From 4ad0144ff2ea327fd34cfcbca8b66857762d8710 Mon Sep 17 00:00:00 2001
From: Niklas Selke <n.selke@fz-juelich.de>
Date: Mon, 5 Jun 2023 15:46:40 +0200
Subject: [PATCH] Reimplemented the pytest fixtures for sample data and
metadata.
---
.gitignore | 2 -
tests/conftest.py | 13 +
tests/create_data_metadata.py | 57 -
tests/test_metrics/conftest.py | 17 +
tests/test_metrics/test_interface.py | 107 +-
tests/test_metrics/test_metrics.py | 71 -
tests/test_metrics/test_stats.py | 44 +-
tests/test_metrics/test_stats_utils.py | 9 +-
tests/time_series.csv | 57855 -----------------------
9 files changed, 129 insertions(+), 58046 deletions(-)
create mode 100644 tests/conftest.py
delete mode 100644 tests/create_data_metadata.py
create mode 100644 tests/test_metrics/conftest.py
delete mode 100644 tests/test_metrics/test_metrics.py
delete mode 100644 tests/time_series.csv
diff --git a/.gitignore b/.gitignore
index c2098da..7753667 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,8 +4,6 @@
# Ignore test files
/.coverage
/tests/.pytest_cache/
-/tests/sample_data/
-/tests/reference_results/
# Ignore package build directory
/toarstats.egg-info/
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..edd9c6d
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,13 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+
+@pytest.fixture(scope="session")
+def data():
+ idx = pd.date_range(start="2011-04-17 09:00", periods=100000, freq="H")
+ rng = np.random.default_rng()
+ values = rng.uniform(13.4, 61.7, len(idx))
+ nan_idx = rng.choice(values.size, int(0.085*values.size), replace=False)
+ values[nan_idx] = np.nan
+ return pd.DataFrame({"values": values}, idx).dropna()
diff --git a/tests/create_data_metadata.py b/tests/create_data_metadata.py
deleted file mode 100644
index e05782d..0000000
--- a/tests/create_data_metadata.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from collections import namedtuple
-from configparser import ConfigParser
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-
-
-def create_sample_data(sample_data_dir):
- sample_data_dir.mkdir(exist_ok=True)
- datetime_index = pd.date_range(start="2011-04-17 09:00", periods=100000,
- freq="H")
- values = np.random.default_rng().uniform(13.4, 61.7, len(datetime_index))
- values[np.random.default_rng().choice(values.size,
- size=int(0.085*values.size),
- replace=False)] = np.nan
- pd.Series(values, datetime_index).dropna().to_csv(
- Path(sample_data_dir, "sample_data.csv"), header=False
- )
- Path(sample_data_dir, "sample_metadata.cfg").write_text(
- "[METADATA]\n"
- "station_lat: 50.906389\n"
- "station_lon: 6.403889\n"
- "station_climatic_zone: 3\n",
- encoding="utf-8"
- )
-
-
-@pytest.fixture
-def sample_data():
- sample_data_file = Path(
- Path(__file__).resolve().parent, "sample_data/sample_data.csv"
- )
- if not sample_data_file.is_file():
- create_sample_data(sample_data_file.parent)
- return pd.read_csv(
- sample_data_file,
- header=None, index_col=0, parse_dates=True
- )
-
-
-@pytest.fixture
-def sample_metadata():
- sample_metadata_file = Path(
- Path(__file__).resolve().parent, "sample_data/sample_metadata.cfg"
- )
- if not sample_metadata_file.is_file():
- create_sample_data(sample_metadata_file.parent)
- parser = ConfigParser()
- with open(sample_metadata_file, encoding="utf-8") as file:
- parser.read_file(file)
- Metadata = namedtuple("Metadata", ["station_lat", "station_lon",
- "station_climatic_zone"])
- return Metadata(parser.getfloat("METADATA", "station_lat"),
- parser.getfloat("METADATA", "station_lon"),
- parser.getint("METADATA", "station_climatic_zone"))
diff --git a/tests/test_metrics/conftest.py b/tests/test_metrics/conftest.py
new file mode 100644
