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raise an exception when use_pdferr is used together with plot_fancy
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@scarlehoff
scarlehoff committed Nov 14, 2023
1 parent 90be1cd commit 8357832
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Showing 2 changed files with 51 additions and 78 deletions.
5 changes: 5 additions & 0 deletions validphys2/src/validphys/checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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']
Expand Down
124 changes: 46 additions & 78 deletions validphys2/src/validphys/dataplots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -623,28 +624,31 @@ 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 = []

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
Expand Down Expand Up @@ -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:
Expand All @@ -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
Expand Down Expand Up @@ -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."""
Expand Down Expand Up @@ -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
Expand All @@ -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
)


Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand Down

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