[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ repos:
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.5.7'
+    rev: 'v0.6.2'
     hooks:
       - id: ruff
         args: ["--fix"]

docs/notebooks/domains.ipynb

@@ -307,7 +307,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from cordex import cordex_domain\n",
     "\n",
     "eur11_cordex = cordex_domain(\"EUR-11\", dummy=\"topo\")"

notebooks/atmospheric-river.ipynb

@@ -1012,7 +1012,6 @@
     "def plot_contourf(da, pole, vmin=None, vmax=None, levels=20):\n",
     "    %matplotlib inline\n",
     "    import cartopy.crs as ccrs\n",
-    "    import cartopy.feature as cf\n",
     "\n",
     "    # use ncar colormaps from https://github.com/hhuangwx/cmaps\n",
     "    # conda install -c conda-forge cmaps\n",

notebooks/domains.ipynb

@@ -2383,7 +2383,6 @@
     "    %matplotlib inline\n",
     "    import cartopy.crs as ccrs\n",
     "    import cartopy.feature as cf\n",
-    "    import matplotlib.patheffects as pe\n",
     "    import matplotlib.pyplot as plt\n",
     "\n",
     "    plt.figure(figsize=(20, 10))\n",
@@ -2478,7 +2477,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from cordex import cordex_domain\n",
     "\n",
     "eur11_cordex = cordex_domain(\"EUR-11\", dummy=\"topo\")"

notebooks/ghg-forcing.ipynb

@@ -79,7 +79,6 @@
     "\n",
     "def plot_species(ds):\n",
     "    \"\"\"plot all species and scenarios\"\"\"\n",
-    "    import matplotlib.pyplot as plt\n",
     "\n",
     "    fig = plt.figure(figsize=(10, 15))\n",
     "    axes = []\n",

notebooks/physics.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {},
    "source": [
     "# Physics"
@@ -10,6 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "acae54e37e7d407bbb7b55eff062a284",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,6 +21,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
    "metadata": {},
    "source": [
     "pyremo contains a simple physics package with common algorithms that were used in a number of legacy fortran programs in and around REMO. For convenience, these algorithms are now implemented in the `pyremo.physics` package. Typical reacurring tasks are, e.g., the derivation relative (`RELHUM`) and specific humidity (`QD`) or liquid water content (`QW`) using temperature and pressure. "
@@ -27,6 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "8dd0d8092fe74a7c96281538738b07e2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,6 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "72eea5119410473aa328ad9291626812",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,6 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "8edb47106e1a46a883d545849b8ab81b",
    "metadata": {},
    "outputs": [
     {
@@ -593,6 +599,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
+   "id": "10185d26023b46108eb7d9f57d49d2b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,6 +609,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "8763a12b2bbd4a93a75aff182afb95dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,6 +619,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "7623eae2785240b9bd12b16a66d81610",
    "metadata": {},
    "outputs": [
     {
@@ -643,6 +652,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
+   "id": "7cdc8c89c7104fffa095e18ddfef8986",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -652,6 +662,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
+   "id": "b118ea5561624da68c537baed56e602f",
    "metadata": {},
    "outputs": [
     {
@@ -1200,6 +1211,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
+   "id": "938c804e27f84196a10c8828c723f798",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1209,6 +1221,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
+   "id": "504fb2a444614c0babb325280ed9130a",
    "metadata": {},
    "outputs": [
     {
@@ -1235,14 +1248,14 @@
     }
    ],
    "source": [
-    "import xarray as xr\n",
     "\n",
     "(abs(qd - ds.QD) / ds.QD).isel(lev=26).plot()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "59bbdb311c014d738909a11f9e486628",
    "metadata": {},
    "outputs": [
     {
@@ -1792,6 +1805,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
+   "id": "b43b363d81ae4b689946ece5c682cd59",
    "metadata": {},
    "outputs": [
     {

notebooks/prepare-test-data.ipynb

@@ -63,7 +63,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xarray as xr\n",
     "\n",
     "ds = xr.open_mfdataset(files, data_vars=\"minimal\", coords=\"minimal\")"
    ]

