Merge pull request #4444 from neutrinoceros/flake8-comp

README.md

@@ -14,7 +14,7 @@
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doc/source/examining/Loading_Generic_Array_Data.ipynb

@@ -84,7 +84,7 @@
    },
    "outputs": [],
    "source": [
-    "data = dict(density=(arr, \"g/cm**3\"))\n",
+    "data = {\"density\": (arr, \"g/cm**3\")}\n",
     "bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])\n",
     "ds = yt.load_uniform_grid(data, arr.shape, length_unit=\"Mpc\", bbox=bbox, nprocs=64)"
    ]
@@ -161,12 +161,12 @@
     "posx_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n",
     "posy_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n",
     "posz_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n",
-    "data = dict(\n",
-    "    density=(np.random.random(size=(64, 64, 64)), \"Msun/kpc**3\"),\n",
-    "    particle_position_x=(posx_arr, \"code_length\"),\n",
-    "    particle_position_y=(posy_arr, \"code_length\"),\n",
-    "    particle_position_z=(posz_arr, \"code_length\"),\n",
-    ")\n",
+    "data = {\n",
+    "    \"density\": (np.random.random(size=(64, 64, 64)), \"Msun/kpc**3\"),\n",
+    "    \"particle_position_x\": (posx_arr, \"code_length\"),\n",
+    "    \"particle_position_y\": (posy_arr, \"code_length\"),\n",
+    "    \"particle_position_z\": (posz_arr, \"code_length\"),\n",
+    "}\n",
     "bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])\n",
     "ds = yt.load_uniform_grid(\n",
     "    data,\n",
@@ -581,18 +581,18 @@
    "outputs": [],
    "source": [
     "grid_data = [\n",
-    "    dict(\n",
-    "        left_edge=[0.0, 0.0, 0.0],\n",
-    "        right_edge=[1.0, 1.0, 1.0],\n",
-    "        level=0,\n",
-    "        dimensions=[32, 32, 32],\n",
-    "    ),\n",
-    "    dict(\n",
-    "        left_edge=[0.25, 0.25, 0.25],\n",
-    "        right_edge=[0.75, 0.75, 0.75],\n",
-    "        level=1,\n",
-    "        dimensions=[32, 32, 32],\n",
-    "    ),\n",
+    "    {\n",
+    "        \"left_edge\": [0.0, 0.0, 0.0],\n",
+    "        \"right_edge\": [1.0, 1.0, 1.0],\n",
+    "        \"level\": 0,\n",
+    "        \"dimensions\": [32, 32, 32],\n",
+    "    },\n",
+    "    {\n",
+    "        \"left_edge\": [0.25, 0.25, 0.25],\n",
+    "        \"right_edge\": [0.75, 0.75, 0.75],\n",
+    "        \"level\": 1,\n",
+    "        \"dimensions\": [32, 32, 32],\n",
+    "    },\n",
     "]"
    ]
   },

pyproject.toml

@@ -289,6 +289,7 @@ select = [
     "E",
     "F",
     "W",
+    "C4",   # flake8-comprehensions
     "B",    # flake8-bugbear
     "G",    # flake8-logging-format
     "YTT",  # flake8-2020

tests/report_failed_answers.py

@@ -333,7 +333,7 @@ def parse_nose_xml(nose_xml):
 
     """
     missing_answers = set()
-    failed_answers = collections.defaultdict(lambda: dict())
+    failed_answers = collections.defaultdict(lambda: {})
     missing_errors = ["No such file or directory", "There is no old answer available"]
     tree = ET.parse(nose_xml)
     testsuite = tree.getroot()

