Ruff lint format

.github/scripts/make_release.py

@@ -5,6 +5,7 @@
 to create the new release.
 
 """
+
 import glob
 import re
 import subprocess

.github/scripts/setup_devbranch.py

@@ -9,6 +9,7 @@
 The changes are not commited to the repository. This is dealt with in the bash script
 `setup_devbranch.sh` (which is also the caller of this script).
 """
+
 import glob
 import json
 import re

.pre-commit-config.yaml

@@ -5,19 +5,17 @@ default_language_version:
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.2.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
-  - repo: https://github.com/pycqa/isort
-    rev: '5.13.2'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.5
     hooks:
-      - id: isort
-        args: ["--profile", "black", "--filter-files"]
-
-  # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: '24.4.2'
-    hooks:
-      - id: black-jupyter
+      # Run the linter, applying safe fixes
+      # TODO: Enable once all existing issues are fixed
+      # - id: ruff-check
+      #   args: [ --fix ]
+      # Run the formatter.
+      - id: ruff-format

climada/engine/calibration_opt.py

@@ -94,33 +94,28 @@ def calib_instance(
             for cnt_, year in years_in_common.iteritems():
                 df_out.loc[df_out["year"] == year, "impact_CLIMADA"] = iys[year]
 
-    else:  # impact per event
-        if df_out.empty | df_out.index.shape[0] == 1:
-            for cnt_, impact in enumerate(impacts.at_event):
-                if cnt_ > 0:
-                    df_out.loc[cnt_] = df_out.loc[0]  # copy info from first row
-                df_out.loc[cnt_, "impact_CLIMADA"] = impact
-                df_out.loc[cnt_, "event_id"] = int(impacts.event_id[cnt_])
-                df_out.loc[cnt_, "event_name"] = impacts.event_name[cnt_]
-                df_out.loc[cnt_, "year"] = dt.datetime.fromordinal(
-                    impacts.date[cnt_]
-                ).year
-                df_out.loc[cnt_, "date"] = impacts.date[cnt_]
-        elif df_out.index.shape[0] == impacts.at_event.shape[0]:
-            for cnt_, (impact, ind) in enumerate(zip(impacts.at_event, df_out.index)):
-                df_out.loc[ind, "impact_CLIMADA"] = impact
-                df_out.loc[ind, "event_id"] = int(impacts.event_id[cnt_])
-                df_out.loc[ind, "event_name"] = impacts.event_name[cnt_]
-                df_out.loc[ind, "year"] = dt.datetime.fromordinal(
-                    impacts.date[cnt_]
-                ).year
-                df_out.loc[ind, "date"] = impacts.date[cnt_]
-        else:
-            raise ValueError(
-                "adding simulated impacts to reported impacts not"
-                " yet implemented. use yearly_impact=True or run"
-                " without init_impact_data."
-            )
+    elif df_out.empty | df_out.index.shape[0] == 1:
+        for cnt_, impact in enumerate(impacts.at_event):
+            if cnt_ > 0:
+                df_out.loc[cnt_] = df_out.loc[0]  # copy info from first row
+            df_out.loc[cnt_, "impact_CLIMADA"] = impact
+            df_out.loc[cnt_, "event_id"] = int(impacts.event_id[cnt_])
+            df_out.loc[cnt_, "event_name"] = impacts.event_name[cnt_]
+            df_out.loc[cnt_, "year"] = dt.datetime.fromordinal(impacts.date[cnt_]).year
+            df_out.loc[cnt_, "date"] = impacts.date[cnt_]
+    elif df_out.index.shape[0] == impacts.at_event.shape[0]:
+        for cnt_, (impact, ind) in enumerate(zip(impacts.at_event, df_out.index)):
+            df_out.loc[ind, "impact_CLIMADA"] = impact
+            df_out.loc[ind, "event_id"] = int(impacts.event_id[cnt_])
+            df_out.loc[ind, "event_name"] = impacts.event_name[cnt_]
+            df_out.loc[ind, "year"] = dt.datetime.fromordinal(impacts.date[cnt_]).year
+            df_out.loc[ind, "date"] = impacts.date[cnt_]
+    else:
+        raise ValueError(
+            "adding simulated impacts to reported impacts not"
+            " yet implemented. use yearly_impact=True or run"
+            " without init_impact_data."
+        )
     if return_cost != "False":
         df_out = calib_cost_calc(df_out, return_cost)
     return df_out
@@ -293,8 +288,7 @@ def init_impact_data(
             # em_data = emdat_impact_event(source_file)
     else:
         raise ValueError(
-            "init_impact_data not yet implemented for other impact_data_sources "
-            "than emdat."
+            "init_impact_data not yet implemented for other impact_data_sources than emdat."
         )
     return em_data
 
