Repository: AI4S2S/s2spy (language: python)

Issue:
Adding a `n` kwarg to `Calendar.add_interval`
While writing the demo notebook, and when trying the new calendar, I often found myself writing a for loop to add multiple intervals.

As writing for loops for this seems a bit silly, we could just wrap the looping into a method. E.g.:

```py
cal = Calendar(anchor="Jan")
cal.add_intervals("precursor", "1W", n=10)  # Add 10 of these intervals
```

I see two options: 

1. A new method `add_intervals`, which internally will just run a `for _ in range(n):` loop and call `add_interval`.
2. Modifying `add_interval` to accept `n` as a kwarg, with the default value of `1`

I am leaning towards option 2 myself.


Discussion / hints:
I think I prefer option 1. An additional option could then be to fill one full year by default unless `n`  is given.

Fix patch:
diff --git a/notebooks/example_label_alignment_RGDR.ipynb b/notebooks/example_label_alignment_RGDR.ipynb
index efd6eaf..e698930 100644
--- a/notebooks/example_label_alignment_RGDR.ipynb
+++ b/notebooks/example_label_alignment_RGDR.ipynb
@@ -1,312 +1,312 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RGDR & traintest\n",
-    "This notebook demonstrates how to use RGDR in a train-test splitting scenario, to get the data ready for training a ML model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we import the required modules and load in the example data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import s2spy.time\n",
-    "import s2spy.rgdr\n",
-    "from s2spy import RGDR\n",
-    "from sklearn.model_selection import ShuffleSplit\n",
-    "import s2spy.traintest\n",
-    "\n",
-    "file_path = '../../s2spy/tests/test_rgdr/test_data'\n",
-    "field = xr.open_dataset(f'{file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')\n",
-    "target_data = xr.open_dataset(f'{file_path}/tf5_nc5_dendo_80d77.nc').sel(cluster=3)\n",
-    "\n",
-    "cal = s2spy.time.AdventCalendar('08-01', freq = \"30d\")\n",
-    "cal = cal.map_to_data(field)\n",
-    "\n",
-    "field_resampled = s2spy.time.resample(cal, field)\n",
-    "target_resampled = s2spy.time.resample(cal, target_data)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As precursor we select the 'sst' DataArray. The target is the (preclustered) 'st' DataArray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "precursor = field_resampled['sst']\n",
-    "target = target_resampled['ts']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define our train-test splitting strategy with \"ShuffleSplit\", and also define the RGDR settings we will use. \n",
-    "\n",
-    "It is essential to use different `RGDR` objects for each split, as the object stores the information required to transform the test data. Here we make a list of different RGDR objects (all with the same settings), with the same length as the number of splits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seed = 1 #same 'randomness'\n",
-    "n_splits = 4\n",
-    "\n",
-    "shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state = seed)\n",
-    "cv = s2spy.traintest.TrainTestSplit(shufflesplit)\n",
-    "\n",
-    "rgdrs = [RGDR(target_intervals=1, lag=5, eps_km=800, alpha=0.10, min_area_km2=0) for _ in range(n_splits)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the traintest cv, we can split the target and precursor data into train and test sets. For easy tracking we put all the splits in a list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target_timeseries_splits = []\n",
-    "precursor_field_splits = []\n",
-    "for x_train, x_test, y_train, y_test in cv.split(precursor, y=target):\n",
-    "    target_timeseries_splits.append(target.sel(anchor_year=x_train[\"anchor_year\"].values))\n",
-    "    precursor_field_splits.append(precursor.sel(anchor_year=x_train[\"anchor_year\"].values))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the `preview_clusters` functionality, we can display what our clusters will look like, for each split.\n",
-    "\n",
-    "Note that there is a single consistent cluster over all splits, but that the top-right plot contains three clusters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(\n",
-    "    ncols=2,\n",
-    "    nrows=n_splits//2,\n",
-    "    figsize=(8, 4),\n",
-    "    gridspec_kw={'hspace':0.6}\n",
-    ")\n",
-    "axs = axs.flatten()\n",
-    "\n",
-    "for split in range(n_splits):\n",
-    "    _ = rgdrs[split].preview_clusters(\n",
-    "        precursor_field_splits[split],\n",
-    "        target_timeseries_splits[split], \n",
-    "        ax=axs[split],\n",
-    "        vmin=-2, vmax=2\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a (list-comprehension) `for` loop, we can fit and apply RGDR to each split:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clustered_precursors = [\n",
-    "    rgdrs[i].fit_transform(precursor_field_splits[i], target_timeseries_splits[i])\n",
-    "    for i in range(n_splits)\n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see the labels of the clusters. These are named based on which `i_interval` was used for the precursor, and then named based on how many clusters there are in each `i_interval`. This leads to long names, which can be inconsistent over splits, as no information is passed between the different RGDR instances to avoid data leakage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-1]\n",
-      "[-1  1]\n",
-      "[-1]\n",
-      "[-2 -1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for el in clustered_precursors:\n",
-    "    print(el.cluster_labels.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, using the `label_alignment` module we can align labels over different splits. `label_alignment` will try to match clusters over different splits, and then _only rename_ the clusters.\n",
-    "\n",
-    "Clusters will be assigned a letter as name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['A']\n",
-      "['A' 'C']\n",
-      "['A']\n",
-      "['A' 'B']\n"
-     ]
-    }
-   ],
-   "source": [
-    "aligned_precursors = s2spy.rgdr.label_alignment.rename_labels(rgdrs, clustered_precursors)\n",
-    "    \n",
-    "for el in aligned_precursors:\n",
-    "    print(el.cluster_labels.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot all the cluster names over the cluster-preview:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(\n",
-    "    ncols=2,\n",
-    "    nrows=n_splits//2,\n",
-    "    figsize=(8, 4),\n",
-    "    gridspec_kw={'hspace':0.6}\n",
-    ")\n",
-    "axs = axs.flatten()\n",
-    "\n",
-    "for split in range(n_splits):\n",
-    "    _ = rgdrs[split].preview_clusters(\n",
-    "        precursor_field_splits[split],\n",
-    "        target_timeseries_splits[split], \n",
-    "        ax=axs[split],\n",
-    "        vmin=-2, vmax=2\n",
-    "    )\n",
-    "    for cl in aligned_precursors[split].cluster_labels:\n",
-    "        axs[split].text(\n",
-    "            x=cl.longitude, y=cl.latitude, s=cl.values,\n",
-    "            fontsize=16, va='center', ha='center',c=\"red\"\n",
-    "        )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ai4s2s",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "a87ac8a6238ac8fffcd070769ea0ffc634a0ba702c52b706c3554dda7777b3fd"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+      "cells": [
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "# RGDR & traintest\n",
+                        "This notebook demonstrates how to use RGDR in a train-test splitting scenario, to get the data ready for training a ML model."
