diff --git a/POLYTEST/POLYTEST.yaml b/POLYTEST/POLYTEST.yaml new file mode 100644 index 00000000..0510989d --- /dev/null +++ b/POLYTEST/POLYTEST.yaml @@ -0,0 +1,56 @@ +# Input YAML configurations for SMEFiT code + +result_ID: POLYTEST +# absolute path where results are stored +result_path: ./ + +# path to common data +data_path: /home/tentori/SMEFIT/smefit_database/Polymode_tests/Polynomial_test + +# path to theory tables, default same as data path +theory_path: /home/tentori/SMEFIT/smefit_database/Polymode_tests/Polynomial_test + +# pQCD order (LO or NLO) +order: LO + +use_theory_covmat: False + +use_t0: True + +poly_mode: True +external_coefficients: + b: a**4 +# SMEFT Expansion Order +use_quad: False + +# Set parameter bounds to previous SCAN result +bounds: Null + +# NS settings +nlive: 500 +lepsilon: 0.01 +target_evidence_unc: 0.01 +target_post_unc: 0.01 +frac_remain: 0.01 +store_raw: false + +# Datasets to include +datasets: + + - poly + + + + +# Coefficients to fit +coefficients: + + ## TOP COEFFICIENTS ## + a: { 'min': 0., 'max': 3.5 } + c: { 'min': 0., 'max': 3 } + + + + +rot_to_fit_basis: null + diff --git a/POLYTEST/posterior.json b/POLYTEST/posterior.json new file mode 100644 index 00000000..33b36df4 --- /dev/null +++ b/POLYTEST/posterior.json @@ -0,0 +1 @@ +{"a": [2.0132298942132287, 1.990352282429752, 2.0004462943291745, 1.9865585074046277, 2.0261207765887423, 2.054946471267457, 2.0264690722220497, 1.9832438773591916, 2.0255177175287074, 1.9794770105530641, 1.9805105952943336, 1.9525468334076195, 2.029060573069893, 1.960538707952879, 2.0303169613880336, 1.9889170711151298, 1.982440321422638, 2.013200483074962, 1.9957364377270903, 1.917983912552362, 2.003390129765003, 2.0378222946042324, 2.052864885020398, 2.0325639413702152, 1.9346083362449908, 2.009543160078648, 2.0157532553585895, 2.000646529656799, 2.0271248224933576, 2.0028117586659238, 1.9730658675927712, 1.9738932597675238, 1.9902528097825427, 1.957780661996151, 1.970626909551563, 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a/Polymode_tests/Polynomial_test/POLYTEST/POLYTEST.yaml b/Polymode_tests/Polynomial_test/POLYTEST/POLYTEST.yaml new file mode 100644 index 00000000..6f86e23c --- /dev/null +++ b/Polymode_tests/Polynomial_test/POLYTEST/POLYTEST.yaml @@ -0,0 +1,56 @@ +# Input YAML configurations for SMEFiT code + +result_ID: POLYTEST +# absolute path where results are stored +result_path: ./ + +# path to common data +data_path: /home/tentori/SMEFIT/smefit_release/Polymode_tests/Polynomial_test + +# path to theory tables, default same as data path +theory_path: /home/tentori/SMEFIT/smefit_release/Polymode_tests/Polynomial_test + +# pQCD order (LO or NLO) +order: LO + +use_theory_covmat: False + +use_t0: True + +poly_mode: True +external_coefficients: + b: a**4 +# SMEFT Expansion Order +use_quad: False + +# Set parameter bounds to previous SCAN result +bounds: Null + +# NS settings +nlive: 500 +lepsilon: 0.1 +target_evidence_unc: 0.1 +target_post_unc: 0.1 +frac_remain: 0.1 +store_raw: false + +# Datasets to include +datasets: + + - poly + + + + +# Coefficients to fit +coefficients: + + ## TOP COEFFICIENTS ## + a: { 'min': 0., 'max': 3.5 } + c: { 'min': 0., 'max': 3 } + + + + +rot_to_fit_basis: null + diff --git a/Polymode_tests/Polynomial_test/POLYTEST/posterior.json b/Polymode_tests/Polynomial_test/POLYTEST/posterior.json new file mode 100644 index 00000000..e7f3909e --- /dev/null +++ b/Polymode_tests/Polynomial_test/POLYTEST/posterior.json @@ -0,0 +1 @@ +{"a": [1.990178731973821, 1.99772594982168, 2.084899160589191, 2.0129363220260386, 2.026554960598265, 1.9709329525520127, 2.020162442169509, 1.994457534104695, 1.9955091992363305, 1.9631277711968311, 2.043196811065704, 1.9972025899335275, 2.0161804314523453, 1.9897763074478267, 2.0570827730824996, 2.00673759631732, 1.9886378809095733, 2.0259644928034257, 2.014614834426446, 2.010128168216872, 1.982602885048177, 1.962114796665727, 2.0197502459669416, 1.9829295632495236, 1.9639186503713746, 2.0089815382228084, 1.9940336231185214, 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1.8374154421286286, 1.6299406053019148, 2.1886990170024823, 2.0370976610715794, 2.058599133707231, 2.0463974103134888, 2.035994643765722, 2.1959327194356684, 2.1822182014171085]} \ No newline at end of file diff --git a/src/smefit/analyze/chi2_utils.py b/src/smefit/analyze/chi2_utils.py index 5699e71d..b8ee4567 100644 --- a/src/smefit/analyze/chi2_utils.py +++ b/src/smefit/analyze/chi2_utils.py @@ -21,6 +21,7 @@ class Chi2tableCalculator: """ def __init__(self, data_info): + self.data_info = data_info self.chi2_df_sm = pd.DataFrame() self.chi2_df_sm_grouped = pd.DataFrame() @@ -64,6 +65,7 @@ def compute(datasets, smeft_predictions): # Compute per experiment cnt = 0 for ndat_exp in datasets.NdataExp: + chi2.append( np.dot( diff[cnt : cnt + ndat_exp], @@ -91,9 +93,6 @@ def compute(datasets, smeft_predictions): ) cnt += ndat_exp - # compute chi2 for the whole dataset - total_chi2_rep = np.einsum("ij,ji->i", diff_rep, covmat_diff_rep) - return ( pd.DataFrame( { @@ -104,7 +103,7 @@ def compute(datasets, smeft_predictions): }, index=datasets.ExpNames, ), - total_chi2_rep / datasets.Commondata.size, + np.sum(chi2_rep, axis=0) / datasets.Commondata.size, ) @staticmethod @@ -369,7 +368,7 @@ def plot_dist(self, chi2_hist, fig_name, figsize=(7, 5)): plt.figure(figsize=figsize) ax = plt.subplot(111) - for label, chi2_list in chi2_hist.items(): + for (label, chi2_list) in chi2_hist.items(): ax.hist( chi2_list, bins="fd", diff --git a/src/smefit/analyze/coefficients_utils.py b/src/smefit/analyze/coefficients_utils.py index a1421c59..e1348639 100644 --- a/src/smefit/analyze/coefficients_utils.py +++ b/src/smefit/analyze/coefficients_utils.py @@ -148,7 +148,7 @@ def _plot_logo(self, ax, extent=[0.8, 0.999, 0.001, 0.30]): ) def _get_suplblots(self, figsize): - groups = self.coeff_info.groupby(level=0, sort=False).count() + groups = self.coeff_info.groupby(level=0).count() _, axs = plt.subplots( groups.size, 1, @@ -180,7 +180,6 @@ def plot_coeffs( colors = plt.get_cmap("tab20") def plot_error_bars(ax, vals, cnt, i, label=None): - ax.errorbar( x=vals.mid, y=Y[cnt] + y_shift[i], @@ -203,13 +202,8 @@ def plot_error_bars(ax, vals, cnt, i, label=None): # loop over fits for i, (fit_name, bound_df) in enumerate(bounds.items()): label = fit_name - bound_df_top_to_bottom = bound_df[g].iloc[ - :, ::-1 - ] # reverse order to plot from top to bottom in ax # loop on coeffs - for cnt, (coeff_name, coeff) in enumerate( - bound_df_top_to_bottom.items() - ): + for cnt, (coeff_name, coeff) in enumerate(bound_df[g].items()): # maybe there are no double solutions key_not_found = f"{coeff_name} posterior is not found in {fit_name}" try: @@ -228,10 +222,9 @@ def plot_error_bars(ax, vals, cnt, i, label=None): except KeyError: pass - # y ticks + # y thicks ax.set_ylim(-2, Y[-1] + 2) - # also position y tick labels from top to bottom - ax.set_yticks(Y[::-1], self.coeff_info[g], fontsize=13) + ax.set_yticks(Y, self.coeff_info[g], fontsize=13) # x grid ax.vlines(0, -2, Y[-1] + 2, ls="dashed", color="black", alpha=0.7) if x_log: @@ -254,16 +247,7 @@ def plot_error_bars(ax, vals, cnt, i, label=None): self._plot_logo(axs[-1]) axs[-1].set_xlabel(r"$c_i/\Lambda^2\ ({\rm TeV}^{-2})$", fontsize=20) - handles, labels = axs[-1].get_legend_handles_labels() - axs[0].legend( - handles, - labels, - loc="lower center", - bbox_to_anchor=(0, 1.1, 1.0, 0.05), - frameon=False, - prop={"size": 13}, - ncol=2, - ) + axs[0].legend(loc=0, frameon=False, prop={"size": 13}) plt.tight_layout() plt.savefig(f"{self.report_folder}/coefficient_central.pdf", dpi=500) plt.savefig(f"{self.report_folder}/coefficient_central.png") @@ -276,6 +260,7 @@ def plot_coeffs_bar( x_log=True, x_min=1e-2, x_max=500, + legend_loc="best", ): """ Plot error bars at given confidence level @@ -301,11 +286,8 @@ def plot_coeffs_bar( df = pd.DataFrame(error) groups, axs = self._get_suplblots(figsize) - for ax, (g, bars) in zip(axs, df.groupby(level=0, sort=False)): - bars_top_to_bottom = bars.iloc[ - ::-1 - ] # reverse order to plot from top to bottom in ax - bars_top_to_bottom.droplevel(0).plot( + for ax, (g, bars) in zip(axs, df.groupby(level=0)): + bars.droplevel(0).plot( kind="barh", width=0.6, ax=ax, @@ -332,18 +314,19 @@ def plot_coeffs_bar( axs[-1].set_xlabel( r"$95\%\ {\rm Confidence\ Level\ Bounds}\ (1/{\rm TeV}^2)$", fontsize=20 ) - axs[0].legend( - loc="lower center", - bbox_to_anchor=(0, 1.1, 1.0, 0.05), - frameon=False, - prop={"size": 13}, - ncol=2, - ) + axs[0].legend(loc=legend_loc, frameon=False, prop={"size": 13}) plt.tight_layout() plt.savefig(f"{self.report_folder}/coefficient_bar.pdf", dpi=500) plt.savefig(f"{self.report_folder}/coefficient_bar.png") - def plot_pull(self, pull, x_min=-3, x_max=3, figsize=(10, 15)): + def plot_pull( + self, + pull, + x_min=-3, + x_max=3, + figsize=(10, 15), + legend_loc="best", + ): """ Plot error bars at given confidence level @@ -364,11 +347,8 @@ def plot_pull(self, pull, x_min=-3, x_max=3, figsize=(10, 15)): df = pd.DataFrame(pull) groups, axs = self._get_suplblots(figsize) - for ax, (g, bars) in zip(axs, df.groupby(level=0, sort=False)): - bars_top_to_bottom = bars.iloc[ - ::-1 - ] # reverse order to plot from top to bottom in ax - bars_top_to_bottom.droplevel(0).plot( + for ax, (g, bars) in zip(axs, df.groupby(level=0)): + bars.droplevel(0).plot( kind="barh", width=0.6, ax=ax, @@ -381,13 +361,7 @@ def plot_pull(self, pull, x_min=-3, x_max=3, figsize=(10, 15)): self._plot_logo(axs[-1]) axs[-1].set_xlabel(r"${\rm Fit\:Residual\:}(\sigma)$", fontsize=20) - axs[0].legend( - loc="lower center", - bbox_to_anchor=(0, 1.1, 1.0, 0.05), - frameon=False, - prop={"size": 13}, - ncol=2, - ) + axs[0].legend(loc=legend_loc, frameon=False, prop={"size": 13}) plt.tight_layout() plt.savefig(f"{self.report_folder}/coefficient_pull.pdf", dpi=500) plt.savefig(f"{self.report_folder}/coefficient_pull.png") @@ -593,6 +567,7 @@ def log_transform(x, delta_shift): filled_start_angle = 90 - 180 / len(self.coeff_info) for i, op_type in enumerate(class_order): + filled_end_angle = ( angle_sweep[i] * 360 + filled_start_angle ) # End angle in degrees @@ -648,39 +623,7 @@ def log_transform(x, delta_shift): ) plt.savefig(f"{self.report_folder}/spider_plot.png", bbox_inches="tight") - def compute_rows_and_columns(self, nrows=-1, ncols=-1): - """Compute number of rows and columns for the plot layout - Parameters - ---------- - nrows : int, optional - Number of rows in the plot layout. Default is -1, which means - that the number of rows will be calculated based on the - number of columns. - ncols : int, optional - Number of columns in the plot layout. Default is -1, which means - that the number of columns will be calculated based on the - number of rows. - Returns - ------- - nrows : int - Number of rows in the plot layout. - ncols : int - Number of columns in the plot layout. - """ - - if nrows == -1 and ncols == -1: # square layout - nrows = ncols = int(np.sqrt(self.npar)) + 1 - elif ncols != -1: # calculate nrows based on ncols - nrows = int(np.ceil(self.npar / ncols)) - else: # calculate ncols based on nrows - ncols = int(np.ceil(self.npar / nrows)) - - if (nrows * ncols) % self.npar == 0: - nrows += 1 # add an extra row to fit the logo - - return nrows, ncols - - def plot_posteriors(self, posteriors, labels, **kwargs): + def plot_posteriors(self, posteriors, labels, disjointed_lists=None): """Plot posteriors histograms. Parameters @@ -689,36 +632,29 @@ def plot_posteriors(self, posteriors, labels, **kwargs): posterior distributions per fit and coefficient labels : list list of fit names - kwargs: dict - keyword arguments for the plot + disjointed_list: list, optional + list of coefficients with double solutions per fit """ colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] - - nrows, ncols = self.compute_rows_and_columns( - kwargs.get("nrows", -1), kwargs.get("ncols", -1) - ) - - subplot_size = 4 - fig = plt.figure(figsize=(ncols * subplot_size, nrows * subplot_size)) - + grid_size = int(np.sqrt(self.npar)) + 1 + fig = plt.figure(figsize=(grid_size * 4, grid_size * 4)) # loop on coefficients - for idx, ((_, l), latex_name) in enumerate(self.coeff_info.items()): - - ax = plt.subplot(nrows, ncols, idx + 1) + for idx, ((_, l), latex_name) in enumerate(self.coeff_info.items()): + try: + ax = plt.subplot(grid_size, grid_size, idx + 1) + except ValueError: + ax = plt.subplot(grid_size, grid_size, idx + 1) # loop on fits for clr_idx, posterior in enumerate(posteriors): if l not in posterior: continue solution = posterior[l] - if ( - kwargs["disjointed_lists"][clr_idx] is not None - and l in kwargs["disjointed_lists"][clr_idx] - ): + if disjointed_lists[clr_idx] is not None and l in disjointed_lists[clr_idx]: solution1, solution2 = split_solution(posterior[l]) - bins_solution1 = np.histogram_bin_edges(solution1, bins="fd") - bins_solution2 = np.histogram_bin_edges(solution2, bins="fd") + bins_solution1 = np.histogram_bin_edges(solution1, bins='fd') + bins_solution2 = np.histogram_bin_edges(solution2, bins='fd') ax.hist( solution, @@ -754,38 +690,28 @@ def plot_posteriors(self, posteriors, labels, **kwargs): if len(axes.get_legend_handles_labels()[0]) > len(lines): lines, labels = axes.get_legend_handles_labels() - # fontsize is normalised to 25 for 5 columns and subplot size 4 - legend_font_size = 25 * (ncols * subplot_size) / 20 - legend_font_size_inch = legend_font_size / 72 # 72 pt = 1 inch - fig.legend( lines, labels, ncol=len(posteriors), - prop={"size": legend_font_size}, + prop={"size": 25 * (grid_size * 4) / 20}, bbox_to_anchor=(0.5, 1.0), loc="upper center", frameon=False, ) - if self.npar % (ncols * nrows) == 0: - ax_logo_nr = self.npar + ncols + if self.npar % grid_size == 0: + ax_logo_nr = self.npar + grid_size else: - ax_logo_nr = self.npar + (ncols - self.npar % ncols) + ax_logo_nr = self.npar + (grid_size - self.npar % grid_size) - ax_logo = plt.subplot(nrows, ncols, ax_logo_nr) + ax_logo = plt.subplot(grid_size, grid_size, ax_logo_nr) plt.axis("off") self._plot_logo(ax_logo, [0, 1, 0.6, 1]) - rel_legend_size = legend_font_size_inch / (nrows * subplot_size) fig.tight_layout( - rect=[ - 0.0, - 0.0, - 1.0, - 1 - 2 * rel_legend_size, - ] # make room for the legend at the top of the figure + rect=[0, 0.05 * (5.0 / grid_size), 1, 1 - 0.08 * (5.0 / grid_size)] ) plt.savefig(f"{self.report_folder}/coefficient_histo.pdf") @@ -815,7 +741,7 @@ def plot_contours_2d( """ if double_solution is None: - double_solution = {f"fit{i+1}": [] for i in range(len(posteriors))} + double_solution = {"fit1": [], "fit2": []} if dofs_show is not None: posteriors = [ @@ -902,9 +828,9 @@ def plot_contours_2d( kde=kde, clr_idx=clr_idx, confidence_level=cl, - double_solution=( - list(double_solution.values())[clr_idx] if kde else None - ), + double_solution=list(double_solution.values())[clr_idx] + if kde + else None, ) hndls_all.append(hndls_contours) @@ -934,7 +860,7 @@ def plot_contours_2d( # in case n_par > 2, put legend outside subplot if n_par > 2: - ax = fig.add_subplot(grid[0, 1]) + ax = fig.add_subplot(grid[0, -1]) ax.axis("off") ax.legend( @@ -949,21 +875,13 @@ def plot_contours_2d( title_fontsize=24, ) - ax_logo = fig.add_subplot(grid[0, -1]) - ax_logo.axis("off") - self._plot_logo(ax_logo, extent=[0.05, 0.95, 0.7, 1]) - - ax.text( - 0.05, - 0.95, - rf"$\mathrm{{Marginalised}}\:{cl}\:\%\:\mathrm{{C.I.