".join(string.replace(' +', ' ').split()) + "]")
-219
-220
-221def _check(condition):
-222    if not condition:
-223        raise Exception("XML file format not supported")
+205    Returns
+206    -------
+207    None
+208    """
+209    pobsstring = create_pobs_string(obsl, name, spec, origin, symbol, enstag)
+210
+211    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+212        fname += '.xml'
+213
+214    if gz:
+215        if not fname.endswith('.gz'):
+216            fname += '.gz'
+217
+218        fp = gzip.open(fname, 'wb')
+219        fp.write(pobsstring.encode('utf-8'))
+220    else:
+221        fp = open(fname, 'w', encoding='utf-8')
+222        fp.write(pobsstring)
+223    fp.close()
 224
 225
-226class _NoTagInDataError(Exception):
-227    """Raised when tag is not in data"""
-228    def __init__(self, tag):
-229        self.tag = tag
-230        super().__init__('Tag %s not in data!' % (self.tag))
-231
-232
-233def _find_tag(dat, tag):
-234    for i in range(len(dat)):
-235        if dat[i].tag == tag:
-236            return i
-237    raise _NoTagInDataError(tag)
-238
-239
-240def _import_array(arr):
-241    name = arr[_find_tag(arr, 'id')].text.strip()
-242    index = _find_tag(arr, 'layout')
-243    try:
-244        sindex = _find_tag(arr, 'symbol')
-245    except _NoTagInDataError:
-246        sindex = 0
-247    if sindex > index:
-248        tmp = _import_data(arr[sindex].tail)
-249    else:
-250        tmp = _import_data(arr[index].tail)
-251
-252    li = arr[index].text.strip()
-253    m = li.split()
-254    if m[1] == "i" and m[2][0] == "f":
-255        nc = int(m[0])
-256        na = int(m[2].lstrip('f'))
-257        _dat = []
-258        mask = []
-259        for a in range(na):
-260            mask += [a]
-261            _dat += [np.array(tmp[1 + a:: na + 1])]
-262        _check(len(tmp[0:: na + 1]) == nc)
-263        return [name, tmp[0:: na + 1], mask, _dat]
-264    elif m[1][0] == 'f' and len(m) < 3:
-265        sh = (int(m[0]), int(m[1].lstrip('f')))
-266        return np.reshape(tmp, sh)
-267    elif any(['f' in s for s in m]):
-268        for si in range(len(m)):
-269            if m[si] == 'f':
-270                break
-271        sh = [int(m[i]) for i in range(si)]
-272        return np.reshape(tmp, sh)
-273    else:
-274        print(name, m)
-275        _check(False)
-276
-277
-278def _import_rdata(rd):
-279    name, idx, mask, deltas = _import_array(rd)
-280    return deltas, name, idx
-281
-282
-283def _import_cdata(cd):
-284    _check(cd[0].tag == "id")
-285    _check(cd[1][0].text.strip() == "cov")
-286    cov = _import_array(cd[1])
-287    grad = _import_array(cd[2])
-288    return cd[0].text.strip(), cov, grad
-289
+226def _import_data(string):
+227    return json.loads("[" + ",".join(string.replace(' +', ' ').split()) + "]")
+228
+229
+230def _check(condition):
+231    if not condition:
+232        raise Exception("XML file format not supported")
+233
+234
+235class _NoTagInDataError(Exception):
+236    """Raised when tag is not in data"""
+237    def __init__(self, tag):
+238        self.tag = tag
+239        super().__init__('Tag %s not in data!' % (self.tag))
+240
+241
+242def _find_tag(dat, tag):
+243    for i in range(len(dat)):
+244        if dat[i].tag == tag:
+245            return i
+246    raise _NoTagInDataError(tag)
+247
+248
+249def _import_array(arr):
+250    name = arr[_find_tag(arr, 'id')].text.strip()
+251    index = _find_tag(arr, 'layout')
+252    try:
+253        sindex = _find_tag(arr, 'symbol')
+254    except _NoTagInDataError:
+255        sindex = 0
+256    if sindex > index:
+257        tmp = _import_data(arr[sindex].tail)
+258    else:
+259        tmp = _import_data(arr[index].tail)
+260
+261    li = arr[index].text.strip()
+262    m = li.split()
+263    if m[1] == "i" and m[2][0] == "f":
+264        nc = int(m[0])
+265        na = int(m[2].lstrip('f'))
+266        _dat = []
+267        mask = []
+268        for a in range(na):
+269            mask += [a]
+270            _dat += [np.array(tmp[1 + a:: na + 1])]
+271        _check(len(tmp[0:: na + 1]) == nc)
+272        return [name, tmp[0:: na + 1], mask, _dat]
+273    elif m[1][0] == 'f' and len(m) < 3:
+274        sh = (int(m[0]), int(m[1].lstrip('f')))
+275        return np.reshape(tmp, sh)
+276    elif any(['f' in s for s in m]):
+277        for si in range(len(m)):
+278            if m[si] == 'f':
+279                break
+280        sh = [int(m[i]) for i in range(si)]
+281        return np.reshape(tmp, sh)
+282    else:
+283        print(name, m)
+284        _check(False)
+285
+286
+287def _import_rdata(rd):
+288    name, idx, mask, deltas = _import_array(rd)
+289    return deltas, name, idx
 290
-291def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
-292    """Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
-293
-294    Tags are not written or recovered automatically.
-295
-296    Parameters
-297    ----------
-298    fname : str
-299        Filename of the input file.
-300    full_output : bool
-301        If True, a dict containing auxiliary information and the data is returned.
-302        If False, only the data is returned as list.
-303    separatior_insertion: str or int
-304        str: replace all occurences of "separator_insertion" within the replica names
-305        by "|%s" % (separator_insertion) when constructing the names of the replica.
-306        int: Insert the separator "|" at the position given by separator_insertion.
-307        None (default): Replica names remain unchanged.
-308    """
-309
-310    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-311        fname += '.xml'
-312    if gz:
-313        if not fname.endswith('.gz'):
-314            fname += '.gz'
-315        with gzip.open(fname, 'r') as fin:
-316            content = fin.read()
-317    else:
-318        if fname.endswith('.gz'):
-319            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-320        with open(fname, 'r') as fin:
-321            content = fin.read()
-322
-323    # parse xml file content
-324    root = et.fromstring(content)
-325
-326    _check(root[2].tag == 'pobs')
-327    pobs = root[2]
-328
-329    version = root[0][1].text.strip()
-330
-331    _check(root[1].tag == 'origin')
-332    file_origin = _etree_to_dict(root[1])['origin']
-333
-334    deltas = []
-335    names = []
-336    idl = []
-337    for i in range(5, len(pobs)):
-338        delta, name, idx = _import_rdata(pobs[i])
-339        deltas.append(delta)
-340        if separator_insertion is None:
-341            pass
-342        elif isinstance(separator_insertion, int):
-343            name = name[:separator_insertion] + '|' + name[separator_insertion:]
-344        elif isinstance(separator_insertion, str):
-345            name = name.replace(separator_insertion, "|%s" % (separator_insertion))
-346        else:
-347            raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
-348        names.append(name)
-349        idl.append(idx)
-350    res = [Obs([d[i] for d in deltas], names, idl=idl) for i in range(len(deltas[0]))]
-351
-352    descriptiond = {}
-353    for i in range(4):
-354        descriptiond[pobs[i].tag] = pobs[i].text.strip()
-355
-356    _check(pobs[4].tag == "nr")
-357
-358    _check(pobs[5].tag == 'array')
-359    if pobs[5][1].tag == 'symbol':
-360        symbol = pobs[5][1].text.strip()
-361        descriptiond['symbol'] = symbol
-362
-363    if full_output:
-364        retd = {}
-365        tool = file_origin.get('tool', None)
-366        if tool:
-367            program = tool['name'] + ' ' + tool['version']
-368        else:
-369            program = ''
-370        retd['program'] = program
-371        retd['version'] = version
-372        retd['who'] = file_origin['who']
-373        retd['date'] = file_origin['date']
-374        retd['host'] = file_origin['host']
-375        retd['description'] = descriptiond
-376        retd['obsdata'] = res
-377        return retd
-378    else:
-379        return res
-380
-381
-382# this is based on Mattia Bruno's implementation at https://github.com/mbruno46/pyobs/blob/master/pyobs/IO/xml.py
-383def import_dobs_string(content, noempty=False, full_output=False, separator_insertion=True):
-384    """Import a list of Obs from a string in the Zeuthen dobs format.
-385
-386    Tags are not written or recovered automatically.
-387
-388    Parameters
-389    ----------
-390    content : str
-391        XML string containing the data
-392    noemtpy : bool
-393        If True, ensembles with no contribution to the Obs are not included.
-394        If False, ensembles are included as written in the file, possibly with vanishing entries.
-395    full_output : bool
-396        If True, a dict containing auxiliary information and the data is returned.
-397        If False, only the data is returned as list.
-398    separatior_insertion: str, int or bool
-399        str: replace all occurences of "separator_insertion" within the replica names
-400        by "|%s" % (separator_insertion) when constructing the names of the replica.
-401        int: Insert the separator "|" at the position given by separator_insertion.
-402        True (default): separator "|" is inserted after len(ensname), assuming that the
-403        ensemble name is a prefix to the replica name.
-404        None or False: No separator is inserted.
-405    """
-406
-407    root = et.fromstring(content)
-408
-409    _check(root.tag == 'OBSERVABLES')
-410    _check(root[0].tag == 'SCHEMA')
-411    version = root[0][1].text.strip()
-412
-413    _check(root[1].tag == 'origin')
-414    file_origin = _etree_to_dict(root[1])['origin']
-415
-416    _check(root[2].tag == 'dobs')
-417
-418    dobs = root[2]
-419
-420    descriptiond = {}
-421    for i in range(3):
-422        descriptiond[dobs[i].tag] = dobs[i].text.strip()
-423
-424    _check(dobs[3].tag == 'array')
-425
-426    symbol = []
-427    if dobs[3][1].tag == 'symbol':
-428        symbol = dobs[3][1].text.strip()
-429        descriptiond['symbol'] = symbol
-430    mean = _import_array(dobs[3])[0]
+291
+292def _import_cdata(cd):
+293    _check(cd[0].tag == "id")
+294    _check(cd[1][0].text.strip() == "cov")
+295    cov = _import_array(cd[1])
+296    grad = _import_array(cd[2])
+297    return cd[0].text.strip(), cov, grad
+298
+299
+300def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
+301    """Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
+302
+303    Tags are not written or recovered automatically.
+304
+305    Parameters
+306    ----------
+307    fname : str
+308        Filename of the input file.
+309    full_output : bool
+310        If True, a dict containing auxiliary information and the data is returned.
+311        If False, only the data is returned as list.
+312    separatior_insertion: str or int
+313        str: replace all occurences of "separator_insertion" within the replica names
+314        by "|%s" % (separator_insertion) when constructing the names of the replica.
+315        int: Insert the separator "|" at the position given by separator_insertion.
+316        None (default): Replica names remain unchanged.
+317
+318    Returns
+319    -------
+320    res : list[Obs]
+321        Imported data
+322    or
+323    res : dict
+324        Imported data and meta-data
+325    """
+326
+327    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+328        fname += '.xml'
+329    if gz:
+330        if not fname.endswith('.gz'):
+331            fname += '.gz'
+332        with gzip.open(fname, 'r') as fin:
+333            content = fin.read()
+334    else:
+335        if fname.endswith('.gz'):
+336            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+337        with open(fname, 'r') as fin:
+338            content = fin.read()
+339
+340    # parse xml file content
+341    root = et.fromstring(content)
+342
+343    _check(root[2].tag == 'pobs')
+344    pobs = root[2]
+345
+346    version = root[0][1].text.strip()
+347
+348    _check(root[1].tag == 'origin')
+349    file_origin = _etree_to_dict(root[1])['origin']
+350
+351    deltas = []
+352    names = []
+353    idl = []
+354    for i in range(5, len(pobs)):
+355        delta, name, idx = _import_rdata(pobs[i])
+356        deltas.append(delta)
+357        if separator_insertion is None:
+358            pass
+359        elif isinstance(separator_insertion, int):
+360            name = name[:separator_insertion] + '|' + name[separator_insertion:]
+361        elif isinstance(separator_insertion, str):
+362            name = name.replace(separator_insertion, "|%s" % (separator_insertion))
+363        else:
+364            raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
+365        names.append(name)
+366        idl.append(idx)
+367    res = [Obs([d[i] for d in deltas], names, idl=idl) for i in range(len(deltas[0]))]
+368
+369    descriptiond = {}
+370    for i in range(4):
+371        descriptiond[pobs[i].tag] = pobs[i].text.strip()
+372
+373    _check(pobs[4].tag == "nr")
+374
+375    _check(pobs[5].tag == 'array')
+376    if pobs[5][1].tag == 'symbol':
+377        symbol = pobs[5][1].text.strip()
+378        descriptiond['symbol'] = symbol
+379
+380    if full_output:
+381        retd = {}
+382        tool = file_origin.get('tool', None)
+383        if tool:
+384            program = tool['name'] + ' ' + tool['version']
+385        else:
+386            program = ''
+387        retd['program'] = program
+388        retd['version'] = version
+389        retd['who'] = file_origin['who']
+390        retd['date'] = file_origin['date']
+391        retd['host'] = file_origin['host']
+392        retd['description'] = descriptiond
+393        retd['obsdata'] = res
+394        return retd
+395    else:
+396        return res
+397
+398
+399# this is based on Mattia Bruno's implementation at https://github.com/mbruno46/pyobs/blob/master/pyobs/IO/xml.py
+400def import_dobs_string(content, noempty=False, full_output=False, separator_insertion=True):
+401    """Import a list of Obs from a string in the Zeuthen dobs format.
+402
+403    Tags are not written or recovered automatically.
+404
+405    Parameters
+406    ----------
+407    content : str
+408        XML string containing the data
+409    noemtpy : bool
+410        If True, ensembles with no contribution to the Obs are not included.
+411        If False, ensembles are included as written in the file, possibly with vanishing entries.
+412    full_output : bool
+413        If True, a dict containing auxiliary information and the data is returned.
+414        If False, only the data is returned as list.
+415    separatior_insertion: str, int or bool
+416        str: replace all occurences of "separator_insertion" within the replica names
+417        by "|%s" % (separator_insertion) when constructing the names of the replica.
+418        int: Insert the separator "|" at the position given by separator_insertion.
+419        True (default): separator "|" is inserted after len(ensname), assuming that the
+420        ensemble name is a prefix to the replica name.
+421        None or False: No separator is inserted.
+422
+423    Returns
+424    -------
+425    res : list[Obs]
+426        Imported data
+427    or
+428    res : dict
+429        Imported data and meta-data
+430    """
 431
-432    _check(dobs[4].tag == "ne")
-433    ne = int(dobs[4].text.strip())
-434    _check(dobs[5].tag == "nc")
-435    nc = int(dobs[5].text.strip())
-436
-437    idld = {}
-438    deltad = {}
-439    covd = {}
-440    gradd = {}
-441    names = []
-442    e_names = []
-443    enstags = {}
-444    for k in range(6, len(list(dobs))):
-445        if dobs[k].tag == "edata":
-446            _check(dobs[k][0].tag == "enstag")
-447            ename = dobs[k][0].text.strip()
-448            e_names.append(ename)
-449            _check(dobs[k][1].tag == "nr")
-450            R = int(dobs[k][1].text.strip())
-451            for i in range(2, 2 + R):
-452                deltas, rname, idx = _import_rdata(dobs[k][i])
-453                if separator_insertion is None or False:
-454                    pass
-455                elif separator_insertion is True:
-456                    if rname.startswith(ename):
-457                        rname = rname[:len(ename)] + '|' + rname[len(ename):]
-458                elif isinstance(separator_insertion, int):
-459                    rname = rname[:separator_insertion] + '|' + rname[separator_insertion:]
-460                elif isinstance(separator_insertion, str):
-461                    rname = rname.replace(separator_insertion, "|%s" % (separator_insertion))
-462                else:
-463                    raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
-464                if '|' in rname:
-465                    new_ename = rname[:rname.index('|')]
-466                else:
-467                    new_ename = ename
-468                enstags[new_ename] = ename
-469                idld[rname] = idx
-470                deltad[rname] = deltas
-471                names.append(rname)
-472        elif dobs[k].tag == "cdata":
-473            cname, cov, grad = _import_cdata(dobs[k])
-474            covd[cname] = cov
-475            if grad.shape[1] == 1:
-476                gradd[cname] = [grad for i in range(len(mean))]
-477            else:
-478                gradd[cname] = grad.T
-479        else:
-480            _check(False)
-481    names = list(set(names))
-482
-483    for name in names:
-484        for i in range(len(deltad[name])):
-485            deltad[name][i] = np.array(deltad[name][i]) + mean[i]
-486
-487    res = []
-488    for i in range(len(mean)):
-489        deltas = []
-490        idl = []
-491        obs_names = []
-492        for name in names:
-493            h = np.unique(deltad[name][i])
-494            if len(h) == 1 and np.all(h == mean[i]) and noempty:
-495                continue
-496            deltas.append(deltad[name][i])
-497            obs_names.append(name)
-498            idl.append(idld[name])
-499        res.append(Obs(deltas, obs_names, idl=idl))
-500        res[-1]._value = mean[i]
-501    _check(len(e_names) == ne)
-502
-503    cnames = list(covd.keys())
-504    for i in range(len(res)):
-505        new_covobs = {name: Covobs(0, covd[name], name, grad=gradd[name][i]) for name in cnames}
-506        if noempty:
-507            for name in cnames:
-508                if np.all(new_covobs[name].grad == 0):
-509                    del new_covobs[name]
-510            cnames_loc = list(new_covobs.keys())
-511        else:
-512            cnames_loc = cnames
-513        for name in cnames_loc:
-514            res[i].names.append(name)
-515            res[i].shape[name] = 1
-516            res[i].idl[name] = []
-517        res[i]._covobs = new_covobs
-518
-519    if symbol:
-520        for i in range(len(res)):
-521            res[i].tag = symbol[i]
-522            if res[i].tag == 'None':
-523                res[i].tag = None
-524    if not noempty:
-525        _check(len(res[0].covobs.keys()) == nc)
-526    if full_output:
-527        retd = {}
-528        tool = file_origin.get('tool', None)
-529        if tool:
-530            program = tool['name'] + ' ' + tool['version']
-531        else:
-532            program = ''
-533        retd['program'] = program
-534        retd['version'] = version
-535        retd['who'] = file_origin['who']
-536        retd['date'] = file_origin['date']
-537        retd['host'] = file_origin['host']
-538        retd['description'] = descriptiond
-539        retd['enstags'] = enstags
-540        retd['obsdata'] = res
-541        return retd
-542    else:
-543        return res
-544
-545
-546def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insertion=True):
-547    """Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
-548
-549    Tags are not written or recovered automatically.
-550
-551    Parameters
-552    ----------
-553    fname : str
-554        Filename of the input file.
-555    noemtpy : bool
-556        If True, ensembles with no contribution to the Obs are not included.
-557        If False, ensembles are included as written in the file.
-558    full_output : bool
-559        If True, a dict containing auxiliary information and the data is returned.
-560        If False, only the data is returned as list.
-561    gz : bool
-562        If True, assumes that data is gzipped. If False, assumes XML file.
-563    separatior_insertion: str, int or bool
-564        str: replace all occurences of "separator_insertion" within the replica names
-565        by "|%s" % (separator_insertion) when constructing the names of the replica.
-566        int: Insert the separator "|" at the position given by separator_insertion.
-567        True (default): separator "|" is inserted after len(ensname), assuming that the
-568        ensemble name is a prefix to the replica name.
-569        None or False: No separator is inserted.
-570    """
-571
-572    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-573        fname += '.xml'
-574    if gz:
-575        if not fname.endswith('.gz'):
-576            fname += '.gz'
-577        with gzip.open(fname, 'r') as fin:
-578            content = fin.read()
-579    else:
-580        if fname.endswith('.gz'):
-581            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-582        with open(fname, 'r') as fin:
-583            content = fin.read()
-584
-585    return import_dobs_string(content, noempty, full_output, separator_insertion=separator_insertion)
-586
-587
-588def _dobsdict_to_xmlstring(d):
-589    if isinstance(d, dict):
-590        iters = ''
-591        for k in d:
-592            if k.startswith('#value'):
-593                for li in d[k]:
-594                    iters += li
-595                return iters + '\n'
-596            elif k.startswith('#'):
-597                for li in d[k]:
-598                    iters += li
-599                iters = '<array>\n' + iters + '<%sarray>\n' % ('/')
-600                return iters
-601            if isinstance(d[k], dict):
-602                iters += '<%s>\n' % (k) + _dobsdict_to_xmlstring(d[k]) + '<%s%s>\n' % ('/', k)
-603            elif isinstance(d[k], str):
-604                if len(d[k]) > 100:
-605                    iters += '<%s>\n ' % (k) + d[k] + ' \n<%s%s>\n' % ('/', k)
-606                else:
-607                    iters += '<%s> ' % (k) + d[k] + ' <%s%s>\n' % ('/', k)
-608            elif isinstance(d[k], list):
-609                tmps = ''
-610                if k in ['edata', 'cdata']:
-611                    for i in range(len(d[k])):
-612                        tmps += '<%s>\n' % (k) + _dobsdict_to_xmlstring(d[k][i]) + '</%s>\n' % (k)
-613                else:
-614                    for i in range(len(d[k])):
-615                        tmps += _dobsdict_to_xmlstring(d[k][i])
-616                iters += tmps
-617            elif isinstance(d[k], (int, float)):
-618                iters += '<%s> ' % (k) + str(d[k]) + ' <%s%s>\n' % ('/', k)
-619            elif not d[k]:
-620                return '\n'
-621            else:
-622                raise Exception('Type', type(d[k]), 'not supported in export!')
-623    else:
-624        raise Exception('Type', type(d), 'not supported in export!')
-625    return iters
-626
-627
-628def _dobsdict_to_xmlstring_spaces(d, space='  '):
-629    s = _dobsdict_to_xmlstring(d)
-630    o = ''
-631    c = 0
-632    cm = False
-633    for li in s.split('\n'):
-634        if li.startswith('<%s' % ('/')):
-635            c -= 1
-636            cm = True
-637        for i in range(c):
-638            o += space
-639        o += li + '\n'
-640        if li.startswith('<') and not cm:
-641            if not '<%s' % ('/') in li:
-642                c += 1
-643        cm = False
-644    return o
-645
-646
-647def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None):
-648    """Generate the string for the export of a list of Obs or structures containing Obs
-649    to a .xml.gz file according to the Zeuthen dobs format.
-650
-651    Tags are not written or recovered automatically. The separator |is removed from the replica names.
-652
-653    Parameters
-654    ----------
-655    obsl : list
-656        List of Obs that will be exported.
-657        The Obs inside a structure do not have to be defined on the same set of configurations,
-658        but the storage requirement is increased, if this is not the case.
-659    name : str
-660        The name of the observable.
-661    spec : str
-662        Optional string that describes the contents of the file.
-663    origin : str
-664        Specify where the data has its origin.
-665    symbol : list
-666        A list of symbols that describe the observables to be written. May be empty.
-667    who : str
-668        Provide the name of the person that exports the data.
-669    enstags : dict
-670        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
-671        Otherwise, the ensemble name is used.
-672    """
-673    if enstags is None:
-674        enstags = {}
-675    od = {}
-676    r_names = []
-677    for o in obsl:
-678        r_names += [name for name in o.names if name.split('|')[0] in o.mc_names]
-679    r_names = sorted(set(r_names))
-680    mc_names = sorted(set([n.split('|')[0] for n in r_names]))
-681    for tmpname in mc_names:
-682        if tmpname not in enstags:
-683            enstags[tmpname] = tmpname
-684    ne = len(set(mc_names))
-685    cov_names = []
-686    for o in obsl:
-687        cov_names += list(o.cov_names)
-688    cov_names = sorted(set(cov_names))
-689    nc = len(set(cov_names))
-690    od['OBSERVABLES'] = {}
-691    od['OBSERVABLES']['SCHEMA'] = {'NAME': 'lattobs', 'VERSION': '1.0'}
-692    if who is None:
-693        who = getpass.getuser()
-694    od['OBSERVABLES']['origin'] = {
-695        'who': who,
-696        'date': str(datetime.datetime.now())[:-7],
-697        'host': socket.gethostname(),
-698        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
-699    od['OBSERVABLES']['dobs'] = {}
-700    pd = od['OBSERVABLES']['dobs']
-701    pd['spec'] = spec
-702    pd['origin'] = origin
-703    pd['name'] = name
-704    pd['array'] = {}
-705    pd['array']['id'] = 'val'
-706    pd['array']['layout'] = '1 f%d' % (len(obsl))
-707    osymbol = ''
-708    if symbol:
-709        if not isinstance(symbol, list):
-710            raise Exception('Symbol has to be a list!')
-711        if not (len(symbol) == 0 or len(symbol) == len(obsl)):
-712            raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
-713        osymbol = symbol[0]
-714        for s in symbol[1:]:
-715            osymbol += ' %s' % s
-716        pd['array']['symbol'] = osymbol
-717
-718    pd['array']['#values'] = ['  '.join(['%1.16e' % o.value for o in obsl])]
-719    pd['ne'] = '%d' % (ne)
-720    pd['nc'] = '%d' % (nc)
-721    pd['edata'] = []
-722    for name in mc_names:
-723        ed = {}
-724        ed['enstag'] = enstags[name]
-725        onames = sorted([n for n in r_names if (n.startswith(name + '|') or n == name)])
-726        nr = len(onames)
-727        ed['nr'] = nr
-728        ed[''] = []
-729
-730        for r in range(nr):
-731            ad = {}
-732            repname = onames[r]
-733            ad['id'] = repname.replace('|', '')
-734            idx = _merge_idx([o.idl.get(repname, []) for o in obsl])
-735            Nconf = len(idx)
-736            layout = '%d i f%d' % (Nconf, len(obsl))
-737            ad['layout'] = layout
-738            data = ''
-739            counters = [0 for o in obsl]
-740            offsets = [o.r_values[repname] - o.value if repname in o.r_values else 0 for o in obsl]
-741            for ci in idx:
-742                data += '%d ' % ci
-743                for oi in range(len(obsl)):
-744                    o = obsl[oi]
-745                    if repname in o.idl:
-746                        if counters[oi] < 0:
-747                            num = offsets[oi]
-748                            if num == 0:
-749                                data += '0 '
-750                            else:
-751                                data += '%1.16e ' % (num)
-752                            continue
-753                        if o.idl[repname][counters[oi]] == ci:
-754                            num = o.deltas[repname][counters[oi]] + offsets[oi]
-755                            if num == 0:
-756                                data += '0 '
-757                            else:
-758                                data += '%1.16e ' % (num)
-759                            counters[oi] += 1
-760                            if counters[oi] >= len(o.idl[repname]):
-761                                counters[oi] = -1
-762                        else:
-763                            num = offsets[oi]
-764                            if num == 0:
-765                                data += '0 '
-766                            else:
-767                                data += '%1.16e ' % (num)
-768                    else:
-769                        data += '0 '
-770                data += '\n'
-771            ad['#data'] = data
-772            ed[''].append(ad)
-773        pd['edata'].append(ed)
-774
-775        allcov = {}
-776        for o in obsl:
-777            for cname in o.cov_names:
-778                if cname in allcov:
-779                    if not np.array_equal(allcov[cname], o.covobs[cname].cov):
-780                        raise Exception('Inconsistent covariance matrices for %s!' % (cname))
-781                else:
-782                    allcov[cname] = o.covobs[cname].cov
-783        pd['cdata'] = []
-784        for cname in cov_names:
-785            cd = {}
-786            cd['id'] = cname
-787
-788            covd = {'id': 'cov'}
-789            if allcov[cname].shape == ():
-790                ncov = 1
-791                covd['layout'] = '1 1 f'
-792                covd['#data'] = '%1.14e' % (allcov[cname])
-793            else:
-794                shape = allcov[cname].shape
-795                assert (shape[0] == shape[1])
-796                ncov = shape[0]
-797                covd['layout'] = '%d %d f' % (ncov, ncov)
-798                ds = ''
-799                for i in range(ncov):
-800                    for j in range(ncov):
-801                        val = allcov[cname][i][j]
-802                        if val == 0:
-803                            ds += '0 '
-804                        else:
-805                            ds += '%1.14e ' % (val)
-806                    ds += '\n'
-807                covd['#data'] = ds
-808
-809            gradd = {'id': 'grad'}
-810            gradd['layout'] = '%d f%d' % (ncov, len(obsl))
-811            ds = ''
-812            for i in range(ncov):
-813                for o in obsl:
-814                    if cname in o.covobs:
-815                        val = o.covobs[cname].grad[i]
-816                        if val != 0:
-817                            ds += '%1.14e ' % (val)
-818                        else:
-819                            ds += '0 '
-820                    else:
-821                        ds += '0 '
-822            gradd['#data'] = ds
-823            cd['array'] = [covd, gradd]
-824            pd['cdata'].append(cd)
+432    root = et.fromstring(content)
+433
+434    _check(root.tag == 'OBSERVABLES')
+435    _check(root[0].tag == 'SCHEMA')
+436    version = root[0][1].text.strip()
+437
+438    _check(root[1].tag == 'origin')
+439    file_origin = _etree_to_dict(root[1])['origin']
+440
+441    _check(root[2].tag == 'dobs')
+442
+443    dobs = root[2]
+444
+445    descriptiond = {}
+446    for i in range(3):
+447        descriptiond[dobs[i].tag] = dobs[i].text.strip()
+448
+449    _check(dobs[3].tag == 'array')
+450
+451    symbol = []
+452    if dobs[3][1].tag == 'symbol':
+453        symbol = dobs[3][1].text.strip()
+454        descriptiond['symbol'] = symbol
+455    mean = _import_array(dobs[3])[0]
+456
+457    _check(dobs[4].tag == "ne")
+458    ne = int(dobs[4].text.strip())
+459    _check(dobs[5].tag == "nc")
+460    nc = int(dobs[5].text.strip())
+461
+462    idld = {}
+463    deltad = {}
+464    covd = {}
+465    gradd = {}
+466    names = []
+467    e_names = []
+468    enstags = {}
+469    for k in range(6, len(list(dobs))):
+470        if dobs[k].tag == "edata":
+471            _check(dobs[k][0].tag == "enstag")
+472            ename = dobs[k][0].text.strip()
+473            e_names.append(ename)
+474            _check(dobs[k][1].tag == "nr")
+475            R = int(dobs[k][1].text.strip())
+476            for i in range(2, 2 + R):
+477                deltas, rname, idx = _import_rdata(dobs[k][i])
+478                if separator_insertion is None or False:
+479                    pass
+480                elif separator_insertion is True:
+481                    if rname.startswith(ename):
+482                        rname = rname[:len(ename)] + '|' + rname[len(ename):]
+483                elif isinstance(separator_insertion, int):
+484                    rname = rname[:separator_insertion] + '|' + rname[separator_insertion:]
+485                elif isinstance(separator_insertion, str):
+486                    rname = rname.replace(separator_insertion, "|%s" % (separator_insertion))
+487                else:
+488                    raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
+489                if '|' in rname:
+490                    new_ename = rname[:rname.index('|')]
+491                else:
+492                    new_ename = ename
+493                enstags[new_ename] = ename
+494                idld[rname] = idx
+495                deltad[rname] = deltas
+496                names.append(rname)
+497        elif dobs[k].tag == "cdata":
+498            cname, cov, grad = _import_cdata(dobs[k])
+499            covd[cname] = cov
+500            if grad.shape[1] == 1:
+501                gradd[cname] = [grad for i in range(len(mean))]
+502            else:
+503                gradd[cname] = grad.T
+504        else:
+505            _check(False)
+506    names = list(set(names))
+507
+508    for name in names:
+509        for i in range(len(deltad[name])):
+510            deltad[name][i] = np.array(deltad[name][i]) + mean[i]
+511
+512    res = []
+513    for i in range(len(mean)):
+514        deltas = []
+515        idl = []
+516        obs_names = []
+517        for name in names:
+518            h = np.unique(deltad[name][i])
+519            if len(h) == 1 and np.all(h == mean[i]) and noempty:
+520                continue
+521            deltas.append(deltad[name][i])
+522            obs_names.append(name)
+523            idl.append(idld[name])
+524        res.append(Obs(deltas, obs_names, idl=idl))
+525        res[-1]._value = mean[i]
+526    _check(len(e_names) == ne)
+527
+528    cnames = list(covd.keys())
+529    for i in range(len(res)):
+530        new_covobs = {name: Covobs(0, covd[name], name, grad=gradd[name][i]) for name in cnames}
+531        if noempty:
+532            for name in cnames:
+533                if np.all(new_covobs[name].grad == 0):
+534                    del new_covobs[name]
+535            cnames_loc = list(new_covobs.keys())
+536        else:
+537            cnames_loc = cnames
+538        for name in cnames_loc:
+539            res[i].names.append(name)
+540            res[i].shape[name] = 1
+541            res[i].idl[name] = []
+542        res[i]._covobs = new_covobs
+543
+544    if symbol:
+545        for i in range(len(res)):
+546            res[i].tag = symbol[i]
+547            if res[i].tag == 'None':
+548                res[i].tag = None
+549    if not noempty:
+550        _check(len(res[0].covobs.keys()) == nc)
+551    if full_output:
+552        retd = {}
+553        tool = file_origin.get('tool', None)
+554        if tool:
+555            program = tool['name'] + ' ' + tool['version']
+556        else:
+557            program = ''
+558        retd['program'] = program
+559        retd['version'] = version
+560        retd['who'] = file_origin['who']
+561        retd['date'] = file_origin['date']
+562        retd['host'] = file_origin['host']
+563        retd['description'] = descriptiond
+564        retd['enstags'] = enstags
+565        retd['obsdata'] = res
+566        return retd
+567    else:
+568        return res
+569
+570
+571def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insertion=True):
+572    """Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
+573
+574    Tags are not written or recovered automatically.
+575
+576    Parameters
+577    ----------
+578    fname : str
+579        Filename of the input file.
+580    noemtpy : bool
+581        If True, ensembles with no contribution to the Obs are not included.
+582        If False, ensembles are included as written in the file.
+583    full_output : bool
+584        If True, a dict containing auxiliary information and the data is returned.
+585        If False, only the data is returned as list.
+586    gz : bool
+587        If True, assumes that data is gzipped. If False, assumes XML file.
+588    separatior_insertion: str, int or bool
+589        str: replace all occurences of "separator_insertion" within the replica names
+590        by "|%s" % (separator_insertion) when constructing the names of the replica.
+591        int: Insert the separator "|" at the position given by separator_insertion.
+592        True (default): separator "|" is inserted after len(ensname), assuming that the
+593        ensemble name is a prefix to the replica name.
+594        None or False: No separator is inserted.
+595
+596    Returns
+597    -------
+598    res : list[Obs]
+599        Imported data
+600    or
+601    res : dict
+602        Imported data and meta-data
+603    """
+604
+605    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+606        fname += '.xml'
+607    if gz:
+608        if not fname.endswith('.gz'):
+609            fname += '.gz'
+610        with gzip.open(fname, 'r') as fin:
+611            content = fin.read()
+612    else:
+613        if fname.endswith('.gz'):
+614            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+615        with open(fname, 'r') as fin:
+616            content = fin.read()
+617
+618    return import_dobs_string(content, noempty, full_output, separator_insertion=separator_insertion)
+619
+620
+621def _dobsdict_to_xmlstring(d):
+622    if isinstance(d, dict):
+623        iters = ''
+624        for k in d:
+625            if k.startswith('#value'):
+626                for li in d[k]:
+627                    iters += li
+628                return iters + '\n'
+629            elif k.startswith('#'):
+630                for li in d[k]:
+631                    iters += li
+632                iters = '<array>\n' + iters + '<%sarray>\n' % ('/')
+633                return iters
+634            if isinstance(d[k], dict):
+635                iters += '<%s>\n' % (k) + _dobsdict_to_xmlstring(d[k]) + '<%s%s>\n' % ('/', k)
+636            elif isinstance(d[k], str):
+637                if len(d[k]) > 100:
+638                    iters += '<%s>\n ' % (k) + d[k] + ' \n<%s%s>\n' % ('/', k)
+639                else:
+640                    iters += '<%s> ' % (k) + d[k] + ' <%s%s>\n' % ('/', k)
+641            elif isinstance(d[k], list):
+642                tmps = ''
+643                if k in ['edata', 'cdata']:
+644                    for i in range(len(d[k])):
+645                        tmps += '<%s>\n' % (k) + _dobsdict_to_xmlstring(d[k][i]) + '</%s>\n' % (k)
+646                else:
+647                    for i in range(len(d[k])):
+648                        tmps += _dobsdict_to_xmlstring(d[k][i])
+649                iters += tmps
+650            elif isinstance(d[k], (int, float)):
+651                iters += '<%s> ' % (k) + str(d[k]) + ' <%s%s>\n' % ('/', k)
+652            elif not d[k]:
+653                return '\n'
+654            else:
+655                raise Exception('Type', type(d[k]), 'not supported in export!')
+656    else:
+657        raise Exception('Type', type(d), 'not supported in export!')
+658    return iters
+659
+660
+661def _dobsdict_to_xmlstring_spaces(d, space='  '):
+662    s = _dobsdict_to_xmlstring(d)
+663    o = ''
+664    c = 0
+665    cm = False
+666    for li in s.split('\n'):
+667        if li.startswith('<%s' % ('/')):
+668            c -= 1
+669            cm = True
+670        for i in range(c):
+671            o += space
+672        o += li + '\n'
+673        if li.startswith('<') and not cm:
+674            if not '<%s' % ('/') in li:
+675                c += 1
+676        cm = False
+677    return o
+678
+679
+680def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None):
+681    """Generate the string for the export of a list of Obs or structures containing Obs
+682    to a .xml.gz file according to the Zeuthen dobs format.
+683
+684    Tags are not written or recovered automatically. The separator |is removed from the replica names.
+685
+686    Parameters
+687    ----------
+688    obsl : list
+689        List of Obs that will be exported.
+690        The Obs inside a structure do not have to be defined on the same set of configurations,
+691        but the storage requirement is increased, if this is not the case.
+692    name : str
+693        The name of the observable.
+694    spec : str
+695        Optional string that describes the contents of the file.
+696    origin : str
+697        Specify where the data has its origin.
+698    symbol : list
+699        A list of symbols that describe the observables to be written. May be empty.
+700    who : str
+701        Provide the name of the person that exports the data.
+702    enstags : dict
+703        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
+704        Otherwise, the ensemble name is used.
+705
+706    Returns
+707    -------
+708    xml_str : str
+709        XML string generated from the data
+710    """
+711    if enstags is None:
+712        enstags = {}
+713    od = {}
+714    r_names = []
+715    for o in obsl:
+716        r_names += [name for name in o.names if name.split('|')[0] in o.mc_names]
+717    r_names = sorted(set(r_names))
+718    mc_names = sorted(set([n.split('|')[0] for n in r_names]))
+719    for tmpname in mc_names:
+720        if tmpname not in enstags:
+721            enstags[tmpname] = tmpname
+722    ne = len(set(mc_names))
+723    cov_names = []
+724    for o in obsl:
+725        cov_names += list(o.cov_names)
+726    cov_names = sorted(set(cov_names))
+727    nc = len(set(cov_names))
+728    od['OBSERVABLES'] = {}
+729    od['OBSERVABLES']['SCHEMA'] = {'NAME': 'lattobs', 'VERSION': '1.0'}
+730    if who is None:
+731        who = getpass.getuser()
+732    od['OBSERVABLES']['origin'] = {
+733        'who': who,
+734        'date': str(datetime.datetime.now())[:-7],
+735        'host': socket.gethostname(),
+736        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
+737    od['OBSERVABLES']['dobs'] = {}
+738    pd = od['OBSERVABLES']['dobs']
+739    pd['spec'] = spec
+740    pd['origin'] = origin
+741    pd['name'] = name
+742    pd['array'] = {}
+743    pd['array']['id'] = 'val'
+744    pd['array']['layout'] = '1 f%d' % (len(obsl))
+745    osymbol = ''
+746    if symbol:
+747        if not isinstance(symbol, list):
+748            raise Exception('Symbol has to be a list!')
+749        if not (len(symbol) == 0 or len(symbol) == len(obsl)):
+750            raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
+751        osymbol = symbol[0]
+752        for s in symbol[1:]:
+753            osymbol += ' %s' % s
+754        pd['array']['symbol'] = osymbol
+755
+756    pd['array']['#values'] = ['  '.join(['%1.16e' % o.value for o in obsl])]
+757    pd['ne'] = '%d' % (ne)
+758    pd['nc'] = '%d' % (nc)
+759    pd['edata'] = []
+760    for name in mc_names:
+761        ed = {}
+762        ed['enstag'] = enstags[name]
+763        onames = sorted([n for n in r_names if (n.startswith(name + '|') or n == name)])
+764        nr = len(onames)
+765        ed['nr'] = nr
+766        ed[''] = []
+767
+768        for r in range(nr):
+769            ad = {}
+770            repname = onames[r]
+771            ad['id'] = repname.replace('|', '')
+772            idx = _merge_idx([o.idl.get(repname, []) for o in obsl])
+773            Nconf = len(idx)
+774            layout = '%d i f%d' % (Nconf, len(obsl))
+775            ad['layout'] = layout
+776            data = ''
+777            counters = [0 for o in obsl]
+778            offsets = [o.r_values[repname] - o.value if repname in o.r_values else 0 for o in obsl]
+779            for ci in idx:
+780                data += '%d ' % ci
+781                for oi in range(len(obsl)):
+782                    o = obsl[oi]
+783                    if repname in o.idl:
+784                        if counters[oi] < 0:
+785                            num = offsets[oi]
+786                            if num == 0:
+787                                data += '0 '
+788                            else:
+789                                data += '%1.16e ' % (num)
+790                            continue
+791                        if o.idl[repname][counters[oi]] == ci:
+792                            num = o.deltas[repname][counters[oi]] + offsets[oi]
+793                            if num == 0:
+794                                data += '0 '
+795                            else:
+796                                data += '%1.16e ' % (num)
+797                            counters[oi] += 1
+798                            if counters[oi] >= len(o.idl[repname]):
+799                                counters[oi] = -1
+800                        else:
+801                            num = offsets[oi]
+802                            if num == 0:
+803                                data += '0 '
+804                            else:
+805                                data += '%1.16e ' % (num)
+806                    else:
+807                        data += '0 '
+808                data += '\n'
+809            ad['#data'] = data
+810            ed[''].append(ad)
+811        pd['edata'].append(ed)
+812
+813        allcov = {}
+814        for o in obsl:
+815            for cname in o.cov_names:
+816                if cname in allcov:
+817                    if not np.array_equal(allcov[cname], o.covobs[cname].cov):
+818                        raise Exception('Inconsistent covariance matrices for %s!' % (cname))
+819                else:
+820                    allcov[cname] = o.covobs[cname].cov
+821        pd['cdata'] = []
+822        for cname in cov_names:
+823            cd = {}
+824            cd['id'] = cname
 825
-826    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dobsdict_to_xmlstring_spaces(od)
-827
-828    return rs
-829
-830
-831def write_dobs(obsl, fname, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None, gz=True):
-832    """Export a list of Obs or structures containing Obs to a .xml.gz file
-833    according to the Zeuthen dobs format.
-834
-835    Tags are not written or recovered automatically. The separator | is removed from the replica names.
-836
-837    Parameters
-838    ----------
-839    obsl : list
-840        List of Obs that will be exported.
-841        The Obs inside a structure do not have to be defined on the same set of configurations,
-842        but the storage requirement is increased, if this is not the case.
-843    fname : str
-844        Filename of the output file.
-845    name : str
-846        The name of the observable.
-847    spec : str
-848        Optional string that describes the contents of the file.
-849    origin : str
-850        Specify where the data has its origin.
-851    symbol : list
-852        A list of symbols that describe the observables to be written. May be empty.
-853    who : str
-854        Provide the name of the person that exports the data.
-855    enstags : dict
-856        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
-857        Otherwise, the ensemble name is used.
-858    gz : bool
-859        If True, the output is a gzipped XML. If False, the output is a XML file.
-860    """
-861    if enstags is None:
-862        enstags = {}
+826            covd = {'id': 'cov'}
+827            if allcov[cname].shape == ():
+828                ncov = 1
+829                covd['layout'] = '1 1 f'
+830                covd['#data'] = '%1.14e' % (allcov[cname])
+831            else:
+832                shape = allcov[cname].shape
+833                assert (shape[0] == shape[1])
+834                ncov = shape[0]
+835                covd['layout'] = '%d %d f' % (ncov, ncov)
+836                ds = ''
+837                for i in range(ncov):
+838                    for j in range(ncov):
+839                        val = allcov[cname][i][j]
+840                        if val == 0:
+841                            ds += '0 '
+842                        else:
+843                            ds += '%1.14e ' % (val)
+844                    ds += '\n'
+845                covd['#data'] = ds
+846
+847            gradd = {'id': 'grad'}
+848            gradd['layout'] = '%d f%d' % (ncov, len(obsl))
+849            ds = ''
+850            for i in range(ncov):
+851                for o in obsl:
+852                    if cname in o.covobs:
+853                        val = o.covobs[cname].grad[i]
+854                        if val != 0:
+855                            ds += '%1.14e ' % (val)
+856                        else:
+857                            ds += '0 '
+858                    else:
+859                        ds += '0 '
+860            gradd['#data'] = ds
+861            cd['array'] = [covd, gradd]
+862            pd['cdata'].append(cd)
 863
-864    dobsstring = create_dobs_string(obsl, name, spec, origin, symbol, who, enstags=enstags)
+864    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dobsdict_to_xmlstring_spaces(od)
 865
-866    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-867        fname += '.xml'
+866    return rs
+867
 868
-869    if gz:
-870        if not fname.endswith('.gz'):
-871            fname += '.gz'
+869def write_dobs(obsl, fname, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None, gz=True):
+870    """Export a list of Obs or structures containing Obs to a .xml.gz file
+871    according to the Zeuthen dobs format.
 872
-873        fp = gzip.open(fname, 'wb')
-874        fp.write(dobsstring.encode('utf-8'))
-875    else:
-876        fp = open(fname, 'w', encoding='utf-8')
-877        fp.write(dobsstring)
-878    fp.close()
+873    Tags are not written or recovered automatically. The separator | is removed from the replica names.
+874
+875    Parameters
+876    ----------
+877    obsl : list
+878        List of Obs that will be exported.
+879        The Obs inside a structure do not have to be defined on the same set of configurations,
+880        but the storage requirement is increased, if this is not the case.
+881    fname : str
+882        Filename of the output file.
+883    name : str
+884        The name of the observable.
+885    spec : str
+886        Optional string that describes the contents of the file.
+887    origin : str
+888        Specify where the data has its origin.
+889    symbol : list
+890        A list of symbols that describe the observables to be written. May be empty.
+891    who : str
+892        Provide the name of the person that exports the data.
+893    enstags : dict
+894        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
+895        Otherwise, the ensemble name is used.
+896    gz : bool
+897        If True, the output is a gzipped XML. If False, the output is a XML file.
+898
+899    Returns
+900    -------
+901    None
+902    """
+903    if enstags is None:
+904        enstags = {}
+905
+906    dobsstring = create_dobs_string(obsl, name, spec, origin, symbol, who, enstags=enstags)
+907
+908    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+909        fname += '.xml'
+910
+911    if gz:
+912        if not fname.endswith('.gz'):
+913            fname += '.gz'
+914
+915        fp = gzip.open(fname, 'wb')
+916        fp.write(dobsstring.encode('utf-8'))
+917    else:
+918        fp = open(fname, 'w', encoding='utf-8')
+919        fp.write(dobsstring)
+920    fp.close()
    @@ -1008,69 +1050,74 @@ 107 A list of symbols that describe the observables to be written. May be empty. 108 enstag : str 109 Enstag that is written to pobs. If None, the ensemble name is used. -110 """ -111 -112 od = {} -113 ename = obsl[0].e_names[0] -114 names = list(obsl[0].deltas.keys()) -115 nr = len(names) -116 onames = [name.replace('|', '') for name in names] -117 for o in obsl: -118 if len(o.e_names) != 1: -119 raise Exception('You try to export dobs to obs!') -120 if o.e_names[0] != ename: -121 raise Exception('You try to export dobs to obs!') -122 if len(o.deltas.keys()) != nr: -123 raise Exception('Incompatible obses in list') -124 od['observables'] = {} -125 od['observables']['schema'] = {'name': 'lattobs', 'version': '1.0'} -126 od['observables']['origin'] = { -127 'who': getpass.getuser(), -128 'date': str(datetime.datetime.now())[:-7], -129 'host': socket.gethostname(), -130 'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}} -131 od['observables']['pobs'] = {} -132 pd = od['observables']['pobs'] -133 pd['spec'] = spec -134 pd['origin'] = origin -135 pd['name'] = name -136 if enstag: -137 if not isinstance(enstag, str): -138 raise Exception('enstag has to be a string!') -139 pd['enstag'] = enstag -140 else: -141 pd['enstag'] = ename -142 pd['nr'] = '%d' % (nr) -143 pd['array'] = [] -144 osymbol = 'cfg' -145 if not isinstance(symbol, list): -146 raise Exception('Symbol has to be a list!') -147 if not (len(symbol) == 0 or len(symbol) == len(obsl)): -148 raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl))) -149 for s in symbol: -150 osymbol += ' %s' % s -151 for r in range(nr): -152 ad = {} -153 ad['id'] = onames[r] -154 Nconf = len(obsl[0].deltas[names[r]]) -155 layout = '%d i f%d' % (Nconf, len(obsl)) -156 ad['layout'] = layout -157 ad['symbol'] = osymbol -158 data = '' -159 for c in range(Nconf): -160 data += '%d ' % obsl[0].idl[names[r]][c] -161 for o in obsl: -162 num = o.deltas[names[r]][c] + o.r_values[names[r]] -163 if num == 0: -164 data += '0 ' -165 else: -166 data += '%1.16e ' % (num) -167 data += '\n' -168 ad['#data'] = data -169 pd['array'].append(ad) -170 -171 rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dict_to_xmlstring_spaces(od) -172 return rs +110 +111 Returns +112 ------- +113 xml_str : str +114 XML formatted string of the input data +115 """ +116 +117 od = {} +118 ename = obsl[0].e_names[0] +119 names = list(obsl[0].deltas.keys()) +120 nr = len(names) +121 onames = [name.replace('|', '') for name in names] +122 for o in obsl: +123 if len(o.e_names) != 1: +124 raise Exception('You try to export dobs to obs!') +125 if o.e_names[0] != ename: +126 raise Exception('You try to export dobs to obs!') +127 if len(o.deltas.keys()) != nr: +128 raise Exception('Incompatible obses in list') +129 od['observables'] = {} +130 od['observables']['schema'] = {'name': 'lattobs', 'version': '1.0'} +131 od['observables']['origin'] = { +132 'who': getpass.getuser(), +133 'date': str(datetime.datetime.now())[:-7], +134 'host': socket.gethostname(), +135 'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}} +136 od['observables']['pobs'] = {} +137 pd = od['observables']['pobs'] +138 pd['spec'] = spec +139 pd['origin'] = origin +140 pd['name'] = name +141 if enstag: +142 if not isinstance(enstag, str): +143 raise Exception('enstag has to be a string!') +144 pd['enstag'] = enstag +145 else: +146 pd['enstag'] = ename +147 pd['nr'] = '%d' % (nr) +148 pd['array'] = [] +149 osymbol = 'cfg' +150 if not isinstance(symbol, list): +151 raise Exception('Symbol has to be a list!') +152 if not (len(symbol) == 0 or len(symbol) == len(obsl)): +153 raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl))) +154 for s in symbol: +155 osymbol += ' %s' % s +156 for r in range(nr): +157 ad = {} +158 ad['id'] = onames[r] +159 Nconf = len(obsl[0].deltas[names[r]]) +160 layout = '%d i f%d' % (Nconf, len(obsl)) +161 ad['layout'] = layout +162 ad['symbol'] = osymbol +163 data = '' +164 for c in range(Nconf): +165 data += '%d ' % obsl[0].idl[names[r]][c] +166 for o in obsl: +167 num = o.deltas[names[r]][c] + o.r_values[names[r]] +168 if num == 0: +169 data += '0 ' +170 else: +171 data += '%1.16e ' % (num) +172 data += '\n' +173 ad['#data'] = data +174 pd['array'].append(ad) +175 +176 rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dict_to_xmlstring_spaces(od) +177 return rs @@ -1096,6 +1143,13 @@ A list of symbols that describe the observables to be written. May be empty.enstag (str): Enstag that is written to pobs. If None, the ensemble name is used. + +
    Returns
    + + @@ -1111,47 +1165,51 @@ Enstag that is written to pobs. If None, the ensemble name is used. - 
    175def write_pobs(obsl, fname, name, spec='', origin='', symbol=[], enstag=None, gz=True):
-176    """Export a list of Obs or structures containing Obs to a .xml.gz file
-177    according to the Zeuthen pobs format.
-178
-179    Tags are not written or recovered automatically. The separator | is removed from the replica names.
-180
-181    Parameters
-182    ----------
-183    obsl : list
-184        List of Obs that will be exported.
-185        The Obs inside a structure have to be defined on the same ensemble.
-186    fname : str
-187        Filename of the output file.
-188    name : str
-189        The name of the observable.
-190    spec : str
-191        Optional string that describes the contents of the file.
-192    origin : str
-193        Specify where the data has its origin.
-194    symbol : list
-195        A list of symbols that describe the observables to be written. May be empty.
-196    enstag : str
-197        Enstag that is written to pobs. If None, the ensemble name is used.
-198    gz : bool
-199        If True, the output is a gzipped xml. If False, the output is an xml file.
-200    """
-201    pobsstring = create_pobs_string(obsl, name, spec, origin, symbol, enstag)
-202
-203    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-204        fname += '.xml'
+            
    180def write_pobs(obsl, fname, name, spec='', origin='', symbol=[], enstag=None, gz=True):
+181    """Export a list of Obs or structures containing Obs to a .xml.gz file
+182    according to the Zeuthen pobs format.
+183
+184    Tags are not written or recovered automatically. The separator | is removed from the replica names.
+185
+186    Parameters
+187    ----------
+188    obsl : list
+189        List of Obs that will be exported.
+190        The Obs inside a structure have to be defined on the same ensemble.
+191    fname : str
+192        Filename of the output file.
+193    name : str
+194        The name of the observable.
+195    spec : str
+196        Optional string that describes the contents of the file.
+197    origin : str
+198        Specify where the data has its origin.
+199    symbol : list
+200        A list of symbols that describe the observables to be written. May be empty.
+201    enstag : str
+202        Enstag that is written to pobs. If None, the ensemble name is used.
+203    gz : bool
+204        If True, the output is a gzipped xml. If False, the output is an xml file.
 205
-206    if gz:
-207        if not fname.endswith('.gz'):
-208            fname += '.gz'
-209
-210        fp = gzip.open(fname, 'wb')
-211        fp.write(pobsstring.encode('utf-8'))
-212    else:
-213        fp = open(fname, 'w', encoding='utf-8')
-214        fp.write(pobsstring)
-215    fp.close()
+206    Returns
+207    -------
+208    None
+209    """
+210    pobsstring = create_pobs_string(obsl, name, spec, origin, symbol, enstag)
+211
+212    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+213        fname += '.xml'
+214
+215    if gz:
+216        if not fname.endswith('.gz'):
+217            fname += '.gz'
+218
+219        fp = gzip.open(fname, 'wb')
+220        fp.write(pobsstring.encode('utf-8'))
+221    else:
+222        fp = open(fname, 'w', encoding='utf-8')
+223        fp.write(pobsstring)
+224    fp.close()
 
    
 
 
@@ -1181,6 +1239,12 @@ Enstag that is written to pobs. If None, the ensemble name is used.
 
