MNT: Test on 3.11 and 3.12 (#999)

* MNT: Test on 3.11 and 3.12

* [RUN] pyupgrade --py38-plus **/*.py

* MNT: Reflect minimum Python support in black config

.circleci/config.yml

@@ -117,6 +117,74 @@ jobs:
           paths:
             - src/coverage/.coverage.py310
 
+  unittest_311:
+    docker:
+      - image: continuumio/miniconda3
+    working_directory: /tmp/src/tedana
+    steps:
+      - checkout
+      - restore_cache:
+          key: conda-py311-v1-{{ checksum "pyproject.toml" }}
+      - run:
+          name: Generate environment
+          command: |
+            apt-get update
+            apt-get install -yqq make
+            if [ ! -d /opt/conda/envs/tedana_py311 ]; then
+              conda create -yq -n tedana_py311 python=3.11
+              source activate tedana_py311
+              pip install .[tests]
+            fi
+      - run:
+          name: Running unit tests
+          command: |
+            source activate tedana_py311
+            make unittest
+            mkdir /tmp/src/coverage
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py311
+      - save_cache:
+          key: conda-py311-v1-{{ checksum "pyproject.toml" }}
+          paths:
+            - /opt/conda/envs/tedana_py311
+      - persist_to_workspace:
+          root: /tmp
+          paths:
+            - src/coverage/.coverage.py311
+
+  unittest_312:
+    docker:
+      - image: continuumio/miniconda3
+    working_directory: /tmp/src/tedana
+    steps:
+      - checkout
+      - restore_cache:
+          key: conda-py312-v1-{{ checksum "pyproject.toml" }}
+      - run:
+          name: Generate environment
+          command: |
+            apt-get update
+            apt-get install -yqq make
+            if [ ! -d /opt/conda/envs/tedana_py312 ]; then
+              conda create -yq -n tedana_py312 python=3.12
+              source activate tedana_py312
+              pip install .[tests]
+            fi
+      - run:
+          name: Running unit tests
+          command: |
+            source activate tedana_py312
+            make unittest
+            mkdir /tmp/src/coverage
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py312
+      - save_cache:
+          key: conda-py312-v1-{{ checksum "pyproject.toml" }}
+          paths:
+            - /opt/conda/envs/tedana_py312
+      - persist_to_workspace:
+          root: /tmp
+          paths:
+            - src/coverage/.coverage.py312
+
   style_check:
     docker:
       - image: continuumio/miniconda3
@@ -310,11 +378,15 @@ workflows:
             - makeenv_38
       - unittest_39
       - unittest_310
+      - unittest_311
+      - unittest_312
       - merge_coverage:
           requires:
             - unittest_38
             - unittest_39
             - unittest_310
+            - unittest_311
+            - unittest_312
             - three-echo
             - four-echo
             - five-echo

docs/conf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 #
 # tedana documentation build configuration file, created by
 # sphinx-quickstart

pyproject.toml

@@ -15,6 +15,8 @@ classifiers = [
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
 ]
 license = {file = "LICENSE"}
 requires-python = ">=3.8"
@@ -101,7 +103,7 @@ version-file = "tedana/_version.py"
 
 [tool.black]
 line-length = 99
-target-version = ['py37']
+target-version = ['py38']
 include = '\.pyi?$'
 exclude = '''
 

tedana/__init__.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Tedana: A Python package for TE-dependent analysis of multi-echo data."""

tedana/bibtex.py

@@ -177,7 +177,7 @@ def get_description_references(description):
         A string containing BibTeX entries, limited only to the citations in the description.
     """
     bibtex_file = op.join(get_resource_path(), "references.bib")
-    with open(bibtex_file, "r") as fo:
+    with open(bibtex_file) as fo:
         bibtex_string = fo.read()
 
     braces_idx = find_braces(bibtex_string)

tedana/io.py

@@ -50,7 +50,7 @@ def default(self, obj):
         if isinstance(obj, set):
             return list(obj)
 
