diff --git a/validphys2/src/validphys/checks.py b/validphys2/src/validphys/checks.py index c2248e1b68..00ee0bcf7d 100644 --- a/validphys2/src/validphys/checks.py +++ b/validphys2/src/validphys/checks.py @@ -52,6 +52,11 @@ def check_pdf_is_montecarlo_or_hessian(pdf, **kwargs): ) +@make_argcheck +def check_not_using_pdferr(use_pdferr, **kwargs): + check(not use_pdferr, "The flag 'use_pdferr' must be off to use this function") + + @make_check def check_know_errors(ns, **kwargs): pdf = ns['pdf'] diff --git a/validphys2/src/validphys/dataplots.py b/validphys2/src/validphys/dataplots.py index d4c56bea7f..176d4527dc 100644 --- a/validphys2/src/validphys/dataplots.py +++ b/validphys2/src/validphys/dataplots.py @@ -23,6 +23,7 @@ from reportengine.figure import figure, figuregen from reportengine.floatformatting import format_number from validphys import plotutils +from validphys.checks import check_not_using_pdferr from validphys.core import CutsPolicy, MCStats, cut_mask from validphys.coredata import KIN_NAMES from validphys.plotoptions import get_info, kitable, transform_result @@ -387,13 +388,13 @@ def _plot_fancy_impl( lb = labellist[normalize_to] ax.set_ylabel(f"Ratio to {lb if lb else norm_result.label}") - ax.legend().set_zorder(100000) ax.set_xlabel(info.xlabel) fig.tight_layout() yield fig +@check_not_using_pdferr @check_normalize_to @figuregen def plot_fancy(one_or_more_results, commondata, cuts, normalize_to: (int, str, type(None)) = None): @@ -623,19 +624,20 @@ def plot_datasets_chi2(groups_data, groups_chi2): each_dataset_chi2_pdfs = collect("each_dataset_chi2", ("pdfs",)) + @figure def plot_datasets_pdfs_chi2(data, each_dataset_chi2_pdfs, pdfs): """ Plot the chi² of all datasets with bars, and for different pdfs. """ - + chi2_pdfs = list(each_dataset_chi2_pdfs) - - pdf_dict = {dataset.name: - [chi2_pdfs[i][j] for i in range(len(chi2_pdfs))] - for j, dataset in enumerate(data) - } + + pdf_dict = { + dataset.name: [chi2_pdfs[i][j] for i in range(len(chi2_pdfs))] + for j, dataset in enumerate(data) + } vals = [] collabels = [] @@ -643,8 +645,10 @@ def plot_datasets_pdfs_chi2(data, each_dataset_chi2_pdfs, pdfs): for ds, val in pdf_dict.items(): vals.append([chi2.central_result / chi2.ndata for chi2 in val]) collabels.append(ds) - - fig, ax = plotutils.barplot(np.array(vals).T, collabels, datalabels=[f'$\chi^2$, {str(pdf)}' for pdf in pdfs]) + + fig, ax = plotutils.barplot( + np.array(vals).T, collabels, datalabels=[rf"$\chi^2$, {str(pdf)}" for pdf in pdfs] + ) ax.set_title(r"$\chi^2$ distribution for datasets") ax.legend() return fig @@ -793,10 +797,7 @@ def plot_training_validation(fit, replica_data, replica_filters=None): """ training, valid = zip(*((dt.training, dt.validation) for dt in replica_data)) fig, ax = plotutils.subplots( - figsize=( - max(mpl.rcParams.get("figure.figsize")), - max(mpl.rcParams.get("figure.figsize")), - ) + figsize=(max(mpl.rcParams.get("figure.figsize")), max(mpl.rcParams.get("figure.figsize"))) ) ax.plot(training, valid, marker="o", linestyle="none", markersize=5, zorder=100) if replica_filters: @@ -808,20 +809,10 @@ def plot_training_validation(fit, replica_data, replica_filters=None): ax.set_xlabel(r"$\chi^2/N_{dat}$ training") ax.set_ylabel(r"$\chi^2/N_{dat}$ validation") - min_max_lims = [ - min([*ax.get_xlim(), *ax.get_ylim()]), - max([*ax.get_xlim(), *ax.get_ylim()]), - ] + min_max_lims = [min([*ax.get_xlim(), *ax.get_ylim()]), max([*ax.get_xlim(), *ax.get_ylim()])] ax.plot(min_max_lims, min_max_lims, ":k") - ax.plot( - np.mean(training), - np.mean(valid), - marker="s", - color="red", - markersize=7, - zorder=1000, - ) + ax.plot(np.mean(training), np.mean(valid), marker="s", color="red", markersize=7, zorder=1000) ax.set_aspect("equal") return fig @@ -936,68 +927,61 @@ def plot_smpdf(pdf, dataset, obs_pdf_correlations, mark_threshold: float = 0.9): else: cmap = cm.viridis - #TODO: vmin vmax should be global or by figure? + # TODO: vmin vmax should be global or by figure? vmin, vmax = min(plotting_var), max(plotting_var) if info.x_scale == 'log': norm = mcolors.LogNorm(vmin, vmax) else: norm = mcolors.Normalize(vmin, vmax) - table["__plotting_var"] = plotting_var sm = cm.ScalarMappable(cmap=cmap, norm=norm) figby = sane_groupby_iter(table, info.figure_by) for same_vals, fb in figby: - grid = fullgrid[ np.asarray(fb.index),...] + grid = fullgrid[np.asarray(fb.index), ...] - - #Use the maximum absolute correlation for plotting purposes + # Use the