  • gz (bool):
 If True, the output is a gzipped xml. If False, the output is an xml file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    @@ -1196,95 +1260,103 @@ If True, the output is a gzipped xml. If False, the output is an xml file. - 
    292def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
-293    """Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
-294
-295    Tags are not written or recovered automatically.
-296
-297    Parameters
-298    ----------
-299    fname : str
-300        Filename of the input file.
-301    full_output : bool
-302        If True, a dict containing auxiliary information and the data is returned.
-303        If False, only the data is returned as list.
-304    separatior_insertion: str or int
-305        str: replace all occurences of "separator_insertion" within the replica names
-306        by "|%s" % (separator_insertion) when constructing the names of the replica.
-307        int: Insert the separator "|" at the position given by separator_insertion.
-308        None (default): Replica names remain unchanged.
-309    """
-310
-311    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-312        fname += '.xml'
-313    if gz:
-314        if not fname.endswith('.gz'):
-315            fname += '.gz'
-316        with gzip.open(fname, 'r') as fin:
-317            content = fin.read()
-318    else:
-319        if fname.endswith('.gz'):
-320            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-321        with open(fname, 'r') as fin:
-322            content = fin.read()
-323
-324    # parse xml file content
-325    root = et.fromstring(content)
-326
-327    _check(root[2].tag == 'pobs')
-328    pobs = root[2]
-329
-330    version = root[0][1].text.strip()
-331
-332    _check(root[1].tag == 'origin')
-333    file_origin = _etree_to_dict(root[1])['origin']
-334
-335    deltas = []
-336    names = []
-337    idl = []
-338    for i in range(5, len(pobs)):
-339        delta, name, idx = _import_rdata(pobs[i])
-340        deltas.append(delta)
-341        if separator_insertion is None:
-342            pass
-343        elif isinstance(separator_insertion, int):
-344            name = name[:separator_insertion] + '|' + name[separator_insertion:]
-345        elif isinstance(separator_insertion, str):
-346            name = name.replace(separator_insertion, "|%s" % (separator_insertion))
-347        else:
-348            raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
-349        names.append(name)
-350        idl.append(idx)
-351    res = [Obs([d[i] for d in deltas], names, idl=idl) for i in range(len(deltas[0]))]
-352
-353    descriptiond = {}
-354    for i in range(4):
-355        descriptiond[pobs[i].tag] = pobs[i].text.strip()
-356
-357    _check(pobs[4].tag == "nr")
-358
-359    _check(pobs[5].tag == 'array')
-360    if pobs[5][1].tag == 'symbol':
-361        symbol = pobs[5][1].text.strip()
-362        descriptiond['symbol'] = symbol
-363
-364    if full_output:
-365        retd = {}
-366        tool = file_origin.get('tool', None)
-367        if tool:
-368            program = tool['name'] + ' ' + tool['version']
-369        else:
-370            program = ''
-371        retd['program'] = program
-372        retd['version'] = version
-373        retd['who'] = file_origin['who']
-374        retd['date'] = file_origin['date']
-375        retd['host'] = file_origin['host']
-376        retd['description'] = descriptiond
-377        retd['obsdata'] = res
-378        return retd
-379    else:
-380        return res
+            
    301def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
+302    """Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
+303
+304    Tags are not written or recovered automatically.
+305
+306    Parameters
+307    ----------
+308    fname : str
+309        Filename of the input file.
+310    full_output : bool
+311        If True, a dict containing auxiliary information and the data is returned.
+312        If False, only the data is returned as list.
+313    separatior_insertion: str or int
+314        str: replace all occurences of "separator_insertion" within the replica names
+315        by "|%s" % (separator_insertion) when constructing the names of the replica.
+316        int: Insert the separator "|" at the position given by separator_insertion.
+317        None (default): Replica names remain unchanged.
+318
+319    Returns
+320    -------
+321    res : list[Obs]
+322        Imported data
+323    or
+324    res : dict
+325        Imported data and meta-data
+326    """
+327
+328    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+329        fname += '.xml'
+330    if gz:
+331        if not fname.endswith('.gz'):
+332            fname += '.gz'
+333        with gzip.open(fname, 'r') as fin:
+334            content = fin.read()
+335    else:
+336        if fname.endswith('.gz'):
+337            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+338        with open(fname, 'r') as fin:
+339            content = fin.read()
+340
+341    # parse xml file content
+342    root = et.fromstring(content)
+343
+344    _check(root[2].tag == 'pobs')
+345    pobs = root[2]
+346
+347    version = root[0][1].text.strip()
+348
+349    _check(root[1].tag == 'origin')
+350    file_origin = _etree_to_dict(root[1])['origin']
+351
+352    deltas = []
+353    names = []
+354    idl = []
+355    for i in range(5, len(pobs)):
+356        delta, name, idx = _import_rdata(pobs[i])
+357        deltas.append(delta)
+358        if separator_insertion is None:
+359            pass
+360        elif isinstance(separator_insertion, int):
+361            name = name[:separator_insertion] + '|' + name[separator_insertion:]
+362        elif isinstance(separator_insertion, str):
+363            name = name.replace(separator_insertion, "|%s" % (separator_insertion))
+364        else:
+365            raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
+366        names.append(name)
+367        idl.append(idx)
+368    res = [Obs([d[i] for d in deltas], names, idl=idl) for i in range(len(deltas[0]))]
+369
+370    descriptiond = {}
+371    for i in range(4):
+372        descriptiond[pobs[i].tag] = pobs[i].text.strip()
+373
+374    _check(pobs[4].tag == "nr")
+375
+376    _check(pobs[5].tag == 'array')
+377    if pobs[5][1].tag == 'symbol':
+378        symbol = pobs[5][1].text.strip()
+379        descriptiond['symbol'] = symbol
+380
+381    if full_output:
+382        retd = {}
+383        tool = file_origin.get('tool', None)
+384        if tool:
+385            program = tool['name'] + ' ' + tool['version']
+386        else:
+387            program = ''
+388        retd['program'] = program
+389        retd['version'] = version
+390        retd['who'] = file_origin['who']
+391        retd['date'] = file_origin['date']
+392        retd['host'] = file_origin['host']
+393        retd['description'] = descriptiond
+394        retd['obsdata'] = res
+395        return retd
+396    else:
+397        return res
 
    
 
 
@@ -1306,6 +1378,16 @@ by "|%s" % (separator_insertion) when constructing the names of the replica.
 int: Insert the separator "|" at the position given by separator_insertion.
 None (default): Replica names remain unchanged.
 
+
+
    Returns
    
+
+
      
+
    • res (list[Obs]):
+Imported data
      • 
+
    • or
      • 
+
    • res (dict):
+Imported data and meta-data
      • 
+
    @@ -1321,167 +1403,175 @@ None (default): Replica names remain unchanged. - 
    384def import_dobs_string(content, noempty=False, full_output=False, separator_insertion=True):
-385    """Import a list of Obs from a string in the Zeuthen dobs format.
-386
-387    Tags are not written or recovered automatically.
-388
-389    Parameters
-390    ----------
-391    content : str
-392        XML string containing the data
-393    noemtpy : bool
-394        If True, ensembles with no contribution to the Obs are not included.
-395        If False, ensembles are included as written in the file, possibly with vanishing entries.
-396    full_output : bool
-397        If True, a dict containing auxiliary information and the data is returned.
-398        If False, only the data is returned as list.
-399    separatior_insertion: str, int or bool
-400        str: replace all occurences of "separator_insertion" within the replica names
-401        by "|%s" % (separator_insertion) when constructing the names of the replica.
-402        int: Insert the separator "|" at the position given by separator_insertion.
-403        True (default): separator "|" is inserted after len(ensname), assuming that the
-404        ensemble name is a prefix to the replica name.
-405        None or False: No separator is inserted.
-406    """
-407
-408    root = et.fromstring(content)
-409
-410    _check(root.tag == 'OBSERVABLES')
-411    _check(root[0].tag == 'SCHEMA')
-412    version = root[0][1].text.strip()
-413
-414    _check(root[1].tag == 'origin')
-415    file_origin = _etree_to_dict(root[1])['origin']
-416
-417    _check(root[2].tag == 'dobs')
-418
-419    dobs = root[2]
-420
-421    descriptiond = {}
-422    for i in range(3):
-423        descriptiond[dobs[i].tag] = dobs[i].text.strip()
-424
-425    _check(dobs[3].tag == 'array')
-426
-427    symbol = []
-428    if dobs[3][1].tag == 'symbol':
-429        symbol = dobs[3][1].text.strip()
-430        descriptiond['symbol'] = symbol
-431    mean = _import_array(dobs[3])[0]
+            
    401def import_dobs_string(content, noempty=False, full_output=False, separator_insertion=True):
+402    """Import a list of Obs from a string in the Zeuthen dobs format.
+403
+404    Tags are not written or recovered automatically.
+405
+406    Parameters
+407    ----------
+408    content : str
+409        XML string containing the data
+410    noemtpy : bool
+411        If True, ensembles with no contribution to the Obs are not included.
+412        If False, ensembles are included as written in the file, possibly with vanishing entries.
+413    full_output : bool
+414        If True, a dict containing auxiliary information and the data is returned.
+415        If False, only the data is returned as list.
+416    separatior_insertion: str, int or bool
+417        str: replace all occurences of "separator_insertion" within the replica names
+418        by "|%s" % (separator_insertion) when constructing the names of the replica.
+419        int: Insert the separator "|" at the position given by separator_insertion.
+420        True (default): separator "|" is inserted after len(ensname), assuming that the
+421        ensemble name is a prefix to the replica name.
+422        None or False: No separator is inserted.
+423
+424    Returns
+425    -------
+426    res : list[Obs]
+427        Imported data
+428    or
+429    res : dict
+430        Imported data and meta-data
+431    """
 432
-433    _check(dobs[4].tag == "ne")
-434    ne = int(dobs[4].text.strip())
-435    _check(dobs[5].tag == "nc")
-436    nc = int(dobs[5].text.strip())
-437
-438    idld = {}
-439    deltad = {}
-440    covd = {}
-441    gradd = {}
-442    names = []
-443    e_names = []
-444    enstags = {}
-445    for k in range(6, len(list(dobs))):
-446        if dobs[k].tag == "edata":
-447            _check(dobs[k][0].tag == "enstag")
-448            ename = dobs[k][0].text.strip()
-449            e_names.append(ename)
-450            _check(dobs[k][1].tag == "nr")
-451            R = int(dobs[k][1].text.strip())
-452            for i in range(2, 2 + R):
-453                deltas, rname, idx = _import_rdata(dobs[k][i])
-454                if separator_insertion is None or False:
-455                    pass
-456                elif separator_insertion is True:
-457                    if rname.startswith(ename):
-458                        rname = rname[:len(ename)] + '|' + rname[len(ename):]
-459                elif isinstance(separator_insertion, int):
-460                    rname = rname[:separator_insertion] + '|' + rname[separator_insertion:]
-461                elif isinstance(separator_insertion, str):
-462                    rname = rname.replace(separator_insertion, "|%s" % (separator_insertion))
-463                else:
-464                    raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
-465                if '|' in rname:
-466                    new_ename = rname[:rname.index('|')]
-467                else:
-468                    new_ename = ename
-469                enstags[new_ename] = ename
-470                idld[rname] = idx
-471                deltad[rname] = deltas
-472                names.append(rname)
-473        elif dobs[k].tag == "cdata":
-474            cname, cov, grad = _import_cdata(dobs[k])
-475            covd[cname] = cov
-476            if grad.shape[1] == 1:
-477                gradd[cname] = [grad for i in range(len(mean))]
-478            else:
-479                gradd[cname] = grad.T
-480        else:
-481            _check(False)
-482    names = list(set(names))
-483
-484    for name in names:
-485        for i in range(len(deltad[name])):
-486            deltad[name][i] = np.array(deltad[name][i]) + mean[i]
-487
-488    res = []
-489    for i in range(len(mean)):
-490        deltas = []
-491        idl = []
-492        obs_names = []
-493        for name in names:
-494            h = np.unique(deltad[name][i])
-495            if len(h) == 1 and np.all(h == mean[i]) and noempty:
-496                continue
-497            deltas.append(deltad[name][i])
-498            obs_names.append(name)
-499            idl.append(idld[name])
-500        res.append(Obs(deltas, obs_names, idl=idl))
-501        res[-1]._value = mean[i]
-502    _check(len(e_names) == ne)
-503
-504    cnames = list(covd.keys())
-505    for i in range(len(res)):
-506        new_covobs = {name: Covobs(0, covd[name], name, grad=gradd[name][i]) for name in cnames}
-507        if noempty:
-508            for name in cnames:
-509                if np.all(new_covobs[name].grad == 0):
-510                    del new_covobs[name]
-511            cnames_loc = list(new_covobs.keys())
-512        else:
-513            cnames_loc = cnames
-514        for name in cnames_loc:
-515            res[i].names.append(name)
-516            res[i].shape[name] = 1
-517            res[i].idl[name] = []
-518        res[i]._covobs = new_covobs
-519
-520    if symbol:
-521        for i in range(len(res)):
-522            res[i].tag = symbol[i]
-523            if res[i].tag == 'None':
-524                res[i].tag = None
-525    if not noempty:
-526        _check(len(res[0].covobs.keys()) == nc)
-527    if full_output:
-528        retd = {}
-529        tool = file_origin.get('tool', None)
-530        if tool:
-531            program = tool['name'] + ' ' + tool['version']
-532        else:
-533            program = ''
-534        retd['program'] = program
-535        retd['version'] = version
-536        retd['who'] = file_origin['who']
-537        retd['date'] = file_origin['date']
-538        retd['host'] = file_origin['host']
-539        retd['description'] = descriptiond
-540        retd['enstags'] = enstags
-541        retd['obsdata'] = res
-542        return retd
-543    else:
-544        return res
+433    root = et.fromstring(content)
+434
+435    _check(root.tag == 'OBSERVABLES')
+436    _check(root[0].tag == 'SCHEMA')
+437    version = root[0][1].text.strip()
+438
+439    _check(root[1].tag == 'origin')
+440    file_origin = _etree_to_dict(root[1])['origin']
+441
+442    _check(root[2].tag == 'dobs')
+443
+444    dobs = root[2]
+445
+446    descriptiond = {}
+447    for i in range(3):
+448        descriptiond[dobs[i].tag] = dobs[i].text.strip()
+449
+450    _check(dobs[3].tag == 'array')
+451
+452    symbol = []
+453    if dobs[3][1].tag == 'symbol':
+454        symbol = dobs[3][1].text.strip()
+455        descriptiond['symbol'] = symbol
+456    mean = _import_array(dobs[3])[0]
+457
+458    _check(dobs[4].tag == "ne")
+459    ne = int(dobs[4].text.strip())
+460    _check(dobs[5].tag == "nc")
+461    nc = int(dobs[5].text.strip())
+462
+463    idld = {}
+464    deltad = {}
+465    covd = {}
+466    gradd = {}
+467    names = []
+468    e_names = []
+469    enstags = {}
+470    for k in range(6, len(list(dobs))):
+471        if dobs[k].tag == "edata":
+472            _check(dobs[k][0].tag == "enstag")
+473            ename = dobs[k][0].text.strip()
+474            e_names.append(ename)
+475            _check(dobs[k][1].tag == "nr")
+476            R = int(dobs[k][1].text.strip())
+477            for i in range(2, 2 + R):
+478                deltas, rname, idx = _import_rdata(dobs[k][i])
+479                if separator_insertion is None or False:
+480                    pass
+481                elif separator_insertion is True:
+482                    if rname.startswith(ename):
+483                        rname = rname[:len(ename)] + '|' + rname[len(ename):]
+484                elif isinstance(separator_insertion, int):
+485                    rname = rname[:separator_insertion] + '|' + rname[separator_insertion:]
+486                elif isinstance(separator_insertion, str):
+487                    rname = rname.replace(separator_insertion, "|%s" % (separator_insertion))
+488                else:
+489                    raise Exception("separator_insertion has to be string or int, is ", type(separator_insertion))
+490                if '|' in rname:
+491                    new_ename = rname[:rname.index('|')]
+492                else:
+493                    new_ename = ename
+494                enstags[new_ename] = ename
+495                idld[rname] = idx
+496                deltad[rname] = deltas
+497                names.append(rname)
+498        elif dobs[k].tag == "cdata":
+499            cname, cov, grad = _import_cdata(dobs[k])
+500            covd[cname] = cov
+501            if grad.shape[1] == 1:
+502                gradd[cname] = [grad for i in range(len(mean))]
+503            else:
+504                gradd[cname] = grad.T
+505        else:
+506            _check(False)
+507    names = list(set(names))
+508
+509    for name in names:
+510        for i in range(len(deltad[name])):
+511            deltad[name][i] = np.array(deltad[name][i]) + mean[i]
+512
+513    res = []
+514    for i in range(len(mean)):
+515        deltas = []
+516        idl = []
+517        obs_names = []
+518        for name in names:
+519            h = np.unique(deltad[name][i])
+520            if len(h) == 1 and np.all(h == mean[i]) and noempty:
+521                continue
+522            deltas.append(deltad[name][i])
+523            obs_names.append(name)
+524            idl.append(idld[name])
+525        res.append(Obs(deltas, obs_names, idl=idl))
+526        res[-1]._value = mean[i]
+527    _check(len(e_names) == ne)
+528
+529    cnames = list(covd.keys())
+530    for i in range(len(res)):
+531        new_covobs = {name: Covobs(0, covd[name], name, grad=gradd[name][i]) for name in cnames}
+532        if noempty:
+533            for name in cnames:
+534                if np.all(new_covobs[name].grad == 0):
+535                    del new_covobs[name]
+536            cnames_loc = list(new_covobs.keys())
+537        else:
+538            cnames_loc = cnames
+539        for name in cnames_loc:
+540            res[i].names.append(name)
+541            res[i].shape[name] = 1
+542            res[i].idl[name] = []
+543        res[i]._covobs = new_covobs
+544
+545    if symbol:
+546        for i in range(len(res)):
+547            res[i].tag = symbol[i]
+548            if res[i].tag == 'None':
+549                res[i].tag = None
+550    if not noempty:
+551        _check(len(res[0].covobs.keys()) == nc)
+552    if full_output:
+553        retd = {}
+554        tool = file_origin.get('tool', None)
+555        if tool:
+556            program = tool['name'] + ' ' + tool['version']
+557        else:
+558            program = ''
+559        retd['program'] = program
+560        retd['version'] = version
+561        retd['who'] = file_origin['who']
+562        retd['date'] = file_origin['date']
+563        retd['host'] = file_origin['host']
+564        retd['description'] = descriptiond
+565        retd['enstags'] = enstags
+566        retd['obsdata'] = res
+567        return retd
+568    else:
+569        return res
 
    
 
 
@@ -1508,6 +1598,16 @@ True (default): separator "|" is inserted after len(ensname), assuming that the
 ensemble name is a prefix to the replica name.
 None or False: No separator is inserted.
 
+
+
    Returns
    
+
+
      
+
    • res (list[Obs]):
+Imported data
      • 
+
    • or
      • 
+
    • res (dict):
+Imported data and meta-data
      • 
+
    @@ -1523,46 +1623,54 @@ None or False: No separator is inserted. - 
    547def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insertion=True):
-548    """Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
-549
-550    Tags are not written or recovered automatically.
-551
-552    Parameters
-553    ----------
-554    fname : str
-555        Filename of the input file.
-556    noemtpy : bool
-557        If True, ensembles with no contribution to the Obs are not included.
-558        If False, ensembles are included as written in the file.
-559    full_output : bool
-560        If True, a dict containing auxiliary information and the data is returned.
-561        If False, only the data is returned as list.
-562    gz : bool
-563        If True, assumes that data is gzipped. If False, assumes XML file.
-564    separatior_insertion: str, int or bool
-565        str: replace all occurences of "separator_insertion" within the replica names
-566        by "|%s" % (separator_insertion) when constructing the names of the replica.
-567        int: Insert the separator "|" at the position given by separator_insertion.
-568        True (default): separator "|" is inserted after len(ensname), assuming that the
-569        ensemble name is a prefix to the replica name.
-570        None or False: No separator is inserted.
-571    """
-572
-573    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-574        fname += '.xml'
-575    if gz:
-576        if not fname.endswith('.gz'):
-577            fname += '.gz'
-578        with gzip.open(fname, 'r') as fin:
-579            content = fin.read()
-580    else:
-581        if fname.endswith('.gz'):
-582            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-583        with open(fname, 'r') as fin:
-584            content = fin.read()
-585
-586    return import_dobs_string(content, noempty, full_output, separator_insertion=separator_insertion)
+            
    572def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insertion=True):
+573    """Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
+574
+575    Tags are not written or recovered automatically.
+576
+577    Parameters
+578    ----------
+579    fname : str
+580        Filename of the input file.
+581    noemtpy : bool
+582        If True, ensembles with no contribution to the Obs are not included.
+583        If False, ensembles are included as written in the file.
+584    full_output : bool
+585        If True, a dict containing auxiliary information and the data is returned.
+586        If False, only the data is returned as list.
+587    gz : bool
+588        If True, assumes that data is gzipped. If False, assumes XML file.
+589    separatior_insertion: str, int or bool
+590        str: replace all occurences of "separator_insertion" within the replica names
+591        by "|%s" % (separator_insertion) when constructing the names of the replica.
+592        int: Insert the separator "|" at the position given by separator_insertion.
+593        True (default): separator "|" is inserted after len(ensname), assuming that the
+594        ensemble name is a prefix to the replica name.
+595        None or False: No separator is inserted.
+596
+597    Returns
+598    -------
+599    res : list[Obs]
+600        Imported data
+601    or
+602    res : dict
+603        Imported data and meta-data
+604    """
+605
+606    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+607        fname += '.xml'
+608    if gz:
+609        if not fname.endswith('.gz'):
+610            fname += '.gz'
+611        with gzip.open(fname, 'r') as fin:
+612            content = fin.read()
+613    else:
+614        if fname.endswith('.gz'):
+615            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+616        with open(fname, 'r') as fin:
+617            content = fin.read()
+618
+619    return import_dobs_string(content, noempty, full_output, separator_insertion=separator_insertion)
 
    
 
 
@@ -1591,6 +1699,16 @@ True (default): separator "|" is inserted after len(ensname), assuming that the
 ensemble name is a prefix to the replica name.
 None or False: No separator is inserted.
 
+
+
    Returns
    
+
+
      
+
    • res (list[Obs]):
+Imported data
      • 
+
    • or
      • 
+
    • res (dict):
+Imported data and meta-data
      • 
+
    @@ -1606,188 +1724,193 @@ None or False: No separator is inserted. - 
    648def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None):
-649    """Generate the string for the export of a list of Obs or structures containing Obs
-650    to a .xml.gz file according to the Zeuthen dobs format.
-651
-652    Tags are not written or recovered automatically. The separator |is removed from the replica names.
-653
-654    Parameters
-655    ----------
-656    obsl : list
-657        List of Obs that will be exported.
-658        The Obs inside a structure do not have to be defined on the same set of configurations,
-659        but the storage requirement is increased, if this is not the case.
-660    name : str
-661        The name of the observable.
-662    spec : str
-663        Optional string that describes the contents of the file.
-664    origin : str
-665        Specify where the data has its origin.
-666    symbol : list
-667        A list of symbols that describe the observables to be written. May be empty.
-668    who : str
-669        Provide the name of the person that exports the data.
-670    enstags : dict
-671        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
-672        Otherwise, the ensemble name is used.
-673    """
-674    if enstags is None:
-675        enstags = {}
-676    od = {}
-677    r_names = []
-678    for o in obsl:
-679        r_names += [name for name in o.names if name.split('|')[0] in o.mc_names]
-680    r_names = sorted(set(r_names))
-681    mc_names = sorted(set([n.split('|')[0] for n in r_names]))
-682    for tmpname in mc_names:
-683        if tmpname not in enstags:
-684            enstags[tmpname] = tmpname
-685    ne = len(set(mc_names))
-686    cov_names = []
-687    for o in obsl:
-688        cov_names += list(o.cov_names)
-689    cov_names = sorted(set(cov_names))
-690    nc = len(set(cov_names))
-691    od['OBSERVABLES'] = {}
-692    od['OBSERVABLES']['SCHEMA'] = {'NAME': 'lattobs', 'VERSION': '1.0'}
-693    if who is None:
-694        who = getpass.getuser()
-695    od['OBSERVABLES']['origin'] = {
-696        'who': who,
-697        'date': str(datetime.datetime.now())[:-7],
-698        'host': socket.gethostname(),
-699        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
-700    od['OBSERVABLES']['dobs'] = {}
-701    pd = od['OBSERVABLES']['dobs']
-702    pd['spec'] = spec
-703    pd['origin'] = origin
-704    pd['name'] = name
-705    pd['array'] = {}
-706    pd['array']['id'] = 'val'
-707    pd['array']['layout'] = '1 f%d' % (len(obsl))
-708    osymbol = ''
-709    if symbol:
-710        if not isinstance(symbol, list):
-711            raise Exception('Symbol has to be a list!')
-712        if not (len(symbol) == 0 or len(symbol) == len(obsl)):
-713            raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
-714        osymbol = symbol[0]
-715        for s in symbol[1:]:
-716            osymbol += ' %s' % s
-717        pd['array']['symbol'] = osymbol
-718
-719    pd['array']['#values'] = ['  '.join(['%1.16e' % o.value for o in obsl])]
-720    pd['ne'] = '%d' % (ne)
-721    pd['nc'] = '%d' % (nc)
-722    pd['edata'] = []
-723    for name in mc_names:
-724        ed = {}
-725        ed['enstag'] = enstags[name]
-726        onames = sorted([n for n in r_names if (n.startswith(name + '|') or n == name)])
-727        nr = len(onames)
-728        ed['nr'] = nr
-729        ed[''] = []
-730
-731        for r in range(nr):
-732            ad = {}
-733            repname = onames[r]
-734            ad['id'] = repname.replace('|', '')
-735            idx = _merge_idx([o.idl.get(repname, []) for o in obsl])
-736            Nconf = len(idx)
-737            layout = '%d i f%d' % (Nconf, len(obsl))
-738            ad['layout'] = layout
-739            data = ''
-740            counters = [0 for o in obsl]
-741            offsets = [o.r_values[repname] - o.value if repname in o.r_values else 0 for o in obsl]
-742            for ci in idx:
-743                data += '%d ' % ci
-744                for oi in range(len(obsl)):
-745                    o = obsl[oi]
-746                    if repname in o.idl:
-747                        if counters[oi] < 0:
-748                            num = offsets[oi]
-749                            if num == 0:
-750                                data += '0 '
-751                            else:
-752                                data += '%1.16e ' % (num)
-753                            continue
-754                        if o.idl[repname][counters[oi]] == ci:
-755                            num = o.deltas[repname][counters[oi]] + offsets[oi]
-756                            if num == 0:
-757                                data += '0 '
-758                            else:
-759                                data += '%1.16e ' % (num)
-760                            counters[oi] += 1
-761                            if counters[oi] >= len(o.idl[repname]):
-762                                counters[oi] = -1
-763                        else:
-764                            num = offsets[oi]
-765                            if num == 0:
-766                                data += '0 '
-767                            else:
-768                                data += '%1.16e ' % (num)
-769                    else:
-770                        data += '0 '
-771                data += '\n'
-772            ad['#data'] = data
-773            ed[''].append(ad)
-774        pd['edata'].append(ed)
-775
-776        allcov = {}
-777        for o in obsl:
-778            for cname in o.cov_names:
-779                if cname in allcov:
-780                    if not np.array_equal(allcov[cname], o.covobs[cname].cov):
-781                        raise Exception('Inconsistent covariance matrices for %s!' % (cname))
-782                else:
-783                    allcov[cname] = o.covobs[cname].cov
-784        pd['cdata'] = []
-785        for cname in cov_names:
-786            cd = {}
-787            cd['id'] = cname
-788
-789            covd = {'id': 'cov'}
-790            if allcov[cname].shape == ():
-791                ncov = 1
-792                covd['layout'] = '1 1 f'
-793                covd['#data'] = '%1.14e' % (allcov[cname])
-794            else:
-795                shape = allcov[cname].shape
-796                assert (shape[0] == shape[1])
-797                ncov = shape[0]
-798                covd['layout'] = '%d %d f' % (ncov, ncov)
-799                ds = ''
-800                for i in range(ncov):
-801                    for j in range(ncov):
-802                        val = allcov[cname][i][j]
-803                        if val == 0:
-804                            ds += '0 '
-805                        else:
-806                            ds += '%1.14e ' % (val)
-807                    ds += '\n'
-808                covd['#data'] = ds
-809
-810            gradd = {'id': 'grad'}
-811            gradd['layout'] = '%d f%d' % (ncov, len(obsl))
-812            ds = ''
-813            for i in range(ncov):
-814                for o in obsl:
-815                    if cname in o.covobs:
-816                        val = o.covobs[cname].grad[i]
-817                        if val != 0:
-818                            ds += '%1.14e ' % (val)
-819                        else:
-820                            ds += '0 '
-821                    else:
-822                        ds += '0 '
-823            gradd['#data'] = ds
-824            cd['array'] = [covd, gradd]
-825            pd['cdata'].append(cd)
+            
    681def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None):
+682    """Generate the string for the export of a list of Obs or structures containing Obs
+683    to a .xml.gz file according to the Zeuthen dobs format.
+684
+685    Tags are not written or recovered automatically. The separator |is removed from the replica names.
+686
+687    Parameters
+688    ----------
+689    obsl : list
+690        List of Obs that will be exported.
+691        The Obs inside a structure do not have to be defined on the same set of configurations,
+692        but the storage requirement is increased, if this is not the case.
+693    name : str
+694        The name of the observable.
+695    spec : str
+696        Optional string that describes the contents of the file.
+697    origin : str
+698        Specify where the data has its origin.
+699    symbol : list
+700        A list of symbols that describe the observables to be written. May be empty.
+701    who : str
+702        Provide the name of the person that exports the data.
+703    enstags : dict
+704        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
+705        Otherwise, the ensemble name is used.
+706
+707    Returns
+708    -------
+709    xml_str : str
+710        XML string generated from the data
+711    """
+712    if enstags is None:
+713        enstags = {}
+714    od = {}
+715    r_names = []
+716    for o in obsl:
+717        r_names += [name for name in o.names if name.split('|')[0] in o.mc_names]
+718    r_names = sorted(set(r_names))
+719    mc_names = sorted(set([n.split('|')[0] for n in r_names]))
+720    for tmpname in mc_names:
+721        if tmpname not in enstags:
+722            enstags[tmpname] = tmpname
+723    ne = len(set(mc_names))
+724    cov_names = []
+725    for o in obsl:
+726        cov_names += list(o.cov_names)
+727    cov_names = sorted(set(cov_names))
+728    nc = len(set(cov_names))
+729    od['OBSERVABLES'] = {}
+730    od['OBSERVABLES']['SCHEMA'] = {'NAME': 'lattobs', 'VERSION': '1.0'}
+731    if who is None:
+732        who = getpass.getuser()
+733    od['OBSERVABLES']['origin'] = {
+734        'who': who,
+735        'date': str(datetime.datetime.now())[:-7],
+736        'host': socket.gethostname(),
+737        'tool': {'name': 'pyerrors', 'version': pyerrorsversion.__version__}}
+738    od['OBSERVABLES']['dobs'] = {}
+739    pd = od['OBSERVABLES']['dobs']
+740    pd['spec'] = spec
+741    pd['origin'] = origin
+742    pd['name'] = name
+743    pd['array'] = {}
+744    pd['array']['id'] = 'val'
+745    pd['array']['layout'] = '1 f%d' % (len(obsl))
+746    osymbol = ''
+747    if symbol:
+748        if not isinstance(symbol, list):
+749            raise Exception('Symbol has to be a list!')
+750        if not (len(symbol) == 0 or len(symbol) == len(obsl)):
+751            raise Exception('Symbol has to be a list of lenght 0 or %d!' % (len(obsl)))
+752        osymbol = symbol[0]
+753        for s in symbol[1:]:
+754            osymbol += ' %s' % s
+755        pd['array']['symbol'] = osymbol
+756
+757    pd['array']['#values'] = ['  '.join(['%1.16e' % o.value for o in obsl])]
+758    pd['ne'] = '%d' % (ne)
+759    pd['nc'] = '%d' % (nc)
+760    pd['edata'] = []
+761    for name in mc_names:
+762        ed = {}
+763        ed['enstag'] = enstags[name]
+764        onames = sorted([n for n in r_names if (n.startswith(name + '|') or n == name)])
+765        nr = len(onames)
+766        ed['nr'] = nr
+767        ed[''] = []
+768
+769        for r in range(nr):
+770            ad = {}
+771            repname = onames[r]
+772            ad['id'] = repname.replace('|', '')
+773            idx = _merge_idx([o.idl.get(repname, []) for o in obsl])
+774            Nconf = len(idx)
+775            layout = '%d i f%d' % (Nconf, len(obsl))
+776            ad['layout'] = layout
+777            data = ''
+778            counters = [0 for o in obsl]
+779            offsets = [o.r_values[repname] - o.value if repname in o.r_values else 0 for o in obsl]
+780            for ci in idx:
+781                data += '%d ' % ci
+782                for oi in range(len(obsl)):
+783                    o = obsl[oi]
+784                    if repname in o.idl:
+785                        if counters[oi] < 0:
+786                            num = offsets[oi]
+787                            if num == 0:
+788                                data += '0 '
+789                            else:
+790                                data += '%1.16e ' % (num)
+791                            continue
+792                        if o.idl[repname][counters[oi]] == ci:
+793                            num = o.deltas[repname][counters[oi]] + offsets[oi]
+794                            if num == 0:
+795                                data += '0 '
+796                            else:
+797                                data += '%1.16e ' % (num)
+798                            counters[oi] += 1
+799                            if counters[oi] >= len(o.idl[repname]):
+800                                counters[oi] = -1
+801                        else:
+802                            num = offsets[oi]
+803                            if num == 0:
+804                                data += '0 '
+805                            else:
+806                                data += '%1.16e ' % (num)
+807                    else:
+808                        data += '0 '
+809                data += '\n'
+810            ad['#data'] = data
+811            ed[''].append(ad)
+812        pd['edata'].append(ed)
+813
+814        allcov = {}
+815        for o in obsl:
+816            for cname in o.cov_names:
+817                if cname in allcov:
+818                    if not np.array_equal(allcov[cname], o.covobs[cname].cov):
+819                        raise Exception('Inconsistent covariance matrices for %s!' % (cname))
+820                else:
+821                    allcov[cname] = o.covobs[cname].cov
+822        pd['cdata'] = []
+823        for cname in cov_names:
+824            cd = {}
+825            cd['id'] = cname
 826
-827    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dobsdict_to_xmlstring_spaces(od)
-828
-829    return rs
+827            covd = {'id': 'cov'}
+828            if allcov[cname].shape == ():
+829                ncov = 1
+830                covd['layout'] = '1 1 f'
+831                covd['#data'] = '%1.14e' % (allcov[cname])
+832            else:
+833                shape = allcov[cname].shape
+834                assert (shape[0] == shape[1])
+835                ncov = shape[0]
+836                covd['layout'] = '%d %d f' % (ncov, ncov)
+837                ds = ''
+838                for i in range(ncov):
+839                    for j in range(ncov):
+840                        val = allcov[cname][i][j]
+841                        if val == 0:
+842                            ds += '0 '
+843                        else:
+844                            ds += '%1.14e ' % (val)
+845                    ds += '\n'
+846                covd['#data'] = ds
+847
+848            gradd = {'id': 'grad'}
+849            gradd['layout'] = '%d f%d' % (ncov, len(obsl))
+850            ds = ''
+851            for i in range(ncov):
+852                for o in obsl:
+853                    if cname in o.covobs:
+854                        val = o.covobs[cname].grad[i]
+855                        if val != 0:
+856                            ds += '%1.14e ' % (val)
+857                        else:
+858                            ds += '0 '
+859                    else:
+860                        ds += '0 '
+861            gradd['#data'] = ds
+862            cd['array'] = [covd, gradd]
+863            pd['cdata'].append(cd)
+864
+865    rs = '<?xml version="1.0" encoding="utf-8"?>\n' + _dobsdict_to_xmlstring_spaces(od)
+866
+867    return rs
 
    
 
 
@@ -1817,6 +1940,13 @@ Provide the name of the person that exports the data.
 Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
 Otherwise, the ensemble name is used.
 
+
+
    Returns
    
+
+
      
+
    • xml_str (str):
+XML string generated from the data
      • 
+
    @@ -1832,54 +1962,58 @@ Otherwise, the ensemble name is used. - 
    832def write_dobs(obsl, fname, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None, gz=True):
-833    """Export a list of Obs or structures containing Obs to a .xml.gz file
-834    according to the Zeuthen dobs format.
-835
-836    Tags are not written or recovered automatically. The separator | is removed from the replica names.
-837
-838    Parameters
-839    ----------
-840    obsl : list
-841        List of Obs that will be exported.
-842        The Obs inside a structure do not have to be defined on the same set of configurations,
-843        but the storage requirement is increased, if this is not the case.
-844    fname : str
-845        Filename of the output file.
-846    name : str
-847        The name of the observable.
-848    spec : str
-849        Optional string that describes the contents of the file.
-850    origin : str
-851        Specify where the data has its origin.
-852    symbol : list
-853        A list of symbols that describe the observables to be written. May be empty.
-854    who : str
-855        Provide the name of the person that exports the data.
-856    enstags : dict
-857        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
-858        Otherwise, the ensemble name is used.
-859    gz : bool
-860        If True, the output is a gzipped XML. If False, the output is a XML file.
-861    """
-862    if enstags is None:
-863        enstags = {}
-864
-865    dobsstring = create_dobs_string(obsl, name, spec, origin, symbol, who, enstags=enstags)
-866
-867    if not fname.endswith('.xml') and not fname.endswith('.gz'):
-868        fname += '.xml'
-869
-870    if gz:
-871        if not fname.endswith('.gz'):
-872            fname += '.gz'
+            
    870def write_dobs(obsl, fname, name, spec='dobs v1.0', origin='', symbol=[], who=None, enstags=None, gz=True):
+871    """Export a list of Obs or structures containing Obs to a .xml.gz file
+872    according to the Zeuthen dobs format.
 873
-874        fp = gzip.open(fname, 'wb')
-875        fp.write(dobsstring.encode('utf-8'))
-876    else:
-877        fp = open(fname, 'w', encoding='utf-8')
-878        fp.write(dobsstring)
-879    fp.close()
+874    Tags are not written or recovered automatically. The separator | is removed from the replica names.
+875
+876    Parameters
+877    ----------
+878    obsl : list
+879        List of Obs that will be exported.
+880        The Obs inside a structure do not have to be defined on the same set of configurations,
+881        but the storage requirement is increased, if this is not the case.
+882    fname : str
+883        Filename of the output file.
+884    name : str
+885        The name of the observable.
+886    spec : str
+887        Optional string that describes the contents of the file.
+888    origin : str
+889        Specify where the data has its origin.
+890    symbol : list
+891        A list of symbols that describe the observables to be written. May be empty.
+892    who : str
+893        Provide the name of the person that exports the data.
+894    enstags : dict
+895        Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
+896        Otherwise, the ensemble name is used.
+897    gz : bool
+898        If True, the output is a gzipped XML. If False, the output is a XML file.
+899
+900    Returns
+901    -------
+902    None
+903    """
+904    if enstags is None:
+905        enstags = {}
+906
+907    dobsstring = create_dobs_string(obsl, name, spec, origin, symbol, who, enstags=enstags)
+908
+909    if not fname.endswith('.xml') and not fname.endswith('.gz'):
+910        fname += '.xml'
+911
+912    if gz:
+913        if not fname.endswith('.gz'):
+914            fname += '.gz'
+915
+916        fp = gzip.open(fname, 'wb')
+917        fp.write(dobsstring.encode('utf-8'))
+918    else:
+919        fp = open(fname, 'w', encoding='utf-8')
+920        fp.write(dobsstring)
+921    fp.close()
 
    
 
 
@@ -1913,6 +2047,12 @@ Otherwise, the ensemble name is used.
 
  • gz (bool):
 If True, the output is a gzipped XML. If False, the output is a XML file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    diff --git a/docs/pyerrors/input/hadrons.html b/docs/pyerrors/input/hadrons.html index 77d311cd..1db5836f 100644 --- a/docs/pyerrors/input/hadrons.html +++ b/docs/pyerrors/input/hadrons.html @@ -176,393 +176,418 @@ 74 two-point function. The gammas argument dominateds over meson. 75 idl : range 76 If specified only configurations in the given range are read in. - 77 ''' - 78 - 79 files, idx = _get_files(path, filestem, idl) - 80 - 81 tree = meson.rsplit('_')[0] - 82 if gammas is not None: - 83 h5file = h5py.File(path + '/' + files[0], "r") - 84 found_meson = None - 85 for key in h5file[tree].keys(): - 86 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: - 87 found_meson = key - 88 break - 89 h5file.close() - 90 if found_meson: - 91 meson = found_meson - 92 else: - 93 raise Exception("Source Sink combination " + str(gammas) + " not found.") - 94 - 95 corr_data = [] - 96 infos = [] - 97 for hd5_file in files: - 98 h5file = h5py.File(path + '/' + hd5_file, "r") - 99 if not tree + '/' + meson in h5file: -100 raise Exception("Entry '" + meson + "' not contained in the files.") -101 raw_data = h5file[tree + '/' + meson + '/corr'] -102 real_data = raw_data[:]["re"].astype(np.double) -103 corr_data.append(real_data) -104 if not infos: -105 for k, i in h5file[tree + '/' + meson].attrs.items(): -106 infos.append(k + ': ' + i[0].decode()) -107 h5file.close() -108 corr_data = np.array(corr_data) -109 -110 l_obs = [] -111 for c in corr_data.T: -112 l_obs.append(Obs([c], [ens_id], idl=[idx])) -113 -114 corr = Corr(l_obs) -115 corr.tag = r", ".join(infos) -116 return corr -117 + 77 + 78 Returns + 79 ------- + 80 corr : Corr + 81 Correlator of the source sink combination in question. + 82 ''' + 83 + 84 files, idx = _get_files(path, filestem, idl) + 85 + 86 tree = meson.rsplit('_')[0] + 87 if gammas is not None: + 88 h5file = h5py.File(path + '/' + files[0], "r") + 89 found_meson = None + 90 for key in h5file[tree].keys(): + 91 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: + 92 found_meson = key + 93 break + 94 h5file.close() + 95 if found_meson: + 96 meson = found_meson + 97 else: + 98 raise Exception("Source Sink combination " + str(gammas) + " not found.") + 99 +100 corr_data = [] +101 infos = [] +102 for hd5_file in files: +103 h5file = h5py.File(path + '/' + hd5_file, "r") +104 if not tree + '/' + meson in h5file: +105 raise Exception("Entry '" + meson + "' not contained in the files.") +106 raw_data = h5file[tree + '/' + meson + '/corr'] +107 real_data = raw_data[:]["re"].astype(np.double) +108 corr_data.append(real_data) +109 if not infos: +110 for k, i in h5file[tree + '/' + meson].attrs.items(): +111 infos.append(k + ': ' + i[0].decode()) +112 h5file.close() +113 corr_data = np.array(corr_data) +114 +115 l_obs = [] +116 for c in corr_data.T: +117 l_obs.append(Obs([c], [ens_id], idl=[idx])) 118 -119def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None): -120 """Read hadrons DistillationContraction hdf5 files in given directory structure -121 -122 Parameters -123 ----------------- -124 path : str -125 path to the directories to read -126 ens_id : str -127 name of the ensemble, required for internal bookkeeping -128 diagrams : list -129 List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"]. -130 idl : range -131 If specified only configurations in the given range are read in. -132 """ -133 -134 res_dict = {} -135 -136 directories, idx = _get_files(path, "data", idl) +119 corr = Corr(l_obs) +120 corr.tag = r", ".join(infos) +121 return corr +122 +123 +124def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None): +125 """Read hadrons DistillationContraction hdf5 files in given directory structure +126 +127 Parameters +128 ----------------- +129 path : str +130 path to the directories to read +131 ens_id : str +132 name of the ensemble, required for internal bookkeeping +133 diagrams : list +134 List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"]. +135 idl : range +136 If specified only configurations in the given range are read in. 137 -138 explore_path = Path(path + "/" + directories[0]) -139 -140 for explore_file in explore_path.iterdir(): -141 if explore_file.is_file(): -142 stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1] -143 else: -144 continue +138 Returns +139 ------- +140 result : dict +141 extracted DistillationContration data +142 """ +143 +144 res_dict = {} 145 -146 file_list = [] -147 for dir in directories: -148 tmp_path = Path(path + "/" + dir) -149 file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5") -150 -151 corr_data = {} -152 -153 for diagram in diagrams: -154 corr_data[diagram] = [] +146 directories, idx = _get_files(path, "data", idl) +147 +148 explore_path = Path(path + "/" + directories[0]) +149 +150 for explore_file in explore_path.iterdir(): +151 if explore_file.is_file(): +152 stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1] +153 else: +154 continue 155 -156 for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))): -157 h5file = h5py.File(hd5_file) -158 -159 if n_file == 0: -160 if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...": -161 raise Exception("Routine is only implemented for files containing inversions on all timeslices.") +156 file_list = [] +157 for dir in directories: +158 tmp_path = Path(path + "/" + dir) +159 file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5") +160 +161 corr_data = {} 162 -163 Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0] -164 -165 identifier = [] -166 for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1): -167 encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file)) -168 full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_") -169 my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3]) -170 identifier.append(my_tuple) -171 identifier = tuple(identifier) -172 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. -173 -174 for diagram in diagrams: -175 -176 if diagram == "triangle" and "Identity" not in str(identifier): -177 part = "im" -178 else: -179 part = "re" -180 -181 real_data = np.zeros(Nt) -182 for x0 in range(Nt): -183 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double) -184 real_data += np.roll(raw_data, -x0) -185 real_data /= Nt -186 -187 corr_data[diagram].append(real_data) -188 h5file.close() -189 -190 res_dict[str(identifier)] = {} -191 -192 for diagram in diagrams: -193 -194 tmp_data = np.array(corr_data[diagram]) -195 -196 l_obs = [] -197 for c in tmp_data.T: -198 l_obs.append(Obs([c], [ens_id], idl=[idx])) +163 for diagram in diagrams: +164 corr_data[diagram] = [] +165 +166 for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))): +167 h5file = h5py.File(hd5_file) +168 +169 if n_file == 0: +170 if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...": +171 raise Exception("Routine is only implemented for files containing inversions on all timeslices.") +172 +173 Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0] +174 +175 identifier = [] +176 for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1): +177 encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file)) +178 full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_") +179 my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3]) +180 identifier.append(my_tuple) +181 identifier = tuple(identifier) +182 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. +183 +184 for diagram in diagrams: +185 +186 if diagram == "triangle" and "Identity" not in str(identifier): +187 part = "im" +188 else: +189 part = "re" +190 +191 real_data = np.zeros(Nt) +192 for x0 in range(Nt): +193 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double) +194 real_data += np.roll(raw_data, -x0) +195 real_data /= Nt +196 +197 corr_data[diagram].append(real_data) +198 h5file.close() 199 -200 corr = Corr(l_obs) -201 corr.tag = str(identifier) -202 -203 res_dict[str(identifier)][diagram] = corr -204 -205 return res_dict -206 -207 -208class Npr_matrix(np.ndarray): +200 res_dict[str(identifier)] = {} +201 +202 for diagram in diagrams: +203 +204 tmp_data = np.array(corr_data[diagram]) +205 +206 l_obs = [] +207 for c in tmp_data.T: +208 l_obs.append(Obs([c], [ens_id], idl=[idx])) 209 -210 def __new__(cls, input_array, mom_in=None, mom_out=None): -211 obj = np.asarray(input_array).view(cls) -212 obj.mom_in = mom_in -213 obj.mom_out = mom_out -214 return obj -215 -216 @property -217 def g5H(self): -218 """Gamma_5 hermitean conjugate +210 corr = Corr(l_obs) +211 corr.tag = str(identifier) +212 +213 res_dict[str(identifier)][diagram] = corr +214 +215 return res_dict +216 +217 +218class Npr_matrix(np.ndarray): 219 -220 Uses the fact that the propagator is gamma5 hermitean, so just the -221 in and out momenta of the propagator are exchanged. -222 """ -223 return Npr_matrix(self, -224 mom_in=self.mom_out, -225 mom_out=self.mom_in) -226 -227 def _propagate_mom(self, other, name): -228 s_mom = getattr(self, name, None) -229 o_mom = getattr(other, name, None) -230 if s_mom is not None and o_mom is not None: -231 if not np.allclose(s_mom, o_mom): -232 raise Exception(name + ' does not match.') -233 return o_mom if o_mom is not None else s_mom -234 -235 def __matmul__(self, other): -236 return self.__new__(Npr_matrix, -237 super().__matmul__(other), -238 self._propagate_mom(other, 'mom_in'), -239 self._propagate_mom(other, 'mom_out')) -240 -241 def __array_finalize__(self, obj): -242 if obj is None: -243 return -244 self.mom_in = getattr(obj, 'mom_in', None) -245 self.mom_out = getattr(obj, 'mom_out', None) -246 -247 -248def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): -249 """Read hadrons ExternalLeg hdf5 file and output an array of CObs +220 def __new__(cls, input_array, mom_in=None, mom_out=None): +221 obj = np.asarray(input_array).view(cls) +222 obj.mom_in = mom_in +223 obj.mom_out = mom_out +224 return obj +225 +226 @property +227 def g5H(self): +228 """Gamma_5 hermitean conjugate +229 +230 Uses the fact that the propagator is gamma5 hermitean, so just the +231 in and out momenta of the propagator are exchanged. +232 """ +233 return Npr_matrix(self, +234 mom_in=self.mom_out, +235 mom_out=self.mom_in) +236 +237 def _propagate_mom(self, other, name): +238 s_mom = getattr(self, name, None) +239 o_mom = getattr(other, name, None) +240 if s_mom is not None and o_mom is not None: +241 if not np.allclose(s_mom, o_mom): +242 raise Exception(name + ' does not match.') +243 return o_mom if o_mom is not None else s_mom +244 +245 def __matmul__(self, other): +246 return self.__new__(Npr_matrix, +247 super().__matmul__(other), +248 self._propagate_mom(other, 'mom_in'), +249 self._propagate_mom(other, 'mom_out')) 250 -251 Parameters -252 ---------- -253 path : str -254 path to the files to read -255 filestem : str -256 namestem of the files to read -257 ens_id : str -258 name of the ensemble, required for internal bookkeeping -259 idl : range -260 If specified only configurations in the given range are read in. -261 """ -262 -263 files, idx = _get_files(path, filestem, idl) -264 -265 mom = None -266 -267 corr_data = [] -268 for hd5_file in files: -269 file = h5py.File(path + '/' + hd5_file, "r") -270 raw_data = file['ExternalLeg/corr'][0][0].view('complex') -271 corr_data.append(raw_data) -272 if mom is None: -273 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -274 file.close() -275 corr_data = np.array(corr_data) -276 -277 rolled_array = np.rollaxis(corr_data, 0, 5) -278 -279 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -280 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -281 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -282 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -283 matrix[si, sj, ci, cj] = CObs(real, imag) -284 -285 return Npr_matrix(matrix, mom_in=mom) -286 -287 -288def read_Bilinear_hd5(path, filestem, ens_id, idl=None): -289 """Read hadrons Bilinear hdf5 file and output an array of CObs -290 -291 Parameters -292 ---------- -293 path : str -294 path to the files to read -295 filestem : str -296 namestem of the files to read -297 ens_id : str -298 name of the ensemble, required for internal bookkeeping -299 idl : range -300 If specified only configurations in the given range are read in. -301 """ +251 def __array_finalize__(self, obj): +252 if obj is None: +253 return +254 self.mom_in = getattr(obj, 'mom_in', None) +255 self.mom_out = getattr(obj, 'mom_out', None) +256 +257 +258def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): +259 """Read hadrons ExternalLeg hdf5 file and output an array of CObs +260 +261 Parameters +262 ---------- +263 path : str +264 path to the files to read +265 filestem : str +266 namestem of the files to read +267 ens_id : str +268 name of the ensemble, required for internal bookkeeping +269 idl : range +270 If specified only configurations in the given range are read in. +271 +272 Returns +273 ------- +274 result : Npr_matrix +275 read Cobs-matrix +276 """ +277 +278 files, idx = _get_files(path, filestem, idl) +279 +280 mom = None +281 +282 corr_data = [] +283 for hd5_file in files: +284 file = h5py.File(path + '/' + hd5_file, "r") +285 raw_data = file['ExternalLeg/corr'][0][0].view('complex') +286 corr_data.append(raw_data) +287 if mom is None: +288 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +289 file.close() +290 corr_data = np.array(corr_data) +291 +292 rolled_array = np.rollaxis(corr_data, 0, 5) +293 +294 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +295 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +296 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +297 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +298 matrix[si, sj, ci, cj] = CObs(real, imag) +299 +300 return Npr_matrix(matrix, mom_in=mom) +301 302 -303 files, idx = _get_files(path, filestem, idl) -304 -305 mom_in = None -306 mom_out = None -307 -308 corr_data = {} -309 for hd5_file in files: -310 file = h5py.File(path + '/' + hd5_file, "r") -311 for i in range(16): -312 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') -313 if name not in corr_data: -314 corr_data[name] = [] -315 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') -316 corr_data[name].append(raw_data) -317 if mom_in is None: -318 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -319 if mom_out is None: -320 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) -321 -322 file.close() -323 -324 result_dict = {} -325 -326 for key, data in corr_data.items(): -327 local_data = np.array(data) -328 -329 rolled_array = np.rollaxis(local_data, 0, 5) -330 -331 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -332 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -333 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -334 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -335 matrix[si, sj, ci, cj] = CObs(real, imag) -336 -337 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) -338 -339 return result_dict -340 +303def read_Bilinear_hd5(path, filestem, ens_id, idl=None): +304 """Read hadrons Bilinear hdf5 file and output an array of CObs +305 +306 Parameters +307 ---------- +308 path : str +309 path to the files to read +310 filestem : str +311 namestem of the files to read +312 ens_id : str +313 name of the ensemble, required for internal bookkeeping +314 idl : range +315 If specified only configurations in the given range are read in. +316 +317 Returns +318 ------- +319 result_dict: dict[Npr_matrix] +320 extracted Bilinears +321 """ +322 +323 files, idx = _get_files(path, filestem, idl) +324 +325 mom_in = None +326 mom_out = None +327 +328 corr_data = {} +329 for hd5_file in files: +330 file = h5py.File(path + '/' + hd5_file, "r") +331 for i in range(16): +332 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') +333 if name not in corr_data: +334 corr_data[name] = [] +335 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') +336 corr_data[name].append(raw_data) +337 if mom_in is None: +338 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +339 if mom_out is None: +340 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) 341 -342def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): -343 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs -344 -345 Parameters -346 ---------- -347 path : str -348 path to the files to read -349 filestem : str -350 namestem of the files to read -351 ens_id : str -352 name of the ensemble, required for internal bookkeeping -353 idl : range -354 If specified only configurations in the given range are read in. -355 vertices : list -356 Vertex functions to be extracted. -357 """ +342 file.close() +343 +344 result_dict = {} +345 +346 for key, data in corr_data.items(): +347 local_data = np.array(data) +348 +349 rolled_array = np.rollaxis(local_data, 0, 5) +350 +351 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +352 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +353 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +354 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +355 matrix[si, sj, ci, cj] = CObs(real, imag) +356 +357 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) 358 -359 files, idx = _get_files(path, filestem, idl) +359 return result_dict 360 -361 mom_in = None -362 mom_out = None -363 -364 vertex_names = [] -365 for vertex in vertices: -366 vertex_names += _get_lorentz_names(vertex) -367 -368 corr_data = {} -369 -370 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' -371 -372 for hd5_file in files: -373 file = h5py.File(path + '/' + hd5_file, "r") -374 -375 for i in range(32): -376 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) -377 if name in vertex_names: -378 if name not in corr_data: -379 corr_data[name] = [] -380 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') -381 corr_data[name].append(raw_data) -382 if mom_in is None: -383 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -384 if mom_out is None: -385 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) -386 -387 file.close() +361 +362def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): +363 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs +364 +365 Parameters +366 ---------- +367 path : str +368 path to the files to read +369 filestem : str +370 namestem of the files to read +371 ens_id : str +372 name of the ensemble, required for internal bookkeeping +373 idl : range +374 If specified only configurations in the given range are read in. +375 vertices : list +376 Vertex functions to be extracted. +377 +378 Returns +379 ------- +380 result_dict : dict +381 extracted fourquark matrizes +382 """ +383 +384 files, idx = _get_files(path, filestem, idl) +385 +386 mom_in = None +387 mom_out = None 388 -389 intermediate_dict = {} -390 -391 for vertex in vertices: -392 lorentz_names = _get_lorentz_names(vertex) -393 for v_name in lorentz_names: -394 if v_name in [('SigmaXY', 'SigmaZT'), -395 ('SigmaXT', 'SigmaYZ'), -396 ('SigmaYZ', 'SigmaXT'), -397 ('SigmaZT', 'SigmaXY')]: -398 sign = -1 -399 else: -400 sign = 1 -401 if vertex not in intermediate_dict: -402 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) -403 else: -404 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) -405 -406 result_dict = {} -407 -408 for key, data in intermediate_dict.items(): -409 -410 rolled_array = np.moveaxis(data, 0, 8) +389 vertex_names = [] +390 for vertex in vertices: +391 vertex_names += _get_lorentz_names(vertex) +392 +393 corr_data = {} +394 +395 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' +396 +397 for hd5_file in files: +398 file = h5py.File(path + '/' + hd5_file, "r") +399 +400 for i in range(32): +401 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) +402 if name in vertex_names: +403 if name not in corr_data: +404 corr_data[name] = [] +405 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') +406 corr_data[name].append(raw_data) +407 if mom_in is None: +408 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +409 if mom_out is None: +410 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) 411 -412 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -413 for index in np.ndindex(rolled_array.shape[:-1]): -414 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) -415 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) -416 matrix[index] = CObs(real, imag) -417 -418 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) -419 -420 return result_dict -421 -422 -423def _get_lorentz_names(name): -424 lorentz_index = ['X', 'Y', 'Z', 'T'] -425 -426 res = [] -427 -428 if name == "TT": -429 for i in range(4): -430 for j in range(i + 1, 4): -431 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) -432 return res -433 -434 if name == "TTtilde": -435 for i in range(4): -436 for j in range(i + 1, 4): -437 for k in range(4): -438 for o in range(k + 1, 4): -439 fac = epsilon_tensor_rank4(i, j, k, o) -440 if not np.isclose(fac, 0.0): -441 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) -442 return res -443 -444 assert len(name) == 2 -445 -446 if 'S' in name or 'P' in name: -447 if not set(name) <= set(['S', 'P']): -448 raise Exception("'" + name + "' is not a Lorentz scalar") -449 -450 g_names = {'S': 'Identity', -451 'P': 'Gamma5'} +412 file.close() +413 +414 intermediate_dict = {} +415 +416 for vertex in vertices: +417 lorentz_names = _get_lorentz_names(vertex) +418 for v_name in lorentz_names: +419 if v_name in [('SigmaXY', 'SigmaZT'), +420 ('SigmaXT', 'SigmaYZ'), +421 ('SigmaYZ', 'SigmaXT'), +422 ('SigmaZT', 'SigmaXY')]: +423 sign = -1 +424 else: +425 sign = 1 +426 if vertex not in intermediate_dict: +427 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) +428 else: +429 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) +430 +431 result_dict = {} +432 +433 for key, data in intermediate_dict.items(): +434 +435 rolled_array = np.moveaxis(data, 0, 8) +436 +437 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +438 for index in np.ndindex(rolled_array.shape[:-1]): +439 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) +440 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) +441 matrix[index] = CObs(real, imag) +442 +443 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +444 +445 return result_dict +446 +447 +448def _get_lorentz_names(name): +449 lorentz_index = ['X', 'Y', 'Z', 'T'] +450 +451 res = [] 452 -453 res.append((g_names[name[0]], g_names[name[1]])) -454 -455 else: -456 if not set(name) <= set(['V', 'A']): -457 raise Exception("'" + name + "' is not a Lorentz scalar") +453 if name == "TT": +454 for i in range(4): +455 for j in range(i + 1, 4): +456 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) +457 return res 458 -459 for ind in lorentz_index: -460 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', -461 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) -462 -463 return res +459 if name == "TTtilde": +460 for i in range(4): +461 for j in range(i + 1, 4): +462 for k in range(4): +463 for o in range(k + 1, 4): +464 fac = epsilon_tensor_rank4(i, j, k, o) +465 if not np.isclose(fac, 0.0): +466 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) +467 return res +468 +469 assert len(name) == 2 +470 +471 if 'S' in name or 'P' in name: +472 if not set(name) <= set(['S', 'P']): +473 raise Exception("'" + name + "' is not a Lorentz scalar") +474 +475 g_names = {'S': 'Identity', +476 'P': 'Gamma5'} +477 +478 res.append((g_names[name[0]], g_names[name[1]])) +479 +480 else: +481 if not set(name) <= set(['V', 'A']): +482 raise Exception("'" + name + "' is not a Lorentz scalar") +483 +484 for ind in lorentz_index: +485 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', +486 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) +487 +488 return res @@ -599,46 +624,51 @@ 75 two-point function. The gammas argument dominateds over meson. 76 idl : range 77 If specified only configurations in the given range are read in. - 78 ''' - 79 - 80 files, idx = _get_files(path, filestem, idl) - 81 - 82 tree = meson.rsplit('_')[0] - 83 if gammas is not None: - 84 h5file = h5py.File(path + '/' + files[0], "r") - 85 found_meson = None - 86 for key in h5file[tree].keys(): - 87 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: - 88 found_meson = key - 89 break - 90 h5file.close() - 91 if found_meson: - 92 meson = found_meson - 93 else: - 94 raise Exception("Source Sink combination " + str(gammas) + " not found.") - 95 - 96 corr_data = [] - 97 infos = [] - 98 for hd5_file in files: - 99 h5file = h5py.File(path + '/' + hd5_file, "r") -100 if not tree + '/' + meson in h5file: -101 raise Exception("Entry '" + meson + "' not contained in the files.") -102 raw_data = h5file[tree + '/' + meson + '/corr'] -103 real_data = raw_data[:]["re"].astype(np.double) -104 corr_data.append(real_data) -105 if not infos: -106 for k, i in h5file[tree + '/' + meson].attrs.items(): -107 infos.append(k + ': ' + i[0].decode()) -108 h5file.close() -109 corr_data = np.array(corr_data) -110 -111 l_obs = [] -112 for c in corr_data.T: -113 l_obs.append(Obs([c], [ens_id], idl=[idx])) -114 -115 corr = Corr(l_obs) -116 corr.tag = r", ".join(infos) -117 return corr + 78 + 79 Returns + 80 ------- + 81 corr : Corr + 82 Correlator of the source sink combination in question. + 83 ''' + 84 + 85 files, idx = _get_files(path, filestem, idl) + 86 + 87 tree = meson.rsplit('_')[0] + 88 if gammas is not None: + 89 h5file = h5py.File(path + '/' + files[0], "r") + 90 found_meson = None + 91 for key in h5file[tree].keys(): + 92 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: + 93 found_meson = key + 94 break + 95 h5file.close() + 96 if found_meson: + 97 meson = found_meson + 98 else: + 99 raise Exception("Source Sink combination " + str(gammas) + " not found.") +100 +101 corr_data = [] +102 infos = [] +103 for hd5_file in files: +104 h5file = h5py.File(path + '/' + hd5_file, "r") +105 if not tree + '/' + meson in h5file: +106 raise Exception("Entry '" + meson + "' not contained in the files.") +107 raw_data = h5file[tree + '/' + meson + '/corr'] +108 real_data = raw_data[:]["re"].astype(np.double) +109 corr_data.append(real_data) +110 if not infos: +111 for k, i in h5file[tree + '/' + meson].attrs.items(): +112 infos.append(k + ': ' + i[0].decode()) +113 h5file.close() +114 corr_data = np.array(corr_data) +115 +116 l_obs = [] +117 for c in corr_data.T: +118 l_obs.append(Obs([c], [ens_id], idl=[idx])) +119 +120 corr = Corr(l_obs) +121 corr.tag = r", ".join(infos) +122 return corr @@ -664,6 +694,13 @@ two-point function. The gammas argument dominateds over meson.
  • idl (range): If specified only configurations in the given range are read in.
    • + +
    Returns
    + + @@ -679,93 +716,98 @@ If specified only configurations in the given range are read in. - 
    120def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None):
-121    """Read hadrons DistillationContraction hdf5 files in given directory structure
-122
-123    Parameters
-124    -----------------
-125    path : str
-126        path to the directories to read
-127    ens_id : str
-128        name of the ensemble, required for internal bookkeeping
-129    diagrams : list
-130        List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].
-131    idl : range
-132        If specified only configurations in the given range are read in.
-133    """
-134
-135    res_dict = {}
-136
-137    directories, idx = _get_files(path, "data", idl)
+            
    125def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None):
+126    """Read hadrons DistillationContraction hdf5 files in given directory structure
+127
+128    Parameters
+129    -----------------
+130    path : str
+131        path to the directories to read
+132    ens_id : str
+133        name of the ensemble, required for internal bookkeeping
+134    diagrams : list
+135        List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].
+136    idl : range
+137        If specified only configurations in the given range are read in.
 138
-139    explore_path = Path(path + "/" + directories[0])
-140
-141    for explore_file in explore_path.iterdir():
-142        if explore_file.is_file():
-143            stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1]
-144        else:
-145            continue
+139    Returns
+140    -------
+141    result : dict
+142        extracted DistillationContration data
+143    """
+144
+145    res_dict = {}
 146
-147        file_list = []
-148        for dir in directories:
-149            tmp_path = Path(path + "/" + dir)
-150            file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5")
-151
-152        corr_data = {}
-153
-154        for diagram in diagrams:
-155            corr_data[diagram] = []
+147    directories, idx = _get_files(path, "data", idl)
+148
+149    explore_path = Path(path + "/" + directories[0])
+150
+151    for explore_file in explore_path.iterdir():
+152        if explore_file.is_file():
+153            stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1]
+154        else:
+155            continue
 156
-157        for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))):
-158            h5file = h5py.File(hd5_file)
-159
-160            if n_file == 0:
-161                if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...":
-162                    raise Exception("Routine is only implemented for files containing inversions on all timeslices.")
+157        file_list = []
+158        for dir in directories:
+159            tmp_path = Path(path + "/" + dir)
+160            file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5")
+161
+162        corr_data = {}
 163
-164                Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0]
-165
-166                identifier = []
-167                for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1):
-168                    encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file))
-169                    full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_")
-170                    my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3])
-171                    identifier.append(my_tuple)
-172                identifier = tuple(identifier)
-173                # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case.
-174
-175            for diagram in diagrams:
-176
-177                if diagram == "triangle" and "Identity" not in str(identifier):
-178                    part = "im"
-179                else:
-180                    part = "re"
-181
-182                real_data = np.zeros(Nt)
-183                for x0 in range(Nt):
-184                    raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double)
-185                    real_data += np.roll(raw_data, -x0)
-186                real_data /= Nt
-187
-188                corr_data[diagram].append(real_data)
-189            h5file.close()
-190
-191        res_dict[str(identifier)] = {}
-192
-193        for diagram in diagrams:
-194
-195            tmp_data = np.array(corr_data[diagram])
-196
-197            l_obs = []
-198            for c in tmp_data.T:
-199                l_obs.append(Obs([c], [ens_id], idl=[idx]))
+164        for diagram in diagrams:
+165            corr_data[diagram] = []
+166
+167        for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))):
+168            h5file = h5py.File(hd5_file)
+169
+170            if n_file == 0:
+171                if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...":
+172                    raise Exception("Routine is only implemented for files containing inversions on all timeslices.")
+173
+174                Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0]
+175
+176                identifier = []
+177                for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1):
+178                    encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file))
+179                    full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_")
+180                    my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3])
+181                    identifier.append(my_tuple)
+182                identifier = tuple(identifier)
+183                # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case.
+184
+185            for diagram in diagrams:
+186
+187                if diagram == "triangle" and "Identity" not in str(identifier):
+188                    part = "im"
+189                else:
+190                    part = "re"
+191
+192                real_data = np.zeros(Nt)
+193                for x0 in range(Nt):
+194                    raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double)
+195                    real_data += np.roll(raw_data, -x0)
+196                real_data /= Nt
+197
+198                corr_data[diagram].append(real_data)
+199            h5file.close()
 200
-201            corr = Corr(l_obs)
-202            corr.tag = str(identifier)
-203
-204            res_dict[str(identifier)][diagram] = corr
-205
-206    return res_dict
+201        res_dict[str(identifier)] = {}
+202
+203        for diagram in diagrams:
+204
+205            tmp_data = np.array(corr_data[diagram])
+206
+207            l_obs = []
+208            for c in tmp_data.T:
+209                l_obs.append(Obs([c], [ens_id], idl=[idx]))
+210
+211            corr = Corr(l_obs)
+212            corr.tag = str(identifier)
+213
+214            res_dict[str(identifier)][diagram] = corr
+215
+216    return res_dict
 
    
 
 
@@ -783,6 +825,13 @@ List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].idl (range):
 If specified only configurations in the given range are read in.
 
+
+
    Returns
    
+
+
      
+
    • result (dict):
+extracted DistillationContration data
      • 
+
    @@ -798,44 +847,44 @@ If specified only configurations in the given range are read in. - 
    209class Npr_matrix(np.ndarray):
-210
-211    def __new__(cls, input_array, mom_in=None, mom_out=None):
-212        obj = np.asarray(input_array).view(cls)
-213        obj.mom_in = mom_in
-214        obj.mom_out = mom_out
-215        return obj
-216
-217    @property
-218    def g5H(self):
-219        """Gamma_5 hermitean conjugate
+            
    219class Npr_matrix(np.ndarray):
 220
-221        Uses the fact that the propagator is gamma5 hermitean, so just the
-222        in and out momenta of the propagator are exchanged.
-223        """
-224        return Npr_matrix(self,
-225                          mom_in=self.mom_out,
-226                          mom_out=self.mom_in)
-227
-228    def _propagate_mom(self, other, name):
-229        s_mom = getattr(self, name, None)
-230        o_mom = getattr(other, name, None)
-231        if s_mom is not None and o_mom is not None:
-232            if not np.allclose(s_mom, o_mom):
-233                raise Exception(name + ' does not match.')
-234        return o_mom if o_mom is not None else s_mom
-235
-236    def __matmul__(self, other):
-237        return self.__new__(Npr_matrix,
-238                            super().__matmul__(other),
-239                            self._propagate_mom(other, 'mom_in'),
-240                            self._propagate_mom(other, 'mom_out'))
-241
-242    def __array_finalize__(self, obj):
-243        if obj is None:
-244            return
-245        self.mom_in = getattr(obj, 'mom_in', None)
-246        self.mom_out = getattr(obj, 'mom_out', None)
+221    def __new__(cls, input_array, mom_in=None, mom_out=None):
+222        obj = np.asarray(input_array).view(cls)
+223        obj.mom_in = mom_in
+224        obj.mom_out = mom_out
+225        return obj
+226
+227    @property
+228    def g5H(self):
+229        """Gamma_5 hermitean conjugate
+230
+231        Uses the fact that the propagator is gamma5 hermitean, so just the
+232        in and out momenta of the propagator are exchanged.
+233        """
+234        return Npr_matrix(self,
+235                          mom_in=self.mom_out,
+236                          mom_out=self.mom_in)
+237
+238    def _propagate_mom(self, other, name):
+239        s_mom = getattr(self, name, None)
+240        o_mom = getattr(other, name, None)
+241        if s_mom is not None and o_mom is not None:
+242            if not np.allclose(s_mom, o_mom):
+243                raise Exception(name + ' does not match.')
+244        return o_mom if o_mom is not None else s_mom
+245
+246    def __matmul__(self, other):
+247        return self.__new__(Npr_matrix,
+248                            super().__matmul__(other),
+249                            self._propagate_mom(other, 'mom_in'),
+250                            self._propagate_mom(other, 'mom_out'))
+251
+252    def __array_finalize__(self, obj):
+253        if obj is None:
+254            return
+255        self.mom_in = getattr(obj, 'mom_in', None)
+256        self.mom_out = getattr(obj, 'mom_out', None)
 
    
 
 
@@ -1090,44 +1139,49 @@ in and out momenta of the propagator are exchanged.
    - 
    249def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
-250    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
-251
-252    Parameters
-253    ----------
-254    path : str
-255        path to the files to read
-256    filestem : str
-257        namestem of the files to read
-258    ens_id : str
-259        name of the ensemble, required for internal bookkeeping
-260    idl : range
-261        If specified only configurations in the given range are read in.
-262    """
-263
-264    files, idx = _get_files(path, filestem, idl)
-265
-266    mom = None
-267
-268    corr_data = []
-269    for hd5_file in files:
-270        file = h5py.File(path + '/' + hd5_file, "r")
-271        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
-272        corr_data.append(raw_data)
-273        if mom is None:
-274            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-275        file.close()
-276    corr_data = np.array(corr_data)
-277
-278    rolled_array = np.rollaxis(corr_data, 0, 5)
-279
-280    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-281    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-282        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-283        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-284        matrix[si, sj, ci, cj] = CObs(real, imag)
-285
-286    return Npr_matrix(matrix, mom_in=mom)
+            
    259def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
+260    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
+261
+262    Parameters
+263    ----------
+264    path : str
+265        path to the files to read
+266    filestem : str
+267        namestem of the files to read
+268    ens_id : str
+269        name of the ensemble, required for internal bookkeeping
+270    idl : range
+271        If specified only configurations in the given range are read in.
+272
+273    Returns
+274    -------
+275    result : Npr_matrix
+276        read Cobs-matrix
+277    """
+278
+279    files, idx = _get_files(path, filestem, idl)
+280
+281    mom = None
+282
+283    corr_data = []
+284    for hd5_file in files:
+285        file = h5py.File(path + '/' + hd5_file, "r")
+286        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
+287        corr_data.append(raw_data)
+288        if mom is None:
+289            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+290        file.close()
+291    corr_data = np.array(corr_data)
+292
+293    rolled_array = np.rollaxis(corr_data, 0, 5)
+294
+295    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+296    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+297        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+298        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+299        matrix[si, sj, ci, cj] = CObs(real, imag)
+300
+301    return Npr_matrix(matrix, mom_in=mom)
 
    
 
 
@@ -1145,6 +1199,13 @@ name of the ensemble, required for internal bookkeeping
 
  • idl (range):
 If specified only configurations in the given range are read in.
    • 
 
+
+
    Returns
    
+
+
      
+
    • result (Npr_matrix):
+read Cobs-matrix
      • 
+
    @@ -1160,58 +1221,63 @@ If specified only configurations in the given range are read in. - 
    289def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
-290    """Read hadrons Bilinear hdf5 file and output an array of CObs
-291
-292    Parameters
-293    ----------
-294    path : str
-295        path to the files to read
-296    filestem : str
-297        namestem of the files to read
-298    ens_id : str
-299        name of the ensemble, required for internal bookkeeping
-300    idl : range
-301        If specified only configurations in the given range are read in.
-302    """
-303
-304    files, idx = _get_files(path, filestem, idl)
-305
-306    mom_in = None
-307    mom_out = None
-308
-309    corr_data = {}
-310    for hd5_file in files:
-311        file = h5py.File(path + '/' + hd5_file, "r")
-312        for i in range(16):
-313            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
-314            if name not in corr_data:
-315                corr_data[name] = []
-316            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
-317            corr_data[name].append(raw_data)
-318            if mom_in is None:
-319                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-320            if mom_out is None:
-321                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
-322
-323        file.close()
-324
-325    result_dict = {}
-326
-327    for key, data in corr_data.items():
-328        local_data = np.array(data)
-329
-330        rolled_array = np.rollaxis(local_data, 0, 5)
-331
-332        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-333        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-334            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-335            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-336            matrix[si, sj, ci, cj] = CObs(real, imag)
-337
-338        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
-339
-340    return result_dict
+            
    304def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
+305    """Read hadrons Bilinear hdf5 file and output an array of CObs
+306
+307    Parameters
+308    ----------
+309    path : str
+310        path to the files to read
+311    filestem : str
+312        namestem of the files to read
+313    ens_id : str
+314        name of the ensemble, required for internal bookkeeping
+315    idl : range
+316        If specified only configurations in the given range are read in.
+317
+318    Returns
+319    -------
+320    result_dict: dict[Npr_matrix]
+321        extracted Bilinears
+322    """
+323
+324    files, idx = _get_files(path, filestem, idl)
+325
+326    mom_in = None
+327    mom_out = None
+328
+329    corr_data = {}
+330    for hd5_file in files:
+331        file = h5py.File(path + '/' + hd5_file, "r")
+332        for i in range(16):
+333            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
+334            if name not in corr_data:
+335                corr_data[name] = []
+336            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
+337            corr_data[name].append(raw_data)
+338            if mom_in is None:
+339                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+340            if mom_out is None:
+341                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+342
+343        file.close()
+344
+345    result_dict = {}
+346
+347    for key, data in corr_data.items():
+348        local_data = np.array(data)
+349
+350        rolled_array = np.rollaxis(local_data, 0, 5)
+351
+352        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+353        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+354            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+355            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+356            matrix[si, sj, ci, cj] = CObs(real, imag)
+357
+358        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+359
+360    return result_dict
 
    
 
 
@@ -1229,6 +1295,13 @@ name of the ensemble, required for internal bookkeeping
 
  • idl (range):
 If specified only configurations in the given range are read in.
    • 
 
+
+
    Returns
    
+
+
      
+
    • result_dict (dict[Npr_matrix]):
+extracted Bilinears
      • 
+
    @@ -1244,85 +1317,90 @@ If specified only configurations in the given range are read in. - 
    343def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
-344    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
-345
-346    Parameters
-347    ----------
-348    path : str
-349        path to the files to read
-350    filestem : str
-351        namestem of the files to read
-352    ens_id : str
-353        name of the ensemble, required for internal bookkeeping
-354    idl : range
-355        If specified only configurations in the given range are read in.
-356    vertices : list
-357        Vertex functions to be extracted.
-358    """
-359
-360    files, idx = _get_files(path, filestem, idl)
-361
-362    mom_in = None
-363    mom_out = None
-364
-365    vertex_names = []
-366    for vertex in vertices:
-367        vertex_names += _get_lorentz_names(vertex)
-368
-369    corr_data = {}
-370
-371    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
-372
-373    for hd5_file in files:
-374        file = h5py.File(path + '/' + hd5_file, "r")
-375
-376        for i in range(32):
-377            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
-378            if name in vertex_names:
-379                if name not in corr_data:
-380                    corr_data[name] = []
-381                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
-382                corr_data[name].append(raw_data)
-383                if mom_in is None:
-384                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-385                if mom_out is None:
-386                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
-387
-388        file.close()
+            
    363def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
+364    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
+365
+366    Parameters
+367    ----------
+368    path : str
+369        path to the files to read
+370    filestem : str
+371        namestem of the files to read
+372    ens_id : str
+373        name of the ensemble, required for internal bookkeeping
+374    idl : range
+375        If specified only configurations in the given range are read in.
+376    vertices : list
+377        Vertex functions to be extracted.
+378
+379    Returns
+380    -------
+381    result_dict : dict
+382        extracted fourquark matrizes
+383    """
+384
+385    files, idx = _get_files(path, filestem, idl)
+386
+387    mom_in = None
+388    mom_out = None
 389
-390    intermediate_dict = {}
-391
-392    for vertex in vertices:
-393        lorentz_names = _get_lorentz_names(vertex)
-394        for v_name in lorentz_names:
-395            if v_name in [('SigmaXY', 'SigmaZT'),
-396                          ('SigmaXT', 'SigmaYZ'),
-397                          ('SigmaYZ', 'SigmaXT'),
-398                          ('SigmaZT', 'SigmaXY')]:
-399                sign = -1
-400            else:
-401                sign = 1
-402            if vertex not in intermediate_dict:
-403                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
-404            else:
-405                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
-406
-407    result_dict = {}
-408
-409    for key, data in intermediate_dict.items():
-410
-411        rolled_array = np.moveaxis(data, 0, 8)
+390    vertex_names = []
+391    for vertex in vertices:
+392        vertex_names += _get_lorentz_names(vertex)
+393
+394    corr_data = {}
+395
+396    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
+397
+398    for hd5_file in files:
+399        file = h5py.File(path + '/' + hd5_file, "r")
+400
+401        for i in range(32):
+402            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
+403            if name in vertex_names:
+404                if name not in corr_data:
+405                    corr_data[name] = []
+406                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
+407                corr_data[name].append(raw_data)
+408                if mom_in is None:
+409                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+410                if mom_out is None:
+411                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
 412
-413        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-414        for index in np.ndindex(rolled_array.shape[:-1]):
-415            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
-416            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
-417            matrix[index] = CObs(real, imag)
-418
-419        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
-420
-421    return result_dict
+413        file.close()
+414
+415    intermediate_dict = {}
+416
+417    for vertex in vertices:
+418        lorentz_names = _get_lorentz_names(vertex)
+419        for v_name in lorentz_names:
+420            if v_name in [('SigmaXY', 'SigmaZT'),
+421                          ('SigmaXT', 'SigmaYZ'),
+422                          ('SigmaYZ', 'SigmaXT'),
+423                          ('SigmaZT', 'SigmaXY')]:
+424                sign = -1
+425            else:
+426                sign = 1
+427            if vertex not in intermediate_dict:
+428                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
+429            else:
+430                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
+431
+432    result_dict = {}
+433
+434    for key, data in intermediate_dict.items():
+435
+436        rolled_array = np.moveaxis(data, 0, 8)
+437
+438        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+439        for index in np.ndindex(rolled_array.shape[:-1]):
+440            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
+441            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
+442            matrix[index] = CObs(real, imag)
+443
+444        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+445
+446    return result_dict
 
    
 
 
@@ -1342,6 +1420,13 @@ If specified only configurations in the given range are read in.
 