notebooks/preproc-dev.ipynb

@@ -1068,7 +1068,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pyintorg import interface as intf\n",
     "\n",
     "\n",
     "def horizontal_dims(da):\n",
@@ -1343,7 +1342,7 @@
     "    Makes sure that the selected tos time range is large enough for resampling.\n",
     "\n",
     "    \"\"\"\n",
-    "    from datetime import date, datetime, timedelta\n",
+    "    from datetime import datetime, timedelta\n",
     "\n",
     "    import cftime\n",
     "\n",

notebooks/preprocessing-cf.ipynb

@@ -58,7 +58,7 @@
     "# from dask_jobqueue import SLURMCluster\n",
     "import dask\n",
     "import xarray as xr\n",
-    "from dask.distributed import Client, progress\n",
+    "from dask.distributed import Client\n",
     "\n",
     "import pyremo as pr\n",
     "\n",

notebooks/production-analysis.ipynb

@@ -44,7 +44,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import xarray as xr\n",
     "\n",
     "%matplotlib inline\n",
@@ -502,7 +501,7 @@
     }
    ],
    "source": [
-    "from pyremo.analysis import analysis, obs, plot"
+    "from pyremo.analysis import analysis, plot"
    ]
   },
   {

notebooks/remo-cmor-dev-json.ipynb

@@ -19,7 +19,6 @@
    "outputs": [],
    "source": [
     "import glob\n",
-    "import os\n",
     "\n",
     "import cordex as cx\n",
     "\n",
@@ -2516,7 +2515,6 @@
     }
    ],
    "source": [
-    "import cf_xarray as cfxr\n",
     "\n",
     "out.cf.add_bounds(\"time\").cf.bounds"
    ]

notebooks/remo-cmor-dev.ipynb

@@ -31,20 +31,14 @@
     }
    ],
    "source": [
-    "import datetime as dt\n",
     "import glob\n",
     "\n",
     "# from urllib.request import urlopen\n",
-    "import json\n",
-    "import os\n",
-    "import sys\n",
     "\n",
     "# from datetime import datetime as dt\n",
     "from datetime import timedelta as td\n",
     "\n",
     "# import cf_xarray as cfxr\n",
-    "import cftime as cfdt\n",
-    "import cmor\n",
     "import cordex as cx\n",
     "import numpy as np\n",
     "import pandas as pd\n",

notebooks/remo-dataset.ipynb

@@ -500,7 +500,7 @@
     }
    ],
    "source": [
-    "from dask.distributed import Client, progress\n",
+    "from dask.distributed import Client\n",
     "\n",
     "client = Client()\n",
     "client"
@@ -18668,9 +18668,7 @@
    "execution_count": 25,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import xesmf as xe"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",

notebooks/remo-dataset2.ipynb

@@ -41,16 +41,10 @@
    "source": [
     "# import PyRemo as pr\n",
     "# from matplotlib import pyplot as plt\n",
-    "import dask\n",
-    "import fsspec\n",
     "\n",
     "# from tqdm.autonotebook import tqdm\n",
-    "import intake\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
     "import xarray as xr\n",
-    "from dask.diagnostics import progress\n",
     "\n",
     "import pyremo as pr\n",
     "\n",
@@ -3218,7 +3212,6 @@
     "    Function to regrid data bilinearly to a coarser grid\n",
     "    \"\"\"\n",
     "\n",
-    "    import xesmf as xe\n",
     "\n",
     "    return xe.Regridder(finer, coarser, mode)"
    ]
@@ -35342,7 +35335,6 @@
     }
    ],
    "source": [
-    "from dask.distributed import progress\n",
     "\n",
     "%time temps_mean_ = temps_mean.persist()  # start computation\n",
     "%time progress(temps_mean_)"
@@ -35973,7 +35965,6 @@
     }
    ],
    "source": [
-    "from dask.distributed import progress\n",
     "\n",
     "%time temps_cycle_ = temps_cycle.persist()  # start computation\n",
     "%time progress(temps_cycle_)"
@@ -40656,9 +40647,7 @@
    "execution_count": 75,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import xesmf as xe"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",