Greatly increased test coverage

Increased test coverage and changed main function name to make it shorter

.coveragerc

@@ -1,3 +1,4 @@
+# type: ignore
 # .coveragerc to control coverage.py
 [run]
 branch = True
@@ -31,3 +32,4 @@ omit =
     */algorithms_educational.py
     */algorithms_old.py
     */smawk_old.py
+    */utils_for_test.py

.pylintrc

@@ -1,8 +1,10 @@
 [MASTER]
 disable= missing-module-docstring, missing-class-docstring, missing-function-docstring, missing-final-newline, C0325
+ignore-paths=.coveragerc
+ignore=.coveragerc
 [FORMAT]
 max-line-length=200
 
 # allow 1 or 2 length variable names
 good-names-rgxs=^[_a-zGW][_a-z0-9L]?$
-good-names=R,D,T,D_row,S,A,F,H,F_vals, F_val, H_vals, M, SMALL_VAL, LARGE_VAL, MicroaggWilberCalculator_edu, N_vals, N
+good-names=R,D,T,D_row,S,A,F,H,F_vals, F_val, H_vals, M, SMALL_VAL, LARGE_VAL, MicroaggWilberCalculator_edu, N_vals, N, setUp, tearDown

setup.cfg

@@ -98,6 +98,10 @@ testpaths = tests
 #     slow: mark tests as slow (deselect with '-m "not slow"')
 #     system: mark end-to-end system tests
 
+[tool.pylint.MASTER]
+ignore = .coveragerc
+ignore-paths = .coveragerc
+
 [devpi:upload]
 # Options for the devpi: PyPI server and packaging tool
 # VCS export must be deactivated since we are using setuptools-scm
@@ -162,3 +166,4 @@ omit =
     */algorithms_educational.py
     */algorithms_old.py
     */smawk_old.py
+    */utils_for_test.py

src/microagg1d/__init__.py

@@ -1,3 +1,3 @@
-from microagg1d.main import optimal_univariate_microaggregation_1d
+from microagg1d.main import univariate_microaggregation
 
-microagg1d = optimal_univariate_microaggregation_1d  # shorthand for the above
+microagg1d = univariate_microaggregation  # shorthand for the above

src/microagg1d/cost_maxdist.py

@@ -29,7 +29,7 @@ def __init__(self, arr, k, F_vals):
         self.SMALL_VAL = (arr[-1] - arr[0]) * n
         self.LARGE_VAL = self.SMALL_VAL * (1 + n)
 
-    def calc(self, i, j):  # i <-> j interchanged is not a bug!
+    def calc(self, i, j):
         """This function computes the w_{ij} values introduced"""
         if j <= i:
             return np.inf

src/microagg1d/cost_round.py

@@ -33,7 +33,7 @@ def __init__(self, arr, k, F_vals):
         self.SMALL_VAL = (arr[-1] - arr[0]) * n
         self.LARGE_VAL = self.SMALL_VAL * (1 + n)
 
-    def calc(self, i, j):  # i <-> j interchanged is not a bug!
+    def calc(self, i, j):
         """This function computes the w_{ij} values introduced"""
         if j <= i:
             return np.inf
@@ -88,7 +88,7 @@ def __init__(self, arr, k, F_vals):
         self.SMALL_VAL = (arr[-1] - arr[0]) * n
         self.LARGE_VAL = self.SMALL_VAL * (1 + n)
 
-    def calc(self, i, j):  # i <-> j interchanged is not a bug!
+    def calc(self, i, j):
         """This function computes the w_{ij} values introduced"""
         if j <= i:
             return np.inf

src/microagg1d/cost_sae.py

@@ -44,7 +44,7 @@ def __init__(self, arr, k, F_vals):
         self.SMALL_VAL = (arr[-1] - arr[0]) * n
         self.LARGE_VAL = self.SMALL_VAL * (1 + n)
 
-    def calc(self, i, j):  # i <-> j interchanged is not a bug!
+    def calc(self, i, j):
         """This function computes the w_{ij} values introduced"""
         if j <= i:
             return np.inf

src/microagg1d/cost_sse.py

@@ -279,7 +279,7 @@ def compute_sse_sorted_stable(v, clusters_sorted):
     r = 0
     while r < len(v):
         r = left
-        while clusters_sorted[left] == clusters_sorted[r] and r < len(v):
+        while r < len(v) and clusters_sorted[left] == clusters_sorted[r]:
             r += 1
         # r-=1
         mean = np.mean(v[left:r])

src/microagg1d/main.py

@@ -22,7 +22,7 @@ def undo_argsort(sorted_arr, sort_order):
     return sorted_arr[revert]
 
 
-def optimal_univariate_microaggregation_1d(x, k, method="auto", stable=1, cost="sse"):
+def univariate_microaggregation(x, k, method="auto", stable=1, cost="sse"):
     """Performs optimal 1d univariate microaggregation"""
     x = np.squeeze(np.asarray(x))
     assert len(x.shape) == 1, "provided array is not 1d"
@@ -70,4 +70,6 @@ def optimal_univariate_microaggregation_1d(x, k, method="auto", stable=1, cost="
         clusters = _rounddown_user(x, k, method)
     elif cost == "maxdist":
         clusters = _maxdist_user(x, k, method)
+    else:
+        raise NotImplementedError("Should not be reachable")
     return undo_argsort(clusters, order)