maximum absolute correlation for plotting purposes absgrid = np.max(np.abs(grid), axis=0) - mark_mask = absgrid > np.max(absgrid)*mark_threshold + mark_mask = absgrid > np.max(absgrid) * mark_threshold label = info.group_label(same_vals, info.figure_by) - #TODO: PY36ScalarMappable - #TODO Improve title? + # TODO: PY36ScalarMappable + # TODO Improve title? title = f"{info.dataset_label} {label if label else ''}\n[{pdf.label}]" - #Start plotting - w,h = mpl.rcParams["figure.figsize"] - h*=2.5 - fig, axes = plotutils.subplots(nrows=nf, sharex=True, figsize=(w,h), sharey=True) + # Start plotting + w, h = mpl.rcParams["figure.figsize"] + h *= 2.5 + fig, axes = plotutils.subplots(nrows=nf, sharex=True, figsize=(w, h), sharey=True) fig.suptitle(title) colors = sm.to_rgba(fb["__plotting_var"]) for flindex, (ax, fl) in enumerate(zip(axes, fls)): - for i,color in enumerate(colors): - ax.plot(x, grid[i,flindex,:].T, color=color) - + for i, color in enumerate(colors): + ax.plot(x, grid[i, flindex, :].T, color=color) - flmask = mark_mask[flindex,:] + flmask = mark_mask[flindex, :] ranges = split_ranges(x, flmask, filter_falses=True) for r in ranges: ax.axvspan(r[0], r[-1], color='#eeeeff') - ax.set_ylabel("$%s$"%basis.elementlabel(fl)) + ax.set_ylabel("$%s$" % basis.elementlabel(fl)) ax.set_xscale(scale_from_grid(obs_pdf_correlations)) - ax.set_ylim(-1,1) + ax.set_ylim(-1, 1) ax.set_xlim(x[0], x[-1]) ax.set_xlabel('$x$') - cbar = fig.colorbar( - sm, - ax=axes.ravel().tolist(), - label=info.xlabel, - aspect=100, - ) + cbar = fig.colorbar(sm, ax=axes.ravel().tolist(), label=info.xlabel, aspect=100) if categorical: cbar.set_ticks(np.linspace(0.5, num_categories - 0.5, num_categories)) cbar.ax.set_yticklabels(categorical_keys) - #TODO: Fix title for this - #fig.tight_layout() + # TODO: Fix title for this + # fig.tight_layout() yield fig + @figure def plot_obscorrs(corrpair_datasets, obs_obs_correlations, pdf): """NOTE: EXPERIMENTAL. Plot the correlation matrix between a pair of datasets.""" @@ -1075,10 +1059,7 @@ def _check_same_posdataset_name(dataspecs_posdataset): @figure @_check_same_posdataset_name def plot_dataspecs_positivity( - dataspecs_speclabel, - dataspecs_positivity_predictions, - dataspecs_posdataset, - pos_use_kin=False, + dataspecs_speclabel, dataspecs_positivity_predictions, dataspecs_posdataset, pos_use_kin=False ): """Like :py:meth:`plot_positivity` except plots positivity for each element of dataspecs, allowing positivity predictions to be generated with @@ -1087,10 +1068,7 @@ def plot_dataspecs_positivity( # we checked the positivity set matches between dataspecs so this is fine posset = dataspecs_posdataset[0] return plot_positivity( - dataspecs_speclabel, - dataspecs_positivity_predictions, - posset, - pos_use_kin, + dataspecs_speclabel, dataspecs_positivity_predictions, posset, pos_use_kin ) @@ -1283,25 +1261,21 @@ def plot_xq2( highlight_datasets = set() def next_options(): - #Get the colors + # Get the colors prop_settings = mpl.rcParams['axes.prop_cycle'] - #Apparently calling the object gives us an infinite cycler + # Apparently calling the object gives us an infinite cycler settings_cycler = prop_settings() - #So far, I don't understand how this is done with mpl "cycler" - #objects, or wether I like it. So far this is godd enough - for markeropts, settings in zip(plotutils.marker_iter_plot(), settings_cycler): - #Override last with first - options = { - 'linestyle': 'none', - **markeropts, - **settings, - } + # So far, I don't understand how this is done with mpl "cycler" + # objects, or wether I like it. So far this is godd enough + for markeropts, settings in zip(plotutils.marker_iter_plot(), settings_cycler): + # Override last with first + options = {'linestyle': 'none', **markeropts, **settings} yield options next_opts = next_options() key_options = {} - for (experiment, commondata, fitted, masked, group) in dataset_inputs_by_groups_xq2map: + for experiment, commondata, fitted, masked, group in dataset_inputs_by_groups_xq2map: info = get_info(commondata) if marker_by == 'process type': key = info.process_description @@ -1341,13 +1315,7 @@ def next_options(): else: # This is to get the label key coords = [], [] - ax.plot( - *coords, - label=key, - markeredgewidth=1, - markeredgecolor=None, - **key_options[key], - ) + ax.plot(*coords, label=key, markeredgewidth=1, markeredgecolor=None, **key_options[key]) # Iterate again so highlights are printed on top. for key in xh: