Update version number, changelog and notebooks for v1.6.0 release

.zenodo.json

@@ -20,7 +20,6 @@
             "affiliation": "University of Oxford",
             "orcid": "0000-0001-9786-3723"
         },
-
         {
             "name": "Kukulies, Julia",
             "affiliation": "NSF National Center for Atmospheric Research",
@@ -66,7 +65,6 @@
             "affiliation": "Leibniz Institute for Tropospheric Research, Leipzig (Germany)",
             "orcid": "0000-0002-4524-8152"
         },
-
         {
             "name": "Pfeifer, Nils",
             "affiliation": "Leibniz Institute for Tropospheric Research, Leipzig (Germany)",
@@ -81,6 +79,10 @@
             "name": "Zhang, Xin",
             "affiliation": "Nanjing University of Information Science & Technology (China)",
             "orcid": "0000-0002-1756-6620"
+        },
+        {
+            "name": "Ziegner, Fabian", 
+            "affiliation":"Leibniz Institute for Tropospheric Research, Leipzig (Germany)"
         }
     ],
     "license": "BSD-3-Clause",

CHANGELOG.md

@@ -1,5 +1,24 @@
 ### Tobac Changelog
 
+_**Version 1.6.0:**_
+
+**Enhancements for Users**
+
+- MAJOR CHANGE: Feature detection and segmentation have been changed to use xarray DataArrays for representation of spatial fields instead of Iris Cubes. This change will allow for increased flexibility for input data and ease development. Support for Iris Cube input is maintained through a conversion wrapper. [#354](https://github.com/tobac-project/tobac/pull/354) & [#417](https://github.com/tobac-project/tobac/pull/417)
+- Some differences in the output of feature detection are present in the output from feature detection: when using xarray, the column names for coordinates will use the variable names, while with iris the columns will use the standard names. This differences reflects the different ways in which coordinates are named in xarray and iris, and can be overridden by the user using the `use_standard_names` parameter. [#489](https://github.com/tobac-project/tobac/pull/489)
+
+**Bug fixes**
+
+- An issue with datetime output from feature detection being converted to `cftime` format erroneously was fixed, and datetime formats should now match the formatting in the input field. Time matching within a tolerance range now works with any combination of datetime formats [#489](https://github.com/tobac-project/tobac/pull/489)
+
+**Documentation**
+
+- Example notebooks have been converted to use `xarray`, and have had minor enhancements to descriptions and visualisations. A notebook showing the legacy approach using Iris Cube input is retained [#487](https://github.com/tobac-project/tobac/pull/487)
+
+**Internal Enhancements**
+
+- `tobac.utils.internal.basic` was restructured into `coordinates` and `label_props` utils, and new `datetime` and `generators` utils were added [#489](https://github.com/tobac-project/tobac/pull/489)
+
 _**Version 1.5.5:**_
 
 **Bug fixes**

doc/bulk_statistics/notebooks/compute_statistics_during_segmentation.ipynb

@@ -13,7 +13,14 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:16.501369Z",
+     "iopub.status.busy": "2025-04-30T07:18:16.501086Z",
+     "iopub.status.idle": "2025-04-30T07:18:17.565771Z",
+     "shell.execute_reply": "2025-04-30T07:18:17.565431Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Import libraries\n",
@@ -32,7 +39,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:17.567849Z",
+     "iopub.status.busy": "2025-04-30T07:18:17.567643Z",
+     "iopub.status.idle": "2025-04-30T07:18:17.569643Z",
+     "shell.execute_reply": "2025-04-30T07:18:17.569414Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Disable a few warnings:\n",
@@ -47,13 +61,20 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:17.570755Z",
+     "iopub.status.busy": "2025-04-30T07:18:17.570666Z",
+     "iopub.status.idle": "2025-04-30T07:18:19.524830Z",
+     "shell.execute_reply": "2025-04-30T07:18:19.523995Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "using tobac version 1.5.5\n"
+      "using tobac version 1.6.0\n"
      ]
     }
    ],
@@ -67,7 +88,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:19.567555Z",
+     "iopub.status.busy": "2025-04-30T07:18:19.567028Z",
+     "iopub.status.idle": "2025-04-30T07:18:19.580798Z",
+     "shell.execute_reply": "2025-04-30T07:18:19.580503Z"
+    }
+   },
    "outputs": [],
    "source": [
     "data_out=Path('../../../examples')\n",
@@ -89,7 +117,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:19.582191Z",
+     "iopub.status.busy": "2025-04-30T07:18:19.582100Z",
+     "iopub.status.idle": "2025-04-30T07:18:19.584081Z",
+     "shell.execute_reply": "2025-04-30T07:18:19.583837Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set up directory to save output:\n",
@@ -104,7 +139,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:19.585354Z",
+     "iopub.status.busy": "2025-04-30T07:18:19.585255Z",
+     "iopub.status.idle": "2025-04-30T07:18:20.646076Z",
+     "shell.execute_reply": "2025-04-30T07:18:20.645629Z"
+    }
+   },
    "outputs": [],
    "source": [
     "Precip = xr.open_dataset(data_file[0])[\"surface_precipitation_average\"]"
@@ -120,7 +162,14 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:20.648419Z",
+     "iopub.status.busy": "2025-04-30T07:18:20.648078Z",
+     "iopub.status.idle": "2025-04-30T07:18:20.728846Z",
+     "shell.execute_reply": "2025-04-30T07:18:20.728484Z"
+    }
+   },
    "outputs": [],
    "source": [
     "parameters_features = {}\n",
@@ -139,13 +188,26 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:20.730511Z",
+     "iopub.status.busy": "2025-04-30T07:18:20.730416Z",
+     "iopub.status.idle": "2025-04-30T07:18:21.584926Z",
+     "shell.execute_reply": "2025-04-30T07:18:21.584671Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "starting feature detection based on multiple thresholds\n",
+      "starting feature detection based on multiple thresholds\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "feature detection done\n",
       "features saved\n"
      ]
@@ -179,7 +241,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:21.586340Z",
+     "iopub.status.busy": "2025-04-30T07:18:21.586188Z",
+     "iopub.status.idle": "2025-04-30T07:18:21.590613Z",
+     "shell.execute_reply": "2025-04-30T07:18:21.590392Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Dictionary containing keyword arguments for segmentation step:\n",
@@ -208,7 +277,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:21.591702Z",
+     "iopub.status.busy": "2025-04-30T07:18:21.591636Z",
+     "iopub.status.idle": "2025-04-30T07:18:21.593200Z",
+     "shell.execute_reply": "2025-04-30T07:18:21.592985Z"
+    }
+   },
    "outputs": [],
    "source": [
     "statistics = {}\n",
@@ -227,7 +303,14 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:21.594255Z",
+     "iopub.status.busy": "2025-04-30T07:18:21.594194Z",
+     "iopub.status.idle": "2025-04-30T07:18:21.595656Z",
+     "shell.execute_reply": "2025-04-30T07:18:21.595446Z"
+    }
+   },
    "outputs": [],
    "source": [
     "statistics[\"percentiles\"] = (np.percentile, {\"q\": [95, 99]})"
@@ -236,14 +319,33 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:21.596645Z",
+     "iopub.status.busy": "2025-04-30T07:18:21.596565Z",
+     "iopub.status.idle": "2025-04-30T07:18:22.769124Z",
+     "shell.execute_reply": "2025-04-30T07:18:22.768880Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting segmentation based on surface precipitation\n",
-      "segmentation based on surface precipitation performed, start saving results to files\n",
+      "Starting segmentation based on surface precipitation\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "segmentation based on surface precipitation performed, start saving results to files\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "segmentation surface precipitation performed and saved\n"
      ]
     }
@@ -272,7 +374,14 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-30T07:18:22.770491Z",
+     "iopub.status.busy": "2025-04-30T07:18:22.770404Z",
+     "iopub.status.idle": "2025-04-30T07:18:22.780761Z",
+     "shell.execute_reply": "2025-04-30T07:18:22.780565Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -487,11 +596,6 @@
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:tobathon_2025]",
-   "language": "python",
-   "name": "conda-env-tobathon_2025-py"
-  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",