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "First we import the required modules and load in the example data."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 1,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "import xarray as xr\n",
+                        "import matplotlib.pyplot as plt\n",
+                        "import s2spy.time\n",
+                        "import s2spy.rgdr\n",
+                        "from s2spy import RGDR\n",
+                        "from sklearn.model_selection import ShuffleSplit\n",
+                        "import s2spy.traintest\n",
+                        "\n",
+                        "file_path = '../../s2spy/tests/test_rgdr/test_data'\n",
+                        "field = xr.open_dataset(f'{file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')\n",
+                        "target_data = xr.open_dataset(f'{file_path}/tf5_nc5_dendo_80d77.nc').sel(cluster=3)\n",
+                        "\n",
+                        "cal = s2spy.time.AdventCalendar('08-01', freq = \"30d\")\n",
+                        "cal = cal.map_to_data(field)\n",
+                        "\n",
+                        "field_resampled = s2spy.time.resample(cal, field)\n",
+                        "target_resampled = s2spy.time.resample(cal, target_data)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "As precursor we select the 'sst' DataArray. The target is the (preclustered) 'st' DataArray."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 2,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "precursor = field_resampled['sst']\n",
+                        "target = target_resampled['ts']"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "We define our train-test splitting strategy with \"ShuffleSplit\", and also define the RGDR settings we will use. \n",
+                        "\n",
+                        "It is essential to use different `RGDR` objects for each split, as the object stores the information required to transform the test data. Here we make a list of different RGDR objects (all with the same settings), with the same length as the number of splits."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 3,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "seed = 1 #same 'randomness'\n",
+                        "n_splits = 4\n",
+                        "\n",
+                        "shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state = seed)\n",
+                        "cv = s2spy.traintest.TrainTestSplit(shufflesplit)\n",
+                        "\n",
+                        "rgdrs = [RGDR(target_intervals=1, lag=5, eps_km=800, alpha=0.10, min_area_km2=0) for _ in range(n_splits)]"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Using the traintest cv, we can split the target and precursor data into train and test sets. For easy tracking we put all the splits in a list."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 4,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "target_timeseries_splits = []\n",
+                        "precursor_field_splits = []\n",
+                        "for x_train, x_test, y_train, y_test in cv.split(precursor, y=target):\n",
+                        "    target_timeseries_splits.append(target.sel(anchor_year=x_train[\"anchor_year\"].values))\n",
+                        "    precursor_field_splits.append(precursor.sel(anchor_year=x_train[\"anchor_year\"].values))"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Using the `preview_clusters` functionality, we can display what our clusters will look like, for each split.\n",
+                        "\n",
+                        "Note that there is a single consistent cluster over all splits, but that the top-right plot contains three clusters."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 5,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 576x288 with 8 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, axs = plt.subplots(\n",
+                        "    ncols=2,\n",
+                        "    nrows=n_splits//2,\n",
+                        "    figsize=(8, 4),\n",
+                        "    gridspec_kw={'hspace':0.6}\n",
+                        ")\n",
+                        "axs = axs.flatten()\n",
+                        "\n",
+                        "for split in range(n_splits):\n",
+                        "    _ = rgdrs[split].preview_clusters(\n",
+                        "        precursor_field_splits[split],\n",
+                        "        target_timeseries_splits[split], \n",
+                        "        ax=axs[split],\n",
+                        "        vmin=-2, vmax=2\n",
+                        "    )"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "With a (list-comprehension) `for` loop, we can fit and apply RGDR to each split:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 6,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "clustered_precursors = [\n",
+                        "    rgdrs[i].fit_transform(precursor_field_splits[i], target_timeseries_splits[i])\n",
+                        "    for i in range(n_splits)\n",
+                        "    ]"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "We can see the labels of the clusters. These are named based on which `i_interval` was used for the precursor, and then named based on how many clusters there are in each `i_interval`. This leads to long names, which can be inconsistent over splits, as no information is passed between the different RGDR instances to avoid data leakage."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 7,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "name": "stdout",
+                              "output_type": "stream",
+                              "text": [
+                                    "[-1]\n",
+                                    "[-1  1]\n",
+                                    "[-1]\n",
+                                    "[-2 -1]\n"
+                              ]
+                        }
+                  ],
+                  "source": [
+                        "for el in clustered_precursors:\n",
+                        "    print(el.cluster_labels.values)"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "However, using the `label_alignment` module we can align labels over different splits. `label_alignment` will try to match clusters over different splits, and then _only rename_ the clusters.\n",
+                        "\n",
+                        "Clusters will be assigned a letter as name."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 8,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "name": "stdout",
+                              "output_type": "stream",
+                              "text": [
+                                    "['A']\n",
+                                    "['A' 'C']\n",
+                                    "['A']\n",
+                                    "['A' 'B']\n"
+                              ]
+                        }
+                  ],
+                  "source": [
+                        "aligned_precursors = s2spy.rgdr.label_alignment.rename_labels(rgdrs, clustered_precursors)\n",
+                        "    \n",
+                        "for el in aligned_precursors:\n",
+                        "    print(el.cluster_labels.values)"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "We can plot all the cluster names over the cluster-preview:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 9,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 576x288 with 8 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, axs = plt.subplots(\n",
+                        "    ncols=2,\n",
+                        "    nrows=n_splits//2,\n",
+                        "    figsize=(8, 4),\n",
+                        "    gridspec_kw={'hspace':0.6}\n",
+                        ")\n",
+                        "axs = axs.flatten()\n",
+                        "\n",
+                        "for split in range(n_splits):\n",
+                        "    _ = rgdrs[split].preview_clusters(\n",
+                        "        precursor_field_splits[split],\n",
+                        "        target_timeseries_splits[split], \n",
+                        "        ax=axs[split],\n",
+                        "        vmin=-2, vmax=2\n",
+                        "    )\n",
+                        "    for cl in aligned_precursors[split].cluster_labels:\n",
+                        "        axs[split].text(\n",
+                        "            x=cl.longitude, y=cl.latitude, s=cl.values,\n",
+                        "            fontsize=16, va='center', ha='center',c=\"red\"\n",
+                        "        )\n"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": null,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": []
