fix lints

atlas_densities/app/cell_densities.py

@@ -811,7 +811,7 @@ def measurements_to_average_densities(
 @click.option(
     "--min-data-points",
     type=int,
-    default=5,
+    default=1,
     help="minimum number of datapoints required for running the linear regression.",
     show_default=True,
 )

atlas_densities/densities/fitting.py

@@ -429,7 +429,7 @@ def _optimize_func(x, coefficient):
     )
     ss_reg = np.sum((_optimize_func(xdata, parameters[0][0]) - np.mean(ydata)) ** 2)
     ss_tot = np.sum((np.array(ydata) - _optimize_func(xdata, parameters[0][0])) ** 2) + ss_reg
-    L.debug(f"Length of xdata = {xdata}.The ss_reg is {ss_reg} and ss_tot is {ss_tot}\n")
+    L.debug("Length of xdata = %s.  The ss_reg is %s and ss_tot is %s", xdata, ss_reg, ss_tot)
     # if total sum of square is null, no variance can be explained by the fitting
     return {
         "coefficient": parameters[0][0],
@@ -488,7 +488,7 @@ def compute_fitting_coefficients(
         The "standard_deviation" value is the standard deviation of the coefficient value.
         The "r_square" value is the coefficient of determination of the coefficient value.
     """
-
+    # pylint: disable=too-many-locals
     if len(densities.index) != len(average_intensities.index) or np.any(
         densities.index != average_intensities.index
     ):
@@ -557,7 +557,10 @@ def compute_fitting_coefficients(
         for cell_type in tqdm(cell_types):
             cloud = clouds[group_name][cell_type]
             L.debug(
-                f"The length of training data for {group_name} and {cell_type} is {cloud['xdata']}"
+                "The length of training data for %s and %s is %s",
+                group_name,
+                cell_type,
+                cloud["xdata"],
             )
             result[group_name][cell_type] = linear_fitting_xy(
                 cloud["xdata"], cloud["ydata"], cloud["sigma"], min_data_points
@@ -801,6 +804,7 @@ def linear_fitting(  # pylint: disable=too-many-arguments
             fitting_coefficients: dict returned by
                 :fun:`atlas_densities.densities.fitting.compute_fitting_coefficients`.
     """
+    # pylint: disable=too-many-locals
     assert group_ids_config is not None
     L.info("Checking input data frames sanity ...")
     _check_average_densities_sanity(average_densities)
@@ -852,8 +856,11 @@ def linear_fitting(  # pylint: disable=too-many-arguments
 
     L.info("Computing fitting coefficients ...")
     fitting_coefficients = compute_fitting_coefficients(
-        groups, average_intensities, densities.drop(densities.index[indexes]),
-    min_data_points=min_data_points)
+        groups,
+        average_intensities,
+        densities.drop(densities.index[indexes]),
+        min_data_points=min_data_points,
+    )
     L.info("Fitting unknown average densities ...")
     fit_unknown_densities(groups, average_intensities, densities, fitting_coefficients)
 

tests/densities/test_fitting.py

@@ -274,17 +274,23 @@ def test_compute_average_intensities(region_map, hierarchy_info):
 
 def test_linear_fitting_xy():
     min_data_points = 1
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 1.0, 1.0], min_data_points=min_data_points)
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 1.0, 1.0], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert np.allclose(actual["r_square"], 1.0)
     assert not np.isinf(actual["standard_deviation"])
 
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 1.0, 4.0], [1.0, 1.0, 1e-5], min_data_points=min_data_points)
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 1.0, 4.0], [1.0, 1.0, 1e-5], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert not np.isinf(actual["standard_deviation"])
     assert np.allclose(actual["r_square"], 0.89286)
 
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 0.0, 1.0], min_data_points=min_data_points)
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 0.0, 1.0], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert not np.isinf(actual["standard_deviation"])
     assert np.allclose(actual["r_square"], 1.0)
@@ -320,7 +326,9 @@ def test_compute_fitting_coefficients(hierarchy_info):
     data = get_fitting_input_data_(hierarchy_info)
 
     actual = tested.compute_fitting_coefficients(
-        data["groups"], data["intensities"], data["densities"],
+        data["groups"],
+        data["intensities"],
+        data["densities"],
         min_data_points=1,
     )
 
@@ -342,24 +350,33 @@ def test_compute_fitting_coefficients_exceptions(hierarchy_info):
     data["densities"].drop(index=["Central lobule"], inplace=True)
 
     with pytest.raises(AtlasDensitiesError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"],
-        min_data_points=1,
-                                            )
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
     data = get_fitting_input_data_(hierarchy_info)
     data["densities"].drop(columns=["pv+"], inplace=True)
 
     with pytest.raises(AtlasDensitiesError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"],
-        min_data_points=1,
-                                            )
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
     data = get_fitting_input_data_(hierarchy_info)
     data["densities"].at["Lobule II", "pv+_standard_deviation"] = np.nan
     with pytest.raises(AssertionError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"],
-        min_data_points=1,
-                                            )
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
 
 @pytest.fixture
@@ -533,6 +550,7 @@ def test_linear_fitting(group_ids_config):
             data["average_densities"],
             data["homogenous_regions"],
             group_ids_config=group_ids_config,
+            min_data_points=1,
         )
         warnings_ = [w for w in warnings_ if isinstance(w.message, AtlasDensitiesWarning)]
         # Three warnings for recording NaN coefficients, three warnings for using them