Parametrize functions and lower performance thresholds

Author: marthacryan

packages/python/plotly/plotly/tests/test_core/test_graph_objs/test_performance.py

@@ -20,57 +20,67 @@ def test_performance_b64_scatter3d():
     z_list = z.tolist()
     c_list = c.tolist()
     list_start = time.time()
-    fig = go.Figure(
-        data=[
-            go.Scatter3d(
-                x=x_list,
-                y=y_list,
-                z=z_list,
-                marker=dict(color=c_list),
-                mode="markers",
-                opacity=0.2,
-            )
-        ]
-    )
+    fig = go.Figure(data=[go.Scatter3d(
+        x=x_list,
+        y=y_list,
+        z=z_list,
+        marker=dict(color=c_list),
+        mode="markers",
+        opacity=0.2,
+    )])
+    fig.show()
     list_time_elapsed = time.time() - list_start
 
     # Test the performance with base64 arrays
     np_start = time.time()
-    fig = go.Scatter3d(
+    fig = go.Figure(data=[go.Scatter3d(
         x=x,
         y=y,
         z=z,
         marker=dict(color=c),
         mode="markers",
         opacity=0.2,
-    )
+    )])
+    fig.show()
     np_time_elapsed = time.time() - np_start
 
     # np should be faster than lists
-    assert (np_time_elapsed / list_time_elapsed) < 0.005
-
+    assert (np_time_elapsed / list_time_elapsed) < 0.5
 
-def test_performance_b64_float64():
-    np_arr_1 = np.random.random(10000)
-    np_arr_2 = np.random.random(10000)
+FLOAT_TEST_CASES = [
+    (
+        "float32", # dtype
+        0.45 # difference threshold
+    ),
+    (
+        'float64',
+        0.55
+    )
+]
+@pytest.mark.parametrize('dtype, expected_size_difference', FLOAT_TEST_CASES)
+def test_performance_b64_float(dtype, expected_size_difference):
+    np_arr_1 = np.random.random(10000).astype(dtype)
+    np_arr_2 = np.random.random(10000).astype(dtype)
     list_1 = np_arr_1.tolist()
     list_2 = np_arr_2.tolist()
 
     # Test the performance of the base64 arrays
     np_start = time.time()
-    fig = go.Scatter(x=np_arr_1, y=np_arr_2)
+    fig = go.Figure(data=[go.Scatter(x=np_arr_1, y=np_arr_2)])
+    fig.show()
     np_time_elapsed = time.time() - np_start
 
     # Test the performance of the normal lists
     list_start = time.time()
-    fig = go.Scatter(x=list_1, y=list_2)
+    fig = go.Figure(data=[go.Scatter(x=list_1, y=list_2)])
+    fig.show()
     list_time_elapsed = time.time() - list_start
 
     # np should be faster than lists
-    assert (np_time_elapsed / list_time_elapsed) < 0.3
+    assert (np_time_elapsed / list_time_elapsed) < expected_size_difference
 
 
-DTYPE_TEST_CASES = [
+INT_SIZE_PERFORMANCE_TEST_CASES = [
     (
         "uint8", # dtype
         256, # max_val
@@ -82,8 +92,8 @@ def test_performance_b64_float64():
         900000
     )
 ]
-@pytest.mark.parametrize('dtype, max_val, expected_size_difference', DTYPE_TEST_CASES)
-def test_size_performance_b64_uint8(dtype, max_val, expected_size_difference):
+@pytest.mark.parametrize('dtype, max_val, expected_size_difference', INT_SIZE_PERFORMANCE_TEST_CASES)
+def test_size_performance_b64_int(dtype, max_val, expected_size_difference):
     np_arr_1 = (np.random.random(100000) * max_val).astype(dtype)
     np_arr_2 = (np.random.random(100000) * max_val).astype(dtype)