index 0000000..56c109a
--- /dev/null
+++ b/tests/test_metrics/conftest.py
@@ -0,0 +1,17 @@
+from collections import namedtuple
+import pytest
+
+from toarstats.metrics.stats_utils import create_reference_series
+
+
+@pytest.fixture(scope="session")
+def ref_data(data):
+ return create_reference_series(data.index)
+
+
+@pytest.fixture(scope="session")
+def metadata():
+ Metadata = namedtuple(
+ "Metadata", ["station_lat", "station_lon", "station_climatic_zone"]
+ )
+ return Metadata(50.906389, 6.403889, "cool temperate moist")
diff --git a/tests/test_metrics/test_interface.py b/tests/test_metrics/test_interface.py
index d730839..ad1dc63 100644
--- a/tests/test_metrics/test_interface.py
+++ b/tests/test_metrics/test_interface.py
@@ -1,38 +1,81 @@
import numpy as np
import pandas as pd
+import pytest
from toarstats.metrics.interface import calculate_statistics
+from toarstats.metrics.constants import (
+ ALLOWED_SAMPLING_VALUES, STATISTICS_LIST
+)
-class TestCalculateStatistics:
- def test_calculate_statistics_simple_input(self):
- data = pd.DataFrame(
- range(10), index=pd.date_range(start="2000", periods=10, freq="H")
- )
- result = calculate_statistics("daily", "mean", data)
- expected_result = pd.DataFrame(
- {"mean": 4.5}, index=pd.DatetimeIndex(["2000"], freq="D")
- )
- pd.testing.assert_frame_equal(result, expected_result)
-
- def test_calculate_statistics_different_min_data_capture(self):
- data = pd.DataFrame(
- [1, 2, np.nan, 4, 5, np.nan, 7, np.nan, np.nan, np.nan],
- index=pd.date_range(start="2000", periods=10, freq="H")
- )
- result = calculate_statistics("daily", "mean", data,
- min_data_capture=0.75)
- expected_result = pd.DataFrame(
- {"mean": np.nan}, index=pd.DatetimeIndex(["2000"], freq="D")
- )
- pd.testing.assert_frame_equal(result, expected_result)
- data = pd.DataFrame(
- range(24),
- index=pd.date_range(start="2000", periods=24, freq="H")
- )
- result = calculate_statistics("daily", "diurnal_cycle", data)
- expected_result = pd.DataFrame(
- {"diurnal_cycle": [float(num) for num in range(24)]},
- index=pd.date_range(start="2000", periods=24, freq="H")
- )
- pd.testing.assert_frame_equal(result, expected_result)
+def test_calculate_statistics_simple_input():
+ data = pd.DataFrame(
+ range(10), index=pd.date_range(start="2000", periods=10, freq="H")
+ )
+ result = calculate_statistics("daily", "mean", data)
+ expected_result = pd.DataFrame(
+ {"mean": 4.5}, index=pd.DatetimeIndex(["2000"], freq="D")
+ )
+ pd.testing.assert_frame_equal(result, expected_result)
+
+
+def test_calculate_statistics_different_min_data_capture():
+ data = pd.DataFrame(
+ [1, 2, np.nan, 4, 5, np.nan, 7, np.nan, np.nan, np.nan],
+ index=pd.date_range(start="2000", periods=10, freq="H")
+ )
+ result = calculate_statistics("daily", "mean", data, min_data_capture=0.75)
+ expected_result = pd.DataFrame(
+ {"mean": np.nan}, index=pd.DatetimeIndex(["2000"], freq="D")
+ )
+ pd.testing.assert_frame_equal(result, expected_result)
+
+ data = pd.DataFrame(
+ range(24), index=pd.date_range(start="2000", periods=24, freq="H")
+ )
+ result = calculate_statistics("daily", "diurnal_cycle", data)
+ expected_result = pd.DataFrame(
+ {"diurnal_cycle": [float(num) for num in range(24)]},
+ index=pd.date_range(start="2000", periods=24, freq="H")
+ )
+ pd.testing.assert_frame_equal(result, expected_result)
+
+
+@pytest.mark.parametrize("sampling", ALLOWED_SAMPLING_VALUES)
+@pytest.mark.parametrize("statistic", STATISTICS_LIST)
+@pytest.mark.slow
+def test_calculate_statistics_run_all_statistics_with_all_samplings(
+ sampling, statistic, data, metadata
+):
+ if statistic == "drmdmax1h" and sampling == "monthly":
+ with pytest.raises(
+ ValueError,
