diff --git a/tests/test_metrics/test_metrics.py b/tests/test_metrics/test_metrics.py
index d7b68939d0b0b28d4aa6687306dfb9d8e6408612..e5020b5f997d16b4573a59b139cadb18338da6c5 100644
--- a/tests/test_metrics/test_metrics.py
+++ b/tests/test_metrics/test_metrics.py
@@ -1,20 +1,3 @@
-"""Tests for the metrics subpackage as a whole.
-
-This module contains tests to check if everything from older package
-versions is implemented and if the results are still the same.
-
-This module contains the following functions:
-create_sample_data - create sample data
-get_all_statistics - get all implemented statistics
-get_all_samplings - get all implemented samplings
-sample_data - get sample data frame
-sample_metadata - get sample metadata
-
-This module contains the following tests:
-test_all_statistics_and_samplings_from_old_versions_implemented
-test_results_match_reference_results
-"""
-
import ast
from collections import namedtuple
from configparser import ConfigParser
@@ -29,10 +12,6 @@ from toarstats.metrics.stats_utils import STATS_LIST
def create_sample_data(sample_data_dir):
- """Create sample data.
-
- :param sample_data_dir: path to the sample data directory
- """
sample_data_dir.mkdir(exist_ok=True)
datetime_index = pd.date_range(start="2011-04-17 09:00", periods=100000,
freq="H")
@@ -53,18 +32,10 @@ def create_sample_data(sample_data_dir):
def get_all_statistics():
- """Get all implemented statistics.
-
- :return: A set of all implemented statistics
- """
return set(STATS_LIST)
def get_all_samplings():
- """Get all implemented samplings.
-
- :return: A set of all implemented samplings
- """
samplings = set()
for file in Path(Path(__file__).resolve().parents[2],
"toarstats/metrics").glob("*.py"):
@@ -81,10 +52,6 @@ def get_all_samplings():
@pytest.fixture
def sample_data():
- """Get sample data frame.
-
- :return: A data frame with sample data
- """
sample_data_file = Path(
Path(__file__).resolve().parent, "sample_data/sample_data.csv"
)
@@ -98,10 +65,6 @@ def sample_data():
@pytest.fixture
def sample_metadata():
- """Get sample metadata.
-
- :return: A ``namedtuple`` with the sample metadata information
- """
parser = ConfigParser()
with open(Path(Path(__file__).resolve().parent,
"sample_data/sample_metadata.cfg"),
@@ -115,7 +78,6 @@ def sample_metadata():
def test_all_statistics_and_samplings_from_old_versions_implemented():
- """Test if all old statistics and samplings are implemented."""
old_statistics = set()
old_samplings = set()
for file in Path(Path(__file__).resolve().parent,
@@ -131,7 +93,6 @@ def test_all_statistics_and_samplings_from_old_versions_implemented():
@pytest.mark.parametrize("statistic", sorted(get_all_statistics()))
def test_results_match_reference_results(statistic, sampling, sample_data,
sample_metadata):
- """Test if the results match old package versions."""
if statistic == "drmdmax1h" and sampling == "monthly":
with pytest.raises(ValueError, match="The drmdmax1h statistic cannot"
" be evaluated with monthly"
diff --git a/tests/test_metrics/test_stats_utils.py b/tests/test_metrics/test_stats_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0970043f582cd472970935849d2abe0b6180b8a0
--- /dev/null
+++ b/tests/test_metrics/test_stats_utils.py
@@ -0,0 +1,48 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from toarstats.metrics.stats_utils import (
+ get_growing_season, get_seasons, harmonize_time, kth_highest, prepare_data
+)
+
+
+def test_get_growing_season():
+ with pytest.raises(
+ KeyError,
+ match="wheat-cool_temperate_le30-SH not included in SEASON_DICT"
+ ):
+ growing_season = get_growing_season("wheat", "cool temperate", -15)
+
+
+def test_get_seasons():
+ seasons = get_seasons(
+ None, ["mean", "median"], None, ["DJF", "MAM"], None, True, False
+ )
+ assert seasons == [["DJF", "MAM"], ["DJF", "MAM"]]
+
+
+def test_harmonize_time():
+ record_list = harmonize_time([{"ser": pd.Series()}], "monthly")
+ for record in record_list:
+ pd.testing.assert_index_equal(
+ record["ser"].index, pd.DatetimeIndex([])
+ )
+
+
+def test_kth_highest():
+ ser = kth_highest(
+ pd.Series(index=pd.DatetimeIndex([])), None, "annual", 26, 0.75
+ )
+ pd.testing.assert_series_equal(
+ ser, pd.Series(index=pd.DatetimeIndex([], ser.index.freq), dtype=float)
+ )
+
+
+def test_prepare_data():
+ record = prepare_data(
+ pd.Series(index=pd.DatetimeIndex([])),
+ pd.Series(index=pd.DatetimeIndex(["2000"])),
+ "seasonal", ["DJF"], "mean"
+ )
+ assert record[0]["ser"].index == pd.DatetimeIndex(["2000"])
diff --git a/toarstats/metrics/input_checks.py b/toarstats/metrics/input_checks.py
index 3bd3d179e53e523c3a958887df4fbd18cf5276d8..37365b33701de513abaa1900b62ac074aa38ace9 100644
--- a/toarstats/metrics/input_checks.py
+++ b/toarstats/metrics/input_checks.py
@@ -228,7 +228,9 @@ def check_data(data_in, datetimes_in, values_in):
check_index(index_out)
check_values(values_out)
if index_out.size != values_out.size:
- raise ValueError("Datetime index and values must have the same length")
+ raise ValueError(
+ "Datetime index and values must have the same length"
+ ) # pragma: no cover
if index_out.tz:
index_out = index_out.tz_localize(None)
return pd.Series(values_out, index_out)
diff --git a/toarstats/metrics/ozone_metrics.py b/toarstats/metrics/ozone_metrics.py
index 23ae283c5a19b6fc73228cf5ac49a72ef07bb107..6bdcec77449d6d1ad4a2de0c880b5c43f8f4c92e 100644
--- a/toarstats/metrics/ozone_metrics.py
+++ b/toarstats/metrics/ozone_metrics.py
@@ -278,7 +278,7 @@ def drmdmax1h(ser, ref, mtype, metadata, seasons, min_data_capture):
tmp1["ser"] = tmp2
res = [tmp1]
elif mtype == "monthly":
- raise ValueError("Invalid mtype")
+ raise ValueError("Invalid mtype") # pragma: no cover
else:
tmpres = stat_processor_1("drmdmax1h", tmp2, tmp1ref, mtype, seasons,
func=resample_with_date)
diff --git a/toarstats/metrics/stats_utils.py b/toarstats/metrics/stats_utils.py
index b80f60a7eff867e7ea71868b385666491de765c7..c6528cf646fc57fe40026f888760af1e5fbf9047 100644
--- a/toarstats/metrics/stats_utils.py
+++ b/toarstats/metrics/stats_utils.py
@@ -399,7 +399,7 @@ def resample(ser, ref, sampling, how, mincount=0, minfrac=None,
else:
try:
ser_tmp = getattr(ser_resample, how)()
- except AttributeError:
+ except AttributeError: # pragma: no cover
ser_tmp = ser_resample.apply(how)
fcov = (count / ref.resample(sampling).count()
if minfrac is not None or how == "sum" else None)