yt/config.py

@@ -4,58 +4,58 @@
 
 ytcfg_defaults = {}
 
-ytcfg_defaults["yt"] = dict(
-    serialize=False,
-    only_deserialize=False,
-    time_functions=False,
-    colored_logs=False,
-    suppress_stream_logging=False,
-    stdout_stream_logging=False,
-    log_level=20,
-    inline=False,
-    num_threads=-1,
-    store_parameter_files=False,
-    parameter_file_store="parameter_files.csv",
-    maximum_stored_datasets=500,
-    skip_dataset_cache=True,
-    load_field_plugins=False,
-    plugin_filename="my_plugins.py",
-    parallel_traceback=False,
-    pasteboard_repo="",
-    reconstruct_index=True,
-    test_storage_dir="/does/not/exist",
-    test_data_dir="/does/not/exist",
-    enzo_db="",
-    notebook_password="",
-    answer_testing_tolerance=3,
-    answer_testing_bitwise=False,
-    gold_standard_filename="gold311",
-    local_standard_filename="local001",
-    answer_tests_url="http://answers.yt-project.org/{1}_{2}",
-    sketchfab_api_key="None",
-    imagebin_api_key="e1977d9195fe39e",
-    imagebin_upload_url="https://api.imgur.com/3/image",
-    imagebin_delete_url="https://api.imgur.com/3/image/{delete_hash}",
-    curldrop_upload_url="http://use.yt/upload",
-    thread_field_detection=False,
-    ignore_invalid_unit_operation_errors=False,
-    chunk_size=1000,
-    xray_data_dir="/does/not/exist",
-    supp_data_dir="/does/not/exist",
-    default_colormap="cmyt.arbre",
-    ray_tracing_engine="yt",
-    internals=dict(
-        within_testing=False,
-        within_pytest=False,
-        parallel=False,
-        strict_requires=False,
-        global_parallel_rank=0,
-        global_parallel_size=1,
-        topcomm_parallel_rank=0,
-        topcomm_parallel_size=1,
-        command_line=False,
-    ),
-)
+ytcfg_defaults["yt"] = {
+    "serialize": False,
+    "only_deserialize": False,
+    "time_functions": False,
+    "colored_logs": False,
+    "suppress_stream_logging": False,
+    "stdout_stream_logging": False,
+    "log_level": 20,
+    "inline": False,
+    "num_threads": -1,
+    "store_parameter_files": False,
+    "parameter_file_store": "parameter_files.csv",
+    "maximum_stored_datasets": 500,
+    "skip_dataset_cache": True,
+    "load_field_plugins": False,
+    "plugin_filename": "my_plugins.py",
+    "parallel_traceback": False,
+    "pasteboard_repo": "",
+    "reconstruct_index": True,
+    "test_storage_dir": "/does/not/exist",
+    "test_data_dir": "/does/not/exist",
+    "enzo_db": "",
+    "notebook_password": "",
+    "answer_testing_tolerance": 3,
+    "answer_testing_bitwise": False,
+    "gold_standard_filename": "gold311",
+    "local_standard_filename": "local001",
+    "answer_tests_url": "http://answers.yt-project.org/{1}_{2}",
+    "sketchfab_api_key": "None",
+    "imagebin_api_key": "e1977d9195fe39e",
+    "imagebin_upload_url": "https://api.imgur.com/3/image",
+    "imagebin_delete_url": "https://api.imgur.com/3/image/{delete_hash}",
+    "curldrop_upload_url": "http://use.yt/upload",
+    "thread_field_detection": False,
+    "ignore_invalid_unit_operation_errors": False,
+    "chunk_size": 1000,
+    "xray_data_dir": "/does/not/exist",
+    "supp_data_dir": "/does/not/exist",
+    "default_colormap": "cmyt.arbre",
+    "ray_tracing_engine": "yt",
+    "internals": {
+        "within_testing": False,
+        "within_pytest": False,
+        "parallel": False,
+        "strict_requires": False,
+        "global_parallel_rank": 0,
+        "global_parallel_size": 1,
+        "topcomm_parallel_rank": 0,
+        "topcomm_parallel_size": 1,
+        "command_line": False,
+    },
+}
 
 
 # For backward compatibility, do not use these vars internally in yt

yt/data_objects/analyzer_objects.py

@@ -26,10 +26,10 @@ def __repr__(self):
 
 def analysis_task(params=None):
     if params is None:
-        params = tuple()
+        params = ()
 
     def create_new_class(func):
-        cls = type(func.__name__, (AnalysisTask,), dict(eval=func, _params=params))
+        cls = type(func.__name__, (AnalysisTask,), {"eval": func, "_params": params})
         return cls
 
     return create_new_class
@@ -67,7 +67,7 @@ class QuantityProxy(AnalysisTask):
     def __repr__(self):
         # Stolen from YTDataContainer.__repr__
         s = f"{self.__class__.__name__}: "
-        s += ", ".join(["%s" % [arg for arg in self.args]])
+        s += ", ".join(["%s" % list(self.args)])
         s += ", ".join(f"{k}={v}" for k, v in self.kwargs.items())
         return s
 