}}$", - fontsize=24, - transform=ax.transAxes, - verticalalignment="top", + ax.set_title( + rf"$\mathrm{{Marginalised}}\:{cl}\:\%\:\mathrm{{C.L.\:intervals}}$", + fontsize=18, ) - - fig.savefig(f"{self.report_folder}/contours_2d.pdf", bbox_inches="tight") - fig.savefig(f"{self.report_folder}/contours_2d.png", bbox_inches="tight") + grid.tight_layout(fig) + fig.savefig(f"{self.report_folder}/contours_2d.pdf") + fig.savefig(f"{self.report_folder}/contours_2d.png") def write_cl_table(self, bounds, round_val=3): """Coefficients latex table""" diff --git a/src/smefit/analyze/fisher.py b/src/smefit/analyze/fisher.py index 2d813ece..001d836c 100644 --- a/src/smefit/analyze/fisher.py +++ b/src/smefit/analyze/fisher.py @@ -1,12 +1,8 @@ # -*- coding: utf-8 -*- -import matplotlib as mpl -import matplotlib.patches as mpatches import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib import colors -from matplotlib.legend_handler import HandlerPatch -from matplotlib.patches import Polygon from mpl_toolkits.axes_grid1 import make_axes_locatable from rich.progress import track @@ -14,37 +10,6 @@ from .pca import impose_constrain -class HandlerTriangle(HandlerPatch): - def create_artists( - self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans - ): - center = (width / 2 - xdescent, height / 2 - ydescent) - size = min(width, height) / 2 - # Define the lower-left triangle vertices - - if orig_handle.xy[0, 0] < orig_handle.xy[1, 0]: - vertices = [ - (center[0] - size, center[1] - size), # Bottom-left - (center[0] + size, center[1] - size), # Bottom-right - (center[0] - size, center[1] + size), # Top-left - ] - else: - # (upper-right) - vertices = [ - (center[0] + size, center[1] + size), # Top-right - (center[0] - size, center[1] + size), # Top-left - (center[0] + size, center[1] - size), # Bottom-right - ] - p = mpatches.Polygon( - vertices, - closed=True, - facecolor=orig_handle.get_facecolor(), - edgecolor=orig_handle.get_edgecolor(), - ) - p.set_transform(trans) - return [p] - - class FisherCalculator: """Computes and writes the Fisher information table, and plots heat map. @@ -63,6 +28,7 @@ class FisherCalculator: """ def __init__(self, coefficients, datasets, compute_quad): + self.coefficients = coefficients self.free_parameters = self.coefficients.free_parameters.index self.datasets = datasets @@ -88,6 +54,7 @@ def compute_linear(self): fisher_tab = [] cnt = 0 for ndat in self.datasets.NdataExp: + fisher_row = np.zeros(self.free_parameters.size) idxs = slice(cnt, cnt + ndat) sigma = self.new_LinearCorrections[:, idxs] fisher_row = np.diag(sigma @ self.datasets.InvCovMat[idxs, idxs] @ sigma.T) @@ -351,235 +318,42 @@ def color(value, thr_val=10): ) return L - @staticmethod - def unify_fishers(df, df_other): - - if df_other is None or df is None: - return None - - # Get the union of row and column indices - all_rows = df.index.union(df_other.index) - all_columns = df.columns.union(df_other.columns) - - # Reindex both DataFrames to have the same rows and columns - df = df.reindex(index=all_rows, columns=all_columns, fill_value=0) - df_other = df_other.reindex(index=all_rows, columns=all_columns, fill_value=0) - - return df, df_other - - @staticmethod - def set_ticks(ax, yticks, xticks, latex_names, x_labels): - ax.set_yticks(yticks, labels=latex_names[::-1], fontsize=15) - ax.set_xticks( - xticks, - labels=x_labels, - rotation=90, - fontsize=15, - ) - ax.xaxis.set_ticks_position("top") - ax.tick_params(which="major", top=False, bottom=False, left=False) - ax.set_xticks(xticks - 0.5, minor=True) - ax.set_yticks(yticks - 0.5, minor=True) - ax.tick_params(which="minor", bottom=False) - ax.grid(visible=True, which="minor", alpha=0.2) - - @staticmethod - def plot_values(ax, dfs, cmap, norm, labels=None): - """ - Plot the values of the Fisher information. - - Parameters - ---------- - ax: matplotlib.axes.Axes - axes object - dfs: list - list of pandas.DataFrame - cmap: matplotlib.colors.LinearSegmentedColormap - colour map - norm: matplotlib.colors.BoundaryNorm - normalisation of colorbar - labels: list, optional - label elements for legend - """ - - df_1 = dfs[0] - df_2 = dfs[1] if len(dfs) > 1 else None - cols, rows = df_1.shape - - # Initialize the delta shift for text positioning - delta_shift = 0 - - for i, row in enumerate(df_1.values.T): - for j, elem_1 in enumerate(row): - - # start filling from the top left corner - x, y = j, rows - 1 - i - ec_1 = "black" - - # if two fishers must be plotted together - if df_2 is not None: - - elem_2 = df_2.values.T[i, j] - - # move position numbers - delta_shift = 0.2 - - # highlight operators that exist in one but not the other - ec_1 = "C1" if elem_2 == 0 and elem_1 > 0 else "black" - - if elem_2 > 0: - ax.text( - x + delta_shift, - y + delta_shift, - f"{elem_2:.1f}", - va="center", - ha="center", - fontsize=10, - ) - - # Create a triangle patch for the second element - triangle2 = Polygon( - [ - [x + 0.5, y - 0.5], - [x + 0.5, y + 0.5], - [x - 0.5, y + 0.5], - ], - closed=True, - facecolor=cmap(norm(elem_2)), - edgecolor="black", - ) - ax.add_patch(triangle2) - - if elem_1 > 0: - - ax.text( - x - delta_shift, - y - delta_shift, - f"{elem_1:.1f}", - va="center", - ha="center", - fontsize=10, - ) - if df_2 is not None: - - # Create a triangle patch for the first element - triangle1 = Polygon( - [ - [x - 0.5, y - 0.5], - [x + 0.5, y - 0.5], - [x - 0.5, y + 0.5], - ], - closed=True, - facecolor=cmap(norm(elem_1)), - edgecolor=ec_1, - ) - ax.add_patch(triangle1) - - # Create legend elements for the patches - legend_elements = [ - mpatches.Polygon( - [[-0.5, -0.5], [0.5, -0.5], [-0.5, 0.5]], - closed=True, - fc="none", - edgecolor="black", - label=labels[0], - ), - mpatches.Polygon( - [[0.5, -0.5], [0.5, 0.5], [0.5, 0.5]], - closed=True, - fc="none", - edgecolor="black", - label=labels[1], - ), - ] - # Add the legend to the plot - ax.legend( - handles=legend_elements, - loc="upper center", - fontsize=25, - frameon=False, - ncol=2, - handler_map={mpatches.Polygon: HandlerTriangle()}, - bbox_to_anchor=(0.5, -0.02), - ) - else: - # Create a rectangle patch for the first element - rectangle = Polygon( - [ - [x - 0.5, y - 0.5], - [x + 0.5, y - 0.5], - [x + 0.5, y + 0.5], - [x - 0.5, y + 0.5], - ], - closed=True, - ec="grey", - color=cmap(norm(elem_1)), - ) - ax.add_patch(rectangle) - - # Set the x and y limits of the plot - ax.set_xlim(0, cols - 0.5) - ax.set_ylim(0, rows - 0.5) - # Set the aspect ratio of the plot to be equal - ax.set_aspect("equal", adjustable="box") - - def plot_heatmap( + def plot( self, latex_names, fig_name, title=None, - other=None, summary_only=True, figsize=(11, 15), - labels=None, - column_names=None, ): + """Plot the heat map of Fisher table. - fisher_df = self.summary_table if summary_only else self.lin_fisher - quad_fisher_df = self.summary_HOtable if summary_only else self.quad_fisher - - if other is not None: - - fisher_df_other = other.summary_table if summary_only else other.lin_fisher - quad_fisher_df_other = ( - other.summary_HOtable if summary_only else other.quad_fisher - ) - # unify the fisher tables and fill missing values by zeros - fisher_dfs = self.unify_fishers(fisher_df, fisher_df_other) - - # reshuffle the tables according to the latex names ordering - fisher_dfs = [ - fisher[latex_names.index.get_level_values(level=1)] - for fisher in fisher_dfs - ] - - if quad_fisher_df is not None: - quad_fisher_dfs = self.unify_fishers( - quad_fisher_df, quad_fisher_df_other - ) - - # reshuffle the tables according to the latex names ordering - quad_fisher_dfs = [ - fisher[latex_names.index.get_level_values(level=1)] - for fisher in quad_fisher_dfs - ] - + Parameters + ---------- + latex_names : list + list of coefficients latex names + fig_name: str + figure path + summary_only: + if True plot the fisher grouped per datsets, + else the fine grained dataset per dataset + figsize : tuple + figure size + title: str, None + plot title + """ + if summary_only: + fisher_df = self.summary_table + quad_fisher_df = self.summary_HOtable else: - fisher_dfs = [fisher_df[latex_names.index.get_level_values(level=1)]] - if quad_fisher_df is not None: - quad_fisher_dfs = [ - quad_fisher_df[latex_names.index.get_level_values(level=1)] - ] - - # reshuffle column name ordering - if column_names is not None: - custom_ordering = [list(column.keys())[0] for column in column_names] - fisher_dfs = [fisher_df.loc[custom_ordering] for fisher_df in fisher_dfs] - x_labels = [list(column.values())[0] for column in column_names] + fisher_df = self.lin_fisher + quad_fisher_df = self.quad_fisher + + fig = plt.figure(figsize=figsize) + if quad_fisher_df is not None: + ax = fig.add_subplot(121) else: - x_labels = [ - f"\\rm{{{name}}}".replace("_", "\\_") for name in fisher_df.index - ] + ax = plt.gca() # colour map cmap_full = plt.get_cmap("Blues") @@ -589,43 +363,56 @@ def plot_heatmap( ) norm = colors.BoundaryNorm(np.arange(110, step=10), cmap.N) - fig = plt.figure(figsize=figsize) - if quad_fisher_df is not None: - ax = fig.add_subplot(121) - else: - ax = plt.gca() - - self.plot_values(ax, fisher_dfs, cmap, norm, labels) + # ticks + yticks = np.arange(fisher_df.shape[1]) + xticks = np.arange(fisher_df.shape[0]) + x_labels = [f"\\rm{{{name}}}".replace("_", "\\_") for name in fisher_df.index] + + def set_ticks(ax): + ax.set_yticks(yticks, labels=latex_names, fontsize=15) + ax.set_xticks( + xticks, + labels=x_labels, + rotation=90, + fontsize=15, + ) + ax.tick_params(which="major", top=False, bottom=False, left=False) + # minor grid + ax.set_xticks(xticks - 0.5, minor=True) + ax.set_yticks(yticks - 0.5, minor=True) + ax.tick_params(which="minor", bottom=False) + ax.grid(visible=True, which="minor", alpha=0.2) + + def plot_values(ax, df): + for i, row in enumerate(df.values.T): + for j, elem in enumerate(row): + if elem > 0: + ax.text( + j, + i, + f"{elem:.1f}", + va="center", + ha="center", + fontsize=8, + ) - self.set_ticks( - ax, - np.arange(fisher_dfs[0].shape[1]), - np.arange(fisher_dfs[0].shape[0]), - latex_names, - x_labels, - ) + cax = ax.matshow(fisher_df.values.T, cmap=cmap, norm=norm) + plot_values(ax, fisher_df) + set_ticks(ax) ax.set_title(r"\rm Linear", fontsize=20, y=-0.08) cax1 = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.5) - colour_bar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax1) + colour_bar = fig.colorbar(cax, cax=cax1) if quad_fisher_df is not None: ax = fig.add_subplot(122) - self.plot_values(ax, quad_fisher_dfs, cmap, norm, labels) - - self.set_ticks( - ax, - np.arange(quad_fisher_dfs[0].shape[1]), - np.arange(quad_fisher_dfs[0].shape[0]), - latex_names, - x_labels, - ) + cax = ax.matshow(quad_fisher_df.values.T, cmap=cmap, norm=norm) + plot_values(ax, quad_fisher_df) + set_ticks(ax) ax.set_title(r"\rm Quadratic", fontsize=20, y=-0.08) - cax1 = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.5) - colour_bar = fig.colorbar( - mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax1 - ) + cax1 = make_axes_locatable(ax).append_axes("right", size="10%", pad=0.1) + colour_bar = fig.colorbar(cax, cax=cax1) - fig.subplots_adjust(top=0.9) + fig.subplots_adjust(top=0.85) colour_bar.set_label( r"${\rm Normalized\ Value}$", diff --git a/src/smefit/analyze/html_utils.py b/src/smefit/analyze/html_utils.py index 7dcd7c82..844e409e 100644 --- a/src/smefit/analyze/html_utils.py +++ b/src/smefit/analyze/html_utils.py @@ -81,7 +81,7 @@ def write_html_container(title, figs=None, links=None, dataFrame=None): + " \n" ) if links is not None: - for file, label in links: + for (file, label) in links: text += "