+            }
+      ],
+      "metadata": {
+            "kernelspec": {
+                  "display_name": "ai4s2s",
+                  "language": "python",
+                  "name": "python3"
+            },
+            "language_info": {
+                  "codemirror_mode": {
+                        "name": "ipython",
+                        "version": 3
+                  },
+                  "file_extension": ".py",
+                  "mimetype": "text/x-python",
+                  "name": "python",
+                  "nbconvert_exporter": "python",
+                  "pygments_lexer": "ipython3",
+                  "version": "3.10.5"
+            },
+            "orig_nbformat": 4,
+            "vscode": {
+                  "interpreter": {
+                        "hash": "a87ac8a6238ac8fffcd070769ea0ffc634a0ba702c52b706c3554dda7777b3fd"
+                  }
+            }
+      },
+      "nbformat": 4,
+      "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/notebooks/tutorial_RGDR.ipynb b/notebooks/tutorial_RGDR.ipynb
index b57122d..c058f90 100644
--- a/notebooks/tutorial_RGDR.ipynb
+++ b/notebooks/tutorial_RGDR.ipynb
@@ -1,1800 +1,1800 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using RGDR\n",
-    "This notebook outlines the current status of the RGDR implementation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import s2spy.time\n",
-    "import s2spy.rgdr\n",
-    "from s2spy import RGDR"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we will load in some example data, and resample them using `s2spy`'s `Calendar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_path = '../tests/test_rgdr/test_data'\n",
-    "field = xr.open_dataset(f'{file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')\n",
-    "target = xr.open_dataset(f'{file_path}/tf5_nc5_dendo_80d77.nc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create calendar, map it to dataset and visualize it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 504x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cal = s2spy.time.Calendar(anchor=\"08-01\")\n",
-    "cal.add_intervals(\"target\", length=\"1W\", n=4)\n",
-    "cal.add_intervals(\"precursor\", length=\"1W\", n=4)\n",
-    "\n",
-    "cal = cal.map_to_data(field)\n",
-    "cal.visualize(n_years=3, relative_dates=True)\n",
-    "_ = plt.title(\"Calendar used in this tutorial\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Resample data with calendar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "field_resampled: xr.Dataset = s2spy.time.resample(cal, field)\n",
-    "target_resampled: xr.Dataset = s2spy.time.resample(cal, target)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The target timeseries comes from already pre-clustered land surface temperature data.\n",
-    "RGDR requires `DataArray`s as input, as otherwise it is ambiguous which variable should be used.\n",
-    "\n",
-    "When initializing RGDR, the target interval(s) and lag have to be specified as well.\n",
-    "The precursor interval(s) will be determined from the target intervals and lag.\n",
-    "The maximum possible lag will depend on the number of available precursor intervals.\n",
-    "\n",
-    "In the illustration you see the algorithm that is used to calculate the correlation in RGDR, when you specify a single target interval:\n",
-    "\n",
-    "![Single target](../notebooks/single_target_RGDR.svg)\n",
-    "\n",
-    "Below we use the first target interval, and a lag of `4`. This makes the precursor interval index `-4`. An example with multiple targets follows later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-4]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target_timeseries = target_resampled.sel(cluster=3)[\"ts\"]\n",
-    "precursor_field = field_resampled[\"sst\"]\n",
-    "\n",
-    "target_intervals = 1\n",
-    "lag = 4\n",
-    "rgdr = RGDR(\n",
-    "    target_intervals=target_intervals,\n",
-    "    lag=lag,\n",
-    "    eps_km=600,\n",
-    "    alpha=0.05,\n",
-    "    min_area_km2=0\n",
-    ")\n",
-    "rgdr.precursor_intervals"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using `.preview_correlation` we can visualize the correlation and p-value map of the target timeseries and precursor field.\n",
-    "\n",
-    "The red hashing shows where p-value `<` alpha"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x144 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
-    "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
-    "rgdr.fit(precursor_field, target_timeseries)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the preview utilities we can compare two RGDR setups, and see what the effect on the generated clusters will be.\n",
-    "\n",
-    "Note that the second figure has an extra cluster, due to the minimum area argument having a smaller value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x2602845d6c0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 864x144 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
-    "\n",
-    "RGDR(\n",
-    "    target_intervals, lag,\n",
-    "    eps_km=600, alpha=0.01, min_area_km2=600**2\n",
-    ").preview_clusters(precursor_field, target_timeseries, ax=ax1, vmin=-2, vmax=2)\n",
-    "\n",
-    "RGDR(\n",
-    "    target_intervals, lag,\n",
-    "    eps_km=600, alpha=0.01, min_area_km2=None\n",
-    ").preview_clusters(precursor_field, target_timeseries, ax=ax2, vmin=-2, vmax=2)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once you are satisfied with the selected parameters, the `.fit` method can be used to fit the RGDR clustering to a precursor field.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rgdr.fit(precursor_field, target_timeseries)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`.transform` can then be used to return the data reduced to clusters.\n",
-    "\n",
-    "Note that only the data for the specified precursor interval index (`-4`) is returned:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
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-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 40, i_interval: 1, cluster_labels: 5)&gt;\n",
-       "292.6 288.8 285.7 292.2 295.8 293.0 ... 296.1 292.2 289.7 286.0 292.0 296.1\n",
-       "Coordinates:\n",
-       "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
-       "  * i_interval      (i_interval) int64 -4\n",
-       "    target          (i_interval) bool False\n",
-       "  * cluster_labels  (cluster_labels) int16 -3 -2 -1 1 2\n",
-       "    latitude        (cluster_labels) float64 28.7 37.5 45.58 37.5 32.5\n",
-       "    longitude       (cluster_labels) float64 233.7 232.5 215.4 180.0 195.0\n",
-       "Attributes:\n",
-       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
-       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 40</li><li><span class='xr-has-index'>i_interval</span>: 1</li><li><span class='xr-has-index'>cluster_labels</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b174c8ef-4a97-4183-bbae-0071de395f9a' class='xr-array-in' type='checkbox' ><label for='section-b174c8ef-4a97-4183-bbae-0071de395f9a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>292.6 288.8 285.7 292.2 295.8 293.0 ... 292.2 289.7 286.0 292.0 296.1</span></div><div class='xr-array-data'><pre>array([[[292.64360842, 288.83886719, 285.69343776, 292.18593924,\n",
-       "         295.78289032]],\n",
-       "\n",
-       "       [[292.95298255, 288.98946272, 285.44787169, 292.703319  ,\n",
-       "         296.40889304]],\n",
-       "\n",
-       "       [[293.43979268, 289.64865112, 286.11178587, 293.12209865,\n",
-       "         295.74627577]],\n",
-       "\n",
-       "       [[295.21637352, 290.48266602, 287.87270857, 291.64146205,\n",
-       "         295.859072  ]],\n",
-       "\n",
-       "       [[292.67704122, 290.12355695, 286.76372838, 291.76468985,\n",
-       "         297.28726959]],\n",
-       "\n",
-       "       [[292.91546231, 289.75571551, 286.47284713, 293.70135934,\n",
-       "         296.4305736 ]],\n",
-       "\n",
-       "       [[291.78717907, 288.1461574 , 284.13377209, 293.47771345,\n",
-       "         297.18304116]],\n",
-       "...\n",
-       "       [[293.63715878, 289.76275635, 285.09576475, 292.27376883,\n",
-       "         295.43231092]],\n",
-       "\n",
-       "       [[291.99310375, 288.2394104 , 284.89413012, 290.64766366,\n",
-       "         297.45475878]],\n",
-       "\n",
-       "       [[291.7683638 , 288.8591701 , 284.45586965, 291.0320173 ,\n",
-       "         296.97395979]],\n",
-       "\n",
-       "       [[292.04618863, 288.69692993, 284.74314085, 290.97675868,\n",
-       "         294.98297119]],\n",
-       "\n",
-       "       [[293.04918065, 289.23656355, 284.82654826, 291.44362749,\n",