@@ -103,6 +103,6 @@ def create_quantity_proxy(quantity_object):
     params = args[1:]
     if kwargs is not None:
         params += kwargs
-    dd = dict(_params=params, quantity_name=quantity_object[0])
+    dd = {"_params": params, "quantity_name": quantity_object[0]}
     cls = type(quantity_object[0], (QuantityProxy,), dd)
     return cls

yt/data_objects/construction_data_containers.py

@@ -2516,7 +2516,7 @@ def _export_ply(
             DLE = self.ds.domain_left_edge
             DRE = self.ds.domain_right_edge
             bounds = [(DLE[i], DRE[i]) for i in range(3)]
-        elif any([not all([isinstance(be, YTArray) for be in b]) for b in bounds]):
+        elif any(not all(isinstance(be, YTArray) for be in b) for b in bounds):
             bounds = [
                 tuple(
                     be if isinstance(be, YTArray) else self.ds.quan(be, "code_length")

yt/data_objects/level_sets/contour_finder.py

@@ -60,7 +60,7 @@ def identify_contours(data_source, field, min_val, max_val, cached_fields=None):
         contour_ids[pg.parent_grid_id].append((sl, ff))
         pbar.update(i + 1)
     pbar.finish()
-    rv = dict()
+    rv = {}
     rv.update(contour_ids)
     # NOTE: Because joins can appear in both a "final join" and a subsequent
     # "join", we can't know for sure how many unique joins there are without

yt/data_objects/level_sets/tests/test_clump_finding.py

@@ -124,8 +124,8 @@ def test_clump_tree_save():
     ds2 = load(fn)
 
     # compare clumps in the tree
-    t1 = [c for c in master_clump]
-    t2 = [c for c in ds2.tree]
+    t1 = list(master_clump)
+    t2 = list(ds2.tree)
     mt1 = ds.arr([c.info["cell_mass"][1] for c in t1])
     mt2 = ds2.arr([c["clump", "cell_mass"] for c in t2])
     it1 = np.array(np.argsort(mt1).astype(int))
@@ -139,8 +139,8 @@ def test_clump_tree_save():
         assert_array_equal(ct1["all", "particle_mass"], ct2["all", "particle_mass"])
 
     # compare leaf clumps
-    c1 = [c for c in leaf_clumps]
-    c2 = [c for c in ds2.leaves]
+    c1 = list(leaf_clumps)
+    c2 = list(ds2.leaves)
     mc1 = ds.arr([c.info["cell_mass"][1] for c in c1])
     mc2 = ds2.arr([c["clump", "cell_mass"] for c in c2])
     ic1 = np.array(np.argsort(mc1).astype(int))

yt/data_objects/particle_filters.py

@@ -52,11 +52,11 @@ def available(self, field_list):
         return all((self.filtered_type, field) in field_list for field in self.requires)
 
     def missing(self, field_list):
-        return list(
+        return [
             (self.filtered_type, field)
             for field in self.requires
             if (self.filtered_type, field) not in field_list
-        )
+        ]
 
     def wrap_func(self, field_name, old_fi):
         new_fi = copy.copy(old_fi)

yt/data_objects/particle_trajectories.py

@@ -337,7 +337,7 @@ def trajectory_from_index(self, index):
         if not np.any(mask):
             print("The particle index %d is not in the list!" % (index))
             raise IndexError
-        fields = [field for field in sorted(self.field_data.keys())]
+        fields = sorted(self.field_data.keys())
         traj = {}
         traj["particle_time"] = self.times
         traj["particle_index"] = index
@@ -362,7 +362,7 @@ def write_out(self, filename_base):
         >>> trajs = ParticleTrajectories(my_fns, indices)
         >>> trajs.write_out("orbit_trajectory")
         """
-        fields = [field for field in sorted(self.field_data.keys())]
+        fields = sorted(self.field_data.keys())
         num_fields = len(fields)
         first_str = "# particle_time\t" + "\t".join(fields) + "\n"
         template_str = "%g\t" * num_fields + "%g\n"
@@ -402,6 +402,6 @@ def write_out_h5(self, filename):
         fid.create_dataset("particle_indices", dtype=np.int64, data=self.indices)
         fid.close()
         self.times.write_hdf5(filename, dataset_name="particle_times")
-        fields = [field for field in sorted(self.field_data.keys())]
+        fields = sorted(self.field_data.keys())
         for field in fields:
             self[field].write_hdf5(filename, dataset_name=f"{field}")

