diff --git a/invisible_cities/reco/icaro_components.py b/invisible_cities/reco/krmap_functions.py
similarity index 100%
rename from invisible_cities/reco/icaro_components.py
rename to invisible_cities/reco/krmap_functions.py
diff --git a/invisible_cities/reco/icaro_components_test.py b/invisible_cities/reco/krmap_functions_test.py
similarity index 81%
rename from invisible_cities/reco/icaro_components_test.py
rename to invisible_cities/reco/krmap_functions_test.py
index f2bbfb933..fcb5cbd34 100644
--- a/invisible_cities/reco/icaro_components_test.py
+++ b/invisible_cities/reco/krmap_functions_test.py
@@ -8,7 +8,7 @@
 from hypothesis             import given, settings
 from hypothesis.strategies  import floats, integers
 
-from .. reco                import icaro_components as icarcomp
+from .. reco                import krmap_functions as krf
 from ..types.symbols        import KrFitFunction
 from .. evm.ic_containers   import FitFunction
 from .. core.fit_functions  import expo
@@ -21,7 +21,7 @@
 def test_lin_function_output_values(x_min, x_max, a, b):
     x = np.array([x_min, x_max])
     y = a + b * x
-    a_test, b_test = icarcomp.lin_seed(x, y)
+    a_test, b_test = krf.lin_seed(x, y)
 
     assert np.isclose(a_test, a)
     assert np.isclose(b_test, b)
@@ -34,7 +34,7 @@ def test_lin_function_output_values(x_min, x_max, a, b):
 def test_expo_seed_output_values(zmin, zmax, elt, e0):
     x = np.array([zmin, zmax])
     y = e0 * np.exp(-x / elt)
-    e0_test, elt_test = icarcomp.expo_seed(x, y)
+    e0_test, elt_test = krf.expo_seed(x, y)
 
     assert np.isclose( e0_test,  e0, rtol=0.1)
     assert np.isclose(elt_test, elt, rtol=0.1)
@@ -48,9 +48,9 @@ def sample_df():
 
 
 def test_select_fit_variables(sample_df):
-    x_linear,  y_linear  = icarcomp.select_fit_variables(KrFitFunction.linear,  sample_df)
-    x_expo,    y_expo    = icarcomp.select_fit_variables(KrFitFunction.expo,    sample_df)
-    x_log_lin, y_log_lin = icarcomp.select_fit_variables(KrFitFunction.log_lin, sample_df)
+    x_linear,  y_linear  = krf.select_fit_variables(KrFitFunction.linear,  sample_df)
+    x_expo,    y_expo    = krf.select_fit_variables(KrFitFunction.expo,    sample_df)
+    x_log_lin, y_log_lin = krf.select_fit_variables(KrFitFunction.log_lin, sample_df)
 
     # First return the same for the 3 cases
     assert (x_expo    == x_linear).all()
@@ -71,9 +71,9 @@ def test_get_function_and_seed_lt_with_data(x_min, x_max, steps, e0, lt):
     x = np.linspace(x_min, x_max, steps)
     y = expo(x, e0, lt)
 
-    fit_func_lin,         seed_func_lin = icarcomp.get_function_and_seed_lt(KrFitFunction.linear)
-    fit_func_expo,       seed_func_expo = icarcomp.get_function_and_seed_lt(KrFitFunction.expo)
-    fit_func_log_lin, seed_func_log_lin = icarcomp.get_function_and_seed_lt(KrFitFunction.log_lin)
+    fit_func_lin,         seed_func_lin = krf.get_function_and_seed_lt(KrFitFunction.linear)
+    fit_func_expo,       seed_func_expo = krf.get_function_and_seed_lt(KrFitFunction.expo)
+    fit_func_log_lin, seed_func_log_lin = krf.get_function_and_seed_lt(KrFitFunction.log_lin)
 
     popt_lin, _     = so.curve_fit(fit_func_lin,     x, y, p0=seed_func_lin    (x, y))
     popt_expo, _    = so.curve_fit(fit_func_expo,    x, y, p0=seed_func_expo   (x, y))
@@ -93,7 +93,7 @@ def test_transform_parameters(a, b):
     fit_output = FitFunction(values=[a, b], errors=errors, cov=np.array([[0.04, 0.02], [0.02, 0.04]]),
                              fn=None, chi2=None, pvalue=None, infodict=None, mesg=None, ier=None)
 
-    transformed_par, transformed_err, transformed_cov = icarcomp.transform_parameters(fit_output)
+    transformed_par, transformed_err, transformed_cov = krf.transform_parameters(fit_output)
 
     E0_expected   = np.exp(-a)
     s_E0_expected = np.abs(E0_expected * errors[0])