-       "         296.55028207]],\n",
-       "\n",
-       "       [[291.88359926, 288.49036952, 285.1243235 , 293.42803737,\n",
-       "         296.09576852]],\n",
-       "\n",
-       "       [[292.20558477, 289.72281756, 285.97866954, 292.00179618,\n",
-       "         296.0643267 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8284e4ac-4b19-465b-b915-4024901a5fc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8284e4ac-4b19-465b-b915-4024901a5fc6' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2018 2017 2016 ... 1981 1980 1979</div><input id='attrs-6e6f762c-5044-4ee7-a495-9c1378d7d012' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e6f762c-5044-4ee7-a495-9c1378d7d012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df565c35-532e-469c-b1db-6412c027c430' class='xr-var-data-in' type='checkbox'><label for='data-df565c35-532e-469c-b1db-6412c027c430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007,\n",
-       "       2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995,\n",
-       "       1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983,\n",
-       "       1982, 1981, 1980, 1979], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4</div><input id='attrs-e6c73670-8c4a-4942-99e1-9e005efb2a6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6c73670-8c4a-4942-99e1-9e005efb2a6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72db7d5c-86b4-4a25-8945-ff1d73a1517c' class='xr-var-data-in' type='checkbox'><label for='data-72db7d5c-86b4-4a25-8945-ff1d73a1517c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-be3b826e-f9c0-402f-b944-5fe70f5e20f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be3b826e-f9c0-402f-b944-5fe70f5e20f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c76b370-0cdc-4eca-b004-87657e14e616' class='xr-var-data-in' type='checkbox'><label for='data-4c76b370-0cdc-4eca-b004-87657e14e616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-3 -2 -1 1 2</div><input id='attrs-4dcadffa-2aba-4da8-8e8c-d9b90647716a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dcadffa-2aba-4da8-8e8c-d9b90647716a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac1953d-6981-4060-8c13-819d3665bbf1' class='xr-var-data-in' type='checkbox'><label for='data-aac1953d-6981-4060-8c13-819d3665bbf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3, -2, -1,  1,  2], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.7 37.5 45.58 37.5 32.5</div><input id='attrs-70aca189-af8f-4b0a-81f5-0b3aa214b038' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70aca189-af8f-4b0a-81f5-0b3aa214b038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7edcc9c-421e-4f83-a7ff-458fa16715d7' class='xr-var-data-in' type='checkbox'><label for='data-b7edcc9c-421e-4f83-a7ff-458fa16715d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.70332394, 37.5       , 45.57956704, 37.5       , 32.5       ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>233.7 232.5 215.4 180.0 195.0</div><input id='attrs-ee029c0c-a39a-4e99-9b6b-a91ee285b98c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee029c0c-a39a-4e99-9b6b-a91ee285b98c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb8a1f37-81ec-40ce-9afd-108065ec2073' class='xr-var-data-in' type='checkbox'><label for='data-eb8a1f37-81ec-40ce-9afd-108065ec2073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([233.70332394, 232.5       , 215.38064945, 180.        ,\n",
-       "       195.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0ccb294-85cc-4b38-850d-9a086589337b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0ccb294-85cc-4b38-850d-9a086589337b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
+      "cells": [
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "### Using RGDR\n",
+                        "This notebook outlines the current status of the RGDR implementation."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 1,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "import xarray as xr\n",
+                        "import matplotlib.pyplot as plt\n",
+                        "import s2spy.time\n",
+                        "import s2spy.rgdr\n",
+                        "from s2spy import RGDR"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "First we will load in some example data, and resample them using `s2spy`'s `Calendar`."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 2,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "file_path = '../tests/test_rgdr/test_data'\n",
+                        "field = xr.open_dataset(f'{file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')\n",
+                        "target = xr.open_dataset(f'{file_path}/tf5_nc5_dendo_80d77.nc')"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Create calendar, map it to dataset and visualize it."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 3,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 504x288 with 1 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "cal = s2spy.time.Calendar(anchor=\"08-01\")\n",
+                        "cal.add_intervals(\"target\", length=\"1W\", n=4)\n",
+                        "cal.add_intervals(\"precursor\", length=\"1W\", n=4)\n",
+                        "\n",
+                        "cal = cal.map_to_data(field)\n",
+                        "cal.visualize(n_years=3, relative_dates=True)\n",
+                        "_ = plt.title(\"Calendar used in this tutorial\")"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Resample data with calendar."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 4,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "field_resampled: xr.Dataset = s2spy.time.resample(cal, field)\n",
+                        "target_resampled: xr.Dataset = s2spy.time.resample(cal, target)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "### The target timeseries comes from already pre-clustered land surface temperature data.\n",
+                        "RGDR requires `DataArray`s as input, as otherwise it is ambiguous which variable should be used.\n",
+                        "\n",
+                        "When initializing RGDR, the target interval(s) and lag have to be specified as well.\n",
+                        "The precursor interval(s) will be determined from the target intervals and lag.\n",
+                        "The maximum possible lag will depend on the number of available precursor intervals.\n",
+                        "\n",
+                        "In the illustration you see the algorithm that is used to calculate the correlation in RGDR, when you specify a single target interval:\n",
+                        "\n",
+                        "![Single target](../notebooks/single_target_RGDR.svg)\n",
+                        "\n",
+                        "Below we use the first target interval, and a lag of `4`. This makes the precursor interval index `-4`. An example with multiple targets follows later."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 5,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "[-4]"
+                                    ]
+                              },
+                              "execution_count": 5,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        }
+                  ],
+                  "source": [
+                        "target_timeseries = target_resampled.sel(cluster=3)[\"ts\"]\n",
+                        "precursor_field = field_resampled[\"sst\"]\n",
+                        "\n",
+                        "target_intervals = 1\n",
+                        "lag = 4\n",
+                        "rgdr = RGDR(\n",
+                        "    target_intervals=target_intervals,\n",
+                        "    lag=lag,\n",
+                        "    eps_km=600,\n",
+                        "    alpha=0.05,\n",
+                        "    min_area_km2=0\n",
+                        ")\n",
+                        "rgdr.precursor_intervals"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Using `.preview_correlation` we can visualize the correlation and p-value map of the target timeseries and precursor field.\n",
+                        "\n",
+                        "The red hashing shows where p-value `<` alpha"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 6,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 864x144 with 4 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
+                        "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
+                        "rgdr.fit(precursor_field, target_timeseries)"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Using the preview utilities we can compare two RGDR setups, and see what the effect on the generated clusters will be.\n",
+                        "\n",
+                        "Note that the second figure has an extra cluster, due to the minimum area argument having a smaller value."