  • vertices (list):
 Vertex functions to be extracted.
    • 
 
+
+
    Returns
    
+
+
      
+
    • result_dict (dict):
+extracted fourquark matrizes
      • 
+
    diff --git a/docs/pyerrors/input/json.html b/docs/pyerrors/input/json.html index 469291f5..7766dd8f 100644 --- a/docs/pyerrors/input/json.html +++ b/docs/pyerrors/input/json.html @@ -122,673 +122,727 @@ 29 indent : int 30 Specify the indentation level of the json file. None or 0 is permissible and 31 saves disk space. - 32 """ - 33 - 34 def _gen_data_d_from_list(ol): - 35 dl = [] - 36 No = len(ol) - 37 for name in ol[0].mc_names: - 38 ed = {} - 39 ed['id'] = name - 40 ed['replica'] = [] - 41 for r_name in ol[0].e_content[name]: - 42 rd = {} - 43 rd['name'] = r_name - 44 rd['deltas'] = [] - 45 offsets = [o.r_values[r_name] - o.value for o in ol] - 46 deltas = np.column_stack([ol[oi].deltas[r_name] + offsets[oi] for oi in range(No)]) - 47 for i in range(len(ol[0].idl[r_name])): - 48 rd['deltas'].append([ol[0].idl[r_name][i]]) - 49 rd['deltas'][-1] += deltas[i].tolist() - 50 ed['replica'].append(rd) - 51 dl.append(ed) - 52 return dl - 53 - 54 def _gen_cdata_d_from_list(ol): - 55 dl = [] - 56 for name in ol[0].cov_names: - 57 ed = {} - 58 ed['id'] = name - 59 ed['layout'] = str(ol[0].covobs[name].cov.shape).lstrip('(').rstrip(')').rstrip(',') - 60 ed['cov'] = list(np.ravel(ol[0].covobs[name].cov)) - 61 ncov = ol[0].covobs[name].cov.shape[0] - 62 ed['grad'] = [] - 63 for i in range(ncov): - 64 ed['grad'].append([]) - 65 for o in ol: - 66 ed['grad'][-1].append(o.covobs[name].grad[i][0]) - 67 dl.append(ed) - 68 return dl - 69 - 70 def write_Obs_to_dict(o): - 71 d = {} - 72 d['type'] = 'Obs' - 73 d['layout'] = '1' - 74 if o.tag: - 75 d['tag'] = [o.tag] - 76 if o.reweighted: - 77 d['reweighted'] = o.reweighted - 78 d['value'] = [o.value] - 79 data = _gen_data_d_from_list([o]) - 80 if len(data) > 0: - 81 d['data'] = data - 82 cdata = _gen_cdata_d_from_list([o]) - 83 if len(cdata) > 0: - 84 d['cdata'] = cdata - 85 return d - 86 - 87 def write_List_to_dict(ol): - 88 _assert_equal_properties(ol) - 89 d = {} - 90 d['type'] = 'List' - 91 d['layout'] = '%d' % len(ol) - 92 taglist = [o.tag for o in ol] - 93 if np.any([tag is not None for tag in taglist]): - 94 d['tag'] = taglist - 95 if ol[0].reweighted: - 96 d['reweighted'] = ol[0].reweighted - 97 d['value'] = [o.value for o in ol] - 98 data = _gen_data_d_from_list(ol) - 99 if len(data) > 0: -100 d['data'] = data -101 cdata = _gen_cdata_d_from_list(ol) -102 if len(cdata) > 0: -103 d['cdata'] = cdata -104 return d -105 -106 def write_Array_to_dict(oa): -107 ol = np.ravel(oa) -108 _assert_equal_properties(ol) -109 d = {} -110 d['type'] = 'Array' -111 d['layout'] = str(oa.shape).lstrip('(').rstrip(')').rstrip(',') -112 taglist = [o.tag for o in ol] -113 if np.any([tag is not None for tag in taglist]): -114 d['tag'] = taglist -115 if ol[0].reweighted: -116 d['reweighted'] = ol[0].reweighted -117 d['value'] = [o.value for o in ol] -118 data = _gen_data_d_from_list(ol) -119 if len(data) > 0: -120 d['data'] = data -121 cdata = _gen_cdata_d_from_list(ol) -122 if len(cdata) > 0: -123 d['cdata'] = cdata -124 return d -125 -126 def _nan_Obs_like(obs): -127 samples = [] -128 names = [] -129 idl = [] -130 for key, value in obs.idl.items(): -131 samples.append([np.nan] * len(value)) -132 names.append(key) -133 idl.append(value) -134 my_obs = Obs(samples, names, idl) -135 my_obs._covobs = obs._covobs -136 for name in obs._covobs: -137 my_obs.names.append(name) -138 my_obs.reweighted = obs.reweighted -139 return my_obs -140 -141 def write_Corr_to_dict(my_corr): -142 first_not_none = next(i for i, j in enumerate(my_corr.content) if np.all(j)) -143 dummy_array = np.empty((my_corr.N, my_corr.N), dtype=object) -144 dummy_array[:] = _nan_Obs_like(my_corr.content[first_not_none].ravel()[0]) -145 content = [o if o is not None else dummy_array for o in my_corr.content] -146 dat = write_Array_to_dict(np.array(content, dtype=object)) -147 dat['type'] = 'Corr' -148 corr_meta_data = str(my_corr.tag) -149 if 'tag' in dat.keys(): -150 dat['tag'].append(corr_meta_data) -151 else: -152 dat['tag'] = [corr_meta_data] -153 taglist = dat['tag'] -154 dat['tag'] = {} # tag is now a dictionary, that contains the previous taglist in the key "tag" -155 dat['tag']['tag'] = taglist -156 if my_corr.prange is not None: -157 dat['tag']['prange'] = my_corr.prange -158 return dat -159 -160 if not isinstance(ol, list): -161 ol = [ol] -162 -163 d = {} -164 d['program'] = 'pyerrors %s' % (pyerrorsversion.__version__) -165 d['version'] = '1.1' -166 d['who'] = getpass.getuser() -167 d['date'] = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S %z') -168 d['host'] = socket.gethostname() + ', ' + platform.platform() -169 -170 if description: -171 d['description'] = description -172 -173 d['obsdata'] = [] -174 for io in ol: -175 if isinstance(io, Obs): -176 d['obsdata'].append(write_Obs_to_dict(io)) -177 elif isinstance(io, list): -178 d['obsdata'].append(write_List_to_dict(io)) -179 elif isinstance(io, np.ndarray): -180 d['obsdata'].append(write_Array_to_dict(io)) -181 elif isinstance(io, Corr): -182 d['obsdata'].append(write_Corr_to_dict(io)) -183 else: -184 raise Exception("Unkown datatype.") -185 -186 def _jsonifier(o): -187 if isinstance(o, np.int64): -188 return int(o) -189 raise TypeError('%r is not JSON serializable' % o) + 32 + 33 Returns + 34 ------- + 35 json_string : str + 36 String for export to .json(.gz) file + 37 """ + 38 + 39 def _gen_data_d_from_list(ol): + 40 dl = [] + 41 No = len(ol) + 42 for name in ol[0].mc_names: + 43 ed = {} + 44 ed['id'] = name + 45 ed['replica'] = [] + 46 for r_name in ol[0].e_content[name]: + 47 rd = {} + 48 rd['name'] = r_name + 49 rd['deltas'] = [] + 50 offsets = [o.r_values[r_name] - o.value for o in ol] + 51 deltas = np.column_stack([ol[oi].deltas[r_name] + offsets[oi] for oi in range(No)]) + 52 for i in range(len(ol[0].idl[r_name])): + 53 rd['deltas'].append([ol[0].idl[r_name][i]]) + 54 rd['deltas'][-1] += deltas[i].tolist() + 55 ed['replica'].append(rd) + 56 dl.append(ed) + 57 return dl + 58 + 59 def _gen_cdata_d_from_list(ol): + 60 dl = [] + 61 for name in ol[0].cov_names: + 62 ed = {} + 63 ed['id'] = name + 64 ed['layout'] = str(ol[0].covobs[name].cov.shape).lstrip('(').rstrip(')').rstrip(',') + 65 ed['cov'] = list(np.ravel(ol[0].covobs[name].cov)) + 66 ncov = ol[0].covobs[name].cov.shape[0] + 67 ed['grad'] = [] + 68 for i in range(ncov): + 69 ed['grad'].append([]) + 70 for o in ol: + 71 ed['grad'][-1].append(o.covobs[name].grad[i][0]) + 72 dl.append(ed) + 73 return dl + 74 + 75 def write_Obs_to_dict(o): + 76 d = {} + 77 d['type'] = 'Obs' + 78 d['layout'] = '1' + 79 if o.tag: + 80 d['tag'] = [o.tag] + 81 if o.reweighted: + 82 d['reweighted'] = o.reweighted + 83 d['value'] = [o.value] + 84 data = _gen_data_d_from_list([o]) + 85 if len(data) > 0: + 86 d['data'] = data + 87 cdata = _gen_cdata_d_from_list([o]) + 88 if len(cdata) > 0: + 89 d['cdata'] = cdata + 90 return d + 91 + 92 def write_List_to_dict(ol): + 93 _assert_equal_properties(ol) + 94 d = {} + 95 d['type'] = 'List' + 96 d['layout'] = '%d' % len(ol) + 97 taglist = [o.tag for o in ol] + 98 if np.any([tag is not None for tag in taglist]): + 99 d['tag'] = taglist +100 if ol[0].reweighted: +101 d['reweighted'] = ol[0].reweighted +102 d['value'] = [o.value for o in ol] +103 data = _gen_data_d_from_list(ol) +104 if len(data) > 0: +105 d['data'] = data +106 cdata = _gen_cdata_d_from_list(ol) +107 if len(cdata) > 0: +108 d['cdata'] = cdata +109 return d +110 +111 def write_Array_to_dict(oa): +112 ol = np.ravel(oa) +113 _assert_equal_properties(ol) +114 d = {} +115 d['type'] = 'Array' +116 d['layout'] = str(oa.shape).lstrip('(').rstrip(')').rstrip(',') +117 taglist = [o.tag for o in ol] +118 if np.any([tag is not None for tag in taglist]): +119 d['tag'] = taglist +120 if ol[0].reweighted: +121 d['reweighted'] = ol[0].reweighted +122 d['value'] = [o.value for o in ol] +123 data = _gen_data_d_from_list(ol) +124 if len(data) > 0: +125 d['data'] = data +126 cdata = _gen_cdata_d_from_list(ol) +127 if len(cdata) > 0: +128 d['cdata'] = cdata +129 return d +130 +131 def _nan_Obs_like(obs): +132 samples = [] +133 names = [] +134 idl = [] +135 for key, value in obs.idl.items(): +136 samples.append([np.nan] * len(value)) +137 names.append(key) +138 idl.append(value) +139 my_obs = Obs(samples, names, idl) +140 my_obs._covobs = obs._covobs +141 for name in obs._covobs: +142 my_obs.names.append(name) +143 my_obs.reweighted = obs.reweighted +144 return my_obs +145 +146 def write_Corr_to_dict(my_corr): +147 first_not_none = next(i for i, j in enumerate(my_corr.content) if np.all(j)) +148 dummy_array = np.empty((my_corr.N, my_corr.N), dtype=object) +149 dummy_array[:] = _nan_Obs_like(my_corr.content[first_not_none].ravel()[0]) +150 content = [o if o is not None else dummy_array for o in my_corr.content] +151 dat = write_Array_to_dict(np.array(content, dtype=object)) +152 dat['type'] = 'Corr' +153 corr_meta_data = str(my_corr.tag) +154 if 'tag' in dat.keys(): +155 dat['tag'].append(corr_meta_data) +156 else: +157 dat['tag'] = [corr_meta_data] +158 taglist = dat['tag'] +159 dat['tag'] = {} # tag is now a dictionary, that contains the previous taglist in the key "tag" +160 dat['tag']['tag'] = taglist +161 if my_corr.prange is not None: +162 dat['tag']['prange'] = my_corr.prange +163 return dat +164 +165 if not isinstance(ol, list): +166 ol = [ol] +167 +168 d = {} +169 d['program'] = 'pyerrors %s' % (pyerrorsversion.__version__) +170 d['version'] = '1.1' +171 d['who'] = getpass.getuser() +172 d['date'] = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S %z') +173 d['host'] = socket.gethostname() + ', ' + platform.platform() +174 +175 if description: +176 d['description'] = description +177 +178 d['obsdata'] = [] +179 for io in ol: +180 if isinstance(io, Obs): +181 d['obsdata'].append(write_Obs_to_dict(io)) +182 elif isinstance(io, list): +183 d['obsdata'].append(write_List_to_dict(io)) +184 elif isinstance(io, np.ndarray): +185 d['obsdata'].append(write_Array_to_dict(io)) +186 elif isinstance(io, Corr): +187 d['obsdata'].append(write_Corr_to_dict(io)) +188 else: +189 raise Exception("Unkown datatype.") 190 -191 if indent: -192 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_SINGLE_LINE_ARRAY) -193 else: -194 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_COMPACT) +191 def _jsonifier(o): +192 if isinstance(o, np.int64): +193 return int(o) +194 raise TypeError('%r is not JSON serializable' % o) 195 -196 -197def dump_to_json(ol, fname, description='', indent=1, gz=True): -198 """Export a list of Obs or structures containing Obs to a .json(.gz) file -199 -200 Parameters -201 ---------- -202 ol : list -203 List of objects that will be exported. At the moment, these objects can be -204 either of: Obs, list, numpy.ndarray, Corr. -205 All Obs inside a structure have to be defined on the same set of configurations. -206 fname : str -207 Filename of the output file. -208 description : str -209 Optional string that describes the contents of the json file. -210 indent : int -211 Specify the indentation level of the json file. None or 0 is permissible and -212 saves disk space. -213 gz : bool -214 If True, the output is a gzipped json. If False, the output is a json file. -215 """ -216 -217 jsonstring = create_json_string(ol, description, indent) -218 -219 if not fname.endswith('.json') and not fname.endswith('.gz'): -220 fname += '.json' -221 -222 if gz: -223 if not fname.endswith('.gz'): -224 fname += '.gz' +196 if indent: +197 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_SINGLE_LINE_ARRAY) +198 else: +199 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_COMPACT) +200 +201 +202def dump_to_json(ol, fname, description='', indent=1, gz=True): +203 """Export a list of Obs or structures containing Obs to a .json(.gz) file +204 +205 Parameters +206 ---------- +207 ol : list +208 List of objects that will be exported. At the moment, these objects can be +209 either of: Obs, list, numpy.ndarray, Corr. +210 All Obs inside a structure have to be defined on the same set of configurations. +211 fname : str +212 Filename of the output file. +213 description : str +214 Optional string that describes the contents of the json file. +215 indent : int +216 Specify the indentation level of the json file. None or 0 is permissible and +217 saves disk space. +218 gz : bool +219 If True, the output is a gzipped json. If False, the output is a json file. +220 +221 Returns +222 ------- +223 Null +224 """ 225 -226 fp = gzip.open(fname, 'wb') -227 fp.write(jsonstring.encode('utf-8')) -228 else: -229 fp = open(fname, 'w', encoding='utf-8') -230 fp.write(jsonstring) -231 fp.close() -232 -233 -234def _parse_json_dict(json_dict, verbose=True, full_output=False): -235 """Reconstruct a list of Obs or structures containing Obs from a dict that -236 was built out of a json string. -237 -238 The following structures are supported: Obs, list, numpy.ndarray, Corr -239 If the list contains only one element, it is unpacked from the list. -240 -241 Parameters -242 ---------- -243 json_string : str -244 json string containing the data. -245 verbose : bool -246 Print additional information that was written to the file. -247 full_output : bool -248 If True, a dict containing auxiliary information and the data is returned. -249 If False, only the data is returned. -250 """ -251 -252 def _gen_obsd_from_datad(d): -253 retd = {} -254 if d: -255 retd['names'] = [] -256 retd['idl'] = [] -257 retd['deltas'] = [] -258 for ens in d: -259 for rep in ens['replica']: -260 rep_name = rep['name'] -261 if len(rep_name) > len(ens["id"]): -262 if rep_name[len(ens["id"])] != "|": -263 tmp_list = list(rep_name) -264 tmp_list = tmp_list[:len(ens["id"])] + ["|"] + tmp_list[len(ens["id"]):] -265 rep_name = ''.join(tmp_list) -266 retd['names'].append(rep_name) -267 retd['idl'].append([di[0] for di in rep['deltas']]) -268 retd['deltas'].append(np.array([di[1:] for di in rep['deltas']])) -269 return retd -270 -271 def _gen_covobsd_from_cdatad(d): -272 retd = {} -273 for ens in d: -274 retl = [] -275 name = ens['id'] -276 layouts = ens.get('layout', '1').strip() -277 layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0] -278 cov = np.reshape(ens['cov'], layout) -279 grad = ens['grad'] -280 nobs = len(grad[0]) -281 for i in range(nobs): -282 retl.append({'name': name, 'cov': cov, 'grad': [g[i] for g in grad]}) -283 retd[name] = retl -284 return retd -285 -286 def get_Obs_from_dict(o): -287 layouts = o.get('layout', '1').strip() -288 if layouts != '1': -289 raise Exception("layout is %s has to be 1 for type Obs." % (layouts), RuntimeWarning) +226 jsonstring = create_json_string(ol, description, indent) +227 +228 if not fname.endswith('.json') and not fname.endswith('.gz'): +229 fname += '.json' +230 +231 if gz: +232 if not fname.endswith('.gz'): +233 fname += '.gz' +234 +235 fp = gzip.open(fname, 'wb') +236 fp.write(jsonstring.encode('utf-8')) +237 else: +238 fp = open(fname, 'w', encoding='utf-8') +239 fp.write(jsonstring) +240 fp.close() +241 +242 +243def _parse_json_dict(json_dict, verbose=True, full_output=False): +244 """Reconstruct a list of Obs or structures containing Obs from a dict that +245 was built out of a json string. +246 +247 The following structures are supported: Obs, list, numpy.ndarray, Corr +248 If the list contains only one element, it is unpacked from the list. +249 +250 Parameters +251 ---------- +252 json_string : str +253 json string containing the data. +254 verbose : bool +255 Print additional information that was written to the file. +256 full_output : bool +257 If True, a dict containing auxiliary information and the data is returned. +258 If False, only the data is returned. +259 +260 Returns +261 ------- +262 result : list[Obs] +263 reconstructed list of observables from the json string +264 or +265 result : Obs +266 only one observable if the list only has one entry +267 or +268 result : dict +269 if full_output=True +270 """ +271 +272 def _gen_obsd_from_datad(d): +273 retd = {} +274 if d: +275 retd['names'] = [] +276 retd['idl'] = [] +277 retd['deltas'] = [] +278 for ens in d: +279 for rep in ens['replica']: +280 rep_name = rep['name'] +281 if len(rep_name) > len(ens["id"]): +282 if rep_name[len(ens["id"])] != "|": +283 tmp_list = list(rep_name) +284 tmp_list = tmp_list[:len(ens["id"])] + ["|"] + tmp_list[len(ens["id"]):] +285 rep_name = ''.join(tmp_list) +286 retd['names'].append(rep_name) +287 retd['idl'].append([di[0] for di in rep['deltas']]) +288 retd['deltas'].append(np.array([di[1:] for di in rep['deltas']])) +289 return retd 290 -291 values = o['value'] -292 od = _gen_obsd_from_datad(o.get('data', {})) -293 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) -294 -295 if od: -296 ret = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl']) -297 ret._value = values[0] -298 else: -299 ret = Obs([], [], means=[]) -300 ret._value = values[0] -301 for name in cd: -302 co = cd[name][0] -303 ret._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) -304 ret.names.append(co['name']) +291 def _gen_covobsd_from_cdatad(d): +292 retd = {} +293 for ens in d: +294 retl = [] +295 name = ens['id'] +296 layouts = ens.get('layout', '1').strip() +297 layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0] +298 cov = np.reshape(ens['cov'], layout) +299 grad = ens['grad'] +300 nobs = len(grad[0]) +301 for i in range(nobs): +302 retl.append({'name': name, 'cov': cov, 'grad': [g[i] for g in grad]}) +303 retd[name] = retl +304 return retd 305 -306 ret.reweighted = o.get('reweighted', False) -307 ret.tag = o.get('tag', [None])[0] -308 return ret -309 -310 def get_List_from_dict(o): -311 layouts = o.get('layout', '1').strip() -312 layout = int(layouts) -313 values = o['value'] -314 od = _gen_obsd_from_datad(o.get('data', {})) -315 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) -316 -317 ret = [] -318 taglist = o.get('tag', layout * [None]) -319 for i in range(layout): -320 if od: -321 ret.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl'])) -322 ret[-1]._value = values[i] -323 else: -324 ret.append(Obs([], [], means=[])) -325 ret[-1]._value = values[i] -326 print('Created Obs with means= ', values[i]) -327 for name in cd: -328 co = cd[name][i] -329 ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) -330 ret[-1].names.append(co['name']) -331 -332 ret[-1].reweighted = o.get('reweighted', False) -333 ret[-1].tag = taglist[i] -334 return ret -335 -336 def get_Array_from_dict(o): -337 layouts = o.get('layout', '1').strip() -338 layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0] -339 N = np.prod(layout) -340 values = o['value'] -341 od = _gen_obsd_from_datad(o.get('data', {})) -342 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) -343 -344 ret = [] -345 taglist = o.get('tag', N * [None]) -346 for i in range(N): -347 if od: -348 ret.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl'])) -349 ret[-1]._value = values[i] -350 else: -351 ret.append(Obs([], [], means=[])) -352 ret[-1]._value = values[i] -353 for name in cd: -354 co = cd[name][i] -355 ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) -356 ret[-1].names.append(co['name']) -357 ret[-1].reweighted = o.get('reweighted', False) -358 ret[-1].tag = taglist[i] -359 return np.reshape(ret, layout) -360 -361 def get_Corr_from_dict(o): -362 if isinstance(o.get('tag'), list): # supports the old way -363 taglist = o.get('tag') # This had to be modified to get the taglist from the dictionary -364 temp_prange = None -365 elif isinstance(o.get('tag'), dict): -366 tagdic = o.get('tag') -367 taglist = tagdic['tag'] -368 if 'prange' in tagdic: -369 temp_prange = tagdic['prange'] +306 def get_Obs_from_dict(o): +307 layouts = o.get('layout', '1').strip() +308 if layouts != '1': +309 raise Exception("layout is %s has to be 1 for type Obs." % (layouts), RuntimeWarning) +310 +311 values = o['value'] +312 od = _gen_obsd_from_datad(o.get('data', {})) +313 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) +314 +315 if od: +316 ret = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl']) +317 ret._value = values[0] +318 else: +319 ret = Obs([], [], means=[]) +320 ret._value = values[0] +321 for name in cd: +322 co = cd[name][0] +323 ret._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) +324 ret.names.append(co['name']) +325 +326 ret.reweighted = o.get('reweighted', False) +327 ret.tag = o.get('tag', [None])[0] +328 return ret +329 +330 def get_List_from_dict(o): +331 layouts = o.get('layout', '1').strip() +332 layout = int(layouts) +333 values = o['value'] +334 od = _gen_obsd_from_datad(o.get('data', {})) +335 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) +336 +337 ret = [] +338 taglist = o.get('tag', layout * [None]) +339 for i in range(layout): +340 if od: +341 ret.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl'])) +342 ret[-1]._value = values[i] +343 else: +344 ret.append(Obs([], [], means=[])) +345 ret[-1]._value = values[i] +346 print('Created Obs with means= ', values[i]) +347 for name in cd: +348 co = cd[name][i] +349 ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) +350 ret[-1].names.append(co['name']) +351 +352 ret[-1].reweighted = o.get('reweighted', False) +353 ret[-1].tag = taglist[i] +354 return ret +355 +356 def get_Array_from_dict(o): +357 layouts = o.get('layout', '1').strip() +358 layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0] +359 N = np.prod(layout) +360 values = o['value'] +361 od = _gen_obsd_from_datad(o.get('data', {})) +362 cd = _gen_covobsd_from_cdatad(o.get('cdata', {})) +363 +364 ret = [] +365 taglist = o.get('tag', N * [None]) +366 for i in range(N): +367 if od: +368 ret.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl'])) +369 ret[-1]._value = values[i] 370 else: -371 temp_prange = None -372 else: -373 raise Exception("The tag is not a list or dict") -374 -375 corr_tag = taglist[-1] -376 tmp_o = o -377 tmp_o['tag'] = taglist[:-1] -378 if len(tmp_o['tag']) == 0: -379 del tmp_o['tag'] -380 dat = get_Array_from_dict(tmp_o) -381 my_corr = Corr([None if np.isnan(o.ravel()[0].value) else o for o in list(dat)]) -382 if corr_tag != 'None': -383 my_corr.tag = corr_tag -384 -385 my_corr.prange = temp_prange -386 return my_corr -387 -388 prog = json_dict.get('program', '') -389 version = json_dict.get('version', '') -390 who = json_dict.get('who', '') -391 date = json_dict.get('date', '') -392 host = json_dict.get('host', '') -393 if prog and verbose: -394 print('Data has been written using %s.' % (prog)) -395 if version and verbose: -396 print('Format version %s' % (version)) -397 if np.any([who, date, host] and verbose): -398 print('Written by %s on %s on host %s' % (who, date, host)) -399 description = json_dict.get('description', '') -400 if description and verbose: -401 print() -402 print('Description: ', description) -403 obsdata = json_dict['obsdata'] -404 ol = [] -405 for io in obsdata: -406 if io['type'] == 'Obs': -407 ol.append(get_Obs_from_dict(io)) -408 elif io['type'] == 'List': -409 ol.append(get_List_from_dict(io)) -410 elif io['type'] == 'Array': -411 ol.append(get_Array_from_dict(io)) -412 elif io['type'] == 'Corr': -413 ol.append(get_Corr_from_dict(io)) -414 else: -415 raise Exception("Unknown datatype.") -416 -417 if full_output: -418 retd = {} -419 retd['program'] = prog -420 retd['version'] = version -421 retd['who'] = who -422 retd['date'] = date -423 retd['host'] = host -424 retd['description'] = description -425 retd['obsdata'] = ol -426 -427 return retd -428 else: -429 if len(obsdata) == 1: -430 ol = ol[0] -431 -432 return ol -433 -434 -435def import_json_string(json_string, verbose=True, full_output=False): -436 """Reconstruct a list of Obs or structures containing Obs from a json string. -437 -438 The following structures are supported: Obs, list, numpy.ndarray, Corr -439 If the list contains only one element, it is unpacked from the list. -440 -441 Parameters -442 ---------- -443 json_string : str -444 json string containing the data. -445 verbose : bool -446 Print additional information that was written to the file. -447 full_output : bool -448 If True, a dict containing auxiliary information and the data is returned. -449 If False, only the data is returned. -450 """ +371 ret.append(Obs([], [], means=[])) +372 ret[-1]._value = values[i] +373 for name in cd: +374 co = cd[name][i] +375 ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad']) +376 ret[-1].names.append(co['name']) +377 ret[-1].reweighted = o.get('reweighted', False) +378 ret[-1].tag = taglist[i] +379 return np.reshape(ret, layout) +380 +381 def get_Corr_from_dict(o): +382 if isinstance(o.get('tag'), list): # supports the old way +383 taglist = o.get('tag') # This had to be modified to get the taglist from the dictionary +384 temp_prange = None +385 elif isinstance(o.get('tag'), dict): +386 tagdic = o.get('tag') +387 taglist = tagdic['tag'] +388 if 'prange' in tagdic: +389 temp_prange = tagdic['prange'] +390 else: +391 temp_prange = None +392 else: +393 raise Exception("The tag is not a list or dict") +394 +395 corr_tag = taglist[-1] +396 tmp_o = o +397 tmp_o['tag'] = taglist[:-1] +398 if len(tmp_o['tag']) == 0: +399 del tmp_o['tag'] +400 dat = get_Array_from_dict(tmp_o) +401 my_corr = Corr([None if np.isnan(o.ravel()[0].value) else o for o in list(dat)]) +402 if corr_tag != 'None': +403 my_corr.tag = corr_tag +404 +405 my_corr.prange = temp_prange +406 return my_corr +407 +408 prog = json_dict.get('program', '') +409 version = json_dict.get('version', '') +410 who = json_dict.get('who', '') +411 date = json_dict.get('date', '') +412 host = json_dict.get('host', '') +413 if prog and verbose: +414 print('Data has been written using %s.' % (prog)) +415 if version and verbose: +416 print('Format version %s' % (version)) +417 if np.any([who, date, host] and verbose): +418 print('Written by %s on %s on host %s' % (who, date, host)) +419 description = json_dict.get('description', '') +420 if description and verbose: +421 print() +422 print('Description: ', description) +423 obsdata = json_dict['obsdata'] +424 ol = [] +425 for io in obsdata: +426 if io['type'] == 'Obs': +427 ol.append(get_Obs_from_dict(io)) +428 elif io['type'] == 'List': +429 ol.append(get_List_from_dict(io)) +430 elif io['type'] == 'Array': +431 ol.append(get_Array_from_dict(io)) +432 elif io['type'] == 'Corr': +433 ol.append(get_Corr_from_dict(io)) +434 else: +435 raise Exception("Unknown datatype.") +436 +437 if full_output: +438 retd = {} +439 retd['program'] = prog +440 retd['version'] = version +441 retd['who'] = who +442 retd['date'] = date +443 retd['host'] = host +444 retd['description'] = description +445 retd['obsdata'] = ol +446 +447 return retd +448 else: +449 if len(obsdata) == 1: +450 ol = ol[0] 451 -452 return _parse_json_dict(json.loads(json_string), verbose, full_output) +452 return ol 453 454 -455def load_json(fname, verbose=True, gz=True, full_output=False): -456 """Import a list of Obs or structures containing Obs from a .json(.gz) file. +455def import_json_string(json_string, verbose=True, full_output=False): +456 """Reconstruct a list of Obs or structures containing Obs from a json string. 457 458 The following structures are supported: Obs, list, numpy.ndarray, Corr 459 If the list contains only one element, it is unpacked from the list. 460 461 Parameters 462 ---------- -463 fname : str -464 Filename of the input file. +463 json_string : str +464 json string containing the data. 465 verbose : bool 466 Print additional information that was written to the file. -467 gz : bool -468 If True, assumes that data is gzipped. If False, assumes JSON file. -469 full_output : bool -470 If True, a dict containing auxiliary information and the data is returned. -471 If False, only the data is returned. -472 """ -473 if not fname.endswith('.json') and not fname.endswith('.gz'): -474 fname += '.json' -475 if gz: -476 if not fname.endswith('.gz'): -477 fname += '.gz' -478 with gzip.open(fname, 'r') as fin: -479 d = json.load(fin) -480 else: -481 if fname.endswith('.gz'): -482 warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning) -483 with open(fname, 'r', encoding='utf-8') as fin: -484 d = json.loads(fin.read()) +467 full_output : bool +468 If True, a dict containing auxiliary information and the data is returned. +469 If False, only the data is returned. +470 +471 Returns +472 ------- +473 result : list[Obs] +474 reconstructed list of observables from the json string +475 or +476 result : Obs +477 only one observable if the list only has one entry +478 or +479 result : dict +480 if full_output=True +481 """ +482 +483 return _parse_json_dict(json.loads(json_string), verbose, full_output) +484 485 -486 return _parse_json_dict(d, verbose, full_output) -487 +486def load_json(fname, verbose=True, gz=True, full_output=False): +487 """Import a list of Obs or structures containing Obs from a .json(.gz) file. 488 -489def _ol_from_dict(ind, reps='DICTOBS'): -490 """Convert a dictionary of Obs objects to a list and a dictionary that contains -491 placeholders instead of the Obs objects. -492 -493 Parameters -494 ---------- -495 ind : dict -496 Dict of JSON valid structures and objects that will be exported. -497 At the moment, these object can be either of: Obs, list, numpy.ndarray, Corr. -498 All Obs inside a structure have to be defined on the same set of configurations. -499 reps : str -500 Specify the structure of the placeholder in exported dict to be reps[0-9]+. -501 """ -502 -503 obstypes = (Obs, Corr, np.ndarray) -504 -505 if not reps.isalnum(): -506 raise Exception('Placeholder string has to be alphanumeric!') -507 ol = [] -508 counter = 0 -509 -510 def dict_replace_obs(d): -511 nonlocal ol -512 nonlocal counter -513 x = {} -514 for k, v in d.items(): -515 if isinstance(v, dict): -516 v = dict_replace_obs(v) -517 elif isinstance(v, list) and all([isinstance(o, Obs) for o in v]): -518 v = obslist_replace_obs(v) -519 elif isinstance(v, list): -520 v = list_replace_obs(v) -521 elif isinstance(v, obstypes): -522 ol.append(v) -523 v = reps + '%d' % (counter) -524 counter += 1 -525 elif isinstance(v, str): -526 if bool(re.match(r'%s[0-9]+' % (reps), v)): -527 raise Exception('Dict contains string %s that matches the placeholder! %s Cannot be savely exported.' % (v, reps)) -528 x[k] = v -529 return x +489 The following structures are supported: Obs, list, numpy.ndarray, Corr +490 If the list contains only one element, it is unpacked from the list. +491 +492 Parameters +493 ---------- +494 fname : str +495 Filename of the input file. +496 verbose : bool +497 Print additional information that was written to the file. +498 gz : bool +499 If True, assumes that data is gzipped. If False, assumes JSON file. +500 full_output : bool +501 If True, a dict containing auxiliary information and the data is returned. +502 If False, only the data is returned. +503 +504 Returns +505 ------- +506 result : list[Obs] +507 reconstructed list of observables from the json string +508 or +509 result : Obs +510 only one observable if the list only has one entry +511 or +512 result : dict +513 if full_output=True +514 """ +515 if not fname.endswith('.json') and not fname.endswith('.gz'): +516 fname += '.json' +517 if gz: +518 if not fname.endswith('.gz'): +519 fname += '.gz' +520 with gzip.open(fname, 'r') as fin: +521 d = json.load(fin) +522 else: +523 if fname.endswith('.gz'): +524 warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning) +525 with open(fname, 'r', encoding='utf-8') as fin: +526 d = json.loads(fin.read()) +527 +528 return _parse_json_dict(d, verbose, full_output) +529 530 -531 def list_replace_obs(li): -532 nonlocal ol -533 nonlocal counter -534 x = [] -535 for e in li: -536 if isinstance(e, list): -537 e = list_replace_obs(e) -538 elif isinstance(e, list) and all([isinstance(o, Obs) for o in e]): -539 e = obslist_replace_obs(e) -540 elif isinstance(e, dict): -541 e = dict_replace_obs(e) -542 elif isinstance(e, obstypes): -543 ol.append(e) -544 e = reps + '%d' % (counter) -545 counter += 1 -546 elif isinstance(e, str): -547 if bool(re.match(r'%s[0-9]+' % (reps), e)): -548 raise Exception('Dict contains string %s that matches the placeholder! %s Cannot be savely exported.' % (e, reps)) -549 x.append(e) -550 return x +531def _ol_from_dict(ind, reps='DICTOBS'): +532 """Convert a dictionary of Obs objects to a list and a dictionary that contains +533 placeholders instead of the Obs objects. +534 +535 Parameters +536 ---------- +537 ind : dict +538 Dict of JSON valid structures and objects that will be exported. +539 At the moment, these object can be either of: Obs, list, numpy.ndarray, Corr. +540 All Obs inside a structure have to be defined on the same set of configurations. +541 reps : str +542 Specify the structure of the placeholder in exported dict to be reps[0-9]+. +543 """ +544 +545 obstypes = (Obs, Corr, np.ndarray) +546 +547 if not reps.isalnum(): +548 raise Exception('Placeholder string has to be alphanumeric!') +549 ol = [] +550 counter = 0 551 -552 def obslist_replace_obs(li): +552 def dict_replace_obs(d): 553 nonlocal ol 554 nonlocal counter -555 il = [] -556 for e in li: -557 il.append(e) -558 -559 ol.append(il) -560 x = reps + '%d' % (counter) -561 counter += 1 -562 return x -563 -564 nd = dict_replace_obs(ind) -565 -566 return ol, nd -567 -568 -569def dump_dict_to_json(od, fname, description='', indent=1, reps='DICTOBS', gz=True): -570 """Export a dict of Obs or structures containing Obs to a .json(.gz) file -571 -572 Parameters -573 ---------- -574 od : dict -575 Dict of JSON valid structures and objects that will be exported. -576 At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr. -577 All Obs inside a structure have to be defined on the same set of configurations. -578 fname : str -579 Filename of the output file. -580 description : str -581 Optional string that describes the contents of the json file. -582 indent : int -583 Specify the indentation level of the json file. None or 0 is permissible and -584 saves disk space. -585 reps : str -586 Specify the structure of the placeholder in exported dict to be reps[0-9]+. -587 gz : bool -588 If True, the output is a gzipped json. If False, the output is a json file. -589 """ -590 -591 if not isinstance(od, dict): -592 raise Exception('od has to be a dictionary. Did you want to use dump_to_json?') +555 x = {} +556 for k, v in d.items(): +557 if isinstance(v, dict): +558 v = dict_replace_obs(v) +559 elif isinstance(v, list) and all([isinstance(o, Obs) for o in v]): +560 v = obslist_replace_obs(v) +561 elif isinstance(v, list): +562 v = list_replace_obs(v) +563 elif isinstance(v, obstypes): +564 ol.append(v) +565 v = reps + '%d' % (counter) +566 counter += 1 +567 elif isinstance(v, str): +568 if bool(re.match(r'%s[0-9]+' % (reps), v)): +569 raise Exception('Dict contains string %s that matches the placeholder! %s Cannot be savely exported.' % (v, reps)) +570 x[k] = v +571 return x +572 +573 def list_replace_obs(li): +574 nonlocal ol +575 nonlocal counter +576 x = [] +577 for e in li: +578 if isinstance(e, list): +579 e = list_replace_obs(e) +580 elif isinstance(e, list) and all([isinstance(o, Obs) for o in e]): +581 e = obslist_replace_obs(e) +582 elif isinstance(e, dict): +583 e = dict_replace_obs(e) +584 elif isinstance(e, obstypes): +585 ol.append(e) +586 e = reps + '%d' % (counter) +587 counter += 1 +588 elif isinstance(e, str): +589 if bool(re.match(r'%s[0-9]+' % (reps), e)): +590 raise Exception('Dict contains string %s that matches the placeholder! %s Cannot be savely exported.' % (e, reps)) +591 x.append(e) +592 return x 593 -594 infostring = ('This JSON file contains a python dictionary that has been parsed to a list of structures. ' -595 'OBSDICT contains the dictionary, where Obs or other structures have been replaced by ' -596 '' + reps + '[0-9]+. The field description contains the additional description of this JSON file. ' -597 'This file may be parsed to a dict with the pyerrors routine load_json_dict.') -598 -599 desc_dict = {'INFO': infostring, 'OBSDICT': {}, 'description': description} -600 ol, desc_dict['OBSDICT'] = _ol_from_dict(od, reps=reps) -601 -602 dump_to_json(ol, fname, description=desc_dict, indent=indent, gz=gz) -603 -604 -605def _od_from_list_and_dict(ol, ind, reps='DICTOBS'): -606 """Parse a list of Obs or structures containing Obs and an accompanying -607 dict, where the structures have been replaced by placeholders to a -608 dict that contains the structures. +594 def obslist_replace_obs(li): +595 nonlocal ol +596 nonlocal counter +597 il = [] +598 for e in li: +599 il.append(e) +600 +601 ol.append(il) +602 x = reps + '%d' % (counter) +603 counter += 1 +604 return x +605 +606 nd = dict_replace_obs(ind) +607 +608 return ol, nd 609 -610 The following structures are supported: Obs, list, numpy.ndarray, Corr -611 -612 Parameters -613 ---------- -614 ol : list -615 List of objects - -616 At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr. -617 All Obs inside a structure have to be defined on the same set of configurations. -618 ind : dict -619 Dict that defines the structure of the resulting dict and contains placeholders -620 reps : str -621 Specify the structure of the placeholder in imported dict to be reps[0-9]+. -622 """ -623 if not reps.isalnum(): -624 raise Exception('Placeholder string has to be alphanumeric!') -625 -626 counter = 0 -627 -628 def dict_replace_string(d): -629 nonlocal counter -630 nonlocal ol -631 x = {} -632 for k, v in d.items(): -633 if isinstance(v, dict): -634 v = dict_replace_string(v) -635 elif isinstance(v, list): -636 v = list_replace_string(v) -637 elif isinstance(v, str) and bool(re.match(r'%s[0-9]+' % (reps), v)): -638 index = int(v[len(reps):]) -639 v = ol[index] -640 counter += 1 -641 x[k] = v -642 return x -643 -644 def list_replace_string(li): -645 nonlocal counter -646 nonlocal ol -647 x = [] -648 for e in li: -649 if isinstance(e, list): -650 e = list_replace_string(e) -651 elif isinstance(e, dict): -652 e = dict_replace_string(e) -653 elif isinstance(e, str) and bool(re.match(r'%s[0-9]+' % (reps), e)): -654 index = int(e[len(reps):]) -655 e = ol[index] -656 counter += 1 -657 x.append(e) -658 return x -659 -660 nd = dict_replace_string(ind) -661 -662 if counter == 0: -663 raise Exception('No placeholder has been replaced! Check if reps is set correctly.') -664 -665 return nd -666 -667 -668def load_json_dict(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'): -669 """Import a dict of Obs or structures containing Obs from a .json(.gz) file. -670 -671 The following structures are supported: Obs, list, numpy.ndarray, Corr -672 -673 Parameters -674 ---------- -675 fname : str -676 Filename of the input file. -677 verbose : bool -678 Print additional information that was written to the file. -679 gz : bool -680 If True, assumes that data is gzipped. If False, assumes JSON file. -681 full_output : bool -682 If True, a dict containing auxiliary information and the data is returned. -683 If False, only the data is returned. -684 reps : str -685 Specify the structure of the placeholder in imported dict to be reps[0-9]+. -686 """ -687 indata = load_json(fname, verbose=verbose, gz=gz, full_output=True) -688 description = indata['description']['description'] -689 indict = indata['description']['OBSDICT'] -690 ol = indata['obsdata'] -691 od = _od_from_list_and_dict(ol, indict, reps=reps) -692 -693 if full_output: -694 indata['description'] = description -695 indata['obsdata'] = od -696 return indata -697 else: -698 return od +610 +611def dump_dict_to_json(od, fname, description='', indent=1, reps='DICTOBS', gz=True): +612 """Export a dict of Obs or structures containing Obs to a .json(.gz) file +613 +614 Parameters +615 ---------- +616 od : dict +617 Dict of JSON valid structures and objects that will be exported. +618 At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr. +619 All Obs inside a structure have to be defined on the same set of configurations. +620 fname : str +621 Filename of the output file. +622 description : str +623 Optional string that describes the contents of the json file. +624 indent : int +625 Specify the indentation level of the json file. None or 0 is permissible and +626 saves disk space. +627 reps : str +628 Specify the structure of the placeholder in exported dict to be reps[0-9]+. +629 gz : bool +630 If True, the output is a gzipped json. If False, the output is a json file. +631 +632 Returns +633 ------- +634 None +635 """ +636 +637 if not isinstance(od, dict): +638 raise Exception('od has to be a dictionary. Did you want to use dump_to_json?') +639 +640 infostring = ('This JSON file contains a python dictionary that has been parsed to a list of structures. ' +641 'OBSDICT contains the dictionary, where Obs or other structures have been replaced by ' +642 '' + reps + '[0-9]+. The field description contains the additional description of this JSON file. ' +643 'This file may be parsed to a dict with the pyerrors routine load_json_dict.') +644 +645 desc_dict = {'INFO': infostring, 'OBSDICT': {}, 'description': description} +646 ol, desc_dict['OBSDICT'] = _ol_from_dict(od, reps=reps) +647 +648 dump_to_json(ol, fname, description=desc_dict, indent=indent, gz=gz) +649 +650 +651def _od_from_list_and_dict(ol, ind, reps='DICTOBS'): +652 """Parse a list of Obs or structures containing Obs and an accompanying +653 dict, where the structures have been replaced by placeholders to a +654 dict that contains the structures. +655 +656 The following structures are supported: Obs, list, numpy.ndarray, Corr +657 +658 Parameters +659 ---------- +660 ol : list +661 List of objects - +662 At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr. +663 All Obs inside a structure have to be defined on the same set of configurations. +664 ind : dict +665 Dict that defines the structure of the resulting dict and contains placeholders +666 reps : str +667 Specify the structure of the placeholder in imported dict to be reps[0-9]+. +668 """ +669 if not reps.isalnum(): +670 raise Exception('Placeholder string has to be alphanumeric!') +671 +672 counter = 0 +673 +674 def dict_replace_string(d): +675 nonlocal counter +676 nonlocal ol +677 x = {} +678 for k, v in d.items(): +679 if isinstance(v, dict): +680 v = dict_replace_string(v) +681 elif isinstance(v, list): +682 v = list_replace_string(v) +683 elif isinstance(v, str) and bool(re.match(r'%s[0-9]+' % (reps), v)): +684 index = int(v[len(reps):]) +685 v = ol[index] +686 counter += 1 +687 x[k] = v +688 return x +689 +690 def list_replace_string(li): +691 nonlocal counter +692 nonlocal ol +693 x = [] +694 for e in li: +695 if isinstance(e, list): +696 e = list_replace_string(e) +697 elif isinstance(e, dict): +698 e = dict_replace_string(e) +699 elif isinstance(e, str) and bool(re.match(r'%s[0-9]+' % (reps), e)): +700 index = int(e[len(reps):]) +701 e = ol[index] +702 counter += 1 +703 x.append(e) +704 return x +705 +706 nd = dict_replace_string(ind) +707 +708 if counter == 0: +709 raise Exception('No placeholder has been replaced! Check if reps is set correctly.') +710 +711 return nd +712 +713 +714def load_json_dict(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'): +715 """Import a dict of Obs or structures containing Obs from a .json(.gz) file. +716 +717 The following structures are supported: Obs, list, numpy.ndarray, Corr +718 +719 Parameters +720 ---------- +721 fname : str +722 Filename of the input file. +723 verbose : bool +724 Print additional information that was written to the file. +725 gz : bool +726 If True, assumes that data is gzipped. If False, assumes JSON file. +727 full_output : bool +728 If True, a dict containing auxiliary information and the data is returned. +729 If False, only the data is returned. +730 reps : str +731 Specify the structure of the placeholder in imported dict to be reps[0-9]+. +732 +733 Returns +734 ------- +735 data : Obs / list / Corr +736 Read data +737 or +738 data : dict +739 Read data and meta-data +740 """ +741 indata = load_json(fname, verbose=verbose, gz=gz, full_output=True) +742 description = indata['description']['description'] +743 indict = indata['description']['OBSDICT'] +744 ol = indata['obsdata'] +745 od = _od_from_list_and_dict(ol, indict, reps=reps) +746 +747 if full_output: +748 indata['description'] = description +749 indata['obsdata'] = od +750 return indata +751 else: +752 return od @@ -819,169 +873,174 @@ 30 indent : int 31 Specify the indentation level of the json file. None or 0 is permissible and 32 saves disk space. - 33 """ - 34 - 35 def _gen_data_d_from_list(ol): - 36 dl = [] - 37 No = len(ol) - 38 for name in ol[0].mc_names: - 39 ed = {} - 40 ed['id'] = name - 41 ed['replica'] = [] - 42 for r_name in ol[0].e_content[name]: - 43 rd = {} - 44 rd['name'] = r_name - 45 rd['deltas'] = [] - 46 offsets = [o.r_values[r_name] - o.value for o in ol] - 47 deltas = np.column_stack([ol[oi].deltas[r_name] + offsets[oi] for oi in range(No)]) - 48 for i in range(len(ol[0].idl[r_name])): - 49 rd['deltas'].append([ol[0].idl[r_name][i]]) - 50 rd['deltas'][-1] += deltas[i].tolist() - 51 ed['replica'].append(rd) - 52 dl.append(ed) - 53 return dl - 54 - 55 def _gen_cdata_d_from_list(ol): - 56 dl = [] - 57 for name in ol[0].cov_names: - 58 ed = {} - 59 ed['id'] = name - 60 ed['layout'] = str(ol[0].covobs[name].cov.shape).lstrip('(').rstrip(')').rstrip(',') - 61 ed['cov'] = list(np.ravel(ol[0].covobs[name].cov)) - 62 ncov = ol[0].covobs[name].cov.shape[0] - 63 ed['grad'] = [] - 64 for i in range(ncov): - 65 ed['grad'].append([]) - 66 for o in ol: - 67 ed['grad'][-1].append(o.covobs[name].grad[i][0]) - 68 dl.append(ed) - 69 return dl - 70 - 71 def write_Obs_to_dict(o): - 72 d = {} - 73 d['type'] = 'Obs' - 74 d['layout'] = '1' - 75 if o.tag: - 76 d['tag'] = [o.tag] - 77 if o.reweighted: - 78 d['reweighted'] = o.reweighted - 79 d['value'] = [o.value] - 80 data = _gen_data_d_from_list([o]) - 81 if len(data) > 0: - 82 d['data'] = data - 83 cdata = _gen_cdata_d_from_list([o]) - 84 if len(cdata) > 0: - 85 d['cdata'] = cdata - 86 return d - 87 - 88 def write_List_to_dict(ol): - 89 _assert_equal_properties(ol) - 90 d = {} - 91 d['type'] = 'List' - 92 d['layout'] = '%d' % len(ol) - 93 taglist = [o.tag for o in ol] - 94 if np.any([tag is not None for tag in taglist]): - 95 d['tag'] = taglist - 96 if ol[0].reweighted: - 97 d['reweighted'] = ol[0].reweighted - 98 d['value'] = [o.value for o in ol] - 99 data = _gen_data_d_from_list(ol) -100 if len(data) > 0: -101 d['data'] = data -102 cdata = _gen_cdata_d_from_list(ol) -103 if len(cdata) > 0: -104 d['cdata'] = cdata -105 return d -106 -107 def write_Array_to_dict(oa): -108 ol = np.ravel(oa) -109 _assert_equal_properties(ol) -110 d = {} -111 d['type'] = 'Array' -112 d['layout'] = str(oa.shape).lstrip('(').rstrip(')').rstrip(',') -113 taglist = [o.tag for o in ol] -114 if np.any([tag is not None for tag in taglist]): -115 d['tag'] = taglist -116 if ol[0].reweighted: -117 d['reweighted'] = ol[0].reweighted -118 d['value'] = [o.value for o in ol] -119 data = _gen_data_d_from_list(ol) -120 if len(data) > 0: -121 d['data'] = data -122 cdata = _gen_cdata_d_from_list(ol) -123 if len(cdata) > 0: -124 d['cdata'] = cdata -125 return d -126 -127 def _nan_Obs_like(obs): -128 samples = [] -129 names = [] -130 idl = [] -131 for key, value in obs.idl.items(): -132 samples.append([np.nan] * len(value)) -133 names.append(key) -134 idl.append(value) -135 my_obs = Obs(samples, names, idl) -136 my_obs._covobs = obs._covobs -137 for name in obs._covobs: -138 my_obs.names.append(name) -139 my_obs.reweighted = obs.reweighted -140 return my_obs -141 -142 def write_Corr_to_dict(my_corr): -143 first_not_none = next(i for i, j in enumerate(my_corr.content) if np.all(j)) -144 dummy_array = np.empty((my_corr.N, my_corr.N), dtype=object) -145 dummy_array[:] = _nan_Obs_like(my_corr.content[first_not_none].ravel()[0]) -146 content = [o if o is not None else dummy_array for o in my_corr.content] -147 dat = write_Array_to_dict(np.array(content, dtype=object)) -148 dat['type'] = 'Corr' -149 corr_meta_data = str(my_corr.tag) -150 if 'tag' in dat.keys(): -151 dat['tag'].append(corr_meta_data) -152 else: -153 dat['tag'] = [corr_meta_data] -154 taglist = dat['tag'] -155 dat['tag'] = {} # tag is now a dictionary, that contains the previous taglist in the key "tag" -156 dat['tag']['tag'] = taglist -157 if my_corr.prange is not None: -158 dat['tag']['prange'] = my_corr.prange -159 return dat -160 -161 if not isinstance(ol, list): -162 ol = [ol] -163 -164 d = {} -165 d['program'] = 'pyerrors %s' % (pyerrorsversion.__version__) -166 d['version'] = '1.1' -167 d['who'] = getpass.getuser() -168 d['date'] = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S %z') -169 d['host'] = socket.gethostname() + ', ' + platform.platform() -170 -171 if description: -172 d['description'] = description -173 -174 d['obsdata'] = [] -175 for io in ol: -176 if isinstance(io, Obs): -177 d['obsdata'].append(write_Obs_to_dict(io)) -178 elif isinstance(io, list): -179 d['obsdata'].append(write_List_to_dict(io)) -180 elif isinstance(io, np.ndarray): -181 d['obsdata'].append(write_Array_to_dict(io)) -182 elif isinstance(io, Corr): -183 d['obsdata'].append(write_Corr_to_dict(io)) -184 else: -185 raise Exception("Unkown datatype.") -186 -187 def _jsonifier(o): -188 if isinstance(o, np.int64): -189 return int(o) -190 raise TypeError('%r is not JSON serializable' % o) + 33 + 34 Returns + 35 ------- + 36 json_string : str + 37 String for export to .json(.gz) file + 38 """ + 39 + 40 def _gen_data_d_from_list(ol): + 41 dl = [] + 42 No = len(ol) + 43 for name in ol[0].mc_names: + 44 ed = {} + 45 ed['id'] = name + 46 ed['replica'] = [] + 47 for r_name in ol[0].e_content[name]: + 48 rd = {} + 49 rd['name'] = r_name + 50 rd['deltas'] = [] + 51 offsets = [o.r_values[r_name] - o.value for o in ol] + 52 deltas = np.column_stack([ol[oi].deltas[r_name] + offsets[oi] for oi in range(No)]) + 53 for i in range(len(ol[0].idl[r_name])): + 54 rd['deltas'].append([ol[0].idl[r_name][i]]) + 55 rd['deltas'][-1] += deltas[i].tolist() + 56 ed['replica'].append(rd) + 57 dl.append(ed) + 58 return dl + 59 + 60 def _gen_cdata_d_from_list(ol): + 61 dl = [] + 62 for name in ol[0].cov_names: + 63 ed = {} + 64 ed['id'] = name + 65 ed['layout'] = str(ol[0].covobs[name].cov.shape).lstrip('(').rstrip(')').rstrip(',') + 66 ed['cov'] = list(np.ravel(ol[0].covobs[name].cov)) + 67 ncov = ol[0].covobs[name].cov.shape[0] + 68 ed['grad'] = [] + 69 for i in range(ncov): + 70 ed['grad'].append([]) + 71 for o in ol: + 72 ed['grad'][-1].append(o.covobs[name].grad[i][0]) + 73 dl.append(ed) + 74 return dl + 75 + 76 def write_Obs_to_dict(o): + 77 d = {} + 78 d['type'] = 'Obs' + 79 d['layout'] = '1' + 80 if o.tag: + 81 d['tag'] = [o.tag] + 82 if o.reweighted: + 83 d['reweighted'] = o.reweighted + 84 d['value'] = [o.value] + 85 data = _gen_data_d_from_list([o]) + 86 if len(data) > 0: + 87 d['data'] = data + 88 cdata = _gen_cdata_d_from_list([o]) + 89 if len(cdata) > 0: + 90 d['cdata'] = cdata + 91 return d + 92 + 93 def write_List_to_dict(ol): + 94 _assert_equal_properties(ol) + 95 d = {} + 96 d['type'] = 'List' + 97 d['layout'] = '%d' % len(ol) + 98 taglist = [o.tag for o in ol] + 99 if np.any([tag is not None for tag in taglist]): +100 d['tag'] = taglist +101 if ol[0].reweighted: +102 d['reweighted'] = ol[0].reweighted +103 d['value'] = [o.value for o in ol] +104 data = _gen_data_d_from_list(ol) +105 if len(data) > 0: +106 d['data'] = data +107 cdata = _gen_cdata_d_from_list(ol) +108 if len(cdata) > 0: +109 d['cdata'] = cdata +110 return d +111 +112 def write_Array_to_dict(oa): +113 ol = np.ravel(oa) +114 _assert_equal_properties(ol) +115 d = {} +116 d['type'] = 'Array' +117 d['layout'] = str(oa.shape).lstrip('(').rstrip(')').rstrip(',') +118 taglist = [o.tag for o in ol] +119 if np.any([tag is not None for tag in taglist]): +120 d['tag'] = taglist +121 if ol[0].reweighted: +122 d['reweighted'] = ol[0].reweighted +123 d['value'] = [o.value for o in ol] +124 data = _gen_data_d_from_list(ol) +125 if len(data) > 0: +126 d['data'] = data +127 cdata = _gen_cdata_d_from_list(ol) +128 if len(cdata) > 0: +129 d['cdata'] = cdata +130 return d +131 +132 def _nan_Obs_like(obs): +133 samples = [] +134 names = [] +135 idl = [] +136 for key, value in obs.idl.items(): +137 samples.append([np.nan] * len(value)) +138 names.append(key) +139 idl.append(value) +140 my_obs = Obs(samples, names, idl) +141 my_obs._covobs = obs._covobs +142 for name in obs._covobs: +143 my_obs.names.append(name) +144 my_obs.reweighted = obs.reweighted +145 return my_obs +146 +147 def write_Corr_to_dict(my_corr): +148 first_not_none = next(i for i, j in enumerate(my_corr.content) if np.all(j)) +149 dummy_array = np.empty((my_corr.N, my_corr.N), dtype=object) +150 dummy_array[:] = _nan_Obs_like(my_corr.content[first_not_none].ravel()[0]) +151 content = [o if o is not None else dummy_array for o in my_corr.content] +152 dat = write_Array_to_dict(np.array(content, dtype=object)) +153 dat['type'] = 'Corr' +154 corr_meta_data = str(my_corr.tag) +155 if 'tag' in dat.keys(): +156 dat['tag'].append(corr_meta_data) +157 else: +158 dat['tag'] = [corr_meta_data] +159 taglist = dat['tag'] +160 dat['tag'] = {} # tag is now a dictionary, that contains the previous taglist in the key "tag" +161 dat['tag']['tag'] = taglist +162 if my_corr.prange is not None: +163 dat['tag']['prange'] = my_corr.prange +164 return dat +165 +166 if not isinstance(ol, list): +167 ol = [ol] +168 +169 d = {} +170 d['program'] = 'pyerrors %s' % (pyerrorsversion.__version__) +171 d['version'] = '1.1' +172 d['who'] = getpass.getuser() +173 d['date'] = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S %z') +174 d['host'] = socket.gethostname() + ', ' + platform.platform() +175 +176 if description: +177 d['description'] = description +178 +179 d['obsdata'] = [] +180 for io in ol: +181 if isinstance(io, Obs): +182 d['obsdata'].append(write_Obs_to_dict(io)) +183 elif isinstance(io, list): +184 d['obsdata'].append(write_List_to_dict(io)) +185 elif isinstance(io, np.ndarray): +186 d['obsdata'].append(write_Array_to_dict(io)) +187 elif isinstance(io, Corr): +188 d['obsdata'].append(write_Corr_to_dict(io)) +189 else: +190 raise Exception("Unkown datatype.") 191 -192 if indent: -193 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_SINGLE_LINE_ARRAY) -194 else: -195 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_COMPACT) +192 def _jsonifier(o): +193 if isinstance(o, np.int64): +194 return int(o) +195 raise TypeError('%r is not JSON serializable' % o) +196 +197 if indent: +198 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_SINGLE_LINE_ARRAY) +199 else: +200 return json.dumps(d, indent=indent, ensure_ascii=False, default=_jsonifier, write_mode=json.WM_COMPACT) @@ -1001,6 +1060,13 @@ Optional string that describes the contents of the json file. Specify the indentation level of the json file. None or 0 is permissible and saves disk space. + +
    Returns
    + + @@ -1016,41 +1082,45 @@ saves disk space. - 
    198def dump_to_json(ol, fname, description='', indent=1, gz=True):
-199    """Export a list of Obs or structures containing Obs to a .json(.gz) file
-200
-201    Parameters
-202    ----------
-203    ol : list
-204        List of objects that will be exported. At the moment, these objects can be
-205        either of: Obs, list, numpy.ndarray, Corr.
-206        All Obs inside a structure have to be defined on the same set of configurations.
-207    fname : str
-208        Filename of the output file.
-209    description : str
-210        Optional string that describes the contents of the json file.
-211    indent : int
-212        Specify the indentation level of the json file. None or 0 is permissible and
-213        saves disk space.
-214    gz : bool
-215        If True, the output is a gzipped json. If False, the output is a json file.
-216    """
-217
-218    jsonstring = create_json_string(ol, description, indent)
-219
-220    if not fname.endswith('.json') and not fname.endswith('.gz'):
-221        fname += '.json'
-222
-223    if gz:
-224        if not fname.endswith('.gz'):
-225            fname += '.gz'
+            
    203def dump_to_json(ol, fname, description='', indent=1, gz=True):
+204    """Export a list of Obs or structures containing Obs to a .json(.gz) file
+205
+206    Parameters
+207    ----------
+208    ol : list
+209        List of objects that will be exported. At the moment, these objects can be
+210        either of: Obs, list, numpy.ndarray, Corr.
+211        All Obs inside a structure have to be defined on the same set of configurations.
+212    fname : str
+213        Filename of the output file.
+214    description : str
+215        Optional string that describes the contents of the json file.
+216    indent : int
+217        Specify the indentation level of the json file. None or 0 is permissible and
+218        saves disk space.
+219    gz : bool
+220        If True, the output is a gzipped json. If False, the output is a json file.
+221
+222    Returns
+223    -------
+224    Null
+225    """
 226
-227        fp = gzip.open(fname, 'wb')
-228        fp.write(jsonstring.encode('utf-8'))
-229    else:
-230        fp = open(fname, 'w', encoding='utf-8')
-231        fp.write(jsonstring)
-232    fp.close()
+227    jsonstring = create_json_string(ol, description, indent)
+228
+229    if not fname.endswith('.json') and not fname.endswith('.gz'):
+230        fname += '.json'
+231
+232    if gz:
+233        if not fname.endswith('.gz'):
+234            fname += '.gz'
+235
+236        fp = gzip.open(fname, 'wb')
+237        fp.write(jsonstring.encode('utf-8'))
+238    else:
+239        fp = open(fname, 'w', encoding='utf-8')
+240        fp.write(jsonstring)
+241    fp.close()
 
    
 
 
@@ -1073,6 +1143,12 @@ saves disk space.
 
  • gz (bool):
 If True, the output is a gzipped json. If False, the output is a json file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • Null
      • 
+
    @@ -1088,24 +1164,35 @@ If True, the output is a gzipped json. If False, the output is a json file. - 
    436def import_json_string(json_string, verbose=True, full_output=False):
-437    """Reconstruct a list of Obs or structures containing Obs from a json string.
-438
-439    The following structures are supported: Obs, list, numpy.ndarray, Corr
-440    If the list contains only one element, it is unpacked from the list.
-441
-442    Parameters
-443    ----------
-444    json_string : str
-445        json string containing the data.
-446    verbose : bool
-447        Print additional information that was written to the file.
-448    full_output : bool
-449        If True, a dict containing auxiliary information and the data is returned.
-450        If False, only the data is returned.
-451    """
-452
-453    return _parse_json_dict(json.loads(json_string), verbose, full_output)
+            
    456def import_json_string(json_string, verbose=True, full_output=False):
+457    """Reconstruct a list of Obs or structures containing Obs from a json string.
+458
+459    The following structures are supported: Obs, list, numpy.ndarray, Corr
+460    If the list contains only one element, it is unpacked from the list.
+461
+462    Parameters
+463    ----------
+464    json_string : str
+465        json string containing the data.
+466    verbose : bool
+467        Print additional information that was written to the file.
+468    full_output : bool
+469        If True, a dict containing auxiliary information and the data is returned.
+470        If False, only the data is returned.
+471
+472    Returns
+473    -------
+474    result : list[Obs]
+475        reconstructed list of observables from the json string
+476    or
+477    result : Obs
+478        only one observable if the list only has one entry
+479    or
+480    result : dict
+481        if full_output=True
+482    """
+483
+484    return _parse_json_dict(json.loads(json_string), verbose, full_output)
 
    
 
 
@@ -1125,6 +1212,19 @@ Print additional information that was written to the file.
 If True, a dict containing auxiliary information and the data is returned.
 If False, only the data is returned.
 
+
+
    Returns
    
+
+
      
+
    • result (list[Obs]):
+reconstructed list of observables from the json string
      • 
+
    • or
      • 
+
    • result (Obs):
+only one observable if the list only has one entry
      • 
+
    • or
      • 
+
    • result (dict):
+if full_output=True
      • 
+
    @@ -1140,38 +1240,49 @@ If False, only the data is returned. - 
    456def load_json(fname, verbose=True, gz=True, full_output=False):
-457    """Import a list of Obs or structures containing Obs from a .json(.gz) file.
-458
-459    The following structures are supported: Obs, list, numpy.ndarray, Corr
-460    If the list contains only one element, it is unpacked from the list.
-461
-462    Parameters
-463    ----------
-464    fname : str
-465        Filename of the input file.
-466    verbose : bool
-467        Print additional information that was written to the file.
-468    gz : bool
-469        If True, assumes that data is gzipped. If False, assumes JSON file.
-470    full_output : bool
-471        If True, a dict containing auxiliary information and the data is returned.
-472        If False, only the data is returned.
-473    """
-474    if not fname.endswith('.json') and not fname.endswith('.gz'):
-475        fname += '.json'
-476    if gz:
-477        if not fname.endswith('.gz'):
-478            fname += '.gz'
-479        with gzip.open(fname, 'r') as fin:
-480            d = json.load(fin)
-481    else:
-482        if fname.endswith('.gz'):
-483            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-484        with open(fname, 'r', encoding='utf-8') as fin:
-485            d = json.loads(fin.read())
-486
-487    return _parse_json_dict(d, verbose, full_output)
+            
    487def load_json(fname, verbose=True, gz=True, full_output=False):
+488    """Import a list of Obs or structures containing Obs from a .json(.gz) file.
+489
+490    The following structures are supported: Obs, list, numpy.ndarray, Corr
+491    If the list contains only one element, it is unpacked from the list.
+492
+493    Parameters
+494    ----------
+495    fname : str
+496        Filename of the input file.
+497    verbose : bool
+498        Print additional information that was written to the file.
+499    gz : bool
+500        If True, assumes that data is gzipped. If False, assumes JSON file.
+501    full_output : bool
+502        If True, a dict containing auxiliary information and the data is returned.
+503        If False, only the data is returned.
+504
+505    Returns
+506    -------
+507    result : list[Obs]
+508        reconstructed list of observables from the json string
+509    or
+510    result : Obs
+511        only one observable if the list only has one entry
+512    or
+513    result : dict
+514        if full_output=True
+515    """
+516    if not fname.endswith('.json') and not fname.endswith('.gz'):
+517        fname += '.json'
+518    if gz:
+519        if not fname.endswith('.gz'):
+520            fname += '.gz'
+521        with gzip.open(fname, 'r') as fin:
+522            d = json.load(fin)
+523    else:
+524        if fname.endswith('.gz'):
+525            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+526        with open(fname, 'r', encoding='utf-8') as fin:
+527            d = json.loads(fin.read())
+528
+529    return _parse_json_dict(d, verbose, full_output)
 
    
 
 
@@ -1193,6 +1304,19 @@ If True, assumes that data is gzipped. If False, assumes JSON file.
 If True, a dict containing auxiliary information and the data is returned.
 If False, only the data is returned.
 
+
+
    Returns
    
+
+
      
+
    • result (list[Obs]):
+reconstructed list of observables from the json string
      • 
+
    • or
      • 
+
    • result (Obs):
+only one observable if the list only has one entry
      • 
+
    • or
      • 
+
    • result (dict):
+if full_output=True
      • 
+
    @@ -1208,40 +1332,44 @@ If False, only the data is returned. - 
    570def dump_dict_to_json(od, fname, description='', indent=1, reps='DICTOBS', gz=True):
-571    """Export a dict of Obs or structures containing Obs to a .json(.gz) file
-572
-573    Parameters
-574    ----------
-575    od : dict
-576        Dict of JSON valid structures and objects that will be exported.
-577        At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.
-578        All Obs inside a structure have to be defined on the same set of configurations.
-579    fname : str
-580        Filename of the output file.
-581    description : str
-582        Optional string that describes the contents of the json file.
-583    indent : int
-584        Specify the indentation level of the json file. None or 0 is permissible and
-585        saves disk space.
-586    reps : str
-587        Specify the structure of the placeholder in exported dict to be reps[0-9]+.
-588    gz : bool
-589        If True, the output is a gzipped json. If False, the output is a json file.
-590    """
-591
-592    if not isinstance(od, dict):
-593        raise Exception('od has to be a dictionary. Did you want to use dump_to_json?')
-594
-595    infostring = ('This JSON file contains a python dictionary that has been parsed to a list of structures. '
-596                  'OBSDICT contains the dictionary, where Obs or other structures have been replaced by '
-597                  '' + reps + '[0-9]+. The field description contains the additional description of this JSON file. '
-598                  'This file may be parsed to a dict with the pyerrors routine load_json_dict.')
-599
-600    desc_dict = {'INFO': infostring, 'OBSDICT': {}, 'description': description}
-601    ol, desc_dict['OBSDICT'] = _ol_from_dict(od, reps=reps)
-602
-603    dump_to_json(ol, fname, description=desc_dict, indent=indent, gz=gz)
+            
    612def dump_dict_to_json(od, fname, description='', indent=1, reps='DICTOBS', gz=True):
+613    """Export a dict of Obs or structures containing Obs to a .json(.gz) file
+614
+615    Parameters
+616    ----------
+617    od : dict
+618        Dict of JSON valid structures and objects that will be exported.
+619        At the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.
+620        All Obs inside a structure have to be defined on the same set of configurations.
+621    fname : str
+622        Filename of the output file.
+623    description : str
+624        Optional string that describes the contents of the json file.
+625    indent : int
+626        Specify the indentation level of the json file. None or 0 is permissible and
+627        saves disk space.
+628    reps : str
+629        Specify the structure of the placeholder in exported dict to be reps[0-9]+.
+630    gz : bool
+631        If True, the output is a gzipped json. If False, the output is a json file.
+632
+633    Returns
+634    -------
+635    None
+636    """
+637
+638    if not isinstance(od, dict):
+639        raise Exception('od has to be a dictionary. Did you want to use dump_to_json?')
+640
+641    infostring = ('This JSON file contains a python dictionary that has been parsed to a list of structures. '
+642                  'OBSDICT contains the dictionary, where Obs or other structures have been replaced by '
+643                  '' + reps + '[0-9]+. The field description contains the additional description of this JSON file. '
+644                  'This file may be parsed to a dict with the pyerrors routine load_json_dict.')
+645
+646    desc_dict = {'INFO': infostring, 'OBSDICT': {}, 'description': description}
+647    ol, desc_dict['OBSDICT'] = _ol_from_dict(od, reps=reps)
+648
+649    dump_to_json(ol, fname, description=desc_dict, indent=indent, gz=gz)
 
    
 
 
@@ -1266,6 +1394,12 @@ Specify the structure of the placeholder in exported dict to be reps[0-9]+.
 
  • gz (bool):
 If True, the output is a gzipped json. If False, the output is a json file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    @@ -1281,37 +1415,45 @@ If True, the output is a gzipped json. If False, the output is a json file. - 
    669def load_json_dict(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):
-670    """Import a dict of Obs or structures containing Obs from a .json(.gz) file.
-671
-672    The following structures are supported: Obs, list, numpy.ndarray, Corr
-673
-674    Parameters
-675    ----------
-676    fname : str
-677        Filename of the input file.
-678    verbose : bool
-679        Print additional information that was written to the file.
-680    gz : bool
-681        If True, assumes that data is gzipped. If False, assumes JSON file.
-682    full_output : bool
-683        If True, a dict containing auxiliary information and the data is returned.
-684        If False, only the data is returned.
-685    reps : str
-686        Specify the structure of the placeholder in imported dict to be reps[0-9]+.
-687    """
-688    indata = load_json(fname, verbose=verbose, gz=gz, full_output=True)
-689    description = indata['description']['description']
-690    indict = indata['description']['OBSDICT']
-691    ol = indata['obsdata']
-692    od = _od_from_list_and_dict(ol, indict, reps=reps)
-693
-694    if full_output:
-695        indata['description'] = description
-696        indata['obsdata'] = od
-697        return indata
-698    else:
-699        return od
+            
    715def load_json_dict(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):
+716    """Import a dict of Obs or structures containing Obs from a .json(.gz) file.
+717
+718    The following structures are supported: Obs, list, numpy.ndarray, Corr
+719
+720    Parameters
+721    ----------
+722    fname : str
+723        Filename of the input file.
+724    verbose : bool
+725        Print additional information that was written to the file.
+726    gz : bool
+727        If True, assumes that data is gzipped. If False, assumes JSON file.
+728    full_output : bool
+729        If True, a dict containing auxiliary information and the data is returned.
+730        If False, only the data is returned.
+731    reps : str
+732        Specify the structure of the placeholder in imported dict to be reps[0-9]+.
+733
+734    Returns
+735    -------
+736    data : Obs / list / Corr
+737        Read data
+738    or
+739    data : dict
+740        Read data and meta-data
+741    """
+742    indata = load_json(fname, verbose=verbose, gz=gz, full_output=True)
+743    description = indata['description']['description']
+744    indict = indata['description']['OBSDICT']
+745    ol = indata['obsdata']
+746    od = _od_from_list_and_dict(ol, indict, reps=reps)
+747
+748    if full_output:
+749        indata['description'] = description
+750        indata['obsdata'] = od
+751        return indata
+752    else:
+753        return od
 
    
 
 
@@ -1334,6 +1476,16 @@ If False, only the data is returned.
 
  • reps (str):
 Specify the structure of the placeholder in imported dict to be reps[0-9]+.
    • 
 
+
+
    Returns
    
+
+
      
+
    • data (Obs / list / Corr):
+Read data
      • 
+
    • or
      • 
+
    • data (dict):
+Read data and meta-data
      • 
+
    diff --git a/docs/pyerrors/input/misc.html b/docs/pyerrors/input/misc.html index 307135a1..d7729156 100644 --- a/docs/pyerrors/input/misc.html +++ b/docs/pyerrors/input/misc.html @@ -85,7 +85,7 @@ 7 8 9def read_pbp(path, prefix, **kwargs): - 10 """Read pbp format from given folder structure. Returns a list of length nrw + 10 """Read pbp format from given folder structure. 11 12 Parameters 13 ---------- @@ -93,111 +93,116 @@ 15 list which contains the first config to be read for each replicum 16 r_stop : list 17 list which contains the last config to be read for each replicum - 18 """ - 19 - 20 ls = [] - 21 for (dirpath, dirnames, filenames) in os.walk(path): - 22 ls.extend(filenames) - 23 break + 18 + 19 Returns + 20 ------- + 21 result : list[Obs] + 22 list of observables read + 23 """ 24 - 25 if not ls: - 26 raise Exception('Error, directory not found') - 27 - 28 # Exclude files with different names - 29 for exc in ls: - 30 if not fnmatch.fnmatch(exc, prefix + '*.dat'): - 31 ls = list(set(ls) - set([exc])) - 32 if len(ls) > 1: - 33 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) - 34 replica = len(ls) - 35 - 36 if 'r_start' in kwargs: - 37 r_start = kwargs.get('r_start') - 38 if len(r_start) != replica: - 39 raise Exception('r_start does not match number of replicas') - 40 # Adjust Configuration numbering to python index - 41 r_start = [o - 1 if o else None for o in r_start] - 42 else: - 43 r_start = [None] * replica - 44 - 45 if 'r_stop' in kwargs: - 46 r_stop = kwargs.get('r_stop') - 47 if len(r_stop) != replica: - 48 raise Exception('r_stop does not match number of replicas') - 49 else: - 50 r_stop = [None] * replica - 51 - 52 print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='') - 53 - 54 print_err = 0 - 55 if 'print_err' in kwargs: - 56 print_err = 1 - 57 print() + 25 ls = [] + 26 for (dirpath, dirnames, filenames) in os.walk(path): + 27 ls.extend(filenames) + 28 break + 29 + 30 if not ls: + 31 raise Exception('Error, directory not found') + 32 + 33 # Exclude files with different names + 34 for exc in ls: + 35 if not fnmatch.fnmatch(exc, prefix + '*.dat'): + 36 ls = list(set(ls) - set([exc])) + 37 if len(ls) > 1: + 38 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) + 39 replica = len(ls) + 40 + 41 if 'r_start' in kwargs: + 42 r_start = kwargs.get('r_start') + 43 if len(r_start) != replica: + 44 raise Exception('r_start does not match number of replicas') + 45 # Adjust Configuration numbering to python index + 46 r_start = [o - 1 if o else None for o in r_start] + 47 else: + 48 r_start = [None] * replica + 49 + 50 if 'r_stop' in kwargs: + 51 r_stop = kwargs.get('r_stop') + 52 if len(r_stop) != replica: + 53 raise Exception('r_stop does not match number of replicas') + 54 else: + 55 r_stop = [None] * replica + 56 + 57 print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='') 58 - 59 deltas = [] - 60 - 61 for rep in range(replica): - 62 tmp_array = [] - 63 with open(path + '/' + ls[rep], 'rb') as fp: - 64 - 65 t = fp.read(4) # number of reweighting factors - 66 if rep == 0: - 67 nrw = struct.unpack('i', t)[0] - 68 for k in range(nrw): - 69 deltas.append([]) - 70 else: - 71 if nrw != struct.unpack('i', t)[0]: - 72 raise Exception('Error: different number of factors for replicum', rep) - 73 - 74 for k in range(nrw): - 75 tmp_array.append([]) - 76 - 77 # This block is necessary for openQCD1.6 ms1 files - 78 nfct = [] - 79 for i in range(nrw): - 80 t = fp.read(4) - 81 nfct.append(struct.unpack('i', t)[0]) - 82 print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting - 83 - 84 nsrc = [] - 85 for i in range(nrw): - 86 t = fp.read(4) - 87 nsrc.append(struct.unpack('i', t)[0]) + 59 print_err = 0 + 60 if 'print_err' in kwargs: + 61 print_err = 1 + 62 print() + 63 + 64 deltas = [] + 65 + 66 for rep in range(replica): + 67 tmp_array = [] + 68 with open(path + '/' + ls[rep], 'rb') as fp: + 69 + 70 t = fp.read(4) # number of reweighting factors + 71 if rep == 0: + 72 nrw = struct.unpack('i', t)[0] + 73 for k in range(nrw): + 74 deltas.append([]) + 75 else: + 76 if nrw != struct.unpack('i', t)[0]: + 77 raise Exception('Error: different number of factors for replicum', rep) + 78 + 79 for k in range(nrw): + 80 tmp_array.append([]) + 81 + 82 # This block is necessary for openQCD1.6 ms1 files + 83 nfct = [] + 84 for i in range(nrw): + 85 t = fp.read(4) + 86 nfct.append(struct.unpack('i', t)[0]) + 87 print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting 88 - 89 # body - 90 while True: + 89 nsrc = [] + 90 for i in range(nrw): 91 t = fp.read(4) - 92 if len(t) < 4: - 93 break - 94 if print_err: - 95 config_no = struct.unpack('i', t) - 96 for i in range(nrw): - 97 tmp_nfct = 1.0 - 98 for j in range(nfct[i]): - 99 t = fp.read(8 * nsrc[i]) -100 t = fp.read(8 * nsrc[i]) -101 tmp_rw = struct.unpack('d' * nsrc[i], t) -102 tmp_nfct *= np.mean(np.asarray(tmp_rw)) -103 if print_err: -104 print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw))) -105 print('Sources:', np.asarray(tmp_rw)) -106 print('Partial factor:', tmp_nfct) -107 tmp_array[i].append(tmp_nfct) -108 -109 for k in range(nrw): -110 deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]]) -111 -112 rep_names = [] -113 for entry in ls: -114 truncated_entry = entry.split('.')[0] -115 idx = truncated_entry.index('r') -116 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) -117 print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources') -118 result = [] -119 for t in range(nrw): -120 result.append(Obs(deltas[t], rep_names)) -121 -122 return result + 92 nsrc.append(struct.unpack('i', t)[0]) + 93 + 94 # body + 95 while True: + 96 t = fp.read(4) + 97 if len(t) < 4: + 98 break + 99 if print_err: +100 config_no = struct.unpack('i', t) +101 for i in range(nrw): +102 tmp_nfct = 1.0 +103 for j in range(nfct[i]): +104 t = fp.read(8 * nsrc[i]) +105 t = fp.read(8 * nsrc[i]) +106 tmp_rw = struct.unpack('d' * nsrc[i], t) +107 tmp_nfct *= np.mean(np.asarray(tmp_rw)) +108 if print_err: +109 print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw))) +110 print('Sources:', np.asarray(tmp_rw)) +111 print('Partial factor:', tmp_nfct) +112 tmp_array[i].append(tmp_nfct) +113 +114 for k in range(nrw): +115 deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]]) +116 +117 rep_names = [] +118 for entry in ls: +119 truncated_entry = entry.split('.')[0] +120 idx = truncated_entry.index('r') +121 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) +122 print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources') +123 result = [] +124 for t in range(nrw): +125 result.append(Obs(deltas[t], rep_names)) +126 +127 return result @@ -214,7 +219,7 @@ 
     10def read_pbp(path, prefix, **kwargs):
- 11    """Read pbp format from given folder structure. Returns a list of length nrw
+ 11    """Read pbp format from given folder structure.
  12
  13    Parameters
  14    ----------
@@ -222,115 +227,120 @@
  16        list which contains the first config to be read for each replicum
  17    r_stop : list
  18        list which contains the last config to be read for each replicum
- 19    """
- 20
- 21    ls = []
- 22    for (dirpath, dirnames, filenames) in os.walk(path):
- 23        ls.extend(filenames)
- 24        break
+ 19
+ 20    Returns
+ 21    -------
+ 22    result : list[Obs]
+ 23        list of observables read
+ 24    """
  25
- 26    if not ls:
- 27        raise Exception('Error, directory not found')
- 28
- 29    # Exclude files with different names
- 30    for exc in ls:
- 31        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
- 32            ls = list(set(ls) - set([exc]))
- 33    if len(ls) > 1:
- 34        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
- 35    replica = len(ls)
- 36
- 37    if 'r_start' in kwargs:
- 38        r_start = kwargs.get('r_start')
- 39        if len(r_start) != replica:
- 40            raise Exception('r_start does not match number of replicas')
- 41        # Adjust Configuration numbering to python index
- 42        r_start = [o - 1 if o else None for o in r_start]
- 43    else:
- 44        r_start = [None] * replica
- 45
- 46    if 'r_stop' in kwargs:
- 47        r_stop = kwargs.get('r_stop')
- 48        if len(r_stop) != replica:
- 49            raise Exception('r_stop does not match number of replicas')
- 50    else:
- 51        r_stop = [None] * replica
- 52
- 53    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
- 54
- 55    print_err = 0
- 56    if 'print_err' in kwargs:
- 57        print_err = 1
- 58        print()
+ 26    ls = []
+ 27    for (dirpath, dirnames, filenames) in os.walk(path):
+ 28        ls.extend(filenames)
+ 29        break
+ 30
+ 31    if not ls:
+ 32        raise Exception('Error, directory not found')
+ 33
+ 34    # Exclude files with different names
+ 35    for exc in ls:
+ 36        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
+ 37            ls = list(set(ls) - set([exc]))
+ 38    if len(ls) > 1:
+ 39        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
+ 40    replica = len(ls)
+ 41
+ 42    if 'r_start' in kwargs:
+ 43        r_start = kwargs.get('r_start')
+ 44        if len(r_start) != replica:
+ 45            raise Exception('r_start does not match number of replicas')
+ 46        # Adjust Configuration numbering to python index
+ 47        r_start = [o - 1 if o else None for o in r_start]
+ 48    else:
+ 49        r_start = [None] * replica
+ 50
+ 51    if 'r_stop' in kwargs:
+ 52        r_stop = kwargs.get('r_stop')
+ 53        if len(r_stop) != replica:
+ 54            raise Exception('r_stop does not match number of replicas')
+ 55    else:
+ 56        r_stop = [None] * replica
+ 57
+ 58    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
  59
- 60    deltas = []
- 61
- 62    for rep in range(replica):
- 63        tmp_array = []
- 64        with open(path + '/' + ls[rep], 'rb') as fp:
- 65
- 66            t = fp.read(4)  # number of reweighting factors
- 67            if rep == 0:
- 68                nrw = struct.unpack('i', t)[0]
- 69                for k in range(nrw):
- 70                    deltas.append([])
- 71            else:
- 72                if nrw != struct.unpack('i', t)[0]:
- 73                    raise Exception('Error: different number of factors for replicum', rep)
- 74
- 75            for k in range(nrw):
- 76                tmp_array.append([])
- 77
- 78            # This block is necessary for openQCD1.6 ms1 files
- 79            nfct = []
- 80            for i in range(nrw):
- 81                t = fp.read(4)
- 82                nfct.append(struct.unpack('i', t)[0])
- 83            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
- 84
- 85            nsrc = []
- 86            for i in range(nrw):
- 87                t = fp.read(4)
- 88                nsrc.append(struct.unpack('i', t)[0])
+ 60    print_err = 0
+ 61    if 'print_err' in kwargs:
+ 62        print_err = 1
+ 63        print()
+ 64
+ 65    deltas = []
+ 66
+ 67    for rep in range(replica):
+ 68        tmp_array = []
+ 69        with open(path + '/' + ls[rep], 'rb') as fp:
+ 70
+ 71            t = fp.read(4)  # number of reweighting factors
+ 72            if rep == 0:
+ 73                nrw = struct.unpack('i', t)[0]
+ 74                for k in range(nrw):
+ 75                    deltas.append([])
+ 76            else:
+ 77                if nrw != struct.unpack('i', t)[0]:
+ 78                    raise Exception('Error: different number of factors for replicum', rep)
+ 79
+ 80            for k in range(nrw):
+ 81                tmp_array.append([])
+ 82
+ 83            # This block is necessary for openQCD1.6 ms1 files
+ 84            nfct = []
+ 85            for i in range(nrw):
+ 86                t = fp.read(4)
+ 87                nfct.append(struct.unpack('i', t)[0])
+ 88            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
  89
- 90            # body
- 91            while True:
+ 90            nsrc = []
+ 91            for i in range(nrw):
  92                t = fp.read(4)
- 93                if len(t) < 4:
- 94                    break
- 95                if print_err:
- 96                    config_no = struct.unpack('i', t)
- 97                for i in range(nrw):
- 98                    tmp_nfct = 1.0
- 99                    for j in range(nfct[i]):
-100                        t = fp.read(8 * nsrc[i])
-101                        t = fp.read(8 * nsrc[i])
-102                        tmp_rw = struct.unpack('d' * nsrc[i], t)
-103                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
-104                        if print_err:
-105                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
-106                            print('Sources:', np.asarray(tmp_rw))
-107                            print('Partial factor:', tmp_nfct)
-108                    tmp_array[i].append(tmp_nfct)
-109
-110            for k in range(nrw):
-111                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
-112
-113    rep_names = []
-114    for entry in ls:
-115        truncated_entry = entry.split('.')[0]
-116        idx = truncated_entry.index('r')
-117        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-118    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
-119    result = []
-120    for t in range(nrw):
-121        result.append(Obs(deltas[t], rep_names))
-122
-123    return result
+ 93                nsrc.append(struct.unpack('i', t)[0])
+ 94
+ 95            # body
+ 96            while True:
+ 97                t = fp.read(4)
+ 98                if len(t) < 4:
+ 99                    break
+100                if print_err:
+101                    config_no = struct.unpack('i', t)
+102                for i in range(nrw):
+103                    tmp_nfct = 1.0
+104                    for j in range(nfct[i]):
+105                        t = fp.read(8 * nsrc[i])
+106                        t = fp.read(8 * nsrc[i])
+107                        tmp_rw = struct.unpack('d' * nsrc[i], t)
+108                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
+109                        if print_err:
+110                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
+111                            print('Sources:', np.asarray(tmp_rw))
+112                            print('Partial factor:', tmp_nfct)
+113                    tmp_array[i].append(tmp_nfct)
+114
+115            for k in range(nrw):
+116                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
+117
+118    rep_names = []
+119    for entry in ls:
+120        truncated_entry = entry.split('.')[0]
+121        idx = truncated_entry.index('r')
+122        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+123    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
+124    result = []
+125    for t in range(nrw):
+126        result.append(Obs(deltas[t], rep_names))
+127
+128    return result
    -

    Read pbp format from given folder structure. Returns a list of length nrw

    +

    Read pbp format from given folder structure.

