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Minimum W2 value for all datasets # chi2 history stored for each `log_freq` epochs diff --git a/src/nnusf/cli/plot.py b/src/nnusf/cli/plot.py index c150e056..7b7436cc 100644 --- a/src/nnusf/cli/plot.py +++ b/src/nnusf/cli/plot.py @@ -137,7 +137,7 @@ def sub_fit(model, runcard, destination): @subcommand.command("sf") -@click.argument("source", type=click.Path(path_type=pathlib.Path, exists=True)) +@click.argument("dataset", type=click.Path(path_type=pathlib.Path, exists=True)) @click.option( "-k", "--kind", @@ -153,14 +153,14 @@ def sub_fit(model, runcard, destination): default=pathlib.Path.cwd().absolute() / "plots", help="Alternative destination path to store the resulting plots (default: $PWD/plots).", ) -def sub_sf(source, kind, destination): +def sub_sf(dataset, kind, destination): """Plots structure functions.""" - sf.main(source, kind, destination) + sf.main(dataset, kind, destination) @subcommand.command("matching_dataset") -@click.argument("source", type=click.Path(path_type=pathlib.Path, exists=True)) +@click.argument("dataset", type=click.Path(path_type=pathlib.Path, exists=True)) @click.option( "-d", "--destination", @@ -168,7 +168,9 @@ def sub_sf(source, kind, destination): default=pathlib.Path.cwd().absolute() / "plots", help="Alternative destination path to store the resulting plots (default: $PWD/plots).", ) -def sub_matching_dataset(source, destination): - """Plots the matching datasets along with the actual data""" +def sub_matching_dataset(dataset, destination): + """Plots the matching datasets along with the actual data. - matching.main(source, destination) + eg: nnu plot matching_dataset commondata/data/DATA_NUTEV_F2_MATCHING.csv + """ + matching.main(dataset, destination) diff --git a/src/nnusf/cli/theory.py b/src/nnusf/cli/theory.py index dadace84..ebd84198 100644 --- a/src/nnusf/cli/theory.py +++ b/src/nnusf/cli/theory.py @@ -84,9 +84,18 @@ def sub_sub_hiq(data, destination): @option_dest def sub_sub_all(data, destination): """Full datasets runcards""" - runcards.dvst(data, destination=destination) + runcards.dvst(data, destination=destination, activate_scale_var=False) +@sub_runcards.command("th_err") +@click.argument( + "data", nargs=-1, type=click.Path(exists=True, path_type=pathlib.Path) +) +@option_dest +def sub_sub_all(data, destination): + """Full datasets runcards with 7 points perescription scale variations""" + runcards.dvst(data, destination=destination, activate_scale_var=True) + @subcommand.command("by") @click.argument( "observables", nargs=-1, type=click.Choice(bodek_yang.load.load().members) diff --git a/src/nnusf/data/loader.py b/src/nnusf/data/loader.py index f58f58d9..98c8ceae 100644 --- a/src/nnusf/data/loader.py +++ b/src/nnusf/data/loader.py @@ -245,11 +245,20 @@ def build_covariance_matrix( """ if "_MATCHING" in dataset_name: - dataset_name = "MATCH_" + dataset_name.removesuffix("_MATCHING") - nrep_predictions = np.load( - f"{commondata_path}/matching/{dataset_name}.npy" - ) - covmat = np.cov(nrep_predictions[mask_predictions]) + sv_variations = [] + for variation in pathlib.Path(f"{commondata_path}/matching/").iterdir(): + if dataset_name in variation.stem: + # central scale + if "xif1_xir1" in variation.stem: + nrep_predictions = np.load(variation) + else: + sv_variations.append(np.load(variation)) + # build th shift + th_shift = (sv_variations - nrep_predictions[:,0]).T + # build covaraince + pdf_covmat = np.cov(nrep_predictions[mask_predictions]) + th_covamt = np.cov(th_shift[mask_predictions]) + covmat = np.sqrt(th_covamt ** 2 + pdf_covmat ** 2) return clip_covmat(covmat, dataset_name) else: diagonal = np.power(unc_df["stat"], 2) diff --git a/src/nnusf/data/matching_grids.py b/src/nnusf/data/matching_grids.py index 670d3114..a69e2534 100644 --- a/src/nnusf/data/matching_grids.py +++ b/src/nnusf/data/matching_grids.py @@ -15,7 +15,7 @@ import yaml from .. import utils -from ..theory.predictions import pdf_error +from ..theory.predictions import pdf_error, theory_error from .utils import ( MAP_OBS_PID, build_obs_dict, @@ -58,13 +58,14 @@ def proton_boundary_conditions( if grids is not None: obsdic_list = [] for grid in grids: - grid_name = grid.stem[6:-13] + grid_name = grid.stem[6:-4] _logger.info(f"Generating BC data for '{grid_name}'.") - obstype = grid_name.split("_")[-1] + obstype = grid_name.split("_")[1] obspid = MAP_OBS_PID[obstype] obsdic = build_obs_dict(obstype, [None], obspid) - obsdic_list.append(obsdic) + if obsdic not in obsdic_list: + obsdic_list.append(obsdic) main( grid, pdf, @@ -175,13 +176,11 @@ def main( with tempfile.TemporaryDirectory() as tmpdir: tmpdir = pathlib.Path(tmpdir).absolute() - grid_name = grids.stem[6:-13] - obs = grid_name.split("_")[-1] - new_name = f"{grid_name}_MATCHING" - experiment = grid_name.split("_")[0] + full_grid_name = grids.stem[6:-4] + experiment, obs, _, xif, xir = full_grid_name.split("_") + new_name = f"{experiment}_{obs}_MATCHING" new_experiment = f"{experiment}_MATCHING" - # if grids.suffix == ".tar.gz": if str(grids).endswith(".tar.gz"): utils.extract_tar(grids, tmpdir) grids = tmpdir / "grids" @@ -195,32 +194,40 @@ def main( if "pineappl" not in gpath.name: continue grid = pineappl.grid.Grid.read(gpath) - prediction = pdf_error(grid, pdf, kin_grid["x"], reshape=False) + if xif == "xif1" and xir == "xir1": + prediction = pdf_error(grid, pdf, kin_grid["x"], reshape=False) + else: + prescription = [(float(xir[3:]), float(xif[3:]))] + prediction = theory_error( + grid, pdf, prescription, kin_grid["x"], reshape=False + ) full_pred.append(prediction[0]) pred = np.average(full_pred, axis=0) - # Select only predictions for Replicas_0 in data - data_pd = pd.DataFrame({"data": pred[:, 0]}) + # store data only for unvaried matching grids + if xif == "xif1" and xir == "xir1": + # data_pd = pd.DataFrame({"data": np.median(pred, axis=1)}) + # Select only predictions for Replicas_0 to be the Central Value + data_pd = pd.DataFrame({"data": pred[:, 0]}) - # Dump the kinematics into CSV - dump_kinematics(destination, kin_grid, new_experiment, is_xsec) + # Dump the kinematics into CSV + dump_kinematics(destination, kin_grid, new_experiment, is_xsec) - # Dump the central (replica) data into CSV - central_val_folder = destination.joinpath("data") - central_val_folder.mkdir(exist_ok=True) - write_to_csv(central_val_folder, f"DATA_{new_name}", data_pd) + # Dump the central (replica) data into CSV + central_val_folder = destination.joinpath("data") + central_val_folder.mkdir(exist_ok=True) + write_to_csv(central_val_folder, f"DATA_{new_name}", data_pd) - # Dump the dummy uncertainties into CSV - dump_uncertainties(destination, new_name, n_points) + # Dump the dummy uncertainties into CSV + dump_uncertainties(destination, new_name, n_points) # Dump the predictions for the REST of the replicas as NPY pred_folder = destination.joinpath("matching") pred_folder.mkdir(exist_ok=True) - mat_dest = (pred_folder / f"MATCH_{grid_name}").with_suffix(".npy") - np.save(mat_dest, pred) + np.save(pred_folder / f"{full_grid_name}.npy", pred) - msg = f"The matching/BC grid for {grid_name} are stored in " - msg += f"'{destination.absolute().relative_to(pathlib.Path.cwd())}'" + msg = f"The matching/BC grid for {full_grid_name} are stored in " + msg += f"'{destination.absolute()}'" _logger.info(msg) diff --git a/src/nnusf/plot/matching.py b/src/nnusf/plot/matching.py index bdeb480d..cc692fab 100644 --- a/src/nnusf/plot/matching.py +++ b/src/nnusf/plot/matching.py @@ -36,13 +36,23 @@ def main( data_name = "_".join(dataset.stem.split("_")[1:]) exp_name = data_name.removesuffix("_MATCHING") - data = loader.Loader(data_name, dataset.parents[1]).table + full_data = loader.Loader(data_name, dataset.parents[1]) + data = full_data.table data_exp = loader.Loader(exp_name, dataset.parents[1]).table - temp_name = "MATCH_" + data_name.removesuffix("_MATCHING") - nrep_predictions = np.load(f"{dataset.parents[1]}/matching/{temp_name}.npy") - - data["std"] = nrep_predictions.std(axis=1) + # bould the sv error + sv_variations = [] + for variation in pathlib.Path(f"{dataset.parents[1]}/matching/").iterdir(): + if data_name in variation.stem: + # central scale + if "xif1_xir1" in variation.stem: + nrep_predictions = np.load(variation) + else: + sv_variations.append(np.load(variation)) + th_shift = (sv_variations - nrep_predictions[:, 0]).T + data["sv_err"] = np.sqrt(np.diag(np.cov(th_shift))) + + data["total_err"] = np.sqrt(np.diag(full_data.covariance_matrix)) # np.testing.assert_allclose(data["data"], nrep_predictions.mean(axis=1), atol=1e-6) # np.testing.assert_allclose(nrep_predictions.T[0], nrep_predictions.mean(axis=1), atol=1e-6) @@ -62,20 +72,40 @@ def main( central = fixed_x_data["data"] central_exp = fixed_x_data_exp["data"] - std = fixed_x_data["std"] + total_err = fixed_x_data["total_err"] + sv_err = fixed_x_data["sv_err"] std_exp = np.sqrt( fixed_x_data_exp["syst"] ** 2 + fixed_x_data_exp["stat"] ** 2 ) plt.figure() - plt.errorbar(fixed_x_data["Q2"], central, yerr=std, fmt="o") plt.errorbar( - fixed_x_data_exp["Q2"], central_exp, yerr=std_exp, fmt="x" + fixed_x_data["Q2"], + central, + yerr=total_err, + fmt="o", + capsize=5, + label="yadism (tot err)", + ) + plt.errorbar( + fixed_x_data["Q2"], + central, + yerr=sv_err, + fmt="o", + label="yadism (sv err)", + ) + plt.errorbar( + fixed_x_data_exp["Q2"], + central_exp, + yerr=std_exp, + fmt="x", + label="data", ) plt.title(f"{data_name}, x={x}. x_exp={x_exp:3f}") plt.xlabel("$Q^2$") plt.xscale("log") + plt.legend() plt.tight_layout() plt.savefig(preds_dest / f"{data_name}_x_{x}.pdf") diff --git a/src/nnusf/sffit/compute_expchi2.py b/src/nnusf/sffit/compute_expchi2.py index c6be9e84..9d1bb2cf 100644 --- a/src/nnusf/sffit/compute_expchi2.py +++ b/src/nnusf/sffit/compute_expchi2.py @@ -67,13 +67,17 @@ def compute_exp_chi2(datainfo, nn_layers, optimizer_parameters, **kwargs): for datname, chi2_value in chi2_values.items() if "_loss" in datname } + # Compute the experimental Chi2 only for real data + expreal_losses = [ + chi2_real_dataset + for n, chi2_real_dataset in chi2_values.items() + if (("_MATCHING" not in n) and ("_loss" in n)) + ] + expreal_pt = [v for n, v in nb_dpts_dataset.items() if "_MATCHING" not in n] + normalized_chi2s["total_chi2"] = chi2_values["loss"] / sum( nb_dpts_dataset.values() ) - - # Compute the experimental Chi2 only for real data - expreal_losses = [v for n, v in chi2_values.items() if "_MATCHING" not in n] - expreal_pt = [v for n, v in nb_dpts_dataset.items() if "_MATCHING" not in n] normalized_chi2s["tot_chi2_real"] = sum(expreal_losses) / sum(expreal_pt) return normalized_chi2s diff --git a/src/nnusf/theory/defs.py b/src/nnusf/theory/defs.py index 549e11c9..ed87c448 100644 --- a/src/nnusf/theory/defs.py +++ b/src/nnusf/theory/defs.py @@ -5,7 +5,11 @@ sfmap = dict(F2="F2_total", FL="FL_total", F3="F3_total") xsmap = dict( - PROTONBC="XSCHORUSCC", CHORUS="XSCHORUSCC", NUTEV="XSNUTEVNU", CDHSW="XSCHORUSCC", FW="FW" + PROTONBC="XSCHORUSCC", + CHORUS="XSCHORUSCC", + NUTEV="XSNUTEVNU", + CDHSW="XSCHORUSCC", + FW="FW", ) projectiles = dict(NU="neutrino", NB="antineutrino") targets = {1: "proton", 20: "neon", 100: "marble", 56: "iron", 208: "lead"} @@ -16,3 +20,15 @@ nine_points = list(itertools.product(three_points, three_points)) """Nine points prescription for scale variations (as couples, referred to ``(fact, ren)`` scales).""" + +seven_points = [ + (0.5, 0.5), + (1.0, 0.5), + (2.0, 1.0), + (1.0, 1.0), + (0.5, 1.0), + (1.0, 2.0), + (2.0, 