@@ -378,17 +372,16 @@ def calib_all(
     # prepare impact data
     if isinstance(impact_data_source, pd.DataFrame):
         df_impact_data = impact_data_source
+    elif list(impact_data_source.keys()) == ["emdat"]:
+        df_impact_data = init_impact_data(
+            hazard_type,
+            region_ids,
+            year_range,
+            impact_data_source["emdat"],
+            year_range[-1],
+        )
     else:
-        if list(impact_data_source.keys()) == ["emdat"]:
-            df_impact_data = init_impact_data(
-                hazard_type,
-                region_ids,
-                year_range,
-                impact_data_source["emdat"],
-                year_range[-1],
-            )
-        else:
-            raise ValueError("other impact data sources not yet implemented.")
+        raise ValueError("other impact data sources not yet implemented.")
     params_generator = (
         dict(zip(param_full_dict, x))
         for x in itertools.product(*param_full_dict.values())
@@ -460,17 +453,16 @@ def calib_optimize(
     # prepare impact data
     if isinstance(impact_data_source, pd.DataFrame):
         df_impact_data = impact_data_source
+    elif list(impact_data_source.keys()) == ["emdat"]:
+        df_impact_data = init_impact_data(
+            hazard_type,
+            region_ids,
+            year_range,
+            impact_data_source["emdat"],
+            year_range[-1],
+        )
     else:
-        if list(impact_data_source.keys()) == ["emdat"]:
-            df_impact_data = init_impact_data(
-                hazard_type,
-                region_ids,
-                year_range,
-                impact_data_source["emdat"],
-                year_range[-1],
-            )
-        else:
-            raise ValueError("other impact data sources not yet implemented.")
+        raise ValueError("other impact data sources not yet implemented.")
 
     # definie specific function to
     def specific_calib(values):

climada/engine/cost_benefit.py

@@ -25,9 +25,9 @@
 import logging
 from typing import Dict, Optional, Tuple, Union
 
-import matplotlib.colors as colors
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib import colors
 from matplotlib.patches import FancyArrowPatch, Rectangle
 from tabulate import tabulate
 
@@ -661,7 +661,7 @@ def plot_waterfall(
         ent_future,
         risk_func=risk_aai_agg,
         axis=None,
-        **kwargs
+        **kwargs,
     ):
         """Plot waterfall graph at future with given risk metric. Can be called
         before and after calc().
@@ -740,7 +740,7 @@ def plot_waterfall(
             2,
             height=(risk_dev - curr_risk) / norm_fact,
             bottom=curr_risk / norm_fact,
-            **kwargs
+            **kwargs,
         )
         axis.text(
             2,
@@ -755,7 +755,7 @@ def plot_waterfall(
             3,
             height=(fut_risk - risk_dev) / norm_fact,
             bottom=risk_dev / norm_fact,
-            **kwargs
+            **kwargs,
         )
         axis.text(
             3,
@@ -799,7 +799,7 @@ def plot_arrow_averted(
         risk_func=risk_aai_agg,
         disc_rates=None,
         imp_time_depen=1,
-        **kwargs
+        **kwargs,
     ):
         """Plot waterfall graph with accumulated values from present to future
         year. Call after calc() with save_imp=True.
@@ -877,7 +877,7 @@ def plot_arrow_averted(
             tot_benefit,
             bars[3].get_height() * norm_fact,
             norm_fact,
-            **kwargs
+            **kwargs,
         )
 
     def plot_waterfall_accumulated(
@@ -888,7 +888,7 @@ def plot_waterfall_accumulated(
         risk_func=risk_aai_agg,
         imp_time_depen=1,
         axis=None,
-        **kwargs
+        **kwargs,
     ):
         """Plot waterfall graph with accumulated values from present to future
         year. Call after calc() with save_imp=True. Provide same inputs as in calc.
@@ -976,7 +976,7 @@ def plot_waterfall_accumulated(
             2,
             height=(risk_dev - risk_curr) / norm_fact,
             bottom=risk_curr / norm_fact,
-            **kwargs
+            **kwargs,
         )
         axis.text(
             2,
@@ -991,7 +991,7 @@ def plot_waterfall_accumulated(
             3,
             height=(risk_tot - risk_dev) / norm_fact,
             bottom=risk_dev / norm_fact,
-            **kwargs
+            **kwargs,
         )
         axis.text(
             3,
@@ -1395,7 +1395,7 @@ def _plot_list_cost_ben(cb_list, axis=None, **kwargs):
                         cb_res.benefit[meas_n] / norm_fact,
                         1 / cb_res.cost_ben_ratio[meas_n],
                         color=cb_res.color_rgb[meas_n],
-                        **kwargs
+                        **kwargs,
                     )
                 )
 
