diff --git a/nnpdf_data/nnpdf_data/filter_utils/utils.py b/nnpdf_data/nnpdf_data/filter_utils/utils.py index d59c197f86..e38dacb70a 100644 --- a/nnpdf_data/nnpdf_data/filter_utils/utils.py +++ b/nnpdf_data/nnpdf_data/filter_utils/utils.py @@ -1,11 +1,373 @@ """ -Module contains general utility functions for general use in implementing datasets. +General Python utilities for commondata implementation. + +This module provides helpful functions that automate a few +tasks that are regularly needed for the implementation of +experimental data to the commondata format. If there are +any additional functions that could be added here as they +could simplify some repetitve tasks, please do suggest. + +Before the usage of any functions, it is recommended to read +the docstrings of the function to understand the inputs and +outputs. """ +from math import sqrt + import numpy as np +from numpy.linalg import eig import yaml +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + semi_diff = (delta_plus + delta_minus) / 2 + average = (delta_plus - delta_minus) / 2 + se_delta = semi_diff + se_sigma = sqrt(average * average + 2 * semi_diff * semi_diff) + return se_delta, se_sigma + + +def percentage_to_absolute(percentage, value): + r"""Compute the absolute value of uncertainty from percentage. + + Parameters + ---------- + percentage : string/float + Experimental datasets can provide the percentage + uncertainties with a % sign or without one. + The function will autostrip % sign and convert to + a float type in case the percentage uncertainty + comes with a % sign. Else, it will directly perform + the computation. + value : float + The data point + + Returns + ------- + absolute : float + The absolute value of the uncertainty + + """ + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + + +def cormat_to_covmat(err_list, cormat_list): + r"""Convert correlation matrix elements to covariance + matrix elements. + + Parameters + ---------- + err_list : list + A one dimensional list which contains the uncertainty + associated to each data point in order. + cormat_list : list + A one dimensional list which contains the elements of + the correlation matrix row by row. Since experimental + datasets provide these matrices in a list form, this + simplifies the implementation for the user. + + Returns + ------- + covmat_list : list + A one dimensional list which contains the elements of + the covariance matrix row by row. + + """ + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(err_list) + b = i % len(err_list) + covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) + return covmat_list + + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + covmat_list : list + A one dimensional list which contains the elements of + the covariance matrix row by row. Since experimental + datasets provide these matrices in a list form, this + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + in an instance. For example, if a single covariance matrix + of a normalized distribution is being processed, the input + would be 1. If a covariance matrix contains pertains to + 3 normalized datasets (i.e. cross covmat for 3 + distributions), the input would be 3. The default value is + 0 for when the covariance matrix pertains to an absolute + distribution. + + Returns + ------- + artunc : list + A two dimensional matrix (given as a list of lists) + which contains artificial uncertainties to be added + to the commondata. i^th row (or list) contains the + artificial uncertainties of the i^th data point. + + """ + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if not psd_check: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() + + +def cross_cormat_to_covmat(row_err_list, col_err_list, cormat_list): + r"""Convert cross correlation matrix elements + (i.e. those between different different variables or + observables) to covariance matrix elements. + + Parameters + ---------- + row_err_list : list + A one dimensional list which contains the uncertainty + associated to each data point of the variable that is + given on the vertical axis. + col_err_list : list + A one dimensional list which contains the uncertainty + associated to each data point of the variable that is + given on the horizontal axis. + cormat_list : list + A one dimensional list which contains the elements of + the correlation matrix row by row. Since experimental + datasets provide these matrices in a list form, this + simplifies the implementation for the user. + + Returns + ------- + covmat_list : list + A one dimensional list which contains the elements of + the covariance matrix row by row. + + """ + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(col_err_list) + b = i % len(col_err_list) + covmat_list.append(cormat_list[i] * row_err_list[a] * col_err_list[b]) + return covmat_list + + +def matlist_to_matrix(rows, columns, mat_list): + r"""Convert a 1d list to a 2d matrix. + + Note: This utils function is not strictly needed for + data implementation, however, it is provided for + the aid of the user due to how matrices are treated + throughout all the other functions. This function + allows the user to convert a list that contains the + elemnets of matrix row by row to a proper matrix, if + need be for any reason. + + Parameters + ---------- + rows : int + No. of rows in the matrix + columns : int + No. of columns in the matrix + mat_list : list + A one dimensional list which contains the elements of + the matrix row by row. + + Returns + ------- + matrix : numpy.ndarray + The matrix as a numpy 2d array. + + """ + if rows * columns == len(mat_list): + matrix = np.zeros((rows, columns)) + for i in range(rows): + for j in range(columns): + matrix[i][j] = mat_list[j + i * columns] + matrix = np.array(matrix) + return matrix + else: + raise Exception('rows * columns != len(mat_list)') + + +def concat_matrices(rows, columns, list_of_matrices): + r"""Join smaller matrices into a large matrix. + + This function aims to simplify the process of joining multiple + smaller matrices into one large matrix. Such a need could arise, + for instance, when cross variable covariance matrices are provided + but the user needs to join all the matrices to generate the full + covariance matrix corresponding to the entire dataset. + + Parameters + ---------- + rows : int + No. of rows of matrices to be concatenated. E.g., if 6 + matrices: A, B, C, D, E, F need to be joined as + [[A, B, C], + [D, E, F]], + the number of rows would be 2. + columns : int + No. of columns of matrices to be concatenated. In the + above example, this would be 3. + list_of_matrices : list + A list of