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 7,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "<matplotlib.collections.QuadMesh at 0x2602845d6c0>"
+                                    ]
+                              },
+                              "execution_count": 7,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        },
+                        {
+                              "data": {
+                                    "image/png": "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",
+                                    "text/plain": [
+                                          "<Figure size 864x144 with 4 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
+                        "\n",
+                        "RGDR(\n",
+                        "    target_intervals, lag,\n",
+                        "    eps_km=600, alpha=0.01, min_area_km2=600**2\n",
+                        ").preview_clusters(precursor_field, target_timeseries, ax=ax1, vmin=-2, vmax=2)\n",
+                        "\n",
+                        "RGDR(\n",
+                        "    target_intervals, lag,\n",
+                        "    eps_km=600, alpha=0.01, min_area_km2=None\n",
+                        ").preview_clusters(precursor_field, target_timeseries, ax=ax2, vmin=-2, vmax=2)\n"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Once you are satisfied with the selected parameters, the `.fit` method can be used to fit the RGDR clustering to a precursor field.\n"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 8,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": [
+                        "rgdr.fit(precursor_field, target_timeseries)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "`.transform` can then be used to return the data reduced to clusters.\n",
+                        "\n",
+                        "Note that only the data for the specified precursor interval index (`-4`) is returned:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 9,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
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+                                          "  grid-column: 1;\n",
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+                                          "\n",
+                                          ".xr-attrs dt:hover span {\n",
+                                          "  display: inline-block;\n",
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+                                          "  padding-right: 10px;\n",
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+                                          "\n",
+                                          ".xr-attrs dd {\n",
+                                          "  grid-column: 2;\n",
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+                                          "\n",
+                                          ".xr-icon-database,\n",
+                                          ".xr-icon-file-text2 {\n",
+                                          "  display: inline-block;\n",
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+                                          "  stroke: currentColor;\n",
+                                          "  fill: currentColor;\n",
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+                                          "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 40, i_interval: 1, cluster_labels: 5)&gt;\n",
+                                          "292.6 288.8 285.7 292.2 295.8 293.0 ... 296.1 292.2 289.7 286.0 292.0 296.1\n",
+                                          "Coordinates:\n",
+                                          "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
+                                          "  * i_interval      (i_interval) int64 -4\n",
+                                          "    target          (i_interval) bool False\n",
+                                          "  * cluster_labels  (cluster_labels) int16 -3 -2 -1 1 2\n",
+                                          "    latitude        (cluster_labels) float64 28.7 37.5 45.58 37.5 32.5\n",
+                                          "    longitude       (cluster_labels) float64 233.7 232.5 215.4 180.0 195.0\n",
+                                          "Attributes:\n",
+                                          "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
+                                          "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 40</li><li><span class='xr-has-index'>i_interval</span>: 1</li><li><span class='xr-has-index'>cluster_labels</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b174c8ef-4a97-4183-bbae-0071de395f9a' class='xr-array-in' type='checkbox' ><label for='section-b174c8ef-4a97-4183-bbae-0071de395f9a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>292.6 288.8 285.7 292.2 295.8 293.0 ... 292.2 289.7 286.0 292.0 296.1</span></div><div class='xr-array-data'><pre>array([[[292.64360842, 288.83886719, 285.69343776, 292.18593924,\n",
+                                          "         295.78289032]],\n",
+                                          "\n",
+                                          "       [[292.95298255, 288.98946272, 285.44787169, 292.703319  ,\n",
+                                          "         296.40889304]],\n",
+                                          "\n",
+                                          "       [[293.43979268, 289.64865112, 286.11178587, 293.12209865,\n",
+                                          "         295.74627577]],\n",
+                                          "\n",
+                                          "       [[295.21637352, 290.48266602, 287.87270857, 291.64146205,\n",
+                                          "         295.859072  ]],\n",
+                                          "\n",
+                                          "       [[292.67704122, 290.12355695, 286.76372838, 291.76468985,\n",
+                                          "         297.28726959]],\n",
+                                          "\n",
+                                          "       [[292.91546231, 289.75571551, 286.47284713, 293.70135934,\n",
+                                          "         296.4305736 ]],\n",
+                                          "\n",
+                                          "       [[291.78717907, 288.1461574 , 284.13377209, 293.47771345,\n",
+                                          "         297.18304116]],\n",
+                                          "...\n",
+                                          "       [[293.63715878, 289.76275635, 285.09576475, 292.27376883,\n",
+                                          "         295.43231092]],\n",
+                                          "\n",
+                                          "       [[291.99310375, 288.2394104 , 284.89413012, 290.64766366,\n",
+                                          "         297.45475878]],\n",
+                                          "\n",
+                                          "       [[291.7683638 , 288.8591701 , 284.45586965, 291.0320173 ,\n",
+                                          "         296.97395979]],\n",
+                                          "\n",
+                                          "       [[292.04618863, 288.69692993, 284.74314085, 290.97675868,\n",
+                                          "         294.98297119]],\n",
+                                          "\n",
+                                          "       [[293.04918065, 289.23656355, 284.82654826, 291.44362749,\n",
+                                          "         296.55028207]],\n",
+                                          "\n",
+                                          "       [[291.88359926, 288.49036952, 285.1243235 , 293.42803737,\n",
+                                          "         296.09576852]],\n",
+                                          "\n",
+                                          "       [[292.20558477, 289.72281756, 285.97866954, 292.00179618,\n",
+                                          "         296.0643267 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8284e4ac-4b19-465b-b915-4024901a5fc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8284e4ac-4b19-465b-b915-4024901a5fc6' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2018 2017 2016 ... 1981 1980 1979</div><input id='attrs-6e6f762c-5044-4ee7-a495-9c1378d7d012' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e6f762c-5044-4ee7-a495-9c1378d7d012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df565c35-532e-469c-b1db-6412c027c430' class='xr-var-data-in' type='checkbox'><label for='data-df565c35-532e-469c-b1db-6412c027c430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007,\n",
+                                          "       2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995,\n",
+                                          "       1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983,\n",
+                                          "       1982, 1981, 1980, 1979], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4</div><input id='attrs-e6c73670-8c4a-4942-99e1-9e005efb2a6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6c73670-8c4a-4942-99e1-9e005efb2a6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72db7d5c-86b4-4a25-8945-ff1d73a1517c' class='xr-var-data-in' type='checkbox'><label for='data-72db7d5c-86b4-4a25-8945-ff1d73a1517c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-be3b826e-f9c0-402f-b944-5fe70f5e20f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be3b826e-f9c0-402f-b944-5fe70f5e20f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c76b370-0cdc-4eca-b004-87657e14e616' class='xr-var-data-in' type='checkbox'><label for='data-4c76b370-0cdc-4eca-b004-87657e14e616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-3 -2 -1 1 2</div><input id='attrs-4dcadffa-2aba-4da8-8e8c-d9b90647716a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dcadffa-2aba-4da8-8e8c-d9b90647716a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac1953d-6981-4060-8c13-819d3665bbf1' class='xr-var-data-in' type='checkbox'><label for='data-aac1953d-6981-4060-8c13-819d3665bbf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3, -2, -1,  1,  2], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.7 37.5 45.58 37.5 32.5</div><input id='attrs-70aca189-af8f-4b0a-81f5-0b3aa214b038' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70aca189-af8f-4b0a-81f5-0b3aa214b038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7edcc9c-421e-4f83-a7ff-458fa16715d7' class='xr-var-data-in' type='checkbox'><label for='data-b7edcc9c-421e-4f83-a7ff-458fa16715d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.70332394, 37.5       , 45.57956704, 37.5       , 32.5       ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>233.7 232.5 215.4 180.0 195.0</div><input id='attrs-ee029c0c-a39a-4e99-9b6b-a91ee285b98c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee029c0c-a39a-4e99-9b6b-a91ee285b98c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb8a1f37-81ec-40ce-9afd-108065ec2073' class='xr-var-data-in' type='checkbox'><label for='data-eb8a1f37-81ec-40ce-9afd-108065ec2073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([233.70332394, 232.5       , 215.38064945, 180.        ,\n",
+                                          "       195.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0ccb294-85cc-4b38-850d-9a086589337b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0ccb294-85cc-4b38-850d-9a086589337b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
+                                    ],
+                                    "text/plain": [
+                                          "<xarray.DataArray 'sst' (anchor_year: 40, i_interval: 1, cluster_labels: 5)>\n",
+                                          "292.6 288.8 285.7 292.2 295.8 293.0 ... 296.1 292.2 289.7 286.0 292.0 296.1\n",
+                                          "Coordinates:\n",
+                                          "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
+                                          "  * i_interval      (i_interval) int64 -4\n",
+                                          "    target          (i_interval) bool False\n",
+                                          "  * cluster_labels  (cluster_labels) int16 -3 -2 -1 1 2\n",
+                                          "    latitude        (cluster_labels) float64 28.7 37.5 45.58 37.5 32.5\n",
+                                          "    longitude       (cluster_labels) float64 233.7 232.5 215.4 180.0 195.0\n",
+                                          "Attributes:\n",
+                                          "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
+                                          "    coordinates:  Latitudes and longitudes are geographical centers associate..."
