some whitespaces in tests

Author: slayoo

tests/test_bin_grid.py

@@ -100,30 +100,44 @@ def test_invalid_grid():
 
     @staticmethod
     def test_histogram_1d():
+        # arrange
         n_data = 1000
         grid = ppmc.BinGrid(100,"linear",0,1000)
         vals = np.random.random(n_data)*1000
         weights = np.ones(n_data)
+        hist, bin_edges = np.histogram(vals, bins=grid.edges)
+
+        # act
         data = ppmc.histogram_1d(grid, vals, weights)
-        hist,bin_edges = np.histogram(vals, bins=grid.edges)
+
+        # assert
         np.testing.assert_array_almost_equal(data, hist/(bin_edges[1]-bin_edges[0]))
 
     @staticmethod
     def test_histogram_2d_linear_linear():
+        # arrange
         n_data = 1000
-        x_grid = ppmc.BinGrid(15,"linear",0,1000)
-        y_grid = ppmc.BinGrid(12,"linear",0,500)
+        x_grid = ppmc.BinGrid(15, "linear", 0, 1000)
+        y_grid = ppmc.BinGrid(12, "linear", 0, 500)
         x_vals = np.random.random(n_data)*1000
         y_vals = np.random.random(n_data)*500
         weights = np.random.random(n_data)
-        data = ppmc.histogram_2d(x_grid, x_vals, y_grid, y_vals, weights)
-        data_numpy,bin_edges_x,bin_edges_y = np.histogram2d(x_vals, y_vals,
-             bins=[x_grid.edges,y_grid.edges], weights=weights)
+        data_numpy, bin_edges_x, bin_edges_y = np.histogram2d(
+            x_vals, y_vals,
+            bins=[x_grid.edges, y_grid.edges],
+            weights=weights
+        )
         cell_size = (bin_edges_x[1]-bin_edges_x[0]) * (bin_edges_y[1]-bin_edges_y[0])
+
+        # act
+        data = ppmc.histogram_2d(x_grid, x_vals, y_grid, y_vals, weights)
+
+        # assert
         np.testing.assert_array_almost_equal(np.array(data), data_numpy/cell_size, decimal=15)
 
     @staticmethod
     def test_histogram_2d_linear_log():
+        # arrange
         n_data = 100
         y_data_min = .1
         y_data_max = 10.
@@ -132,26 +146,41 @@ def test_histogram_2d_linear_log():
         x_vals = np.random.random(n_data)*1000
         y_vals = y_data_min*10**(np.log10(y_data_max/y_data_min) * np.random.random(n_data))
         weights = np.random.random(n_data)
-        data = ppmc.histogram_2d(x_grid, x_vals, y_grid, y_vals, weights)
-        data_numpy,bin_edges_x,bin_edges_y = np.histogram2d(x_vals, y_vals,
-             bins=[x_grid.edges,y_grid.edges], weights=weights)
+        data_numpy, bin_edges_x, bin_edges_y = np.histogram2d(
+            x_vals, y_vals,
+            bins=[x_grid.edges, y_grid.edges],
+            weights=weights
+        )
         cell_size = (bin_edges_x[1]-bin_edges_x[0]) * np.log(bin_edges_y[1]/bin_edges_y[0])
+
+        # act
+        data = ppmc.histogram_2d(x_grid, x_vals, y_grid, y_vals, weights)
+
+        # assert
         np.testing.assert_array_almost_equal(np.array(data), data_numpy/cell_size, decimal=15)
 
     @staticmethod
     def test_histogram_2d_log_log():
+        # arrange
         n_data = 100
         x_data_min = 1
         x_data_max = 1000
         y_data_min = .1
         y_data_max = 10.
         x_grid = ppmc.BinGrid(15,"log", x_data_min, x_data_max)
         y_grid = ppmc.BinGrid(12,"log", y_data_min, y_data_max)
-        x_vals = x_data_min*10**(np.log10(x_data_max/x_data_min) * np.random.random(n_data))
-        y_vals = y_data_min*10**(np.log10(y_data_max/y_data_min) * np.random.random(n_data))
+        x_vals = x_data_min * 10**(np.log10(x_data_max / x_data_min) * np.random.random(n_data))
+        y_vals = y_data_min * 10**(np.log10(y_data_max / y_data_min) * np.random.random(n_data))
         weights = np.random.random(n_data)
+        data_numpy, bin_edges_x, bin_edges_y = np.histogram2d(
+            x_vals, y_vals,
+            bins=[x_grid.edges, y_grid.edges],
+            weights=weights
+        )
+        cell_size = np.log(bin_edges_x[1] / bin_edges_x[0]) * np.log(bin_edges_y[1] / bin_edges_y[0])
+
+        # act
         data = ppmc.histogram_2d(x_grid, x_vals, y_grid, y_vals, weights)
-        data_numpy,bin_edges_x,bin_edges_y = np.histogram2d(x_vals, y_vals,
-             bins=[x_grid.edges,y_grid.edges], weights=weights)
-        cell_size = np.log(bin_edges_x[1]/bin_edges_x[0]) * np.log(bin_edges_y[1]/bin_edges_y[0])
+
+        # assert
         np.testing.assert_array_almost_equal(np.array(data), data_numpy/cell_size, decimal=13)