@@ -1489,7 +1489,7 @@ def _plot_averted_arrow(axis, bar_4, tot_benefit, risk_tot, norm_fact, **kwargs)
                     bar_top[0] - (bar_top[0] - bar_bottom[0]) / 2,
                     risk_tot / norm_fact - arrow_len,
                 ),
-                **kwargs
+                **kwargs,
             )
         )
 

climada/engine/forecast.py

@@ -476,7 +476,7 @@ def _plot_imp_map(
                 coord[:, 0],
                 c=array_im,
                 transform=ccrs.PlateCarree(),
-                **kwargs
+                **kwargs,
             )
             if shapes:
                 # add warning regions
@@ -882,7 +882,7 @@ def _plot_exc_prob(
                 coord[:, 0],
                 c=array_im,
                 transform=ccrs.PlateCarree(),
-                **kwargs
+                **kwargs,
             )
             if shapes:
                 # add warning regions

climada/engine/impact.py

@@ -35,12 +35,12 @@
 import contextily as ctx
 import geopandas as gpd
 import h5py
-import matplotlib.animation as animation
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import xlsxwriter
 from deprecation import deprecated
+from matplotlib import animation
 from matplotlib.colors import Normalize
 from pandas.api.types import is_string_dtype
 from pyproj import CRS as pyprojCRS
@@ -188,8 +188,7 @@ def __init__(
             )
         if len(self.coord_exp) != len(self.eai_exp):
             raise AttributeError(
-                "Number of exposures points is different from"
-                "number of eai_exp values"
+                "Number of exposures points is different fromnumber of eai_exp values"
             )
         if imp_mat is not None:
             self.imp_mat = imp_mat
@@ -214,8 +213,7 @@ def calc(
     ):
         """This function is deprecated, use ``ImpactCalc.impact`` instead."""
         LOGGER.warning(
-            "The use of Impact().calc() is deprecated."
-            " Use ImpactCalc().impact() instead."
+            "The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead."
         )
         from climada.engine.impact_calc import (  # pylint: disable=import-outside-toplevel
             ImpactCalc,
@@ -1238,8 +1236,9 @@ def plot_rp_imp(
 
         impacts_stats_vals = impacts_stats.values[:, 1:].T.astype(float)
         if not log10_scale:
-            min_impact, max_impact = np.nanmin(impacts_stats_vals), np.nanmax(
-                impacts_stats_vals
+            min_impact, max_impact = (
+                np.nanmin(impacts_stats_vals),
+                np.nanmax(impacts_stats_vals),
             )
             kwargs.update(
                 {
@@ -1471,7 +1470,6 @@ def write_csr(group, name, value):
 
         # Open file in write mode
         with h5py.File(file_path, "w") as file:
-
             # Now write all attributes
             # NOTE: Remove leading underscore to write '_tot_value' as regular attribute
             for name, value in self.__dict__.items():
@@ -1612,8 +1610,7 @@ def from_excel(cls, file_name):
     def read_excel(self, *args, **kwargs):
         """This function is deprecated, use Impact.from_excel instead."""
         LOGGER.warning(
-            "The use of Impact.read_excel is deprecated."
-            "Use Impact.from_excel instead."
+            "The use of Impact.read_excel is deprecated.Use Impact.from_excel instead."
         )
         self.__dict__ = Impact.from_excel(*args, **kwargs).__dict__
 
@@ -1671,7 +1668,6 @@ def from_hdf5(cls, file_path: Union[str, Path]):
         """
         kwargs = dict()
         with h5py.File(file_path, "r") as file:
-
             # Impact matrix
             if "imp_mat" in file:
                 impact_matrix = file["imp_mat"]

climada/engine/impact_calc.py

@@ -196,8 +196,7 @@ def impact(
         )
         if insured:
             LOGGER.info(
-                "cover and/or deductible columns detected,"
-                " going to calculate insured impact"
+                "cover and/or deductible columns detected, going to calculate insured impact"
             )
             # TODO: make a better impact matrix generator for insured impacts when
             # the impact matrix is already present

climada/engine/test/test_cost_benefit.py

@@ -39,7 +39,6 @@
 from climada.entity.measures.base import LOGGER as ILOG
 from climada.hazard.base import Hazard
 from climada.test import get_test_file
-from climada.util.api_client import Client
 from climada.util.constants import ENT_DEMO_FUTURE, ENT_DEMO_TODAY
 
 ENT_TEST_MAT = get_test_file("demo_today", file_format="MAT-file")

climada/engine/test/test_impact.py

@@ -1207,7 +1207,6 @@ def test__exp_build_event(self):
 
 
 class TestMatchCentroids(unittest.TestCase):
-
     def test_match_centroids(self):
         "Test that hazard centroids get assigned correctly"
         exp = ENT.exposures

climada/engine/test/test_impact_calc.py

@@ -35,7 +35,6 @@
 from climada.entity.entity_def import Entity
 from climada.hazard.base import Centroids, Hazard
 from climada.test import get_test_file
-from climada.util.api_client import Client
 from climada.util.config import Config
 from climada.util.constants import DEMO_DIR, ENT_DEMO_TODAY
 

climada/engine/unsequa/calc_delta_climate.py

@@ -235,8 +235,7 @@ def uncertainty(
 
         if unc_sample.samples_df.empty:
             raise ValueError(
-                "No sample was found. Please create one first"
-                "using UncImpact.make_sample(N)"
+                "No sample was found. Please create one firstusing UncImpact.make_sample(N)"
             )
 
         # copy may not be needed, but is kept to prevent potential