    Parameters
    @@ -340,6 +350,13 @@ list which contains the first config to be read for each replicum
  • r_stop (list): list which contains the last config to be read for each replicum
    • + +
    Returns
    + +
      +
    • result (list[Obs]): +list of observables read
      • +
    diff --git a/docs/pyerrors/input/openQCD.html b/docs/pyerrors/input/openQCD.html index e61d699f..9cef2ade 100644 --- a/docs/pyerrors/input/openQCD.html +++ b/docs/pyerrors/input/openQCD.html @@ -138,1094 +138,1124 @@ 42 files performed if given. 43 print_err : bool 44 Print additional information that is useful for debugging. - 45 """ - 46 known_oqcd_versions = ['1.4', '1.6', '2.0'] - 47 if not (version in known_oqcd_versions): - 48 raise Exception('Unknown openQCD version defined!') - 49 print("Working with openQCD version " + version) - 50 if 'postfix' in kwargs: - 51 postfix = kwargs.get('postfix') - 52 else: - 53 postfix = '' - 54 ls = [] - 55 for (dirpath, dirnames, filenames) in os.walk(path): - 56 ls.extend(filenames) - 57 break - 58 - 59 if not ls: - 60 raise Exception(f"Error, directory '{path}' not found") - 61 if 'files' in kwargs: - 62 ls = kwargs.get('files') - 63 else: - 64 for exc in ls: - 65 if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): - 66 ls = list(set(ls) - set([exc])) - 67 if len(ls) > 1: - 68 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) - 69 replica = len(ls) - 70 - 71 if 'r_start' in kwargs: - 72 r_start = kwargs.get('r_start') - 73 if len(r_start) != replica: - 74 raise Exception('r_start does not match number of replicas') - 75 r_start = [o if o else None for o in r_start] - 76 else: - 77 r_start = [None] * replica - 78 - 79 if 'r_stop' in kwargs: - 80 r_stop = kwargs.get('r_stop') - 81 if len(r_stop) != replica: - 82 raise Exception('r_stop does not match number of replicas') - 83 else: - 84 r_stop = [None] * replica - 85 - 86 if 'r_step' in kwargs: - 87 r_step = kwargs.get('r_step') + 45 + 46 Returns + 47 ------- + 48 rwms : Obs + 49 Reweighting factors read + 50 """ + 51 known_oqcd_versions = ['1.4', '1.6', '2.0'] + 52 if not (version in known_oqcd_versions): + 53 raise Exception('Unknown openQCD version defined!') + 54 print("Working with openQCD version " + version) + 55 if 'postfix' in kwargs: + 56 postfix = kwargs.get('postfix') + 57 else: + 58 postfix = '' + 59 ls = [] + 60 for (dirpath, dirnames, filenames) in os.walk(path): + 61 ls.extend(filenames) + 62 break + 63 + 64 if not ls: + 65 raise Exception(f"Error, directory '{path}' not found") + 66 if 'files' in kwargs: + 67 ls = kwargs.get('files') + 68 else: + 69 for exc in ls: + 70 if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): + 71 ls = list(set(ls) - set([exc])) + 72 if len(ls) > 1: + 73 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) + 74 replica = len(ls) + 75 + 76 if 'r_start' in kwargs: + 77 r_start = kwargs.get('r_start') + 78 if len(r_start) != replica: + 79 raise Exception('r_start does not match number of replicas') + 80 r_start = [o if o else None for o in r_start] + 81 else: + 82 r_start = [None] * replica + 83 + 84 if 'r_stop' in kwargs: + 85 r_stop = kwargs.get('r_stop') + 86 if len(r_stop) != replica: + 87 raise Exception('r_stop does not match number of replicas') 88 else: - 89 r_step = 1 + 89 r_stop = [None] * replica 90 - 91 print('Read reweighting factors from', prefix[:-1], ',', - 92 replica, 'replica', end='') - 93 - 94 if names is None: - 95 rep_names = [] - 96 for entry in ls: - 97 truncated_entry = entry - 98 suffixes = [".dat", ".rwms", ".ms1"] - 99 for suffix in suffixes: - 100 if truncated_entry.endswith(suffix): - 101 truncated_entry = truncated_entry[0:-len(suffix)] - 102 idx = truncated_entry.index('r') - 103 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) - 104 else: - 105 rep_names = names - 106 - 107 print_err = 0 - 108 if 'print_err' in kwargs: - 109 print_err = 1 - 110 print() + 91 if 'r_step' in kwargs: + 92 r_step = kwargs.get('r_step') + 93 else: + 94 r_step = 1 + 95 + 96 print('Read reweighting factors from', prefix[:-1], ',', + 97 replica, 'replica', end='') + 98 + 99 if names is None: + 100 rep_names = [] + 101 for entry in ls: + 102 truncated_entry = entry + 103 suffixes = [".dat", ".rwms", ".ms1"] + 104 for suffix in suffixes: + 105 if truncated_entry.endswith(suffix): + 106 truncated_entry = truncated_entry[0:-len(suffix)] + 107 idx = truncated_entry.index('r') + 108 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) + 109 else: + 110 rep_names = names 111 - 112 deltas = [] - 113 - 114 configlist = [] - 115 r_start_index = [] - 116 r_stop_index = [] - 117 - 118 for rep in range(replica): - 119 tmp_array = [] - 120 with open(path + '/' + ls[rep], 'rb') as fp: - 121 - 122 t = fp.read(4) # number of reweighting factors - 123 if rep == 0: - 124 nrw = struct.unpack('i', t)[0] - 125 if version == '2.0': - 126 nrw = int(nrw / 2) - 127 for k in range(nrw): - 128 deltas.append([]) - 129 else: - 130 if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): - 131 raise Exception('Error: different number of reweighting factors for replicum', rep) - 132 - 133 for k in range(nrw): - 134 tmp_array.append([]) - 135 - 136 # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files - 137 nfct = [] - 138 if version in ['1.6', '2.0']: - 139 for i in range(nrw): - 140 t = fp.read(4) - 141 nfct.append(struct.unpack('i', t)[0]) - 142 else: - 143 for i in range(nrw): - 144 nfct.append(1) - 145 - 146 nsrc = [] - 147 for i in range(nrw): - 148 t = fp.read(4) - 149 nsrc.append(struct.unpack('i', t)[0]) - 150 if version == '2.0': - 151 if not struct.unpack('i', fp.read(4))[0] == 0: - 152 print('something is wrong!') - 153 - 154 configlist.append([]) - 155 while True: - 156 t = fp.read(4) - 157 if len(t) < 4: - 158 break - 159 config_no = struct.unpack('i', t)[0] - 160 configlist[-1].append(config_no) - 161 for i in range(nrw): - 162 if (version == '2.0'): - 163 tmpd = _read_array_openQCD2(fp) - 164 tmpd = _read_array_openQCD2(fp) - 165 tmp_rw = tmpd['arr'] - 166 tmp_nfct = 1.0 - 167 for j in range(tmpd['n'][0]): - 168 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j]))) - 169 if print_err: - 170 print(config_no, i, j, - 171 np.mean(np.exp(-np.asarray(tmp_rw[j]))), - 172 np.std(np.exp(-np.asarray(tmp_rw[j])))) - 173 print('Sources:', - 174 np.exp(-np.asarray(tmp_rw[j]))) - 175 print('Partial factor:', tmp_nfct) - 176 elif version == '1.6' or version == '1.4': - 177 tmp_nfct = 1.0 - 178 for j in range(nfct[i]): - 179 t = fp.read(8 * nsrc[i]) - 180 t = fp.read(8 * nsrc[i]) - 181 tmp_rw = struct.unpack('d' * nsrc[i], t) - 182 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw))) - 183 if print_err: - 184 print(config_no, i, j, - 185 np.mean(np.exp(-np.asarray(tmp_rw))), - 186 np.std(np.exp(-np.asarray(tmp_rw)))) - 187 print('Sources:', np.exp(-np.asarray(tmp_rw))) - 188 print('Partial factor:', tmp_nfct) - 189 tmp_array[i].append(tmp_nfct) - 190 - 191 diffmeas = configlist[-1][-1] - configlist[-1][-2] - 192 configlist[-1] = [item // diffmeas for item in configlist[-1]] - 193 if configlist[-1][0] > 1 and diffmeas > 1: - 194 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') - 195 offset = configlist[-1][0] - 1 - 196 configlist[-1] = [item - offset for item in configlist[-1]] - 197 - 198 if r_start[rep] is None: - 199 r_start_index.append(0) - 200 else: - 201 try: - 202 r_start_index.append(configlist[-1].index(r_start[rep])) - 203 except ValueError: - 204 raise Exception('Config %d not in file with range [%d, %d]' % ( - 205 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None - 206 - 207 if r_stop[rep] is None: - 208 r_stop_index.append(len(configlist[-1]) - 1) - 209 else: - 210 try: - 211 r_stop_index.append(configlist[-1].index(r_stop[rep])) - 212 except ValueError: - 213 raise Exception('Config %d not in file with range [%d, %d]' % ( - 214 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None - 215 - 216 for k in range(nrw): - 217 deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step]) - 218 - 219 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): - 220 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) - 221 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] - 222 if np.any([step != 1 for step in stepsizes]): - 223 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) - 224 - 225 print(',', nrw, 'reweighting factors with', nsrc, 'sources') - 226 result = [] - 227 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] - 228 - 229 for t in range(nrw): - 230 result.append(Obs(deltas[t], rep_names, idl=idl)) - 231 return result - 232 + 112 print_err = 0 + 113 if 'print_err' in kwargs: + 114 print_err = 1 + 115 print() + 116 + 117 deltas = [] + 118 + 119 configlist = [] + 120 r_start_index = [] + 121 r_stop_index = [] + 122 + 123 for rep in range(replica): + 124 tmp_array = [] + 125 with open(path + '/' + ls[rep], 'rb') as fp: + 126 + 127 t = fp.read(4) # number of reweighting factors + 128 if rep == 0: + 129 nrw = struct.unpack('i', t)[0] + 130 if version == '2.0': + 131 nrw = int(nrw / 2) + 132 for k in range(nrw): + 133 deltas.append([]) + 134 else: + 135 if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): + 136 raise Exception('Error: different number of reweighting factors for replicum', rep) + 137 + 138 for k in range(nrw): + 139 tmp_array.append([]) + 140 + 141 # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files + 142 nfct = [] + 143 if version in ['1.6', '2.0']: + 144 for i in range(nrw): + 145 t = fp.read(4) + 146 nfct.append(struct.unpack('i', t)[0]) + 147 else: + 148 for i in range(nrw): + 149 nfct.append(1) + 150 + 151 nsrc = [] + 152 for i in range(nrw): + 153 t = fp.read(4) + 154 nsrc.append(struct.unpack('i', t)[0]) + 155 if version == '2.0': + 156 if not struct.unpack('i', fp.read(4))[0] == 0: + 157 print('something is wrong!') + 158 + 159 configlist.append([]) + 160 while True: + 161 t = fp.read(4) + 162 if len(t) < 4: + 163 break + 164 config_no = struct.unpack('i', t)[0] + 165 configlist[-1].append(config_no) + 166 for i in range(nrw): + 167 if (version == '2.0'): + 168 tmpd = _read_array_openQCD2(fp) + 169 tmpd = _read_array_openQCD2(fp) + 170 tmp_rw = tmpd['arr'] + 171 tmp_nfct = 1.0 + 172 for j in range(tmpd['n'][0]): + 173 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j]))) + 174 if print_err: + 175 print(config_no, i, j, + 176 np.mean(np.exp(-np.asarray(tmp_rw[j]))), + 177 np.std(np.exp(-np.asarray(tmp_rw[j])))) + 178 print('Sources:', + 179 np.exp(-np.asarray(tmp_rw[j]))) + 180 print('Partial factor:', tmp_nfct) + 181 elif version == '1.6' or version == '1.4': + 182 tmp_nfct = 1.0 + 183 for j in range(nfct[i]): + 184 t = fp.read(8 * nsrc[i]) + 185 t = fp.read(8 * nsrc[i]) + 186 tmp_rw = struct.unpack('d' * nsrc[i], t) + 187 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw))) + 188 if print_err: + 189 print(config_no, i, j, + 190 np.mean(np.exp(-np.asarray(tmp_rw))), + 191 np.std(np.exp(-np.asarray(tmp_rw)))) + 192 print('Sources:', np.exp(-np.asarray(tmp_rw))) + 193 print('Partial factor:', tmp_nfct) + 194 tmp_array[i].append(tmp_nfct) + 195 + 196 diffmeas = configlist[-1][-1] - configlist[-1][-2] + 197 configlist[-1] = [item // diffmeas for item in configlist[-1]] + 198 if configlist[-1][0] > 1 and diffmeas > 1: + 199 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') + 200 offset = configlist[-1][0] - 1 + 201 configlist[-1] = [item - offset for item in configlist[-1]] + 202 + 203 if r_start[rep] is None: + 204 r_start_index.append(0) + 205 else: + 206 try: + 207 r_start_index.append(configlist[-1].index(r_start[rep])) + 208 except ValueError: + 209 raise Exception('Config %d not in file with range [%d, %d]' % ( + 210 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None + 211 + 212 if r_stop[rep] is None: + 213 r_stop_index.append(len(configlist[-1]) - 1) + 214 else: + 215 try: + 216 r_stop_index.append(configlist[-1].index(r_stop[rep])) + 217 except ValueError: + 218 raise Exception('Config %d not in file with range [%d, %d]' % ( + 219 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None + 220 + 221 for k in range(nrw): + 222 deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step]) + 223 + 224 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): + 225 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) + 226 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] + 227 if np.any([step != 1 for step in stepsizes]): + 228 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) + 229 + 230 print(',', nrw, 'reweighting factors with', nsrc, 'sources') + 231 result = [] + 232 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] 233 - 234def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs): - 235 """Extract t0 from given .ms.dat files. Returns t0 as Obs. - 236 - 237 It is assumed that all boundary effects have - 238 sufficiently decayed at x0=xmin. - 239 The data around the zero crossing of t^2<E> - 0.3 - 240 is fitted with a linear function - 241 from which the exact root is extracted. - 242 - 243 It is assumed that one measurement is performed for each config. - 244 If this is not the case, the resulting idl, as well as the handling - 245 of r_start, r_stop and r_step is wrong and the user has to correct - 246 this in the resulting observable. + 234 for t in range(nrw): + 235 result.append(Obs(deltas[t], rep_names, idl=idl)) + 236 return result + 237 + 238 + 239def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs): + 240 """Extract t0 from given .ms.dat files. Returns t0 as Obs. + 241 + 242 It is assumed that all boundary effects have + 243 sufficiently decayed at x0=xmin. + 244 The data around the zero crossing of t^2<E> - 0.3 + 245 is fitted with a linear function + 246 from which the exact root is extracted. 247 - 248 Parameters - 249 ---------- - 250 path : str - 251 Path to .ms.dat files - 252 prefix : str - 253 Ensemble prefix - 254 dtr_read : int - 255 Determines how many trajectories should be skipped - 256 when reading the ms.dat files. - 257 Corresponds to dtr_cnfg / dtr_ms in the openQCD input file. - 258 xmin : int - 259 First timeslice where the boundary - 260 effects have sufficiently decayed. - 261 spatial_extent : int - 262 spatial extent of the lattice, required for normalization. - 263 fit_range : int - 264 Number of data points left and right of the zero - 265 crossing to be included in the linear fit. (Default: 5) - 266 r_start : list - 267 list which contains the first config to be read for each replicum. - 268 r_stop : list - 269 list which contains the last config to be read for each replicum. - 270 r_step : int - 271 integer that defines a fixed step size between two measurements (in units of configs) - 272 If not given, r_step=1 is assumed. - 273 plaquette : bool - 274 If true extract the plaquette estimate of t0 instead. - 275 names : list - 276 list of names that is assigned to the data according according - 277 to the order in the file list. Use careful, if you do not provide file names! - 278 files : list - 279 list which contains the filenames to be read. No automatic detection of - 280 files performed if given. - 281 plot_fit : bool - 282 If true, the fit for the extraction of t0 is shown together with the data. - 283 assume_thermalization : bool - 284 If True: If the first record divided by the distance between two measurements is larger than - 285 1, it is assumed that this is due to thermalization and the first measurement belongs - 286 to the first config (default). - 287 If False: The config numbers are assumed to be traj_number // difference - 288 """ - 289 - 290 ls = [] - 291 for (dirpath, dirnames, filenames) in os.walk(path): - 292 ls.extend(filenames) - 293 break - 294 - 295 if not ls: - 296 raise Exception('Error, directory not found') - 297 - 298 if 'files' in kwargs: - 299 ls = kwargs.get('files') - 300 else: - 301 for exc in ls: - 302 if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'): - 303 ls = list(set(ls) - set([exc])) - 304 if len(ls) > 1: - 305 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) - 306 replica = len(ls) + 248 It is assumed that one measurement is performed for each config. + 249 If this is not the case, the resulting idl, as well as the handling + 250 of r_start, r_stop and r_step is wrong and the user has to correct + 251 this in the resulting observable. + 252 + 253 Parameters + 254 ---------- + 255 path : str + 256 Path to .ms.dat files + 257 prefix : str + 258 Ensemble prefix + 259 dtr_read : int + 260 Determines how many trajectories should be skipped + 261 when reading the ms.dat files. + 262 Corresponds to dtr_cnfg / dtr_ms in the openQCD input file. + 263 xmin : int + 264 First timeslice where the boundary + 265 effects have sufficiently decayed. + 266 spatial_extent : int + 267 spatial extent of the lattice, required for normalization. + 268 fit_range : int + 269 Number of data points left and right of the zero + 270 crossing to be included in the linear fit. (Default: 5) + 271 r_start : list + 272 list which contains the first config to be read for each replicum. + 273 r_stop : list + 274 list which contains the last config to be read for each replicum. + 275 r_step : int + 276 integer that defines a fixed step size between two measurements (in units of configs) + 277 If not given, r_step=1 is assumed. + 278 plaquette : bool + 279 If true extract the plaquette estimate of t0 instead. + 280 names : list + 281 list of names that is assigned to the data according according + 282 to the order in the file list. Use careful, if you do not provide file names! + 283 files : list + 284 list which contains the filenames to be read. No automatic detection of + 285 files performed if given. + 286 plot_fit : bool + 287 If true, the fit for the extraction of t0 is shown together with the data. + 288 assume_thermalization : bool + 289 If True: If the first record divided by the distance between two measurements is larger than + 290 1, it is assumed that this is due to thermalization and the first measurement belongs + 291 to the first config (default). + 292 If False: The config numbers are assumed to be traj_number // difference + 293 + 294 Returns + 295 ------- + 296 t0 : Obs + 297 Extracted t0 + 298 """ + 299 + 300 ls = [] + 301 for (dirpath, dirnames, filenames) in os.walk(path): + 302 ls.extend(filenames) + 303 break + 304 + 305 if not ls: + 306 raise Exception('Error, directory not found') 307 - 308 if 'r_start' in kwargs: - 309 r_start = kwargs.get('r_start') - 310 if len(r_start) != replica: - 311 raise Exception('r_start does not match number of replicas') - 312 r_start = [o if o else None for o in r_start] - 313 else: - 314 r_start = [None] * replica - 315 - 316 if 'r_stop' in kwargs: - 317 r_stop = kwargs.get('r_stop') - 318 if len(r_stop) != replica: - 319 raise Exception('r_stop does not match number of replicas') - 320 else: - 321 r_stop = [None] * replica - 322 - 323 if 'r_step' in kwargs: - 324 r_step = kwargs.get('r_step') - 325 else: - 326 r_step = 1 - 327 - 328 print('Extract t0 from', prefix, ',', replica, 'replica') - 329 - 330 if 'names' in kwargs: - 331 rep_names = kwargs.get('names') - 332 else: - 333 rep_names = [] - 334 for entry in ls: - 335 truncated_entry = entry.split('.')[0] - 336 idx = truncated_entry.index('r') - 337 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) - 338 - 339 Ysum = [] - 340 - 341 configlist = [] - 342 r_start_index = [] - 343 r_stop_index = [] - 344 - 345 for rep in range(replica): - 346 - 347 with open(path + '/' + ls[rep], 'rb') as fp: - 348 t = fp.read(12) - 349 header = struct.unpack('iii', t) - 350 if rep == 0: - 351 dn = header[0] - 352 nn = header[1] - 353 tmax = header[2] - 354 elif dn != header[0] or nn != header[1] or tmax != header[2]: - 355 raise Exception('Replica parameters do not match.') + 308 if 'files' in kwargs: + 309 ls = kwargs.get('files') + 310 else: + 311 for exc in ls: + 312 if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'): + 313 ls = list(set(ls) - set([exc])) + 314 if len(ls) > 1: + 315 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) + 316 replica = len(ls) + 317 + 318 if 'r_start' in kwargs: + 319 r_start = kwargs.get('r_start') + 320 if len(r_start) != replica: + 321 raise Exception('r_start does not match number of replicas') + 322 r_start = [o if o else None for o in r_start] + 323 else: + 324 r_start = [None] * replica + 325 + 326 if 'r_stop' in kwargs: + 327 r_stop = kwargs.get('r_stop') + 328 if len(r_stop) != replica: + 329 raise Exception('r_stop does not match number of replicas') + 330 else: + 331 r_stop = [None] * replica + 332 + 333 if 'r_step' in kwargs: + 334 r_step = kwargs.get('r_step') + 335 else: + 336 r_step = 1 + 337 + 338 print('Extract t0 from', prefix, ',', replica, 'replica') + 339 + 340 if 'names' in kwargs: + 341 rep_names = kwargs.get('names') + 342 else: + 343 rep_names = [] + 344 for entry in ls: + 345 truncated_entry = entry.split('.')[0] + 346 idx = truncated_entry.index('r') + 347 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) + 348 + 349 Ysum = [] + 350 + 351 configlist = [] + 352 r_start_index = [] + 353 r_stop_index = [] + 354 + 355 for rep in range(replica): 356 - 357 t = fp.read(8) - 358 if rep == 0: - 359 eps = struct.unpack('d', t)[0] - 360 print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps) - 361 elif eps != struct.unpack('d', t)[0]: - 362 raise Exception('Values for eps do not match among replica.') - 363 - 364 Ysl = [] - 365 - 366 configlist.append([]) - 367 while True: - 368 t = fp.read(4) - 369 if (len(t) < 4): - 370 break - 371 nc = struct.unpack('i', t)[0] - 372 configlist[-1].append(nc) + 357 with open(path + '/' + ls[rep], 'rb') as fp: + 358 t = fp.read(12) + 359 header = struct.unpack('iii', t) + 360 if rep == 0: + 361 dn = header[0] + 362 nn = header[1] + 363 tmax = header[2] + 364 elif dn != header[0] or nn != header[1] or tmax != header[2]: + 365 raise Exception('Replica parameters do not match.') + 366 + 367 t = fp.read(8) + 368 if rep == 0: + 369 eps = struct.unpack('d', t)[0] + 370 print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps) + 371 elif eps != struct.unpack('d', t)[0]: + 372 raise Exception('Values for eps do not match among replica.') 373 - 374 t = fp.read(8 * tmax * (nn + 1)) - 375 if kwargs.get('plaquette'): - 376 if nc % dtr_read == 0: - 377 Ysl.append(struct.unpack('d' * tmax * (nn + 1), t)) - 378 t = fp.read(8 * tmax * (nn + 1)) - 379 if not kwargs.get('plaquette'): - 380 if nc % dtr_read == 0: - 381 Ysl.append(struct.unpack('d' * tmax * (nn + 1), t)) - 382 t = fp.read(8 * tmax * (nn + 1)) + 374 Ysl = [] + 375 + 376 configlist.append([]) + 377 while True: + 378 t = fp.read(4) + 379 if (len(t) < 4): + 380 break + 381 nc = struct.unpack('i', t)[0] + 382 configlist[-1].append(nc) 383 - 384 Ysum.append([]) - 385 for i, item in enumerate(Ysl): - 386 Ysum[-1].append([np.mean(item[current + xmin: - 387 current + tmax - xmin]) - 388 for current in range(0, len(item), tmax)]) - 389 - 390 diffmeas = configlist[-1][-1] - configlist[-1][-2] - 391 configlist[-1] = [item // diffmeas for item in configlist[-1]] - 392 if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1: - 393 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') - 394 offset = configlist[-1][0] - 1 - 395 configlist[-1] = [item - offset for item in configlist[-1]] - 396 - 397 if r_start[rep] is None: - 398 r_start_index.append(0) - 399 else: - 400 try: - 401 r_start_index.append(configlist[-1].index(r_start[rep])) - 402 except ValueError: - 403 raise Exception('Config %d not in file with range [%d, %d]' % ( - 404 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None - 405 - 406 if r_stop[rep] is None: - 407 r_stop_index.append(len(configlist[-1]) - 1) - 408 else: - 409 try: - 410 r_stop_index.append(configlist[-1].index(r_stop[rep])) - 411 except ValueError: - 412 raise Exception('Config %d not in file with range [%d, %d]' % ( - 413 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None - 414 - 415 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): - 416 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) - 417 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] - 418 if np.any([step != 1 for step in stepsizes]): - 419 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) - 420 - 421 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] - 422 t2E_dict = {} - 423 for n in range(nn + 1): - 424 samples = [] - 425 for nrep, rep in enumerate(Ysum): - 426 samples.append([]) - 427 for cnfg in rep: - 428 samples[-1].append(cnfg[n]) - 429 samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step] - 430 new_obs = Obs(samples, rep_names, idl=idl) - 431 t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3 - 432 - 433 zero_crossing = np.argmax(np.array( - 434 [o.value for o in t2E_dict.values()]) > 0.0) - 435 - 436 x = list(t2E_dict.keys())[zero_crossing - fit_range: - 437 zero_crossing + fit_range] - 438 y = list(t2E_dict.values())[zero_crossing - fit_range: - 439 zero_crossing + fit_range] - 440 [o.gamma_method() for o in y] - 441 - 442 fit_result = fit_lin(x, y) - 443 - 444 if kwargs.get('plot_fit'): - 445 plt.figure() - 446 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0) - 447 ax0 = plt.subplot(gs[0]) - 448 xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2] - 449 ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2] - 450 [o.gamma_method() for o in ymore] - 451 ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x') - 452 xplot = np.linspace(np.min(x), np.max(x)) - 453 yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot] - 454 [yi.gamma_method() for yi in yplot] - 455 ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot]) - 456 retval = (-fit_result[0] / fit_result[1]) - 457 retval.gamma_method() - 458 ylim = ax0.get_ylim() - 459 ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4) - 460 ax0.set_ylim(ylim) - 461 ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $') - 462 xlim = ax0.get_xlim() - 463 - 464 fit_res = [fit_result[0] + fit_result[1] * xi for xi in x] - 465 residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y]) - 466 ax1 = plt.subplot(gs[1]) - 467 ax1.plot(x, residuals, 'ko', ls='none', markersize=5) - 468 ax1.tick_params(direction='out') - 469 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True) - 470 ax1.axhline(y=0.0, ls='--', color='k') - 471 ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k') - 472 ax1.set_xlim(xlim) - 473 ax1.set_ylabel('Residuals') - 474 ax1.set_xlabel(r'$t/a^2$') - 475 - 476 plt.draw() - 477 return -fit_result[0] / fit_result[1] - 478 - 479 - 480def _parse_array_openQCD2(d, n, size, wa, quadrupel=False): - 481 arr = [] - 482 if d == 2: - 483 for i in range(n[0]): - 484 tmp = wa[i * n[1]:(i + 1) * n[1]] - 485 if quadrupel: - 486 tmp2 = [] - 487 for j in range(0, len(tmp), 2): - 488 tmp2.append(tmp[j]) - 489 arr.append(tmp2) - 490 else: - 491 arr.append(np.asarray(tmp)) - 492 - 493 else: - 494 raise Exception('Only two-dimensional arrays supported!') - 495 - 496 return arr - 497 - 498 - 499def _read_array_openQCD2(fp): - 500 t = fp.read(4) - 501 d = struct.unpack('i', t)[0] - 502 t = fp.read(4 * d) - 503 n = struct.unpack('%di' % (d), t) - 504 t = fp.read(4) - 505 size = struct.unpack('i', t)[0] - 506 if size == 4: - 507 types = 'i' - 508 elif size == 8: - 509 types = 'd' - 510 elif size == 16: - 511 types = 'dd' - 512 else: - 513 raise Exception("Type for size '" + str(size) + "' not known.") - 514 m = n[0] - 515 for i in range(1, d): - 516 m *= n[i] - 517 - 518 t = fp.read(m * size) - 519 tmp = struct.unpack('%d%s' % (m, types), t) - 520 - 521 arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True) - 522 return {'d': d, 'n': n, 'size': size, 'arr': arr} - 523 - 524 - 525def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs): - 526 """Read the topologial charge based on openQCD gradient flow measurements. + 384 t = fp.read(8 * tmax * (nn + 1)) + 385 if kwargs.get('plaquette'): + 386 if nc % dtr_read == 0: + 387 Ysl.append(struct.unpack('d' * tmax * (nn + 1), t)) + 388 t = fp.read(8 * tmax * (nn + 1)) + 389 if not kwargs.get('plaquette'): + 390 if nc % dtr_read == 0: + 391 Ysl.append(struct.unpack('d' * tmax * (nn + 1), t)) + 392 t = fp.read(8 * tmax * (nn + 1)) + 393 + 394 Ysum.append([]) + 395 for i, item in enumerate(Ysl): + 396 Ysum[-1].append([np.mean(item[current + xmin: + 397 current + tmax - xmin]) + 398 for current in range(0, len(item), tmax)]) + 399 + 400 diffmeas = configlist[-1][-1] - configlist[-1][-2] + 401 configlist[-1] = [item // diffmeas for item in configlist[-1]] + 402 if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1: + 403 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') + 404 offset = configlist[-1][0] - 1 + 405 configlist[-1] = [item - offset for item in configlist[-1]] + 406 + 407 if r_start[rep] is None: + 408 r_start_index.append(0) + 409 else: + 410 try: + 411 r_start_index.append(configlist[-1].index(r_start[rep])) + 412 except ValueError: + 413 raise Exception('Config %d not in file with range [%d, %d]' % ( + 414 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None + 415 + 416 if r_stop[rep] is None: + 417 r_stop_index.append(len(configlist[-1]) - 1) + 418 else: + 419 try: + 420 r_stop_index.append(configlist[-1].index(r_stop[rep])) + 421 except ValueError: + 422 raise Exception('Config %d not in file with range [%d, %d]' % ( + 423 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None + 424 + 425 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): + 426 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) + 427 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] + 428 if np.any([step != 1 for step in stepsizes]): + 429 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) + 430 + 431 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] + 432 t2E_dict = {} + 433 for n in range(nn + 1): + 434 samples = [] + 435 for nrep, rep in enumerate(Ysum): + 436 samples.append([]) + 437 for cnfg in rep: + 438 samples[-1].append(cnfg[n]) + 439 samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step] + 440 new_obs = Obs(samples, rep_names, idl=idl) + 441 t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3 + 442 + 443 zero_crossing = np.argmax(np.array( + 444 [o.value for o in t2E_dict.values()]) > 0.0) + 445 + 446 x = list(t2E_dict.keys())[zero_crossing - fit_range: + 447 zero_crossing + fit_range] + 448 y = list(t2E_dict.values())[zero_crossing - fit_range: + 449 zero_crossing + fit_range] + 450 [o.gamma_method() for o in y] + 451 + 452 fit_result = fit_lin(x, y) + 453 + 454 if kwargs.get('plot_fit'): + 455 plt.figure() + 456 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0) + 457 ax0 = plt.subplot(gs[0]) + 458 xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2] + 459 ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2] + 460 [o.gamma_method() for o in ymore] + 461 ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x') + 462 xplot = np.linspace(np.min(x), np.max(x)) + 463 yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot] + 464 [yi.gamma_method() for yi in yplot] + 465 ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot]) + 466 retval = (-fit_result[0] / fit_result[1]) + 467 retval.gamma_method() + 468 ylim = ax0.get_ylim() + 469 ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4) + 470 ax0.set_ylim(ylim) + 471 ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $') + 472 xlim = ax0.get_xlim() + 473 + 474 fit_res = [fit_result[0] + fit_result[1] * xi for xi in x] + 475 residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y]) + 476 ax1 = plt.subplot(gs[1]) + 477 ax1.plot(x, residuals, 'ko', ls='none', markersize=5) + 478 ax1.tick_params(direction='out') + 479 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True) + 480 ax1.axhline(y=0.0, ls='--', color='k') + 481 ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k') + 482 ax1.set_xlim(xlim) + 483 ax1.set_ylabel('Residuals') + 484 ax1.set_xlabel(r'$t/a^2$') + 485 + 486 plt.draw() + 487 return -fit_result[0] / fit_result[1] + 488 + 489 + 490def _parse_array_openQCD2(d, n, size, wa, quadrupel=False): + 491 arr = [] + 492 if d == 2: + 493 for i in range(n[0]): + 494 tmp = wa[i * n[1]:(i + 1) * n[1]] + 495 if quadrupel: + 496 tmp2 = [] + 497 for j in range(0, len(tmp), 2): + 498 tmp2.append(tmp[j]) + 499 arr.append(tmp2) + 500 else: + 501 arr.append(np.asarray(tmp)) + 502 + 503 else: + 504 raise Exception('Only two-dimensional arrays supported!') + 505 + 506 return arr + 507 + 508 + 509def _read_array_openQCD2(fp): + 510 t = fp.read(4) + 511 d = struct.unpack('i', t)[0] + 512 t = fp.read(4 * d) + 513 n = struct.unpack('%di' % (d), t) + 514 t = fp.read(4) + 515 size = struct.unpack('i', t)[0] + 516 if size == 4: + 517 types = 'i' + 518 elif size == 8: + 519 types = 'd' + 520 elif size == 16: + 521 types = 'dd' + 522 else: + 523 raise Exception("Type for size '" + str(size) + "' not known.") + 524 m = n[0] + 525 for i in range(1, d): + 526 m *= n[i] 527 - 528 Parameters - 529 ---------- - 530 path : str - 531 path of the measurement files - 532 prefix : str - 533 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. - 534 Ignored if file names are passed explicitly via keyword files. - 535 c : double - 536 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. - 537 dtr_cnfg : int - 538 (optional) parameter that specifies the number of measurements - 539 between two configs. - 540 If it is not set, the distance between two measurements - 541 in the file is assumed to be the distance between two configurations. - 542 steps : int - 543 (optional) Distance between two configurations in units of trajectories / - 544 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given - 545 version : str - 546 Either openQCD or sfqcd, depending on the data. - 547 L : int - 548 spatial length of the lattice in L/a. - 549 HAS to be set if version != sfqcd, since openQCD does not provide - 550 this in the header - 551 r_start : list - 552 list which contains the first config to be read for each replicum. - 553 r_stop : list - 554 list which contains the last config to be read for each replicum. - 555 files : list - 556 specify the exact files that need to be read - 557 from path, practical if e.g. only one replicum is needed - 558 postfix : str - 559 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files - 560 names : list - 561 Alternative labeling for replicas/ensembles. - 562 Has to have the appropriate length. - 563 Zeuthen_flow : bool - 564 (optional) If True, the Zeuthen flow is used for Qtop. Only possible - 565 for version=='sfqcd' If False, the Wilson flow is used. - 566 integer_charge : bool - 567 If True, the charge is rounded towards the nearest integer on each config. - 568 """ - 569 - 570 return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs) - 571 - 572 - 573def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs): - 574 """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details. - 575 - 576 Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step. - 577 - 578 Parameters - 579 ---------- - 580 path : str - 581 path of the measurement files - 582 prefix : str - 583 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. - 584 Ignored if file names are passed explicitly via keyword files. - 585 c : double - 586 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. - 587 dtr_cnfg : int - 588 (optional) parameter that specifies the number of measurements - 589 between two configs. - 590 If it is not set, the distance between two measurements - 591 in the file is assumed to be the distance between two configurations. - 592 steps : int - 593 (optional) Distance between two configurations in units of trajectories / - 594 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given - 595 r_start : list - 596 list which contains the first config to be read for each replicum. - 597 r_stop : list - 598 list which contains the last config to be read for each replicum. - 599 files : list - 600 specify the exact files that need to be read - 601 from path, practical if e.g. only one replicum is needed - 602 names : list - 603 Alternative labeling for replicas/ensembles. - 604 Has to have the appropriate length. - 605 postfix : str - 606 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files - 607 Zeuthen_flow : bool - 608 (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used. - 609 """ - 610 - 611 if c != 0.3: - 612 raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.") - 613 - 614 plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs) - 615 C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs) - 616 L = plaq.tag["L"] - 617 T = plaq.tag["T"] - 618 - 619 if T != L: - 620 raise Exception("The required lattice norm is only implemented for T=L at the moment.") - 621 - 622 if Zeuthen_flow is not True: - 623 raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.") - 624 - 625 t = (c * L) ** 2 / 8 - 626 - 627 normdict = {4: 0.012341170468270, - 628 6: 0.010162691462430, - 629 8: 0.009031614807931, - 630 10: 0.008744966371393, - 631 12: 0.008650917856809, - 632 14: 8.611154391267955E-03, - 633 16: 0.008591758449508, - 634 20: 0.008575359627103, - 635 24: 0.008569387847540, - 636 28: 8.566803713382559E-03, - 637 32: 0.008565541650006, - 638 40: 8.564480684962046E-03, - 639 48: 8.564098025073460E-03, - 640 64: 8.563853943383087E-03} + 528 t = fp.read(m * size) + 529 tmp = struct.unpack('%d%s' % (m, types), t) + 530 + 531 arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True) + 532 return {'d': d, 'n': n, 'size': size, 'arr': arr} + 533 + 534 + 535def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs): + 536 """Read the topologial charge based on openQCD gradient flow measurements. + 537 + 538 Parameters + 539 ---------- + 540 path : str + 541 path of the measurement files + 542 prefix : str + 543 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. + 544 Ignored if file names are passed explicitly via keyword files. + 545 c : double + 546 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. + 547 dtr_cnfg : int + 548 (optional) parameter that specifies the number of measurements + 549 between two configs. + 550 If it is not set, the distance between two measurements + 551 in the file is assumed to be the distance between two configurations. + 552 steps : int + 553 (optional) Distance between two configurations in units of trajectories / + 554 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given + 555 version : str + 556 Either openQCD or sfqcd, depending on the data. + 557 L : int + 558 spatial length of the lattice in L/a. + 559 HAS to be set if version != sfqcd, since openQCD does not provide + 560 this in the header + 561 r_start : list + 562 list which contains the first config to be read for each replicum. + 563 r_stop : list + 564 list which contains the last config to be read for each replicum. + 565 files : list + 566 specify the exact files that need to be read + 567 from path, practical if e.g. only one replicum is needed + 568 postfix : str + 569 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files + 570 names : list + 571 Alternative labeling for replicas/ensembles. + 572 Has to have the appropriate length. + 573 Zeuthen_flow : bool + 574 (optional) If True, the Zeuthen flow is used for Qtop. Only possible + 575 for version=='sfqcd' If False, the Wilson flow is used. + 576 integer_charge : bool + 577 If True, the charge is rounded towards the nearest integer on each config. + 578 + 579 Returns + 580 ------- + 581 result : Obs + 582 Read topological charge + 583 """ + 584 + 585 return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs) + 586 + 587 + 588def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs): + 589 """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details. + 590 + 591 Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step. + 592 + 593 Parameters + 594 ---------- + 595 path : str + 596 path of the measurement files + 597 prefix : str + 598 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. + 599 Ignored if file names are passed explicitly via keyword files. + 600 c : double + 601 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. + 602 dtr_cnfg : int + 603 (optional) parameter that specifies the number of measurements + 604 between two configs. + 605 If it is not set, the distance between two measurements + 606 in the file is assumed to be the distance between two configurations. + 607 steps : int + 608 (optional) Distance between two configurations in units of trajectories / + 609 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given + 610 r_start : list + 611 list which contains the first config to be read for each replicum. + 612 r_stop : list + 613 list which contains the last config to be read for each replicum. + 614 files : list + 615 specify the exact files that need to be read + 616 from path, practical if e.g. only one replicum is needed + 617 names : list + 618 Alternative labeling for replicas/ensembles. + 619 Has to have the appropriate length. + 620 postfix : str + 621 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files + 622 Zeuthen_flow : bool + 623 (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used. + 624 """ + 625 + 626 if c != 0.3: + 627 raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.") + 628 + 629 plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs) + 630 C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs) + 631 L = plaq.tag["L"] + 632 T = plaq.tag["T"] + 633 + 634 if T != L: + 635 raise Exception("The required lattice norm is only implemented for T=L at the moment.") + 636 + 637 if Zeuthen_flow is not True: + 638 raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.") + 639 + 640 t = (c * L) ** 2 / 8 641 - 642 return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L] - 643 - 644 - 645def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs): - 646 """Read a flow observable based on openQCD gradient flow measurements. - 647 - 648 Parameters - 649 ---------- - 650 path : str - 651 path of the measurement files - 652 prefix : str - 653 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. - 654 Ignored if file names are passed explicitly via keyword files. - 655 c : double - 656 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. - 657 dtr_cnfg : int - 658 (optional) parameter that specifies the number of measurements - 659 between two configs. - 660 If it is not set, the distance between two measurements - 661 in the file is assumed to be the distance between two configurations. - 662 steps : int - 663 (optional) Distance between two configurations in units of trajectories / - 664 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given - 665 version : str - 666 Either openQCD or sfqcd, depending on the data. - 667 obspos : int - 668 position of the obeservable in the measurement file. Only relevant for sfqcd files. - 669 sum_t : bool - 670 If true sum over all timeslices, if false only take the value at T/2. - 671 L : int - 672 spatial length of the lattice in L/a. - 673 HAS to be set if version != sfqcd, since openQCD does not provide - 674 this in the header - 675 r_start : list - 676 list which contains the first config to be read for each replicum. - 677 r_stop : list - 678 list which contains the last config to be read for each replicum. - 679 files : list - 680 specify the exact files that need to be read - 681 from path, practical if e.g. only one replicum is needed - 682 names : list - 683 Alternative labeling for replicas/ensembles. - 684 Has to have the appropriate length. - 685 postfix : str - 686 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files - 687 Zeuthen_flow : bool - 688 (optional) If True, the Zeuthen flow is used for Qtop. Only possible - 689 for version=='sfqcd' If False, the Wilson flow is used. - 690 integer_charge : bool - 691 If True, the charge is rounded towards the nearest integer on each config. - 692 """ - 693 known_versions = ["openQCD", "sfqcd"] - 694 - 695 if version not in known_versions: - 696 raise Exception("Unknown openQCD version.") - 697 if "steps" in kwargs: - 698 steps = kwargs.get("steps") - 699 if version == "sfqcd": - 700 if "L" in kwargs: - 701 supposed_L = kwargs.get("L") - 702 else: - 703 supposed_L = None - 704 postfix = ".gfms.dat" - 705 else: - 706 if "L" not in kwargs: - 707 raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.") - 708 else: - 709 L = kwargs.get("L") - 710 postfix = ".ms.dat" - 711 - 712 if "postfix" in kwargs: - 713 postfix = kwargs.get("postfix") + 642 normdict = {4: 0.012341170468270, + 643 6: 0.010162691462430, + 644 8: 0.009031614807931, + 645 10: 0.008744966371393, + 646 12: 0.008650917856809, + 647 14: 8.611154391267955E-03, + 648 16: 0.008591758449508, + 649 20: 0.008575359627103, + 650 24: 0.008569387847540, + 651 28: 8.566803713382559E-03, + 652 32: 0.008565541650006, + 653 40: 8.564480684962046E-03, + 654 48: 8.564098025073460E-03, + 655 64: 8.563853943383087E-03} + 656 + 657 return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L] + 658 + 659 + 660def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs): + 661 """Read a flow observable based on openQCD gradient flow measurements. + 662 + 663 Parameters + 664 ---------- + 665 path : str + 666 path of the measurement files + 667 prefix : str + 668 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat. + 669 Ignored if file names are passed explicitly via keyword files. + 670 c : double + 671 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L. + 672 dtr_cnfg : int + 673 (optional) parameter that specifies the number of measurements + 674 between two configs. + 675 If it is not set, the distance between two measurements + 676 in the file is assumed to be the distance between two configurations. + 677 steps : int + 678 (optional) Distance between two configurations in units of trajectories / + 679 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given + 680 version : str + 681 Either openQCD or sfqcd, depending on the data. + 682 obspos : int + 683 position of the obeservable in the measurement file. Only relevant for sfqcd files. + 684 sum_t : bool + 685 If true sum over all timeslices, if false only take the value at T/2. + 686 L : int + 687 spatial length of the lattice in L/a. + 688 HAS to be set if version != sfqcd, since openQCD does not provide + 689 this in the header + 690 r_start : list + 691 list which contains the first config to be read for each replicum. + 692 r_stop : list + 693 list which contains the last config to be read for each replicum. + 694 files : list + 695 specify the exact files that need to be read + 696 from path, practical if e.g. only one replicum is needed + 697 names : list + 698 Alternative labeling for replicas/ensembles. + 699 Has to have the appropriate length. + 700 postfix : str + 701 postfix of the file to read, e.g. '.gfms.dat' for openQCD-files + 702 Zeuthen_flow : bool + 703 (optional) If True, the Zeuthen flow is used for Qtop. Only possible + 704 for version=='sfqcd' If False, the Wilson flow is used. + 705 integer_charge : bool + 706 If True, the charge is rounded towards the nearest integer on each config. + 707 + 708 Returns + 709 ------- + 710 result : Obs + 711 flow observable specified + 712 """ + 713 known_versions = ["openQCD", "sfqcd"] 714 - 715 if "files" in kwargs: - 716 files = kwargs.get("files") - 717 postfix = '' - 718 else: - 719 found = [] - 720 files = [] - 721 for (dirpath, dirnames, filenames) in os.walk(path + "/"): - 722 found.extend(filenames) - 723 break - 724 for f in found: - 725 if fnmatch.fnmatch(f, prefix + "*" + postfix): - 726 files.append(f) - 727 - 728 files = sorted(files) - 729 - 730 if 'r_start' in kwargs: - 731 r_start = kwargs.get('r_start') - 732 if len(r_start) != len(files): - 733 raise Exception('r_start does not match number of replicas') - 734 r_start = [o if o else None for o in r_start] - 735 else: - 736 r_start = [None] * len(files) - 737 - 738 if 'r_stop' in kwargs: - 739 r_stop = kwargs.get('r_stop') - 740 if len(r_stop) != len(files): - 741 raise Exception('r_stop does not match number of replicas') - 742 else: - 743 r_stop = [None] * len(files) - 744 rep_names = [] - 745 - 746 zeuthen = kwargs.get('Zeuthen_flow', False) - 747 if zeuthen and version not in ['sfqcd']: - 748 raise Exception('Zeuthen flow can only be used for version==sfqcd') + 715 if version not in known_versions: + 716 raise Exception("Unknown openQCD version.") + 717 if "steps" in kwargs: + 718 steps = kwargs.get("steps") + 719 if version == "sfqcd": + 720 if "L" in kwargs: + 721 supposed_L = kwargs.get("L") + 722 else: + 723 supposed_L = None + 724 postfix = ".gfms.dat" + 725 else: + 726 if "L" not in kwargs: + 727 raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.") + 728 else: + 729 L = kwargs.get("L") + 730 postfix = ".ms.dat" + 731 + 732 if "postfix" in kwargs: + 733 postfix = kwargs.get("postfix") + 734 + 735 if "files" in kwargs: + 736 files = kwargs.get("files") + 737 postfix = '' + 738 else: + 739 found = [] + 740 files = [] + 741 for (dirpath, dirnames, filenames) in os.walk(path + "/"): + 742 found.extend(filenames) + 743 break + 744 for f in found: + 745 if fnmatch.fnmatch(f, prefix + "*" + postfix): + 746 files.append(f) + 747 + 748 files = sorted(files) 749 - 750 r_start_index = [] - 751 r_stop_index = [] - 752 deltas = [] - 753 configlist = [] - 754 if not zeuthen: - 755 obspos += 8 - 756 for rep, file in enumerate(files): - 757 with open(path + "/" + file, "rb") as fp: - 758 - 759 Q = [] - 760 traj_list = [] - 761 if version in ['sfqcd']: - 762 t = fp.read(12) - 763 header = struct.unpack('<iii', t) - 764 zthfl = header[0] # Zeuthen flow -> if it's equal to 2 it means that the Zeuthen flow is also 'measured' (apart from the Wilson flow) - 765 ncs = header[1] # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's - 766 tmax = header[2] # lattice T/a - 767 - 768 t = fp.read(12) - 769 Ls = struct.unpack('<iii', t) - 770 if (Ls[0] == Ls[1] and Ls[1] == Ls[2]): - 771 L = Ls[0] - 772 if not (supposed_L == L) and supposed_L: - 773 raise Exception("It seems the length given in the header and by you contradict each other") - 774 else: - 775 raise Exception("Found more than one spatial length in header!") - 776 - 777 t = fp.read(16) - 778 header2 = struct.unpack('<dd', t) - 779 tol = header2[0] - 780 cmax = header2[1] # highest value of c used - 781 - 782 if c > cmax: - 783 raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol)) - 784 - 785 if (zthfl == 2): - 786 nfl = 2 # number of flows - 787 else: - 788 nfl = 1 - 789 iobs = 8 * nfl # number of flow observables calculated - 790 - 791 while True: - 792 t = fp.read(4) - 793 if (len(t) < 4): - 794 break - 795 traj_list.append(struct.unpack('i', t)[0]) # trajectory number when measurement was done + 750 if 'r_start' in kwargs: + 751 r_start = kwargs.get('r_start') + 752 if len(r_start) != len(files): + 753 raise Exception('r_start does not match number of replicas') + 754 r_start = [o if o else None for o in r_start] + 755 else: + 756 r_start = [None] * len(files) + 757 + 758 if 'r_stop' in kwargs: + 759 r_stop = kwargs.get('r_stop') + 760 if len(r_stop) != len(files): + 761 raise Exception('r_stop does not match number of replicas') + 762 else: + 763 r_stop = [None] * len(files) + 764 rep_names = [] + 765 + 766 zeuthen = kwargs.get('Zeuthen_flow', False) + 767 if zeuthen and version not in ['sfqcd']: + 768 raise Exception('Zeuthen flow can only be used for version==sfqcd') + 769 + 770 r_start_index = [] + 771 r_stop_index = [] + 772 deltas = [] + 773 configlist = [] + 774 if not zeuthen: + 775 obspos += 8 + 776 for rep, file in enumerate(files): + 777 with open(path + "/" + file, "rb") as fp: + 778 + 779 Q = [] + 780 traj_list = [] + 781 if version in ['sfqcd']: + 782 t = fp.read(12) + 783 header = struct.unpack('<iii', t) + 784 zthfl = header[0] # Zeuthen flow -> if it's equal to 2 it means that the Zeuthen flow is also 'measured' (apart from the Wilson flow) + 785 ncs = header[1] # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's + 786 tmax = header[2] # lattice T/a + 787 + 788 t = fp.read(12) + 789 Ls = struct.unpack('<iii', t) + 790 if (Ls[0] == Ls[1] and Ls[1] == Ls[2]): + 791 L = Ls[0] + 792 if not (supposed_L == L) and supposed_L: + 793 raise Exception("It seems the length given in the header and by you contradict each other") + 794 else: + 795 raise Exception("Found more than one spatial length in header!") 796 - 797 for j in range(ncs + 1): - 798 for i in range(iobs): - 799 t = fp.read(8 * tmax) - 800 if (i == obspos): # determines the flow observable -> i=0 <-> Zeuthen flow - 801 Q.append(struct.unpack('d' * tmax, t)) - 802 - 803 else: - 804 t = fp.read(12) - 805 header = struct.unpack('<iii', t) - 806 # step size in integration steps "dnms" - 807 dn = header[0] - 808 # number of measurements, so "ntot"/dn - 809 nn = header[1] - 810 # lattice T/a - 811 tmax = header[2] - 812 - 813 t = fp.read(8) - 814 eps = struct.unpack('d', t)[0] - 815 - 816 while True: - 817 t = fp.read(4) - 818 if (len(t) < 4): - 819 break - 820 traj_list.append(struct.unpack('i', t)[0]) - 821 # Wsl - 822 t = fp.read(8 * tmax * (nn + 1)) - 823 # Ysl - 824 t = fp.read(8 * tmax * (nn + 1)) - 825 # Qsl, which is asked for in this method - 826 t = fp.read(8 * tmax * (nn + 1)) - 827 # unpack the array of Qtops, - 828 # on each timeslice t=0,...,tmax-1 and the - 829 # measurement number in = 0...nn (see README.qcd1) - 830 tmpd = struct.unpack('d' * tmax * (nn + 1), t) - 831 Q.append(tmpd) + 797 t = fp.read(16) + 798 header2 = struct.unpack('<dd', t) + 799 tol = header2[0] + 800 cmax = header2[1] # highest value of c used + 801 + 802 if c > cmax: + 803 raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol)) + 804 + 805 if (zthfl == 2): + 806 nfl = 2 # number of flows + 807 else: + 808 nfl = 1 + 809 iobs = 8 * nfl # number of flow observables calculated + 810 + 811 while True: + 812 t = fp.read(4) + 813 if (len(t) < 4): + 814 break + 815 traj_list.append(struct.unpack('i', t)[0]) # trajectory number when measurement was done + 816 + 817 for j in range(ncs + 1): + 818 for i in range(iobs): + 819 t = fp.read(8 * tmax) + 820 if (i == obspos): # determines the flow observable -> i=0 <-> Zeuthen flow + 821 Q.append(struct.unpack('d' * tmax, t)) + 822 + 823 else: + 824 t = fp.read(12) + 825 header = struct.unpack('<iii', t) + 826 # step size in integration steps "dnms" + 827 dn = header[0] + 828 # number of measurements, so "ntot"/dn + 829 nn = header[1] + 830 # lattice T/a + 831 tmax = header[2] 832 - 833 if len(np.unique(np.diff(traj_list))) != 1: - 834 raise Exception("Irregularities in stepsize found") - 835 else: - 836 if 'steps' in kwargs: - 837 if steps != traj_list[1] - traj_list[0]: - 838 raise Exception("steps and the found stepsize are not the same") - 839 else: - 840 steps = traj_list[1] - traj_list[0] - 841 - 842 configlist.append([tr // steps // dtr_cnfg for tr in traj_list]) - 843 if configlist[-1][0] > 1: - 844 offset = configlist[-1][0] - 1 - 845 warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % ( - 846 offset, offset * steps)) - 847 configlist[-1] = [item - offset for item in configlist[-1]] - 848 - 849 if r_start[rep] is None: - 850 r_start_index.append(0) - 851 else: - 852 try: - 853 r_start_index.append(configlist[-1].index(r_start[rep])) - 854 except ValueError: - 855 raise Exception('Config %d not in file with range [%d, %d]' % ( - 856 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None - 857 - 858 if r_stop[rep] is None: - 859 r_stop_index.append(len(configlist[-1]) - 1) - 860 else: - 861 try: - 862 r_stop_index.append(configlist[-1].index(r_stop[rep])) - 863 except ValueError: - 864 raise Exception('Config %d not in file with range [%d, %d]' % ( - 865 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None - 866 - 867 if version in ['sfqcd']: - 868 cstepsize = cmax / ncs - 869 index_aim = round(c / cstepsize) - 870 else: - 871 t_aim = (c * L) ** 2 / 8 - 872 index_aim = round(t_aim / eps / dn) - 873 - 874 Q_sum = [] - 875 for i, item in enumerate(Q): - 876 if sum_t is True: - 877 Q_sum.append([sum(item[current:current + tmax]) - 878 for current in range(0, len(item), tmax)]) - 879 else: - 880 Q_sum.append([item[int(tmax / 2)]]) - 881 Q_top = [] - 882 if version in ['sfqcd']: - 883 for i in range(len(Q_sum) // (ncs + 1)): - 884 Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0]) - 885 else: - 886 for i in range(len(Q) // dtr_cnfg): - 887 Q_top.append(Q_sum[dtr_cnfg * i][index_aim]) - 888 if len(Q_top) != len(traj_list) // dtr_cnfg: - 889 raise Exception("qtops and traj_list dont have the same length") - 890 - 891 if kwargs.get('integer_charge', False): - 892 Q_top = [round(q) for q in Q_top] + 833 t = fp.read(8) + 834 eps = struct.unpack('d', t)[0] + 835 + 836 while True: + 837 t = fp.read(4) + 838 if (len(t) < 4): + 839 break + 840 traj_list.append(struct.unpack('i', t)[0]) + 841 # Wsl + 842 t = fp.read(8 * tmax * (nn + 1)) + 843 # Ysl + 844 t = fp.read(8 * tmax * (nn + 1)) + 845 # Qsl, which is asked for in this method + 846 t = fp.read(8 * tmax * (nn + 1)) + 847 # unpack the array of Qtops, + 848 # on each timeslice t=0,...,tmax-1 and the + 849 # measurement number in = 0...nn (see README.qcd1) + 850 tmpd = struct.unpack('d' * tmax * (nn + 1), t) + 851 Q.append(tmpd) + 852 + 853 if len(np.unique(np.diff(traj_list))) != 1: + 854 raise Exception("Irregularities in stepsize found") + 855 else: + 856 if 'steps' in kwargs: + 857 if steps != traj_list[1] - traj_list[0]: + 858 raise Exception("steps and the found stepsize are not the same") + 859 else: + 860 steps = traj_list[1] - traj_list[0] + 861 + 862 configlist.append([tr // steps // dtr_cnfg for tr in traj_list]) + 863 if configlist[-1][0] > 1: + 864 offset = configlist[-1][0] - 1 + 865 warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % ( + 866 offset, offset * steps)) + 867 configlist[-1] = [item - offset for item in configlist[-1]] + 868 + 869 if r_start[rep] is None: + 870 r_start_index.append(0) + 871 else: + 872 try: + 873 r_start_index.append(configlist[-1].index(r_start[rep])) + 874 except ValueError: + 875 raise Exception('Config %d not in file with range [%d, %d]' % ( + 876 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None + 877 + 878 if r_stop[rep] is None: + 879 r_stop_index.append(len(configlist[-1]) - 1) + 880 else: + 881 try: + 882 r_stop_index.append(configlist[-1].index(r_stop[rep])) + 883 except ValueError: + 884 raise Exception('Config %d not in file with range [%d, %d]' % ( + 885 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None + 886 + 887 if version in ['sfqcd']: + 888 cstepsize = cmax / ncs + 889 index_aim = round(c / cstepsize) + 890 else: + 891 t_aim = (c * L) ** 2 / 8 + 892 index_aim = round(t_aim / eps / dn) 893 - 894 truncated_file = file[:-len(postfix)] - 895 - 896 if "names" not in kwargs: - 897 try: - 898 idx = truncated_file.index('r') - 899 except Exception: - 900 if "names" not in kwargs: - 901 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") - 902 ens_name = truncated_file[:idx] - 903 rep_names.append(ens_name + '|' + truncated_file[idx:]) - 904 else: - 905 names = kwargs.get("names") - 906 rep_names = names - 907 deltas.append(Q_top) - 908 - 909 idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))] - 910 deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))] - 911 result = Obs(deltas, rep_names, idl=idl) - 912 result.tag = {"T": tmax - 1, - 913 "L": L} - 914 return result + 894 Q_sum = [] + 895 for i, item in enumerate(Q): + 896 if sum_t is True: + 897 Q_sum.append([sum(item[current:current + tmax]) + 898 for current in range(0, len(item), tmax)]) + 899 else: + 900 Q_sum.append([item[int(tmax / 2)]]) + 901 Q_top = [] + 902 if version in ['sfqcd']: + 903 for i in range(len(Q_sum) // (ncs + 1)): + 904 Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0]) + 905 else: + 906 for i in range(len(Q) // dtr_cnfg): + 907 Q_top.append(Q_sum[dtr_cnfg * i][index_aim]) + 908 if len(Q_top) != len(traj_list) // dtr_cnfg: + 909 raise Exception("qtops and traj_list dont have the same length") + 910 + 911 if kwargs.get('integer_charge', False): + 912 Q_top = [round(q) for q in Q_top] + 913 + 914 truncated_file = file[:-len(postfix)] 915 - 916 - 917def qtop_projection(qtop, target=0): - 918 """Returns the projection to the topological charge sector defined by target. - 919 - 920 Parameters - 921 ---------- - 922 path : Obs - 923 Topological charge. - 924 target : int - 925 Specifies the topological sector to be reweighted to (default 0) - 926 """ - 927 if qtop.reweighted: - 928 raise Exception('You can not use a reweighted observable for reweighting!') - 929 - 930 proj_qtop = [] - 931 for n in qtop.deltas: - 932 proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]])) - 933 - 934 reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names]) - 935 return reto + 916 if "names" not in kwargs: + 917 try: + 918 idx = truncated_file.index('r') + 919 except Exception: + 920 if "names" not in kwargs: + 921 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") + 922 ens_name = truncated_file[:idx] + 923 rep_names.append(ens_name + '|' + truncated_file[idx:]) + 924 else: + 925 names = kwargs.get("names") + 926 rep_names = names + 927 deltas.append(Q_top) + 928 + 929 idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))] + 930 deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))] + 931 result = Obs(deltas, rep_names, idl=idl) + 932 result.tag = {"T": tmax - 1, + 933 "L": L} + 934 return result + 935 936 - 937 - 938def read_qtop_sector(path, prefix, c, target=0, **kwargs): - 939 """Constructs reweighting factors to a specified topological sector. - 940 - 941 Parameters - 942 ---------- - 943 path : str - 944 path of the measurement files - 945 prefix : str - 946 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat - 947 c : double - 948 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L - 949 target : int - 950 Specifies the topological sector to be reweighted to (default 0) - 951 dtr_cnfg : int - 952 (optional) parameter that specifies the number of trajectories - 953 between two configs. - 954 if it is not set, the distance between two measurements - 955 in the file is assumed to be the distance between two configurations. - 956 steps : int - 957 (optional) Distance between two configurations in units of trajectories / - 958 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given - 959 version : str - 960 version string of the openQCD (sfqcd) version used to create - 961 the ensemble. Default is 2.0. May also be set to sfqcd. - 962 L : int - 963 spatial length of the lattice in L/a. - 964 HAS to be set if version != sfqcd, since openQCD does not provide - 965 this in the header - 966 r_start : list - 967 offset of the first ensemble, making it easier to match - 968 later on with other Obs - 969 r_stop : list - 970 last configurations that need to be read (per replicum) - 971 files : list - 972 specify the exact files that need to be read - 973 from path, practical if e.g. only one replicum is needed - 974 names : list - 975 Alternative labeling for replicas/ensembles. - 976 Has to have the appropriate length - 977 Zeuthen_flow : bool - 978 (optional) If True, the Zeuthen flow is used for Qtop. Only possible - 979 for version=='sfqcd' If False, the Wilson flow is used. - 980 """ - 981 - 982 if not isinstance(target, int): - 983 raise Exception("'target' has to be an integer.") - 984 - 985 kwargs['integer_charge'] = True - 986 qtop = read_qtop(path, prefix, c, **kwargs) - 987 - 988 return qtop_projection(qtop, target=target) - 989 - 990 - 991def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs): - 992 """ - 993 Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data. - 994 - 995 Parameters - 996 ---------- - 997 path : str - 998 The directory to search for the files in. - 999 prefix : str -1000 The prefix to match the files against. -1001 qc : str -1002 The quark combination extension to match the files against. -1003 corr : str -1004 The correlator to extract data for. -1005 sep : str, optional -1006 The separator to use when parsing the replika names. -1007 **kwargs -1008 Additional keyword arguments. The following keyword arguments are recognized: -1009 -1010 - names (List[str]): A list of names to use for the replicas. + 937def qtop_projection(qtop, target=0): + 938 """Returns the projection to the topological charge sector defined by target. + 939 + 940 Parameters + 941 ---------- + 942 path : Obs + 943 Topological charge. + 944 target : int + 945 Specifies the topological sector to be reweighted to (default 0) + 946 + 947 Returns + 948 ------- + 949 reto : Obs + 950 projection to the topological charge sector defined by target + 951 """ + 952 if qtop.reweighted: + 953 raise Exception('You can not use a reweighted observable for reweighting!') + 954 + 955 proj_qtop = [] + 956 for n in qtop.deltas: + 957 proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]])) + 958 + 959 reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names]) + 960 return reto + 961 + 962 + 963def read_qtop_sector(path, prefix, c, target=0, **kwargs): + 964 """Constructs reweighting factors to a specified topological sector. + 965 + 966 Parameters + 967 ---------- + 968 path : str + 969 path of the measurement files + 970 prefix : str + 971 prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat + 972 c : double + 973 Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L + 974 target : int + 975 Specifies the topological sector to be reweighted to (default 0) + 976 dtr_cnfg : int + 977 (optional) parameter that specifies the number of trajectories + 978 between two configs. + 979 if it is not set, the distance between two measurements + 980 in the file is assumed to be the distance between two configurations. + 981 steps : int + 982 (optional) Distance between two configurations in units of trajectories / + 983 cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given + 984 version : str + 985 version string of the openQCD (sfqcd) version used to create + 986 the ensemble. Default is 2.0. May also be set to sfqcd. + 987 L : int + 988 spatial length of the lattice in L/a. + 989 HAS to be set if version != sfqcd, since openQCD does not provide + 990 this in the header + 991 r_start : list + 992 offset of the first ensemble, making it easier to match + 993 later on with other Obs + 994 r_stop : list + 995 last configurations that need to be read (per replicum) + 996 files : list + 997 specify the exact files that need to be read + 998 from path, practical if e.g. only one replicum is needed + 999 names : list +1000 Alternative labeling for replicas/ensembles. +1001 Has to have the appropriate length +1002 Zeuthen_flow : bool +1003 (optional) If True, the Zeuthen flow is used for Qtop. Only possible +1004 for version=='sfqcd' If False, the Wilson flow is used. +1005 +1006 Returns +1007 ------- +1008 reto : Obs +1009 projection to the topological charge sector defined by target +1010 """ 1011 -1012 Returns -1013 ------- -1014 Corr -1015 A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators. -1016 or -1017 CObs -1018 A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators. +1012 if not isinstance(target, int): +1013 raise Exception("'target' has to be an integer.") +1014 +1015 kwargs['integer_charge'] = True +1016 qtop = read_qtop(path, prefix, c, **kwargs) +1017 +1018 return qtop_projection(qtop, target=target) 1019 1020 -1021 Raises -1022 ------ -1023 FileNotFoundError -1024 If no files matching the specified prefix and quark combination extension are found in the specified directory. -1025 IOError -1026 If there is an error reading a file. -1027 struct.error -1028 If there is an error unpacking binary data. -1029 """ -1030 -1031 found = [] -1032 files = [] -1033 names = [] -1034 for (dirpath, dirnames, filenames) in os.walk(path + "/"): -1035 found.extend(filenames) -1036 break -1037 -1038 for f in found: -1039 if fnmatch.fnmatch(f, prefix + "*.ms5_xsf_" + qc + ".dat"): -1040 files.append(f) -1041 if not sep == "": -1042 names.append(prefix + "|r" + f.split(".")[0].split(sep)[1]) -1043 else: -1044 names.append(prefix) -1045 files = sorted(files) -1046 -1047 if "names" in kwargs: -1048 names = kwargs.get("names") -1049 else: -1050 names = sorted(names) -1051 -1052 cnfgs = [] -1053 realsamples = [] -1054 imagsamples = [] -1055 repnum = 0 -1056 for file in files: -1057 with open(path + "/" + file, "rb") as fp: -1058 -1059 t = fp.read(8) -1060 kappa = struct.unpack('d', t)[0] -1061 t = fp.read(8) -1062 csw = struct.unpack('d', t)[0] -1063 t = fp.read(8) -1064 dF = struct.unpack('d', t)[0] -1065 t = fp.read(8) -1066 zF = struct.unpack('d', t)[0] +1021def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs): +1022 """ +1023 Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data. +1024 +1025 Parameters +1026 ---------- +1027 path : str +1028 The directory to search for the files in. +1029 prefix : str +1030 The prefix to match the files against. +1031 qc : str +1032 The quark combination extension to match the files against. +1033 corr : str +1034 The correlator to extract data for. +1035 sep : str, optional +1036 The separator to use when parsing the replika names. +1037 **kwargs +1038 Additional keyword arguments. The following keyword arguments are recognized: +1039 +1040 - names (List[str]): A list of names to use for the replicas. +1041 +1042 Returns +1043 ------- +1044 Corr +1045 A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators. +1046 or +1047 CObs +1048 A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators. +1049 +1050 +1051 Raises +1052 ------ +1053 FileNotFoundError +1054 If no files matching the specified prefix and quark combination extension are found in the specified directory. +1055 IOError +1056 If there is an error reading a file. +1057 struct.error +1058 If there is an error unpacking binary data. +1059 """ +1060 +1061 found = [] +1062 files = [] +1063 names = [] +1064 for (dirpath, dirnames, filenames) in os.walk(path + "/"): +1065 found.extend(filenames) +1066 break 1067 -1068 t = fp.read(4) -1069 tmax = struct.unpack('i', t)[0] -1070 t = fp.read(4) -1071 bnd = struct.unpack('i', t)[0] -1072 -1073 placesBI = ["gS", "gP", -1074 "gA", "gV", -1075 "gVt", "lA", -1076 "lV", "lVt", -1077 "lT", "lTt"] -1078 placesBB = ["g1", "l1"] -1079 -1080 # the chunks have the following structure: -1081 # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles -1082 -1083 chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2) -1084 packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2) -1085 cnfgs.append([]) -1086 realsamples.append([]) -1087 imagsamples.append([]) -1088 for t in range(tmax): -1089 realsamples[repnum].append([]) -1090 imagsamples[repnum].append([]) -1091 -1092 while True: -1093 cnfgt = fp.read(chunksize) -1094 if not cnfgt: -1095 break -1096 asascii = struct.unpack(packstr, cnfgt) -1097 cnfg = asascii[0] -1098 cnfgs[repnum].append(cnfg) -1099 -1100 if corr not in placesBB: -1101 tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax] -1102 else: -1103 tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2] -1104 -1105 corrres = [[], []] -1106 for i in range(len(tmpcorr)): -1107 corrres[i % 2].append(tmpcorr[i]) -1108 for t in range(int(len(tmpcorr) / 2)): -1109 realsamples[repnum][t].append(corrres[0][t]) -1110 for t in range(int(len(tmpcorr) / 2)): -1111 imagsamples[repnum][t].append(corrres[1][t]) -1112 repnum += 1 -1113 -1114 s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t])) -1115 for rep in range(1, repnum): -1116 s += ", " + str(len(realsamples[rep][t])) -1117 s += " samples" -1118 print(s) -1119 print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd) -1120 -1121 # we have the data now... but we need to re format the whole thing and put it into Corr objects. -1122 -1123 compObs = [] -1124 -1125 for t in range(int(len(tmpcorr) / 2)): -1126 compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs), -1127 Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs))) -1128 -1129 if len(compObs) == 1: -1130 return compObs[0] -1131 else: -1132 return Corr(compObs) +1068 for f in found: +1069 if fnmatch.fnmatch(f, prefix + "*.ms5_xsf_" + qc + ".dat"): +1070 files.append(f) +1071 if not sep == "": +1072 names.append(prefix + "|r" + f.split(".")[0].split(sep)[1]) +1073 else: +1074 names.append(prefix) +1075 files = sorted(files) +1076 +1077 if "names" in kwargs: +1078 names = kwargs.get("names") +1079 else: +1080 names = sorted(names) +1081 +1082 cnfgs = [] +1083 realsamples = [] +1084 imagsamples = [] +1085 repnum = 0 +1086 for file in files: +1087 with open(path + "/" + file, "rb") as fp: +1088 +1089 t = fp.read(8) +1090 kappa = struct.unpack('d', t)[0] +1091 t = fp.read(8) +1092 csw = struct.unpack('d', t)[0] +1093 t = fp.read(8) +1094 dF = struct.unpack('d', t)[0] +1095 t = fp.read(8) +1096 zF = struct.unpack('d', t)[0] +1097 +1098 t = fp.read(4) +1099 tmax = struct.unpack('i', t)[0] +1100 t = fp.read(4) +1101 bnd = struct.unpack('i', t)[0] +1102 +1103 placesBI = ["gS", "gP", +1104 "gA", "gV", +1105 "gVt", "lA", +1106 "lV", "lVt", +1107 "lT", "lTt"] +1108 placesBB = ["g1", "l1"] +1109 +1110 # the chunks have the following structure: +1111 # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles +1112 +1113 chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2) +1114 packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2) +1115 cnfgs.append([]) +1116 realsamples.append([]) +1117 imagsamples.append([]) +1118 for t in range(tmax): +1119 realsamples[repnum].append([]) +1120 imagsamples[repnum].append([]) +1121 +1122 while True: +1123 cnfgt = fp.read(chunksize) +1124 if not cnfgt: +1125 break +1126 asascii = struct.unpack(packstr, cnfgt) +1127 cnfg = asascii[0] +1128 cnfgs[repnum].append(cnfg) +1129 +1130 if corr not in placesBB: +1131 tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax] +1132 else: +1133 tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2] +1134 +1135 corrres = [[], []] +1136 for i in range(len(tmpcorr)): +1137 corrres[i % 2].append(tmpcorr[i]) +1138 for t in range(int(len(tmpcorr) / 2)): +1139 realsamples[repnum][t].append(corrres[0][t]) +1140 for t in range(int(len(tmpcorr) / 2)): +1141 imagsamples[repnum][t].append(corrres[1][t]) +1142 repnum += 1 +1143 +1144 s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t])) +1145 for rep in range(1, repnum): +1146 s += ", " + str(len(realsamples[rep][t])) +1147 s += " samples" +1148 print(s) +1149 print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd) +1150 +1151 # we have the data now... but we need to re format the whole thing and put it into Corr objects. +1152 +1153 compObs = [] +1154 +1155 for t in range(int(len(tmpcorr) / 2)): +1156 compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs), +1157 Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs))) +1158 +1159 if len(compObs) == 1: +1160 return compObs[0] +1161 else: +1162 return Corr(compObs)
    @@ -1271,193 +1301,198 @@ 43 files performed if given. 44 print_err : bool 45 Print additional information that is useful for debugging. - 46 """ - 47 known_oqcd_versions = ['1.4', '1.6', '2.0'] - 48 if not (version in known_oqcd_versions): - 49 raise Exception('Unknown openQCD version defined!') - 50 print("Working with openQCD version " + version) - 51 if 'postfix' in kwargs: - 52 postfix = kwargs.get('postfix') - 53 else: - 54 postfix = '' - 55 ls = [] - 56 for (dirpath, dirnames, filenames) in os.walk(path): - 57 ls.extend(filenames) - 58 break - 59 - 60 if not ls: - 61 raise Exception(f"Error, directory '{path}' not found") - 62 if 'files' in kwargs: - 63 ls = kwargs.get('files') - 64 else: - 65 for exc in ls: - 66 if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): - 67 ls = list(set(ls) - set([exc])) - 68 if len(ls) > 1: - 69 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) - 70 replica = len(ls) - 71 - 72 if 'r_start' in kwargs: - 73 r_start = kwargs.get('r_start') - 74 if len(r_start) != replica: - 75 raise Exception('r_start does not match number of replicas') - 76 r_start = [o if o else None for o in r_start] - 77 else: - 78 r_start = [None] * replica - 79 - 80 if 'r_stop' in kwargs: - 81 r_stop = kwargs.get('r_stop') - 82 if len(r_stop) != replica: - 83 raise Exception('r_stop does not match number of replicas') - 84 else: - 85 r_stop = [None] * replica - 86 - 87 if 'r_step' in kwargs: - 88 r_step = kwargs.get('r_step') + 46 + 47 Returns + 48 ------- + 49 rwms : Obs + 50 Reweighting factors read + 51 """ + 52 known_oqcd_versions = ['1.4', '1.6', '2.0'] + 53 if not (version in known_oqcd_versions): + 54 raise Exception('Unknown openQCD version defined!') + 55 print("Working with openQCD version " + version) + 56 if 'postfix' in kwargs: + 57 postfix = kwargs.get('postfix') + 58 else: + 59 postfix = '' + 60 ls = [] + 61 for (dirpath, dirnames, filenames) in os.walk(path): + 62 ls.extend(filenames) + 63 break + 64 + 65 if not ls: + 66 raise Exception(f"Error, directory '{path}' not found") + 67 if 'files' in kwargs: + 68 ls = kwargs.get('files') + 69 else: + 70 for exc in ls: + 71 if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): + 72 ls = list(set(ls) - set([exc])) + 73 if len(ls) > 1: + 74 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) + 75 replica = len(ls) + 76 + 77 if 'r_start' in kwargs: + 78 r_start = kwargs.get('r_start') + 79 if len(r_start) != replica: + 80 raise Exception('r_start does not match number of replicas') + 81 r_start = [o if o else None for o in r_start] + 82 else: + 83 r_start = [None] * replica + 84 + 85 if 'r_stop' in kwargs: + 86 r_stop = kwargs.get('r_stop') + 87 if len(r_stop) != replica: + 88 raise Exception('r_stop does not match number of replicas') 89 else: - 90 r_step = 1 + 90 r_stop = [None] * replica 91 - 92 print('Read reweighting factors from', prefix[:-1], ',', - 93 replica, 'replica', end='') - 94 - 95 if names is None: - 96 rep_names = [] - 97 for entry in ls: - 98 truncated_entry = entry - 99 suffixes = [".dat", ".rwms", ".ms1"] -100 for suffix in suffixes: -101 if truncated_entry.endswith(suffix): -102 truncated_entry = truncated_entry[0:-len(suffix)] -103 idx = truncated_entry.index('r') -104 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) -105 else: -106 rep_names = names -107 -108 print_err = 0 -109 if 'print_err' in kwargs: -110 print_err = 1 -111 print() + 92 if 'r_step' in kwargs: + 93 r_step = kwargs.get('r_step') + 94 else: + 95 r_step = 1 + 96 + 97 print('Read reweighting factors from', prefix[:-1], ',', + 98 replica, 'replica', end='') + 99 +100 if names is None: +101 rep_names = [] +102 for entry in ls: +103 truncated_entry = entry +104 suffixes = [".dat", ".rwms", ".ms1"] +105 for suffix in suffixes: +106 if truncated_entry.endswith(suffix): +107 truncated_entry = truncated_entry[0:-len(suffix)] +108 idx = truncated_entry.index('r') +109 rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) +110 else: +111 rep_names = names 112 -113 deltas = [] -114 -115 configlist = [] -116 r_start_index = [] -117 r_stop_index = [] -118 -119 for rep in range(replica): -120 tmp_array = [] -121 with open(path + '/' + ls[rep], 'rb') as fp: -122 -123 t = fp.read(4) # number of reweighting factors -124 if rep == 0: -125 nrw = struct.unpack('i', t)[0] -126 if version == '2.0': -127 nrw = int(nrw / 2) -128 for k in range(nrw): -129 deltas.append([]) -130 else: -131 if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): -132 raise Exception('Error: different number of reweighting factors for replicum', rep) -133 -134 for k in range(nrw): -135 tmp_array.append([]) -136 -137 # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files -138 nfct = [] -139 if version in ['1.6', '2.0']: -140 for i in range(nrw): -141 t = fp.read(4) -142 nfct.append(struct.unpack('i', t)[0]) -143 else: -144 for i in range(nrw): -145 nfct.append(1) -146 -147 nsrc = [] -148 for i in range(nrw): -149 t = fp.read(4) -150 nsrc.append(struct.unpack('i', t)[0]) -151 if version == '2.0': -152 if not struct.unpack('i', fp.read(4))[0] == 0: -153 print('something is wrong!') -154 -155 configlist.append([]) -156 while True: -157 t = fp.read(4) -158 if len(t) < 4: -159 break -160 config_no = struct.unpack('i', t)[0] -161 configlist[-1].append(config_no) -162 for i in range(nrw): -163 if (version == '2.0'): -164 tmpd = _read_array_openQCD2(fp) -165 tmpd = _read_array_openQCD2(fp) -166 tmp_rw = tmpd['arr'] -167 tmp_nfct = 1.0 -168 for j in range(tmpd['n'][0]): -169 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j]))) -170 if print_err: -171 print(config_no, i, j, -172 np.mean(np.exp(-np.asarray(tmp_rw[j]))), -173 np.std(np.exp(-np.asarray(tmp_rw[j])))) -174 print('Sources:', -175 np.exp(-np.asarray(tmp_rw[j]))) -176 print('Partial factor:', tmp_nfct) -177 elif version == '1.6' or version == '1.4': -178 tmp_nfct = 1.0 -179 for j in range(nfct[i]): -180 t = fp.read(8 * nsrc[i]) -181 t = fp.read(8 * nsrc[i]) -182 tmp_rw = struct.unpack('d' * nsrc[i], t) -183 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw))) -184 if print_err: -185 print(config_no, i, j, -186 np.mean(np.exp(-np.asarray(tmp_rw))), -187 np.std(np.exp(-np.asarray(tmp_rw)))) -188 print('Sources:', np.exp(-np.asarray(tmp_rw))) -189 print('Partial factor:', tmp_nfct) -190 tmp_array[i].append(tmp_nfct) -191 -192 diffmeas = configlist[-1][-1] - configlist[-1][-2] -193 configlist[-1] = [item // diffmeas for item in configlist[-1]] -194 if configlist[-1][0] > 1 and diffmeas > 1: -195 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') -196 offset = configlist[-1][0] - 1 -197 configlist[-1] = [item - offset for item in configlist[-1]] -198 -199 if r_start[rep] is None: -200 r_start_index.append(0) -201 else: -202 try: -203 r_start_index.append(configlist[-1].index(r_start[rep])) -204 except ValueError: -205 raise Exception('Config %d not in file with range [%d, %d]' % ( -206 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None -207 -208 if r_stop[rep] is None: -209 r_stop_index.append(len(configlist[-1]) - 1) -210 else: -211 try: -212 r_stop_index.append(configlist[-1].index(r_stop[rep])) -213 except ValueError: -214 raise Exception('Config %d not in file with range [%d, %d]' % ( -215 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None -216 -217 for k in range(nrw): -218 deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step]) -219 -220 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): -221 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) -222 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] -223 if np.any([step != 1 for step in stepsizes]): -224 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) -225 -226 print(',', nrw, 'reweighting factors with', nsrc, 'sources') -227 result = [] -228 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] -229 -230 for t in range(nrw): -231 result.append(Obs(deltas[t], rep_names, idl=idl)) -232 return result +113 print_err = 0 +114 if 'print_err' in kwargs: +115 print_err = 1 +116 print() +117 +118 deltas = [] +119 +120 configlist = [] +121 r_start_index = [] +122 r_stop_index = [] +123 +124 for rep in range(replica): +125 tmp_array = [] +126 with open(path + '/' + ls[rep], 'rb') as fp: +127 +128 t = fp.read(4) # number of reweighting factors +129 if rep == 0: +130 nrw = struct.unpack('i', t)[0] +131 if version == '2.0': +132 nrw = int(nrw / 2) +133 for k in range(nrw): +134 deltas.append([]) +135 else: +136 if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): +137 raise Exception('Error: different number of reweighting factors for replicum', rep) +138 +139 for k in range(nrw): +140 tmp_array.append([]) +141 +142 # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files +143 nfct = [] +144 if version in ['1.6', '2.0']: +145 for i in range(nrw): +146 t = fp.read(4) +147 nfct.append(struct.unpack('i', t)[0]) +148 else: +149 for i in range(nrw): +150 nfct.append(1) +151 +152 nsrc = [] +153 for i in range(nrw): +154 t = fp.read(4) +155 nsrc.append(struct.unpack('i', t)[0]) +156 if version == '2.0': +157 if not struct.unpack('i', fp.read(4))[0] == 0: +158 print('something is wrong!') +159 +160 configlist.append([]) +161 while True: +162 t = fp.read(4) +163 if len(t) < 4: +164 break +165 config_no = struct.unpack('i', t)[0] +166 configlist[-1].append(config_no) +167 for i in range(nrw): +168 if (version == '2.0'): +169 tmpd = _read_array_openQCD2(fp) +170 tmpd = _read_array_openQCD2(fp) +171 tmp_rw = tmpd['arr'] +172 tmp_nfct = 1.0 +173 for j in range(tmpd['n'][0]): +174 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j]))) +175 if print_err: +176 print(config_no, i, j, +177 np.mean(np.exp(-np.asarray(tmp_rw[j]))), +178 np.std(np.exp(-np.asarray(tmp_rw[j])))) +179 print('Sources:', +180 np.exp(-np.asarray(tmp_rw[j]))) +181 print('Partial factor:', tmp_nfct) +182 elif version == '1.6' or version == '1.4': +183 tmp_nfct = 1.0 +184 for j in range(nfct[i]): +185 t = fp.read(8 * nsrc[i]) +186 t = fp.read(8 * nsrc[i]) +187 tmp_rw = struct.unpack('d' * nsrc[i], t) +188 tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw))) +189 if print_err: +190 print(config_no, i, j, +191 np.mean(np.exp(-np.asarray(tmp_rw))), +192 np.std(np.exp(-np.asarray(tmp_rw)))) +193 print('Sources:', np.exp(-np.asarray(tmp_rw))) +194 print('Partial factor:', tmp_nfct) +195 tmp_array[i].append(tmp_nfct) +196 +197 diffmeas = configlist[-1][-1] - configlist[-1][-2] +198 configlist[-1] = [item // diffmeas for item in configlist[-1]] +199 if configlist[-1][0] > 1 and diffmeas > 1: +200 warnings.warn('Assume thermalization and that the first measurement belongs to the first config.') +201 offset = configlist[-1][0] - 1 +202 configlist[-1] = [item - offset for item in configlist[-1]] +203 +204 if r_start[rep] is None: +205 r_start_index.append(0) +206 else: +207 try: +208 r_start_index.append(configlist[-1].index(r_start[rep])) +209 except ValueError: +210 raise Exception('Config %d not in file with range [%d, %d]' % ( +211 r_start[rep], configlist[-1][0], configlist[-1][-1])) from None +212 +213 if r_stop[rep] is None: +214 r_stop_index.append(len(configlist[-1]) - 1) +215 else: +216 try: +217 r_stop_index.append(configlist[-1].index(r_stop[rep])) +218 except ValueError: +219 raise Exception('Config %d not in file with range [%d, %d]' % ( +220 r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None +221 +222 for k in range(nrw): +223 deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step]) +224 +225 if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]): +226 raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist]) +227 stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist] +228 if np.any([step != 1 for step in stepsizes]): +229 warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning) +230 +231 print(',', nrw, 'reweighting factors with', nsrc, 'sources') +232 result = [] +233 idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)] +234 +235 for t in range(nrw): +236 result.append(Obs(deltas[t], rep_names, idl=idl)) +237 return result @@ -1492,6 +1527,13 @@ files performed if given.
  • print_err (bool): Print additional information that is useful for debugging.
    • + +
    Returns
    + + @@ -1507,250 +1549,255 @@ Print additional information that is useful for debugging. - 
    235def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
-236    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
-237
-238    It is assumed that all boundary effects have
-239    sufficiently decayed at x0=xmin.
-240    The data around the zero crossing of t^2<E> - 0.3
-241    is fitted with a linear function
-242    from which the exact root is extracted.
-243
-244    It is assumed that one measurement is performed for each config.
-245    If this is not the case, the resulting idl, as well as the handling
-246    of r_start, r_stop and r_step is wrong and the user has to correct
-247    this in the resulting observable.
+            
    240def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
+241    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
+242
+243    It is assumed that all boundary effects have
+244    sufficiently decayed at x0=xmin.
+245    The data around the zero crossing of t^2<E> - 0.3
+246    is fitted with a linear function
+247    from which the exact root is extracted.
 248
-249    Parameters
-250    ----------
-251    path : str
-252        Path to .ms.dat files
-253    prefix : str
-254        Ensemble prefix
-255    dtr_read : int
-256        Determines how many trajectories should be skipped
-257        when reading the ms.dat files.
-258        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
-259    xmin : int
-260        First timeslice where the boundary
-261        effects have sufficiently decayed.
-262    spatial_extent : int
-263        spatial extent of the lattice, required for normalization.
-264    fit_range : int
-265        Number of data points left and right of the zero
-266        crossing to be included in the linear fit. (Default: 5)
-267    r_start : list
-268        list which contains the first config to be read for each replicum.
-269    r_stop : list
-270        list which contains the last config to be read for each replicum.
-271    r_step : int
-272        integer that defines a fixed step size between two measurements (in units of configs)
-273        If not given, r_step=1 is assumed.
-274    plaquette : bool
-275        If true extract the plaquette estimate of t0 instead.
-276    names : list
-277        list of names that is assigned to the data according according
-278        to the order in the file list. Use careful, if you do not provide file names!
-279    files : list
-280        list which contains the filenames to be read. No automatic detection of
-281        files performed if given.
-282    plot_fit : bool
-283        If true, the fit for the extraction of t0 is shown together with the data.
-284    assume_thermalization : bool
-285        If True: If the first record divided by the distance between two measurements is larger than
-286        1, it is assumed that this is due to thermalization and the first measurement belongs
-287        to the first config (default).
-288        If False: The config numbers are assumed to be traj_number // difference
-289    """
-290
-291    ls = []
-292    for (dirpath, dirnames, filenames) in os.walk(path):
-293        ls.extend(filenames)
-294        break
-295
-296    if not ls:
-297        raise Exception('Error, directory not found')
-298
-299    if 'files' in kwargs:
-300        ls = kwargs.get('files')
-301    else:
-302        for exc in ls:
-303            if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
-304                ls = list(set(ls) - set([exc]))
-305        if len(ls) > 1:
-306            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
-307    replica = len(ls)
+249    It is assumed that one measurement is performed for each config.
+250    If this is not the case, the resulting idl, as well as the handling
+251    of r_start, r_stop and r_step is wrong and the user has to correct
+252    this in the resulting observable.
+253
+254    Parameters
+255    ----------
+256    path : str
+257        Path to .ms.dat files
+258    prefix : str
+259        Ensemble prefix
+260    dtr_read : int
+261        Determines how many trajectories should be skipped
+262        when reading the ms.dat files.
+263        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
+264    xmin : int
+265        First timeslice where the boundary
+266        effects have sufficiently decayed.
+267    spatial_extent : int
+268        spatial extent of the lattice, required for normalization.
+269    fit_range : int
+270        Number of data points left and right of the zero
+271        crossing to be included in the linear fit. (Default: 5)
+272    r_start : list
+273        list which contains the first config to be read for each replicum.
+274    r_stop : list
+275        list which contains the last config to be read for each replicum.
+276    r_step : int
+277        integer that defines a fixed step size between two measurements (in units of configs)
+278        If not given, r_step=1 is assumed.
+279    plaquette : bool
+280        If true extract the plaquette estimate of t0 instead.
+281    names : list
+282        list of names that is assigned to the data according according
+283        to the order in the file list. Use careful, if you do not provide file names!
+284    files : list
+285        list which contains the filenames to be read. No automatic detection of
+286        files performed if given.
+287    plot_fit : bool
+288        If true, the fit for the extraction of t0 is shown together with the data.
+289    assume_thermalization : bool
+290        If True: If the first record divided by the distance between two measurements is larger than
+291        1, it is assumed that this is due to thermalization and the first measurement belongs
+292        to the first config (default).
+293        If False: The config numbers are assumed to be traj_number // difference
+294
+295    Returns
+296    -------
+297    t0 : Obs
+298        Extracted t0
+299    """
+300
+301    ls = []
+302    for (dirpath, dirnames, filenames) in os.walk(path):
+303        ls.extend(filenames)
+304        break
+305
+306    if not ls:
+307        raise Exception('Error, directory not found')
 308
-309    if 'r_start' in kwargs:
-310        r_start = kwargs.get('r_start')
-311        if len(r_start) != replica:
-312            raise Exception('r_start does not match number of replicas')
-313        r_start = [o if o else None for o in r_start]
-314    else:
-315        r_start = [None] * replica
-316
-317    if 'r_stop' in kwargs:
-318        r_stop = kwargs.get('r_stop')
-319        if len(r_stop) != replica:
-320            raise Exception('r_stop does not match number of replicas')
-321    else:
-322        r_stop = [None] * replica
-323
-324    if 'r_step' in kwargs:
-325        r_step = kwargs.get('r_step')
-326    else:
-327        r_step = 1
-328
-329    print('Extract t0 from', prefix, ',', replica, 'replica')
-330
-331    if 'names' in kwargs:
-332        rep_names = kwargs.get('names')
-333    else:
-334        rep_names = []
-335        for entry in ls:
-336            truncated_entry = entry.split('.')[0]
-337            idx = truncated_entry.index('r')
-338            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-339
-340    Ysum = []
-341
-342    configlist = []
-343    r_start_index = []
-344    r_stop_index = []
-345
-346    for rep in range(replica):
-347
-348        with open(path + '/' + ls[rep], 'rb') as fp:
-349            t = fp.read(12)
-350            header = struct.unpack('iii', t)
-351            if rep == 0:
-352                dn = header[0]
-353                nn = header[1]
-354                tmax = header[2]
-355            elif dn != header[0] or nn != header[1] or tmax != header[2]:
-356                raise Exception('Replica parameters do not match.')
+309    if 'files' in kwargs:
+310        ls = kwargs.get('files')
+311    else:
+312        for exc in ls:
+313            if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
+314                ls = list(set(ls) - set([exc]))
+315        if len(ls) > 1:
+316            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
+317    replica = len(ls)
+318
+319    if 'r_start' in kwargs:
+320        r_start = kwargs.get('r_start')
+321        if len(r_start) != replica:
+322            raise Exception('r_start does not match number of replicas')
+323        r_start = [o if o else None for o in r_start]
+324    else:
+325        r_start = [None] * replica
+326
+327    if 'r_stop' in kwargs:
+328        r_stop = kwargs.get('r_stop')
+329        if len(r_stop) != replica:
+330            raise Exception('r_stop does not match number of replicas')
+331    else:
+332        r_stop = [None] * replica
+333
+334    if 'r_step' in kwargs:
+335        r_step = kwargs.get('r_step')
+336    else:
+337        r_step = 1
+338
+339    print('Extract t0 from', prefix, ',', replica, 'replica')
+340
+341    if 'names' in kwargs:
+342        rep_names = kwargs.get('names')
+343    else:
+344        rep_names = []
+345        for entry in ls:
+346            truncated_entry = entry.split('.')[0]
+347            idx = truncated_entry.index('r')
+348            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+349
+350    Ysum = []
+351
+352    configlist = []
+353    r_start_index = []
+354    r_stop_index = []
+355
+356    for rep in range(replica):
 357
-358            t = fp.read(8)
-359            if rep == 0:
-360                eps = struct.unpack('d', t)[0]
-361                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
-362            elif eps != struct.unpack('d', t)[0]:
-363                raise Exception('Values for eps do not match among replica.')
-364
-365            Ysl = []
-366
-367            configlist.append([])
-368            while True:
-369                t = fp.read(4)
-370                if (len(t) < 4):
-371                    break
-372                nc = struct.unpack('i', t)[0]
-373                configlist[-1].append(nc)
+358        with open(path + '/' + ls[rep], 'rb') as fp:
+359            t = fp.read(12)
+360            header = struct.unpack('iii', t)
+361            if rep == 0:
+362                dn = header[0]
+363                nn = header[1]
+364                tmax = header[2]
+365            elif dn != header[0] or nn != header[1] or tmax != header[2]:
+366                raise Exception('Replica parameters do not match.')
+367
+368            t = fp.read(8)
+369            if rep == 0:
+370                eps = struct.unpack('d', t)[0]
+371                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
+372            elif eps != struct.unpack('d', t)[0]:
+373                raise Exception('Values for eps do not match among replica.')
 374
-375                t = fp.read(8 * tmax * (nn + 1))
-376                if kwargs.get('plaquette'):
-377                    if nc % dtr_read == 0:
-378                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-379                t = fp.read(8 * tmax * (nn + 1))
-380                if not kwargs.get('plaquette'):
-381                    if nc % dtr_read == 0:
-382                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-383                t = fp.read(8 * tmax * (nn + 1))
+375            Ysl = []
+376
+377            configlist.append([])
+378            while True:
+379                t = fp.read(4)
+380                if (len(t) < 4):
+381                    break
+382                nc = struct.unpack('i', t)[0]
+383                configlist[-1].append(nc)
 384
-385        Ysum.append([])
-386        for i, item in enumerate(Ysl):
-387            Ysum[-1].append([np.mean(item[current + xmin:
-388                             current + tmax - xmin])
-389                            for current in range(0, len(item), tmax)])
-390
-391        diffmeas = configlist[-1][-1] - configlist[-1][-2]
-392        configlist[-1] = [item // diffmeas for item in configlist[-1]]
-393        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
-394            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
-395            offset = configlist[-1][0] - 1
-396            configlist[-1] = [item - offset for item in configlist[-1]]
-397
-398        if r_start[rep] is None:
-399            r_start_index.append(0)
-400        else:
-401            try:
-402                r_start_index.append(configlist[-1].index(r_start[rep]))
-403            except ValueError:
-404                raise Exception('Config %d not in file with range [%d, %d]' % (
-405                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
-406
-407        if r_stop[rep] is None:
-408            r_stop_index.append(len(configlist[-1]) - 1)
-409        else:
-410            try:
-411                r_stop_index.append(configlist[-1].index(r_stop[rep]))
-412            except ValueError:
-413                raise Exception('Config %d not in file with range [%d, %d]' % (
-414                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
-415
-416    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
-417        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-418    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
-419    if np.any([step != 1 for step in stepsizes]):
-420        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
-421
-422    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
-423    t2E_dict = {}
-424    for n in range(nn + 1):
-425        samples = []
-426        for nrep, rep in enumerate(Ysum):
-427            samples.append([])
-428            for cnfg in rep:
-429                samples[-1].append(cnfg[n])
-430            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
-431        new_obs = Obs(samples, rep_names, idl=idl)
-432        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
-433
-434    zero_crossing = np.argmax(np.array(
-435        [o.value for o in t2E_dict.values()]) > 0.0)
-436
-437    x = list(t2E_dict.keys())[zero_crossing - fit_range:
-438                              zero_crossing + fit_range]
-439    y = list(t2E_dict.values())[zero_crossing - fit_range:
-440                                zero_crossing + fit_range]
-441    [o.gamma_method() for o in y]
-442
-443    fit_result = fit_lin(x, y)
-444
-445    if kwargs.get('plot_fit'):
-446        plt.figure()
-447        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-448        ax0 = plt.subplot(gs[0])
-449        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-450        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-451        [o.gamma_method() for o in ymore]
-452        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
-453        xplot = np.linspace(np.min(x), np.max(x))
-454        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
-455        [yi.gamma_method() for yi in yplot]
-456        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
-457        retval = (-fit_result[0] / fit_result[1])
-458        retval.gamma_method()
-459        ylim = ax0.get_ylim()
-460        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
-461        ax0.set_ylim(ylim)
-462        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
-463        xlim = ax0.get_xlim()
-464
-465        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
-466        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
-467        ax1 = plt.subplot(gs[1])
-468        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-469        ax1.tick_params(direction='out')
-470        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-471        ax1.axhline(y=0.0, ls='--', color='k')
-472        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
-473        ax1.set_xlim(xlim)
-474        ax1.set_ylabel('Residuals')
-475        ax1.set_xlabel(r'$t/a^2$')
-476
-477        plt.draw()
-478    return -fit_result[0] / fit_result[1]
+385                t = fp.read(8 * tmax * (nn + 1))
+386                if kwargs.get('plaquette'):
+387                    if nc % dtr_read == 0:
+388                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+389                t = fp.read(8 * tmax * (nn + 1))
+390                if not kwargs.get('plaquette'):
+391                    if nc % dtr_read == 0:
+392                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+393                t = fp.read(8 * tmax * (nn + 1))
+394
+395        Ysum.append([])
+396        for i, item in enumerate(Ysl):
+397            Ysum[-1].append([np.mean(item[current + xmin:
+398                             current + tmax - xmin])
+399                            for current in range(0, len(item), tmax)])
+400
+401        diffmeas = configlist[-1][-1] - configlist[-1][-2]
+402        configlist[-1] = [item // diffmeas for item in configlist[-1]]
+403        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
+404            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+405            offset = configlist[-1][0] - 1
+406            configlist[-1] = [item - offset for item in configlist[-1]]
+407
+408        if r_start[rep] is None:
+409            r_start_index.append(0)
+410        else:
+411            try:
+412                r_start_index.append(configlist[-1].index(r_start[rep]))
+413            except ValueError:
+414                raise Exception('Config %d not in file with range [%d, %d]' % (
+415                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+416
+417        if r_stop[rep] is None:
+418            r_stop_index.append(len(configlist[-1]) - 1)
+419        else:
+420            try:
+421                r_stop_index.append(configlist[-1].index(r_stop[rep]))
+422            except ValueError:
+423                raise Exception('Config %d not in file with range [%d, %d]' % (
+424                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+425
+426    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+427        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+428    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+429    if np.any([step != 1 for step in stepsizes]):
+430        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+431
+432    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+433    t2E_dict = {}
+434    for n in range(nn + 1):
+435        samples = []
+436        for nrep, rep in enumerate(Ysum):
+437            samples.append([])
+438            for cnfg in rep:
+439                samples[-1].append(cnfg[n])
+440            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
+441        new_obs = Obs(samples, rep_names, idl=idl)
+442        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
+443
+444    zero_crossing = np.argmax(np.array(
+445        [o.value for o in t2E_dict.values()]) > 0.0)
+446
+447    x = list(t2E_dict.keys())[zero_crossing - fit_range:
+448                              zero_crossing + fit_range]
+449    y = list(t2E_dict.values())[zero_crossing - fit_range:
+450                                zero_crossing + fit_range]
+451    [o.gamma_method() for o in y]
+452
+453    fit_result = fit_lin(x, y)
+454
+455    if kwargs.get('plot_fit'):
+456        plt.figure()
+457        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+458        ax0 = plt.subplot(gs[0])
+459        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+460        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+461        [o.gamma_method() for o in ymore]
+462        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
+463        xplot = np.linspace(np.min(x), np.max(x))
+464        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
+465        [yi.gamma_method() for yi in yplot]
+466        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
+467        retval = (-fit_result[0] / fit_result[1])
+468        retval.gamma_method()
+469        ylim = ax0.get_ylim()
+470        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
+471        ax0.set_ylim(ylim)
+472        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
+473        xlim = ax0.get_xlim()
+474
+475        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
+476        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
+477        ax1 = plt.subplot(gs[1])
+478        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+479        ax1.tick_params(direction='out')
+480        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+481        ax1.axhline(y=0.0, ls='--', color='k')
+482        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
+483        ax1.set_xlim(xlim)
+484        ax1.set_ylabel('Residuals')
+485        ax1.set_xlabel(r'$t/a^2$')
+486
+487        plt.draw()
+488    return -fit_result[0] / fit_result[1]
 