+ match="The drmdmax1h statistic cannot be evaluated with"
+ " monthly sampling"
+ ):
+ result = calculate_statistics(sampling, statistic, data, metadata)
+ else:
+ result = calculate_statistics(sampling, statistic, data, metadata)
+ if (("aot40" in statistic or "w126" in statistic)
+ and sampling == "seasonal"):
+ expected_num_columns = 2
+ elif statistic == "diurnal_cycle" and sampling == "monthly":
+ expected_num_columns = 12
+ elif sampling == "seasonal":
+ expected_num_columns = 4
+ elif sampling == "vegseason":
+ expected_num_columns = 2
+ else:
+ expected_num_columns = 1
+ if statistic == "drmdmax1h" and sampling not in {"daily", "monthly"}:
+ expected_num_columns *= 2
+ elif statistic == "nvgtall":
+ expected_num_columns *= 6
+ elif statistic == "percentiles1":
+ expected_num_columns *= 3
+ elif statistic == "percentiles2":
+ if sampling in {"daily", "monthly", "seasonal", "vegseason"}:
+ expected_num_columns *= 7
+ else:
+ expected_num_columns *= 9
+ assert len(result.columns) == expected_num_columns
diff --git a/tests/test_metrics/test_metrics.py b/tests/test_metrics/test_metrics.py
deleted file mode 100644
index 3cb78c7..0000000
--- a/tests/test_metrics/test_metrics.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import ast
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-
-from tests.create_data_metadata import sample_data, sample_metadata
-from toarstats.metrics.interface import calculate_statistics
-from toarstats.metrics.stats_utils import STATS_LIST
-
-
-def get_all_statistics():
- return set(STATS_LIST)
-
-
-def get_all_samplings():
- samplings = set()
- for file in Path(Path(__file__).resolve().parents[2],
- "toarstats/metrics").glob("*.py"):
- for node in ast.parse(file.read_text(), file).body:
- if (isinstance(node, ast.Assign)
- and isinstance(node.value, ast.Dict)
- and node.targets[0].id == "RSTAGS"):
- samplings.update([sampling.value
- for sampling in node.value.keys])
- if "seasonal" in samplings and "vegseason" not in samplings:
- samplings.add("vegseason")
- return samplings
-
-
-def test_all_statistics_and_samplings_from_old_versions_implemented():
- old_statistics = set()
- old_samplings = set()
- for file in Path(Path(__file__).resolve().parent,
- "reference_results").rglob("*.csv"):
- statistic, sampling = file.stem.split("-")
- old_statistics.add(statistic)
- old_samplings.add(sampling)
- assert (old_statistics <= get_all_statistics()
- and old_samplings <= get_all_samplings())
-
-
-@pytest.mark.parametrize("sampling", sorted(get_all_samplings()))
-@pytest.mark.parametrize("statistic", sorted(get_all_statistics()))
-def test_results_match_reference_results(statistic, sampling, sample_data,
- sample_metadata):
- if statistic == "drmdmax1h" and sampling == "monthly":
- with pytest.raises(ValueError, match="The drmdmax1h statistic cannot"
- " be evaluated with monthly"
- " sampling"):
- result = calculate_statistics(sampling, statistic, sample_data,
- sample_metadata)
- else:
- result = calculate_statistics(sampling, statistic, sample_data,
- sample_metadata)
- for file in Path(
- Path(__file__).resolve().parent, "reference_results"
- ).rglob(f"{statistic}-{sampling}.csv"):
- expected_result = pd.read_csv(file, header=None, index_col=0,
- parse_dates=True)
- pd.testing.assert_index_equal(result.index, expected_result.index,
- check_names=False)
- if statistic == "w90" and sampling == "seasonal":
- with pytest.raises(AssertionError):
- np.testing.assert_allclose(result.values,
- expected_result.values,
- rtol=1e-13)
- else:
- np.testing.assert_allclose(result.values,
- expected_result.values, rtol=1e-13)
diff --git a/tests/test_metrics/test_stats.py b/tests/test_metrics/test_stats.py
