Change code linewidth limit from 79 to 90

netatmoqc/apps/clustering/app.py

@@ -18,17 +18,10 @@ from flask_caching import Cache
 from server import server
 
 from netatmoqc.clustering import cluster_netatmo_obs, sort_df_by_cluster_size
-from netatmoqc.config_parser import (
-    ParsedConfig,
-    UndefinedConfigValue,
-    read_config,
-)
+from netatmoqc.config_parser import ParsedConfig, UndefinedConfigValue, read_config
 from netatmoqc.domains import Domain
 from netatmoqc.dtgs import Dtg
-from netatmoqc.load_data import (
-    read_netatmo_data_for_dtg,
-    remove_irregular_stations,
-)
+from netatmoqc.load_data import read_netatmo_data_for_dtg, remove_irregular_stations
 from netatmoqc.logs import CustomFormatter
 from netatmoqc.metrics import haversine_distance
 from netatmoqc.plots import make_clustering_fig
@@ -46,9 +39,7 @@ app = dash.Dash(
     name="clustering",
     server=server,
     url_base_pathname="/clustering/",
-    meta_tags=[
-        {"name": "viewport", "content": "width=device-width, initial-scale=1"}
-    ],
+    meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}],
 )
 
 # Fix duplicate log items
@@ -106,13 +97,9 @@ def generate_obs_weights_panel():
         # Get defaults from config file if defined. Use the ones
         # defined in the calls to this function otherwise.
         try:
-            default_from_config = config.get_clustering_opt("obs_weights", {})[
-                var_name
-            ]
+            default_from_config = config.get_clustering_opt("obs_weights", {})[var_name]
             if default_from_config is UndefinedConfigValue:
-                raise AttributeError(
-                    'obs_weights not defined for "{}"'.format(var_name)
-                )
+                raise AttributeError('obs_weights not defined for "{}"'.format(var_name))
             default = default_from_config
             logger.debug(
                 'Using config file default "%s" for "%s" weight',
@@ -189,9 +176,7 @@ def generate_control_card():
             html.P("Clustering method"),
             dcc.Dropdown(
                 id="method-select",
-                options=[
-                    {"label": i, "value": i} for i in allowed_cluster_methods
-                ],
+                options=[{"label": i, "value": i} for i in allowed_cluster_methods],
                 value=allowed_cluster_methods[0],
             ),
             html.Br(),
@@ -298,9 +283,7 @@ def generate_control_card():
                         id="outlier_rm_method_div",
                         children=[
                             html.Br(),
-                            html.P(
-                                "Post-Clustering Outlier Removal (Optional)"
-                            ),
+                            html.P("Post-Clustering Outlier Removal (Optional)"),
                             dcc.Dropdown(
                                 id="outlier_rm_method",
                                 options=[{"label": "None", "value": None}]
@@ -577,9 +560,7 @@ def read_data_df(str_date, cycle):
 @app.callback(
     [
         Output(component_id="eps_div", component_property="style"),
-        Output(
-            component_id="min_cluster_size_div", component_property="style"
-        ),
+        Output(component_id="min_cluster_size_div", component_property="style"),
     ],
     [Input(component_id="method-select", component_property="value")],
 )
@@ -676,9 +657,7 @@ def run_clustering_and_make_plot(
     start_read_data = time.time()
     df = read_data_df(date, cycle)
     end_read_data = time.time()
-    logger.info(
-        "Done reading data. Elapsed: %.1fs", end_read_data - start_read_data
-    )
+    logger.info("Done reading data. Elapsed: %.1fs", end_read_data - start_read_data)
 