-        return super(CustomEncoder, self).default(obj)
+        return super().default(obj)
 
 
 class OutputGenerator:
@@ -454,7 +454,7 @@ def load_json(path: str) -> dict:
     FileNotFoundError if the file does not exist
     IsADirectoryError if the path is a directory instead of a file
     """
-    with open(path, "r") as f:
+    with open(path) as f:
         try:
             data = json.load(f)
         except json.decoder.JSONDecodeError:
@@ -943,7 +943,7 @@ def fname_to_component_list(fname: str) -> List[int]:
         else:
             raise ValueError(f"Cannot determine a components column in file {fname}")
 
-    with open(fname, "r") as fp:
+    with open(fname) as fp:
         contents = fp.read()
         return str_to_component_list(contents)
 

tedana/reporting/html_report.py

@@ -40,7 +40,7 @@ def _generate_buttons(out_dir, io_generator):
 
     buttons_template_name = "report_carpet_buttons_template.html"
     buttons_template_path = resource_path.joinpath(buttons_template_name)
-    with open(str(buttons_template_path), "r") as buttons_file:
+    with open(str(buttons_template_path)) as buttons_file:
         buttons_tpl = Template(buttons_file.read())
 
     buttons_html = buttons_tpl.substitute(
@@ -87,7 +87,7 @@ def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, boke
 
     body_template_name = "report_body_template.html"
     body_template_path = resource_path.joinpath(body_template_name)
-    with open(str(body_template_path), "r") as body_file:
+    with open(str(body_template_path)) as body_file:
         body_tpl = Template(body_file.read())
     body = body_tpl.substitute(
         content=bokeh_id,
@@ -114,7 +114,7 @@ def _save_as_html(body):
     resource_path = Path(__file__).resolve().parent.joinpath("data", "html")
     head_template_name = "report_head_template.html"
     head_template_path = resource_path.joinpath(head_template_name)
-    with open(str(head_template_path), "r") as head_file:
+    with open(str(head_template_path)) as head_file:
         head_tpl = Template(head_file.read())
 
     html = head_tpl.substitute(version=__version__, bokehversion=bokehversion, body=body)
@@ -127,7 +127,7 @@ def _generate_info_table(info_dict):
 
     info_template_name = "report_info_table_template.html"
     info_template_path = resource_path.joinpath(info_template_name)
-    with open(str(info_template_path), "r") as info_file:
+    with open(str(info_template_path)) as info_file:
         info_tpl = Template(info_file.read())
 
     info_dict = info_dict["GeneratedBy"][0]
@@ -273,7 +273,7 @@ def get_elbow_val(elbow_prefix):
     with open(opj(io_generator.out_dir, f"{io_generator.prefix}report.txt"), "r+") as f:
         about = f.read()
 
-    with open(opj(io_generator.out_dir, f"{io_generator.prefix}references.bib"), "r") as f:
+    with open(opj(io_generator.out_dir, f"{io_generator.prefix}references.bib")) as f:
         references = f.read()
 
     # Read info table

tedana/selection/component_selector.py

@@ -106,7 +106,7 @@ def validate_tree(tree):
         raise TreeError("\n" + f"Decision tree missing required fields: {missing_keys}")
 
     # Warn if unused fields exist
-    unused_keys = set(tree.keys()) - set(tree_expected_keys) - set(["used_metrics"])
+    unused_keys = set(tree.keys()) - set(tree_expected_keys) - {"used_metrics"}
     # Make sure some fields don't trigger a warning; hacky, sorry
     ok_to_not_use = (
         "reconstruct_from",
@@ -133,7 +133,7 @@ def validate_tree(tree):
             continue
 