    
 
 
@@ -1809,6 +1856,13 @@ If True: If the first record divided by the distance between two measurements is
 to the first config (default).
 If False: The config numbers are assumed to be traj_number // difference
 
+
+
    Returns
    
+
+
      
+
    • t0 (Obs):
+Extracted t0
      • 
+
    @@ -1824,52 +1878,57 @@ If False: The config numbers are assumed to be traj_number // difference - 
    526def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
-527    """Read the topologial charge based on openQCD gradient flow measurements.
-528
-529    Parameters
-530    ----------
-531    path : str
-532        path of the measurement files
-533    prefix : str
-534        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-535        Ignored if file names are passed explicitly via keyword files.
-536    c : double
-537        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-538    dtr_cnfg : int
-539        (optional) parameter that specifies the number of measurements
-540        between two configs.
-541        If it is not set, the distance between two measurements
-542        in the file is assumed to be the distance between two configurations.
-543    steps : int
-544        (optional) Distance between two configurations in units of trajectories /
-545         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-546    version : str
-547        Either openQCD or sfqcd, depending on the data.
-548    L : int
-549        spatial length of the lattice in L/a.
-550        HAS to be set if version != sfqcd, since openQCD does not provide
-551        this in the header
-552    r_start : list
-553        list which contains the first config to be read for each replicum.
-554    r_stop : list
-555        list which contains the last config to be read for each replicum.
-556    files : list
-557        specify the exact files that need to be read
-558        from path, practical if e.g. only one replicum is needed
-559    postfix : str
-560        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-561    names : list
-562        Alternative labeling for replicas/ensembles.
-563        Has to have the appropriate length.
-564    Zeuthen_flow : bool
-565        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-566        for version=='sfqcd' If False, the Wilson flow is used.
-567    integer_charge : bool
-568        If True, the charge is rounded towards the nearest integer on each config.
-569    """
-570
-571    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
+            
    536def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
+537    """Read the topologial charge based on openQCD gradient flow measurements.
+538
+539    Parameters
+540    ----------
+541    path : str
+542        path of the measurement files
+543    prefix : str
+544        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+545        Ignored if file names are passed explicitly via keyword files.
+546    c : double
+547        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+548    dtr_cnfg : int
+549        (optional) parameter that specifies the number of measurements
+550        between two configs.
+551        If it is not set, the distance between two measurements
+552        in the file is assumed to be the distance between two configurations.
+553    steps : int
+554        (optional) Distance between two configurations in units of trajectories /
+555         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+556    version : str
+557        Either openQCD or sfqcd, depending on the data.
+558    L : int
+559        spatial length of the lattice in L/a.
+560        HAS to be set if version != sfqcd, since openQCD does not provide
+561        this in the header
+562    r_start : list
+563        list which contains the first config to be read for each replicum.
+564    r_stop : list
+565        list which contains the last config to be read for each replicum.
+566    files : list
+567        specify the exact files that need to be read
+568        from path, practical if e.g. only one replicum is needed
+569    postfix : str
+570        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+571    names : list
+572        Alternative labeling for replicas/ensembles.
+573        Has to have the appropriate length.
+574    Zeuthen_flow : bool
+575        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+576        for version=='sfqcd' If False, the Wilson flow is used.
+577    integer_charge : bool
+578        If True, the charge is rounded towards the nearest integer on each config.
+579
+580    Returns
+581    -------
+582    result : Obs
+583        Read topological charge
+584    """
+585
+586    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
 
    
 
 
@@ -1917,6 +1976,13 @@ for version=='sfqcd' If False, the Wilson flow is used.
 