the matrices that have to concatenated row by + row. In the above example, this would be [A, B, C, D, E, F]. + The matrices themselves need to be provided as a list of lists, + or a numpy 2d array. If the user has the matrix in a 1d row by + row form, use matList_to_matrix() to convert it. It is assumed + the user verifies that all the input matrices have the correct + dimensions. Matrices with incompatible dimensions will lead to + undesired behavior. + + Returns + ------- + final_mat_list : list + A one dimensional list which contains the elements of + the final, fully concatenated matrix row by row. + + """ + for i in range(len(list_of_matrices)): + list_of_matrices[i] = np.array(list_of_matrices[i]) + col_list = [] + for i in range(rows): + row_list = [] + for j in range(columns): + row_list.append(list_of_matrices[j + i * columns]) + col_list.append(np.concatenate(tuple(row_list), axis=1)) + final_mat = np.concatenate(tuple(col_list), axis=0) + final_mat_list = [] + for i in range(len(final_mat)): + for j in range(len(final_mat[i])): + final_mat_list.append(final_mat[i][j]) + return final_mat_list + + +def trimat_to_fullmat(mode, tri_mat_list): + r"""Convert a list of values of a triangular matrix + to a symmetric matrix. + + Experimental datasets can provide the entries of + correlation or covariance matrices as a triangular + matrix, as these matrices are symmetric by their + very nature. This function can convert these list to + a complete symmetric matrix, that can be used for the + dataset implementation. + + mode : bool + Enter 0 or 1 based on the following scenarios: + Use mode 0 if matrix entries are given row by + row such as: + 0 1 2 3 + 4 5 6 + 7 8 + 9 + Use mode 1 if the matrix entries are given column + by column such as: + 0 1 3 6 + 2 4 7 + 5 8 + 9 + Please note that the numbers above (0-9) are not + entries of the matrix but rather the index of the + entries of the list which contains the elements of + the triangular matrix. + tri_mat_list : list + A list containing the elements of the triangular matrix, + for example, for a 4*4 matrix, the list of + triangular matrix entries could be: + [a, b, c, d, e, f, g, h, i, j] + + Returns + ------- + mat_list : list + A one dimensional list which contains the elements of + the fully populated, symmetric matrix row by row. + + """ + dim = int((np.sqrt(1 + 8 * len(tri_mat_list)) - 1) / 2) + matrix = np.zeros((dim, dim)) + if mode == 0: + for i in range(dim): + for j in range(i + 1): + list_el = len(tri_mat_list) - 1 - ((i * (i + 1)) // 2 + j) + if i == j: + matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] + else: + matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] + matrix[dim - 1 - j][dim - 1 - i] = tri_mat_list[list_el] + elif mode == 1: + for i in range(dim): + for j in range(i + 1): + list_el = (i * (i + 1)) // 2 + j + if i == j: + matrix[i][j] = tri_mat_list[list_el] + else: + matrix[i][j] = tri_mat_list[list_el] + matrix[j][i] = tri_mat_list[list_el] + else: + raise Exception('Mode should be 0 or 1, refer to the function for usage') + mat_list = [] + for i in range(dim): + for j in range(dim): + mat_list.append(matrix[i][j]) + return mat_list + + def correlation_to_covariance(correlation, uncertainties): """ Converts a correlation matrix into a covariance matrix diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py index ff222f5fea..b0606649fe 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py index 0655765b51..6ddd20bef2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/artunc.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/artunc.py index 25e6371f66..e8de287406 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/artunc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/artunc.py @@ -1,9 +1,9 @@ -import yaml import numpy as np -from utils import covmat_to_artunc as cta -from utils import matlist_to_matrix as mtm -from utils import concat_matrices as cm +import yaml +from nnpdf_data.filter_utils.utils import concat_matrices as cm +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import matlist_to_matrix as mtm covmat_mtt = [] covmat_mtt_norm = [] @@ -22,7 +22,7 @@ covmat_496 = [] covmat_497 = [] -covariance_matrix_mtt="rawdata/Table464.yaml" +covariance_matrix_mtt = "rawdata/Table464.yaml" with open(covariance_matrix_mtt, 'r') as file1: input1 = yaml.safe_load(file1) for i in range(81): @@ -30,7 +30,7 @@ artunc_mtt = cta(9, covmat_mtt) -covariance_matrix_mtt_norm="rawdata/Table462.yaml" +covariance_matrix_mtt_norm = "rawdata/Table462.yaml" with open(covariance_matrix_mtt_norm, 'r') as file2: input2 = yaml.safe_load(file2) for i in range(81): @@ -38,14 +38,14 @@ artunc_mtt_norm = cta(9, covmat_mtt_norm, 1) -covariance_matrix_ytt="rawdata/Table476.yaml" +covariance_matrix_ytt = "rawdata/Table476.yaml" with open(covariance_matrix_ytt, 'r') as file3: input3 = yaml.safe_load(file3) for i in range(144): covmat_ytt.append(input3['dependent_variables'][0]['values'][i]['value']) artunc_ytt = cta(12, covmat_ytt) -covariance_matrix_ytt_norm="rawdata/Table474.yaml" +covariance_matrix_ytt_norm = "rawdata/Table474.yaml" with open(covariance_matrix_ytt_norm, 'r') as file4: input4 = yaml.safe_load(file4) for i in range(144): @@ -92,7 +92,21 @@ covmat_506.append(input10['dependent_variables'][0]['values'][i]['value']) covmat_506 = mtm(3, 3, covmat_506) -covmat_mtt_ytt = cm(3, 3, [covmat_501, covmat_502t, covmat_504t, covmat_502, covmat_503, covmat_505t, covmat_504, covmat_505, covmat_506]) +covmat_mtt_ytt = cm( + 3, + 3, + [ + covmat_501, + covmat_502t, + covmat_504t, + covmat_502, + covmat_503, + covmat_505t, + covmat_504, + covmat_505, + covmat_506, + ], +) artunc_mtt_ytt = cta(11, covmat_mtt_ytt) # abs mtt-ytt-norm @@ -135,5 +149,19 @@ covmat_497.append(input16['dependent_variables'][0]['values'][i]['value']) covmat_497 = mtm(3, 3, covmat_497) -covmat_mtt_ytt_norm = cm(3, 3, [covmat_492, covmat_493t, covmat_495t, covmat_493, covmat_494, covmat_496t, covmat_495, covmat_496, covmat_497]) +covmat_mtt_ytt_norm = cm( + 3, + 3, + [ + covmat_492, + covmat_493t, + covmat_495t, + covmat_493, + covmat_494, + covmat_496t, + covmat_495, + covmat_496, + covmat_497, + ], +) artunc_mtt_ytt_norm = cta(11, covmat_mtt_ytt_norm, 1) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py deleted file mode 100644 index dac59de537..0000000000 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py +++ /dev/null @@ -1,369 +0,0 @@ -""" -Python utilities for commondata implementation. - -This module provides helpful functions that automate a few -tasks that are regularly needed for the implementation of -experimental data to the