refactor

preliz/__init__.py

@@ -3,7 +3,6 @@
 
 Tools to help you pick a prior
 """
-import logging
 from os import path as os_path
 
 from matplotlib import rcParams
@@ -18,13 +17,6 @@
 
 __version__ = "0.11.0"
 
-_log = logging.getLogger("preliz")
-
-if not logging.root.handlers:
-    _log.setLevel(logging.INFO)
-    if len(_log.handlers) == 0:
-        handler = logging.StreamHandler()
-        _log.addHandler(handler)
 
 # Allow legend outside plot in maxent to be included when saving a figure
 # We may want to make this more explicit by having preliz.rcParams
@@ -37,4 +29,4 @@
 style.core.reload_library()
 
 # clean namespace
-del logging, os_path, rcParams, _preliz_style_path, _log
+del os_path, rcParams, _preliz_style_path

preliz/internal/distribution_helper.py

@@ -12,7 +12,7 @@ def from_precision(precision):
 
 
 def to_precision(sigma):
-    precision = 1 / sigma**2
+    precision = 1 / (eps + sigma**2)
     return precision
 
 
@@ -148,38 +148,15 @@ def num_kurtosis(dist):
 }
 
 
-def get_distributions(dist_names=None, exclude=None):
+def get_distributions(dist_names=None):
 
     if dist_names is None:
         all_distributions = modules["preliz.distributions"].__all__
     else:
         all_distributions = dist_names
 
-    if exclude is None:
-        exclude = []
-    if exclude == "auto":
-        exclude = [
-            "Beta",
-            "BetaScaled",
-            "Triangular",
-            "TruncatedNormal",
-            "Uniform",
-            "VonMises",
-            "Categorical",
-            "DiscreteUniform",
-            "HyperGeometric",
-            "zeroInflatedBinomial",
-            "ZeroInflatedNegativeBinomial",
-            "ZeroInflatedPoisson",
-            "MvNormal",
-            "Mixture",
-        ]
-
     distributions = []
     for a_dist in all_distributions:
         dist = getattr(modules["preliz.distributions"], a_dist)()
-        if dist.__class__.__name__ not in exclude:
-            distributions.append(dist)
-    if exclude:
-        return exclude, distributions
+        distributions.append(dist)
     return distributions

preliz/internal/plot_helper.py

@@ -5,16 +5,14 @@
 try:
     from IPython import get_ipython
     from ipywidgets import FloatSlider, IntSlider, FloatText, IntText, Checkbox, ToggleButton
-    from pymc import sample_prior_predictive
 except ImportError:
     pass
 
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import _pylab_helpers, get_backend
 from matplotlib.ticker import MaxNLocator
-from .logging import disable_pymc_sampling_logs
-from .narviz import hdi, kde
+from preliz.internal.narviz import hdi, kde
 
 
 def plot_pointinterval(distribution, interval="hdi", levels=None, rotated=False, ax=None):
@@ -425,63 +423,6 @@ def looper(*args, **kwargs):
     return looper
 
 
-def bambi_plot_decorator(func, iterations, kind_plot, references, plot_func):
-    def looper(*args, **kwargs):
-        kwargs.pop("__resample__")
-        x_min = kwargs.pop("__x_min__")
-        x_max = kwargs.pop("__x_max__")
-        if not kwargs.pop("__set_xlim__"):
-            x_min = None
-            x_max = None
-            auto = True
-        else:
-            auto = False
-
-        model = func(*args, **kwargs)
-        model.build()
-        with disable_pymc_sampling_logs():
-            idata = model.prior_predictive(iterations)
-        results = (
-            idata["prior_predictive"].stack(sample=("chain", "draw"))[model.response_name].values.T
-        )
-
-        _, ax = plt.subplots()
-        ax.set_xlim(x_min, x_max, auto=auto)
-        if plot_func is None:
-            plot_repr(results, kind_plot, references, iterations, ax)
-        else:
-            plot_func(results, ax)
-
-    return looper
-
-
-def pymc_plot_decorator(func, iterations, kind_plot, references, plot_func):
-    def looper(*args, **kwargs):
-        kwargs.pop("__resample__")
-        x_min = kwargs.pop("__x_min__")
-        x_max = kwargs.pop("__x_max__")
-        if not kwargs.pop("__set_xlim__"):
-            x_min = None
-            x_max = None
-            auto = True
-        else:
-            auto = False
-        with func(*args, **kwargs) as model:
-            obs_name = model.observed_RVs[0].name
-            with disable_pymc_sampling_logs():
-                idata = sample_prior_predictive(samples=iterations)
-            results = idata["prior_predictive"].stack(sample=("chain", "draw"))[obs_name].values.T
-
-        _, ax = plt.subplots()
-        ax.set_xlim(x_min, x_max, auto=auto)
-        if plot_func is None:
-            plot_repr(results, kind_plot, references, iterations, ax)
-        else:
-            plot_func(results, ax)
-
-    return looper
-
-
 def plot_repr(results, kind_plot, references, iterations, ax):
     alpha = max(0.01, 1 - iterations * 0.009)