+                                    ]
+                              },
+                              "execution_count": 9,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        }
+                  ],
+                  "source": [
+                        "clustered_data = rgdr.transform(precursor_field)\n",
+                        "xr.set_options(display_expand_data=False) # Hide the full data repr\n",
+                        "clustered_data"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Now we can plot the data for each cluster (blue for positively correlated lcusters, red for negatively correlated ones):"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 10,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "[<matplotlib.lines.Line2D at 0x26028538430>]"
+                                    ]
+                              },
+                              "execution_count": 10,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        },
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 432x288 with 1 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "import matplotlib.pyplot as plt\n",
+                        "clustered_data = clustered_data  - clustered_data.mean(dim=\"anchor_year\")\n",
+                        "kwargs = {'x': 'anchor_year', 'add_legend': False}\n",
+                        "clustered_data.sel(cluster_labels=1).plot.line(c='tab:blue', **kwargs)\n",
+                        "clustered_data.sel(cluster_labels=2).plot.line(c='tab:blue', **kwargs)\n",
+                        "clustered_data.sel(cluster_labels=-1).plot.line(c='tab:red', **kwargs)\n",
+                        "clustered_data.sel(cluster_labels=-2).plot.line(c='tab:red', **kwargs)\n",
+                        "clustered_data.sel(cluster_labels=-3).plot.line(c='tab:red', **kwargs)"
+                  ]
+            },
+            {
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "The `RGDR` object will also contain the correlation maps, p-values, and cluster maps.\n",
+                        "\n",
+                        "These are all `xr.DataArray` objects, and can easily be plot using the builtin methods:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 11,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/html": [
+                                          "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+                                          "<defs>\n",
+                                          "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+                                          "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+                                          "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+                                          "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+                                          "</symbol>\n",
+                                          "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+                                          "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+                                          "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+                                          "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+                                          "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+                                          "</symbol>\n",
+                                          "</defs>\n",
+                                          "</svg>\n",
+                                          "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+                                          " *\n",
+                                          " */\n",
+                                          "\n",
+                                          ":root {\n",
+                                          "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+                                          "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+                                          "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+                                          "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+                                          "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+                                          "  --xr-background-color: var(--jp-layout-color0, white);\n",
+                                          "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+                                          "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+                                          "}\n",
+                                          "\n",
+                                          "html[theme=dark],\n",
+                                          "body[data-theme=dark],\n",
+                                          "body.vscode-dark {\n",
+                                          "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+                                          "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+                                          "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+                                          "  --xr-border-color: #1F1F1F;\n",
+                                          "  --xr-disabled-color: #515151;\n",
+                                          "  --xr-background-color: #111111;\n",
+                                          "  --xr-background-color-row-even: #111111;\n",
+                                          "  --xr-background-color-row-odd: #313131;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-wrap {\n",
+                                          "  display: block !important;\n",
+                                          "  min-width: 300px;\n",
+                                          "  max-width: 700px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-text-repr-fallback {\n",
+                                          "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+                                          "  display: none;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-header {\n",
+                                          "  padding-top: 6px;\n",
+                                          "  padding-bottom: 6px;\n",
+                                          "  margin-bottom: 4px;\n",
+                                          "  border-bottom: solid 1px var(--xr-border-color);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-header > div,\n",
+                                          ".xr-header > ul {\n",
+                                          "  display: inline;\n",
+                                          "  margin-top: 0;\n",
+                                          "  margin-bottom: 0;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-obj-type,\n",
+                                          ".xr-array-name {\n",
+                                          "  margin-left: 2px;\n",
+                                          "  margin-right: 10px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-obj-type {\n",
+                                          "  color: var(--xr-font-color2);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-sections {\n",
+                                          "  padding-left: 0 !important;\n",
+                                          "  display: grid;\n",
+                                          "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-item {\n",
+                                          "  display: contents;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-item input {\n",
+                                          "  display: none;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-item input + label {\n",
+                                          "  color: var(--xr-disabled-color);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-item input:enabled + label {\n",
+                                          "  cursor: pointer;\n",
+                                          "  color: var(--xr-font-color2);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-item input:enabled + label:hover {\n",
+                                          "  color: var(--xr-font-color0);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary {\n",
+                                          "  grid-column: 1;\n",
+                                          "  color: var(--xr-font-color2);\n",
+                                          "  font-weight: 500;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary > span {\n",
+                                          "  display: inline-block;\n",
+                                          "  padding-left: 0.5em;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in:disabled + label {\n",
+                                          "  color: var(--xr-font-color2);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in + label:before {\n",
+                                          "  display: inline-block;\n",
+                                          "  content: '►';\n",
+                                          "  font-size: 11px;\n",
+                                          "  width: 15px;\n",
+                                          "  text-align: center;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in:disabled + label:before {\n",
+                                          "  color: var(--xr-disabled-color);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in:checked + label:before {\n",