yt/data_objects/profiles.py

@@ -1395,7 +1395,7 @@ def create_profile(
 
             if isinstance(field_ex[0], tuple):
                 field_ex = [data_source.ds.quan(*f) for f in field_ex]
-            if any([exi is None for exi in field_ex]):
+            if any(exi is None for exi in field_ex):
                 try:
                     ds_extrema = data_source.quantities.extrema(bin_field)
                 except AttributeError:
@@ -1462,7 +1462,7 @@ def create_profile(
         if mi <= 0 and l:
             raise YTIllDefinedBounds(mi, ma)
         args += [f, n, mi, ma, l]
-    kwargs = dict(weight_field=weight_field)
+    kwargs = {"weight_field": weight_field}
     if cls is ParticleProfile:
         kwargs["deposition"] = deposition
     if override_bins is not None:
@@ -1472,7 +1472,7 @@ def create_profile(
     obj.accumulation = accumulation
     obj.fractional = fractional
     if fields is not None:
-        obj.add_fields([field for field in fields])
+        obj.add_fields(list(fields))
     for field in fields:
         if fractional:
             obj.field_data[field] /= obj.field_data[field].sum()

yt/data_objects/tests/test_ellipsoid.py

@@ -36,7 +36,7 @@ def test_ellipsoid():
         ABC[:, 0] = 0.1
         for i in range(12):
             for c in cs:
-                A, B, C = reversed(sorted(ABC[:, i]))
+                A, B, C = sorted(ABC[:, i], reverse=True)
                 A = max(A, min_dx[0])
                 B = max(B, min_dx[1])
                 C = max(C, min_dx[2])

yt/data_objects/tests/test_exclude_functions.py

@@ -30,7 +30,7 @@ def test_exclude_below():
 def test_exclude_nan():
     test_array = np.nan * np.ones((10, 10, 10))
     test_array[1, 1, :] = 1
-    data = dict(density=test_array)
+    data = {"density": test_array}
     ds = load_uniform_grid(data, test_array.shape, length_unit="cm", nprocs=1)
     ad = ds.all_data()
     no_nan_ds = ad.exclude_nan(("gas", "density"))
@@ -41,7 +41,7 @@ def test_equal():
     test_array = np.ones((10, 10, 10))
     test_array[1, 1, :] = 2.0
     test_array[2, 1, :] = 3.0
-    data = dict(density=test_array)
+    data = {"density": test_array}
     ds = load_uniform_grid(data, test_array.shape, length_unit="cm", nprocs=1)
     ad = ds.all_data()
     no_ones = ad.exclude_equal(("gas", "density"), 1.0)
@@ -54,7 +54,7 @@ def test_inside_outside():
     test_array = np.ones((10, 10, 10))
     test_array[1, 1, :] = 2.0
     test_array[2, 1, :] = 3.0
-    data = dict(density=test_array)
+    data = {"density": test_array}
     ds = load_uniform_grid(data, test_array.shape, length_unit="cm", nprocs=1)
     ad = ds.all_data()
 

yt/data_objects/tests/test_refinement.py

@@ -12,18 +12,18 @@ def setup_fake_refby():
     n3 = 2
 
     grid_data = [
-        dict(
-            left_edge=[0.0, 0.0, 0.0],
-            right_edge=[1.0, np.pi, np.pi * 2.0],
-            level=0,
-            dimensions=np.array([n1, n2, n3]),
-        ),
-        dict(
-            left_edge=[0.0, 0.0, 0.0],
-            right_edge=[0.5, np.pi, np.pi * 2.0],
-            level=1,
-            dimensions=refine_by * [n1 / 2.0, n2, n3],
-        ),
+        {
+            "left_edge": [0.0, 0.0, 0.0],
+            "right_edge": [1.0, np.pi, np.pi * 2.0],
+            "level": 0,
+            "dimensions": np.array([n1, n2, n3]),
+        },
+        {
+            "left_edge": [0.0, 0.0, 0.0],
+            "right_edge": [0.5, np.pi, np.pi * 2.0],
+            "level": 1,
+            "dimensions": refine_by * [n1 / 2.0, n2, n3],
+        },
     ]
 
     for g in grid_data: