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feat(distributions): add logweibull, ricegamma, and logricegamma

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Add three new continuous random variables for log-domain and
linear-domain clutter modeling with compound Gamma-Rice structure.

Fix numerical stability of k_dist._logpdf and logk._log_kve via a
three-regime log(kve) asymptotic (direct / large-z Hankel / large-order
Gamma); replace quad-based k_dist._cdf with Gauss-Laguerre quadrature.

Fix fitter: use np.asarray instead of np.abs in fit(), pass fit_params
to goodness_of_fit so the observed-data statistic reuses fitted params.
Skip non-finite quantiles in QQ plots. Add plot_qq_plots_sns(); rename
histogram_with_fits_seaborn() to histogram_with_fits_sns(). Add unit
tests for logweibull and logricegamma.
This commit is contained in:
neutonsevero
2026-05-07 11:55:33 -03:00
parent c59bc55fe5
commit 346d85c4f7
3 changed files with 1007 additions and 57 deletions
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118
etc/fitting/fitter.py
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@@ -8,6 +8,12 @@ from scipy.stats import rv_continuous, goodness_of_fit
from plotly.subplots import make_subplots
def set_plot_style():
sns.set_style("whitegrid")
sns.set_context("paper", font_scale=1.25)
@dataclass
class DistributionSummary:
"""
@@ -181,7 +187,7 @@ class Fitter:
Mapping of distribution name → summary (test_result is None
until validate() is called).
"""
data_flat = np.abs(data).flatten()
data_flat = np.asarray(data).flatten()
self._last_data_flat = data_flat
for dist in self.dist_list:
@@ -213,8 +219,16 @@ class Fitter:
data_flat = self._last_data_flat
for dist in self.dist_list:
_summary = self._dist[dist.name]
# Build fit_params dict so goodness_of_fit reuses the already-fitted
# parameters for the observed-data statistic instead of re-fitting.
shape_names = [s.strip() for s in dist.shapes.split(',')] if dist.shapes else []
all_param_names = shape_names + ['loc', 'scale']
fit_params_dict = dict(zip(all_param_names, _summary.fit_result_params))
test_result = goodness_of_fit(
dist, data_flat, statistic=_summary.statistic_method, **kwargs
dist, data_flat,
statistic=_summary.statistic_method,
fit_params=fit_params_dict,
**kwargs,
)
_summary.test_result = test_result
self._dist[dist.name] = _summary
@@ -276,13 +290,21 @@ class Fitter:
plotting_positions, *summary.fit_result_params
)
# Drop NaN/inf quantiles that arise when ppf fails to converge
valid = np.isfinite(theoretical_quantiles)
if not valid.any():
print(f"Distribution '{dist_name}': all theoretical quantiles are non-finite. Skipping QQ plot.")
continue
theoretical_quantiles = theoretical_quantiles[valid]
sorted_data_plot = sorted_data[valid]
# Create QQ plot in each subplot
row = (list(self._dist.keys()).index(dist_name) // num_cols) + 1
col = (list(self._dist.keys()).index(dist_name) % num_cols) + 1
fig.add_trace(
go.Scatter(
x=theoretical_quantiles,
y=sorted_data,
y=sorted_data_plot,
mode="markers",
name=dist_name,
),
@@ -290,8 +312,8 @@ class Fitter:
col=col,
)
# Add a reference line y=x
min_val = min(theoretical_quantiles.min(), sorted_data.min())
max_val = max(theoretical_quantiles.max(), sorted_data.max())
min_val = min(theoretical_quantiles.min(), sorted_data_plot.min())
max_val = max(theoretical_quantiles.max(), sorted_data_plot.max())
fig.add_trace(
go.Scatter(
x=[min_val, max_val],
@@ -322,6 +344,88 @@ class Fitter:
showlegend=False,
autosize=True,
)
return fig
def plot_qq_plots_sns(self, method: str = "hazen"):
"""
Generate QQ plots for each fitted distribution using seaborn/matplotlib.
Parameters
----------
method : str
Plotting positions formula. Either 'hazen' (default) or 'filliben'.
"""
if not hasattr(self, "_last_data_flat"):
raise RuntimeError("No data available. Call fit() first.")
if method not in ("hazen", "filliben"):
raise ValueError(f"method must be 'hazen' or 'filliben', got '{method}'.")
set_plot_style()
dist_names = list(self._dist.keys())
num_dists = len(dist_names)
num_cols = 2
num_rows = (num_dists + 1) // 2
dot_color = sns.color_palette()[0]
fig, axes = plt.subplots(num_rows, num_cols, figsize=(6 * num_cols, 5 * num_rows))
axes = np.array(axes).flatten()
sorted_data = np.sort(self._last_data_flat)
n = len(sorted_data)
i = np.arange(1, n + 1)
if method == "hazen":
plotting_positions = (i - 0.5) / n
else:
plotting_positions = (i - 0.3175) / (n + 0.365)
plotting_positions[0] = 1 - 0.5 ** (1 / n)
plotting_positions[-1] = 0.5 ** (1 / n)
for idx, (dist_name, summary) in enumerate(self):
ax = axes[idx]
if summary.test_result is None:
ax.set_title(dist_name)
ax.text(0.5, 0.5, "Not validated", ha="center", va="center",
transform=ax.transAxes)
continue
theoretical_quantiles = summary.distribution_object.ppf(
plotting_positions, *summary.fit_result_params
)
valid = np.isfinite(theoretical_quantiles)
if not valid.any():
ax.set_title(dist_name)
ax.text(0.5, 0.5, "All quantiles non-finite", ha="center", va="center",
transform=ax.transAxes)
continue
tq = theoretical_quantiles[valid]
sd = sorted_data[valid]
ax.scatter(tq, sd, color=dot_color, s=8, alpha=0.6, linewidths=0)
ref_min = min(tq.min(), sd.min())
ref_max = max(tq.max(), sd.max())
ax.plot([ref_min, ref_max], [ref_min, ref_max], "k--", linewidth=1)
pvalue = summary.pvalue
stat_color = "green" if pvalue > 0.05 else "red"
ax.text(0.95, 0.05,
f"{summary.statistic_method}={summary.gof_statistic:.4f}",
transform=ax.transAxes, ha="right", va="bottom",
fontsize=9, color=stat_color)
ax.set_title(dist_name)
ax.set_xlabel("Theoretical Quantiles")
ax.set_ylabel("Empirical Quantiles")
# Hide unused axes
for idx in range(num_dists, len(axes)):
axes[idx].set_visible(False)
fig.suptitle(f"QQ Plots of Fitted Distributions ({method})", y=1.01)
plt.tight_layout()
return fig
def histogram_with_fits(self):
@@ -389,7 +493,7 @@ class Fitter:
return fig
def histogram_with_fits_seaborn(self):
def histogram_with_fits_sns(self):
"""
Generate a histogram of the data with overlaid PDFs of each fitted distribution using seaborn.
Requires that fit() has been called to populate parameters.
@@ -422,7 +526,7 @@ class Fitter:
ax.plot(
x,
pdf_values,
label=f"{summary.distribution_name} --- p={summary.pvalue:.4f}",
label=f"{summary.distribution_name}",
)
# put title in top left, make it smaller, change it font to sans and put in light gray
ax.set_title(