Update pre-commit hooks to use latest versions of ruff and mypy (#236)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: andersy005

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
       - id: check-yaml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.5.5"
+    rev: "v0.6.4"
     hooks:
       - id: ruff
         args: ["--fix"]
@@ -26,13 +26,12 @@ repos:
       - id: prettier
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.0
+    rev: v1.11.2
     hooks:
       - id: mypy
         additional_dependencies: [
             # Type stubs
             types-setuptools,
-            types-pkg_resources,
             # Dependencies that are typed
             numpy,
             xarray,

doc/demo.ipynb

@@ -26,6 +26,7 @@
    "outputs": [],
    "source": [
     "import xarray as xr\n",
+    "\n",
     "import xbatcher"
    ]
   },
@@ -46,12 +47,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "store = \"az://carbonplan-share/example_cmip6_data.zarr\"\n",
+    "store = 'az://carbonplan-share/example_cmip6_data.zarr'\n",
     "ds = xr.open_dataset(\n",
     "    store,\n",
-    "    engine=\"zarr\",\n",
+    "    engine='zarr',\n",
     "    chunks={},\n",
-    "    backend_kwargs={\"storage_options\": {\"account_name\": \"carbonplan\"}},\n",
+    "    backend_kwargs={'storage_options': {'account_name': 'carbonplan'}},\n",
     ")\n",
     "\n",
     "# the attributes contain a lot of useful information, but clutter the print out when we inspect the outputs\n",
@@ -98,10 +99,10 @@
     "\n",
     "bgen = xbatcher.BatchGenerator(\n",
     "    ds=ds,\n",
-    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
+    "    input_dims={'time': n_timepoint_in_each_sample},\n",
     ")\n",
     "\n",
-    "print(f\"{len(bgen)} batches\")"
+    "print(f'{len(bgen)} batches')"
    ]
   },
   {
@@ -133,7 +134,7 @@
    "outputs": [],
    "source": [
     "expected_n_batch = len(ds.time) / n_timepoint_in_each_sample\n",
-    "print(f\"Expecting {expected_n_batch} batches, getting {len(bgen)} batches\")"
+    "print(f'Expecting {expected_n_batch} batches, getting {len(bgen)} batches')"
    ]
   },
   {
@@ -153,7 +154,7 @@
    "source": [
     "expected_batch_size = len(ds.lat) * len(ds.lon)\n",
     "print(\n",
-    "    f\"Expecting {expected_batch_size} samples per batch, getting {len(batch.sample)} samples per batch\"\n",
+    "    f'Expecting {expected_batch_size} samples per batch, getting {len(batch.sample)} samples per batch'\n",
     ")"
    ]
   },
@@ -179,12 +180,12 @@
     "\n",
     "bgen = xbatcher.BatchGenerator(\n",
     "    ds=ds,\n",
-    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
-    "    batch_dims={\"time\": n_timepoint_in_each_batch},\n",
+    "    input_dims={'time': n_timepoint_in_each_sample},\n",
+    "    batch_dims={'time': n_timepoint_in_each_batch},\n",
     "    concat_input_dims=True,\n",
     ")\n",
     "\n",
-    "print(f\"{len(bgen)} batches\")"
+    "print(f'{len(bgen)} batches')"
    ]
   },
   {
@@ -217,11 +218,11 @@
    "source": [
     "n_timepoint_in_batch = 31\n",
     "\n",
-    "bgen = xbatcher.BatchGenerator(ds=ds, input_dims={\"time\": n_timepoint_in_batch})\n",
+    "bgen = xbatcher.BatchGenerator(ds=ds, input_dims={'time': n_timepoint_in_batch})\n",
     "\n",
     "for batch in bgen:\n",
-    "    print(f\"last time point in ds    is {ds.time[-1].values}\")\n",
-    "    print(f\"last time point in batch is {batch.time[-1].values}\")\n",
+    "    print(f'last time point in ds    is {ds.time[-1].values}')\n",
+    "    print(f'last time point in batch is {batch.time[-1].values}')\n",
     "batch"
    ]
   },
@@ -249,15 +250,15 @@
     "\n",
     "bgen = xbatcher.BatchGenerator(\n",
     "    ds=ds,\n",
-    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
-    "    batch_dims={\"time\": n_timepoint_in_each_batch},\n",
+    "    input_dims={'time': n_timepoint_in_each_sample},\n",
+    "    batch_dims={'time': n_timepoint_in_each_batch},\n",
     "    concat_input_dims=True,\n",
-    "    input_overlap={\"time\": input_overlap},\n",
+    "    input_overlap={'time': input_overlap},\n",
     ")\n",
     "\n",
     "batch = bgen[0]\n",
     "\n",
-    "print(f\"{len(bgen)} batches\")\n",