index df0ca77..a3a6f37 100644
--- a/tests/test_metrics/test_stats.py
+++ b/tests/test_metrics/test_stats.py
@@ -9,20 +9,10 @@ from toarstats.metrics.constants import (
)
from toarstats.metrics.defaults import DEFAULT_MIN_DATA_CAPTURE
from toarstats.metrics.interface import calculate_statistics
-from toarstats.metrics.stats_utils import create_reference_series
-
-data = pd.read_csv(
- "tests/time_series.csv", header=None, names=[None, "values"], index_col=0,
- parse_dates=True, date_format="%Y-%m-%d %H:%M:%S"
-)
-ref_data = create_reference_series(data.index)
-metadata = {"station_lat": 50.906389,
- "station_lon": 6.403889,
- "station_climatic_zone": "cool temperate moist"}
@pytest.mark.slow
-def test_value_count():
+def test_value_count(data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "count", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -41,7 +31,7 @@ def test_value_count():
@pytest.mark.slow
-def test_data_capture():
+def test_data_capture(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "data_capture", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -67,7 +57,7 @@ def test_data_capture():
@pytest.mark.slow
-def test_average_values():
+def test_average_values(data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "mean", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -93,7 +83,7 @@ def test_average_values():
@pytest.mark.slow
-def test_standard_deviation():
+def test_standard_deviation(data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "stddev", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -119,7 +109,7 @@ def test_standard_deviation():
@pytest.mark.slow
-def test_median():
+def test_median(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "median", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -151,7 +141,7 @@ def test_median():
@pytest.mark.slow
-def test_maximum():
+def test_maximum(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "max", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -183,7 +173,7 @@ def test_maximum():
@pytest.mark.slow
-def test_minimum():
+def test_minimum(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "min", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -215,7 +205,7 @@ def test_minimum():
@pytest.mark.slow
-def test_perc05():
+def test_perc05(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p05", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -247,7 +237,7 @@ def test_perc05():
@pytest.mark.slow
-def test_perc10():
+def test_perc10(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p10", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -279,7 +269,7 @@ def test_perc10():
@pytest.mark.slow
-def test_perc25():
+def test_perc25(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p25", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -311,7 +301,7 @@ def test_perc25():
@pytest.mark.slow
-def test_perc75():
+def test_perc75(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p75", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -343,7 +333,7 @@ def test_perc75():
@pytest.mark.slow
-def test_perc90():
+def test_perc90(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p90", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -375,7 +365,7 @@ def test_perc90():
@pytest.mark.slow
-def test_perc95():
+def test_perc95(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p95", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -407,7 +397,7 @@ def test_perc95():
@pytest.mark.slow
-def test_perc98():
+def test_perc98(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p98", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -439,7 +429,7 @@ def test_perc98():
@pytest.mark.slow
-def test_perc99():