     n_obs = len(df.index)
     if n_obs == 0:
@@ -784,22 +763,14 @@ def run_clustering_and_make_plot(
         data,
         dcc.Markdown("**{:.2f}**".format(silhouette_score)),
         dcc.Markdown("**{}**".format(n_clusters)),
+        dcc.Markdown("**{} ({:.2f}%)**".format(n_accepted, 100.0 * n_accepted / n_obs)),
         dcc.Markdown(
-            "**{} ({:.2f}%)**".format(n_accepted, 100.0 * n_accepted / n_obs)
-        ),
-        dcc.Markdown(
-            "**{} ({:.2f}%)**".format(
-                n_rm_clustering, 100.0 * n_rm_clustering / n_obs
-            )
-        ),
-        dcc.Markdown(
-            "**{} ({:.2f}%)**".format(
-                n_rm_refining, 100.0 * n_rm_refining / n_obs
-            )
+            "**{} ({:.2f}%)**".format(n_rm_clustering, 100.0 * n_rm_clustering / n_obs)
         ),
         dcc.Markdown(
-            "**{} ({:.2f}%)**".format(noise_count, 100.0 * noise_count / n_obs)
+            "**{} ({:.2f}%)**".format(n_rm_refining, 100.0 * n_rm_refining / n_obs)
         ),
+        dcc.Markdown("**{} ({:.2f}%)**".format(noise_count, 100.0 * noise_count / n_obs)),
     )
 
 
@@ -809,9 +780,7 @@ def run_clustering_and_make_plot(
     [
         # Set clickmode='event+select' in the figure layout, and then
         # use 'selectedData' here instead of 'clickData'
-        Input(
-            component_id="clustering_plot", component_property="selectedData"
-        ),
+        Input(component_id="clustering_plot", component_property="selectedData"),
     ],
 )
 def geodist_upon_pt_pair_selection(selected_data):

netatmoqc/apps/scattergeo_timeseries/app.py

@@ -15,10 +15,7 @@ from server import server
 
 from netatmoqc.config_parser import read_config
 from netatmoqc.domains import Domain
-from netatmoqc.load_data import (
-    read_netatmo_data_for_dtg,
-    remove_irregular_stations,
-)
+from netatmoqc.load_data import read_netatmo_data_for_dtg, remove_irregular_stations
 from netatmoqc.logs import CustomFormatter
 from netatmoqc.plots import generate_single_frame, init_fig_dict
 
@@ -34,9 +31,7 @@ app = dash.Dash(
     name="scattergeo_timeseries",
     server=server,
     url_base_pathname="/scattergeo_timeseries/",
-    meta_tags=[
-        {"name": "viewport", "content": "width=device-width, initial-scale=1"}
-    ],
+    meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}],
 )
 
 
@@ -92,9 +87,7 @@ def generate_control_card():
             html.Br(),
             html.Div(
                 id="plot-btn-outer",
-                children=html.Button(
-                    id="plot-btn", children="Make Plot", n_clicks=0
-                ),
+                children=html.Button(id="plot-btn", children="Make Plot", n_clicks=0),
             ),
         ],
     )
@@ -149,9 +142,7 @@ def prepare_animation(n_clicks, start_date, end_date, dataset_var):
     if n_clicks == 0:
         return domain.get_fig(max_ngrid=0)
 
-    fig_dict, sliders_dict = init_fig_dict(
-        domain, dataset_var, frame_duration=300
-    )
+    fig_dict, sliders_dict = init_fig_dict(domain, dataset_var, frame_duration=300)
 
     # Determine map boundaries and max/min plotted values
     minval_dataset_var = float("inf")
@@ -172,9 +163,7 @@ def prepare_animation(n_clicks, start_date, end_date, dataset_var):
 
     for idtg, dtg in enumerate(pd.date_range(start_date, end_date, freq="3H")):
         logger.info("Reading data for %s", dtg)
-        df = read_netatmo_data_for_dtg(
-            dtg, rootdir=config.general.data_rootdir
-        )
+        df = read_netatmo_data_for_dtg(dtg, rootdir=config.general.data_rootdir)
         df, _ = remove_irregular_stations(df)
         logger.debug("    * Done. Now adding frame.")
 