  • integer_charge (bool):
 If True, the charge is rounded towards the nearest integer on each config.
    • 
 
+
+
    Returns
    
+
+
      
+
    • result (Obs):
+Read topological charge
      • 
+
    @@ -1932,76 +1998,76 @@ If True, the charge is rounded towards the nearest integer on each config. - 
    574def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
-575    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
-576
-577    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
-578
-579    Parameters
-580    ----------
-581    path : str
-582        path of the measurement files
-583    prefix : str
-584        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-585        Ignored if file names are passed explicitly via keyword files.
-586    c : double
-587        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-588    dtr_cnfg : int
-589        (optional) parameter that specifies the number of measurements
-590        between two configs.
-591        If it is not set, the distance between two measurements
-592        in the file is assumed to be the distance between two configurations.
-593    steps : int
-594        (optional) Distance between two configurations in units of trajectories /
-595         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-596    r_start : list
-597        list which contains the first config to be read for each replicum.
-598    r_stop : list
-599        list which contains the last config to be read for each replicum.
-600    files : list
-601        specify the exact files that need to be read
-602        from path, practical if e.g. only one replicum is needed
-603    names : list
-604        Alternative labeling for replicas/ensembles.
-605        Has to have the appropriate length.
-606    postfix : str
-607        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-608    Zeuthen_flow : bool
-609        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
-610    """
-611
-612    if c != 0.3:
-613        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
-614
-615    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
-616    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
-617    L = plaq.tag["L"]
-618    T = plaq.tag["T"]
-619
-620    if T != L:
-621        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
-622
-623    if Zeuthen_flow is not True:
-624        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
-625
-626    t = (c * L) ** 2 / 8
-627
-628    normdict = {4: 0.012341170468270,
-629                6: 0.010162691462430,
-630                8: 0.009031614807931,
-631                10: 0.008744966371393,
-632                12: 0.008650917856809,
-633                14: 8.611154391267955E-03,
-634                16: 0.008591758449508,
-635                20: 0.008575359627103,
-636                24: 0.008569387847540,
-637                28: 8.566803713382559E-03,
-638                32: 0.008565541650006,
-639                40: 8.564480684962046E-03,
-640                48: 8.564098025073460E-03,
-641                64: 8.563853943383087E-03}
+            
    589def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
+590    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+591
+592    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
+593
+594    Parameters
+595    ----------
+596    path : str
+597        path of the measurement files
+598    prefix : str
+599        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+600        Ignored if file names are passed explicitly via keyword files.
+601    c : double
+602        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+603    dtr_cnfg : int
+604        (optional) parameter that specifies the number of measurements
+605        between two configs.
+606        If it is not set, the distance between two measurements
+607        in the file is assumed to be the distance between two configurations.
+608    steps : int
+609        (optional) Distance between two configurations in units of trajectories /
+610         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+611    r_start : list
+612        list which contains the first config to be read for each replicum.
+613    r_stop : list
+614        list which contains the last config to be read for each replicum.
+615    files : list
+616        specify the exact files that need to be read
+617        from path, practical if e.g. only one replicum is needed
+618    names : list
+619        Alternative labeling for replicas/ensembles.
+620        Has to have the appropriate length.
+621    postfix : str
+622        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+623    Zeuthen_flow : bool
+624        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
+625    """
+626
+627    if c != 0.3:
+628        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
+629
+630    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+631    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+632    L = plaq.tag["L"]
+633    T = plaq.tag["T"]
+634
+635    if T != L:
+636        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
+637
+638    if Zeuthen_flow is not True:
+639        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
+640
+641    t = (c * L) ** 2 / 8
 642
-643    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
+643    normdict = {4: 0.012341170468270,
+644                6: 0.010162691462430,
+645                8: 0.009031614807931,
+646                10: 0.008744966371393,
+647                12: 0.008650917856809,
+648                14: 8.611154391267955E-03,
+649                16: 0.008591758449508,
+650                20: 0.008575359627103,
+651                24: 0.008569387847540,
+652                28: 8.566803713382559E-03,
+653                32: 0.008565541650006,
+654                40: 8.564480684962046E-03,
+655                48: 8.564098025073460E-03,
+656                64: 8.563853943383087E-03}
+657
+658    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
 
    
 
 
@@ -2057,25 +2123,30 @@ postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    - 
    918def qtop_projection(qtop, target=0):
-919    """Returns the projection to the topological charge sector defined by target.
-920
-921    Parameters
-922    ----------
-923    path : Obs
-924        Topological charge.
-925    target : int
-926        Specifies the topological sector to be reweighted to (default 0)
-927    """
-928    if qtop.reweighted:
-929        raise Exception('You can not use a reweighted observable for reweighting!')
-930
-931    proj_qtop = []
-932    for n in qtop.deltas:
-933        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
-934
-935    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
-936    return reto
+            
    938def qtop_projection(qtop, target=0):
+939    """Returns the projection to the topological charge sector defined by target.
+940
+941    Parameters
+942    ----------
+943    path : Obs
+944        Topological charge.
+945    target : int
+946        Specifies the topological sector to be reweighted to (default 0)
+947
+948    Returns
+949    -------
+950    reto : Obs
+951        projection to the topological charge sector defined by target
+952    """
+953    if qtop.reweighted:
+954        raise Exception('You can not use a reweighted observable for reweighting!')
+955
+956    proj_qtop = []
+957    for n in qtop.deltas:
+958        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
+959
+960    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
+961    return reto
 
    
 
 
@@ -2089,6 +2160,13 @@ Topological charge.
 
  • target (int):
 Specifies the topological sector to be reweighted to (default 0)
    • 
 
+
+
    Returns
    
+
+
      
+
    • reto (Obs):
+projection to the topological charge sector defined by target
      • 
+
    @@ -2104,57 +2182,62 @@ Specifies the topological sector to be reweighted to (default 0) - 
    939def read_qtop_sector(path, prefix, c, target=0, **kwargs):
-940    """Constructs reweighting factors to a specified topological sector.
-941
-942    Parameters
-943    ----------
-944    path : str
-945        path of the measurement files
-946    prefix : str
-947        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
-948    c : double
-949        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
-950    target : int
-951        Specifies the topological sector to be reweighted to (default 0)
-952    dtr_cnfg : int
-953        (optional) parameter that specifies the number of trajectories
-954        between two configs.
-955        if it is not set, the distance between two measurements
-956        in the file is assumed to be the distance between two configurations.
-957    steps : int
-958        (optional) Distance between two configurations in units of trajectories /
-959         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-960    version : str
-961        version string of the openQCD (sfqcd) version used to create
-962        the ensemble. Default is 2.0. May also be set to sfqcd.
-963    L : int
-964        spatial length of the lattice in L/a.
-965        HAS to be set if version != sfqcd, since openQCD does not provide
-966        this in the header
-967    r_start : list
-968        offset of the first ensemble, making it easier to match
-969        later on with other Obs
-970    r_stop : list
-971        last configurations that need to be read (per replicum)
-972    files : list
-973        specify the exact files that need to be read
-974        from path, practical if e.g. only one replicum is needed
-975    names : list
-976        Alternative labeling for replicas/ensembles.
-977        Has to have the appropriate length
-978    Zeuthen_flow : bool
-979        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-980        for version=='sfqcd' If False, the Wilson flow is used.
-981    """
-982
-983    if not isinstance(target, int):
-984        raise Exception("'target' has to be an integer.")
-985
-986    kwargs['integer_charge'] = True
-987    qtop = read_qtop(path, prefix, c, **kwargs)
-988
-989    return qtop_projection(qtop, target=target)
+            
     964def read_qtop_sector(path, prefix, c, target=0, **kwargs):
+ 965    """Constructs reweighting factors to a specified topological sector.
+ 966
+ 967    Parameters
+ 968    ----------
+ 969    path : str
+ 970        path of the measurement files
+ 971    prefix : str
+ 972        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
+ 973    c : double
+ 974        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
+ 975    target : int
+ 976        Specifies the topological sector to be reweighted to (default 0)
+ 977    dtr_cnfg : int
+ 978        (optional) parameter that specifies the number of trajectories
+ 979        between two configs.
+ 980        if it is not set, the distance between two measurements
+ 981        in the file is assumed to be the distance between two configurations.
+ 982    steps : int
+ 983        (optional) Distance between two configurations in units of trajectories /
+ 984         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 985    version : str
+ 986        version string of the openQCD (sfqcd) version used to create
+ 987        the ensemble. Default is 2.0. May also be set to sfqcd.
+ 988    L : int
+ 989        spatial length of the lattice in L/a.
+ 990        HAS to be set if version != sfqcd, since openQCD does not provide
+ 991        this in the header
+ 992    r_start : list
+ 993        offset of the first ensemble, making it easier to match
+ 994        later on with other Obs
+ 995    r_stop : list
+ 996        last configurations that need to be read (per replicum)
+ 997    files : list
+ 998        specify the exact files that need to be read
+ 999        from path, practical if e.g. only one replicum is needed
+1000    names : list
+1001        Alternative labeling for replicas/ensembles.
+1002        Has to have the appropriate length
+1003    Zeuthen_flow : bool
+1004        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+1005        for version=='sfqcd' If False, the Wilson flow is used.
+1006
+1007    Returns
+1008    -------
+1009    reto : Obs
+1010        projection to the topological charge sector defined by target
+1011    """
+1012
+1013    if not isinstance(target, int):
+1014        raise Exception("'target' has to be an integer.")
+1015
+1016    kwargs['integer_charge'] = True
+1017    qtop = read_qtop(path, prefix, c, **kwargs)
+1018
+1019    return qtop_projection(qtop, target=target)
 
    
 
 
@@ -2201,6 +2284,13 @@ Has to have the appropriate length
 (optional) If True, the Zeuthen flow is used for Qtop. Only possible
 for version=='sfqcd' If False, the Wilson flow is used.
 
+
+
    Returns
    
+
+
      
+
    • reto (Obs):
+projection to the topological charge sector defined by target
      • 
+
    @@ -2216,148 +2306,148 @@ for version=='sfqcd' If False, the Wilson flow is used. - 
     992def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
- 993    """
- 994    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
- 995
- 996    Parameters
- 997    ----------
- 998    path : str
- 999        The directory to search for the files in.
-1000    prefix : str
-1001        The prefix to match the files against.
-1002    qc : str
-1003        The quark combination extension to match the files against.
-1004    corr : str
-1005        The correlator to extract data for.
-1006    sep : str, optional
-1007        The separator to use when parsing the replika names.
-1008    **kwargs
-1009        Additional keyword arguments. The following keyword arguments are recognized:
-1010
-1011        - names (List[str]): A list of names to use for the replicas.
-1012
-1013    Returns
-1014    -------
-1015    Corr
-1016        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
-1017    or
-1018    CObs
-1019        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
-1020
-1021
-1022    Raises
-1023    ------
-1024    FileNotFoundError
-1025        If no files matching the specified prefix and quark combination extension are found in the specified directory.
-1026    IOError
-1027        If there is an error reading a file.
-1028    struct.error
-1029        If there is an error unpacking binary data.
-1030    """
-1031
-1032    found = []
-1033    files = []
-1034    names = []
-1035    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
-1036        found.extend(filenames)
-1037        break
-1038
-1039    for f in found:
-1040        if fnmatch.fnmatch(f, prefix + "*.ms5_xsf_" + qc + ".dat"):
-1041            files.append(f)
-1042            if not sep == "":
-1043                names.append(prefix + "|r" + f.split(".")[0].split(sep)[1])
-1044            else:
-1045                names.append(prefix)
-1046    files = sorted(files)
-1047
-1048    if "names" in kwargs:
-1049        names = kwargs.get("names")
-1050    else:
-1051        names = sorted(names)
-1052
-1053    cnfgs = []
-1054    realsamples = []
-1055    imagsamples = []
-1056    repnum = 0
-1057    for file in files:
-1058        with open(path + "/" + file, "rb") as fp:
-1059
-1060            t = fp.read(8)
-1061            kappa = struct.unpack('d', t)[0]
-1062            t = fp.read(8)
-1063            csw = struct.unpack('d', t)[0]
-1064            t = fp.read(8)
-1065            dF = struct.unpack('d', t)[0]
-1066            t = fp.read(8)
-1067            zF = struct.unpack('d', t)[0]
+            
    1022def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
+1023    """
+1024    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
+1025
+1026    Parameters
+1027    ----------
+1028    path : str
+1029        The directory to search for the files in.
+1030    prefix : str
+1031        The prefix to match the files against.
+1032    qc : str
+1033        The quark combination extension to match the files against.
+1034    corr : str
+1035        The correlator to extract data for.
+1036    sep : str, optional
+1037        The separator to use when parsing the replika names.
+1038    **kwargs
+1039        Additional keyword arguments. The following keyword arguments are recognized:
+1040
+1041        - names (List[str]): A list of names to use for the replicas.
+1042
+1043    Returns
+1044    -------
+1045    Corr
+1046        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
+1047    or
+1048    CObs
+1049        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
+1050
+1051
+1052    Raises
+1053    ------
+1054    FileNotFoundError
+1055        If no files matching the specified prefix and quark combination extension are found in the specified directory.
+1056    IOError
+1057        If there is an error reading a file.
+1058    struct.error
+1059        If there is an error unpacking binary data.
+1060    """
+1061
+1062    found = []
+1063    files = []
+1064    names = []
+1065    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
+1066        found.extend(filenames)
+1067        break
 1068
-1069            t = fp.read(4)
-1070            tmax = struct.unpack('i', t)[0]
-1071            t = fp.read(4)
-1072            bnd = struct.unpack('i', t)[0]
-1073
-1074            placesBI = ["gS", "gP",
-1075                        "gA", "gV",
-1076                        "gVt", "lA",
-1077                        "lV", "lVt",
-1078                        "lT", "lTt"]
-1079            placesBB = ["g1", "l1"]
-1080
-1081            # the chunks have the following structure:
-1082            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
-1083
-1084            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
-1085            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
-1086            cnfgs.append([])
-1087            realsamples.append([])
-1088            imagsamples.append([])
-1089            for t in range(tmax):
-1090                realsamples[repnum].append([])
-1091                imagsamples[repnum].append([])
-1092
-1093            while True:
-1094                cnfgt = fp.read(chunksize)
-1095                if not cnfgt:
-1096                    break
-1097                asascii = struct.unpack(packstr, cnfgt)
-1098                cnfg = asascii[0]
-1099                cnfgs[repnum].append(cnfg)
-1100
-1101                if corr not in placesBB:
-1102                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
-1103                else:
-1104                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
-1105
-1106                corrres = [[], []]
-1107                for i in range(len(tmpcorr)):
-1108                    corrres[i % 2].append(tmpcorr[i])
-1109                for t in range(int(len(tmpcorr) / 2)):
-1110                    realsamples[repnum][t].append(corrres[0][t])
-1111                for t in range(int(len(tmpcorr) / 2)):
-1112                    imagsamples[repnum][t].append(corrres[1][t])
-1113        repnum += 1
-1114
-1115    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
-1116    for rep in range(1, repnum):
-1117        s += ", " + str(len(realsamples[rep][t]))
-1118    s += " samples"
-1119    print(s)
-1120    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
-1121
-1122    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
-1123
-1124    compObs = []
-1125
-1126    for t in range(int(len(tmpcorr) / 2)):
-1127        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
-1128                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
-1129
-1130    if len(compObs) == 1:
-1131        return compObs[0]
-1132    else:
-1133        return Corr(compObs)
+1069    for f in found:
+1070        if fnmatch.fnmatch(f, prefix + "*.ms5_xsf_" + qc + ".dat"):
+1071            files.append(f)
+1072            if not sep == "":
+1073                names.append(prefix + "|r" + f.split(".")[0].split(sep)[1])
+1074            else:
+1075                names.append(prefix)
+1076    files = sorted(files)
+1077
+1078    if "names" in kwargs:
+1079        names = kwargs.get("names")
+1080    else:
+1081        names = sorted(names)
+1082
+1083    cnfgs = []
+1084    realsamples = []
+1085    imagsamples = []
+1086    repnum = 0
+1087    for file in files:
+1088        with open(path + "/" + file, "rb") as fp:
+1089
+1090            t = fp.read(8)
+1091            kappa = struct.unpack('d', t)[0]
+1092            t = fp.read(8)
+1093            csw = struct.unpack('d', t)[0]
+1094            t = fp.read(8)
+1095            dF = struct.unpack('d', t)[0]
+1096            t = fp.read(8)
+1097            zF = struct.unpack('d', t)[0]
+1098
+1099            t = fp.read(4)
+1100            tmax = struct.unpack('i', t)[0]
+1101            t = fp.read(4)
+1102            bnd = struct.unpack('i', t)[0]
+1103
+1104            placesBI = ["gS", "gP",
+1105                        "gA", "gV",
+1106                        "gVt", "lA",
+1107                        "lV", "lVt",
+1108                        "lT", "lTt"]
+1109            placesBB = ["g1", "l1"]
+1110
+1111            # the chunks have the following structure:
+1112            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
+1113
+1114            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
+1115            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
+1116            cnfgs.append([])
+1117            realsamples.append([])
+1118            imagsamples.append([])
+1119            for t in range(tmax):
+1120                realsamples[repnum].append([])
+1121                imagsamples[repnum].append([])
+1122
+1123            while True:
+1124                cnfgt = fp.read(chunksize)
+1125                if not cnfgt:
+1126                    break
+1127                asascii = struct.unpack(packstr, cnfgt)
+1128                cnfg = asascii[0]
+1129                cnfgs[repnum].append(cnfg)
+1130
+1131                if corr not in placesBB:
+1132                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
+1133                else:
+1134                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
+1135
+1136                corrres = [[], []]
+1137                for i in range(len(tmpcorr)):
+1138                    corrres[i % 2].append(tmpcorr[i])
+1139                for t in range(int(len(tmpcorr) / 2)):
+1140                    realsamples[repnum][t].append(corrres[0][t])
+1141                for t in range(int(len(tmpcorr) / 2)):
+1142                    imagsamples[repnum][t].append(corrres[1][t])
+1143        repnum += 1
+1144
+1145    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
+1146    for rep in range(1, repnum):
+1147        s += ", " + str(len(realsamples[rep][t]))
+1148    s += " samples"
+1149    print(s)
+1150    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
+1151
+1152    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
+1153
+1154    compObs = []
+1155
+1156    for t in range(int(len(tmpcorr) / 2)):
+1157        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
+1158                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
+1159
+1160    if len(compObs) == 1:
+1161        return compObs[0]
+1162    else:
+1163        return Corr(compObs)
 
    
 
 
diff --git a/docs/pyerrors/input/pandas.html b/docs/pyerrors/input/pandas.html
index af4857f6..7c1526c8 100644
--- a/docs/pyerrors/input/pandas.html
+++ b/docs/pyerrors/input/pandas.html
@@ -109,133 +109,151 @@
      22        How to behave if table already exists. Options 'fail', 'replace', 'append'.
  23    gz : bool
  24        If True the json strings are gzipped.
- 25    """
- 26    se_df = _serialize_df(df, gz=gz)
- 27    con = sqlite3.connect(db)
- 28    se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs)
- 29    con.close()
- 30
- 31
- 32def read_sql(sql, db, auto_gamma=False, **kwargs):
- 33    """Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
+ 25
+ 26    Returns
+ 27    -------
+ 28    None
+ 29    """
+ 30    se_df = _serialize_df(df, gz=gz)
+ 31    con = sqlite3.connect(db)
+ 32    se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs)
+ 33    con.close()
  34
- 35    Parameters
- 36    ----------
- 37    sql : str
- 38        SQL query to be executed.
- 39    db : str
- 40        Path to the sqlite database.
- 41    auto_gamma : bool
- 42        If True applies the gamma_method to all imported Obs objects with the default parameters for
- 43        the error analysis. Default False.
- 44    """
- 45    con = sqlite3.connect(db)
- 46    extract_df = pd.read_sql(sql, con, **kwargs)
- 47    con.close()
- 48    return _deserialize_df(extract_df, auto_gamma=auto_gamma)
- 49
- 50
- 51def dump_df(df, fname, gz=True):
- 52    """Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
- 53
- 54    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized
- 55    json format of pyerrors.
- 56
- 57    Parameters
- 58    ----------
- 59    df : pandas.DataFrame
- 60        Dataframe to be dumped to a file.
- 61    fname : str
- 62        Filename of the output file.
- 63    gz : bool
- 64        If True, the output is a gzipped csv file. If False, the output is a csv file.
- 65    """
- 66    out = _serialize_df(df, gz=False)
- 67
- 68    if not fname.endswith('.csv'):
- 69        fname += '.csv'
- 70
- 71    if gz is True:
- 72        if not fname.endswith('.gz'):
- 73            fname += '.gz'
- 74        out.to_csv(fname, index=False, compression='gzip')
- 75    else:
- 76        out.to_csv(fname, index=False)
- 77
- 78
- 79def load_df(fname, auto_gamma=False, gz=True):
- 80    """Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
- 81
- 82    Parameters
- 83    ----------
- 84    fname : str
- 85        Filename of the input file.
- 86    auto_gamma : bool
- 87        If True applies the gamma_method to all imported Obs objects with the default parameters for
- 88        the error analysis. Default False.
- 89    gz : bool
- 90        If True, assumes that data is gzipped. If False, assumes JSON file.
- 91    """
- 92    if not fname.endswith('.csv') and not fname.endswith('.gz'):
- 93        fname += '.csv'
+ 35
+ 36def read_sql(sql, db, auto_gamma=False, **kwargs):
+ 37    """Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
+ 38
+ 39    Parameters
+ 40    ----------
+ 41    sql : str
+ 42        SQL query to be executed.
+ 43    db : str
+ 44        Path to the sqlite database.
+ 45    auto_gamma : bool
+ 46        If True applies the gamma_method to all imported Obs objects with the default parameters for
+ 47        the error analysis. Default False.
+ 48
+ 49    Returns
+ 50    -------
+ 51    data : pandas.DataFrame
+ 52        Dataframe with the content of the sqlite database.
+ 53    """
+ 54    con = sqlite3.connect(db)
+ 55    extract_df = pd.read_sql(sql, con, **kwargs)
+ 56    con.close()
+ 57    return _deserialize_df(extract_df, auto_gamma=auto_gamma)
+ 58
+ 59
+ 60def dump_df(df, fname, gz=True):
+ 61    """Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
+ 62
+ 63    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized
+ 64    json format of pyerrors.
+ 65
+ 66    Parameters
+ 67    ----------
+ 68    df : pandas.DataFrame
+ 69        Dataframe to be dumped to a file.
+ 70    fname : str
+ 71        Filename of the output file.
+ 72    gz : bool
+ 73        If True, the output is a gzipped csv file. If False, the output is a csv file.
+ 74
+ 75    Returns
+ 76    -------
+ 77    None
+ 78    """
+ 79    out = _serialize_df(df, gz=False)
+ 80
+ 81    if not fname.endswith('.csv'):
+ 82        fname += '.csv'
+ 83
+ 84    if gz is True:
+ 85        if not fname.endswith('.gz'):
+ 86            fname += '.gz'
+ 87        out.to_csv(fname, index=False, compression='gzip')
+ 88    else:
+ 89        out.to_csv(fname, index=False)
+ 90
+ 91
+ 92def load_df(fname, auto_gamma=False, gz=True):
+ 93    """Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
  94
- 95    if gz is True:
- 96        if not fname.endswith('.gz'):
- 97            fname += '.gz'
- 98        with gzip.open(fname) as f:
- 99            re_import = pd.read_csv(f)
-100    else:
-101        if fname.endswith('.gz'):
-102            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-103        re_import = pd.read_csv(fname)
+ 95    Parameters
+ 96    ----------
+ 97    fname : str
+ 98        Filename of the input file.
+ 99    auto_gamma : bool
+100        If True applies the gamma_method to all imported Obs objects with the default parameters for
+101        the error analysis. Default False.
+102    gz : bool
+103        If True, assumes that data is gzipped. If False, assumes JSON file.
 104
-105    return _deserialize_df(re_import, auto_gamma=auto_gamma)
-106
-107
-108def _serialize_df(df, gz=False):
-109    """Serializes all Obs or Corr valued columns into json strings according to the pyerrors json specification.
-110
-111    Parameters
-112    ----------
-113    df : pandas.DataFrame
-114        DataFrame to be serilized.
-115    gz: bool
-116        gzip the json string representation. Default False.
-117    """
-118    out = df.copy()
-119    for column in out:
-120        if isinstance(out[column][0], (Obs, Corr)):
-121            out[column] = out[column].transform(lambda x: create_json_string(x, indent=0))
-122            if gz is True:
-123                out[column] = out[column].transform(lambda x: gzip.compress(x.encode('utf-8')))
-124    return out
+105    Returns
+106    -------
+107    data : pandas.DataFrame
+108        Dataframe with the content of the sqlite database.
+109    """
+110    if not fname.endswith('.csv') and not fname.endswith('.gz'):
+111        fname += '.csv'
+112
+113    if gz is True:
+114        if not fname.endswith('.gz'):
+115            fname += '.gz'
+116        with gzip.open(fname) as f:
+117            re_import = pd.read_csv(f)
+118    else:
+119        if fname.endswith('.gz'):
+120            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+121        re_import = pd.read_csv(fname)
+122
+123    return _deserialize_df(re_import, auto_gamma=auto_gamma)
+124
 125
-126
-127def _deserialize_df(df, auto_gamma=False):
-128    """Deserializes all pyerrors json strings into Obs or Corr objects according to the pyerrors json specification.
-129
-130    Parameters
-131    ----------
-132    df : pandas.DataFrame
-133        DataFrame to be deserilized.
-134    auto_gamma : bool
-135        If True applies the gamma_method to all imported Obs objects with the default parameters for
-136        the error analysis. Default False.
-137
-138    Notes:
-139    ------
-140    In case any column of the DataFrame is gzipped it is gunzipped in the process.
-141    """
-142    for column in df.select_dtypes(include="object"):
-143        if isinstance(df[column][0], bytes):
-144            if df[column][0].startswith(b"\x1f\x8b\x08\x00"):
-145                df[column] = df[column].transform(lambda x: gzip.decompress(x).decode('utf-8'))
-146        if isinstance(df[column][0], str):
-147            if '"program":' in df[column][0][:20]:
-148                df[column] = df[column].transform(lambda x: import_json_string(x, verbose=False))
-149                if auto_gamma is True:
-150                    df[column].apply(lambda x: x.gamma_method())
-151    return df
+126def _serialize_df(df, gz=False):
+127    """Serializes all Obs or Corr valued columns into json strings according to the pyerrors json specification.
+128
+129    Parameters
+130    ----------
+131    df : pandas.DataFrame
+132        DataFrame to be serilized.
+133    gz: bool
+134        gzip the json string representation. Default False.
+135    """
+136    out = df.copy()
+137    for column in out:
+138        if isinstance(out[column][0], (Obs, Corr)):
+139            out[column] = out[column].transform(lambda x: create_json_string(x, indent=0))
+140            if gz is True:
+141                out[column] = out[column].transform(lambda x: gzip.compress(x.encode('utf-8')))
+142    return out
+143
+144
+145def _deserialize_df(df, auto_gamma=False):
+146    """Deserializes all pyerrors json strings into Obs or Corr objects according to the pyerrors json specification.
+147
+148    Parameters
+149    ----------
+150    df : pandas.DataFrame
+151        DataFrame to be deserilized.
+152    auto_gamma : bool
+153        If True applies the gamma_method to all imported Obs objects with the default parameters for
+154        the error analysis. Default False.
+155
+156    Notes:
+157    ------
+158    In case any column of the DataFrame is gzipped it is gunzipped in the process.
+159    """
+160    for column in df.select_dtypes(include="object"):
+161        if isinstance(df[column][0], bytes):
+162            if df[column][0].startswith(b"\x1f\x8b\x08\x00"):
+163                df[column] = df[column].transform(lambda x: gzip.decompress(x).decode('utf-8'))
+164        if isinstance(df[column][0], str):
+165            if '"program":' in df[column][0][:20]:
+166                df[column] = df[column].transform(lambda x: import_json_string(x, verbose=False))
+167                if auto_gamma is True:
+168                    df[column].apply(lambda x: x.gamma_method())
+169    return df
    @@ -266,11 +284,15 @@ 23 How to behave if table already exists. Options 'fail', 'replace', 'append'. 24 gz : bool 25 If True the json strings are gzipped. -26 """ -27 se_df = _serialize_df(df, gz=gz) -28 con = sqlite3.connect(db) -29 se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs) -30 con.close() +26 +27 Returns +28 ------- +29 None +30 """ +31 se_df = _serialize_df(df, gz=gz) +32 con = sqlite3.connect(db) +33 se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs) +34 con.close() @@ -290,6 +312,12 @@ How to behave if table already exists. Options 'fail', 'replace', 'append'.
  • gz (bool): If True the json strings are gzipped.
    • + +
    Returns
    + + @@ -305,23 +333,28 @@ If True the json strings are gzipped. - 
    33def read_sql(sql, db, auto_gamma=False, **kwargs):
-34    """Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
-35
-36    Parameters
-37    ----------
-38    sql : str
-39        SQL query to be executed.
-40    db : str
-41        Path to the sqlite database.
-42    auto_gamma : bool
-43        If True applies the gamma_method to all imported Obs objects with the default parameters for
-44        the error analysis. Default False.
-45    """
-46    con = sqlite3.connect(db)
-47    extract_df = pd.read_sql(sql, con, **kwargs)
-48    con.close()
-49    return _deserialize_df(extract_df, auto_gamma=auto_gamma)
+            
    37def read_sql(sql, db, auto_gamma=False, **kwargs):
+38    """Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
+39
+40    Parameters
+41    ----------
+42    sql : str
+43        SQL query to be executed.
+44    db : str
+45        Path to the sqlite database.
+46    auto_gamma : bool
+47        If True applies the gamma_method to all imported Obs objects with the default parameters for
+48        the error analysis. Default False.
+49
+50    Returns
+51    -------
+52    data : pandas.DataFrame
+53        Dataframe with the content of the sqlite database.
+54    """
+55    con = sqlite3.connect(db)
+56    extract_df = pd.read_sql(sql, con, **kwargs)
+57    con.close()
+58    return _deserialize_df(extract_df, auto_gamma=auto_gamma)
 
    
 
 
@@ -338,6 +371,13 @@ Path to the sqlite database.
 If True applies the gamma_method to all imported Obs objects with the default parameters for
 the error analysis. Default False.
 
+
+
    Returns
    
+
+
      
+
    • data (pandas.DataFrame):
+Dataframe with the content of the sqlite database.
      • 
+
    @@ -353,32 +393,36 @@ the error analysis. Default False. - 
    52def dump_df(df, fname, gz=True):
-53    """Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
-54
-55    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized
-56    json format of pyerrors.
-57
-58    Parameters
-59    ----------
-60    df : pandas.DataFrame
-61        Dataframe to be dumped to a file.
-62    fname : str
-63        Filename of the output file.
-64    gz : bool
-65        If True, the output is a gzipped csv file. If False, the output is a csv file.
-66    """
-67    out = _serialize_df(df, gz=False)
-68
-69    if not fname.endswith('.csv'):
-70        fname += '.csv'
-71
-72    if gz is True:
-73        if not fname.endswith('.gz'):
-74            fname += '.gz'
-75        out.to_csv(fname, index=False, compression='gzip')
-76    else:
-77        out.to_csv(fname, index=False)
+            
    61def dump_df(df, fname, gz=True):
+62    """Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
+63
+64    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized
+65    json format of pyerrors.
+66
+67    Parameters
+68    ----------
+69    df : pandas.DataFrame
+70        Dataframe to be dumped to a file.
+71    fname : str
+72        Filename of the output file.
+73    gz : bool
+74        If True, the output is a gzipped csv file. If False, the output is a csv file.
+75
+76    Returns
+77    -------
+78    None
+79    """
+80    out = _serialize_df(df, gz=False)
+81
+82    if not fname.endswith('.csv'):
+83        fname += '.csv'
+84
+85    if gz is True:
+86        if not fname.endswith('.gz'):
+87            fname += '.gz'
+88        out.to_csv(fname, index=False, compression='gzip')
+89    else:
+90        out.to_csv(fname, index=False)
 
    
 
 
@@ -397,6 +441,12 @@ Filename of the output file.
 
  • gz (bool):
 If True, the output is a gzipped csv file. If False, the output is a csv file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • None
      • 
+
    @@ -412,33 +462,38 @@ If True, the output is a gzipped csv file. If False, the output is a csv file. - 
     80def load_df(fname, auto_gamma=False, gz=True):
- 81    """Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
- 82
- 83    Parameters
- 84    ----------
- 85    fname : str
- 86        Filename of the input file.
- 87    auto_gamma : bool
- 88        If True applies the gamma_method to all imported Obs objects with the default parameters for
- 89        the error analysis. Default False.
- 90    gz : bool
- 91        If True, assumes that data is gzipped. If False, assumes JSON file.
- 92    """
- 93    if not fname.endswith('.csv') and not fname.endswith('.gz'):
- 94        fname += '.csv'
+            
     93def load_df(fname, auto_gamma=False, gz=True):
+ 94    """Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
  95
- 96    if gz is True:
- 97        if not fname.endswith('.gz'):
- 98            fname += '.gz'
- 99        with gzip.open(fname) as f:
-100            re_import = pd.read_csv(f)
-101    else:
-102        if fname.endswith('.gz'):
-103            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
-104        re_import = pd.read_csv(fname)
+ 96    Parameters
+ 97    ----------
+ 98    fname : str
+ 99        Filename of the input file.
+100    auto_gamma : bool
+101        If True applies the gamma_method to all imported Obs objects with the default parameters for
+102        the error analysis. Default False.
+103    gz : bool
+104        If True, assumes that data is gzipped. If False, assumes JSON file.
 105
-106    return _deserialize_df(re_import, auto_gamma=auto_gamma)
+106    Returns
+107    -------
+108    data : pandas.DataFrame
+109        Dataframe with the content of the sqlite database.
+110    """
+111    if not fname.endswith('.csv') and not fname.endswith('.gz'):
+112        fname += '.csv'
+113
+114    if gz is True:
+115        if not fname.endswith('.gz'):
+116            fname += '.gz'
+117        with gzip.open(fname) as f:
+118            re_import = pd.read_csv(f)
+119    else:
+120        if fname.endswith('.gz'):
+121            warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
+122        re_import = pd.read_csv(fname)
+123
+124    return _deserialize_df(re_import, auto_gamma=auto_gamma)
 
    
 
 
@@ -455,6 +510,13 @@ the error analysis. Default False.
 
  • gz (bool):
 If True, assumes that data is gzipped. If False, assumes JSON file.
    • 
 