+def test_perc99(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "p99", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -471,7 +461,7 @@ def test_perc99():
@pytest.mark.slow
-def test_percentiles1():
+def test_percentiles1(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "percentiles1", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
@@ -508,7 +498,7 @@ def test_percentiles1():
@pytest.mark.slow
-def test_percentiles2():
+def test_percentiles2(data, ref_data, metadata):
for sampling in ALLOWED_SAMPLING_VALUES:
result = calculate_statistics(sampling, "percentiles2", data, metadata)
offset = RSTAGS["seasonal" if sampling == "vegseason" else sampling]
diff --git a/tests/test_metrics/test_stats_utils.py b/tests/test_metrics/test_stats_utils.py
index 0970043..6162622 100644
--- a/tests/test_metrics/test_stats_utils.py
+++ b/tests/test_metrics/test_stats_utils.py
@@ -1,12 +1,17 @@
-import numpy as np
import pandas as pd
import pytest
+from toarstats.metrics.constants import STATISTICS_LIST
from toarstats.metrics.stats_utils import (
- get_growing_season, get_seasons, harmonize_time, kth_highest, prepare_data
+ get_growing_season, get_seasons, harmonize_time, kth_highest, prepare_data,
+ STATS_LIST
)
+def test_statistics_list_is_complete():
+ assert sorted(STATISTICS_LIST) == sorted(STATS_LIST)
+
+
def test_get_growing_season():
with pytest.raises(
KeyError,
diff --git a/tests/time_series.csv b/tests/time_series.csv
deleted file mode 100644
index bef6089..0000000
--- a/tests/time_series.csv
+++ /dev/null
@@ -1,57855 +0,0 @@
-2004-05-11 12:00:00,1.30122
-2004-05-11 13:00:00,0.0
-2004-06-20 23:00:00,4.88559
-2004-06-21 00:00:00,5.48858
-2004-06-21 01:00:00,8.0439
-2004-06-21 02:00:00,6.77827
-2004-06-21 03:00:00,5.79634
-2004-06-21 04:00:00,3.09917
-2004-06-21 05:00:00,7.36973
-2004-06-21 06:00:00,9.40426
-2004-06-21 07:00:00,21.813
-2004-06-21 08:00:00,28.2955
-2004-06-21 09:00:00,33.2873
-2004-06-21 10:00:00,37.1559
-2004-06-21 11:00:00,34.9199
-2004-06-21 12:00:00,35.2392
-2004-06-21 13:00:00,31.4067
-2004-06-21 14:00:00,33.7725
-2004-06-21 15:00:00,32.6959
-2004-06-21 16:00:00,33.0157
-2004-06-21 17:00:00,15.4016
-2004-06-21 18:00:00,26.876
-2004-06-21 19:00:00,19.9088
-2004-06-21 20:00:00,24.2736
-2004-06-21 21:00:00,9.90099
-2004-06-21 22:00:00,9.13209
-2004-06-21 23:00:00,7.52361
-2004-06-22 00:00:00,8.45792
-2004-06-22 01:00:00,9.0965
-2004-06-22 02:00:00,8.6118
-2004-06-22 03:00:00,6.35221
-2004-06-22 04:00:00,9.65288
-2004-06-22 05:00:00,11.1431
-2004-06-22 06:00:00,14.2187
-2004-06-22 07:00:00,23.0671
-2004-06-22 08:00:00,33.3706
-2004-06-22 09:00:00,40.7754
-2004-06-22 10:00:00,41.3312
-2004-06-22 11:00:00,41.8756
-2004-06-22 12:00:00,38.386
-2004-06-22 13:00:00,32.2583
-2004-06-22 14:00:00,33.3229
-2004-06-22 15:00:00,32.8974
-2004-06-22 16:00:00,32.8854
-2004-06-22 17:00:00,17.2351
-2004-06-22 18:00:00,15.2006
-2004-06-22 19:00:00,11.8293
-2004-06-22 20:00:00,8.20931
-2004-06-22 21:00:00,6.68353
-2004-06-22 22:00:00,6.65998
-2004-06-22 23:00:00,5.96175
-2004-06-23 00:00:00,5.7016
-2004-06-23 01:00:00,5.35876
-2004-06-23 02:00:00,8.42234
-2004-06-23 03:00:00,5.40587
-2004-06-23 04:00:00,7.44041
-2004-06-23 05:00:00,10.9777
-2004-06-23 06:00:00,14.3961
-2004-06-23 07:00:00,20.3819
-2004-06-23 08:00:00,30.7681
-2004-06-23 09:00:00,34.6242
-2004-06-23 10:00:00,35.18
-2004-06-23 11:00:00,38.7052
-2004-06-23 12:00:00,35.5825
-2004-06-23 13:00:00,30.6022
-2004-06-23 14:00:00,30.3185
-2004-06-23 15:00:00,31.6668
-2004-06-23 16:00:00,29.9045
-2004-06-23 17:00:00,19.2817
-2004-06-23 18:00:00,9.08498
-2004-06-23 19:00:00,6.41136
-2004-06-23 20:00:00,4.60138
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--
GitLab