Quartile int

preliz/internal/distribution_helper.py

@@ -1,3 +1,4 @@
+import re
 import numpy as np
 from scipy.special import gamma
 
@@ -92,6 +93,22 @@ def valid_distribution(self):
     raise ValueError(f"{self.__class__.__name__} is not supported")
 
 
+def process_extra(input_string):
+    pattern = r"(\w+)\((.*?)\)"
+    matches = re.findall(pattern, input_string)
+    result_dict = {}
+    for match in matches:
+        name = match[0]
+        args = match[1].split(",")
+        arg_dict = {}
+        for arg in args:
+            key, value = arg.split("=")
+            arg_dict[key.strip()] = float(value)
+        result_dict[name] = arg_dict
+
+    return result_dict
+
+
 init_vals = {
     "AsymmetricLaplace": {"kappa": 1.0, "mu": 0.0, "b": 1.0},
     "Beta": {"alpha": 2, "beta": 2},

preliz/internal/optimization.py

@@ -249,6 +249,36 @@ def fit_to_sample(selected_distributions, sample, x_min, x_max):
     return fitted
 
 
+def fit_to_quartile(dist_names, q1, q2, q3, extra_pros):
+    error = np.inf
+
+    for distribution in get_distributions(dist_names):
+        if distribution.__class__.__name__ in extra_pros:
+            distribution._parametrization(**extra_pros[distribution.__class__.__name__])
+            if distribution.__class__.__name__ == "BetaScaled":
+                update_bounds_beta_scaled(
+                    distribution,
+                    extra_pros[distribution.__class__.__name__]["lower"],
+                    extra_pros[distribution.__class__.__name__]["upper"],
+                )
+        if distribution._check_endpoints(q1, q3, raise_error=False):
+
+            none_idx, fixed = get_fixed_params(distribution)
+
+            distribution._fit_moments(
+                mean=q2, sigma=(q3 - q1) / 1.35
+            )  # pylint:disable=protected-access
+
+            optimize_quartile(distribution, (q1, q2, q3), none_idx, fixed)
+
+            r_error, _ = relative_error(distribution, q1, q3, 0.5)
+            if r_error < error:
+                fitted_dist = distribution
+                error = r_error
+
+    return fitted_dist
+
+
 def update_bounds_beta_scaled(dist, x_min, x_max):
     dist.lower = x_min
     dist.upper = x_max

preliz/internal/plot_helper.py

@@ -498,3 +498,20 @@ def check_inside_notebook(need_widget=False):
         tb_as_str = traceback.format_exc()
         # Print only the last line of the traceback, which contains the error message
         print(tb_as_str.strip().rsplit("\n", maxsplit=1)[-1], file=sys.stdout)
+
+
+def representations(fitted_dist, kind_plot, ax):
+    if kind_plot == "pdf":
+        fitted_dist.plot_pdf(pointinterval=True, legend="title", ax=ax)
+        ax.set_yticks([])
+
+        for bound in fitted_dist.support:
+            if np.isfinite(bound):
+                ax.plot(bound, 0, "ko")
+
+    elif kind_plot == "cdf":
+        fitted_dist.plot_cdf(pointinterval=True, legend="title", ax=ax)
+
+    elif kind_plot == "ppf":
+        fitted_dist.plot_ppf(pointinterval=True, legend="title", ax=ax)
+        ax.set_xlim(-0.01, 1)

preliz/tests/quartile_int.ipynb

@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "81849101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget\n",
+    "import pytest\n",
+    "import ipytest\n",
+    "ipytest.autoconfig()\n",
+    "\n",
+    "from preliz import quartile_int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1630c205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%ipytest\n",
+    "\n",
+    "@pytest.mark.parametrize(\"q1, q2, q3, dist_names, figsize\", [\n",
+    "    (1, 2, 3, None, None),  # Test default behavior\n",
+    "    (-1, 2, 3, [\"Poisson\", \"Normal\"], None),  # Test custom list of distributions\n",
+    "    (-1, 2, 3, None, (10, 8)),  # Test custom figsize\n",
+    "])\n",
+    "def test_quartile_int(q1, q2, q3, dist_names, figsize):\n",
+    "    quartile_int(q1, q2, q3, dist_names, figsize)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5b344a7d0839c309585d2ae27435157813d3b4ade1fa431f12bd272ea9135317"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

preliz/tests/test_distributions_helper.py

@@ -0,0 +1,9 @@
+from preliz.internal.distribution_helper import process_extra
+
+
+def test_process_extra():
+    ref0 = {"TruncatedNormal": {"lower": -3, "upper": 3}}
+    ref1 = {"StudentT": {"nu": 3.4}, "Normal": {"mu": 3.0}}
+
+    assert process_extra("TruncatedNormal(lower=-3, upper=3)") == ref0
+    assert process_extra("StudentT(nu=3.4),Normal(mu=3)") == ref1

preliz/unidimensional/__init__.py

@@ -1,7 +1,8 @@
 from .maxent import maxent
 from .mle import mle
 from .quartile import quartile
+from .quartile_int import quartile_int
 from .roulette import roulette
 
 
-__all__ = ["maxent", "mle", "roulette", "quartile"]
+__all__ = ["maxent", "mle", "roulette", "quartile", "quartile_int"]