Add functions to get 3D krypton maps

invisible_cities/core/fit_functions.py

@@ -131,6 +131,36 @@ def power(x, const, pow_):
     return const * np.power(x, pow_)
 
 
+def sigmoid(x          : np.array,
+            scale      : float,
+            inflection : float,
+            slope      : float,
+            offset     : float)->np.array:
+    '''
+    Sigmoid function, it computes the sigmoid of the input array x using the specified
+    parameters for scaling, inflection point, slope, and offset.
+    Parameters
+    ----------
+    x : np.array
+        The input array.
+    scale : float
+        The scaling factor determining the maximum value of the sigmoid function.
+    inflection : float
+        The x-value of the sigmoid's inflection point (where the function value is half of the scale).
+    slope : float
+        The slope parameter that controls the steepness of the sigmoid curve.
+    offset : float
+        The vertical offset added to the sigmoid function.
+    Returns
+    -------
+    np.array
+        Array of computed sigmoid values for x array.
+    '''
+    sigmoid = scale / (1 + np.exp(-slope * (x - inflection))) + offset
+    return sigmoid
+
+
+
 # ###########################################################
 # Tools
 def fit(func, x, y, seed=(), fit_range=None, **kwargs):

invisible_cities/core/fit_functions_test.py

@@ -252,6 +252,34 @@ def test_power_at_zero(a, b):
                     0.)
 
 
+def test_sigmoid_inflection_point(): #en x = inflection, sigm(x) = scale/2 + offset
+    x = np.array(10)
+    scale = 2
+    inflection = 10
+    slope = 2
+    offset = 1.5
+
+    y = fitf.sigmoid(x, scale, inflection, slope, offset)
+
+    expected_sigm = 0.5*scale + offset
+
+    assert np.isclose(y, expected_sigm)
+
+
+def test_sigmoid_monotonically_increasing(): #todos los incrementos en y > 0
+    x = np.linspace(-10, 10, 100)
+    y = fitf.sigmoid(x, scale = 1, inflection = 0, slope = 1, offset = 0)
+
+    assert np.all(np.diff(y) >= 0)
+
+
+def test_sigmoid_shape(): ##input shape = output shape
+    x = np.linspace(-10, 10, 100)
+    y = fitf.sigmoid(x, scale = 1, inflection = 0, slope = 1, offset = 0)
+
+    assert y.shape == x.shape
+
+
 @mark.parametrize("        fn                 pars        ".split(),
                   ((fitf.gauss         , (3.0,  2.0, 0.50)),
                    (fitf.expo          , (6.0,  1.5)),