         # Get a functions parameters and compare to parameters defined in the tree
-        pos = set([p for p, i in sig.parameters.items() if i.default is inspect.Parameter.empty])
+        pos = {p for p, i in sig.parameters.items() if i.default is inspect.Parameter.empty}
         kwargs = set(sig.parameters.keys()) - pos
 
         missing_pos = pos - set(node.get("parameters").keys()) - defaults
@@ -194,11 +194,11 @@ def validate_tree(tree):
         if node.get("kwargs") is not None:
             tagset = set()
             if "tag_if_true" in node.get("kwargs").keys():
-                tagset.update(set([node["kwargs"]["tag_if_true"]]))
+                tagset.update({node["kwargs"]["tag_if_true"]})
             if "tag_if_false" in node.get("kwargs").keys():
-                tagset.update(set([node["kwargs"]["tag_if_false"]]))
+                tagset.update({node["kwargs"]["tag_if_false"]})
             if "tag" in node.get("kwargs").keys():
-                tagset.update(set([node["kwargs"]["tag"]]))
+                tagset.update({node["kwargs"]["tag"]})
             undefined_classification_tags = tagset.difference(set(tree.get("classification_tags")))
             if undefined_classification_tags:
                 LGR.warning(

tedana/selection/selection_nodes.py

@@ -523,7 +523,7 @@ def dec_variance_lessthan_thresholds(
     """
     outputs = {
         "decision_node_idx": selector.current_node_idx,
-        "used_metrics": set([var_metric]),
+        "used_metrics": {var_metric},
         "node_label": None,
         "n_true": None,
         "n_false": None,
@@ -647,7 +647,7 @@ def calc_median(
         "decision_node_idx": selector.current_node_idx,
         "node_label": None,
         label_name: None,
-        "used_metrics": set([metric_name]),
+        "used_metrics": {metric_name},
         "calc_cross_comp_metrics": [label_name],
     }
 
@@ -736,7 +736,7 @@ def calc_kappa_elbow(
         "decision_node_idx": selector.current_node_idx,
         "node_label": None,
         "n_echos": selector.n_echos,
-        "used_metrics": set(["kappa"]),
+        "used_metrics": {"kappa"},
         "calc_cross_comp_metrics": [
             "kappa_elbow_kundu",
             "kappa_allcomps_elbow",
@@ -874,7 +874,7 @@ def calc_rho_elbow(
             "rho_unclassified_elbow",
             "elbow_f05",
         ],
-        "used_metrics": set(["kappa", "rho", "variance explained"]),
+        "used_metrics": {"kappa", "rho", "variance explained"},
         elbow_name: None,
         "rho_allcomps_elbow": None,
         "rho_unclassified_elbow": None,
@@ -1124,8 +1124,8 @@ def dec_reclassify_high_var_comps(
     # predefine all outputs that should be logged
     outputs = {
         "decision_node_idx": selector.current_node_idx,
-        "used_metrics": set(["variance explained"]),
-        "used_cross_comp_metrics": set(["varex_upper_p"]),
+        "used_metrics": {"variance explained"},
+        "used_cross_comp_metrics": {"varex_upper_p"},
         "node_label": None,
         "n_true": None,
         "n_false": None,
@@ -1273,7 +1273,7 @@ def calc_varex_thresh(
         "node_label": None,
         varex_name: None,
         "num_highest_var_comps": num_highest_var_comps,
-        "used_metrics": set(["variance explained"]),
+        "used_metrics": {"variance explained"},
     }
     if (
         isinstance(percentile_thresh, (int, float))

tedana/selection/selection_utils.py

@@ -268,7 +268,7 @@ def comptable_classification_changer(
                 for idx in changeidx:
                     tmpstr = selector.component_table.loc[idx, "classification_tags"]
                     if tmpstr == "" or isinstance(tmpstr, float):
-                        tmpset = set([tag_if])
+                        tmpset = {tag_if}
                     else:
                         tmpset = set(tmpstr.split(","))
                         tmpset.update([tag_if])
@@ -633,11 +633,11 @@ def kappa_elbow_kundu(component_table, n_echos, comps2use=None):
         kappa_nonsig_elbow = getelbow(kappas_nonsig, return_val=True)
 
         kappa_elbow = np.min((kappa_nonsig_elbow, kappa_allcomps_elbow))
-        LGR.info(("Calculating kappa elbow based on min of all and nonsig components."))
+        LGR.info("Calculating kappa elbow based on min of all and nonsig components.")
     else:
         kappa_elbow = kappa_allcomps_elbow
         kappa_nonsig_elbow = None
-        LGR.info(("Calculating kappa elbow based on all components."))
+        LGR.info("Calculating kappa elbow based on all components.")
 
