v0.7.2: Solve issue test_statistic equal to zero

phitter/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.7.1"
+__version__ = "0.7.2"
 
 from .main import PHITTER
 from phitter import continuous

phitter/continuous/phitter_continuous.py

@@ -66,7 +66,7 @@ def test(self, test_function, label: str, distribution):
         validation_test = False
         try:
             test = test_function(distribution, self.continuous_measures)
-            if numpy.isnan(test["test_statistic"]) == False and numpy.isinf(test["test_statistic"]) == False and test["test_statistic"] > 0:
+            if numpy.isnan(test["test_statistic"]) == False and numpy.isinf(test["test_statistic"]) == False and test["test_statistic"] >= 0:
                 self.distribution_results[label] = {
                     "test_statistic": test["test_statistic"],
                     "critical_value": test["critical_value"],

phitter/discrete/phitter_discrete.py

@@ -63,7 +63,7 @@ def test(self, test_function, label: str, distribution):
         validation_test = False
         try:
             test = test_function(distribution, self.discrete_measures)
-            if numpy.isnan(test["test_statistic"]) == False and numpy.isinf(test["test_statistic"]) == False and test["test_statistic"] > 0:
+            if numpy.isnan(test["test_statistic"]) == False and numpy.isinf(test["test_statistic"]) == False and test["test_statistic"] >= 0:
                 self.distribution_results[label] = {
                     "test_statistic": test["test_statistic"],
                     "critical_value": test["critical_value"],

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "phitter"
-version = "0.7.1"
+version = "0.7.2"
 description = "Find the best probability distribution for your dataset"
 authors = [{name = "Sebastián José Herrera Monterrosa", email = "[email protected]"}]
 readme = "README.md"

tests/phitter_local/statistical_tests/singular/test_statistical_tests_discrete.ipynb

@@ -2,19 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "import sys\n",
     "\n",
-    "sys.path.append(\"../../\")\n",
+    "sys.path.append(\"../../../../\")\n",
     "import phitter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,17 +27,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = \"../../datasets_test/discrete/sample_binomial.txt\"\n",
+    "path = \"../../../../datasets_test/discrete/sample_binomial.txt\"\n",
     "data = get_data(path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -67,7 +67,7 @@
        " 'rejected': False}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +90,7 @@
        " 'rejected': False}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,7 +116,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,