Add params_dict attribute (#521)

* add params_dict attribute

* change returns

preliz/distributions/distributions.py

@@ -56,6 +56,13 @@ def __repr__(self):
         else:
             return name
 
+    @property
+    def params_dict(self):
+        if self.is_frozen:
+            return dict(zip(self.param_names, self.params))
+        else:
+            return None
+
     def summary(self, mass=0.94, interval="hdi", fmt=".2f"):
         """
         Namedtuple with the mean, median, standard deviation, and lower and upper bounds

preliz/tests/test_maxent.py

@@ -167,7 +167,7 @@
     ],
 )
 def test_maxent(dist, lower, upper, mass, support, result):
-    _, opt = maxent(dist, lower, upper, mass)
+    maxent(dist, lower, upper, mass)
 
     assert_almost_equal(dist.support, support, 0)
 
@@ -176,8 +176,8 @@ def test_maxent(dist, lower, upper, mass, support, result):
         "HyperGeometric",
         "ZeroInflatedBinomial",
     ]:  # optimization fails to converge, but results are reasonable
-        assert opt.success
-    assert_allclose(opt.x, result, atol=0.001)
+        assert dist.opt.success
+    assert_allclose(dist.opt.x, result, atol=0.001)
 
 
 def test_maxent_plot():

preliz/tests/test_quartile.py

@@ -109,6 +109,6 @@
     ],
 )
 def test_quartile(distribution, q1, q2, q3, result):
-    _, opt = quartile(distribution, q1, q2, q3)
+    quartile(distribution, q1, q2, q3)
 
-    assert_allclose(opt.x, result, atol=0.01)
+    assert_allclose(distribution.opt.x, result, atol=0.01)

preliz/unidimensional/maxent.py

@@ -40,7 +40,8 @@ def maxent(
     Returns
     -------
 
-    axes: matplotlib axes
+    dict: dict with the parameters of the distribution
+    axes: matplotlib axes (only if `plot=True`)
 
     See Also
     --------
@@ -110,6 +111,7 @@ def maxent(
         )
 
     opt = optimize_max_ent(distribution, lower, upper, mass, none_idx, fixed)
+    distribution.opt = opt
 
     r_error, computed_mass = relative_error(distribution, lower, upper, mass)
 
@@ -127,7 +129,9 @@ def maxent(
         else:
             cid = -1
         ax.plot([lower, upper], [0, 0], "o", color=ax.get_lines()[cid].get_c(), alpha=0.5)
-    return ax, opt
+        return distribution, ax
+
+    return distribution
 
 
 def end_points_ints(lower, upper):

preliz/unidimensional/quartile.py

@@ -41,7 +41,8 @@ def quartile(
     Returns
     -------
 
-    axes: matplotlib axes
+    dict: dict with the parameters of the distribution
+    axes: matplotlib axes (only if `plot=True`)
 
     See Also
     --------
@@ -98,6 +99,8 @@ def quartile(
 
     opt = optimize_quartile(distribution, quartiles, none_idx, fixed)
 
+    distribution.opt = opt
+
     r_error, _ = relative_error(distribution, q1, q3, 0.5)
 
     if r_error > 0.01:
@@ -113,4 +116,6 @@ def quartile(
         else:
             cid = -1
         ax.plot(quartiles, [0, 0, 0], "o", color=ax.get_lines()[cid].get_c(), alpha=0.5)
-    return ax, opt
+        return distribution, ax
+
+    return distribution