commondata format. If there are -any additional functions that could be added here as they -could simplify some repetitve tasks, please do suggest. - -Before the usage of any functions, it is recommended to read -the docstrings of the function to understand the inputs and -outputs. - -@author: Tanishq Sharma -""" - -from math import sqrt - -import numpy as np -from numpy.linalg import eig - - -def symmetrize_errors(delta_plus, delta_minus): - r"""Compute the symmterized uncertainty and the shift in data point. - - Parameters - ---------- - delta_plus : float - The top/plus uncertainty with sign - delta_minus : float - The bottom/minus uncertainty with sign - - Returns - ------- - se_delta : float - The value to be added to the data point - se_sigma : float - The symmetrized uncertainty to be used in commondata - - """ - semi_diff = (delta_plus + delta_minus) / 2 - average = (delta_plus - delta_minus) / 2 - se_delta = semi_diff - se_sigma = sqrt(average * average + 2 * semi_diff * semi_diff) - return se_delta, se_sigma - - -def percentage_to_absolute(percentage, value): - r"""Compute the absolute value of uncertainty from percentage. - - Parameters - ---------- - percentage : string/float - Experimental datasets can provide the percentage - uncertainties with a % sign or without one. - The function will autostrip % sign and convert to - a float type in case the percentage uncertainty - comes with a % sign. Else, it will directly perform - the computation. - value : float - The data point - - Returns - ------- - absolute : float - The absolute value of the uncertainty - - """ - if type(percentage) is str: - percentage = float(percentage.replace("%", "")) - absolute = percentage * value * 0.01 - return absolute - else: - absolute = percentage * value * 0.01 - return absolute - - -def cormat_to_covmat(err_list, cormat_list): - r"""Convert correlation matrix elements to covariance - matrix elements. - - Parameters - ---------- - err_list : list - A one dimensional list which contains the uncertainty - associated to each data point in order. - cormat_list : list - A one dimensional list which contains the elements of - the correlation matrix row by row. Since experimental - datasets provide these matrices in a list form, this - simplifies the implementation for the user. - - Returns - ------- - covmat_list : list - A one dimensional list which contains the elements of - the covariance matrix row by row. - - """ - covmat_list = [] - for i in range(len(cormat_list)): - a = i // len(err_list) - b = i % len(err_list) - covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) - return covmat_list - - -def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): - r"""Convert the covariance matrix to a matrix of - artificial uncertainties. - - Parameters - ---------- - ndata : integer - Number of data points - covmat_list : list - A one dimensional list which contains the elements of - the covariance matrix row by row. Since experimental - datasets provide these matrices in a list form, this - simplifies the implementation for the user. - no_of_norm_mat : int - Normalized covariance matrices may have an eigenvalue - of 0 due to the last data point not being linearly - independent. To allow for this, the user should input - the number of normalized matrices that are being treated - in an instance. For example, if a single covariance matrix - of a normalized distribution is being processed, the input - would be 1. If a covariance matrix contains pertains to - 3 normalized datasets (i.e. cross covmat for 3 - distributions), the input would be 3. The default value is - 0 for when the covariance matrix pertains to an absolute - distribution. - - Returns - ------- - artunc : list - A two dimensional matrix (given as a list of lists) - which contains artificial uncertainties to be added - to the commondata. i^th row (or list) contains the - artificial uncertainties of the i^th data point. - - """ - epsilon = -0.0000000001 - neg_eval_count = 0 - psd_check = True - covmat = np.zeros((ndata, ndata)) - artunc = np.zeros((ndata, ndata)) - for i in range(len(covmat_list)): - a = i // ndata - b = i % ndata - covmat[a][b] = covmat_list[i] - eigval, eigvec = eig(covmat) - for j in range(len(eigval)): - if eigval[j] < epsilon: - psd_check = False - elif eigval[j] > epsilon and eigval[j] <= 0: - neg_eval_count = neg_eval_count + 1 - if neg_eval_count == (no_of_norm_mat + 1): - psd_check = False - elif eigval[j] > 0: - continue - if psd_check == False: - raise ValueError('The covariance matrix is not positive-semidefinite') - else: - for i in range(ndata): - for j in range(ndata): - if eigval[j] < 0: - continue - else: - artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) - return artunc.tolist() - - -def cross_cormat_to_covmat(row_err_list, col_err_list, cormat_list): - r"""Convert cross correlation matrix elements - (i.e. those between different different variables or - observables) to covariance matrix elements. - - Parameters - ---------- - row_err_list : list - A one dimensional list which contains the uncertainty - associated to each data point of the variable that is - given on the vertical axis. - col_err_list : list - A one dimensional list which contains the uncertainty - associated to each data point of the variable that is - given on the horizontal axis. - cormat_list : list - A one dimensional list which contains the elements of - the correlation matrix row by row. Since experimental - datasets provide these matrices in a list form, this - simplifies the implementation for the user. - - Returns - ------- - covmat_list : list - A one dimensional list which contains the elements of - the covariance matrix row by row. - - """ - covmat_list = [] - for i in range(len(cormat_list)): - a = i // len(col_err_list) - b = i % len(col_err_list) - covmat_list.append(cormat_list[i] * row_err_list[a] * col_err_list[b]) - return covmat_list - - -def matlist_to_matrix(rows, columns, mat_list): - r"""Convert a 1d list to a 2d matrix. - - Note: This utils function is not strictly needed for - data implementation, however, it is provided for - the aid of the user due to how matrices are treated - throughout all the other functions. This function - allows the user to convert a list that contains the - elemnets of matrix row by row to a proper matrix, if - need be for any reason. - - Parameters - ---------- - rows : int - No. of rows in the matrix - columns : int - No. of columns in the matrix - mat_list : list - A one dimensional list which contains the elements of - the matrix row by row. - - Returns - ------- - matrix : numpy.ndarray - The matrix as a numpy 2d array. - - """ - if rows * columns == len(mat_list): - matrix = np.zeros((rows, columns)) - for i in range(rows): - for j in range(columns): - matrix[i][j] = mat_list[j + i * columns] - matrix = np.array(matrix) - return matrix - else: - raise Exception('rows * columns != len(mat_list)') - - -def concat_matrices(rows, columns, list_of_matrices): - r"""Join smaller matrices into a large matrix. - - This function aims to simplify the process of joining multiple - smaller matrices into one large matrix. Such a need could arise, - for instance, when cross variable covariance matrices are provided - but the user needs to join all the matrices to generate the full - covariance matrix corresponding to the entire dataset. - - Parameters - ---------- - rows : int - No. of rows of matrices to be concatenated. E.g., if 6 - matrices: A, B, C, D, E, F need to be joined as - [[A, B, C], - [D, E, F]], - the number of rows would be 2. - columns : int - No. of columns of matrices to be concatenated. In the - above example, this would be 3. - list_of_matrices : list - A list of the matrices that have to concatenated row by - row. In the above example, this would be [A, B, C, D, E, F]. - The matrices themselves need to be provided as a list of lists, - or a numpy 2d array. If the user has the matrix in a 1d row by - row form, use matList_to_matrix() to convert it. It is assumed - the user verifies that all the input matrices have the correct - dimensions. Matrices with incompatible dimensions will lead to - undesired behavior. - - Returns - ------- - final_mat_list : list - A one dimensional list which contains the elements of - the final, fully concatenated matrix row by row. - - """ - for i in range(len(list_of_matrices)): - list_of_matrices[i] = np.array(list_of_matrices[i]) - col_list = [] - for i in range(rows): - row_list = [] - for j in range(columns): - row_list.append(list_of_matrices[j + i * columns]) - col_list.append(np.concatenate(tuple(row_list), axis=1)) - final_mat = np.concatenate(tuple(col_list), axis=0) - final_mat_list = [] - for i in range(len(final_mat)): - for j in range(len(final_mat[i])): - final_mat_list.append(final_mat[i][j]) - return final_mat_list - - -def trimat_to_fullmat(mode, tri_mat_list): - r"""Convert a list of values of a triangular matrix - to a symmetric matrix. - - Experimental datasets can provide the entries of - correlation or covariance matrices as a triangular - matrix, as these matrices are symmetric by their - very nature. This function can convert these list to - a complete symmetric matrix, that can be used for the - dataset implementation. - - mode : bool - Enter 0 or 1 based on the following scenarios: - Use mode 0 if matrix entries are given row by - row such as: - 0 1 2 3 - 4 5 6 - 7 8 - 9 - Use mode 1 if the matrix entries are given column - by column such as: - 0 1 3 6 - 2 4 7 - 5 8 - 9 - Please note that the numbers above (0-9) are not - entries of the matrix but rather the index of the - entries of the list which contains the elements of - the triangular matrix. - tri_mat_list : list - A list containing the elements of the triangular matrix, - for example, for a 4*4 matrix, the list of - triangular matrix entries could be: - [a, b, c, d, e, f, g, h, i, j] - - Returns - ------- - mat_list : list - A one dimensional list which contains the elements of - the fully populated, symmetric matrix row by row. - - """ - dim = int((np.sqrt(1 + 8 * len(tri_mat_list)) - 1) / 2) - matrix = np.zeros((dim, dim)) - if mode == 0: - for i in range(dim): - for j in range(i + 1): - list_el = len(tri_mat_list) - 1 - ((i * (i + 1)) // 2 + j) - if i == j: - matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] - else: - matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] - matrix[dim - 1 - j][dim - 1 - i] = tri_mat_list[list_el] - elif mode == 1: - for i in range(dim): - for j in range(i + 1): - list_el = (i * (i + 1)) // 2 + j - if i == j: - matrix[i][j] = tri_mat_list[list_el] - else: - matrix[i][j] = tri_mat_list[list_el] - matrix[j][i] = tri_mat_list[list_el] - else: - raise Exception('Mode should be 0 or 1, refer to the function for usage') - mat_list = [] - for i in range(dim): - for j in range(dim): - mat_list.append(matrix[i][j]) - return mat_list diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py index 37b78e0988..f224ee8f3f 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,8 +1,8 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py index 1458e3cd54..e2c2a8fb2a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py index a079949fa8..822d1367e9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py @@ -2,80 +2,80 @@ from numpy.linalg import eig import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import concat_matrices as cm -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import matlist_to_matrix as mtm -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import concat_matrices as cm +from nnpdf_data.filter_utils.utils import cormat_to_covmat as ctc +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import matlist_to_matrix as mtm +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def artunc(): statArr = [] for i in [23, 29, 31, 27]: - with open('rawdata/Table_'+str(i)+'.yaml', 'r') as file: + with open('rawdata/Table_' + str(i) + '.yaml', 'r') as file: input = yaml.safe_load(file) for j in range(len(input['dependent_variables'][0]['values'])): datval = input['dependent_variables'][0]['values'][j]['value'] statperc = input['dependent_variables'][0]['values'][j]['errors'][0]['symerror'] statArr.append(pta(statperc, datval)) - -# mttbar(7)| pTt (8)| yt(5)| yttbar(5) -# mttbar| 179 174t 170t 177t -# pTt | 174 172 168t 173 -# yt | 170 168 167 169 -# yttbar| 177 173t 169t 176 + + # mttbar(7)| pTt (8)| yt(5)| yttbar(5) + # mttbar| 179 174t 170t 177t + # pTt | 174 172 168t 173 + # yt | 170 168 167 169 + # yttbar| 177 173t 169t 176 ml179, ml174, ml170, ml177, ml172, ml168, ml167, ml173, ml169, ml176 = ([] for i in range(10)) - + with open('rawdata/Table_179.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml179.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_174.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml174.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_170.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml170.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_177.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml177.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_172.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml172.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_168.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml168.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_167.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml167.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_173.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml173.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_169.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml169.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_176.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml176.append(input['dependent_variables'][0]['values'][i]['value']) - + mat179 = mtm(7, 7, ml179) mat174 = mtm(8, 7, ml174) mat174t = mat174.transpose() @@ -93,8 +93,28 @@ def artunc(): mat169t = mat169.transpose() mat176 = mtm(5, 5, ml176) - cormatlist = cm(4, 4, [mat179, mat174t, mat170t, mat177t, mat174, mat172, mat168t, mat173, - mat170, mat168, mat167, mat169, mat177, mat173t, mat169t, mat176]) + cormatlist = cm( + 4, + 4, + [ + mat179, + mat174t, + mat170t, + mat177t, + mat174, + mat172, + mat168t, + mat173, + mat170, + mat168, + mat167, + mat169, + mat177, + mat173t, + mat169t, + mat176, + ], + ) covmatlist = ctc(statArr, cormatlist) artunc = cta(25, covmatlist) @@ -104,70 +124,70 @@ def artunc(): def artunc_norm(): statArr = [] for i in [24, 30, 32, 28]: - with open('rawdata/Table_'+str(i)+'.yaml', 'r') as file: + with open('rawdata/Table_' + str(i) + '.yaml', 'r') as file: input = yaml.safe_load(file) for j in range(len(input['dependent_variables'][0]['values'])): datval = input['dependent_variables'][0]['values'][j]['value'] statperc = input['dependent_variables'][0]['values'][j]['errors'][0]['symerror'] statArr.append(pta(statperc, datval)) - -# mttbar(7)| pTt (8)| yt(5)| yttbar(5) -# mttbar| 234 229t 225t 232t -# pTt | 229 227 223t 228 -# yt | 225 223 222 224 -# yttbar| 232 228t 224t 231 + + # mttbar(7)| pTt (8)| yt(5)| yttbar(5) + # mttbar| 234 229t 225t 232t + # pTt | 229 227 223t 228 + # yt | 225 223 222 224 + # yttbar| 232 228t 224t 231 ml234, ml229, ml225, ml232, ml227, ml223, ml222, ml228, ml224, ml231 = ([] for i in range(10)) - + with open('rawdata/Table_234.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml234.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_229.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml229.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_225.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml225.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_232.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml232.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_227.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml227.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_223.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml223.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_222.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml222.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_228.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml228.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_224.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml224.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_231.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml231.append(input['dependent_variables'][0]['values'][i]['value']) - + mat234 = mtm(7, 7, ml234) mat229 = mtm(8, 7, ml229) mat229t = mat229.transpose() @@ -185,8 +205,28 @@ def artunc_norm(): mat224t = mat224.transpose() mat231 = mtm(5, 5, ml231) - cormatlist = cm(4, 4, [mat234, mat229t, mat225t, mat232t, mat229, mat227, mat223t, mat228, - mat225, mat223, mat222, mat224, mat232, mat228t, mat224t, mat231]) + cormatlist = cm( + 4, + 4, + [ + mat234, + mat229t, + mat225t, + mat232t, + mat229, + mat227, + mat223t, + mat228, + mat225, + mat223, + mat222, + mat224, + mat232, + mat228t, + mat224t, + mat231, + ], + ) covmatlist = ctc(statArr, cormatlist) artunc = cta(25, covmatlist, 4) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py index b114623b64..94b2dbb9c0 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py @@ -4,8 +4,8 @@ import artunc import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py index 0d358adae5..0b1c8e4dc1 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py index fcda4a06a5..87a9789fdc 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py index 0cd577753c..c22c00ee8d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,10 +1,10 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import trimat_to_fullmat as ttf +from nnpdf_data.filter_utils.utils import cormat_to_covmat as ctc +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import trimat_to_fullmat as ttf def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py index 5f5c5af484..0399a3049e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py @@ -1,7 +1,7 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py index 8397558851..13509f0a65 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py index d1bbb8538d..86a9664109 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py index ba34402178..d61b7d3375 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MW_VALUE = 80.398 # GeV SQRT_S = 8_000.0 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py index 040b44911f..d5cd43745e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc # MZ_VALUE = 91.1876 # GeV # MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py index 736ea6b856..b7a9b5b465 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,8 +1,8 @@ import numpy import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def artunc(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py index 7f19469816..7f3f74bacc 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py @@ -3,8 +3,8 @@ import artUnc import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py index 00505782ec..55bf4e9062 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py @@ -1,7 +1,7 @@ from manual_impl import artunc, jet_data, jet_sys import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py index 50e3f03421..8a5176db0c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,74 +1,3537 @@ from math import sqrt -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import cormat_to_covmat as ctc +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta -jet_old_impl_list = [['1', 'DIS_1JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '7.042365878823e+01', '1.901438787282e+00', '7.042365878823e-01', '1.000000000000e+00', '7.130250486484e-01', '1.010961455291e+00', '6.718827732504e-01', '9.531044964111e-01', '2.517144109385e-01', '3.572500464504e-01', '2.515885537330e-01', '3.572500464504e-01', '3.521182939412e-01', '5.000000000000e-01', '4.225419527294e-01', '6.000000000000e-01', '2.042286104859e+00', '2.900000000000e+00'], ['2', 'DIS_1JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '3.095350776162e+01', '1.269093818226e+00', '8.666982173254e-01', '2.800000000000e+00', '7.598162606841e-01', '2.452246318915e+00', '1.718173641914e-01', '5.542497004702e-01', '1.910967863178e-01', '6.170584587557e-01', '8.045980207445e-02', '2.599375899303e-01', '1.547675388081e-01', '5.000000000000e-01', '1.857210465697e-01', '6.000000000000e-01', '8.976517250870e-01', '2.900000000000e+00'], ['3', 'DIS_1JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '8.082109035000e+00', '5.172549782400e-01', '2.828738162250e-01', '3.500000000000e+00', '2.743800000000e-01', '3.400000000000e+00', '1.976738222426e-02', '2.447042700083e-01', '3.679736009134e-02', '4.552940319412e-01', '8.082109035000e-03', '1.000000000000e-01', '4.041054517500e-02', '5.000000000000e-01', '4.849265421000e-02', '6.000000000000e-01', '2.343811620150e-01', '2.900000000000e+00'], ['4', 'DIS_1JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '9.125014074304e-01', '1.396127153368e-01', '1.067626646694e-01', '1.170000000000e+01', '4.688994472166e-02', '5.118073262173e+00', '1.519286117216e-03', '1.657483653351e-01', '4.299338659215e-03', '4.704529347867e-01', '1.991738317891e-03', '2.182723557106e-01', '4.562507037152e-03', '5.000000000000e-01', '5.475008444582e-03', '6.000000000000e-01', '2.646254081548e-02', '2.900000000000e+00'], ['5', 'DIS_1JET', '2.350000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.493706846573e+01', '1.648112053972e+00', '-3.296224107944e-01', '-6.000000000000e-01', '5.220401134013e-01', '9.531044964111e-01', '6.074575198510e-01', '1.107945704936e+00', '4.273370774492e-01', '7.782554801857e-01', '1.960665379920e-01', '3.568929749397e-01', '2.746853423286e-01', '5.000000000000e-01', '3.296224107944e-01', '6.000000000000e-01', '1.593174985506e+00', '2.900000000000e+00'], ['6', 'DIS_1JET', '2.350000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.680000000000e+01', '1.098800000000e+00', '9.112000000000e-01', '3.400000000000e+00', '6.432000000000e-01', '2.400000000000e+00', '1.072000000000e-01', '4.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '8.040000000000e-02', '3.000000000000e-01', '1.340000000000e-01', '5.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '7.772000000000e-01', '2.900000000000e+00'], ['7', 'DIS_1JET', '2.350000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.013496240129e+00', '4.628907518485e-01', '3.366478195262e-01', '4.800000000000e+00', '2.492988998672e-01', '3.551005871609e+00', '1.404103000000e-02', '2.000000000000e-01', '3.893063895065e-02', '5.548042274342e-01', '2.505571857565e-02', '3.572500464504e-01', '3.506748120064e-02', '5.000000000000e-01', '4.208097744077e-02', '6.000000000000e-01', '2.033913909637e-01', '2.900000000000e+00'], ['8', 'DIS_1JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.549775241245e-01', '1.299565836669e-01', '3.932896610973e-02', '4.600000000000e+00', '4.505743015308e-02', '5.264739441654e+00', '1.865246959223e-03', '2.182723557106e-01', '7.400622244443e-04', '8.655926074807e-02', '2.564932572373e-03', '3.000000000000e-01', '4.274887620623e-03', '5.000000000000e-01', '5.129865144747e-03', '6.000000000000e-01', '2.479434819961e-02', '2.900000000000e+00'], ['9', 'DIS_1JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.209563915000e+01', '1.562869174500e+00', '7.814345872500e-01', '1.500000000000e+00', '4.972717868530e-01', '9.531044964111e-01', '5.217390000000e-01', '1.000000000000e+00', '4.570802892413e-01', '8.773868536773e-01', '1.562869174500e-01', '3.000000000000e-01', '2.604781957500e-01', '5.000000000000e-01', '3.125738349000e-01', '6.000000000000e-01', '1.510773535350e+00', '2.900000000000e+00'], ['10', 'DIS_1JET', '3.450000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.785563475695e+01', '1.114225390278e+00', '8.635246774655e-01', '3.100000000000e+00', '6.258088126577e-01', '2.249985843976e+00', '1.112556000000e-01', '4.000000000000e-01', '2.259611917827e-01', '8.115922482154e-01', '7.233489456689e-02', '2.596777822442e-01', '1.114225390278e-01', '4.000000000000e-01', '1.671338085417e-01', '6.000000000000e-01', '8.078134079515e-01', '2.900000000000e+00'], ['11', 'DIS_1JET', '3.450000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '6.962069709237e+00', '4.734207402281e-01', '1.322793244755e-01', '1.900000000000e+00', '2.518340918144e-01', '3.606383090020e+00', '1.158001203865e-02', '1.657483653351e-01', '5.928243109147e-02', '8.502285946131e-01', '1.811516043400e-02', '2.601979180124e-01', '2.784827883695e-02', '4.000000000000e-01', '4.177241825542e-02', '6.000000000000e-01', '2.019000215679e-01', '2.900000000000e+00'], ['12', 'DIS_1JET', '3.450000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.702993693220e-01', '1.314152047676e-01', '-2.610898107966e-02', '-3.000000000000e+00', '4.826471687216e-02', '5.562396168725e+00', '1.502894423550e-03', '1.733784592161e-01', '5.667628733710e-03', '6.522043307043e-01', '2.849193515760e-03', '3.273808549327e-01', '3.481197477288e-03', '4.000000000000e-01', '5.221796215932e-03', '6.000000000000e-01', '2.523868171034e-02', '2.900000000000e+00'], ['13', 'DIS_1JET', '5.500000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '4.877557560000e+01', '1.560818419200e+00', '7.316336340000e-01', '1.500000000000e+00', '6.381428053344e-01', '1.308978661724e+00', '3.412584000000e-01', '7.000000000000e-01', '5.616976088752e-01', '1.151596064148e+00', '9.755115120000e-02', '2.000000000000e-01', '1.951023024000e-01', '4.000000000000e-01', '2.926534536000e-01', '6.000000000000e-01', '1.414491692400e+00', '2.900000000000e+00'], ['14', 'DIS_1JET', '5.500000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.690000000000e+01', '1.102900000000e+00', '3.228000000000e-01', '1.200000000000e+00', '5.380000000000e-01', '2.000000000000e+00', '1.076000000000e-01', '4.000000000000e-01', '1.883000000000e-01', '7.000000000000e-01', '2.690000000000e-02', '1.000000000000e-01', '1.076000000000e-01', '4.000000000000e-01', '1.614000000000e-01', '6.000000000000e-01', '7.801000000000e-01', '2.900000000000e+00'], ['15', 'DIS_1JET', '5.500000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.949992050000e+00', '4.849495150500e-01', '2.782497217500e-01', '3.500000000000e+00', '2.943647864470e-01', '3.699002713601e+00', '2.639353425041e-02', '3.319944735090e-01', '6.359993640000e-02', '8.000000000000e-01', '7.949992050000e-03', '1.000000000000e-01', '2.384997615000e-02', '3.000000000000e-01', '4.769995230000e-02', '6.000000000000e-01', '2.305497694500e-01', '2.900000000000e+00'], ['16', 