netatmoqc/argparse_wrapper.py

@@ -56,9 +56,7 @@ def get_parsed_args(program_name):
         fpath = Path(os.getenv("NETATMOQC_CONFIG_PATH", "config.toml"))
         default_conf_path = fpath.resolve(strict=True)
     except FileNotFoundError:
-        default_conf_path = (
-            Path(os.getenv("HOME")) / ".netatmoqc" / "config.toml"
-        )
+        default_conf_path = Path(os.getenv("HOME")) / ".netatmoqc" / "config.toml"
     parser.add_argument(
         "--version", "-v", action="version", version="%(prog)s v" + __version__
     )

netatmoqc/clustering.py

@@ -70,8 +70,7 @@ def sort_df_by_cluster_size(df):
     # mess up the sorting performed above.
     unique_labels = df[original_cluster_label_col].unique()
     _labels_old2new = {
-        old: new
-        for new, old in enumerate(lab for lab in unique_labels if lab >= 0)
+        old: new for new, old in enumerate(lab for lab in unique_labels if lab >= 0)
     }
 
     @np.vectorize
@@ -455,9 +454,7 @@ def cluster_netatmo_obs(df, config, **kwargs):
 
         # Reset df: Only accepted obs will be passed on to the whole-domain
         # clustering. Rejections will be added again to df after that.
-        df_rejected = df_rejoined_split[
-            df_rejoined_split["cluster_label"] < 0
-        ].copy()
+        df_rejected = df_rejoined_split[df_rejoined_split["cluster_label"] < 0].copy()
         cols_to_drop = [c for c in df_rejected.columns if c not in df.columns]
         df = df_rejoined_split
         df = df[~df["id"].isin(df_rejected["id"])].drop(cols_to_drop, axis=1)
@@ -469,16 +466,12 @@ def cluster_netatmo_obs(df, config, **kwargs):
             "DTG=%s: Main clustering over whole domain...",
             df.metadata_dict["dtg"],
         )
-    df = _cluster_netatmo_obs_one_domain(
-        df=df, config=config, domain=domain, **kwargs
-    )
+    df = _cluster_netatmo_obs_one_domain(df=df, config=config, domain=domain, **kwargs)
 
     if df_rejected is not None:
         # Put back eventual obs rejected at the pre-clustering step
         if "original_cluster_label" in df.columns:
-            df_rejected["original_cluster_label"] = df_rejected[
-                "cluster_label"
-            ].copy()
+            df_rejected["original_cluster_label"] = df_rejected["cluster_label"].copy()
         df = pd.concat([df, df_rejected], ignore_index=True)
 
     # Now we're done.

netatmoqc/commands_functions.py

@@ -77,13 +77,9 @@ def cluster_obs_single_dtg(args):
         outdir.mkdir(parents=True)
 
     try:
-        df = read_netatmo_data_for_dtg(
-            dtg, rootdir=config.general.data_rootdir
-        )
+        df = read_netatmo_data_for_dtg(dtg, rootdir=config.general.data_rootdir)
     except DataNotFoundError:
-        logger.warning(
-            "Could not cluster obs for dtg=%s: ", dtg, exc_info=True
-        )
+        logger.warning("Could not cluster obs for dtg=%s: ", dtg, exc_info=True)
         return
 
     df, _ = remove_irregular_stations(df)
@@ -102,9 +98,7 @@ def cluster_obs_single_dtg(args):
         if args.show:
             fig.show(config=DEF_FIGSHOW_CONFIG)
 
-    logger.info(
-        "%sDone with 'cluster' command.%s", logcolor.cyan, logcolor.reset
-    )
+    logger.info("%sDone with 'cluster' command.%s", logcolor.cyan, logcolor.reset)
 
