Cosmetics

invisible_cities/reco/icaro_components.py

@@ -1,9 +1,9 @@
-import numpy        as np
-import pandas       as pd
+import numpy  as np
+import pandas as pd
 
-from ..types.symbols       import KrFitFunction
-from .. evm.ic_containers  import FitFunction
-from .. core.fit_functions import polynom, expo
+from .. types.symbols       import KrFitFunction
+from .. evm.ic_containers   import FitFunction
+from .. core.fit_functions  import polynom, expo
 
 
 def lin_seed(x : np.array, y : np.array):
@@ -80,8 +80,8 @@ def select_fit_variables(fittype : KrFitFunction, dst : pd.DataFrame):
     y_data : pd.Series
         The dependent variable data prepared for fitting.
     '''
-    if   fittype is KrFitFunction.linear : return dst.DT, dst.S2e
-    elif fittype is KrFitFunction.expo   : return dst.DT, dst.S2e
+    if   fittype is KrFitFunction.linear : return dst.DT,         dst.S2e
+    elif fittype is KrFitFunction.expo   : return dst.DT,         dst.S2e
     elif fittype is KrFitFunction.log_lin: return dst.DT, -np.log(dst.S2e)
 
 

invisible_cities/reco/icaro_components_test.py

@@ -14,10 +14,10 @@
 from .. core.fit_functions  import expo
 
 
-@given(floats(min_value= 0, max_value= 10),
-       floats(min_value=10, max_value= 20),
-       floats(min_value= 1, max_value=100),
-       floats(min_value= 0, max_value= 10))
+@given(floats(min_value = 0,  max_value = 10),
+       floats(min_value = 10, max_value = 20),
+       floats(min_value = 1,  max_value = 100),
+       floats(min_value = 0,  max_value = 10))
 def test_lin_function_output_values(x_min, x_max, a, b):
     x = np.array([x_min, x_max])
     y = a + b * x
@@ -32,12 +32,12 @@ def test_lin_function_output_values(x_min, x_max, a, b):
        floats(min_value = 1e4,  max_value = 1e5),
        floats(min_value = 1e4,  max_value = 1e5))
 def test_expo_seed_output_values(zmin, zmax, elt, e0):
-    x = np.array( [ zmin, zmax ] )
-    y = e0 * np.exp( - x / elt )
+    x = np.array([zmin, zmax])
+    y = e0 * np.exp(-x / elt)
     e0_test, elt_test = icarcomp.expo_seed(x, y)
 
-    assert np.isclose(e0_test,    e0, rtol=0.1)
-    assert np.isclose(elt_test,  elt, rtol=0.1)
+    assert np.isclose( e0_test,  e0, rtol=0.1)
+    assert np.isclose(elt_test, elt, rtol=0.1)
 
 
 @pytest.fixture
@@ -79,15 +79,15 @@ def test_get_function_and_seed_lt_with_data(x_min, x_max, steps, e0, lt):
     popt_expo, _    = so.curve_fit(fit_func_expo,    x, y, p0=seed_func_expo   (x, y))
     popt_log_lin, _ = so.curve_fit(fit_func_log_lin, x, y, p0=seed_func_log_lin(x, y))
 
-    assert     np.isclose(popt_lin[0],   popt_expo[0],    rtol=1e-1) # The interceipt should be close between lin and expo
-    assert not np.isclose(popt_lin[1],   popt_expo[1],    rtol=1e-1) # The "lifetime" should be different between lin and expo
+    assert     np.isclose(popt_lin[0], popt_expo[0],    rtol=1e-1) # The interceipt should be close between lin and expo
+    assert not np.isclose(popt_lin[1], popt_expo[1],    rtol=1e-1) # The "lifetime" should be different between lin and expo
 
-    assert     np.isclose(popt_lin[0],   popt_log_lin[0], rtol=1e-10) # The lin and log_lin are the same (the only difference is their
-    assert     np.isclose(popt_lin[1],   popt_log_lin[1], rtol=1e-10) # inputs: s2e or -log(s2e)) so both parameters should be the same
+    assert     np.isclose(popt_lin[0], popt_log_lin[0], rtol=1e-10) # The lin and log_lin are the same (the only difference is their
+    assert     np.isclose(popt_lin[1], popt_log_lin[1], rtol=1e-10) # inputs: s2e or -log(s2e)) so both parameters should be the same
                                                                       # for the purpose of testing this function
 
-@given(floats(min_value=1,  max_value=1e5),
-       floats(min_value=10, max_value=1e5))
+@given(floats(min_value = 1,  max_value = 1e5),
+       floats(min_value = 10, max_value = 1e5))
 def test_transform_parameters(a, b):
     errors     = 0.2*np.array([a, b])
     fit_output = FitFunction(values=[a, b], errors=errors, cov=np.array([[0.04, 0.02], [0.02, 0.04]]),