'DIS_1JET', '5.500000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.561421438570e-01', '1.412634537364e-01', '-7.619665080327e-02', '-8.900000000000e+00', '4.800730113222e-02', '5.596189240424e+00', '1.213193003977e-03', '1.415629191565e-01', '1.211979810973e-03', '1.415629191565e-01', '8.561421438570e-04', '1.000000000000e-01', '1.712284287714e-03', '2.000000000000e-01', '5.136852863142e-03', '6.000000000000e-01', '2.482812217185e-02', '2.900000000000e+00'], ['17', 'DIS_1JET', '2.850000000000e+03', '9.000000000000e+00', '3.190000000000e+02', '4.329996751417e+01', '1.515498862996e+00', '9.525992853119e-01', '2.200000000000e+00', '4.802202307275e-01', '1.110163814455e+00', '1.970436461015e-01', '4.552940319412e-01', '1.971421679245e-01', '4.552940319412e-01', '2.164998375709e-01', '5.000000000000e-01', '4.762996426559e-01', '1.100000000000e+00', '2.597998050850e-01', '6.000000000000e-01', '1.255699057911e+00', '2.900000000000e+00'], ['18', 'DIS_1JET', '2.850000000000e+03', '1.450000000000e+01', '3.190000000000e+02', '2.852850712500e+01', '1.141140285000e+00', '3.993990997500e-01', '1.400000000000e+00', '4.422094385017e-01', '1.550836646595e+00', '2.851425000000e-02', '1.000000000000e-01', '1.581191652889e-01', '5.542497004702e-01', '1.711710427500e-01', '6.000000000000e-01', '3.138135783750e-01', '1.100000000000e+00', '1.711710427500e-01', '6.000000000000e-01', '8.273267066250e-01', '2.900000000000e+00'], ['19', 'DIS_1JET', '2.850000000000e+03', '2.400000000000e+01', '3.190000000000e+02', '1.069999732500e+01', '5.242998689250e-01', '2.888999277750e-01', '2.700000000000e+00', '2.943472566544e-01', '2.752285083237e+00', '1.069465000000e-02', '1.000000000000e-01', '5.930470312414e-02', '5.542497004702e-01', '4.279998930000e-02', '4.000000000000e-01', '1.176999705750e-01', '1.100000000000e+00', '6.419998395000e-02', '6.000000000000e-01', '3.102999224250e-01', '2.900000000000e+00'], ['20', 'DIS_1JET', '2.850000000000e+03', '4.000000000000e+01', '3.190000000000e+02', '2.044081530000e+00', '1.737469300500e-01', '4.292571213000e-02', '2.100000000000e+00', '9.495865837300e-02', '4.647864398158e+00', '1.769341861456e-03', '8.655926074807e-02', '6.132244590000e-03', '3.000000000000e-01', '4.088163060000e-03', '2.000000000000e-01', '2.044081530000e-02', '1.000000000000e+00', '1.226448918000e-02', '6.000000000000e-01', '5.927836437000e-02', '2.900000000000e+00'], ['21', 'DIS_1JET', '1.000000000000e+04', '9.000000000000e+00', '3.190000000000e+02', '2.571395501979e+00', '3.779951387908e-01', '-7.714186505936e-02', '-3.000000000000e+00', '2.217633874200e-02', '8.533627868550e-01', '1.102535035362e-02', '4.246887574694e-01', '4.242064477684e-02', '1.652187526630e+00', '1.277337104494e-02', '4.967485956598e-01', '4.885651453759e-02', '1.900000000000e+00', '1.542837301187e-02', '6.000000000000e-01', '7.457046955738e-02', '2.900000000000e+00'], ['22', 'DIS_1JET', '1.000000000000e+04', '1.450000000000e+01', '3.190000000000e+02', '1.760953607685e+00', '2.887963916604e-01', '1.937048968454e-02', '1.100000000000e+00', '2.485217093133e-02', '1.425434816076e+00', '1.509897970990e-03', '8.655926074807e-02', '1.934579665249e-02', '1.101344240954e+00', '1.299737337559e-02', '7.380872113191e-01', '3.169716493834e-02', '1.800000000000e+00', '1.056572164611e-02', '6.000000000000e-01', '5.106765462288e-02', '2.900000000000e+00'], ['23', 'DIS_1JET', '1.000000000000e+04', '2.400000000000e+01', '3.190000000000e+02', '6.709991612502e-01', '1.449358188300e-01', '-8.655889180127e-02', 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0, + 0, + -1, + 1, + 7, + -36, + 100, + -13, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + -2, + -1, + -2, + -7, + 66, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + -1, + 2, + 2, + -9, + -13, + 100, ] + def sys_breakdown(old_impl, is_jet): sys_breakdown = [[] for i in range(24)] for i in range(24): sys_breakdown[i].append(float(old_impl[i][7])) - sys_breakdown[i].append(float(old_impl[i][9])/2) - sys_breakdown[i].append(float(old_impl[i][9])/2) - sys_breakdown[i].append(float(old_impl[i][11])/2) - sys_breakdown[i].append(float(old_impl[i][11])/2) + sys_breakdown[i].append(float(old_impl[i][9]) / 2) + sys_breakdown[i].append(float(old_impl[i][9]) / 2) + sys_breakdown[i].append(float(old_impl[i][11]) / 2) + sys_breakdown[i].append(float(old_impl[i][11]) / 2) sys_breakdown[i].append(float(old_impl[i][13])) sys_breakdown[i].append(float(old_impl[i][15])) sys_breakdown[i].append(float(old_impl[i][17])) - sys_breakdown[i].append(float(old_impl[i][5])*0.005) if is_jet else sys_breakdown[i].append(float(old_impl[i][19])) + ( + sys_breakdown[i].append(float(old_impl[i][5]) * 0.005) + if is_jet + else sys_breakdown[i].append(float(old_impl[i][19])) + ) sys_breakdown[i].append(float(old_impl[i][21])) return sys_breakdown @@ -81,6 +3544,7 @@ def sym_data(): dijet_data.append(float(dijet_old_impl_list[i][5])) return jet_data, dijet_data + def stat_lists(): jet_stat = [] dijet_stat = [] @@ -89,12 +3553,12 @@ def stat_lists(): dijet_stat.append(float(dijet_old_impl_list[i][6])) return jet_stat, dijet_stat + jet_data, dijet_data = sym_data() jet_stat, dijet_stat = stat_lists() jet_sys = sys_breakdown(jet_old_impl_list, True) dijet_sys = sys_breakdown(dijet_old_impl_list, False) -covmat = ctc(jet_stat + dijet_stat, [a/100 for a in corMatArray]) - -artunc = cta(48, covmat, ) +covmat = ctc(jet_stat + dijet_stat, [a / 100 for a in corMatArray]) +artunc = cta(48, covmat) diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py index 736ea6b856..b7a9b5b465 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,8 +1,8 @@ import numpy import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def artunc(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py index ecf4dff877..c638b42a41 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py @@ -3,8 +3,8 @@ import artUnc import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py index 39f732434d..8c3b997d5b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py @@ -1,7 +1,7 @@ from manual_impl import artunc, dijet_data, dijet_sys import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.filter_utils.utils import percentage_to_absolute as pta def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py index 50e3f03421..8a5176db0c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,74 +1,3537 @@ from math import sqrt -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.filter_utils.utils import cormat_to_covmat as ctc +from nnpdf_data.filter_utils.utils