 
 ########################################
@@ -135,9 +129,7 @@ def _select_stations_single_dtg(dtg, config, args):
         cpu_share = multiprocessing.cpu_count() // proc_family_size
 
     try:
-        df = read_netatmo_data_for_dtg(
-            dtg, rootdir=config.general.data_rootdir
-        )
+        df = read_netatmo_data_for_dtg(dtg, rootdir=config.general.data_rootdir)
     except DataNotFoundError:
         logger.warning("Could not select obs for dtg=%s: ", dtg, exc_info=True)
         return pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
@@ -250,17 +242,12 @@ def select_stations(args):
     # Using a rescued_rejected_stats list to modify the dataframes
     # df_accepted/df_rejected is much faster than modifying
     # the dataframes inside the loop above
-    rescued_rows = df_rejected.loc[
-        df_rejected["id"].isin(rescued_rejected_stats), :
-    ]
+    rescued_rows = df_rejected.loc[df_rejected["id"].isin(rescued_rejected_stats), :]
     df_accepted = df_accepted.append(rescued_rows, ignore_index=True)
     df_rejected = df_rejected.drop(rescued_rows.index)
     if len(rescued_rejected_stats) > 0:
         logger.info(
-            (
-                "    > Rescuing %d stations rejected in "
-                "less than %.1f%% of occurences"
-            ),
+            ("    > Rescuing %d stations rejected in " "less than %.1f%% of occurences"),
             len(rescued_rejected_stats),
             100 * config.commands.select.station_rejection_tol,
         )
@@ -316,9 +303,7 @@ def select_stations(args):
     coarse_grid_factor = config.domain.thinning_grid_coarse_factor
     if coarse_grid_factor > 0:
         domain = Domain.construct_from_dict(config.domain)
-        logger.info(
-            "Thinning accepted obs: Keep only 1 station per support grid point"
-        )
+        logger.info("Thinning accepted obs: Keep only 1 station per support grid point")
         logger.info(
             "    > Support grid spacing: %.1f m (%d x the domain's)",
             domain.thinning_grid.x_spacing,
@@ -345,9 +330,7 @@ def select_stations(args):
         # (c) Thin data keeping only the first entry found at each (i, j).
         #     As we've sorted by rejection rate (lower to higher), all but the
         #     lowest-rejection-rate station at each grid (i, j) will be kept.
-        grid_trimmed_stations = domain.thinning_grid.thin_obs(
-            df_accepted, method="first"
-        )
+        grid_trimmed_stations = domain.thinning_grid.thin_obs(df_accepted, method="first")
 
         # (d) Finally, move to df_rejected those stations that appear in
         #     df_accepted but not in grid_trimmed_stations
@@ -495,9 +478,7 @@ def csv2obsoul(args):
         obsoul_export_params=config.general.obsoul_export_params,
     )
 
-    logger.info(
-        "%sDone with 'csv2obsoul' command.%s", logcolor.cyan, logcolor.reset
-    )
+    logger.info("%sDone with 'csv2obsoul' command.%s", logcolor.cyan, logcolor.reset)
 
 
 ######################################
@@ -531,9 +512,7 @@ def thin_data_from_csv_files(args):
 
     for fpath in file_list:
         if fpath.suffix != ".csv":
-            logger.warning(
-                "Only csv files supported. Skipping file '%s'", fpath
-            )
+            logger.warning("Only csv files supported. Skipping file '%s'", fpath)
             continue
 
         logger.info("Parsing data from file %s", fpath)
@@ -608,9 +587,7 @@ def show(args):
             logger.info("Openning file '%s'", fpath)
             _open_file_with_default_app(fpath)
         else:
-            logger.warning(
-                "Only html and csv files supported. Skipping file '%s'", fpath
-            )
+            logger.warning("Only html and csv files supported. Skipping file '%s'", fpath)
 
     if len(dataframes) > 0:
         fig = show_cmd_get_fig_from_dataframes(args, dataframes, domain)

netatmoqc/config_parser.py

@@ -65,9 +65,7 @@ class UndefinedValueType:
         if self._return_self_on_attr_error:
             return self
         raise AttributeError(
-            "'{}' object has no attribute '{}'".format(
-                self.__class__.__name__, item
-            )
+            "'{}' object has no attribute '{}'".format(self.__class__.__name__, item)
         )
 
     def __copy__(self):
@@ -378,9 +376,7 @@ with config_section("general") as section:
     config_metadata.register("dtgs.end")
     config_metadata.register("dtgs.cycle_length", default="3H")
     # Data cols to ignore when running clustering
-    config_metadata.register(
-        "unclusterable_data_columns", default=["id", "time_utc"]
-    )
+    config_metadata.register("unclusterable_data_columns", default=["id", "time_utc"])
     # Data cols to export when saving obsoul output
     config_metadata.register(
         "obsoul_export_params",
@@ -464,12 +460,8 @@ with config_section("domain") as section:
     # <https://hirlam.org/trac/browser/Harmonie/scr/Harmonie_domains.pm>
     config_metadata.register("nlon", default=900, minval=1, astype=int)
     config_metadata.register("nlat", default=960, minval=1, astype=int)
-    config_metadata.register(
-        "lonc", default=16.763011639, minval=-180, maxval=180
-    )
-    config_metadata.register(
-        "latc", default=63.489212956, minval=-90, maxval=90
-    )
+    config_metadata.register("lonc", default=16.763011639, minval=-180, maxval=180)
+    config_metadata.register("latc", default=63.489212956, minval=-90, maxval=90)
     config_metadata.register("lon0", default=15.0, minval=-180, maxval=180)
     config_metadata.register("lat0", default=63.0, minval=-90, maxval=90)
     config_metadata.register("gsize", default=2500.0, minval=0)
@@ -509,9 +501,7 @@ def _parse_dtg_entires(dtgs_config):
         try:
             dtgs = DtgContainer(dtgs_config["list"], cycle_length=cycle_length)
         except ValueError as err:
-            raise ValueError(
-                "Bad 'dtg.list' or 'dtg.cycle_length' config."
-            ) from err
+            raise ValueError("Bad 'dtg.list' or 'dtg.cycle_length' config.") from err
     else:
         try:
             _ = dtgs_config["start"]
@@ -527,9 +517,7 @@ def _parse_dtg_entires(dtgs_config):
                 cycle_length=cycle_length,
             )
         except (ValueError, TypeError) as err:
-            raise ValueError(
-                "Bad 'dtg.start/end/cycle_length' config."
-            ) from err
+            raise ValueError("Bad 'dtg.start/end/cycle_length' config.") from err
 
     return dtgs
 
@@ -569,9 +557,7 @@ def _raw2parsed(raw, recog_configs=config_metadata, parent_keys=()):
 
         if metadata is UndefinedConfigValue:
             # Leave as is
-            logger.warning(
-                "Config opt '%s' not recognised. Passed as is.", full_key
-            )
+            logger.warning("Config opt '%s' not recognised. Passed as is.", full_key)
             parsed[key] = user_val
             continue
 
@@ -673,13 +659,9 @@ def _fill_defaults(raw, recog_configs=config_metadata, parent_keys=()):
                 user_value = parsed[key]
             except KeyError:
                 if metadata.default is NoDefaultProvided:
-                    logger.debug(
-                        "Missing config opt '%s' with no default", full_key
-                    )
+                    logger.debug("Missing config opt '%s' with no default", full_key)
                 else:
-                    logger.debug(
-                        "Filling defaults for config opt '%s'", full_key
-                    )
+                    logger.debug("Filling defaults for config opt '%s'", full_key)
                     parsed[key] = metadata.default
                     if metadata.astype:
                         parsed[key] = metadata.astype(parsed[key])
@@ -692,8 +674,7 @@ def _fill_defaults(raw, recog_configs=config_metadata, parent_keys=()):
                 parsed[key] = _fill_defaults(user_value, metadata, all_keys)
         else:
             raise TypeError(
-                "Expected only _MetadataDict or ConfigDict, got %s"
-                % (type(metadata))
+                "Expected only _MetadataDict or ConfigDict, got %s" % (type(metadata))
             )
     return parsed
 
@@ -730,9 +711,7 @@ class ParsedConfig:
         self._parsed = _fill_defaults(_raw2parsed(self._raw))
 
         # DTGs are treated a bit specially
-        self._parsed.general.dtgs = _parse_dtg_entires(
-            self._parsed.general.dtgs
-        )
+        self._parsed.general.dtgs = _parse_dtg_entires(self._parsed.general.dtgs)
 
         self._parsed.set_dynamic_flags(False)
 

netatmoqc/domains.py

@@ -49,8 +49,7 @@ class Grid2D:
     def _validated_axis_info(axis):
         if not isinstance(axis, GridAxisConfig):
             raise TypeError(
-                "expected type 'GridAxisConfig', got '%s' instead"
-                % (type(axis).__name__)
+                "expected type 'GridAxisConfig', got '%s' instead" % (type(axis).__name__)
             )
         return axis
 
@@ -295,17 +294,13 @@ class DomainGrid(Grid2D):
     def thin_obs(self, df, method="nearest"):
         """Return df with only one entry per grid point."""
         if method not in ["nearest", "first"]:
-            raise NotImplementedError(
-                "'method' must be one of: 'nearest', 'first'"
-            )
+            raise NotImplementedError("'method' must be one of: 'nearest', 'first'")
 
         if len(df.index) == 0:
             return df
 
         # Add grid (i, j) info
-        icol, jcol = self.lonlat2grid(
-            df["lon"].to_numpy(), df["lat"].to_numpy()
-        )
+        icol, jcol = self.lonlat2grid(df["lon"].to_numpy(), df["lat"].to_numpy())
         df["i"] = icol
         df["j"] = jcol
 
@@ -368,16 +363,12 @@ class Domain:
         def init_proj(ngrid_lonlat, proj_lon0_lat0, grid_spacing):
             """Help routine to initialise domain projection."""
             if self.lmrt and abs(proj_lon0_lat0[1]) > 0:
-                logger.warning(
-                    "lat0 should be 0 if lmrt=True. Resetting lat0 to 0."
-                )
+                logger.warning("lat0 should be 0 if lmrt=True. Resetting lat0 to 0.")
                 proj_lon0_lat0 = (proj_lon0_lat0[0], 0.0)
 
             y_range = ngrid_lonlat[1] * grid_spacing
             return DomainProjection(
-                name=self._auto_choose_projname(
-                    lat0=proj_lon0_lat0[1], y_range=y_range
-                ),
+                name=self._auto_choose_projname(lat0=proj_lon0_lat0[1], y_range=y_range),
                 lon0=proj_lon0_lat0[0],
                 lat0=proj_lon0_lat0[1],
             )
@@ -390,9 +381,7 @@ class Domain:
         def init_grid(ngrid_lonlat, grid_spacing, ezone_ngrid):
             """Help routine to initialise domain grid."""
             # (a) Get projected coords of grid center
-            center_xy = proj.lonlat2xy(
-                lon=center_lonlat[0], lat=center_lonlat[1]
-            )
+            center_xy = proj.lonlat2xy(lon=center_lonlat[0], lat=center_lonlat[1])
             # (b) Grid x-axis
             x_range = ngrid_lonlat[0] * grid_spacing
             xmin = center_xy[0] - 0.5 * x_range
@@ -553,14 +542,9 @@ class Domain:
 
         splits = []
         for ilat in range(nsplit_lat):
-            new_yc = (
-                self.grid.ymin + (ilat + 0.5) * new_nlat * self.grid.y_spacing
-            )
+            new_yc = self.grid.ymin + (ilat + 0.5) * new_nlat * self.grid.y_spacing
             for ilon in range(nsplit_lon):
-                new_xc = (
-                    self.grid.xmin
-                    + (ilon + 0.5) * new_nlon * self.grid.x_spacing
-                )
+                new_xc = self.grid.xmin + (ilon + 0.5) * new_nlon * self.grid.x_spacing
                 new_lonc, new_latc = self.proj.xy2lonlat(new_xc, new_yc)
                 split = self.__class__(
                     name="{} Split {}/{}".format(

netatmoqc/dtgs.py

@@ -233,9 +233,7 @@ class Dtg(datetime):
             )
         if not self.compatible_with_cycle_length(cycle_length):
             raise ValueError(
-                "Dtg {} not compatible with cycle_length {}".format(
-                    self, cycle_length
-                )
+                "Dtg {} not compatible with cycle_length {}".format(self, cycle_length)
             )
         self._cycle_length = cycle_length
 