+                                          "  content: '▼';\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in:checked + label > span {\n",
+                                          "  display: none;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary,\n",
+                                          ".xr-section-inline-details {\n",
+                                          "  padding-top: 4px;\n",
+                                          "  padding-bottom: 4px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-inline-details {\n",
+                                          "  grid-column: 2 / -1;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-details {\n",
+                                          "  display: none;\n",
+                                          "  grid-column: 1 / -1;\n",
+                                          "  margin-bottom: 5px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+                                          "  display: contents;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-array-wrap {\n",
+                                          "  grid-column: 1 / -1;\n",
+                                          "  display: grid;\n",
+                                          "  grid-template-columns: 20px auto;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-array-wrap > label {\n",
+                                          "  grid-column: 1;\n",
+                                          "  vertical-align: top;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-preview {\n",
+                                          "  color: var(--xr-font-color3);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-array-preview,\n",
+                                          ".xr-array-data {\n",
+                                          "  padding: 0 5px !important;\n",
+                                          "  grid-column: 2;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-array-data,\n",
+                                          ".xr-array-in:checked ~ .xr-array-preview {\n",
+                                          "  display: none;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-array-in:checked ~ .xr-array-data,\n",
+                                          ".xr-array-preview {\n",
+                                          "  display: inline-block;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-dim-list {\n",
+                                          "  display: inline-block !important;\n",
+                                          "  list-style: none;\n",
+                                          "  padding: 0 !important;\n",
+                                          "  margin: 0;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-dim-list li {\n",
+                                          "  display: inline-block;\n",
+                                          "  padding: 0;\n",
+                                          "  margin: 0;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-dim-list:before {\n",
+                                          "  content: '(';\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-dim-list:after {\n",
+                                          "  content: ')';\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-dim-list li:not(:last-child):after {\n",
+                                          "  content: ',';\n",
+                                          "  padding-right: 5px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-has-index {\n",
+                                          "  font-weight: bold;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-list,\n",
+                                          ".xr-var-item {\n",
+                                          "  display: contents;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-item > div,\n",
+                                          ".xr-var-item label,\n",
+                                          ".xr-var-item > .xr-var-name span {\n",
+                                          "  background-color: var(--xr-background-color-row-even);\n",
+                                          "  margin-bottom: 0;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-item > .xr-var-name:hover span {\n",
+                                          "  padding-right: 5px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-list > li:nth-child(odd) > div,\n",
+                                          ".xr-var-list > li:nth-child(odd) > label,\n",
+                                          ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+                                          "  background-color: var(--xr-background-color-row-odd);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-name {\n",
+                                          "  grid-column: 1;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-dims {\n",
+                                          "  grid-column: 2;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-dtype {\n",
+                                          "  grid-column: 3;\n",
+                                          "  text-align: right;\n",
+                                          "  color: var(--xr-font-color2);\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-preview {\n",
+                                          "  grid-column: 4;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-name,\n",
+                                          ".xr-var-dims,\n",
+                                          ".xr-var-dtype,\n",
+                                          ".xr-preview,\n",
+                                          ".xr-attrs dt {\n",
+                                          "  white-space: nowrap;\n",
+                                          "  overflow: hidden;\n",
+                                          "  text-overflow: ellipsis;\n",
+                                          "  padding-right: 10px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-name:hover,\n",
+                                          ".xr-var-dims:hover,\n",
+                                          ".xr-var-dtype:hover,\n",
+                                          ".xr-attrs dt:hover {\n",
+                                          "  overflow: visible;\n",
+                                          "  width: auto;\n",
+                                          "  z-index: 1;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-attrs,\n",
+                                          ".xr-var-data {\n",
+                                          "  display: none;\n",
+                                          "  background-color: var(--xr-background-color) !important;\n",
+                                          "  padding-bottom: 5px !important;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+                                          ".xr-var-data-in:checked ~ .xr-var-data {\n",
+                                          "  display: block;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-data > table {\n",
+                                          "  float: right;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-var-name span,\n",
+                                          ".xr-var-data,\n",
+                                          ".xr-attrs {\n",
+                                          "  padding-left: 25px !important;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-attrs,\n",
+                                          ".xr-var-attrs,\n",
+                                          ".xr-var-data {\n",
+                                          "  grid-column: 1 / -1;\n",
+                                          "}\n",
+                                          "\n",
+                                          "dl.xr-attrs {\n",
+                                          "  padding: 0;\n",
+                                          "  margin: 0;\n",
+                                          "  display: grid;\n",
+                                          "  grid-template-columns: 125px auto;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-attrs dt,\n",
+                                          ".xr-attrs dd {\n",
+                                          "  padding: 0;\n",
+                                          "  margin: 0;\n",
+                                          "  float: left;\n",
+                                          "  padding-right: 10px;\n",
+                                          "  width: auto;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-attrs dt {\n",
+                                          "  font-weight: normal;\n",
+                                          "  grid-column: 1;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-attrs dt:hover span {\n",
+                                          "  display: inline-block;\n",
+                                          "  background: var(--xr-background-color);\n",
+                                          "  padding-right: 10px;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-attrs dd {\n",
+                                          "  grid-column: 2;\n",
+                                          "  white-space: pre-wrap;\n",
+                                          "  word-break: break-all;\n",
+                                          "}\n",
+                                          "\n",
+                                          ".xr-icon-database,\n",
