test: inprove converage (#23)

cyeva/core/wind.py

@@ -174,6 +174,7 @@ def get_least_lev_diff(
     return np.abs(lev1 - lev2)
 
 
+@convert_to_ndarray
 def get_least_dir_deflection(
     dir1: Union[int, np.ndarray], dir2: Union[int, np.ndarray], circle_num: int = 8
 ) -> Union[int, np.ndarray]:

tests/case/wind/__init__.py

@@ -4,6 +4,7 @@
     LEAST_ANGLE_DEFLECTION_CASE,
     LEAST_DIR_DEFLECTION_CASE,
     IDENTIFY_DIRECTION8_CASE,
+    IDENTIFY_DIRECTION16_CASE,
     WIND_DIR_SCORE_CASE,
     WIND_DIR_ACCURACY_RATE,
 )

tests/case/wind/wind_directions.py

@@ -13,13 +13,21 @@
     {"angle": 350, "result": 0},
 ]
 
+IDENTIFY_DIRECTION16_CASE = [
+    {"angle": 22, "result": 1},
+    {"angle": 23, "result": 1},
+    {"angle": 91, "result": 4},
+    {"angle": 350, "result": 0},
+]
+
 LEAST_DIR_DEFLECTION_CASE = [
     {"dir1": 0, "dir2": 1, "result": 1},
     {"dir1": 0, "dir2": 7, "result": 1},
     {"dir1": 1, "dir2": 7, "result": 2},
     {"dir1": 2, "dir2": 7, "result": 3},
     {"dir1": 2, "dir2": 6, "result": 4},
     {"dir1": 3, "dir2": 6, "result": 3},
+    {"dir1": [3, 2], "dir2": [6, 6], "result": np.array([3, 4])},
 ]
 
 WIND_DIR_SCORE_CASE = [

tests/case/wind/wind_speed.py

@@ -4,6 +4,7 @@
     {"wspd": 0.1, "result": 0},
     {"wspd": 8.1, "result": 5},
     {"wspd": 38, "result": 13},
+    {"wspd": [38, 8.1], "result": np.array([13, 5])},
 ]
 
 IDENTIFY_SPEED_LEVEL_MAPPING_CASE = [

tests/test_temp.py

@@ -107,9 +107,45 @@ def test_calc_temp_linregress():
 
 
 def test_gather_all_factors():
-    for _, cases in ACCURACY_RATE_CASE.items():
-        for case in cases:
-            obs = case["obs"]
-            fcst = case["fct"]
-
-            TemperatureComparison(obs, fcst)
+    np.random.seed(0)
+
+    obs = np.sin(np.arange(100)) * 20 + np.random.random(100) * 5 * np.random.choice(
+        [1, -1]
+    )
+    fcst = obs + np.random.random(100) * 5 * np.random.choice([1, -1])
+
+    temp = TemperatureComparison(obs, fcst)
+    temp.gather_all_factors()
+
+
+def test_temp_obj():
+    np.random.seed(0)
+
+    obs = np.sin(np.arange(100)) * 20 + np.random.random(100) * 5 * np.random.choice(
+        [1, -1]
+    )
+    fcst = obs + np.random.random(100) * 5 * np.random.choice([1, -1])
+
+    temp = TemperatureComparison(obs, fcst)
+    temp
+    print(temp)
+    assert (
+        temp.__str__()
+        == temp.__repr__()
+        == """<Object:TemperatureComparison(degC)>
+data:
+    observation   forecast
+0     -2.744068  -5.592160
+1     13.253473   9.734786
+2     15.172132  13.729749
+3      0.097984  -2.068456
+4    -17.254324 -21.034857
+..          ...        ...
+95    12.749277   7.898094
+96    16.739190  13.336467
+97     7.491617   7.065139
+98   -15.612338 -15.894429
+99   -20.007614 -22.446803
+
+[100 rows x 2 columns]"""
+    )

tests/test_wind.py

@@ -1,3 +1,8 @@
+from numbers import Number
+from typing import List
+
+import numpy as np
+
 from cyeva.core.wind import (
     get_least_angle_deflection,
     identify_direction,
@@ -12,6 +17,7 @@
     LEAST_ANGLE_DEFLECTION_CASE,
     LEAST_DIR_DEFLECTION_CASE,
     IDENTIFY_DIRECTION8_CASE,
+    IDENTIFY_DIRECTION16_CASE,
     WIND_DIR_SCORE_CASE,
     WIND_DIR_ACCURACY_RATE,
     IDENTIFY_SPEED_LEVEL_GENERAL_CASE,
@@ -42,7 +48,11 @@ def test_get_least_dir_deflection():
         dir1 = case["dir1"]
         dir2 = case["dir2"]
         result = case["result"]
-        assert get_least_dir_deflection(dir1, dir2) == result
+        _result = get_least_dir_deflection(dir1, dir2)
+        if isinstance(result, Number):
+            assert _result == result
+        elif isinstance(result, List) or isinstance(result, np.ndarray):
+            assert (_result == result).all()
 
 
 def test_identify_direction8():
@@ -52,11 +62,23 @@ def test_identify_direction8():
         assert identify_direction(angle, dnum=8) == result
 
 
+def test_identify_direction16():
+    for case in IDENTIFY_DIRECTION16_CASE:
+        angle = case["angle"]
+        result = case["result"]
+        _result = identify_direction(angle, dnum=16)
+        assert _result == result
+
+
 def test_identify_wind_scale():
     for case in IDENTIFY_SPEED_LEVEL_GENERAL_CASE:
         wspd = case["wspd"]
         result = case["result"]
-        assert identify_wind_scale(wspd) == result
+        _result = identify_wind_scale(wspd)
+        if isinstance(result, Number):
+            assert _result == result
+        elif isinstance(result, List) or isinstance(result, np.ndarray):
+            assert (_result == result).all()
 
 
 def test_calc_wind_dir_score():
@@ -202,3 +224,37 @@ def test_filter_wind_scales():
             _result = filter_wind_scales(obs, fct, **parameters)
             for i in range(len(result)):
                 assert (_result[i] == result[i]).all()
+
+
+def test_wind_obj():
+    np.random.seed(0)
+
+    obs_spd = np.random.random(100) * 10
+    obs_dir = np.random.random(100) * 360
+    fct_spd = np.random.random(100) * 10
+    fct_dir = np.random.random(100) * 360
+
+    wind = WindComparison(obs_spd, fct_spd, obs_dir, fct_dir)
+    wind
+    print(wind)
+
+    assert (
+        wind.__str__()
+        == wind.__repr__()
+        == """<Object:WindComparison>
+data:
+    observation_speed  ...  forecast_direction
+0            5.488135  ...          326.359980
+1            7.151894  ...          278.657040
+2            6.027634  ...          119.932255
+3            5.448832  ...           29.196500
+4            4.236548  ...          146.606822
+..                ...  ...                 ...
+95           1.831914  ...          345.233780
+96           5.865129  ...          127.932785
+97           0.201075  ...          128.414481
+98           8.289400  ...            5.878261
+99           0.046955  ...           66.683637
+
+[100 rows x 4 columns]"""
+    )