@@ -314,18 +312,14 @@ class DtgContainer:
         # (i) data and (start, end) are mutually exclusive (avoid ambiguities)
         if data is None:
             if (start is None) or (end is None):
-                raise ValueError(
-                    "Need 'start' and 'end' and if 'data' is not passed"
-                )
+                raise ValueError("Need 'start' and 'end' and if 'data' is not passed")
             # msg_which_input and input_vals will be used later on in the
             # cycle_length validation
             msg_which_input = "'start' and 'end'"
             input_vals = [start, end]
         else:
             if (start is not None) or (end is not None):
-                raise ValueError(
-                    "Pass either just 'data' or both 'start' and 'end'"
-                )
+                raise ValueError("Pass either just 'data' or both 'start' and 'end'")
             msg_which_input = "the elements of 'data'"
             input_vals = data
         # (ii) Decide whether to get cycle_length from
@@ -369,9 +363,7 @@ class DtgContainer:
             self._start = None
             self._end = None
             # Convert data to Dtg for consistency, and make it immutable
-            self._data = tuple(
-                Dtg(d, cycle_length=self.cycle_length) for d in data
-            )
+            self._data = tuple(Dtg(d, cycle_length=self.cycle_length) for d in data)
 
     @property
     def cycle_length(self):
@@ -394,9 +386,7 @@ class DtgContainer:
 
         def calc_nth_item(n):
             if (n > len(self) - 1) or (n < -len(self)):
-                raise IndexError(
-                    "{} index out of range".format(self.__class__.__name__)
-                )
+                raise IndexError("{} index out of range".format(self.__class__.__name__))
             return self._start + sign * (n % len(self)) * self.cycle_length
 
         if isinstance(item, slice):

netatmoqc/hollow_symmetric_matrix.py

@@ -130,10 +130,7 @@ def _data_index_to_matrix_index(n, i_data, check_bounds=True):
         if i_data < 0 or i_data >= n * (n - 1) / 2:
             raise ValueError("Arg 'i_data' not in the range [0, n*(n-1)/2)")
 
-    i = (
-        int(0.5 * ((2 * n + 1) - np.sqrt((2 * n + 1) ** 2 - 8 * (n + i_data))))
-        - 1
-    )
+    i = int(0.5 * ((2 * n + 1) - np.sqrt((2 * n + 1) ** 2 - 8 * (n + i_data)))) - 1
     j = (1 + i_data + i) - (i * (2 * n - i - 1)) // 2
     return i, j
 
@@ -417,9 +414,7 @@ class HollowSymmetricMatrix(np.lib.mixins.NDArrayOperatorsMixin):
             order = 0.5 * (1.0 + np.sqrt(1.0 + 8.0 * n_indep))
             nearest_int_order = int(np.rint(order))
             if abs(order - nearest_int_order) > 1e-8:
-                nearest_n_indep = int(
-                    0.5 * nearest_int_order * (nearest_int_order - 1)
-                )
+                nearest_n_indep = int(0.5 * nearest_int_order * (nearest_int_order - 1))
                 msg = "len(data)={0} incompatible with {1}: {2} elements "
                 msg += "needed for a {3}x{3} matrix (closest option)"
                 msg = msg.format(
@@ -448,8 +443,7 @@ class HollowSymmetricMatrix(np.lib.mixins.NDArrayOperatorsMixin):
                 np.fill_diagonal(data, 0)
         else:
             raise ValueError(
-                "%s: ndim(data) should be 1 or 2. Got %s."
-                % (cls.__name__, np.ndim(data))
+                "%s: ndim(data) should be 1 or 2. Got %s." % (cls.__name__, np.ndim(data))
             )
         return data
 
@@ -556,9 +550,7 @@ class HollowSymmetricMatrix(np.lib.mixins.NDArrayOperatorsMixin):
         if self.stored_as_dense:
             rtn = self._data