+
+
    Returns
    
+
+
      
+
    • data (pandas.DataFrame):
+Dataframe with the content of the sqlite database.
      • 
+
    diff --git a/docs/pyerrors/input/sfcf.html b/docs/pyerrors/input/sfcf.html index 14a86390..16205ce2 100644 --- a/docs/pyerrors/input/sfcf.html +++ b/docs/pyerrors/input/sfcf.html @@ -133,314 +133,320 @@ 55 files: list 56 list of files to be read per replica, default is all. 57 for non-compact output format, hand the folders to be read here. - 58 check_configs: + 58 check_configs: list[list[int]] 59 list of list of supposed configs, eg. [range(1,1000)] 60 for one replicum with 1000 configs - 61 """ - 62 if kwargs.get('im'): - 63 im = 1 - 64 part = 'imaginary' - 65 else: - 66 im = 0 - 67 part = 'real' - 68 if "replica" in kwargs: - 69 reps = kwargs.get("replica") - 70 if corr_type == 'bb': - 71 b2b = True - 72 single = True - 73 elif corr_type == 'bib': - 74 b2b = True - 75 single = False - 76 else: - 77 b2b = False - 78 single = False - 79 compact = True - 80 appended = False - 81 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] - 82 - 83 if version not in known_versions: - 84 raise Exception("This version is not known!") - 85 if (version[-1] == "c"): - 86 appended = False - 87 compact = True - 88 version = version[:-1] - 89 elif (version[-1] == "a"): - 90 appended = True - 91 compact = False - 92 version = version[:-1] - 93 else: - 94 compact = False - 95 appended = False - 96 read = 0 - 97 T = 0 - 98 start = 0 - 99 ls = [] -100 if "replica" in kwargs: -101 ls = reps -102 else: -103 for (dirpath, dirnames, filenames) in os.walk(path): -104 if not appended: -105 ls.extend(dirnames) -106 else: -107 ls.extend(filenames) -108 break -109 if not ls: -110 raise Exception('Error, directory not found') -111 # Exclude folders with different names -112 for exc in ls: -113 if not fnmatch.fnmatch(exc, prefix + '*'): -114 ls = list(set(ls) - set([exc])) -115 -116 if not appended: -117 if len(ls) > 1: -118 # New version, to cope with ids, etc. -119 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) -120 replica = len(ls) -121 else: -122 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) -123 print('Read', part, 'part of', name, 'from', prefix[:-1], -124 ',', replica, 'replica') -125 if 'names' in kwargs: -126 new_names = kwargs.get('names') -127 if len(new_names) != len(set(new_names)): -128 raise Exception("names are not unique!") -129 if len(new_names) != replica: -130 raise Exception('Names does not have the required length', replica) -131 else: -132 new_names = [] -133 if not appended: -134 for entry in ls: -135 try: -136 idx = entry.index('r') -137 except Exception: -138 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -139 -140 if 'ens_name' in kwargs: -141 new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) -142 else: -143 new_names.append(entry[:idx] + '|' + entry[idx:]) -144 else: + 61 + 62 Returns + 63 ------- + 64 result: list[Obs] + 65 list of Observables with length T, observable per timeslice. + 66 bb-type correlators have length 1. + 67 """ + 68 if kwargs.get('im'): + 69 im = 1 + 70 part = 'imaginary' + 71 else: + 72 im = 0 + 73 part = 'real' + 74 if "replica" in kwargs: + 75 reps = kwargs.get("replica") + 76 if corr_type == 'bb': + 77 b2b = True + 78 single = True + 79 elif corr_type == 'bib': + 80 b2b = True + 81 single = False + 82 else: + 83 b2b = False + 84 single = False + 85 compact = True + 86 appended = False + 87 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] + 88 + 89 if version not in known_versions: + 90 raise Exception("This version is not known!") + 91 if (version[-1] == "c"): + 92 appended = False + 93 compact = True + 94 version = version[:-1] + 95 elif (version[-1] == "a"): + 96 appended = True + 97 compact = False + 98 version = version[:-1] + 99 else: +100 compact = False +101 appended = False +102 read = 0 +103 T = 0 +104 start = 0 +105 ls = [] +106 if "replica" in kwargs: +107 ls = reps +108 else: +109 for (dirpath, dirnames, filenames) in os.walk(path): +110 if not appended: +111 ls.extend(dirnames) +112 else: +113 ls.extend(filenames) +114 break +115 if not ls: +116 raise Exception('Error, directory not found') +117 # Exclude folders with different names +118 for exc in ls: +119 if not fnmatch.fnmatch(exc, prefix + '*'): +120 ls = list(set(ls) - set([exc])) +121 +122 if not appended: +123 if len(ls) > 1: +124 # New version, to cope with ids, etc. +125 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) +126 replica = len(ls) +127 else: +128 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) +129 print('Read', part, 'part of', name, 'from', prefix[:-1], +130 ',', replica, 'replica') +131 if 'names' in kwargs: +132 new_names = kwargs.get('names') +133 if len(new_names) != len(set(new_names)): +134 raise Exception("names are not unique!") +135 if len(new_names) != replica: +136 raise Exception('Names does not have the required length', replica) +137 else: +138 new_names = [] +139 if not appended: +140 for entry in ls: +141 try: +142 idx = entry.index('r') +143 except Exception: +144 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") 145 -146 for exc in ls: -147 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -148 ls = list(set(ls) - set([exc])) -149 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -150 for entry in ls: -151 myentry = entry[:-len(name) - 1] -152 try: -153 idx = myentry.index('r') -154 except Exception: -155 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -156 -157 if 'ens_name' in kwargs: -158 new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) -159 else: -160 new_names.append(myentry[:idx] + '|' + myentry[idx:]) -161 idl = [] -162 if not appended: -163 for i, item in enumerate(ls): -164 sub_ls = [] -165 if "files" in kwargs: -166 sub_ls = kwargs.get("files") -167 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -168 else: -169 for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): -170 if compact: -171 sub_ls.extend(filenames) -172 else: -173 sub_ls.extend(dirnames) -174 break -175 if compact: -176 for exc in sub_ls: -177 if not fnmatch.fnmatch(exc, prefix + '*'): -178 sub_ls = list(set(sub_ls) - set([exc])) -179 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -180 else: -181 for exc in sub_ls: -182 if not fnmatch.fnmatch(exc, 'cfg*'): -183 sub_ls = list(set(sub_ls) - set([exc])) -184 sub_ls.sort(key=lambda x: int(x[3:])) -185 rep_idl = [] -186 no_cfg = len(sub_ls) -187 for cfg in sub_ls: -188 try: -189 if compact: -190 rep_idl.append(int(cfg.split(cfg_separator)[-1])) -191 else: -192 rep_idl.append(int(cfg[3:])) -193 except Exception: -194 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) -195 rep_idl.sort() -196 # maybe there is a better way to print the idls -197 print(item, ':', no_cfg, ' configurations') -198 idl.append(rep_idl) -199 # here we have found all the files we need to look into. -200 if i == 0: -201 # here, we want to find the place within the file, -202 # where the correlator we need is stored. -203 # to do so, the pattern needed is put together -204 # from the input values -205 if version == "0.0": -206 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) -207 # if b2b, a second wf is needed -208 if b2b: -209 pattern += ", wf_2 " + str(wf2) -210 qs = quarks.split(" ") -211 pattern += " : " + qs[0] + " - " + qs[1] -212 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -213 for k, line in enumerate(file): -214 if read == 1 and not line.strip() and k > start + 1: -215 break -216 if read == 1 and k >= start: -217 T += 1 -218 if pattern in line: -219 read = 1 -220 start = k + 1 -221 print(str(T) + " entries found.") -222 file.close() -223 else: -224 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -225 if b2b: -226 pattern += '\nwf_2 ' + str(wf2) -227 # and the file is parsed through to find the pattern -228 if compact: -229 file = open(path + '/' + item + '/' + sub_ls[0], "r") -230 else: -231 # for non-compactified versions of the files -232 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -233 -234 content = file.read() -235 match = re.search(pattern, content) -236 if match: -237 start_read = content.count('\n', 0, match.start()) + 5 + b2b -238 end_match = re.search(r'\n\s*\n', content[match.start():]) -239 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b -240 assert T > 0 -241 print(T, 'entries, starting to read in line', start_read) -242 file.close() -243 else: -244 file.close() -245 raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') -246 -247 # we found where the correlator -248 # that is to be read is in the files -249 # after preparing the datastructure -250 # the correlators get parsed into... -251 deltas = [] -252 for j in range(T): -253 deltas.append([]) -254 -255 for t in range(T): -256 deltas[t].append(np.zeros(no_cfg)) -257 if compact: -258 for cfg in range(no_cfg): -259 with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: -260 lines = fp.readlines() -261 # check, if the correlator is in fact -262 # printed completely -263 if (start_read + T > len(lines)): -264 raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") -265 # and start to read the correlator. -266 # the range here is chosen like this, -267 # since this allows for implementing -268 # a security check for every read correlator later... -269 for k in range(start_read - 6, start_read + T): -270 if k == start_read - 5 - b2b: -271 if lines[k].strip() != 'name ' + name: -272 raise Exception('Wrong format', sub_ls[cfg]) -273 if (k >= start_read and k < start_read + T): -274 floats = list(map(float, lines[k].split())) -275 deltas[k - start_read][i][cfg] = floats[-2:][im] -276 else: -277 for cnfg, subitem in enumerate(sub_ls): -278 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: -279 # since the non-compatified files -280 # are typically not so long, -281 # we can iterate over the whole file. -282 # here one can also implement the chekc from above. -283 for k, line in enumerate(fp): -284 if (k >= start_read and k < start_read + T): -285 floats = list(map(float, line.split())) -286 if version == "0.0": -287 deltas[k - start][i][cnfg] = floats[im - single] -288 else: -289 deltas[k - start_read][i][cnfg] = floats[1 + im - single] -290 -291 else: -292 if "files" in kwargs: -293 ls = kwargs.get("files") -294 else: -295 for exc in ls: -296 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -297 ls = list(set(ls) - set([exc])) -298 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -299 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -300 if b2b: -301 pattern += '\nwf_2 ' + str(wf2) -302 for rep, file in enumerate(ls): -303 rep_idl = [] -304 with open(path + '/' + file, 'r') as fp: -305 content = fp.readlines() -306 data_starts = [] -307 for linenumber, line in enumerate(content): -308 if "[run]" in line: -309 data_starts.append(linenumber) -310 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: -311 raise Exception("Irregularities in file structure found, not all runs have the same output length") -312 chunk = content[:data_starts[1]] -313 for linenumber, line in enumerate(chunk): -314 if line.startswith("gauge_name"): -315 gauge_line = linenumber -316 elif line.startswith("[correlator]"): -317 corr_line = linenumber -318 found_pat = "" -319 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: -320 found_pat += li -321 if re.search(pattern, found_pat): -322 start_read = corr_line + 7 + b2b -323 break -324 endline = corr_line + 6 + b2b -325 while not chunk[endline] == "\n": -326 endline += 1 -327 T = endline - start_read -328 if rep == 0: -329 deltas = [] -330 for t in range(T): -331 deltas.append([]) -332 for t in range(T): -333 deltas[t].append(np.zeros(len(data_starts))) -334 # all other chunks should follow the same structure -335 for cnfg in range(len(data_starts)): -336 start = data_starts[cnfg] -337 stop = start + data_starts[1] -338 chunk = content[start:stop] -339 try: -340 rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1])) -341 except Exception: -342 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) -343 -344 found_pat = "" -345 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: -346 found_pat += li -347 if re.search(pattern, found_pat): -348 for t, line in enumerate(chunk[start_read:start_read + T]): -349 floats = list(map(float, line.split())) -350 deltas[t][rep][cnfg] = floats[im + 1 - single] -351 idl.append(rep_idl) -352 -353 if "check_configs" in kwargs: -354 print("Checking for missing configs...") -355 che = kwargs.get("check_configs") -356 if not (len(che) == len(idl)): -357 raise Exception("check_configs has to be the same length as replica!") -358 for r in range(len(idl)): -359 print("checking " + new_names[r]) -360 utils.check_idl(idl[r], che[r]) -361 print("Done") -362 result = [] -363 for t in range(T): -364 result.append(Obs(deltas[t], new_names, idl=idl)) -365 return result +146 if 'ens_name' in kwargs: +147 new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) +148 else: +149 new_names.append(entry[:idx] + '|' + entry[idx:]) +150 else: +151 +152 for exc in ls: +153 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +154 ls = list(set(ls) - set([exc])) +155 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +156 for entry in ls: +157 myentry = entry[:-len(name) - 1] +158 try: +159 idx = myentry.index('r') +160 except Exception: +161 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") +162 +163 if 'ens_name' in kwargs: +164 new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) +165 else: +166 new_names.append(myentry[:idx] + '|' + myentry[idx:]) +167 idl = [] +168 if not appended: +169 for i, item in enumerate(ls): +170 sub_ls = [] +171 if "files" in kwargs: +172 sub_ls = kwargs.get("files") +173 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +174 else: +175 for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): +176 if compact: +177 sub_ls.extend(filenames) +178 else: +179 sub_ls.extend(dirnames) +180 break +181 if compact: +182 for exc in sub_ls: +183 if not fnmatch.fnmatch(exc, prefix + '*'): +184 sub_ls = list(set(sub_ls) - set([exc])) +185 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +186 else: +187 for exc in sub_ls: +188 if not fnmatch.fnmatch(exc, 'cfg*'): +189 sub_ls = list(set(sub_ls) - set([exc])) +190 sub_ls.sort(key=lambda x: int(x[3:])) +191 rep_idl = [] +192 no_cfg = len(sub_ls) +193 for cfg in sub_ls: +194 try: +195 if compact: +196 rep_idl.append(int(cfg.split(cfg_separator)[-1])) +197 else: +198 rep_idl.append(int(cfg[3:])) +199 except Exception: +200 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) +201 rep_idl.sort() +202 # maybe there is a better way to print the idls +203 print(item, ':', no_cfg, ' configurations') +204 idl.append(rep_idl) +205 # here we have found all the files we need to look into. +206 if i == 0: +207 # here, we want to find the place within the file, +208 # where the correlator we need is stored. +209 # to do so, the pattern needed is put together +210 # from the input values +211 if version == "0.0": +212 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) +213 # if b2b, a second wf is needed +214 if b2b: +215 pattern += ", wf_2 " + str(wf2) +216 qs = quarks.split(" ") +217 pattern += " : " + qs[0] + " - " + qs[1] +218 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") +219 for k, line in enumerate(file): +220 if read == 1 and not line.strip() and k > start + 1: +221 break +222 if read == 1 and k >= start: +223 T += 1 +224 if pattern in line: +225 read = 1 +226 start = k + 1 +227 print(str(T) + " entries found.") +228 file.close() +229 else: +230 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) +231 if b2b: +232 pattern += '\nwf_2 ' + str(wf2) +233 # and the file is parsed through to find the pattern +234 if compact: +235 file = open(path + '/' + item + '/' + sub_ls[0], "r") +236 else: +237 # for non-compactified versions of the files +238 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") +239 +240 content = file.read() +241 match = re.search(pattern, content) +242 if match: +243 start_read = content.count('\n', 0, match.start()) + 5 + b2b +244 end_match = re.search(r'\n\s*\n', content[match.start():]) +245 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b +246 assert T > 0 +247 print(T, 'entries, starting to read in line', start_read) +248 file.close() +249 else: +250 file.close() +251 raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') +252 +253 # we found where the correlator +254 # that is to be read is in the files +255 # after preparing the datastructure +256 # the correlators get parsed into... +257 deltas = [] +258 for j in range(T): +259 deltas.append([]) +260 +261 for t in range(T): +262 deltas[t].append(np.zeros(no_cfg)) +263 if compact: +264 for cfg in range(no_cfg): +265 with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: +266 lines = fp.readlines() +267 # check, if the correlator is in fact +268 # printed completely +269 if (start_read + T > len(lines)): +270 raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") +271 # and start to read the correlator. +272 # the range here is chosen like this, +273 # since this allows for implementing +274 # a security check for every read correlator later... +275 for k in range(start_read - 6, start_read + T): +276 if k == start_read - 5 - b2b: +277 if lines[k].strip() != 'name ' + name: +278 raise Exception('Wrong format', sub_ls[cfg]) +279 if (k >= start_read and k < start_read + T): +280 floats = list(map(float, lines[k].split())) +281 deltas[k - start_read][i][cfg] = floats[-2:][im] +282 else: +283 for cnfg, subitem in enumerate(sub_ls): +284 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: +285 # since the non-compatified files +286 # are typically not so long, +287 # we can iterate over the whole file. +288 # here one can also implement the chekc from above. +289 for k, line in enumerate(fp): +290 if (k >= start_read and k < start_read + T): +291 floats = list(map(float, line.split())) +292 if version == "0.0": +293 deltas[k - start][i][cnfg] = floats[im - single] +294 else: +295 deltas[k - start_read][i][cnfg] = floats[1 + im - single] +296 +297 else: +298 if "files" in kwargs: +299 ls = kwargs.get("files") +300 else: +301 for exc in ls: +302 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +303 ls = list(set(ls) - set([exc])) +304 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +305 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) +306 if b2b: +307 pattern += '\nwf_2 ' + str(wf2) +308 for rep, file in enumerate(ls): +309 rep_idl = [] +310 with open(path + '/' + file, 'r') as fp: +311 content = fp.readlines() +312 data_starts = [] +313 for linenumber, line in enumerate(content): +314 if "[run]" in line: +315 data_starts.append(linenumber) +316 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: +317 raise Exception("Irregularities in file structure found, not all runs have the same output length") +318 chunk = content[:data_starts[1]] +319 for linenumber, line in enumerate(chunk): +320 if line.startswith("gauge_name"): +321 gauge_line = linenumber +322 elif line.startswith("[correlator]"): +323 corr_line = linenumber +324 found_pat = "" +325 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: +326 found_pat += li +327 if re.search(pattern, found_pat): +328 start_read = corr_line + 7 + b2b +329 break +330 endline = corr_line + 6 + b2b +331 while not chunk[endline] == "\n": +332 endline += 1 +333 T = endline - start_read +334 if rep == 0: +335 deltas = [] +336 for t in range(T): +337 deltas.append([]) +338 for t in range(T): +339 deltas[t].append(np.zeros(len(data_starts))) +340 # all other chunks should follow the same structure +341 for cnfg in range(len(data_starts)): +342 start = data_starts[cnfg] +343 stop = start + data_starts[1] +344 chunk = content[start:stop] +345 try: +346 rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1])) +347 except Exception: +348 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) +349 +350 found_pat = "" +351 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: +352 found_pat += li +353 if re.search(pattern, found_pat): +354 for t, line in enumerate(chunk[start_read:start_read + T]): +355 floats = list(map(float, line.split())) +356 deltas[t][rep][cnfg] = floats[im + 1 - single] +357 idl.append(rep_idl) +358 +359 if "check_configs" in kwargs: +360 print("Checking for missing configs...") +361 che = kwargs.get("check_configs") +362 if not (len(che) == len(idl)): +363 raise Exception("check_configs has to be the same length as replica!") +364 for r in range(len(idl)): +365 print("checking " + new_names[r]) +366 utils.check_idl(idl[r], che[r]) +367 print("Done") +368 result = [] +369 for t in range(T): +370 result.append(Obs(deltas[t], new_names, idl=idl)) +371 return result @@ -505,314 +511,320 @@ 56 files: list 57 list of files to be read per replica, default is all. 58 for non-compact output format, hand the folders to be read here. - 59 check_configs: + 59 check_configs: list[list[int]] 60 list of list of supposed configs, eg. [range(1,1000)] 61 for one replicum with 1000 configs - 62 """ - 63 if kwargs.get('im'): - 64 im = 1 - 65 part = 'imaginary' - 66 else: - 67 im = 0 - 68 part = 'real' - 69 if "replica" in kwargs: - 70 reps = kwargs.get("replica") - 71 if corr_type == 'bb': - 72 b2b = True - 73 single = True - 74 elif corr_type == 'bib': - 75 b2b = True - 76 single = False - 77 else: - 78 b2b = False - 79 single = False - 80 compact = True - 81 appended = False - 82 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] - 83 - 84 if version not in known_versions: - 85 raise Exception("This version is not known!") - 86 if (version[-1] == "c"): - 87 appended = False - 88 compact = True - 89 version = version[:-1] - 90 elif (version[-1] == "a"): - 91 appended = True - 92 compact = False - 93 version = version[:-1] - 94 else: - 95 compact = False - 96 appended = False - 97 read = 0 - 98 T = 0 - 99 start = 0 -100 ls = [] -101 if "replica" in kwargs: -102 ls = reps -103 else: -104 for (dirpath, dirnames, filenames) in os.walk(path): -105 if not appended: -106 ls.extend(dirnames) -107 else: -108 ls.extend(filenames) -109 break -110 if not ls: -111 raise Exception('Error, directory not found') -112 # Exclude folders with different names -113 for exc in ls: -114 if not fnmatch.fnmatch(exc, prefix + '*'): -115 ls = list(set(ls) - set([exc])) -116 -117 if not appended: -118 if len(ls) > 1: -119 # New version, to cope with ids, etc. -120 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) -121 replica = len(ls) -122 else: -123 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) -124 print('Read', part, 'part of', name, 'from', prefix[:-1], -125 ',', replica, 'replica') -126 if 'names' in kwargs: -127 new_names = kwargs.get('names') -128 if len(new_names) != len(set(new_names)): -129 raise Exception("names are not unique!") -130 if len(new_names) != replica: -131 raise Exception('Names does not have the required length', replica) -132 else: -133 new_names = [] -134 if not appended: -135 for entry in ls: -136 try: -137 idx = entry.index('r') -138 except Exception: -139 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -140 -141 if 'ens_name' in kwargs: -142 new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) -143 else: -144 new_names.append(entry[:idx] + '|' + entry[idx:]) -145 else: + 62 + 63 Returns + 64 ------- + 65 result: list[Obs] + 66 list of Observables with length T, observable per timeslice. + 67 bb-type correlators have length 1. + 68 """ + 69 if kwargs.get('im'): + 70 im = 1 + 71 part = 'imaginary' + 72 else: + 73 im = 0 + 74 part = 'real' + 75 if "replica" in kwargs: + 76 reps = kwargs.get("replica") + 77 if corr_type == 'bb': + 78 b2b = True + 79 single = True + 80 elif corr_type == 'bib': + 81 b2b = True + 82 single = False + 83 else: + 84 b2b = False + 85 single = False + 86 compact = True + 87 appended = False + 88 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] + 89 + 90 if version not in known_versions: + 91 raise Exception("This version is not known!") + 92 if (version[-1] == "c"): + 93 appended = False + 94 compact = True + 95 version = version[:-1] + 96 elif (version[-1] == "a"): + 97 appended = True + 98 compact = False + 99 version = version[:-1] +100 else: +101 compact = False +102 appended = False +103 read = 0 +104 T = 0 +105 start = 0 +106 ls = [] +107 if "replica" in kwargs: +108 ls = reps +109 else: +110 for (dirpath, dirnames, filenames) in os.walk(path): +111 if not appended: +112 ls.extend(dirnames) +113 else: +114 ls.extend(filenames) +115 break +116 if not ls: +117 raise Exception('Error, directory not found') +118 # Exclude folders with different names +119 for exc in ls: +120 if not fnmatch.fnmatch(exc, prefix + '*'): +121 ls = list(set(ls) - set([exc])) +122 +123 if not appended: +124 if len(ls) > 1: +125 # New version, to cope with ids, etc. +126 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) +127 replica = len(ls) +128 else: +129 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) +130 print('Read', part, 'part of', name, 'from', prefix[:-1], +131 ',', replica, 'replica') +132 if 'names' in kwargs: +133 new_names = kwargs.get('names') +134 if len(new_names) != len(set(new_names)): +135 raise Exception("names are not unique!") +136 if len(new_names) != replica: +137 raise Exception('Names does not have the required length', replica) +138 else: +139 new_names = [] +140 if not appended: +141 for entry in ls: +142 try: +143 idx = entry.index('r') +144 except Exception: +145 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") 146 -147 for exc in ls: -148 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -149 ls = list(set(ls) - set([exc])) -150 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -151 for entry in ls: -152 myentry = entry[:-len(name) - 1] -153 try: -154 idx = myentry.index('r') -155 except Exception: -156 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -157 -158 if 'ens_name' in kwargs: -159 new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) -160 else: -161 new_names.append(myentry[:idx] + '|' + myentry[idx:]) -162 idl = [] -163 if not appended: -164 for i, item in enumerate(ls): -165 sub_ls = [] -166 if "files" in kwargs: -167 sub_ls = kwargs.get("files") -168 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -169 else: -170 for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): -171 if compact: -172 sub_ls.extend(filenames) -173 else: -174 sub_ls.extend(dirnames) -175 break -176 if compact: -177 for exc in sub_ls: -178 if not fnmatch.fnmatch(exc, prefix + '*'): -179 sub_ls = list(set(sub_ls) - set([exc])) -180 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -181 else: -182 for exc in sub_ls: -183 if not fnmatch.fnmatch(exc, 'cfg*'): -184 sub_ls = list(set(sub_ls) - set([exc])) -185 sub_ls.sort(key=lambda x: int(x[3:])) -186 rep_idl = [] -187 no_cfg = len(sub_ls) -188 for cfg in sub_ls: -189 try: -190 if compact: -191 rep_idl.append(int(cfg.split(cfg_separator)[-1])) -192 else: -193 rep_idl.append(int(cfg[3:])) -194 except Exception: -195 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) -196 rep_idl.sort() -197 # maybe there is a better way to print the idls -198 print(item, ':', no_cfg, ' configurations') -199 idl.append(rep_idl) -200 # here we have found all the files we need to look into. -201 if i == 0: -202 # here, we want to find the place within the file, -203 # where the correlator we need is stored. -204 # to do so, the pattern needed is put together -205 # from the input values -206 if version == "0.0": -207 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) -208 # if b2b, a second wf is needed -209 if b2b: -210 pattern += ", wf_2 " + str(wf2) -211 qs = quarks.split(" ") -212 pattern += " : " + qs[0] + " - " + qs[1] -213 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -214 for k, line in enumerate(file): -215 if read == 1 and not line.strip() and k > start + 1: -216 break -217 if read == 1 and k >= start: -218 T += 1 -219 if pattern in line: -220 read = 1 -221 start = k + 1 -222 print(str(T) + " entries found.") -223 file.close() -224 else: -225 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -226 if b2b: -227 pattern += '\nwf_2 ' + str(wf2) -228 # and the file is parsed through to find the pattern -229 if compact: -230 file = open(path + '/' + item + '/' + sub_ls[0], "r") -231 else: -232 # for non-compactified versions of the files -233 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -234 -235 content = file.read() -236 match = re.search(pattern, content) -237 if match: -238 start_read = content.count('\n', 0, match.start()) + 5 + b2b -239 end_match = re.search(r'\n\s*\n', content[match.start():]) -240 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b -241 assert T > 0 -242 print(T, 'entries, starting to read in line', start_read) -243 file.close() -244 else: -245 file.close() -246 raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') -247 -248 # we found where the correlator -249 # that is to be read is in the files -250 # after preparing the datastructure -251 # the correlators get parsed into... -252 deltas = [] -253 for j in range(T): -254 deltas.append([]) -255 -256 for t in range(T): -257 deltas[t].append(np.zeros(no_cfg)) -258 if compact: -259 for cfg in range(no_cfg): -260 with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: -261 lines = fp.readlines() -262 # check, if the correlator is in fact -263 # printed completely -264 if (start_read + T > len(lines)): -265 raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") -266 # and start to read the correlator. -267 # the range here is chosen like this, -268 # since this allows for implementing -269 # a security check for every read correlator later... -270 for k in range(start_read - 6, start_read + T): -271 if k == start_read - 5 - b2b: -272 if lines[k].strip() != 'name ' + name: -273 raise Exception('Wrong format', sub_ls[cfg]) -274 if (k >= start_read and k < start_read + T): -275 floats = list(map(float, lines[k].split())) -276 deltas[k - start_read][i][cfg] = floats[-2:][im] -277 else: -278 for cnfg, subitem in enumerate(sub_ls): -279 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: -280 # since the non-compatified files -281 # are typically not so long, -282 # we can iterate over the whole file. -283 # here one can also implement the chekc from above. -284 for k, line in enumerate(fp): -285 if (k >= start_read and k < start_read + T): -286 floats = list(map(float, line.split())) -287 if version == "0.0": -288 deltas[k - start][i][cnfg] = floats[im - single] -289 else: -290 deltas[k - start_read][i][cnfg] = floats[1 + im - single] -291 -292 else: -293 if "files" in kwargs: -294 ls = kwargs.get("files") -295 else: -296 for exc in ls: -297 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -298 ls = list(set(ls) - set([exc])) -299 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -300 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -301 if b2b: -302 pattern += '\nwf_2 ' + str(wf2) -303 for rep, file in enumerate(ls): -304 rep_idl = [] -305 with open(path + '/' + file, 'r') as fp: -306 content = fp.readlines() -307 data_starts = [] -308 for linenumber, line in enumerate(content): -309 if "[run]" in line: -310 data_starts.append(linenumber) -311 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: -312 raise Exception("Irregularities in file structure found, not all runs have the same output length") -313 chunk = content[:data_starts[1]] -314 for linenumber, line in enumerate(chunk): -315 if line.startswith("gauge_name"): -316 gauge_line = linenumber -317 elif line.startswith("[correlator]"): -318 corr_line = linenumber -319 found_pat = "" -320 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: -321 found_pat += li -322 if re.search(pattern, found_pat): -323 start_read = corr_line + 7 + b2b -324 break -325 endline = corr_line + 6 + b2b -326 while not chunk[endline] == "\n": -327 endline += 1 -328 T = endline - start_read -329 if rep == 0: -330 deltas = [] -331 for t in range(T): -332 deltas.append([]) -333 for t in range(T): -334 deltas[t].append(np.zeros(len(data_starts))) -335 # all other chunks should follow the same structure -336 for cnfg in range(len(data_starts)): -337 start = data_starts[cnfg] -338 stop = start + data_starts[1] -339 chunk = content[start:stop] -340 try: -341 rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1])) -342 except Exception: -343 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) -344 -345 found_pat = "" -346 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: -347 found_pat += li -348 if re.search(pattern, found_pat): -349 for t, line in enumerate(chunk[start_read:start_read + T]): -350 floats = list(map(float, line.split())) -351 deltas[t][rep][cnfg] = floats[im + 1 - single] -352 idl.append(rep_idl) -353 -354 if "check_configs" in kwargs: -355 print("Checking for missing configs...") -356 che = kwargs.get("check_configs") -357 if not (len(che) == len(idl)): -358 raise Exception("check_configs has to be the same length as replica!") -359 for r in range(len(idl)): -360 print("checking " + new_names[r]) -361 utils.check_idl(idl[r], che[r]) -362 print("Done") -363 result = [] -364 for t in range(T): -365 result.append(Obs(deltas[t], new_names, idl=idl)) -366 return result +147 if 'ens_name' in kwargs: +148 new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) +149 else: +150 new_names.append(entry[:idx] + '|' + entry[idx:]) +151 else: +152 +153 for exc in ls: +154 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +155 ls = list(set(ls) - set([exc])) +156 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +157 for entry in ls: +158 myentry = entry[:-len(name) - 1] +159 try: +160 idx = myentry.index('r') +161 except Exception: +162 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") +163 +164 if 'ens_name' in kwargs: +165 new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) +166 else: +167 new_names.append(myentry[:idx] + '|' + myentry[idx:]) +168 idl = [] +169 if not appended: +170 for i, item in enumerate(ls): +171 sub_ls = [] +172 if "files" in kwargs: +173 sub_ls = kwargs.get("files") +174 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +175 else: +176 for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): +177 if compact: +178 sub_ls.extend(filenames) +179 else: +180 sub_ls.extend(dirnames) +181 break +182 if compact: +183 for exc in sub_ls: +184 if not fnmatch.fnmatch(exc, prefix + '*'): +185 sub_ls = list(set(sub_ls) - set([exc])) +186 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +187 else: +188 for exc in sub_ls: +189 if not fnmatch.fnmatch(exc, 'cfg*'): +190 sub_ls = list(set(sub_ls) - set([exc])) +191 sub_ls.sort(key=lambda x: int(x[3:])) +192 rep_idl = [] +193 no_cfg = len(sub_ls) +194 for cfg in sub_ls: +195 try: +196 if compact: +197 rep_idl.append(int(cfg.split(cfg_separator)[-1])) +198 else: +199 rep_idl.append(int(cfg[3:])) +200 except Exception: +201 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) +202 rep_idl.sort() +203 # maybe there is a better way to print the idls +204 print(item, ':', no_cfg, ' configurations') +205 idl.append(rep_idl) +206 # here we have found all the files we need to look into. +207 if i == 0: +208 # here, we want to find the place within the file, +209 # where the correlator we need is stored. +210 # to do so, the pattern needed is put together +211 # from the input values +212 if version == "0.0": +213 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) +214 # if b2b, a second wf is needed +215 if b2b: +216 pattern += ", wf_2 " + str(wf2) +217 qs = quarks.split(" ") +218 pattern += " : " + qs[0] + " - " + qs[1] +219 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") +220 for k, line in enumerate(file): +221 if read == 1 and not line.strip() and k > start + 1: +222 break +223 if read == 1 and k >= start: +224 T += 1 +225 if pattern in line: +226 read = 1 +227 start = k + 1 +228 print(str(T) + " entries found.") +229 file.close() +230 else: +231 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) +232 if b2b: +233 pattern += '\nwf_2 ' + str(wf2) +234 # and the file is parsed through to find the pattern +235 if compact: +236 file = open(path + '/' + item + '/' + sub_ls[0], "r") +237 else: +238 # for non-compactified versions of the files +239 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") +240 +241 content = file.read() +242 match = re.search(pattern, content) +243 if match: +244 start_read = content.count('\n', 0, match.start()) + 5 + b2b +245 end_match = re.search(r'\n\s*\n', content[match.start():]) +246 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b +247 assert T > 0 +248 print(T, 'entries, starting to read in line', start_read) +249 file.close() +250 else: +251 file.close() +252 raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') +253 +254 # we found where the correlator +255 # that is to be read is in the files +256 # after preparing the datastructure +257 # the correlators get parsed into... +258 deltas = [] +259 for j in range(T): +260 deltas.append([]) +261 +262 for t in range(T): +263 deltas[t].append(np.zeros(no_cfg)) +264 if compact: +265 for cfg in range(no_cfg): +266 with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: +267 lines = fp.readlines() +268 # check, if the correlator is in fact +269 # printed completely +270 if (start_read + T > len(lines)): +271 raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") +272 # and start to read the correlator. +273 # the range here is chosen like this, +274 # since this allows for implementing +275 # a security check for every read correlator later... +276 for k in range(start_read - 6, start_read + T): +277 if k == start_read - 5 - b2b: +278 if lines[k].strip() != 'name ' + name: +279 raise Exception('Wrong format', sub_ls[cfg]) +280 if (k >= start_read and k < start_read + T): +281 floats = list(map(float, lines[k].split())) +282 deltas[k - start_read][i][cfg] = floats[-2:][im] +283 else: +284 for cnfg, subitem in enumerate(sub_ls): +285 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: +286 # since the non-compatified files +287 # are typically not so long, +288 # we can iterate over the whole file. +289 # here one can also implement the chekc from above. +290 for k, line in enumerate(fp): +291 if (k >= start_read and k < start_read + T): +292 floats = list(map(float, line.split())) +293 if version == "0.0": +294 deltas[k - start][i][cnfg] = floats[im - single] +295 else: +296 deltas[k - start_read][i][cnfg] = floats[1 + im - single] +297 +298 else: +299 if "files" in kwargs: +300 ls = kwargs.get("files") +301 else: +302 for exc in ls: +303 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +304 ls = list(set(ls) - set([exc])) +305 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +306 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) +307 if b2b: +308 pattern += '\nwf_2 ' + str(wf2) +309 for rep, file in enumerate(ls): +310 rep_idl = [] +311 with open(path + '/' + file, 'r') as fp: +312 content = fp.readlines() +313 data_starts = [] +314 for linenumber, line in enumerate(content): +315 if "[run]" in line: +316 data_starts.append(linenumber) +317 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: +318 raise Exception("Irregularities in file structure found, not all runs have the same output length") +319 chunk = content[:data_starts[1]] +320 for linenumber, line in enumerate(chunk): +321 if line.startswith("gauge_name"): +322 gauge_line = linenumber +323 elif line.startswith("[correlator]"): +324 corr_line = linenumber +325 found_pat = "" +326 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: +327 found_pat += li +328 if re.search(pattern, found_pat): +329 start_read = corr_line + 7 + b2b +330 break +331 endline = corr_line + 6 + b2b +332 while not chunk[endline] == "\n": +333 endline += 1 +334 T = endline - start_read +335 if rep == 0: +336 deltas = [] +337 for t in range(T): +338 deltas.append([]) +339 for t in range(T): +340 deltas[t].append(np.zeros(len(data_starts))) +341 # all other chunks should follow the same structure +342 for cnfg in range(len(data_starts)): +343 start = data_starts[cnfg] +344 stop = start + data_starts[1] +345 chunk = content[start:stop] +346 try: +347 rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1])) +348 except Exception: +349 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) +350 +351 found_pat = "" +352 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: +353 found_pat += li +354 if re.search(pattern, found_pat): +355 for t, line in enumerate(chunk[start_read:start_read + T]): +356 floats = list(map(float, line.split())) +357 deltas[t][rep][cnfg] = floats[im + 1 - single] +358 idl.append(rep_idl) +359 +360 if "check_configs" in kwargs: +361 print("Checking for missing configs...") +362 che = kwargs.get("check_configs") +363 if not (len(che) == len(idl)): +364 raise Exception("check_configs has to be the same length as replica!") +365 for r in range(len(idl)): +366 print("checking " + new_names[r]) +367 utils.check_idl(idl[r], che[r]) +368 print("Done") +369 result = [] +370 for t in range(T): +371 result.append(Obs(deltas[t], new_names, idl=idl)) +372 return result @@ -867,9 +879,18 @@ list of replica to be read, default is all
  • files (list): list of files to be read per replica, default is all. for non-compact output format, hand the folders to be read here.
    • -
  • check_configs:: list of list of supposed configs, eg. [range(1,1000)] +
  • check_configs (list[list[int]]): +list of list of supposed configs, eg. [range(1,1000)] for one replicum with 1000 configs
    • + +
    Returns
    + + diff --git a/docs/pyerrors/input/utils.html b/docs/pyerrors/input/utils.html index a6b742d0..18e4bca3 100644 --- a/docs/pyerrors/input/utils.html +++ b/docs/pyerrors/input/utils.html @@ -90,18 +90,24 @@ 10 idl of the current replicum 11 che : list 12 list of configurations to be checked against -13 """ -14 missing = [] -15 for c in che: -16 if c not in idl: -17 missing.append(c) -18 # print missing configurations such that it can directly be parsed to slurm terminal -19 if not (len(missing) == 0): -20 print(len(missing), "configs missing") -21 miss_str = str(missing[0]) -22 for i in missing[1:]: -23 miss_str += "," + str(i) -24 print(miss_str) +13 +14 Returns +15 ------- +16 miss_str : str +17 string with integers of which idls are missing +18 """ +19 missing = [] +20 for c in che: +21 if c not in idl: +22 missing.append(c) +23 # print missing configurations such that it can directly be parsed to slurm terminal +24 if not (len(missing) == 0): +25 print(len(missing), "configs missing") +26 miss_str = str(missing[0]) +27 for i in missing[1:]: +28 miss_str += "," + str(i) +29 print(miss_str) +30 return miss_str @@ -126,18 +132,24 @@ 11 idl of the current replicum 12 che : list 13 list of configurations to be checked against -14 """ -15 missing = [] -16 for c in che: -17 if c not in idl: -18 missing.append(c) -19 # print missing configurations such that it can directly be parsed to slurm terminal -20 if not (len(missing) == 0): -21 print(len(missing), "configs missing") -22 miss_str = str(missing[0]) -23 for i in missing[1:]: -24 miss_str += "," + str(i) -25 print(miss_str) +14 +15 Returns +16 ------- +17 miss_str : str +18 string with integers of which idls are missing +19 """ +20 missing = [] +21 for c in che: +22 if c not in idl: +23 missing.append(c) +24 # print missing configurations such that it can directly be parsed to slurm terminal +25 if not (len(missing) == 0): +26 print(len(missing), "configs missing") +27 miss_str = str(missing[0]) +28 for i in missing[1:]: +29 miss_str += "," + str(i) +30 print(miss_str) +31 return miss_str @@ -151,6 +163,13 @@ idl of the current replicum
  • che (list): list of configurations to be checked against
    • + +
    Returns
    + + diff --git a/docs/pyerrors/misc.html b/docs/pyerrors/misc.html index 444e13fc..0cab6773 100644 --- a/docs/pyerrors/misc.html +++ b/docs/pyerrors/misc.html @@ -101,105 +101,124 @@ 14 name of the file 15 path : str 16 specifies a custom path for the file (default '.') - 17 """ - 18 if 'path' in kwargs: - 19 file_name = kwargs.get('path') + '/' + name + '.p' - 20 else: - 21 file_name = name + '.p' - 22 with open(file_name, 'wb') as fb: - 23 pickle.dump(obj, fb) - 24 - 25 - 26def load_object(path): - 27 """Load object from pickle file. + 17 + 18 Returns + 19 ------- + 20 None + 21 """ + 22 if 'path' in kwargs: + 23 file_name = kwargs.get('path') + '/' + name + '.p' + 24 else: + 25 file_name = name + '.p' + 26 with open(file_name, 'wb') as fb: + 27 pickle.dump(obj, fb) 28 - 29 Parameters - 30 ---------- - 31 path : str - 32 path to the file - 33 """ - 34 with open(path, 'rb') as file: - 35 return pickle.load(file) - 36 + 29 + 30def load_object(path): + 31 """Load object from pickle file. + 32 + 33 Parameters + 34 ---------- + 35 path : str + 36 path to the file 37 - 38def pseudo_Obs(value, dvalue, name, samples=1000): - 39 """Generate an Obs object with given value, dvalue and name for test purposes - 40 - 41 Parameters - 42 ---------- - 43 value : float - 44 central value of the Obs to be generated. - 45 dvalue : float - 46 error of the Obs to be generated. - 47 name : str - 48 name of the ensemble for which the Obs is to be generated. - 49 samples: int - 50 number of samples for the Obs (default 1000). - 51 """ - 52 if dvalue <= 0.0: - 53 return Obs([np.zeros(samples) + value], [name]) - 54 else: - 55 for _ in range(100): - 56 deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)] - 57 deltas -= np.mean(deltas) - 58 deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples) - 59 deltas += value - 60 res = Obs(deltas, [name]) - 61 res.gamma_method(S=2, tau_exp=0) - 62 if abs(res.dvalue - dvalue) < 1e-10 * dvalue: - 63 break - 64 - 65 res._value = float(value) - 66 - 67 return res - 68 - 69 - 70def gen_correlated_data(means, cov, name, tau=0.5, samples=1000): - 71 """ Generate observables with given covariance and autocorrelation times. - 72 - 73 Parameters - 74 ---------- - 75 means : list - 76 list containing the mean value of each observable. - 77 cov : numpy.ndarray - 78 covariance matrix for the data to be generated. - 79 name : str - 80 ensemble name for the data to be geneated. - 81 tau : float or list - 82 can either be a real number or a list with an entry for - 83 every dataset. - 84 samples : int - 85 number of samples to be generated for each observable. - 86 """ - 87 - 88 assert len(means) == cov.shape[-1] - 89 tau = np.asarray(tau) - 90 if np.min(tau) < 0.5: - 91 raise Exception('All integrated autocorrelations have to be >= 0.5.') - 92 - 93 a = (2 * tau - 1) / (2 * tau + 1) - 94 rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples) - 95 - 96 # Normalize samples such that sample variance matches input - 97 norm = np.array([np.var(o, ddof=1) / samples for o in rand.T]) - 98 rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm)) - 99 -100 data = [rand[0]] -101 for i in range(1, samples): -102 data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1]) -103 corr_data = np.array(data) - np.mean(data, axis=0) + means -104 return [Obs([dat], [name]) for dat in corr_data.T] -105 + 38 Returns + 39 ------- + 40 object : Obs + 41 Loaded Object + 42 """ + 43 with open(path, 'rb') as file: + 44 return pickle.load(file) + 45 + 46 + 47def pseudo_Obs(value, dvalue, name, samples=1000): + 48 """Generate an Obs object with given value, dvalue and name for test purposes + 49 + 50 Parameters + 51 ---------- + 52 value : float + 53 central value of the Obs to be generated. + 54 dvalue : float + 55 error of the Obs to be generated. + 56 name : str + 57 name of the ensemble for which the Obs is to be generated. + 58 samples: int + 59 number of samples for the Obs (default 1000). + 60 + 61 Returns + 62 ------- + 63 res : Obs + 64 Generated Observable + 65 """ + 66 if dvalue <= 0.0: + 67 return Obs([np.zeros(samples) + value], [name]) + 68 else: + 69 for _ in range(100): + 70 deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)] + 71 deltas -= np.mean(deltas) + 72 deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples) + 73 deltas += value + 74 res = Obs(deltas, [name]) + 75 res.gamma_method(S=2, tau_exp=0) + 76 if abs(res.dvalue - dvalue) < 1e-10 * dvalue: + 77 break + 78 + 79 res._value = float(value) + 80 + 81 return res + 82 + 83 + 84def gen_correlated_data(means, cov, name, tau=0.5, samples=1000): + 85 """ Generate observables with given covariance and autocorrelation times. + 86 + 87 Parameters + 88 ---------- + 89 means : list + 90 list containing the mean value of each observable. + 91 cov : numpy.ndarray + 92 covariance matrix for the data to be generated. + 93 name : str + 94 ensemble name for the data to be geneated. + 95 tau : float or list + 96 can either be a real number or a list with an entry for + 97 every dataset. + 98 samples : int + 99 number of samples to be generated for each observable. +100 +101 Returns +102 ------- +103 corr_obs : list[Obs] +104 Generated observable list +105 """ 106 -107def _assert_equal_properties(ol, otype=Obs): -108 otype = type(ol[0]) -109 for o in ol[1:]: -110 if not isinstance(o, otype): -111 raise Exception("Wrong data type in list.") -112 for attr in ["reweighted", "e_content", "idl"]: -113 if hasattr(ol[0], attr): -114 if not getattr(ol[0], attr) == getattr(o, attr): -115 raise Exception(f"All Obs in list have to have the same state '{attr}'.") +107 assert len(means) == cov.shape[-1] +108 tau = np.asarray(tau) +109 if np.min(tau) < 0.5: +110 raise Exception('All integrated autocorrelations have to be >= 0.5.') +111 +112 a = (2 * tau - 1) / (2 * tau + 1) +113 rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples) +114 +115 # Normalize samples such that sample variance matches input +116 norm = np.array([np.var(o, ddof=1) / samples for o in rand.T]) +117 rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm)) +118 +119 data = [rand[0]] +120 for i in range(1, samples): +121 data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1]) +122 corr_data = np.array(data) - np.mean(data, axis=0) + means +123 return [Obs([dat], [name]) for dat in corr_data.T] +124 +125 +126def _assert_equal_properties(ol, otype=Obs): +127 otype = type(ol[0]) +128 for o in ol[1:]: +129 if not isinstance(o, otype): +130 raise Exception("Wrong data type in list.") +131 for attr in ["reweighted", "e_content", "idl"]: +132 if hasattr(ol[0], attr): +133 if not getattr(ol[0], attr) == getattr(o, attr): +134 raise Exception(f"All Obs in list have to have the same state '{attr}'.") @@ -226,13 +245,17 @@ 15 name of the file 16 path : str 17 specifies a custom path for the file (default '.') -18 """ -19 if 'path' in kwargs: -20 file_name = kwargs.get('path') + '/' + name + '.p' -21 else: -22 file_name = name + '.p' -23 with open(file_name, 'wb') as fb: -24 pickle.dump(obj, fb) +18 +19 Returns +20 ------- +21 None +22 """ +23 if 'path' in kwargs: +24 file_name = kwargs.get('path') + '/' + name + '.p' +25 else: +26 file_name = name + '.p' +27 with open(file_name, 'wb') as fb: +28 pickle.dump(obj, fb) @@ -248,6 +271,12 @@ name of the file
  • path (str): specifies a custom path for the file (default '.')
    • + +
    Returns
    + + @@ -263,16 +292,21 @@ specifies a custom path for the file (default '.') - 
    27def load_object(path):
-28    """Load object from pickle file.
-29
-30    Parameters
-31    ----------
-32    path : str
-33        path to the file
-34    """
-35    with open(path, 'rb') as file:
-36        return pickle.load(file)
+            
    31def load_object(path):
+32    """Load object from pickle file.
+33
+34    Parameters
+35    ----------
+36    path : str
+37        path to the file
+38
+39    Returns
+40    -------
+41    object : Obs
+42        Loaded Object
+43    """
+44    with open(path, 'rb') as file:
+45        return pickle.load(file)
 
    
 
 
@@ -284,6 +318,13 @@ specifies a custom path for the file (default '.')
 
  • path (str):
 path to the file
    • 
 
+
+
    Returns
    
+
+
      
+
    • object (Obs):
+Loaded Object
      • 
+
    @@ -299,36 +340,41 @@ path to the file - 
    39def pseudo_Obs(value, dvalue, name, samples=1000):
-40    """Generate an Obs object with given value, dvalue and name for test purposes
-41
-42    Parameters
-43    ----------
-44    value : float
-45        central value of the Obs to be generated.
-46    dvalue : float
-47        error of the Obs to be generated.
-48    name : str
-49        name of the ensemble for which the Obs is to be generated.
-50    samples: int
-51        number of samples for the Obs (default 1000).
-52    """
-53    if dvalue <= 0.0:
-54        return Obs([np.zeros(samples) + value], [name])
-55    else:
-56        for _ in range(100):
-57            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
-58            deltas -= np.mean(deltas)
-59            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
-60            deltas += value
-61            res = Obs(deltas, [name])
-62            res.gamma_method(S=2, tau_exp=0)
-63            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
-64                break
-65
-66        res._value = float(value)
-67
-68        return res
+            
    48def pseudo_Obs(value, dvalue, name, samples=1000):
+49    """Generate an Obs object with given value, dvalue and name for test purposes
+50
+51    Parameters
+52    ----------
+53    value : float
+54        central value of the Obs to be generated.
+55    dvalue : float
+56        error of the Obs to be generated.
+57    name : str
+58        name of the ensemble for which the Obs is to be generated.
+59    samples: int
+60        number of samples for the Obs (default 1000).
+61
+62    Returns
+63    -------
+64    res : Obs
+65        Generated Observable
+66    """
+67    if dvalue <= 0.0:
+68        return Obs([np.zeros(samples) + value], [name])
+69    else:
+70        for _ in range(100):
+71            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
+72            deltas -= np.mean(deltas)
+73            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
+74            deltas += value
+75            res = Obs(deltas, [name])
+76            res.gamma_method(S=2, tau_exp=0)
+77            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
+78                break
+79
+80        res._value = float(value)
+81
+82        return res
 
    
 
 
@@ -346,6 +392,13 @@ name of the ensemble for which the Obs is to be generated.
 
  • samples (int):
 number of samples for the Obs (default 1000).
    • 
 
+
+
    Returns
    
+
+
      
+
    • res (Obs):
+Generated Observable
      • 
+
    @@ -361,41 +414,46 @@ number of samples for the Obs (default 1000). - 
     71def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
- 72    """ Generate observables with given covariance and autocorrelation times.
- 73
- 74    Parameters
- 75    ----------
- 76    means : list
- 77        list containing the mean value of each observable.
- 78    cov : numpy.ndarray
- 79        covariance matrix for the data to be generated.
- 80    name : str
- 81        ensemble name for the data to be geneated.
- 82    tau : float or list
- 83        can either be a real number or a list with an entry for
- 84        every dataset.
- 85    samples : int
- 86        number of samples to be generated for each observable.
- 87    """
- 88
- 89    assert len(means) == cov.shape[-1]
- 90    tau = np.asarray(tau)
- 91    if np.min(tau) < 0.5:
- 92        raise Exception('All integrated autocorrelations have to be >= 0.5.')
- 93
- 94    a = (2 * tau - 1) / (2 * tau + 1)
- 95    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
- 96
- 97    # Normalize samples such that sample variance matches input
- 98    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
- 99    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
-100
-101    data = [rand[0]]
-102    for i in range(1, samples):
-103        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
-104    corr_data = np.array(data) - np.mean(data, axis=0) + means
-105    return [Obs([dat], [name]) for dat in corr_data.T]
+            
     85def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
+ 86    """ Generate observables with given covariance and autocorrelation times.
+ 87
+ 88    Parameters
+ 89    ----------
+ 90    means : list
+ 91        list containing the mean value of each observable.
+ 92    cov : numpy.ndarray
+ 93        covariance matrix for the data to be generated.
+ 94    name : str
+ 95        ensemble name for the data to be geneated.
+ 96    tau : float or list
+ 97        can either be a real number or a list with an entry for
+ 98        every dataset.
+ 99    samples : int
+100        number of samples to be generated for each observable.
+101
+102    Returns
+103    -------
+104    corr_obs : list[Obs]
+105        Generated observable list
+106    """
+107
+108    assert len(means) == cov.shape[-1]
+109    tau = np.asarray(tau)
+110    if np.min(tau) < 0.5:
+111        raise Exception('All integrated autocorrelations have to be >= 0.5.')
+112
+113    a = (2 * tau - 1) / (2 * tau + 1)
+114    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
+115
+116    # Normalize samples such that sample variance matches input
+117    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
+118    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
+119
+120    data = [rand[0]]
+121    for i in range(1, samples):
+122        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
+123    corr_data = np.array(data) - np.mean(data, axis=0) + means
+124    return [Obs([dat], [name]) for dat in corr_data.T]
 
    
 
 
@@ -416,6 +474,13 @@ every dataset.
 
  • samples (int):
 number of samples to be generated for each observable.
    • 
 
+
+
    Returns
    
+
+
      
+
    • corr_obs (list[Obs]):
+Generated observable list
      • 
+
    diff --git a/docs/pyerrors/mpm.html b/docs/pyerrors/mpm.html index 4063718f..43f00ae5 100644 --- a/docs/pyerrors/mpm.html +++ b/docs/pyerrors/mpm.html @@ -99,41 +99,46 @@ 21 matrix pencil parameter which filters noise. The optimal value is expected between 22 len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is 23 to len(data)/2 but could possibly suppress more noise (default len(data)//2). -24 """ -25 if isinstance(corrs[0], Obs): -26 data = [corrs] -27 else: -28 data = corrs -29 -30 lengths = [len(d) for d in data] -31 if lengths.count(lengths[0]) != len(lengths): -32 raise Exception('All datasets have to have the same length.') -33 -34 data_sets = len(data) -35 n_data = len(data[0]) -36 -37 if p is None: -38 p = max(n_data // 2, k) -39 if n_data <= p: -40 raise Exception('The pencil p has to be smaller than the number of data samples.') -41 if p < k or n_data - p < k: -42 raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p) -43 -44 # Construct the hankel matrices -45 matrix = [] -46 for n in range(data_sets): -47 matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:])) -48 matrix = np.array(matrix) -49 # Construct y1 and y2 -50 y1 = np.concatenate(matrix[:, :, :p]) -51 y2 = np.concatenate(matrix[:, :, 1:]) -52 # Apply SVD to y2 -53 u, s, vh = svd(y2, **kwargs) -54 # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero -55 z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k] -56 # Return the sorted logarithms of the real eigenvalues as Obs -57 energy_levels = np.log(np.abs(eig(z, **kwargs))) -58 return sorted(energy_levels, key=lambda x: abs(x.value)) +24 +25 Returns +26 ------- +27 energy_levels : list[Obs] +28 Extracted energy levels +29 """ +30 if isinstance(corrs[0], Obs): +31 data = [corrs] +32 else: +33 data = corrs +34 +35 lengths = [len(d) for d in data] +36 if lengths.count(lengths[0]) != len(lengths): +37 raise Exception('All datasets have to have the same length.') +38 +39 data_sets = len(data) +40 n_data = len(data[0]) +41 +42 if p is None: +43 p = max(n_data // 2, k) +44 if n_data <= p: +45 raise Exception('The pencil p has to be smaller than the number of data samples.') +46 if p < k or n_data - p < k: +47 raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p) +48 +49 # Construct the hankel matrices +50 matrix = [] +51 for n in range(data_sets): +52 matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:])) +53 matrix = np.array(matrix) +54 # Construct y1 and y2 +55 y1 = np.concatenate(matrix[:, :, :p]) +56 y2 = np.concatenate(matrix[:, :, 1:]) +57 # Apply SVD to y2 +58 u, s, vh = svd(y2, **kwargs) +59 # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero +60 z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k] +61 # Return the sorted logarithms of the real eigenvalues as Obs +62 energy_levels = np.log(np.abs(eig(z, **kwargs))) +63 return sorted(energy_levels, key=lambda x: abs(x.value)) @@ -166,41 +171,46 @@ 22 matrix pencil parameter which filters noise. The optimal value is expected between 23 len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is 24 to len(data)/2 but could possibly suppress more noise (default len(data)//2). -25 """ -26 if isinstance(corrs[0], Obs): -27 data = [corrs] -28 else: -29 data = corrs -30 -31 lengths = [len(d) for d in data] -32 if lengths.count(lengths[0]) != len(lengths): -33 raise Exception('All datasets have to have the same length.') -34 -35 data_sets = len(data) -36 n_data = len(data[0]) -37 -38 if p is None: -39 p = max(n_data // 2, k) -40 if n_data <= p: -41 raise Exception('The pencil p has to be smaller than the number of data samples.') -42 if p < k or n_data - p < k: -43 raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p) -44 -45 # Construct the hankel matrices -46 matrix = [] -47 for n in range(data_sets): -48 matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:])) -49 matrix = np.array(matrix) -50 # Construct y1 and y2 -51 y1 = np.concatenate(matrix[:, :, :p]) -52 y2 = np.concatenate(matrix[:, :, 1:]) -53 # Apply SVD to y2 -54 u, s, vh = svd(y2, **kwargs) -55 # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero -56 z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k] -57 # Return the sorted logarithms of the real eigenvalues as Obs -58 energy_levels = np.log(np.abs(eig(z, **kwargs))) -59 return sorted(energy_levels, key=lambda x: abs(x.value)) +25 +26 Returns +27 ------- +28 energy_levels : list[Obs] +29 Extracted energy levels +30 """ +31 if isinstance(corrs[0], Obs): +32 data = [corrs] +33 else: +34 data = corrs +35 +36 lengths = [len(d) for d in data] +37 if lengths.count(lengths[0]) != len(lengths): +38 raise Exception('All datasets have to have the same length.') +39 +40 data_sets = len(data) +41 n_data = len(data[0]) +42 +43 if p is None: +44 p = max(n_data // 2, k) +45 if n_data <= p: +46 raise Exception('The pencil p has to be smaller than the number of data samples.') +47 if p < k or n_data - p < k: +48 raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p) +49 +50 # Construct the hankel matrices +51 matrix = [] +52 for n in range(data_sets): +53 matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:])) +54 matrix = np.array(matrix) +55 # Construct y1 and y2 +56 y1 = np.concatenate(matrix[:, :, :p]) +57 y2 = np.concatenate(matrix[:, :, 1:]) +58 # Apply SVD to y2 +59 u, s, vh = svd(y2, **kwargs) +60 # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero +61 z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k] +62 # Return the sorted logarithms of the real eigenvalues as Obs +63 energy_levels = np.log(np.abs(eig(z, **kwargs))) +64 return sorted(energy_levels, key=lambda x: abs(x.value)) @@ -222,6 +232,13 @@ matrix pencil parameter which filters noise. The optimal value is expected betwe len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is to len(data)/2 but could possibly suppress more noise (default len(data)//2). + +
    Returns
    + + diff --git a/docs/pyerrors/roots.html b/docs/pyerrors/roots.html index fdfde017..96acde28 100644 --- a/docs/pyerrors/roots.html +++ b/docs/pyerrors/roots.html @@ -102,7 +102,7 @@ 24 25 Returns 26 ------- -27 Obs +27 res : Obs 28 `Obs` valued root of the function. 29 ''' 30 d_val = np.vectorize(lambda x: x.value)(np.array(d)) @@ -154,7 +154,7 @@ 25 26 Returns 27 ------- -28 Obs +28 res : Obs 29 `Obs` valued root of the function. 30 ''' 31 d_val = np.vectorize(lambda x: x.value)(np.array(d)) @@ -198,7 +198,8 @@ Initial guess for the minimization.

    Returns
    diff --git a/docs/search.js b/docs/search.js index 38f25af0..9e6ea864 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWhat is pyerrors?\n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
      • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
      • \n
    • coherent error propagation for data from different Markov chains.
      • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
      • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
      • \n
    \n\n

    More detailed examples can found in the GitHub repository \"badge\".

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
      • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
      • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
      • \n
    \n\n

    and

    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
      • \n
    \n\n

    where applicable.

    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Basic example

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.

    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

    \n\n

    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Error estimation for multiple ensembles

    \n\n

    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

    \n\n
    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

    \n\n
    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n

    The individual entries of a correlator can be accessed via slicing

    \n\n
    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

    \n\n
    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
      • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
      • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
      • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
      • \n
    • Corr.roll periodically shifts the correlator.
      • \n
    • Corr.reverse reverses the time ordering of the correlator.
      • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
      • \n
    • Corr.reweight reweights the correlator.
      • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
      • \n
    • cholseky for the Cholesky decomposition.
      • \n
    • det for the matrix determinant.
      • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
      • \n
    • eig for eigenvalues of general matrices.
      • \n
    • pinv for the Moore-Penrose pseudoinverse.
      • \n
    • svd for the singular-value-decomposition.
      • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
      • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
      • \n
    • Who did write the file when and on which machine?
      • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
      • \n
    • version is a string that specifies the version of the format.
      • \n
    • who is a string that specifies the user name of the creator of the file.
      • \n
    • date is a string and contains the creation date of the file.
      • \n
    • host is a string and contains the hostname of the machine where the file has been written.
      • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
      • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
      • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
      • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
      • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
      • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
      • \n
    • data is an array that contains the data from MC chains. We will define it below.
      • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
      • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
      • \n
    • replica, an array that contains an entry per replica of the ensemble.
      • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
      • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
      • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
      • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
      • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
      • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
      • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
      • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
      • \n
    • j (int):\nSecond index to be picked.
      • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "

    Checks whether a correlator matrices is symmetric on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "

    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.

    \n\n

    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing

    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
      • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
      • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
        • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
        • \n
      • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
      • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
      • \n
    • All other parameters are identical to the ones of Corr.GEVP.
      • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
      • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
      • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
      • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
      • \n
    • offset (int):\nOffset the equal spacing
      • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
      • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
      • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
      • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
      • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, 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.\nsinh : 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.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
      • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
      • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
      • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
      • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
      • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
      • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
      • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
      • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
      • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
      • \n
    • logscale (bool):\nSets y-axis to logscale.
      • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
      • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
      • \n
    • ylabel (str):\nLabel for the y-axis.
      • \n
    • save (str):\npath to file in which the figure should be saved.
      • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
      • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
      • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
      • \n
    • title (string):\nOptional title of the figure.
      • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
      • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "

    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
      • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
      • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
      • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
      • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
      • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
      • \n
    • chisquare_by_dof (float):\nreduced chisquare.
      • \n
    • p_value (float):\np-value of the fit
      • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
      • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "kind": "function", "doc": "

    \n", "signature": "()"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of floats.
      • \n
    • y (list):\nlist of Obs.
      • \n

    • func (object):\nfit function, has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

      • \n
    • priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
      • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
      • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).\nAt the moment this option only works for prior==None and when no method is given.
      • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
      • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
      • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
      • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
      • \n

    • func (object):\nfunc has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
      • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy

    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
      • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
      • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\ncheck if the residuals of the fit are gaussian distributed.

    \n", "signature": "(x, o_y, func, p):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n", "signature": "(x, y, func, fit_res):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "

    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
      • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "

    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "

    Import a list of Obs from a string in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
      • \n
    • noemtpy (bool):\nIf True, ensembles with no contribution to the Obs are not included.\nIf False, ensembles are included as written in the file, possibly with vanishing entries.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n", "signature": "(content, noempty=False, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • noemtpy (bool):\nIf True, ensembles with no contribution to the Obs are not included.\nIf False, ensembles are included as written in the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n", "signature": "(\tfname,\tnoempty=False,\tfull_output=False,\tgz=True,\tseparator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator |is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
      • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "

    Read hadrons DistillationContraction hdf5 files in given directory structure

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
      • \n
    • shape (tuple of ints):\nShape of created array.
      • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
      • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
      • \n
    • offset (int, optional):\nOffset of array data in buffer.
      • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
      • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
      • \n
    • data (buffer):\nThe array's elements, in memory.
      • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
      • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
      • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
      • \n
    • imag (ndarray):\nImaginary part of the array.
      • \n
    • real (ndarray):\nReal part of the array.
      • \n
    • size (int):\nNumber of elements in the array.
      • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
      • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
      • \n
    • ndim (int):\nThe array's number of dimensions.
      • \n
    • shape (tuple of ints):\nShape of the array.
      • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
      • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
      • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
      • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n

    Second mode:

    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"fullname": "pyerrors.input.hadrons.Npr_matrix.__init__", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.__init__", "kind": "function", "doc": "

    \n", "signature": "()"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    • vertices (list):\nVertex functions to be extracted.
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "

    Read pbp format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
      • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
      • \n
    • version (str):\nversion of openQCD, default 2.0
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
      • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
      • \n
    • prefix (str):\nEnsemble prefix
      • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
      • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
      • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
      • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
      • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
      • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "

    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.

    \n\n

    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
      • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "

    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
      • \n
    • prefix (str):\nThe prefix to match the files against.
      • \n
    • qc (str):\nThe quark combination extension to match the files against.
      • \n
    • corr (str):\nThe correlator to extract data for.
      • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
      • \n

    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:

      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
        • \n
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
      • \n
    • or
      • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
      • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
      • \n
    • IOError: If there is an error reading a file.
      • \n
    • struct.error: If there is an error unpacking binary data.
      • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "

    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "

    Write DataFrame including Obs or Corr valued columns to sqlite database.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
      • \n
    • table_name (str):\nName of the table in the database.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
      • \n
    • gz (bool):\nIf True the json strings are gzipped.
      • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "

    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.

    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "

    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.

    \n\n

    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
      • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "

    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
      • \n
    • prefix (str):\nPrefix of the sfcf files.
      • \n
    • name (str):\nName of the correlation function to read.
      • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
      • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
        • \n
      • 'bb' for boundary-boundary
        • \n
      • 'bib' for boundary-inner-boundary
        • \n
      • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
      • \n
    • wf (int):\nID of wave function
      • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
      • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • ens_name (str):\nreplaces the name of the ensemble
      • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
      • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
      • \n
    • replica (list):\nlist of replica to be read, default is all
      • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
      • \n
    • check_configs:: list of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
      • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "

    Utilities for the input

    \n"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
      • \n
    • che (list):\nlist of configurations to be checked against
      • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • For large matrices this is considerably faster compared to matmul.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
      • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "

    Cholesky decomposition of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
      • \n
    • name (str):\nname of the file
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
      • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "

    Generate an Obs object with given value, dvalue and name for test purposes

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
      • \n
    • dvalue (float):\nerror of the Obs to be generated.
      • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
      • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
      • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
      • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
      • \n
    • name (str):\nensemble name for the data to be geneated.
      • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
      • \n
    • samples (int):\nnumber of samples to be generated for each observable.
      • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
      • \n
    • k (int):\nNumber of states to extract (default 1).
      • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
      • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
      • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
      • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
      • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
      • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
      • \n
    • names (list):\nlist of strings labeling the individual samples
      • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
      • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
      • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "

    Reweight the obs with given rewighting factors.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
      • \n
    • Works only properly when the gamma method was run.
      • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
      • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
      • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "

    Dump the Obs to a file 'name' of chosen format.

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
      • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "

    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
      • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
      • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
      • \n
    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
      • \n
    • obs_b (Obs):\nSecond observable
      • \n
    \n\n
    Notes
    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).

    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "

    Calculates the error covariance matrix of a set of observables.

    \n\n

    WARNING: This function should be used with care, especially for observables with support on multiple\n ensembles with differing autocorrelations. See the notes below for details.

    \n\n

    The gamma method has to be applied first to all observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
      • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
      • \n
    • correlation (bool):\nIf True the correlation matrix instead of the error covariance matrix is returned (default False).
      • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
      • \n
    \n\n
    Notes
    \n\n

    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
      • \n
    • name (str):\nname of the ensemble the samples are defined on.
      • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
      • \n
    \n\n
    Notes
    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "

    Create an Obs based on mean(s) and a covariance matrix

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
      • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
      • \n

    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:

      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
      • \n

    • guess (float):\nInitial guess for the minimization.

      • \n
    \n\n
    Returns
    \n\n
      \n
    • Obs: Obs valued root of the function.
      • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "

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    What is pyerrors?

    \n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
      • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
      • \n
    • coherent error propagation for data from different Markov chains.
      • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
      • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
      • \n
    \n\n

    More detailed examples can found in the GitHub repository \"badge\".

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
      • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
      • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
      • \n
    \n\n

    and

    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
      • \n
    \n\n

    where applicable.

    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Basic example

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.

    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

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    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

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    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

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    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Error estimation for multiple ensembles

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    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

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    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

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    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

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    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n

    The individual entries of a correlator can be accessed via slicing

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    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

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    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
      • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
      • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
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    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
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    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
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    • Corr.plateau extracts a plateau value from the correlator in a given range.
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    • Corr.roll periodically shifts the correlator.
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    • Corr.reverse reverses the time ordering of the correlator.
      • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
      • \n
    • Corr.reweight reweights the correlator.
      • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
      • \n
    • cholseky for the Cholesky decomposition.
      • \n
    • det for the matrix determinant.
      • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
      • \n
    • eig for eigenvalues of general matrices.
      • \n
    • pinv for the Moore-Penrose pseudoinverse.
      • \n
    • svd for the singular-value-decomposition.
      • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
      • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
      • \n
    • Who did write the file when and on which machine?
      • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
      • \n
    • version is a string that specifies the version of the format.
      • \n
    • who is a string that specifies the user name of the creator of the file.
      • \n
    • date is a string and contains the creation date of the file.
      • \n
    • host is a string and contains the hostname of the machine where the file has been written.
      • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
      • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
      • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
      • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
      • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
      • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
      • \n
    • data is an array that contains the data from MC chains. We will define it below.
      • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
      • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
      • \n
    • replica, an array that contains an entry per replica of the ensemble.
      • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
      • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
      • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
      • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
      • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
      • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
      • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
      • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
      • \n
    • j (int):\nSecond index to be picked.
      • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "

    Checks whether a correlator matrices is symmetric on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "

    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.

    \n\n

    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing

    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
      • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
      • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
        • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
        • \n
      • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
      • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
      • \n
    • All other parameters are identical to the ones of Corr.GEVP.
      • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
      • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
      • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
      • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
      • \n
    • offset (int):\nOffset the equal spacing
      • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
      • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
      • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
      • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
      • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, 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.\nsinh : 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.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
      • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
      • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
      • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
      • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
      • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
      • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
      • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
      • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
      • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
      • \n
    • logscale (bool):\nSets y-axis to logscale.
      • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
      • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
      • \n
    • ylabel (str):\nLabel for the y-axis.
      • \n
    • save (str):\npath to file in which the figure should be saved.
      • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
      • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
      • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
      • \n
    • title (string):\nOptional title of the figure.
      • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
      • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "

    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
      • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
      • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
      • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
      • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
      • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
      • \n
    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
      • \n
    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
      • \n
    • chisquare_by_dof (float):\nreduced chisquare.
      • \n
    • p_value (float):\np-value of the fit
      • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
      • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "kind": "function", "doc": "

    \n", "signature": "()"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of floats.
      • \n
    • y (list):\nlist of Obs.
      • \n

    • func (object):\nfit function, has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

      • \n
    • priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
      • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
      • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).\nAt the moment this option only works for prior==None and when no method is given.
      • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
      • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
      • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
      • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
      • \n

    • func (object):\nfunc has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
      • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy.

    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
      • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • fit_parameters (list[Obs]):\nLIist of fitted observables.
      • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\n check if the residuals of the fit are gaussian distributed.

    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, o_y, func, p):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, y, func, fit_res):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "

    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n\n
    Returns
    \n\n
      \n
    • err (np.array(Obs)):\nError band for an array of sample values x
      • \n
    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (List[Obs]):\nExtracted data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • success (int):\nreturns 0 is successful
      • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (dict):\nExtracted meson data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "

    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML formatted string of the input data
      • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "

    Import a list of Obs from a string in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
      • \n
    • noemtpy (bool):\nIf True, ensembles with no contribution to the Obs are not included.\nIf False, ensembles are included as written in the file, possibly with vanishing entries.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(content, noempty=False, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • noemtpy (bool):\nIf True, ensembles with no contribution to the Obs are not included.\nIf False, ensembles are included as written in the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(\tfname,\tnoempty=False,\tfull_output=False,\tgz=True,\tseparator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator |is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML string generated from the data
      • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
      • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr (Corr):\nCorrelator of the source sink combination in question.
      • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "

    Read hadrons DistillationContraction hdf5 files in given directory structure

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (dict):\nextracted DistillationContration data
      • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
      • \n
    • shape (tuple of ints):\nShape of created array.
      • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
      • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
      • \n
    • offset (int, optional):\nOffset of array data in buffer.
      • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
      • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
      • \n
    • data (buffer):\nThe array's elements, in memory.
      • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
      • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
      • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
      • \n
    • imag (ndarray):\nImaginary part of the array.
      • \n
    • real (ndarray):\nReal part of the array.
      • \n
    • size (int):\nNumber of elements in the array.
      • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
      • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
      • \n
    • ndim (int):\nThe array's number of dimensions.
      • \n
    • shape (tuple of ints):\nShape of the array.
      • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
      • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
      • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
      • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n

    Second mode:

    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"fullname": "pyerrors.input.hadrons.Npr_matrix.__init__", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.__init__", "kind": "function", "doc": "

    \n", "signature": "()"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Npr_matrix):\nread Cobs-matrix
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict[Npr_matrix]):\nextracted Bilinears
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    • vertices (list):\nVertex functions to be extracted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict):\nextracted fourquark matrizes
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • json_string (str):\nString for export to .json(.gz) file
      • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Null
      • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (Obs / list / Corr):\nRead data
      • \n
    • or
      • \n
    • data (dict):\nRead data and meta-data
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "

    Read pbp format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of observables read
      • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
      • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
      • \n
    • version (str):\nversion of openQCD, default 2.0
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • rwms (Obs):\nReweighting factors read
      • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
      • \n
    • prefix (str):\nEnsemble prefix
      • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
      • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
      • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
      • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
      • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
      • \n
    \n\n
    Returns
    \n\n
      \n
    • t0 (Obs):\nExtracted t0
      • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Obs):\nRead topological charge
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "

    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.

    \n\n

    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
      • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "

    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
      • \n
    • prefix (str):\nThe prefix to match the files against.
      • \n
    • qc (str):\nThe quark combination extension to match the files against.
      • \n
    • corr (str):\nThe correlator to extract data for.
      • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
      • \n

    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:

      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
        • \n
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
      • \n
    • or
      • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
      • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
      • \n
    • IOError: If there is an error reading a file.
      • \n
    • struct.error: If there is an error unpacking binary data.
      • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "

    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "

    Write DataFrame including Obs or Corr valued columns to sqlite database.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
      • \n
    • table_name (str):\nName of the table in the database.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
      • \n
    • gz (bool):\nIf True the json strings are gzipped.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "

    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.

    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "

    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.

    \n\n

    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "

    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
      • \n
    • prefix (str):\nPrefix of the sfcf files.
      • \n
    • name (str):\nName of the correlation function to read.
      • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
      • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
        • \n
      • 'bb' for boundary-boundary
        • \n
      • 'bib' for boundary-inner-boundary
        • \n
      • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
      • \n
    • wf (int):\nID of wave function
      • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
      • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • ens_name (str):\nreplaces the name of the ensemble
      • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
      • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
      • \n
    • replica (list):\nlist of replica to be read, default is all
      • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
      • \n
    • check_configs (list[list[int]]):\nlist of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
      • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "

    Utilities for the input

    \n"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
      • \n
    • che (list):\nlist of configurations to be checked against
      • \n
    \n\n
    Returns
    \n\n
      \n
    • miss_str (str):\nstring with integers of which idls are missing
      • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • For large matrices this is considerably faster compared to matmul.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
      • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "

    Cholesky decomposition of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
      • \n
    • name (str):\nname of the file
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
      • \n
    \n\n
    Returns
    \n\n
      \n
    • object (Obs):\nLoaded Object
      • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "

    Generate an Obs object with given value, dvalue and name for test purposes

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
      • \n
    • dvalue (float):\nerror of the Obs to be generated.
      • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
      • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nGenerated Observable
      • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
      • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
      • \n
    • name (str):\nensemble name for the data to be geneated.
      • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
      • \n
    • samples (int):\nnumber of samples to be generated for each observable.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr_obs (list[Obs]):\nGenerated observable list
      • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
      • \n
    • k (int):\nNumber of states to extract (default 1).
      • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • energy_levels (list[Obs]):\nExtracted energy levels
      • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
      • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
      • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
      • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
      • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
      • \n
    • names (list):\nlist of strings labeling the individual samples
      • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
      • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
      • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "

    Reweight the obs with given rewighting factors.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
      • \n
    • Works only properly when the gamma method was run.
      • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
      • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
      • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "

    Dump the Obs to a file 'name' of chosen format.

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
      • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "

    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
      • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
      • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
      • \n
    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
      • \n
    • obs_b (Obs):\nSecond observable
      • \n
    \n\n
    Notes
    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).

    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "

    Calculates the error covariance matrix of a set of observables.

    \n\n

    WARNING: This function should be used with care, especially for observables with support on multiple\n ensembles with differing autocorrelations. See the notes below for details.

    \n\n

    The gamma method has to be applied first to all observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
      • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
      • \n
    • correlation (bool):\nIf True the correlation matrix instead of the error covariance matrix is returned (default False).
      • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
      • \n
    \n\n
    Notes
    \n\n

    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
      • \n
    • name (str):\nname of the ensemble the samples are defined on.
      • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
      • \n
    \n\n
    Notes
    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "

    Create an Obs based on mean(s) and a covariance matrix

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
      • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
      • \n

    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:

      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
      • \n

    • guess (float):\nInitial guess for the minimization.

      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nObs valued root of the function.
      • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "

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