import covmat_to_artunc as cta -jet_old_impl_list = [['1', 'DIS_1JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '7.042365878823e+01', '1.901438787282e+00', '7.042365878823e-01', '1.000000000000e+00', '7.130250486484e-01', '1.010961455291e+00', '6.718827732504e-01', '9.531044964111e-01', '2.517144109385e-01', '3.572500464504e-01', '2.515885537330e-01', '3.572500464504e-01', '3.521182939412e-01', '5.000000000000e-01', '4.225419527294e-01', '6.000000000000e-01', '2.042286104859e+00', '2.900000000000e+00'], ['2', 'DIS_1JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '3.095350776162e+01', '1.269093818226e+00', '8.666982173254e-01', '2.800000000000e+00', '7.598162606841e-01', '2.452246318915e+00', '1.718173641914e-01', '5.542497004702e-01', '1.910967863178e-01', '6.170584587557e-01', '8.045980207445e-02', '2.599375899303e-01', '1.547675388081e-01', '5.000000000000e-01', '1.857210465697e-01', '6.000000000000e-01', '8.976517250870e-01', '2.900000000000e+00'], ['3', 'DIS_1JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '8.082109035000e+00', '5.172549782400e-01', '2.828738162250e-01', '3.500000000000e+00', '2.743800000000e-01', '3.400000000000e+00', '1.976738222426e-02', '2.447042700083e-01', '3.679736009134e-02', '4.552940319412e-01', '8.082109035000e-03', '1.000000000000e-01', '4.041054517500e-02', '5.000000000000e-01', '4.849265421000e-02', '6.000000000000e-01', '2.343811620150e-01', '2.900000000000e+00'], ['4', 'DIS_1JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '9.125014074304e-01', '1.396127153368e-01', '1.067626646694e-01', '1.170000000000e+01', '4.688994472166e-02', '5.118073262173e+00', '1.519286117216e-03', '1.657483653351e-01', '4.299338659215e-03', '4.704529347867e-01', '1.991738317891e-03', '2.182723557106e-01', '4.562507037152e-03', '5.000000000000e-01', '5.475008444582e-03', '6.000000000000e-01', '2.646254081548e-02', '2.900000000000e+00'], ['5', 'DIS_1JET', '2.350000000000e+02', '9.000000000000e+00', 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'7.013496240129e+00', '4.628907518485e-01', '3.366478195262e-01', '4.800000000000e+00', '2.492988998672e-01', '3.551005871609e+00', '1.404103000000e-02', '2.000000000000e-01', '3.893063895065e-02', '5.548042274342e-01', '2.505571857565e-02', '3.572500464504e-01', '3.506748120064e-02', '5.000000000000e-01', '4.208097744077e-02', '6.000000000000e-01', '2.033913909637e-01', '2.900000000000e+00'], ['8', 'DIS_1JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.549775241245e-01', '1.299565836669e-01', '3.932896610973e-02', '4.600000000000e+00', '4.505743015308e-02', '5.264739441654e+00', '1.865246959223e-03', '2.182723557106e-01', '7.400622244443e-04', '8.655926074807e-02', '2.564932572373e-03', '3.000000000000e-01', '4.274887620623e-03', '5.000000000000e-01', '5.129865144747e-03', '6.000000000000e-01', '2.479434819961e-02', '2.900000000000e+00'], ['9', 'DIS_1JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.209563915000e+01', 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+ 1, + 0, + 0, + 0, + -2, + -1, + -2, + -7, + 66, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + -1, + 2, + 2, + -9, + -13, + 100, ] + def sys_breakdown(old_impl, is_jet): sys_breakdown = [[] for i in range(24)] for i in range(24): sys_breakdown[i].append(float(old_impl[i][7])) - sys_breakdown[i].append(float(old_impl[i][9])/2) - sys_breakdown[i].append(float(old_impl[i][9])/2) - sys_breakdown[i].append(float(old_impl[i][11])/2) - sys_breakdown[i].append(float(old_impl[i][11])/2) + sys_breakdown[i].append(float(old_impl[i][9]) / 2) + sys_breakdown[i].append(float(old_impl[i][9]) / 2) + sys_breakdown[i].append(float(old_impl[i][11]) / 2) + sys_breakdown[i].append(float(old_impl[i][11]) / 2) sys_breakdown[i].append(float(old_impl[i][13])) sys_breakdown[i].append(float(old_impl[i][15])) sys_breakdown[i].append(float(old_impl[i][17])) - sys_breakdown[i].append(float(old_impl[i][5])*0.005) if is_jet else sys_breakdown[i].append(float(old_impl[i][19])) + ( + sys_breakdown[i].append(float(old_impl[i][5]) * 0.005) + if is_jet + else sys_breakdown[i].append(float(old_impl[i][19])) + ) sys_breakdown[i].append(float(old_impl[i][21])) return sys_breakdown @@ -81,6 +3544,7 @@ def sym_data(): dijet_data.append(float(dijet_old_impl_list[i][5])) return jet_data, dijet_data + def stat_lists(): jet_stat = [] dijet_stat = [] @@ -89,12 +3553,12 @@ def stat_lists(): dijet_stat.append(float(dijet_old_impl_list[i][6])) return jet_stat, dijet_stat + jet_data, dijet_data = sym_data() jet_stat, dijet_stat = stat_lists() jet_sys = sys_breakdown(jet_old_impl_list, True) dijet_sys = sys_breakdown(dijet_old_impl_list, False) -covmat = ctc(jet_stat + dijet_stat, [a/100 for a in corMatArray]) - -artunc = cta(48, covmat, ) +covmat = ctc(jet_stat + dijet_stat, [a / 100 for a in corMatArray]) +artunc = cta(48, covmat) diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py index cec61ffaac..00f70cfbd5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py index 4b0d1a73e5..f0f14cb1a2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py index d63c7f9cab..c61279cbe8 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py index 6a2a2cb286..87c54145aa 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py index d485a62c81..d7eba548ab 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV SQRT_S = 13_000.0 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py index ddab8f5a32..f875fa3478 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py @@ -4,10 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import ( - covmat_to_artunc, - percentage_to_absolute, -) +from nnpdf_data.filter_utils.utils import covmat_to_artunc, percentage_to_absolute MZ_VALUE = 91.1876 # GeV SQRT_S = 7_000.0 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py index a603f11e5b..91195801d9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py index 8edf001649..9bd9881e2e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV SQRT_S = 8_000.0 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py index 11f1899ddb..4ecaf473a7 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py @@ -4,7 +4,7 @@ import pandas as pd import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc +from nnpdf_data.filter_utils.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py index e6c5b3fa0b..66c931644a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py index 1804a8e969..9ecf7b5024 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py index ce56dd4dab..3ab3b10a21 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.filter_utils.utils import symmetrize_errors as se def processData():