2.0), +] +"""Seven points prescription for scale variations (as couples, referred to ``(fact, +ren)`` scales).""" diff --git a/src/nnusf/theory/grids.py b/src/nnusf/theory/grids.py index 4f6c1efb..f2c99098 100644 --- a/src/nnusf/theory/grids.py +++ b/src/nnusf/theory/grids.py @@ -24,6 +24,7 @@ def main(cards: pathlib.Path, destination: pathlib.Path): """ with tempfile.TemporaryDirectory() as tmpdir: tmpdir = pathlib.Path(tmpdir).absolute() + full_data_name = str(cards).split("-")[1][:-4] # extract tar content if cards.suffix == ".tar": @@ -51,10 +52,8 @@ def main(cards: pathlib.Path, destination: pathlib.Path): output.dump_pineappl_to_file(res_path, obs) _logger.info(f"Dumped {res_path.name}") - if "_NU" in data_name or "_NB" in data_name: - data_name = data_name[:-3] file_name = ( - f"grids-{data_name}.tar" if data_name != "" else f"grids.tar" + f"grids-{full_data_name}.tar.gz" if full_data_name != "" else f"grids.tar.gz" ) with tarfile.open(destination / file_name, "w") as tar: for path in grids_dest.iterdir(): diff --git a/src/nnusf/theory/runcards.py b/src/nnusf/theory/runcards.py index 68e8568a..5fc297ed 100644 --- a/src/nnusf/theory/runcards.py +++ b/src/nnusf/theory/runcards.py @@ -11,7 +11,7 @@ from .. import utils from ..data import loader -from . import bodek_yang, data_vs_theory, highq +from . import bodek_yang, data_vs_theory, highq, defs _logger = logging.getLogger(__name__) @@ -104,32 +104,53 @@ def hiq(datasets: list[pathlib.Path], destination: pathlib.Path): ) -def update_theory(name: str, path: pathlib.Path) -> dict: +def update_theory( + name: str, path: pathlib.Path, activate_scale_var: bool +) -> list: """Update theory runcard""" data = loader.Loader(name, path) - th = theory({"MP": float(np.unique(data.table["m_nucleon"]))}) - return th + ths = [] + if activate_scale_var: + # 7 points prescription (xir, xif) + for xif, xir in defs.seven_points: + ths.append( + theory( + { + "MP": float(np.unique(data.table["m_nucleon"])), + "XIF": xif, + "XIR": xir, + } + ) + ) + else: + ths.append(theory({"MP": float(np.unique(data.table["m_nucleon"]))})) + return ths -def dvst(datasets: list[pathlib.Path], destination: pathlib.Path): +def dvst( + datasets: list[pathlib.Path], destination: pathlib.Path, activate_scale_var: bool +): """Generate yadism runcards for all datapoints.""" path = datasets_path(datasets) utils.mkdest(destination) + # Loop on datasets for dataset in datasets: data_name = "_".join(dataset.stem.split("_")[1:]) ocards = data_vs_theory.runcards.observables([data_name], path) - theory_card = update_theory(data_name, path) - with tempfile.TemporaryDirectory() as tmpdir: - tmpdir = pathlib.Path(tmpdir) - utils.write(theory_card, tmpdir / "theory.yaml") - for name, observable_card in ocards.items(): - utils.write(observable_card, tmpdir / f"obs-{name}.yaml") - - tarpath = destination / f"runcards-{data_name}.tar" - with tarfile.open(tarpath, "w") as tar: - for tmppath in tmpdir.iterdir(): - tar.add(tmppath.absolute(), arcname="runcards/" + tmppath.name) - - _logger.info( - f"Runcards have been dumped to '{tarpath.relative_to(pathlib.Path.cwd())}'" - ) + # loop on theories + theory_cards = update_theory(data_name, path, activate_scale_var) + for theory_card in theory_cards: + with tempfile.TemporaryDirectory() as tmpdir: + tmpdir = pathlib.Path(tmpdir) + utils.write(theory_card, tmpdir / "theory.yaml") + for name, observable_card in ocards.items(): + utils.write(observable_card, tmpdir / f"obs-{name}.yaml") + + tarpath = destination / f"runcards-{data_name}_xif{theory_card['XIF']}_xir{theory_card['XIR']}.tar" + with tarfile.open(tarpath, "w") as tar: + for tmppath in tmpdir.iterdir(): + tar.add(tmppath.absolute(), arcname="runcards/" + tmppath.name) + + _logger.info( + f"Runcards have been dumped to '{tarpath.relative_to(pathlib.Path.cwd())}'" + )