+    "print(f'{len(bgen)} batches')\n",
     "batch"
    ]
   },
@@ -283,10 +284,10 @@
     "display(pixel)\n",
     "\n",
     "print(\n",
-    "    f\"sample 1 goes from {pixel.isel(input_batch=0).time[0].values} to {pixel.isel(input_batch=0).time[-1].values}\"\n",
+    "    f'sample 1 goes from {pixel.isel(input_batch=0).time[0].values} to {pixel.isel(input_batch=0).time[-1].values}'\n",
     ")\n",
     "print(\n",
-    "    f\"sample 2 goes from {pixel.isel(input_batch=1).time[0].values} to {pixel.isel(input_batch=1).time[-1].values}\"\n",
+    "    f'sample 2 goes from {pixel.isel(input_batch=1).time[0].values} to {pixel.isel(input_batch=1).time[-1].values}'\n",
     ")"
    ]
   },
@@ -310,28 +311,28 @@
    "outputs": [],
    "source": [
     "bgen = xbatcher.BatchGenerator(\n",
-    "    ds=ds[[\"tasmax\"]].isel(lat=slice(0, 18), lon=slice(0, 18), time=slice(0, 30)),\n",
-    "    input_dims={\"lat\": 9, \"lon\": 9, \"time\": 10},\n",
-    "    batch_dims={\"lat\": 18, \"lon\": 18, \"time\": 15},\n",
+    "    ds=ds[['tasmax']].isel(lat=slice(0, 18), lon=slice(0, 18), time=slice(0, 30)),\n",
+    "    input_dims={'lat': 9, 'lon': 9, 'time': 10},\n",
+    "    batch_dims={'lat': 18, 'lon': 18, 'time': 15},\n",
     "    concat_input_dims=True,\n",
-    "    input_overlap={\"lat\": 8, \"lon\": 8, \"time\": 9},\n",
+    "    input_overlap={'lat': 8, 'lon': 8, 'time': 9},\n",
     ")\n",
     "\n",
     "for i, batch in enumerate(bgen):\n",
-    "    print(f\"batch {i}\")\n",
+    "    print(f'batch {i}')\n",
     "    # make sure the ordering of dimension is consistent\n",
-    "    batch = batch.transpose(\"input_batch\", \"lat_input\", \"lon_input\", \"time_input\")\n",
+    "    batch = batch.transpose('input_batch', 'lat_input', 'lon_input', 'time_input')\n",
     "\n",
     "    # only use the first 9 time points as features, since the last time point is the label to be predicted\n",
     "    features = batch.tasmax.isel(time_input=slice(0, 9))\n",
     "    # select the center pixel at the last time point to be the label to be predicted\n",
     "    # the actual lat/lon/time for each of the sample can be accessed in labels.coords\n",
     "    labels = batch.tasmax.isel(lat_input=5, lon_input=5, time_input=9)\n",
     "\n",
-    "    print(\"feature shape\", features.shape)\n",
-    "    print(\"label shape\", labels.shape)\n",
-    "    print(\"shape of lat of each sample\", labels.coords[\"lat\"].shape)\n",
-    "    print(\"\")"
+    "    print('feature shape', features.shape)\n",
+    "    print('label shape', labels.shape)\n",
+    "    print('shape of lat of each sample', labels.coords['lat'].shape)\n",
+    "    print('')"
    ]
   },
   {
@@ -350,21 +351,21 @@
    "outputs": [],
    "source": [
     "for i, batch in enumerate(bgen):\n",
-    "    print(f\"batch {i}\")\n",
+    "    print(f'batch {i}')\n",
     "    # make sure the ordering of dimension is consistent\n",
-    "    batch = batch.transpose(\"input_batch\", \"lat_input\", \"lon_input\", \"time_input\")\n",
+    "    batch = batch.transpose('input_batch', 'lat_input', 'lon_input', 'time_input')\n",
     "\n",
     "    # only use the first 9 time points as features, since the last time point is the label to be predicted\n",
     "    features = batch.tasmax.isel(time_input=slice(0, 9))\n",
-    "    features = features.stack(features=[\"lat_input\", \"lon_input\", \"time_input\"])\n",
+    "    features = features.stack(features=['lat_input', 'lon_input', 'time_input'])\n",
     "\n",
     "    # select the center pixel at the last time point to be the label to be predicted\n",
     "    # the actual lat/lon/time for each of the sample can be accessed in labels.coords\n",
     "    labels = batch.tasmax.isel(lat_input=5, lon_input=5, time_input=9)\n",
     "\n",
-    "    print(\"feature shape\", features.shape)\n",
-    "    print(\"label shape\", labels.shape)\n",
-    "    print(\"shape of lat of each sample\", labels.coords[\"lat\"].shape, \"\\n\")"
+    "    print('feature shape', features.shape)\n",
+    "    print('label shape', labels.shape)\n",
+    "    print('shape of lat of each sample', labels.coords['lat'].shape, '\\n')"
    ]
   },
   {