\n" + html_link(f"{file}.html", label) if figs is not None: @@ -130,7 +130,7 @@ def dump_html_index(html_report, html_index, report_path, report_title): index_html = current_path.parent.joinpath("assets/index.html") index_css = current_path.parent.joinpath("assets/index.css") shutil.copyfile(index_css, report_path.joinpath("index.css")) - with open(index_html, encoding="utf-8") as f: + with open(index_html, "r", encoding="utf-8") as f: text = f.read() text = text.replace( diff --git a/src/smefit/analyze/latex_tools.py b/src/smefit/analyze/latex_tools.py index 4b9e9c42..f24d05c8 100644 --- a/src/smefit/analyze/latex_tools.py +++ b/src/smefit/analyze/latex_tools.py @@ -6,6 +6,7 @@ def latex_packages(): + L = [ r"\documentclass{article}", r"\usepackage{float}", diff --git a/src/smefit/analyze/pca.py b/src/smefit/analyze/pca.py index 3f411247..9ee05f66 100644 --- a/src/smefit/analyze/pca.py +++ b/src/smefit/analyze/pca.py @@ -11,6 +11,7 @@ from mpl_toolkits.axes_grid1 import make_axes_locatable from ..coefficients import CoefficientManager +from ..compute_theory import flatten from ..loader import load_datasets from .latex_tools import latex_packages @@ -29,6 +30,7 @@ class RotateToPca: """ def __init__(self, loaded_datasets, coefficients, config): + self.loaded_datasets = loaded_datasets self.coefficients = coefficients self.config = config @@ -47,11 +49,11 @@ def from_dict(cls, config): config["data_path"], config["datasets"], config["coefficients"], + config["order"], config["use_quad"], config["use_theory_covmat"], config["use_t0"], config.get("use_multiplicative_prescription", False), - config.get("default_order", "LO"), config.get("theory_path", None), config.get("rot_to_fit_basis", None), config.get("uv_couplings", False), @@ -197,13 +199,9 @@ def impose_constrain(dataset, coefficients, update_quad=False): params[idx] = 1.0 temp_coeffs.set_free_parameters(params) temp_coeffs.set_constraints() + coeff_outer_coeff = np.outer(temp_coeffs.value, temp_coeffs.value) new_quad_corrections.append( - np.einsum( - "ijk,j,k -> i", - dataset.QuadraticCorrections, - temp_coeffs.value, - temp_coeffs.value, - ) + flatten(coeff_outer_coeff) @ dataset.QuadraticCorrections.T ) if update_quad: @@ -241,6 +239,7 @@ class PcaCalculator: """ def __init__(self, datasets, coefficients, latex_names): + self.coefficients = coefficients self.datasets = datasets self.latex_names = latex_names diff --git a/src/smefit/analyze/report.py b/src/smefit/analyze/report.py index 4bf8342b..1d549886 100644 --- a/src/smefit/analyze/report.py +++ b/src/smefit/analyze/report.py @@ -70,47 +70,17 @@ def __init__(self, report_path, result_path, report_config): self.fits.append(fit) self.fits = np.array(self.fits) - # Get names of datasets for each fit - self.dataset_fits = [] - for fit in self.fits: - self.dataset_fits.append([data["name"] for data in fit.config["datasets"]]) - # Loads useful information about data - self.data_info = self._load_grouped_data_info(report_config["data_info"]) + self.data_info = self._load_grouped_info(report_config["data_info"], "datasets") # Loads coefficients grouped with latex name - self.coeff_info = self._load_grouped_coeff_info(report_config["coeff_info"]) + self.coeff_info = self._load_grouped_info( + report_config["coeff_info"], "coefficients" + ) self.html_index = "" self.html_content = "" - def _load_grouped_data_info(self, raw_dict): - """Load grouped info of datasets. - - Only elements appearing at least once in the fit configs are kept. - - Parameters - ---------- - raw_dict: dict - raw dictionary with relevant information - - Returns - _______ - grouped_config: pandas.DataFrame - table with information by group - - """ - out_dict = {} - for group, entries in raw_dict.items(): - out_dict[group] = {} - for val in entries: - if np.any([val[0] in datasets for datasets in self.dataset_fits]): - out_dict[group][val[0]] = val[1] - - if len(out_dict[group]) == 0: - out_dict.pop(group) - return pd.DataFrame(out_dict).stack().swaplevel() - - def _load_grouped_coeff_info(self, raw_dict): - """Load grouped info of coefficients. + def _load_grouped_info(self, raw_dict, key): + """Load grouped info of coefficients and datasets. Only elements appearing at least once in the fit configs are kept. @@ -118,6 +88,8 @@ def _load_grouped_coeff_info(self, raw_dict): ---------- raw_dict: dict raw dictionary with relevant information + key: "datasets" or "coefficients" + key to check Returns _______ @@ -129,7 +101,7 @@ def _load_grouped_coeff_info(self, raw_dict): for group, entries in raw_dict.items(): out_dict[group] = {} for val in entries: - if np.any([val[0] in fit.config["coefficients"] for fit in self.fits]): + if np.any([val[0] in fit.config[key] for fit in self.fits]): out_dict[group][val[0]] = val[1] if len(out_dict[group]) == 0: @@ -181,19 +153,13 @@ def chi2(self, table=True, plot_experiment=None, plot_distribution=None): chi2_dict_group = {} chi2_replica = {} for fit in self.fits: - # This computes the chi2 by taking the mean of the replicas - _, chi2_total_rep = chi2_cal.compute( + chi2_df, chi2_total_rep = chi2_cal.compute( fit.datasets, fit.smeft_predictions, ) - - chi2_df_best, _ = chi2_cal.compute( - fit.datasets, fit.smeft_predictions_best_fit - ) - chi2_replica[fit.label] = chi2_total_rep - chi2_dict[fit.label] = chi2_cal.add_normalized_chi2(chi2_df_best) - chi2_dict_group[fit.label] = chi2_cal.group_chi2_df(chi2_df_best) + chi2_dict[fit.label] = chi2_cal.add_normalized_chi2(chi2_df) + chi2_dict_group[fit.label] = chi2_cal.group_chi2_df(chi2_df) if table: lines = chi2_cal.write(chi2_dict, chi2_dict_group) @@ -268,14 +234,12 @@ def coefficients( bounds_dict = {} for fit in self.fits: bounds_dict[fit.label] = compute_confidence_level( - fit.results["samples"], + fit.results, coeff_plt.coeff_info, fit.has_posterior, - ( - double_solution.get(fit.name, None) - if double_solution is not None - else None - ), + double_solution.get(fit.name, None) + if double_solution is not None + else None, ) if scatter_plot is not None: @@ -326,6 +290,7 @@ def coefficients( # if dbl solution requested, add the confidence intervals, otherwise just # use the sum of the hdi intervals if 1 in dbl_solution: + dbl_op = double_solution.get(fit.name, None) idx = [ np.argwhere( @@ -357,19 +322,15 @@ def coefficients( if posterior_histograms: _logger.info("Plotting : Posterior histograms") disjointed_lists = [ - ( - double_solution.get(fit.name, None) - if double_solution is not None - else None - ) + double_solution.get(fit.name, None) + if double_solution is not None + else None for fit in self.fits ] - posterior_histograms["disjointed_lists"] = disjointed_lists - coeff_plt.plot_posteriors( - [fit.results["samples"] for fit in self.fits], + [fit.results for fit in self.fits], labels=[fit.label for fit in self.fits], - **posterior_histograms, + disjointed_lists=disjointed_lists, ) figs_list.append("coefficient_histo") @@ -384,7 +345,7 @@ def coefficients( coeff_plt.plot_contours_2d( [ ( - fit.results["samples"][fit.coefficients.free_parameters.index], + fit.results[fit.coefficients.free_parameters.index], fit.config["use_quad"], ) for fit in self.fits @@ -427,7 +388,7 @@ def correlations( _logger.info(f"Plotting correlations for: {fit.name}") coeff_to_keep = fit.coefficients.free_parameters.index plot_correlations( - fit.results["samples"][coeff_to_keep], + fit.results[coeff_to_keep], latex_names=self.coeff_info.droplevel(0), fig_name=f"{self.report}/correlations_{fit.name}", title=fit.label if title else None, @@ -492,7 +453,12 @@ def pca( self._append_section("PCA", figs=figs_list, links=links_list) def fisher( - self, norm="coeff", summary_only=True, plot=None, fit_list=None, log=False + self, + norm="coeff", + summary_only=True, + plot=None, + fit_list=None, + log=False, ): """Fisher information table and plots runner. @@ -520,7 +486,6 @@ def fisher( else: fit_list = self.fits - fishers = {} for fit in fit_list: compute_quad = fit.config["use_quad"] fisher_cal = FisherCalculator(fit.coefficients, fit.datasets, compute_quad) @@ -531,13 +496,10 @@ def fisher( fisher_cal.summary_table = fisher_cal.groupby_data( fisher_cal.lin_fisher, self.data_info, norm, log ) - fishers[fit.name] = fisher_cal # if necessary compute the quadratic Fisher if compute_quad: - fisher_cal.compute_quadratic( - fit.results["samples"], fit.smeft_predictions - ) + fisher_cal.compute_quadratic(fit.results, fit.smeft_predictions) fisher_cal.quad_fisher = fisher_cal.normalize( fisher_cal.quad_fisher, norm=norm, log=log ) @@ -545,44 +507,27 @@ def fisher( fisher_cal.quad_fisher, self.data_info, norm, log ) + # Write down the table in latex + free_coeff_config = self.coeff_info.loc[ + :, fit.coefficients.free_parameters.index + ] compile_tex( self.report, - fisher_cal.write_grouped(self.coeff_info, self.data_info, summary_only), + fisher_cal.write_grouped( + free_coeff_config, self.data_info, summary_only + ), f"fisher_{fit.name}", ) links_list.append((f"fisher_{fit.name}", f"Table {fit.label}")) if plot is not None: fit_plot = copy.deepcopy(plot) - fit_plot.pop("together", None) title = fit.label if fit_plot.pop("title") else None - fisher_cal.plot_heatmap( - self.coeff_info, + fisher_cal.plot( + free_coeff_config, f"{self.report}/fisher_heatmap_{fit.name}", title=title, **fit_plot, ) figs_list.append(f"fisher_heatmap_{fit.name}") - - # plot both fishers - if plot.get("together", False): - fisher_1 = fishers[plot["together"][0]] - fisher_2 = fishers[plot["together"][1]] - fit_plot = copy.deepcopy(plot) - fit_plot.pop("together") - - # show title of last fit - title = fit.label if fit_plot.pop("title") else None - - # make heatmap of fisher_1 and fisher_2 - fisher_2.plot_heatmap( - self.coeff_info, - f"{self.report}/fisher_heatmap_both", - title=title, - other=fisher_1, - labels=[fit.label for fit in self.fits], - **fit_plot, - ) - figs_list.append(f"fisher_heatmap_both") - self._append_section("Fisher", figs=figs_list, links=links_list) diff --git a/src/smefit/analyze/spider.py b/src/smefit/analyze/spider.py index e60589b1..6f7f5b04 100644 --- a/src/smefit/analyze/spider.py +++ b/src/smefit/analyze/spider.py @@ -37,10 +37,12 @@ def transform_path_non_affine(self, path): return Path(self.transform(path.vertices), path.codes) class RadarAxes(PolarAxes): + name = "radar" PolarTransform = RadarTransform def __init__(self, *args, **kwargs): + super().__init__(*args, aspect="equal", **kwargs) # rotate plot such that the first axis is at the top self.set_theta_zero_location("N") diff --git a/src/smefit/analyze/summary.py b/src/smefit/analyze/summary.py index 0630de6c..0565f229 100644 --- a/src/smefit/analyze/summary.py +++ b/src/smefit/analyze/summary.py @@ -50,14 +50,11 @@ class SummaryWriter: """ def __init__(self, fits, data_groups, coeff_config): + self.fits = fits self.data_info = data_groups self.coeff_info = coeff_config self.nfits = len(self.fits) - # Get names of datasets for each fit - self.dataset_fits = [] - for fit in self.fits: - self.dataset_fits.append([data["name"] for data in fit.config["datasets"]]) def fit_settings(self): """Fit settings table. @@ -73,6 +70,7 @@ def fit_settings(self): summary_dict["EFT order"] = ( "Qudratic" if fit.config["use_quad"] else "Linear" ) + summary_dict["pQCD"] = fit.config["order"] summary_dict["Replicas"] = fit.n_replica label = fit.label.replace(r"\ ", "").replace(r"\rm", "") summaries[label] = summary_dict @@ -107,9 +105,9 @@ def write_dataset_table(self): L.append(f"\\multirow{{{datasets.shape[0]}}}{{*}}{{{group}}}") for isub, (dataset, link) in enumerate(datasets.items()): temp = r" & \href{" + link + "}{" + dataset + "} " - for data in self.dataset_fits: + for fit in self.fits: temp += " & " - if dataset in data: + if dataset in fit.config["datasets"]: temp += r"$\checkmark$" if isub != datasets.shape[0] - 1: temp += f"\\\\ \\cline{{2-{(2 + self.nfits)}}}" diff --git a/src/smefit/basis_rotation.py b/src/smefit/basis_rotation.py index fbc9eef0..f9a7c90e 100644 --- a/src/smefit/basis_rotation.py +++ b/src/smefit/basis_rotation.py @@ -32,7 +32,7 @@ def rotate_to_fit_basis(lin_dict, quad_dict, rotation_matrix_path): theory dictionary with quadratic operator corrections in the fit basis, emptry if quadratic corrections are not used """ - with open(rotation_matrix_path, encoding="utf-8") as f: + with open(rotation_matrix_path, "r", encoding="utf-8") as f: rot = json.load(f) rotation_matrix = pd.DataFrame( data=rot["matrix"], index=rot["ypars"], columns=rot["xpars"] diff --git a/src/smefit/chi2.py b/src/smefit/chi2.py index 3705867a..229a2b6e 100644 --- a/src/smefit/chi2.py +++ b/src/smefit/chi2.py @@ -1,17 +1,12 @@ # -*- coding: utf-8 -*- """Module for the computation of chi-squared values.""" -import importlib import json -import pathlib -import jax.numpy as jnp import matplotlib.pyplot as plt import numpy as np import scipy.optimize as opt from rich.progress import track - -from smefit.rge.rge import RGE, load_rge_matrix -from smefit.utils import NumpyEncoder +import jax.numpy as jnp from . import compute_theory as pr from .coefficients import CoefficientManager @@ -27,6 +22,7 @@ def compute_chi2( use_quad, use_multiplicative_prescription, use_replica=False, + poly_mode=False ): r""" Compute the :math:`\chi^2`. @@ -41,8 +37,6 @@ def compute_chi2( if True add the |EFT| contribution as a key factor use_quad: bool if True include also |HO| corrections - rgemat: numpy.ndarray - solution matrix of the RGE Returns ------- @@ -52,7 +46,7 @@ def compute_chi2( # compute theory prediction for each point in the dataset theory_predictions = pr.make_predictions( - dataset, coefficients_values, use_quad, use_multiplicative_prescription + dataset, coefficients_values, use_quad, use_multiplicative_prescription, poly_mode ) # compute experimental central values - theory @@ -77,7 +71,7 @@ class Scanner: number of replica to use """ - def __init__(self, run_card, n_replica, scan_points=100): + def __init__(self, run_card, n_replica): self.n_replica = n_replica self.use_quad = run_card["use_quad"] @@ -85,148 +79,34 @@ def __init__(self, run_card, n_replica, scan_points=100): self.use_multiplicative_prescription = run_card.get( "use_multiplicative_prescription", False ) - self.mass_scan = False - self.run_card = run_card - - # set all the coefficients to 0 - self.coefficients = CoefficientManager.from_dict(run_card["coefficients"]) - self.coefficients.set_free_parameters( - np.full(self.coefficients.free_parameters.shape[0], 0) - ) - - self.rge_dict = run_card.get("rge", None) - operators_to_keep = run_card["coefficients"] - self.cutoff_scale = run_card.get("cutoff_scale", None) - - # Here we assume that if a mass is present in the coefficients - # this is a 1 parameter mass scan only - if np.any(self.coefficients.is_mass) and self.rge_dict is not None: - _logger.info("Mass scan detected") - _logger.info("RGE evolution will be computed for each mass in the scan") - self.mass_scan = True - self.rgemat = None - else: - if self.rge_dict is not None and run_card.get("datasets") is not None: - _logger.info("Loading RGE matrix") - self.rgemat, operators_to_keep = load_rge_matrix( - self.rge_dict, - list(operators_to_keep.keys()), - run_card["datasets"], - run_card.get("theory_path", None), - self.cutoff_scale, - ) - _logger.info("The