     # Calculating varex_upper_p
     # Upper limit for variance explained is median across components with high

tedana/tests/test_combine.py

@@ -39,7 +39,7 @@ def test_make_optcom():
     n_voxels, n_echos, n_trs = 20, 3, 10
     n_mask = 5
     data = np.random.random((n_voxels, n_echos, n_trs))
-    mask = np.zeros((n_voxels)).astype(bool)
+    mask = np.zeros(n_voxels).astype(bool)
     mask[:n_mask] = True
     tes = np.array([10, 20, 30])  # E
 
@@ -49,7 +49,7 @@ def test_make_optcom():
     assert comb.shape == (n_voxels, n_trs)
 
     # Voxel-wise T2* estimates
-    t2s = np.random.random((n_voxels))
+    t2s = np.random.random(n_voxels)
     comb = combine.make_optcom(data, tes, mask, t2s=t2s, combmode="t2s")
     assert comb.shape == (n_voxels, n_trs)
 

tedana/tests/test_component_selector.py

@@ -300,8 +300,8 @@ def test_are_only_necessary_metrics_used_warning():
     selector = component_selector.ComponentSelector("minimal", sample_comptable())
 
     # warning when an element of necessary_metrics was not in used_metrics
-    selector.tree["used_metrics"] = set(["A", "B", "C"])
-    selector.necessary_metrics = set(["B", "C", "D"])
+    selector.tree["used_metrics"] = {"A", "B", "C"}
+    selector.necessary_metrics = {"B", "C", "D"}
     selector.are_only_necessary_metrics_used()
 
 

tedana/tests/test_decay.py

@@ -67,7 +67,7 @@ def test__apply_t2s_floor():
     n_voxels, n_echos, n_trs = 100, 5, 25
     echo_times = np.array([2, 23, 54, 75, 96])
     me_data = np.random.random((n_voxels, n_echos, n_trs))
-    t2s = np.random.random((n_voxels)) * 1000
+    t2s = np.random.random(n_voxels) * 1000
     t2s[t2s < 1] = 1  # Crop at 1 ms to be safe
     t2s[0] = 0.001
 
@@ -100,7 +100,7 @@ def test_smoke_fit_decay():
     n_echos = 5
     n_times = 20
     data = np.random.random((n_samples, n_echos, n_times))
-    tes = np.random.random((n_echos)).tolist()
+    tes = np.random.random(n_echos).tolist()
     mask = np.ones(n_samples, dtype=int)
     mask[n_samples // 2 :] = 0
     adaptive_mask = np.random.randint(2, n_echos, size=(n_samples)) * mask
@@ -126,7 +126,7 @@ def test_smoke_fit_decay_curvefit():
     n_echos = 5
     n_times = 20
     data = np.random.random((n_samples, n_echos, n_times))
-    tes = np.random.random((n_echos)).tolist()
+    tes = np.random.random(n_echos).tolist()
     mask = np.ones(n_samples, dtype=int)
     mask[n_samples // 2 :] = 0
     adaptive_mask = np.random.randint(2, n_echos, size=(n_samples)) * mask
@@ -150,7 +150,7 @@ def test_smoke_fit_decay_ts():
     n_echos = 5
     n_times = 20
     data = np.random.random((n_samples, n_echos, n_times))
-    tes = np.random.random((n_echos)).tolist()
+    tes = np.random.random(n_echos).tolist()
     mask = np.ones(n_samples, dtype=int)
     mask[n_samples // 2 :] = 0
     adaptive_mask = np.random.randint(2, n_echos, size=(n_samples)) * mask
@@ -176,7 +176,7 @@ def test_smoke_fit_decay_curvefit_ts():
     n_echos = 5
     n_times = 20
     data = np.random.random((n_samples, n_echos, n_times))
-    tes = np.random.random((n_echos)).tolist()
+    tes = np.random.random(n_echos).tolist()
     mask = np.ones(n_samples, dtype=int)
     mask[n_samples // 2 :] = 0
     adaptive_mask = np.random.randint(2, n_echos, size=(n_samples)) * mask