Piazza pulita 2: removed principal_components_variance_distribution_d…

…ataset as we are not using it

validphys2/src/validphys/closuretest/inconsistent_closuretest/multiclosure_inconsistent.py

@@ -126,35 +126,3 @@ def principal_components_bias_variance_dataset(
 principal_components_bias_variance_datasets = collect(
     "principal_components_bias_variance_dataset", ("data",)
 )
-
-
-def principal_components_variance_distribution_dataset(
-    internal_multiclosure_dataset_loader, principal_components_dataset
-):
-    """
-    TODO
-    """
-
-    closures_th, _, _, _ = internal_multiclosure_dataset_loader
-
-    reps = np.asarray([th.error_members for th in closures_th])
-
-    pc_basis, pc_reps, n_comp = principal_components_dataset
-
-    if n_comp <= 1:
-        return None, n_comp
-    # estimate (PC) pdf covariance matrix (from replicas), shape is (Npc, Npc)
-    covmat_pdf = np.cov(pc_reps)
-    sqrt_covmat_pdf = covmats.sqrt_covmat(covmat_pdf)
-
-    variances = []
-    for i in range(reps.shape[0]):
-        diffs = pc_basis @ (reps[i, :, :] - reps[i, :, :].mean(axis=1, keepdims=True))
-        variances.append([calc_chi2(sqrt_covmat_pdf, diffs)])
-
-    return np.asarray(variances), n_comp
-
-
-principal_components_variance_distribution_datasets = collect(
-    "principal_components_variance_distribution_dataset", ("data",)
-)

validphys2/src/validphys/closuretest/inconsistent_closuretest/multiclosure_inconsistent_output.py

@@ -9,7 +9,6 @@
 
 import pandas as pd
 import numpy as np
-from scipy import stats
 
 from reportengine.figure import figuregen
 from reportengine.table import table
@@ -21,73 +20,6 @@
 )
 
 
-@figuregen
-def plot_bias_distribution_datasets(principal_components_bias_variance_datasets, each_dataset):
-    """
-    TODO
-    """
-    for pc_bias_var_dataset, ds in zip(principal_components_bias_variance_datasets, each_dataset):
-        biases, variances, n_comp = pc_bias_var_dataset
-
-        try:
-            sqrt_rbv = np.sqrt(np.mean(biases) / np.mean(variances))
-            vals = np.linspace(0, 3 * n_comp, 100)
-            chi2_pdf = stats.chi2.pdf(vals, df=n_comp)
-            chi2_cdf = lambda x: stats.chi2.cdf(x, df=n_comp)
-            pvalue_ks = stats.kstest(biases, chi2_cdf).pvalue
-            fig, ax = plotutils.subplots()
-            ax.hist(biases, density=True, bins='auto', label=f"Bias {str(ds)}, p={pvalue_ks:.3f}")
-            ax.plot(vals, chi2_pdf, label=f"chi2, dof={n_comp}")
-            ax.plot([], [], 'ro', label=f"sqrt(Rbv) = {sqrt_rbv:.2f}")
-
-            ax.legend()
-
-            yield fig
-
-        except:
-            fig, ax = plotutils.subplots()
-            ax.plot([], [], label=f"Dataset: {str(ds)}")
-            ax.legend()
-            yield fig
-
-
-@figuregen
-def plot_variance_distribution_datasets(
-    principal_components_variance_distribution_datasets, each_dataset
-):
-    """
-    TODO
-    """
-
-    for pc_var_dataset, ds in zip(
-        principal_components_variance_distribution_datasets, each_dataset
-    ):
-        variances, n_comp = pc_var_dataset
-        try:
-            vals = np.linspace(0, 3 * n_comp, 100)
-            chi2_pdf = stats.chi2.pdf(vals, df=n_comp)
-            chi2_cdf = lambda x: stats.chi2.cdf(x, df=n_comp)
-
-            for i, var_fit in enumerate(variances):
-                pvalue_ks = stats.kstest(var_fit[0], chi2_cdf).pvalue
-
-                fig, ax = plotutils.subplots()
-                ax.hist(
-                    var_fit[0],
-                    density=True,
-                    bins='auto',
-                    label=f"Variance {str(ds)}, p={pvalue_ks:.3f}",
-                )
-                ax.plot(vals, chi2_pdf, label=f"chi2, dof={n_comp}")
-                ax.set_title(f"Fit {i}")
-                ax.legend()
-
-                yield fig
-        except:
-            fig, ax = plotutils.subplots()
-            yield fig
-
-
 @table
 def table_bias_variance_datasets(principal_components_bias_variance_datasets, each_dataset):
     """