def __init__(self, rename=True, invert=False, **params):
# todo from_spec constructor method for this kind of logic
cmap = params.pop("cmap", None)
cmap = pplt.Colormap(cmap) if cmap else cmap
params["cmap"] = cmap
super().__init__(**params)
self._excluded_from_opts += [
"norm",
"sclf",
] # perhaps use leading underscore to exclude?
if self.cmap is None and self.norm is None and self.field is not None:
cmap, norm = CMAP_NORM_DEFAULTS[self.field]
elif self.field is None:
cmap = pplt.Colormap("viridis")
norm = pplt.Norm("linear", 0.0, 1.0)
self.norm = norm
self._cmap = cmap # unreversed cmap
if rename:
cmap.name = self.field + "_default"
if invert:
cmap = cmap.reversed()
self.cmap = cmap
from concurrent import futures
import proplot as pplt
current_dir = Path().cwd()
fit_result = load_fitresult(current_dir / 'output' /
'SecB_tetramer_dimer_batch')
tmp_dir = Path(__file__).parent / 'temp'
tmp_dir.mkdir(exist_ok=True)
if __name__ == '__main__':
report = FitReport(fit_result, temp_dir=tmp_dir)
report.add_standard_figure('peptide_coverage_figure')
report.add_standard_figure('residue_time_scatter_figure')
report.add_standard_figure('residue_scatter_figure')
report.add_standard_figure('dG_scatter_figure', ncols=1, aspect=3)
report.add_standard_figure('ddG_scatter_figure', ncols=1, reference=0)
report.add_standard_figure('linear_bars',
cmap='viridis',
norm=pplt.Norm('linear', 15e3,
35e3)) #todo name from kwargs
report.add_standard_figure('rainbowclouds')
report.add_peptide_uptake_curves()
executor = futures.ProcessPoolExecutor(max_workers=10)
report.generate_figures(executor=executor)
report.generate_latex()
report.generate_pdf(current_dir / 'output' / 'fit_report')
data2 = (state.rand(20, 20) - 0.515).cumsum(axis=0).cumsum(axis=1)
# Figure
fig, axs = pplt.subplots(nrows=2, ncols=2, refwidth=2.2, order='F')
axs.format(suptitle='Diverging normalizer demo')
cmap = pplt.Colormap('DryWet', cut=0.1)
# Diverging norms
i = 0
for data, mode, fair in zip(
(data1, data2),
('positive', 'negative'),
('fair', 'unfair'),
):
for fair in ('fair', 'unfair'):
norm = pplt.Norm('diverging', fair=(fair == 'fair'))
ax = axs[i]
m = ax.contourf(data, cmap=cmap, norm=norm)
ax.colorbar(m, loc='b')
ax.format(title=f'{mode.title()}-skewed + {fair} scaling')
i += 1
# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_discrete:
#
# Discrete levels
# ---------------
#
# By default, proplot uses `~proplot.colors.DiscreteNorm` to "discretize"
# the possible colormap colors for contour and pseudocolor `~proplot.axes.PlotAxes`
# commands (e.g., `~proplot.axes.PlotAxes.contourf`, `~proplot.axes.PlotAxes.pcolor`).