operators generated by the RGE are: ") - _logger.info(list(operators_to_keep.keys())) - else: - self.rgemat = None - self.datasets = load_datasets( run_card["data_path"], run_card["datasets"], - operators_to_keep, + run_card["coefficients"], + run_card["order"], run_card["use_quad"], run_card["use_theory_covmat"], False, self.use_multiplicative_prescription, - run_card.get("default_order", "LO"), run_card.get("theory_path", None), run_card.get("rot_to_fit_basis", None), run_card.get("uv_couplings", False), run_card.get("external_chi2", False), - rgemat=self.rgemat, - cutoff_scale=self.cutoff_scale, + run_card.get("poly_mode", False), + run_card.get("external_coefficients", {}) ) - external_chi2 = run_card.get("external_chi2", None) - - self.chi2_ext = ( - self.load_external_chi2(external_chi2) if external_chi2 else None + # set all the coefficients to 0 + self.coefficients = CoefficientManager.from_dict(run_card["coefficients"]) + self.coefficients.set_free_parameters( + np.full(self.coefficients.free_parameters.shape[0], 0) ) # build empty dict to store results self.chi2_dict = {} for name, row in self.coefficients.free_parameters.iterrows(): self.chi2_dict[name] = {} - self.chi2_dict[name]["x"] = np.linspace( - row.minimum, row.maximum, scan_points - ) - self.chi2_dict[name]["n_datapoints"] = self.datasets.Commondata.size - - def load_external_chi2(self, external_chi2): - """ - Loads the external chi2 modules - - Parameters - ---------- - external_chi2: dict - dict of external chi2s, with the name of the function object as key and the path to the external script - as value - - Returns - ------- - ext_chi2_modules: list - List of external chi2 objects that can be evaluated by passing a coefficients instance - """ - # TODO: this is copied from the Optimiser class, should be moved to a common place - - # dynamical import - ext_chi2_modules = [] - - for class_name, module in external_chi2.items(): - module_path = module["path"] - path = pathlib.Path(module_path) - base_path, stem = path.parent, path.stem - try: - chi2_module = importlib.import_module(stem) - except ModuleNotFoundError: - print( - f"Module {stem} not found in {base_path}. Modify the runcard and rerun, exiting" - ) - exit(1) - - my_chi2_class = getattr(chi2_module, class_name) - - if self.rge_dict is not None: - # Check if dynamic scale - if self.rge_dict["obs_scale"] == "dynamic": - _logger.info( - f"Computing RGE matrix for {class_name} " - f"with initial scale {self.rge_dict['init_scale']}." - ) - # compute RGE matrix - if "scale" not in module: - raise ValueError( - "Dynamic scale requested but no scale provided in the external chi2" - ) - scale = module["scale"] - rge_runner = RGE( - self.coefficients.name, - self.rge_dict["init_scale"], - self.rge_dict.get("smeft_accuracy", "integrate"), - ) - rge_df = rge_runner.RGEmatrix(scale) - gen_operators = list(rge_df.index) - _logger.info("The operators generated by the RGE are: ") - _logger.info(gen_operators) - operators_dict = { - k: {"max": 0.0, "min": 0.0} for k in gen_operators - } - new_coeffs = CoefficientManager.from_dict(operators_dict) - chi2_ext = my_chi2_class(new_coeffs, rge_df.values) - else: - gen_operators = list(self.datasets.OperatorsNames) - # Create dummy coefficients - operators_dict = { - k: {"max": 0.0, "min": 0.0} for k in gen_operators - } - new_coeffs = CoefficientManager.from_dict(operators_dict) - chi2_ext = my_chi2_class(new_coeffs, self.rgemat) - else: - chi2_ext = my_chi2_class(self.coefficients) - - ext_chi2_modules.append(chi2_ext.compute_chi2) - - return ext_chi2_modules + self.chi2_dict[name]["x"] = np.linspace(row.minimum, row.maximum, 100) def regularized_chi2_func(self, coeff, xs, use_replica): r"""Individual :math:`\chi^2` wrappper over series of values. @@ -246,78 +126,20 @@ def regularized_chi2_func(self, coeff, xs, use_replica): """ chi2_list = [] + coeff.is_free = True for x in xs: coeff.value = x self.coefficients.set_constraints() - chi2_tot = compute_chi2( - self.datasets, - self.coefficients.value, - self.use_quad, - self.use_multiplicative_prescription, - use_replica, - ) - # compute chi2 for each external chi2 - if self.chi2_ext is not None: - for chi2_ext in self.chi2_ext: - chi2_ext_i = chi2_ext(self.coefficients.value) - chi2_tot += chi2_ext_i - - chi2_list.append(chi2_tot) - return np.array(chi2_list) - - def chi2_mass_scan(self, coeff, xs): - - chi2_list = [] - for x in xs: - coeff.value = x - self.coefficients.set_constraints() - # compute rge matrix - _logger.info("Loading RGE matrix") - # assuming that the mass is in TeV - self.rge_dict.update({"init_scale": x * 1e3}) - rgemat, operators_to_keep = load_rge_matrix( - self.rge_dict, - list(self.run_card["coefficients"].keys()), - self.run_card["datasets"], - self.run_card.get("theory_path", None), - self.cutoff_scale, - ) - _logger.info("The operators generated by the RGE are: ") - _logger.info(list(operators_to_keep.keys())) - - self.datasets = load_datasets( - self.run_card["data_path"], - self.run_card["datasets"], - operators_to_keep, - self.run_card["use_quad"], - self.run_card["use_theory_covmat"], - False, - self.use_multiplicative_prescription, - self.run_card.get("default_order", "LO"), - self.run_card.get("theory_path", None), - self.run_card.get("rot_to_fit_basis", None), - self.run_card.get("uv_couplings", False), - self.run_card.get("external_chi2", False), - rgemat=rgemat, - cutoff_scale=self.cutoff_scale, - ) - - chi2_tot = compute_chi2( - self.datasets, - self.coefficients.value, - self.use_quad, - self.use_multiplicative_prescription, - use_replica=False, + chi2_list.append( + compute_chi2( + self.datasets, + self.coefficients.value, + self.use_quad, + self.use_multiplicative_prescription, + use_replica, + ) ) - - if self.chi2_ext is not None: - for chi2_ext in self.chi2_ext: - chi2_ext_i = chi2_ext(self.coefficients.value) - chi2_tot += chi2_ext_i - - chi2_list.append(chi2_tot) - - return np.array(chi2_list) + return np.array(chi2_list) / self.datasets.Commondata.size def compute_bounds(self): r"""Compute individual bounds solving. @@ -371,7 +193,6 @@ def chi2_func(xs): def compute_scan(self): r"""Compute the individual :math:`\chi^2` scan for each replica and coefficient.""" # loop on replicas - for rep in track( range(self.n_replica + 1), description="[green]Computing chi2 for each replica...", @@ -384,6 +205,8 @@ def compute_scan(self): self.datasets.OperatorsNames, self.datasets.LinearCorrections, self.datasets.QuadraticCorrections, + self.datasets.OperatorsDictionary, + self.datasets.ExternalCoefficients, self.datasets.ExpNames, self.datasets.NdataExp, self.datasets.InvCovMat, @@ -397,22 +220,11 @@ def compute_scan(self): for coeff in self.coefficients: if coeff.name not in self.chi2_dict: continue - if self.mass_scan: - self.chi2_dict[coeff.name][rep] = self.chi2_mass_scan( - coeff, self.chi2_dict[coeff.name]["x"] - ) - else: - self.chi2_dict[coeff.name][rep] = self.regularized_chi2_func( - coeff, self.chi2_dict[coeff.name]["x"], use_replica - ) - # Parameter is scanned, - # reset to 0 to not affect the next scan + self.chi2_dict[coeff.name][rep] = self.regularized_chi2_func( + coeff, self.chi2_dict[coeff.name]["x"], use_replica + ) coeff.value = 0.0 - - def write_scan(self): - r"""Write the :math:`\chi^2` scan to json file.""" - with open(f"{self.result_path}/chi2_scan.json", "w", encoding="utf-8") as f: - json.dump(self.chi2_dict, f, cls=NumpyEncoder, indent=4) + coeff.is_free = False def plot_scan(self): r"""Plot and save the :math:`\chi^2` scan for each coefficient.""" diff --git a/src/smefit/cli/__init__.py b/src/smefit/cli/__init__.py index 5e3a72e8..618b28e4 100644 --- a/src/smefit/cli/__init__.py +++ b/src/smefit/cli/__init__.py @@ -185,21 +185,13 @@ def post_fit(result_folder: pathlib.Path, n_replica: int, clean_rep: bool): default=False, help="compute also the chi2 bounds", ) -@click.option( - "-s", - "--scan_points", - type=int, - default=100, - required=False, - help="number of points to scan", -) -def scan(fit_card: pathlib.Path, n_replica: int, bounds: bool, scan_points: int): +def scan(fit_card: pathlib.Path, n_replica: int, bounds: bool): r"""Plot individual :math:`\chi^2` profiles for all the free parameters. Usage: smefit SCAN [OPTIONS] path_to_runcard """ runner = Runner.from_file(fit_card.absolute()) - runner.chi2_scan(n_replica, bounds, scan_points) + runner.chi2_scan(n_replica, bounds) @base_command.command("R") @@ -225,19 +217,24 @@ def report(report_card: pathlib.Path): type=float, default=None, required=False, - help="Adjusts the statistical uncertainties according to the specified luminosity. " - "If not specified, the original uncertainties are kept " - "and the central values are fluctuates according to the specified noise level.", + help="Adjusts the statistical uncertainties according to the specified luminosity", ) @click.option( - "--noise", - type=click.Choice(["L0", "L1"], case_sensitive=True), - default="L0", - required=False, - help="Noise level for the projection, choose between L0 or L1. Assumes L0 by default.", + "--closure", + type=bool, + is_flag=True, + default=False, + help="Produces datasets under the SM", ) -def projection(projection_card: pathlib.Path, lumi: float, noise: str): +def projection(projection_card: pathlib.Path, lumi: float, closure: bool): r"""Compute projection for specified dataset""" - projection_setup = Projection.from_config(projection_card) - projection_setup.build_projection(lumi, noise) + if (lumi is not None) ^ closure: + projection_setup = Projection.from_config(projection_card) + projection_setup.build_projection(lumi, closure) + else: + print(lumi, closure) + print( + "Usage: specify exclusively either a luminosity in fb-1 after --lumi or run a SM closure test with --closure" + ) + sys.exit() diff --git a/src/smefit/coefficients.py b/src/smefit/coefficients.py index 14abe226..ff7fe265 100644 --- a/src/smefit/coefficients.py +++ b/src/smefit/coefficients.py @@ -1,4 +1,5 @@ # -*- coding: utf-8 -*- + import numpy as np import pandas as pd @@ -21,24 +22,13 @@ class Coefficient: - if False, the parameter is free, default option - if True, the parameter is fixed to the given value - if dict the parameter is fixed to a function of other coefficients - is_mass : bool - if True, the coefficient is a mass parameter - default False """ - def __init__( - self, name, minimum, maximum, value=None, constrain=False, is_mass=False - ): + def __init__(self, name, minimum, maximum, value=None, constrain=False): self.name = name - # Temporary check for deprecated operator - if name == "Opd": - raise ValueError( - "The operator Opd is deprecated and has been renamed. Use OpBox instead." - ) self.minimum = minimum self.maximum = maximum - self.is_mass = is_mass # determine if the parameter is free self.is_free = False @@ -153,7 +143,6 @@ def __init__(self, input_array): ) self._table.index = np.array([o.name for o in input_array], dtype=str) self.is_free = np.array([o.is_free for o in input_array], dtype=bool) - self.is_mass = np.array([o.is_mass for o in input_array], dtype=bool) # NOTE: this will not be updated. self._objlist = input_array @@ -205,7 +194,6 @@ def from_dict(cls, coefficient_config): property_dict["max"], constrain=constrain, value=property_dict.get("value", None), - is_mass=property_dict.get("is_mass", False), ) ) # make sure elements are sorted by names @@ -238,6 +226,7 @@ def set_constraints(self): # loop pn fixed coefficients for coefficient_fixed in self._objlist[np.invert(self.is_free)]: + # skip coefficient fixed to a single value if coefficient_fixed.constrain is None: continue @@ -264,6 +253,7 @@ def update_constrain(self, inv_rotation): rotation matrix from the original basis to the new_basis """ for coefficient_fixed in self._objlist[~self.is_free]: + # skip coefficient fixed to a single value if coefficient_fixed.constrain is None: continue diff --git a/src/smefit/compute_theory.py b/src/smefit/compute_theory.py index 2b597abd..e4fffa33 100644 --- a/src/smefit/compute_theory.py +++ b/src/smefit/compute_theory.py @@ -3,8 +3,8 @@ """ Module for the generation of theory predictions """ -import jax.numpy as jnp import numpy as np +import jax.numpy as jnp def flatten(quad_mat, axis=0): @@ -23,9 +23,21 @@ def flatten(quad_mat, axis=0): return quad_mat[np.triu_indices(size)] +def string_to_function(expressions,param_dict): + """Substitute to the theory card expression the numerical values, keeping also into account external + defined parameters + + Parameters + ---------- + expressions: the array of theory card dictionary keys, + param_dict: dictionary with the values of the point in the parameter space tried and the external parameters numerical value + """ + return [eval(e,param_dict) for e in expressions ] + def make_predictions( - dataset, coefficients_values, use_quad, use_multiplicative_prescription -): + dataset, coefficients_values, use_quad, use_multiplicative_prescription, poly_mode +): + """ Generate the corrected theory predictions for dataset given a set of |SMEFT| coefficients. @@ -38,35 +50,53 @@ def make_predictions( |EFT| coefficients values use_quad: bool if True include also |HO| corrections - use_multiplicative_prescription: bool - if True add the |EFT| contribution as a k-factor Returns ------- corrected_theory : numpy.ndarray SM + EFT theory predictions """ + if poly_mode: + summed_corrections=[] + #create dictionary of operator and the point tried + dict_point=dict(zip(dataset.OperatorsNames,coefficients_values)) + #extend dictionary evaluating external parametrs + for ext,val in dataset.ExternalCoefficients.items(): + dict_point[ext]=eval(val,{},dict_point) - coefficients_values = jnp.array(coefficients_values) - - summed_corrections = jnp.einsum( - "ij,j->i", dataset.LinearCorrections, coefficients_values - ) - - # Compute total quadratic correction - if use_quad: - summed_quad_corrections = jnp.einsum( - "ijk,j,k -> i", - dataset.QuadraticCorrections, - coefficients_values, - coefficients_values, - ) - - summed_corrections += summed_quad_corrections - - # Sum of SM theory + SMEFT corrections - if use_multiplicative_prescription: - corrected_theory = dataset.SMTheory * (1.0 + summed_corrections) + #OperatorsDictionary[][1] are the labels, [][2] are the numerical coefficients + for op_dict in dataset.OperatorsDictionary: + op_dict_num_values=string_to_function(op_dict[0],dict_point) + summed_corrections.extend(jnp.dot(jnp.array(op_dict_num_values),jnp.array(op_dict[1]))) + #construct the theory precition + if use_multiplicative_prescription: + corrected_theory = jnp.array(dataset.SMTheory)*(1.0 + jnp.array(summed_corrections)) + else: + corrected_theory = jnp.array(dataset.SMTheory) + jnp.array(summed_corrections) + return corrected_theory + else: - corrected_theory = dataset.SMTheory + summed_corrections - return corrected_theory + coefficients_values = jnp.array(coefficients_values) + + # Compute total linear correction + # note @ is slower when running with mpiexec + summed_corrections = jnp.einsum( + "ij,j->i", dataset.LinearCorrections, coefficients_values + ) + + # Compute total quadratic correction + if use_quad: + coeff_outer_coeff = jnp.outer(coefficients_values, coefficients_values) + # note @ is slower when running with mpiexec + summed_quad_corrections = jnp.einsum( + "ij,j->i", dataset.QuadraticCorrections, flatten(coeff_outer_coeff) + ) + summed_corrections += summed_quad_corrections + + # Sum of SM theory + SMEFT corrections + if use_multiplicative_prescription: + corrected_theory = dataset.SMTheory * (1.0 + summed_corrections) + else: + corrected_theory = dataset.SMTheory + summed_corrections + + return corrected_theory diff --git a/src/smefit/covmat.py b/src/smefit/covmat.py index 09453174..67acae56 100644 --- a/src/smefit/covmat.py +++ b/src/smefit/covmat.py @@ -1,4 +1,5 @@ # -*- coding: utf-8 -*- + """ Module containing computation of covariance matrix. Based on @@ -82,6 +83,7 @@ def covmat_from_systematics(stat_errors: list, sys_errors: list): block_diags = [] for dataset_stat_errors, dataset_sys_errors in zip(stat_errors, sys_errors): + # separate out the special uncertainties which can be correlated across # datasets is_intra_dataset_error = dataset_sys_errors.columns.isin(INTRA_DATASET_SYS_NAME) diff --git a/src/smefit/fit_manager.py b/src/smefit/fit_manager.py index b89ced01..fefc5962 100644 --- a/src/smefit/fit_manager.py +++ b/src/smefit/fit_manager.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- import json -import pickle -import jax.numpy as jnp import numpy as np import pandas as pd import yaml @@ -56,7 +54,6 @@ def __init__(self, path, name, label=None): self.has_posterior = self.config.get("has_posterior", True) self.results = None self.datasets = None - self.rgemat = None def __repr__(self): return self.name @@ -72,44 +69,31 @@ def load_results(self): """ file = "results" if self.has_posterior: - file = "fit_results" + file = "posterior" with open(f"{self.path}/{self.name}/{file}.json", encoding="utf-8") as f: results = json.load(f) - # load the rge matrix in the result dir if it exists - try: - with open(f"{self.path}/{self.name}/rge_matrix.pkl", "rb") as f: - rgemats = pickle.load(f) - self.operators_to_keep = {op: {} for op in rgemats[0].index} - self.rgemat = jnp.stack([rgemat.values for rgemat in rgemats]) - - except FileNotFoundError: - print("No RGE matrix found in the result folder, skipping...") - # if the posterior is from single parameter fits # then each distribution might have a different number of samples is_single_param = results.get("single_parameter_fits", False) if is_single_param: + del results["single_parameter_fits"] num_samples = [] - for key in results["samples"].keys(): - num_samples.append(len(results["samples"][key])) + for key in results.keys(): + num_samples.append(len(results[key])) num_samples_min = min(num_samples) - for key in results["samples"].keys(): - results["samples"][key] = np.random.choice( - results["samples"][key], num_samples_min, replace=False + for key in results.keys(): + results[key] = np.random.choice( + results[key], num_samples_min, replace=False ) # TODO: support pariwise posteriors # Be sure columns are sorted, otherwise can't compute theory... - results["samples"] = pd.DataFrame(results["samples"]).sort_index(axis=1) - results["best_fit_point"] = pd.DataFrame( - [results["best_fit_point"]] - ).sort_index(axis=1) - self.results = results + self.results = pd.DataFrame(results).sort_index(axis=1) def load_configuration(self): """Load configuration yaml card. @@ -126,23 +110,21 @@ def load_configuration(self): def load_datasets(self): """Load all datasets.""" - self.datasets = load_datasets( self.config["data_path"], self.config["datasets"], - self.config["coefficients"] - if self.rgemat is None - else self.operators_to_keep, + self.config["coefficients"], + self.config["order"], self.config["use_quad"], self.config["use_theory_covmat"], False, # t0 is not used here because in the report we look at the experimental chi2 self.config.get("use_multiplicative_prescription", False), - self.config.get("default_order", "LO"), self.config.get("theory_path", None), self.config.get("rot_to_fit_basis", None), self.config.get("uv_couplings", False), self.config.get("external_chi2", False), - rgemat=self.rgemat, + self.config.get("poly_mode", False), + self.config.get("external_coefficients", False), ) @property @@ -163,32 +145,14 @@ def smeft_predictions(self): smeft.append( make_predictions( self.datasets, - self.results["samples"].iloc[rep, :], + self.results.iloc[rep, :], self.config["use_quad"], self.config.get("use_multiplicative_prescription", False), + self.config.get("poly_mode", False) ) ) return np.array(smeft) - @property - def smeft_predictions_best_fit(self): - """Compute |SMEFT| predictions for the best fit point. - - Returns - ------- - np.ndarray: - |SMEFT| predictions for the best fit - """ - predictions = make_predictions( - self.datasets, - self.results["best_fit_point"].iloc[0, :], - self.config["use_quad"], - self.config.get("use_multiplicative_prescription", False), - ) - - # Add a dimension to match the shape of the replica predictions - return np.array([predictions]) - @property def coefficients(self): """coefficient manager""" @@ -197,4 +161,4 @@ def coefficients(self): @property def n_replica(self): """Number of replicas""" - return self.results["samples"].shape[0] + return self.results.shape[0] diff --git a/src/smefit/loader.py b/src/smefit/loader.py index e1430d6c..ca4731db 100644 --- a/src/smefit/loader.py +++ b/src/smefit/loader.py @@ -1,9 +1,9 @@ # -*- coding: utf-8 -*- + import json import pathlib from collections import namedtuple -import jax.numpy as jnp import numpy as np import pandas as pd import scipy.linalg as la @@ -23,6 +23,8 @@ "OperatorsNames", "LinearCorrections", "QuadraticCorrections", + "OperatorsDictionary", + "ExternalCoefficients", "ExpNames", "NdataExp", "InvCovMat", @@ -39,15 +41,13 @@ def check_file(path): raise FileNotFoundError(f"File {path} does not exist.") -def check_missing_operators(loaded_corrections, coeff_config): +def check_missing_oparators(loaded_corrections, coeff_config): """Check if all the coefficient in the runcard are also present inside the theory tables.""" loaded_corrections = set(loaded_corrections) missing_operators = [k for k in coeff_config if k not in loaded_corrections] if missing_operators != []: raise ValueError( - f"{missing_operators} not in the theory. Comment it out in setup script and restart.\n" - "In case a cutoff was applied " - f"the operator(s) {missing_operators} did not survive the mask." + f"{missing_operators} not in the theory. Comment it out in setup script and restart." ) @@ -68,6 +68,10 @@ class Loader: if True add the theory covariance matrix to the experimental one rot_to_fit_basis: dict, None matrix rotation to fit basis or None + poly_mode: bool + run in polynomial mode + external_coefficients: + list of coefficients in the theory card that are going to be replaced with the runcard expression """ @@ -85,7 +89,8 @@ def __init__( use_theory_covmat, use_multiplicative_prescription, rot_to_fit_basis, - cutoff_scale, + poly_mode, + external_coefficients ): self._data_folder = self.commondata_path self._sys_folder = self.commondata_path / "systypes" @@ -94,21 +99,31 @@ def __init__( self.setname = setname self.dataspec = {} - ( + if poly_mode: + ( self.dataspec["SM_predictions"], self.dataspec["theory_covmat"], - self.dataspec["lin_corrections"], - self.dataspec["quad_corrections"], - self.dataspec["scales"], - ) = self.load_theory( - self.setname, - operators_to_keep, + self.dataspec["operator_dict"], + + ) = self.load_theory_poly( order, - use_quad, use_theory_covmat, use_multiplicative_prescription, - rot_to_fit_basis, - ) + ) + else: + ( + self.dataspec["SM_predictions"], + self.dataspec["theory_covmat"], + self.dataspec["lin_corrections"], + self.dataspec["quad_corrections"], + ) = self.load_theory( + operators_to_keep, + order, + use_quad, + use_theory_covmat, + use_multiplicative_prescription, + rot_to_fit_basis, + ) ( self.dataspec["central_values"], @@ -117,68 +132,12 @@ def __init__( self.dataspec["stat_error"], self.dataspec["luminosity"], ) = self.load_experimental_data() - - # mask theory and data to ensure only data below the specified cutoff scale is included - self.mask = np.array( - [True] * self.n_data - ) # initial mask retains all datapoints - if cutoff_scale is not None: - self.apply_cutoff_mask(cutoff_scale) - + self.dataspec["external_coefficients"] = external_coefficients if len(self.dataspec["central_values"]) != len(self.dataspec["SM_predictions"]): raise ValueError( f"Number of experimental data points and theory predictions does not match in dataset {self.setname}." ) - def apply_cutoff_mask(self, cutoff_scale): - """ - Updates previously loaded theory and datasets by filtering out - points with scales above the cutoff scale - - Parameters - ---------- - cutoff_scale: flaot - Value of the cutoff scale as specified in the runcard - """ - self.mask = self.dataspec["scales"] < cutoff_scale - - # if all datapoints lie above the cutoff, return - if np.all(~self.mask): - return - - # Apply mask to all relevant theory entries in dataspec - self.dataspec.update( - { - "SM_predictions": self.dataspec["SM_predictions"][self.mask], - "theory_covmat": self.dataspec["theory_covmat"][self.mask, :][ - :, self.mask - ], - "lin_corrections": { - k: v[self.mask] for k, v in self.dataspec["lin_corrections"].items() - }, - "quad_corrections": { - k: v[self.mask] - for k, v in self.dataspec["quad_corrections"].items() - }, - "scales": self.dataspec["scales"][self.mask], - } - ) - - # Single data points satisfy the mask already at this point - if self.n_data == 1: - stat_error = self.dataspec["stat_error"] - else: - stat_error = self.dataspec["stat_error"][self.mask] - - self.dataspec.update( - { - "central_values": self.dataspec["central_values"][self.mask], - "sys_error": self.dataspec["sys_error"].loc[self.mask], - "sys_error_t0": self.dataspec["sys_error_t0"].loc[self.mask], - "stat_error": stat_error, - } - ) - def load_experimental_data(self): """ Load experimental data with corresponding uncertainties @@ -224,7 +183,6 @@ def load_experimental_data(self): indx_mult = np.where(type_sys == "MULT")[0] sys_t0 = np.zeros((num_sys, num_data)) sys_t0[indx_add] = sys_add[indx_add].reshape(sys_t0[indx_add].shape) - sys_t0[indx_mult] = ( sys_mult[indx_mult].reshape(sys_t0[indx_mult].shape) * self.dataspec["SM_predictions"] @@ -252,10 +210,58 @@ def load_experimental_data(self): # here return both exp sys and t0 modified sys return central_values, df, df_t0, stat_error, luminosity + def load_theory_poly( + self, + order, + use_theory_covmat, + use_multiplicative_prescription): + """ + Load theory predictions + + Parameters + ---------- + operators_to_keep: list + list of operators to keep + order: "LO", "NLO" + EFT perturbative order + use_theory_covmat: bool + if True add the theory covariance matrix to the experimental one + + + + Returns + ------- + sm: numpy.ndarray + |SM| predictions + poly_dict: dict + dictionary which labels are the polynomial expression of the correction and the values the contribution to the theory prediction + + + """ + theory_file = self._theory_folder / f"{self.setname}.json" + check_file(theory_file) + with open(theory_file, encoding="utf-8") as f: + raw_th_data = json.load(f) + + + + # save sm prediction at the chosen perturbative order + sm = np.array(raw_th_data[order]["SM"]) - @staticmethod + poly_dict={} + for key, value in raw_th_data[order].items(): + if "SM" not in key: + poly_dict[key] = np.array(value) + if use_multiplicative_prescription: + poly_dict[key] = np.divide(poly_dict[key], sm) + best_sm = np.array(raw_th_data["best_sm"]) + th_cov = np.zeros((best_sm.size, best_sm.size)) + if use_theory_covmat: + th_cov = raw_th_data["theory_cov"] + return raw_th_data["best_sm"], th_cov, poly_dict + def load_theory( - setname, + self, operators_to_keep, order, use_quad, @@ -279,6 +285,7 @@ def load_theory( rotation_matrix: numpy.ndarray rotation matrix from tables basis to fitting basis + Returns ------- sm: numpy.ndarray @@ -287,10 +294,9 @@ def load_theory( dictionary with |NHO| corrections quad_dict: dict dictionary with |HO| corrections, empty if not use_quad - scales: list - list of energy scales for the theory predictions + """ - theory_file = Loader.theory_path / f"{setname}.json" + theory_file = self._theory_folder / f"{self.setname}.json" check_file(theory_file) # load theory predictions with open(theory_file, encoding="utf-8") as f: @@ -300,7 +306,6 @@ def load_theory( lin_dict = {} # save sm prediction at the chosen perturbative order - sm = np.array(raw_th_data[order]["SM"]) # split corrections into a linear and quadratic dict @@ -311,6 +316,7 @@ def load_theory( if use_multiplicative_prescription: quad_dict[key] = np.divide(quad_dict[key], sm) + # linear terms elif "SM" not in key and "*" not in key: lin_dict[key] = np.array(value) @@ -345,21 +351,10 @@ def is_to_keep(op1, op2=None): if is_to_keep(k.split("*")[0], k.split("*")[1]) } best_sm = np.array(raw_th_data["best_sm"]) + th_cov = np.zeros((best_sm.size, best_sm.size)) if use_theory_covmat: - th_cov = np.array(raw_th_data["theory_cov"]) - else: - th_cov = np.zeros((best_sm.size, best_sm.size)) - - # check if scales are present in the theory file - scales = np.array(raw_th_data.get("scales", [None] * len(best_sm))) - - return ( - best_sm, - th_cov, - lin_dict_to_keep, - quad_dict_to_keep, - scales, - ) + th_cov = raw_th_data["theory_cov"] + return raw_th_data["best_sm"], th_cov, lin_dict_to_keep, quad_dict_to_keep @property def n_data(self): @@ -495,11 +490,35 @@ def quad_corrections(self): return self.dataspec["quad_corrections"] -def construct_corrections_matrix_linear( - corrections_list, n_data_tot, sorted_keys, rgemat=None -): + @property + def operator_dict(self): + """ + Dictionary of operator and their coefficients in poly_mode + + Returns + ------- + poly_dict : dict + dictionary with operators formula and their coefficients + """ + return self.dataspec["operator_dict"] + + @property + def external_coefficients(self): + """ + Dictionary of theory card operator that are going to be replaced with the expression in the runcard + + Returns + ------- + external_coeficcients : dict + as per above + """ + return self.dataspec["external_coefficients"] + + +def construct_corrections_matrix(corrections_list, n_data_tot, sorted_keys=None): """ - Constructs the linear EFT corrections. + Construct a unique list of correction name, + with corresponding values. Parameters ---------- @@ -509,98 +528,56 @@ def construct_corrections_matrix_linear( total number of experimental data points sorted_keys: numpy.ndarray list of sorted operator corrections - rgemat: numpy.ndarray, optional - solution matrix of the RGE, shape (k, l, m) with k the number of datapoints, - l the number of generated coefficients and m the number of - original |EFT| coefficients specified in the runcard. Returns ------- + sorted_keys : np.ndarray + unique list of operators for which at least one correction is present corr_values : np.ndarray matrix with correction values (n_data_tot, sorted_keys.size) """ + if sorted_keys is None: + tmp = [ + [ + *c, + ] + for _, c in corrections_list + ] + sorted_keys = np.unique([item for sublist in tmp for item in sublist]) corr_values = np.zeros((n_data_tot, sorted_keys.size)) cnt = 0 # loop on experiments for n_dat, correction_dict in corrections_list: # loop on corrections for key, values in correction_dict.items(): + if "*" in key: + op1, op2 = key.split("*") + if op2 < op1: + key = f"{op2}*{op1}" + idx = np.where(sorted_keys == key)[0][0] corr_values[cnt : cnt + n_dat, idx] = values cnt += n_dat - if rgemat is not None: - corr_values = jnp.einsum("ij, ijk -> ik", corr_values, rgemat) - - return corr_values - - -def construct_corrections_matrix_quadratic( - corrections_list, n_data_tot, sorted_keys, rgemat=None -): - """ - Constructs quadratic EFT corrections. - - Parameters - ---------- - corrections_list : list(dict) - list containing per experiment the number of datapoints and corresponding corrections - n_data_tot : int - total number of experimental data points - sorted_keys: numpy.ndarray - list of sorted operator corrections, shape=(n rg generated coeff,) - or shape=(n original coeff,) in the absence of rgemat - rgemat: numpy.ndarray, optional - solution matrix of the RGE, shape=(k, l, m) with k the number of datapoints, - l the number of generated coefficients under the RG and m the number of - original |EFT| coefficients specified in the runcard. - - Returns - ------- - corr_values : np.ndarray - matrix with correction values (n_data_tot, sorted_keys.size, sorted_keys.size) - """ - - corr_values = np.zeros((n_data_tot, sorted_keys.size, sorted_keys.size)) - cnt = 0 - - # loop on experiments - for n_dat, correction_dict in corrections_list: - # loop on corrections - for key, values in correction_dict.items(): - op1, op2 = key.split("*") - idx1 = np.where(sorted_keys == op1)[0][0] - idx2 = np.where(sorted_keys == op2)[0][0] - - # we want to store the values in the upper triangular part of the matrix - if idx1 > idx2: - idx1, idx2 = idx2, idx1 - - corr_values[cnt : cnt + n_dat, idx1, idx2] = values - cnt += n_dat - - if rgemat is not None: - corr_values = jnp.einsum("ijk, ijl, ikr -> ilr", corr_values, rgemat, rgemat) - - return corr_values + return sorted_keys, corr_values def load_datasets( commondata_path, datasets, operators_to_keep, + order, use_quad, use_theory_covmat, use_t0, use_multiplicative_prescription, - default_order="LO", theory_path=None, rot_to_fit_basis=None, has_uv_couplings=False, has_external_chi2=False, - rgemat=None, - cutoff_scale=None, + poly_mode=False, + external_coefficients={} ): """ Loads experimental data, theory and |SMEFT| corrections into a namedtuple @@ -610,11 +587,11 @@ def load_datasets( commondata_path : str, pathlib.Path path to commondata folder, commondata excluded datasets : list - List of datasets to be loaded + list of datasets to be loaded operators_to_keep: list list of operators for which corrections are loaded - default_order: str - Default perturbative order of the theory predictions + order: "LO", "NLO" + EFT perturbative order use_quad: bool if True loads also |HO| corrections use_theory_covmat: bool @@ -628,13 +605,12 @@ def load_datasets( True for UV fits has_external_chi2: bool, optional True in the presence of external chi2 modules - rgemat: numpy.ndarray, optional - solution matrix of the RGE, shape=(k, l, m) with k the number of datapoints, - l the number of generated coefficients under the RG and m the number of - original |EFT| coefficients specified in the runcard. - cutoff_scale: float, optional - kinematic cutoff scale - """ + poly_mode: bool + True if running in polymode + external_coefficients: dictionary + coefficients in the theory card that are going to be replaced by runcard expressions + + """ exp_data = [] sm_theory = [] @@ -643,40 +619,43 @@ def load_datasets( stat_error = [] lin_corr_list = [] quad_corr_list = [] + operators_dict_list= [] n_data_exp = [] lumi_exp = [] exp_name = [] th_cov = [] Loader.commondata_path = pathlib.Path(commondata_path) - Loader.theory_path = pathlib.Path(theory_path or commondata_path) - - _logger.info(f"Applying cutoff scale: {cutoff_scale} GeV.") - for sset in datasets: - dataset_name = sset.get("name") + if theory_path is not None: + Loader.theory_path = pathlib.Path(theory_path) + else: + Loader.theory_path = pathlib.Path(commondata_path) + for sset in np.unique(datasets): dataset = Loader( - dataset_name, + sset, operators_to_keep, - sset.get("order", default_order), + order, use_quad, use_theory_covmat, use_multiplicative_prescription, rot_to_fit_basis, - cutoff_scale, + poly_mode, + external_coefficients ) - - # skip dataset if all datapoints are above the cutoff scale - if np.all(~dataset.mask): - continue - - exp_name.append(dataset_name) + exp_name.append(sset) n_data_exp.append(dataset.n_data) lumi_exp.append(dataset.lumi) exp_data.extend(dataset.central_values) sm_theory.extend(dataset.sm_prediction) - lin_corr_list.append([dataset.n_data, dataset.lin_corrections]) - quad_corr_list.append([dataset.n_data, dataset.quad_corrections]) + if poly_mode: + lin_corr_list.append([dataset.n_data, {}]) + quad_corr_list.append([dataset.n_data,{}]) + operators_dict_list.append([list(dataset.operator_dict.keys()), list(dataset.operator_dict.values())]) + else: + lin_corr_list.append([dataset.n_data, dataset.lin_corrections]) + quad_corr_list.append([dataset.n_data, dataset.quad_corrections]) + operators_dict_list.append([[],[]]) sys_error_t0.append(dataset.sys_error_t0) sys_error.append(dataset.sys_error) stat_error.append(dataset.stat_error) @@ -685,29 +664,30 @@ def load_datasets( exp_data = np.array(exp_data) n_data_tot = exp_data.size + sorted_keys = None # if uv couplings are present allow for op which are not in the - # theory files (same for external chi2 and rge) - if has_uv_couplings or has_external_chi2 or rgemat is not None: + # theory files + if has_uv_couplings or has_external_chi2: sorted_keys = np.unique((*operators_to_keep,)) - else: - # Construct unique list of operator names entering lin_corr_list - operator_names = [] - for item in lin_corr_list: - operator_names.extend(list(item[1].keys())) - sorted_keys = np.unique(operator_names) - - # operators to keep contains the operators that are present in the runcard - # sorted_keys contains the operators that are present in the theory files - # we need to check that all operators in the runcard are also present in the theory files - check_missing_operators(sorted_keys, operators_to_keep) - - lin_corr_values = construct_corrections_matrix_linear( - lin_corr_list, n_data_tot, sorted_keys, rgemat + operators_names, lin_corr_values = construct_corrections_matrix( + lin_corr_list, n_data_tot, sorted_keys ) + if poly_mode: + operators_names = sorted(operators_to_keep.keys()) + if (not(poly_mode)): + check_missing_oparators(operators_names, operators_to_keep) if use_quad: - quad_corr_values = construct_corrections_matrix_quadratic( - quad_corr_list, n_data_tot, sorted_keys, rgemat + quad_corrections_names = [] + for op1 in operators_names: + for op2 in operators_names: + if ( + f"{op1}*{op2}" not in quad_corrections_names + and f"{op2}*{op1}" not in quad_corrections_names + ): + quad_corrections_names.append(f"{op1}*{op2}") + _, quad_corr_values = construct_corrections_matrix( + quad_corr_list, n_data_tot, np.array(quad_corrections_names) ) else: quad_corr_values = None @@ -731,9 +711,11 @@ def load_datasets( return DataTuple( exp_data, np.array(sm_theory), - sorted_keys, + operators_names, lin_corr_values, quad_corr_values, + operators_dict_list, + external_coefficients, np.array(exp_name), np.array(n_data_exp), np.linalg.inv(fit_covmat), @@ -755,11 +737,15 @@ def get_dataset(datasets, data_name): datasets.SMTheory[posix_in:posix_out], datasets.OperatorsNames, datasets.LinearCorrections[posix_in:posix_out], - ( - datasets.QuadraticCorrections[posix_in:posix_out] - if datasets.QuadraticCorrections is not None - else None - ), + datasets.QuadraticCorrections[posix_in:posix_out] + if datasets.QuadraticCorrections is not None + else None, + [datasets.OperatorsDictionary[idx]] + if datasets.OperatorsDictionary is not None + else None, + datasets.ExternalCoefficients + if datasets.ExternalCoefficients is not None + else None, data_name, ndata, datasets.InvCovMat[posix_in:posix_out].T[posix_in:posix_out], diff --git a/src/smefit/optimize/__init__.py b/src/smefit/optimize/__init__.py index 0216a35d..62c49bcf 100644 --- a/src/smefit/optimize/__init__.py +++ b/src/smefit/optimize/__init__.py @@ -2,16 +2,12 @@ import importlib import json import pathlib +import sys -import numpy as np from rich.style import Style from rich.table import Table -from smefit.rge.rge import RGE -from smefit.utils import NumpyEncoder - from .. import chi2, log -from ..coefficients import CoefficientManager from ..loader import get_dataset try: @@ -21,8 +17,6 @@ except ModuleNotFoundError: run_parallel = False -_logger = log.logging.getLogger(__name__) - class Optimizer: """ @@ -42,12 +36,6 @@ class Optimizer: True for single parameter fits use_multiplicative_prescription: if True uses the multiplicative prescription for the |EFT| corrections. - external_chi2: dict - dict of external chi2 - rgemat: numpy.ndarray - solution matrix of the RGE - rge_dict: dict - dictionary with the RGE input parameter options """ print_rate = 500 @@ -60,14 +48,16 @@ def __init__( use_quad, single_parameter_fits, use_multiplicative_prescription, + poly_mode, + external_coefficients, external_chi2=None, - rgemat=None, - rge_dict=None, ): self.results_path = pathlib.Path(results_path) self.loaded_datasets = loaded_datasets self.coefficients = coefficients self.use_quad = use_quad + self.poly_mode = poly_mode + self.external_coefficients = external_coefficients self.npts = ( self.loaded_datasets.Commondata.size if self.loaded_datasets is not None @@ -77,10 +67,6 @@ def __init__( self.use_multiplicative_prescription = use_multiplicative_prescription self.counter = 0 - # set RGE matrix - self.rgemat = rgemat - self.rge_dict = rge_dict - # load external chi2 modules as amortized objects (fast to evaluate) self.chi2_ext = ( self.load_external_chi2(external_chi2) if external_chi2 else None @@ -104,57 +90,14 @@ def load_external_chi2(self, external_chi2): # dynamical import ext_chi2_modules = [] - for class_name, module in external_chi2.items(): - module_path = module["path"] + for class_name, module_path in external_chi2.items(): + path = pathlib.Path(module_path) base_path, stem = path.parent, path.stem - try: - chi2_module = importlib.import_module(stem) - except ModuleNotFoundError: - print( - f"Module {stem} not found in {base_path}. Adjust and rerun. Exiting the code." - ) - exit(1) + chi2_module = importlib.import_module(stem) my_chi2_class = getattr(chi2_module, class_name) - - if self.rge_dict is not None: - # Check if dynamic scale - if self.rge_dict["obs_scale"] == "dynamic": - _logger.info( - f"Computing RGE matrix for {class_name} " - f"with initial scale {self.rge_dict['init_scale']}." - ) - # compute RGE matrix - if "scale" not in module: - raise ValueError( - "Dynamic scale requested but no scale provided in the external chi2" - ) - scale = module["scale"] - rge_runner = RGE( - self.coefficients.name, - self.rge_dict["init_scale"], - self.rge_dict.get("smeft_accuracy", "integrate"), - ) - rge_df = rge_runner.RGEmatrix(scale) - gen_operators = list(rge_df.index) - _logger.info("The operators generated by the RGE are: ") - _logger.info(gen_operators) - operators_dict = { - k: {"max": 0.0, "min": 0.0} for k in gen_operators - } - new_coeffs = CoefficientManager.from_dict(operators_dict) - chi2_ext = my_chi2_class(new_coeffs, rge_df.values) - else: - gen_operators = list(self.loaded_datasets.OperatorsNames) - # Create dummy coefficients - operators_dict = { - k: {"max": 0.0, "min": 0.0} for k in gen_operators - } - new_coeffs = CoefficientManager.from_dict(operators_dict) - chi2_ext = my_chi2_class(new_coeffs, self.rgemat) - else: - chi2_ext = my_chi2_class(self.coefficients) + chi2_ext = my_chi2_class(self.coefficients) ext_chi2_modules.append(chi2_ext.compute_chi2) @@ -207,6 +150,7 @@ def chi2_func(self, use_replica=False, print_log=True): self.use_quad, self.use_multiplicative_prescription, use_replica, + self.poly_mode ) else: chi2_tot = 0 @@ -227,6 +171,7 @@ def chi2_func(self, use_replica=False, print_log=True): self.use_quad, self.use_multiplicative_prescription, use_replica, + self.poly_mode ) / dataset.NdataExp ) @@ -234,50 +179,14 @@ def chi2_func(self, use_replica=False, print_log=True): return chi2_tot - def dump_fit_result(self, fit_result_file, values): - """ - Dumps the fit results to a json file. - - dump_fit_result gets called repeatedly for single parameter fits, once for each parameter. - `values` contains the samples of the current fit, while previous fit results get loaded - into `tmp` and updated with the current samples. The updated values are then written back - to the file. - - Parameters - ---------- - fit_result_file: PosixPath - path to the fit results file - values: dict - dictionary containing the current fit results - """ - + def dump_posterior(self, posterior_file, values): if self.single_parameter_fits: - if fit_result_file.is_file(): - with open(fit_result_file, encoding="utf-8") as f: + if posterior_file.is_file(): + with open(posterior_file, encoding="utf-8") as f: tmp = json.load(f) - # Get the operator name - coeff = values["free_parameters"][0] - # update the values - tmp["logz"][coeff] = values["logz"] - tmp["max_loglikelihood"][coeff] = values["max_loglikelihood"] - tmp["best_fit_point"][coeff] = values["best_fit_point"][coeff] - tmp["samples"][coeff] = values["samples"][coeff] - tmp["free_parameters"].append(values["free_parameters"][0]) - # update the file with the new values - with open(fit_result_file, "w", encoding="utf-8") as f: - json.dump(tmp, f, indent=4, cls=NumpyEncoder) - + values.update(tmp) else: values["single_parameter_fits"] = True - with open(fit_result_file, "w", encoding="utf-8") as f: - # Get the operator name - coeff = values["free_parameters"][0] - values["logz"] = {coeff: values["logz"]} - values["max_loglikelihood"] = {coeff: values["max_loglikelihood"]} - values["best_fit_point"] = {coeff: values["best_fit_point"][coeff]} - values["samples"] = {coeff: values["samples"][coeff]} - json.dump(values, f, indent=4, cls=NumpyEncoder) - else: - with open(fit_result_file, "w", encoding="utf-8") as f: - json.dump(values, f, indent=4, cls=NumpyEncoder) + with open(posterior_file, "w", encoding="utf-8") as f: + json.dump(values, f) diff --git a/src/smefit/optimize/analytic.py b/src/smefit/optimize/analytic.py index 56f78632..80642382 100644 --- a/src/smefit/optimize/analytic.py +++ b/src/smefit/optimize/analytic.py @@ -4,8 +4,6 @@ from rich.style import Style from rich.table import Table -from smefit.rge.rge import load_rge_matrix - from .. import chi2, log from ..analyze.pca import impose_constrain from ..coefficients import CoefficientManager @@ -49,12 +47,14 @@ def __init__( n_samples, ): super().__init__( - results_path=f"{result_path}/{result_ID}", - loaded_datasets=loaded_datasets, - coefficients=coefficients, + f"{result_path}/{result_ID}", + loaded_datasets, + coefficients, # disble quadratic corrections here use_quad=False, single_parameter_fits=single_parameter_fits, + poly_mode=False, + external_coefficients={}, # this option does not make any difference here use_multiplicative_prescription=False, ) @@ -80,40 +80,20 @@ def from_dict(cls, config): "Analytic solution with quadratic corrections is not available." ) - rge_dict = config.get("rge", None) - operators_to_keep = config["coefficients"] - cutoff_scale = config.get("cutoff_scale", None) - - if rge_dict is not None and config.get("datasets") is not None: - _logger.info("Loading RGE matrix") - rgemat, operators_to_keep = load_rge_matrix( - rge_dict, - list(operators_to_keep.keys()), - config["datasets"], - config.get("theory_path", None), - cutoff_scale, - config.get("result_path", None), - config.get("result_ID", None), - ) - _logger.info("The operators generated by the RGE are: ") - _logger.info(list(operators_to_keep.keys())) - else: - rgemat = None - loaded_datasets = load_datasets( config["data_path"], config["datasets"], - operators_to_keep, + config["coefficients"], + config["order"], False, config["use_theory_covmat"], config["use_t0"], False, - config.get("default_order", "LO"), config.get("theory_path", None), config.get("rot_to_fit_basis", None), config.get("uv_couplings", False), - rgemat=rgemat, - cutoff_scale=config.get("cutoff_scale", None), + config.get("poly_mode", False), + config.get("external_coefficients", {}) ) coefficients = CoefficientManager.from_dict(config["coefficients"]) @@ -142,10 +122,6 @@ def log_result(self, coeff_best, coeff_covmat): def run_sampling(self): """Run sapmling accordying to the analytic solution.""" - fit_result = {} - # add names of the free parameters - fit_result["free_parameters"] = self.coefficients.free_parameters.index.tolist() - # update linear corrections in casde new_LinearCorrections = impose_constrain( self.loaded_datasets, self.coefficients @@ -175,44 +151,19 @@ def run_sampling(self): @ diff_sm ) - # Compute some metrics of the fit result - # Get names of coefficients, including the constrained ones - coeffs_name = sorted(self.coefficients.name) - - # set the best fit point - self.coefficients.set_free_parameters(coeff_best) - self.coefficients.set_constraints() - - fit_result["best_fit_point"] = dict(zip(coeffs_name, self.coefficients.value)) - - # compute max log likelihood - chi2_tot = chi2.compute_chi2( - self.loaded_datasets, - self.coefficients.value, - self.use_quad, - self.use_multiplicative_prescription, - ) - max_logl = float(-0.5 * chi2_tot) - - fit_result["max_loglikelihood"] = max_logl - - gaussian_integral = np.log(np.sqrt(np.linalg.det(2 * np.pi * coeff_covmat))) - # NOTE: current formula for logz is not inluding the prior penalty - logz = gaussian_integral + max_logl - fit_result["logz"] = logz - _logger.warning("The logZ computation is not including the prior penalty.") - # generate samples in case n_samples > 0 if self.n_samples > 0: + self.log_result(coeff_best, coeff_covmat) # sample _logger.info("Sampling solutions ...") - fit_result["samples"] = np.random.multivariate_normal( + samples = np.random.multivariate_normal( coeff_best, coeff_covmat, size=(self.n_samples,) ) - self.save(fit_result) + self.save(samples) else: # record only chi2 if no samples are requested + self.coefficients.set_free_parameters(coeff_best) self.coefficients.set_constraints() @@ -225,11 +176,10 @@ def run_sampling(self): chi2_red = chi2_tot / self.loaded_datasets.Commondata.size with open(self.results_path / "chi2.dat", "a") as f: - f.write(f"{chi2_red} \n") + f.write("{} \n".format(chi2_red)) - def save(self, result): + def save(self, samples): """Save samples to json inside a dictionary: {coff: [replicas values]}. - Saving also some basic information about the fit. Parameters ---------- @@ -237,18 +187,17 @@ def save(self, result): raw samples with shape (n_samples, n_free_param) """ - posterior_samples = {} + values = {} for c in self.coefficients.name: - posterior_samples[c] = [] + values[c] = [] # propagate constrain - for sample in result["samples"]: + for sample in samples: self.coefficients.set_free_parameters(sample) self.coefficients.set_constraints() for c in self.coefficients.name: - posterior_samples[c].append(self.coefficients[c].value) + values[c].append(self.coefficients[c].value) - result["samples"] = posterior_samples - # save fit result - self.dump_fit_result(self.results_path / "fit_results.json", result) + posterior_file = self.results_path / "posterior.json" + self.dump_posterior(posterior_file, values) diff --git a/src/smefit/optimize/mc.py b/src/smefit/optimize/mc.py index 67a65bae..d4f71e6c 100644 --- a/src/smefit/optimize/mc.py +++ b/src/smefit/optimize/mc.py @@ -17,6 +17,7 @@ class MCOptimizer(Optimizer): + """Optimizer specification for |MC|. Parameters @@ -72,6 +73,8 @@ def __init__( use_bounds, minimizer_specs, use_multiplicative_prescription, + poly_mode, + external_coefficients, external_chi2=None, ): super().__init__( @@ -81,6 +84,8 @@ def __init__( use_quad, single_parameter_fits, use_multiplicative_prescription, + poly_mode, + external_coefficients, external_chi2, ) self.chi2_values = [] @@ -121,15 +126,17 @@ def from_dict(cls, config): config["data_path"], config["datasets"], config["coefficients"], + config["order"], config["use_quad"], config["use_theory_covmat"], config["use_t0"], config.get("use_multiplicative_prescription", False), - config.get("default_order", "LO"), config.get("theory_path", None), config.get("rot_to_fit_basis", None), config.get("uv_couplings", False), config.get("external_chi2", False), + config.get("poly_mode", False), + config.get("external_coefficients", {}), ) coefficients = CoefficientManager.from_dict(config["coefficients"]) @@ -179,10 +186,13 @@ def from_dict(cls, config): use_bounds, minimizer_specs, use_multiplicative_prescription, + config["poly_mode"], + config["external_coefficients"], external_chi2, ) def get_status(self, chi2): + if len(self.chi2_values) == 0: self.chi2_values.append(chi2) @@ -311,11 +321,4 @@ def save(self): / f"replica_{self.replica}/coefficients_rep_{self.replica}.json" ) - fit_result = { - "samples": values, - "logz": None, - "max_loglikelihood": None, - "best_fit_point": None, - } - - self.dump_fit_result(posterior_file, fit_result) + self.dump_posterior(posterior_file, values) diff --git a/src/smefit/optimize/ultranest.py b/src/smefit/optimize/ultranest.py index 6f675987..20645826 100644 --- a/src/smefit/optimize/ultranest.py +++ b/src/smefit/optimize/ultranest.py @@ -2,7 +2,6 @@ """Fitting the Wilson coefficients with |NS|""" import time from functools import partial - import jax import jax.numpy as jnp import ultranest @@ -10,8 +9,6 @@ from rich.table import Table from ultranest import stepsampler -from smefit.rge.rge import load_rge_matrix - from .. import chi2, log from ..coefficients import CoefficientManager from ..loader import load_datasets @@ -62,16 +59,7 @@ class USOptimizer(Optimizer): Set to a higher number (0.5) if you know the posterior is simple. store_raw: bool if True store the result to eventually resume the job - vectorized: bool - if True use jax vectorization - float64: bool - if True use float64 precision - external_chi2: dict - dict of external chi2 - rgemat: numpy.ndarray - solution matrix of the RGE - rge_dict: dict - dictionary with the RGE input parameter options + """ print_rate = 5000 @@ -86,6 +74,8 @@ def __init__( single_parameter_fits, pairwise_fits, use_multiplicative_prescription, + poly_mode, + external_coefficients, live_points=500, lepsilon=0.001, target_evidence_unc=0.5, @@ -95,19 +85,17 @@ def __init__( vectorized=False, float64=False, external_chi2=None, - rgemat=None, - rge_dict=None, ): super().__init__( - results_path=f"{result_path}/{result_ID}", - loaded_datasets=loaded_datasets, - coefficients=coefficients, - use_quad=use_quad, - single_parameter_fits=single_parameter_fits, - use_multiplicative_prescription=use_multiplicative_prescription, - external_chi2=external_chi2, - rgemat=rgemat, - rge_dict=rge_dict, + f"{result_path}/{result_ID}", + loaded_datasets, + coefficients, + use_quad, + single_parameter_fits, + use_multiplicative_prescription, + poly_mode, + external_coefficients, + external_chi2, ) self.live_points = live_points self.lepsilon = lepsilon @@ -143,42 +131,22 @@ def from_dict(cls, config): created object """ - rge_dict = config.get("rge", None) - operators_to_keep = config["coefficients"] - cutoff_scale = config.get("cutoff_scale", None) - - if rge_dict is not None and config.get("datasets") is not None: - _logger.info("Loading RGE matrix") - rgemat, operators_to_keep = load_rge_matrix( - rge_dict, - list(operators_to_keep.keys()), - config["datasets"], - config.get("theory_path", None), - cutoff_scale, - config.get("result_path", None), - config.get("result_ID", None), - ) - _logger.info("The operators generated by the RGE are: ") - _logger.info(list(operators_to_keep.keys())) - else: - rgemat = None - if config.get("datasets") is not None: loaded_datasets = load_datasets( config["data_path"], config["datasets"], - operators_to_keep, + config["coefficients"], + config["order"], config["use_quad"], config["use_theory_covmat"], config["use_t0"], config.get("use_multiplicative_prescription", False), - config.get("default_order", "LO"), config.get("theory_path", None), config.get("rot_to_fit_basis", None), config.get("uv_couplings", False), config.get("external_chi2", False), - rgemat=rgemat, - cutoff_scale=cutoff_scale, + config.get("poly_mode", False), + config.get("external_coefficients", {}) ) elif config.get("external_chi2") is not None: loaded_datasets = None @@ -240,6 +208,8 @@ def from_dict(cls, config): single_parameter_fits, pairwise_fits, use_multiplicative_prescription, + config["poly_mode"], + config["external_coefficients"], live_points=nlive, lepsilon=lepsilon, target_evidence_unc=target_evidence_unc, @@ -249,8 +219,6 @@ def from_dict(cls, config): vectorized=vectorized, float64=float64, external_chi2=external_chi2, - rgemat=rgemat, - rge_dict=rge_dict, ) def chi2_func_ns(self, params): @@ -269,16 +237,15 @@ def chi2_func_ns(self, params): params, self.use_quad, self.use_multiplicative_prescription, - use_replica=False, + False, + self.poly_mode ) else: chi2_tot = 0 - if self.chi2_ext is not None: for chi2_ext in self.chi2_ext: chi2_ext_i = chi2_ext(params) chi2_tot += chi2_ext_i - return chi2_tot def compute_fixed_coeff(self, constrain, param_dict): @@ -371,7 +338,7 @@ def run_sampling(self): log_dir = None if self.store_raw: - log_dir = self.results_path / "ultranest_log" + log_dir = self.results_path if self.vectorized: loglikelihood = jax.vmap(self.gaussian_loglikelihood) @@ -415,11 +382,6 @@ def run_sampling(self): log.console.log(f"Time : {((t2 - t1) / 60.0):.3f} minutes") log.console.log(f"Number of samples: {result['samples'].shape[0]}") - if self.store_raw: - _logger.info("Ultranest plots being produced...") - sampler.plot() - _logger.info(f"Ultranest plots produced in {log_dir}") - table = Table( style=Style(color="white"), title_style="bold cyan", title=None ) @@ -434,7 +396,6 @@ def run_sampling(self): def save(self, result): """Save |NS| replicas to json inside a dictionary: {coff: [replicas values]}. - Saving also some basic information about the fit. Parameters ---------- @@ -442,41 +403,23 @@ def save(self, result): result dictionary """ - posterior_samples = {} + values = {} for c in self.coefficients.name: - posterior_samples[c] = [] + values[c] = [] for sample in result["samples"]: self.coefficients.set_free_parameters(sample) self.coefficients.set_constraints() for c in self.coefficients.name: - posterior_samples[c].append(self.coefficients[c].value) + values[c].append(self.coefficients[c].value) if self.pairwise_fits: - fit_results_file = ( + posterior_file = ( self.results_path - / f"fit_results_{self.coefficients.name[0]}_{self.coefficients.name[1]}.json" + / f"posterior_{self.coefficients.name[0]}_{self.coefficients.name[1]}.json" ) else: - fit_results_file = self.results_path / "fit_results.json" - - # self.dump_posterior(posterior_file, values) - - # Writing the fit summary results - logz = result["logz"] - max_loglikelihood = result["maximum_likelihood"]["logl"] - # get the best fit point, including the constrained coefficients - self.coefficients.set_free_parameters(result["maximum_likelihood"]["point"]) - self.coefficients.set_constraints() - best_fit_point = dict(zip(self.coefficients.name, self.coefficients.value)) - - fit_result = { - "samples": posterior_samples, - "logz": logz, - "max_loglikelihood": max_loglikelihood, - "best_fit_point": best_fit_point, - "free_parameters": self.coefficients.free_parameters.index.tolist(), - } - - self.dump_fit_result(fit_results_file, fit_result) + posterior_file = self.results_path / "posterior.json" + + self.dump_posterior(posterior_file, values) diff --git a/src/smefit/postfit/__init__.py b/src/smefit/postfit/__init__.py index 269636b0..ebcbf4eb 100644 --- a/src/smefit/postfit/__init__.py +++ b/src/smefit/postfit/__init__.py @@ -13,6 +13,7 @@ class Postfit: def __init__(self, result_path, single_parameter_fits, chi2_threshold): + self.results_folder = result_path self.finished_replicas = 0 for name in self.results_folder.iterdir(): @@ -25,6 +26,7 @@ def __init__(self, result_path, single_parameter_fits, chi2_threshold): @classmethod def from_file(cls, result_folder): + # load file with open(result_folder / f"{result_folder.stem}.yaml", encoding="utf-8") as f: config = yaml.safe_load(f) @@ -43,6 +45,7 @@ def from_file(cls, result_folder): ) def save(self, nrep): + postfit_res = [] chi2_list = [] coeffs = None # Initialize coeffs to None @@ -85,6 +88,7 @@ def save(self, nrep): self.not_completed = True else: + posterior = {} for i, c in enumerate(coeffs): posterior[c] = list(np.array(postfit_res).T[i, :]) diff --git a/src/smefit/prefit/__init__.py b/src/smefit/prefit/__init__.py index 47f067fd..7eeb7844 100644 --- a/src/smefit/prefit/__init__.py +++ b/src/smefit/prefit/__init__.py @@ -19,14 +19,16 @@ def __init__(self, config): config["data_path"], config["datasets"], config["coefficients"], + config["order"], config["use_quad"], config["use_theory_covmat"], config["use_t0"], False, - config.get("default_order", "LO"), config.get("theory_path", None), config.get("rot_to_fit_basis", None), config.get("uv_couplings", False), + config.get("poly_mode",False), + config.get("external_coefficients",{}) ) def chi2_sm(self): diff --git a/src/smefit/projections/__init__.py b/src/smefit/projections/__init__.py index 93b18bc3..ff5674c0 100644 --- a/src/smefit/projections/__init__.py +++ b/src/smefit/projections/__init__.py @@ -19,10 +19,10 @@ def __init__( self, commondata_path, theory_path, - datasets, + dataset_names, projections_path, coefficients, - default_order, + order, use_quad, use_theory_covmat, rot_to_fit_basis, @@ -30,12 +30,13 @@ def __init__( fred_sys, use_t0, ): + self.commondata_path = commondata_path self.theory_path = theory_path - self.datasets = datasets + self.dataset_names = dataset_names self.projections_path = projections_path self.coefficients = coefficients - self.default_order = default_order + self.order = order self.use_quad = use_quad self.use_theory_covmat = use_theory_covmat self.rot_to_fit_basis = rot_to_fit_basis @@ -45,19 +46,19 @@ def __init__( self.datasets = load_datasets( self.commondata_path, - self.datasets, + self.dataset_names, self.coefficients, + self.order, self.use_quad, self.use_theory_covmat, self.use_t0, False, - self.default_order, theory_path=self.theory_path, ) if self.coefficients: _logger.info( - "Some coefficients are specified in the runcard: EFT correction will be used for the central values" + f"Some coefficients are specified in the runcard: EFT correction will be used for the central values" ) @classmethod @@ -82,10 +83,10 @@ def from_config(cls, projection_card): projections_path = pathlib.Path( projection_config["projections_path"] ).absolute() - datasets = projection_config["datasets"] + dataset_names = projection_config["datasets"] coefficients = projection_config.get("coefficients", []) - default_order = projection_config.get("default_order", "LO") + order = projection_config.get("order", "LO") use_quad = projection_config.get("use_quad", False) use_theory_covmat = projection_config.get("use_theory_covmat", True) rot_to_fit_basis = projection_config.get("rot_to_fit_basis", None) @@ -98,10 +99,10 @@ def from_config(cls, projection_card): return cls( commondata_path, theory_path, - datasets, + dataset_names, projections_path, coefficients, - default_order, + order, use_quad, use_theory_covmat, rot_to_fit_basis, @@ -126,7 +127,7 @@ def compute_cv_projection(self): for coeff in self.datasets.OperatorsNames: coefficient_values.append(self.coefficients[coeff]["value"]) cv = make_predictions( - self.datasets, coefficient_values, self.use_quad, False + self.datasets, coefficient_values, self.use_quad, False, False ) return cv @@ -152,6 +153,7 @@ def rescale_sys(self, sys, fred_sys): is_artificial = is_square & np.any(sys < 0) if is_artificial: + # reconstruct covmat and keep only diagonal components cov_tot = sys @ sys.T sys_diag = np.sqrt(np.diagonal(cov_tot)) @@ -164,6 +166,7 @@ def rescale_sys(self, sys, fred_sys): @staticmethod def rescale_stat(stat, lumi_old, lumi_new): + """ Projects the statistical uncertainties from lumi_old to lumi_new @@ -183,19 +186,15 @@ def rescale_stat(stat, lumi_old, lumi_new): fred_stat = np.sqrt(lumi_old / lumi_new) return stat * fred_stat - def build_projection(self, lumi_new=None, noise="L0"): + def build_projection(self, lumi_new, closure): """ Constructs runcard for projection by updating the central value and statistical and systematic uncertainties Parameters ---------- - lumi_new: float, optional - Adjusts the statistical uncertainties according to the specified luminosity lumi_new. - If not specified, the uncertainties are left unchanged and the central values are fluctuated - according to the noise level - noise: str - Noise level for the projection, choose between L0 or L1 + lumi_new: float + Adjusts the statistical uncertainties according to the specified luminosity closure: bool Set to true for a L1 closure test (no rescaling, only cv gets fluctuated according to original uncertainties) @@ -206,6 +205,7 @@ def build_projection(self, lumi_new=None, noise="L0"): cnt = 0 for dataset_idx, num_data in enumerate(self.datasets.NdataExp): + dataset_name = self.datasets.ExpNames[dataset_idx] path_to_dataset = self.commondata_path / f"{dataset_name}.yaml" @@ -270,7 +270,7 @@ def build_projection(self, lumi_new=None, noise="L0"): th_covmat = self.datasets.ThCovMat[idxs, idxs] - if lumi_new is not None: + if not closure: # if all stats are zero, we only have access to the total error which we rescale by 1/3 (compromise) no_stats = not np.any(stat) if no_stats: @@ -296,9 +296,8 @@ def build_projection(self, lumi_new=None, noise="L0"): # build covmat for projections. Use rescaled uncertainties newcov = covmat_from_systematics([stat_red], [sys_red]) - # Replace old luminosity with new one in the dataset - data_dict["luminosity"] = lumi_new else: # closure test + # we store absolute uncertainties and convert all multipicative uncertainties to additive ones if num_data > 1: @@ -316,11 +315,8 @@ def build_projection(self, lumi_new=None, noise="L0"): if self.use_theory_covmat: newcov += th_covmat - # add Gaussian noise to central values in case of L1 - # and leave them unchanged in case of L0 - cv_projection = cv[idxs] - if noise == "L1": - cv_projection = np.random.multivariate_normal(cv[idxs], newcov) + # add L1 noise to cv + cv_projection = np.random.multivariate_normal(cv[idxs], newcov) # replace cv with updated central values if len(cv_projection) > 1: @@ -332,7 +328,7 @@ def build_projection(self, lumi_new=None, noise="L0"): projection_folder.mkdir(exist_ok=True) if projection_folder != self.commondata_path: - if lumi_new is not None: + if not closure: with open( f"{projection_folder}/{dataset_name}_proj.yaml", "w" ) as file: @@ -347,7 +343,7 @@ def build_projection(self, lumi_new=None, noise="L0"): sys.exit() # copy corresponding theory predictions with _proj appended to filename - if lumi_new is not None: + if not closure: shutil.copy( self.theory_path / f"{dataset_name}.json", self.theory_path / f"{dataset_name}_proj.json", diff --git a/src/smefit/runner.py b/src/smefit/runner.py index 0ac33a4f..0854ddc4 100644 --- a/src/smefit/runner.py +++ b/src/smefit/runner.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- +import importlib import itertools import pathlib -import shutil import subprocess import sys from shutil import copyfile @@ -11,7 +11,6 @@ from .analyze.pca import RotateToPca from .chi2 import Scanner from .log import logging -from .optimize import Optimizer from .optimize.analytic import ALOptimizer from .optimize.mc import MCOptimizer from .optimize.ultranest import USOptimizer @@ -66,9 +65,9 @@ def setup_result_folder(self): else: res_folder_fit = result_folder / result_ID + subprocess.call(f"mkdir -p {result_folder}", shell=True) if res_folder_fit.exists(): _logger.warning(f"{res_folder_fit} already found, overwriting old results") - shutil.rmtree(res_folder_fit) subprocess.call(f"mkdir -p {res_folder_fit}", shell=True) # Copy yaml runcard to results folder or dump it @@ -140,13 +139,13 @@ def ultranest(self, config): # add external modules to paths if "external_chi2" in config: external_chi2 = config["external_chi2"] - for _, module in external_chi2.items(): - module_path = module["path"] + for class_name, module_path in external_chi2.items(): path = pathlib.Path(module_path) base_path, stem = path.parent, path.stem sys.path = [str(base_path)] + sys.path if run_parallel: + comm = MPI.COMM_WORLD rank = comm.Get_rank() if rank == 0: @@ -196,6 +195,11 @@ def single_parameter_analysis(self, optimizer): optimizer to be used (NS, MC or A) """ + def MultipleConstrainError(): + raise ValueError( + "Constrain with multiple coefficients do not make sense, in sigle parameter fits." + ) + config = self.run_card # loop on all the coefficients @@ -205,15 +209,16 @@ def single_parameter_analysis(self, optimizer): # skip contrained coeffs if "constrain" in config["coefficients"][coeff]: - _logger.info("Skipping constrained coefficient %s", coeff) + _logger.info("Skipping contrained coefficient %s", coeff) continue - # We define the new coefficient config for the individual fit + # if there are constrained coefficients, only + # relations in which appears a single indepentent + # coefficient make sense. new_coeff_config = {} - # seach for a relation: loop on all the coefficients + # seach for a realtion: loop on all the coefficients for coeff2, vals in config["coefficients"].items(): - # skip free coefficients if "constrain" not in vals: continue @@ -229,21 +234,19 @@ def single_parameter_analysis(self, optimizer): else [vals["constrain"]] ) + # only single coefficient constrain are supported new_constrain = [] - # Now we redefine the constraints - # We only keep constraints proportional to the current coeff, - # if not we skip the contribution as it is not relevant - # for the current individual fit for addend in constrain: + if len(addend) > 1: + MultipleConstrainError() if coeff in addend: new_constrain.append(addend) + # check that the coefficient appearing in all the addends is the same + elif new_constrain: + MultipleConstrainError() if new_constrain: - new_coeff_config[coeff2] = { - "constrain": new_constrain, - "min": vals["min"], - "max": vals["max"], - } + new_coeff_config[coeff2] = vals # add fitted coefficient new_coeff_config[coeff] = config["coefficients"][coeff] @@ -289,7 +292,7 @@ def run_analysis(self, optimizer): else: self.global_analysis(optimizer) - def chi2_scan(self, n_replica, compute_bounds, scan_points=100): + def chi2_scan(self, n_replica, compute_bounds): r"""Run an individual :math:`\chi^2` scan. Parameters @@ -301,23 +304,8 @@ def chi2_scan(self, n_replica, compute_bounds, scan_points=100): compute_bounds: bool if True compute and save the :math:`\chi^2` bounds. """ - - if "external_chi2" in self.run_card: - external_chi2 = self.run_card["external_chi2"] - for _, module in external_chi2.items(): - module_path = module["path"] - path = pathlib.Path(module_path) - base_path, stem = path.parent, path.stem - if not base_path.exists(): - raise FileNotFoundError( - f"Path {base_path} does not exist. Modify the runcard and rerun. Exiting" - ) - else: - sys.path = [str(base_path)] + sys.path - - scan = Scanner(self.run_card, n_replica, scan_points) + scan = Scanner(self.run_card, n_replica) if compute_bounds: scan.compute_bounds() scan.compute_scan() - scan.write_scan() scan.plot_scan()