added test for prob density

tests/test_diagnostics.py

@@ -89,21 +89,37 @@ def test_score_hist_output_json(spark, gamma_settings_4, params_4, sqlite_con_4)
   
     test chart exported as dictionary is in fact a valid dictionary
     """
-    
+
     altair_installed = True
     try:
         import altair as alt
     except ImportError:
         altair_installed = False
-    
+
     dfpd = pd.read_sql("select * from df", sqlite_con_4)
     df = spark.createDataFrame(dfpd)
     df = df.withColumn("tf_adjusted_match_prob", 1.0 - (f.rand() / 10))
 
     res3 = _calc_probability_density(df, spark=spark, buckets=5)
-
-
-    if (altair_installed):
+
+    if altair_installed:
         assert isinstance(_create_probability_density_plot(res3).to_dict(), dict)
     else:
         assert isinstance(_create_probability_density_plot(res3), dict)
+
+
+def test_prob_density(spark, gamma_settings_4, params_4, sqlite_con_4):
+
+    """
+  
+     a test that checks that probability density is computed correctly. 
+     explicitly define a dataframe with tf_adjusted_match_prob = [0.1, 0.3, 0.5, 0.7, 0.9] 
+     and make sure that the probability density is the correct value (0.2) with all 5 bins
+     
+    """
+
+    dfpd = pd.DataFrame([0.1, 0.3, 0.5, 0.7, 0.9], columns=["tf_adjusted_match_prob"])
+    spdf = spark.createDataFrame(dfpd)
+
+    res = _calc_probability_density(spdf, spark=spark, buckets=5)
+    assert all(value == pytest.approx(0.2) for value in res.normalised.values)