Merge pull request #5 from andrewtavis/add-testing

Tests for data_utils and lctn_utils with version up

CHANGELOG.md

@@ -1,3 +1,20 @@
+### wikirepo 0.1.0 (Feb 23, 2021)
+
+First stable release of wikirepo
+
+Additions include:
+
+- Full documentation of the package
+
+- Virtual environment files
+
+- Bug fixes
+
+- Extensive testing of all modules with GH Actions and Codecov
+
+- Code of conduct and contribution guidelines
+
+
 ### wikirepo 0.0.2 (Dec 8, 2020)
 
 The minimum viable product of wikirepo:

docs/source/conf.py

@@ -24,7 +24,7 @@
 author = "wikirepo developers"
 
 # The full version, including alpha/beta/rc tags
-release = "0.0.2.8"
+release = "0.1.0"
 
 
 # -- General configuration ---------------------------------------------------

setup.py

@@ -9,11 +9,11 @@
 
 setup_args = dict(
     name="wikirepo",
-    version="0.0.2.8",
+    version="0.1.0",
     author="Andrew Tavis McAllister",
     author_email="andrew.t.mcallister@gmail.com",
     classifiers=[
-        "Development Status :: 4 - Beta",
+        "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",

tests/conftest.py

@@ -54,6 +54,7 @@
 timespan = (date(2009, 1, 1), date(2010, 1, 1))
 interval = "yearly"
 
+# Test of values for a given timespan
 df = wikirepo.data.query(
     ents_dict=entities_dict,
     locations=countries,
@@ -94,6 +95,48 @@
     verbose=True,
 )
 
+# Test of most recent values
+df = wikirepo.data.query(
+    ents_dict=entities_dict,
+    locations=countries,
+    depth=depth,
+    timespan=None,
+    interval=None,
+    demographic_props=[
+        "ethnic_div",
+        "life_expectancy",
+        "literacy",
+        "out_of_school_children",
+        "population",
+    ],
+    economic_props=[
+        "gdp_ppp",
+        "gini",
+        "inflation_rate",
+        "median_income",
+        "nom_gdp_per_cap",
+        "nom_gdp",
+        "ppp_gdp_per_cap",
+        "total_reserves",
+        "unemployment",
+    ],
+    electoral_poll_props=False,
+    electoral_result_props=False,
+    geographic_props=["area", "continent", "country",],
+    institutional_props=[
+        "bti_gov_idx",
+        "bti_status_idx",
+        "capital",
+        "fh_category",
+        "human_dev_idx",
+        "org_membership",
+    ],
+    political_props="executive",
+    misc_props="country_abbr",
+    verbose=True,
+)
+df = data_utils.split_col_val_dates(df, col="population")
+
 entities_dict_bundeslands = wd_utils.EntitiesDict()
 depth = 1
 sub_lctns = True
@@ -129,7 +172,12 @@ def ents_dict(request):
     return request.param
 
 
-@pytest.fixture(params=[df])
+@pytest.fixture(params=[bundeslands_dict])
+def lctns_dict(request):
+    return request.param
+
+
+@pytest.fixture(params=[df_bundeslands])
 def df(request):
     return request.param
 

tests/test_data_utils.py

@@ -2,3 +2,70 @@
 Data Utilities Tests
 --------------------
 """
+
+import pandas as pd
+
+from wikirepo.data import data_utils
+
+
+def test_interp_by_subset(df):
+    df_interp = data_utils.interp_by_subset(
+        df=df, depth=1, col_name="sub_abbr", method="pad", limit_direction="both"
+    )
+    assert df_interp["sub_abbr"].isnull().values.any() == False
+
+
+def test_sum_df_prop_vals(df):
+    df.loc[
+        df.loc[(df["sub_lctn"] == "Berlin") & (df["year"] == "2009")].index,
+        "population",
+    ] = 100
+
+    df.loc[
+        df.loc[(df["sub_lctn"] == "Berlin") & (df["year"] == "2010")].index,
+        "population",
+    ] = 100
+
+    df.loc[
+        df.loc[(df["sub_lctn"] == "Hamburg") & (df["year"] == "2009")].index,
+        "population",
+    ] = 100
+
+    df.loc[
+        df.loc[(df["sub_lctn"] == "Hamburg") & (df["year"] == "2010")].index,
+        "population",
+    ] = 100
+
+    df_test = data_utils.sum_df_prop_vals(
+        df=df,
+        target_lctn="Berlin",
+        vals_lctn="Hamburg",
+        lctn_col="sub_lctn",
+        time_col="year",
+        prop_col="population",
+        subtract=False,
+        drop_vals_lctn=True,
+    )
+
+    assert (
+        df_test.loc[df_test.loc[df_test["sub_lctn"] == "Berlin"].index[0], "population"]
+        > df.loc[df.loc[df["sub_lctn"] == "Berlin"].index[0], "population"]
+    )
+    assert "Hamburg" not in list(df_test["sub_lctn"])
+
+    df_test = data_utils.sum_df_prop_vals(
+        df=df,
+        target_lctn="Berlin",
+        vals_lctn="Hamburg",
+        lctn_col="sub_lctn",
+        time_col=None,
+        prop_col="population",
+        subtract=True,
+        drop_vals_lctn=True,
+    )
+
+    assert (
+        df_test.loc[df_test.loc[df_test["sub_lctn"] == "Berlin"].index[0], "population"]
+        < df.loc[df.loc[df["sub_lctn"] == "Berlin"].index[0], "population"]
+    )
+    assert "Hamburg" not in list(df_test["sub_lctn"])

tests/test_lctn_utils.py

@@ -2,3 +2,42 @@
 Location Utilities Tests
 ------------------------
 """
+
+from wikirepo.data import lctn_utils
+
+
+def test_lctn_to_qid_dict():
+    assert type(lctn_utils.lctn_to_qid_dict()) == dict
+
+
+def test_qid_to_lctn_dict():
+    assert type(lctn_utils.qid_to_lctn_dict()) == dict
+
+
+def test_incl_lctn_lbls():
+    assert len(lctn_utils.incl_lctn_lbls("world")) == 1
+    assert len(lctn_utils.incl_lctn_lbls("continent")) == 7
+    assert type(lctn_utils.incl_lctn_lbls("country")) == list
+    assert type(lctn_utils.incl_lctn_lbls("region")) == list
+
+
+def test_incl_lctn_ids():
+    assert type(lctn_utils.incl_lctn_ids()) == list
+
+
+def test_qid_to_lctn_lbl(qid):
+    assert type(lctn_utils.qid_to_lctn_lbl(qid)) == str
+
+
+def test_merge_lctn_dicts(lctns_dict):
+    assert (
+        type(lctn_utils.merge_lctn_dicts(ld1=lctns_dict, ld2=lctns_dict))
+        == lctn_utils.LocationsDict
+    )
+
+
+def test_LocationsDict(ents_dict, lctns_dict):
+    assert len(lctns_dict.key_lbls_list()) == 17  # Germany and all its states
+    assert lctns_dict.get_depth() == 1
+    assert lctns_dict.get_qids_at_depth(depth=0) == ["Q183"]
+    assert type(lctns_dict.key_lbls_at_depth(ents_dict=ents_dict, depth=0)) == list