+                                          ".xr-icon-file-text2 {\n",
+                                          "  display: inline-block;\n",
+                                          "  vertical-align: middle;\n",
+                                          "  width: 1em;\n",
+                                          "  height: 1.5em !important;\n",
+                                          "  stroke-width: 0;\n",
+                                          "  stroke: currentColor;\n",
+                                          "  fill: currentColor;\n",
+                                          "}\n",
+                                          "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 5, longitude: 13)&gt;\n",
+                                          "0.2697 0.2123 0.0529 -0.1931 -0.3189 ... -0.1533 -0.246 -0.3174 -0.4126 -0.4341\n",
+                                          "Coordinates:\n",
+                                          "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
+                                          "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
+                                          "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
+                                          "    cluster     int64 3\n",
+                                          "    tfreq       int64 5\n",
+                                          "    n_clusters  int64 6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14114774-aab0-40e6-aa62-d77f68145afb' class='xr-array-in' type='checkbox' ><label for='section-14114774-aab0-40e6-aa62-d77f68145afb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.2697 0.2123 0.0529 -0.1931 ... -0.246 -0.3174 -0.4126 -0.4341</span></div><div class='xr-array-data'><pre>array([[ 0.26973879,  0.21227671,  0.05289627, -0.19309815, -0.31889021,\n",
+                                          "        -0.47274522, -0.55472119, -0.53583813, -0.56565047, -0.53421202,\n",
+                                          "        -0.41056264, -0.2310342 ,         nan],\n",
+                                          "       [ 0.2037804 ,  0.26253778,  0.19418173,  0.06666334, -0.08516656,\n",
+                                          "        -0.18050655, -0.27848657, -0.35489399, -0.41916154, -0.47261077,\n",
+                                          "        -0.31655439, -0.12650094,         nan],\n",
+                                          "       [ 0.40535219,  0.32321764,  0.16911906,  0.11556145,  0.08321155,\n",
+                                          "         0.07836849,  0.10415046, -0.0085853 , -0.16056509, -0.1003596 ,\n",
+                                          "        -0.22329525, -0.3465616 ,         nan],\n",
+                                          "       [ 0.2630065 ,  0.26032401,  0.35231884,  0.40363035,  0.40450999,\n",
+                                          "         0.31832834,  0.18927728,  0.09921395,  0.01973299, -0.05579345,\n",
+                                          "        -0.24113899, -0.30901333, -0.43695491],\n",
+                                          "       [ 0.21990857,  0.19720435,  0.12089699,  0.10531533,  0.07945109,\n",
+                                          "         0.01934902, -0.0193646 , -0.10691568, -0.15330192, -0.24602795,\n",
+                                          "        -0.31735925, -0.41262283, -0.43411288]])</pre></div></div></li><li class='xr-section-item'><input id='section-e3a869ff-d54c-4ac9-a9e6-f5d9b52bff02' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3a869ff-d54c-4ac9-a9e6-f5d9b52bff02' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.5 42.5 37.5 32.5 27.5</div><input id='attrs-573c8629-c58c-4535-9d78-3a87f9ffd9b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-573c8629-c58c-4535-9d78-3a87f9ffd9b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d0ea2a0-ec6b-4cd3-a6d3-34789c2826d7' class='xr-var-data-in' type='checkbox'><label for='data-1d0ea2a0-ec6b-4cd3-a6d3-34789c2826d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([47.5, 42.5, 37.5, 32.5, 27.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>177.5 182.5 187.5 ... 232.5 237.5</div><input id='attrs-e99ecefd-7768-4d79-879c-fcf7154e577a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e99ecefd-7768-4d79-879c-fcf7154e577a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b134f8-544e-45a5-91a4-1cdf5e7bef52' class='xr-var-data-in' type='checkbox'><label for='data-70b134f8-544e-45a5-91a4-1cdf5e7bef52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
+                                          "       227.5, 232.5, 237.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.088e+05 2.278e+05 ... 2.741e+05</div><input id='attrs-e9d32b8c-7b02-48b7-ae63-4c636bebf5c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9d32b8c-7b02-48b7-ae63-4c636bebf5c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d228b03e-f29b-41a1-8bca-e0407ddfd06d' class='xr-var-data-in' type='checkbox'><label for='data-d228b03e-f29b-41a1-8bca-e0407ddfd06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([208767.02150832, 227829.22531495, 245157.51114987, 260620.0002948 ,\n",
+                                          "       274099.01387082])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-ecf81775-cda5-434f-8c7a-36eaebcefd6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecf81775-cda5-434f-8c7a-36eaebcefd6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eff327f-c005-41ca-8b65-741de3454c50' class='xr-var-data-in' type='checkbox'><label for='data-9eff327f-c005-41ca-8b65-741de3454c50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tfreq</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5d2ef2c-39db-4f0d-b39a-a72e28803262' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5d2ef2c-39db-4f0d-b39a-a72e28803262' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98838557-31ab-4b2b-9b28-abad9eb77c4c' class='xr-var-data-in' type='checkbox'><label for='data-98838557-31ab-4b2b-9b28-abad9eb77c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(5, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_clusters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52592435-3ae0-4243-b411-9a0d91357019' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52592435-3ae0-4243-b411-9a0d91357019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e2c7eb-6e36-4511-b92a-52a820d7ec7c' class='xr-var-data-in' type='checkbox'><label for='data-51e2c7eb-6e36-4511-b92a-52a820d7ec7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(6, dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa1963e0-4c33-4751-8373-182fb245b730' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa1963e0-4c33-4751-8373-182fb245b730' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+                                    ],
+                                    "text/plain": [
+                                          "<xarray.DataArray (latitude: 5, longitude: 13)>\n",
+                                          "0.2697 0.2123 0.0529 -0.1931 -0.3189 ... -0.1533 -0.246 -0.3174 -0.4126 -0.4341\n",
+                                          "Coordinates:\n",
+                                          "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
+                                          "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
+                                          "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
+                                          "    cluster     int64 3\n",
+                                          "    tfreq       int64 ...\n",
+                                          "    n_clusters  int64 ..."
+                                    ]
+                              },
+                              "execution_count": 11,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        }
+                  ],
+                  "source": [
+                        "# View the contents of the corr_map DataArray:\n",
+                        "rgdr.corr_map"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 12,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "<matplotlib.collections.QuadMesh at 0x260285b71c0>"
+                                    ]
+                              },
+                              "execution_count": 12,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        },
+                        {
+                              "data": {
+                                    "image/png": 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LulfS4pR2bncZzKwGZWsLVktl5vhlVnwDqGpqwAtDGyRNIBvasGDYMfcDbwaQ9CpgEvBYrZOORc/Vx8luF0yr2HZCRFw0Btc2szq8tmBNjl9mBRYh1gw235SJiLWShoY29ANnDQ1tABZGxALgU8B3JH2S7E7kMRERtc7b1saVpJnA24DTgOPbeS0za5JAdcZWlZHjl1nxBeSaigHqD21I0zLsPZpztrvn6uvAZ8gex650mqSTgSuBEyPi+eEZ06Cz4wBmbboR07Z/edOFWHXv/U3nnXpjvnkPD9+l7kMFVe0+99W5rr1kz61y5b/qT7s2nffWpU/kuvbjy1c1nfepR1bkuva222+SK/++W2+cK/9YU76nBXvZ12lB/Jo4aUP+ut2mTRdi8j35/pZu+kLzf8fTjm8+dgLc8/6ZTefd8MIdcl17w98ty5V/8LHHm8/73Es+EqMTg01n7dtgWv2Darhn7vCPe7EFYs1g8eJX28ZcSToEeDQiFg3b9TlgB2B3YGPgsyPlj4j5ETEnIuZMnzalXcU0M4m+8eOqpjJqZfwaP8Hxy6xdhga0V0ud0s4B7XsDcyUtJVtvbH9J50XE8sg8D/wH2QReZtYhEvT191VNJeX4ZdYlBumrmjqlbVeOiM9FxMyImE02+v7XEXGUpBkAyqZrPhT4Q7vKYGYNkOibMK5qKiPHL7PuEAFrBvuqpk7pROQ8X9KmZHMXLgY+3IEymNkLhPpK20M1Wo5fZgUSaGiy0EIZk8ZVRFwNXJ1e7z8W1zSzxkiib8L4ThejsBy/zIorgDVlbVyZWYEJ+txzZWZdqcQ9V2ZWYGnMlZlZt4lwz5WZFZDSVAxmZt0mEGsLOM+VI6pZ2QlU3ikXzKzL5Z2hvR3cuDIrOUn0u+fKzLpQgHuuzKyY3HNlZl0pxGC458rMisZjrsysSwWw1gPazaxoJNHXX7xudTOzegLcc2VmBSQ8FYOZdaXsaUH3XJlZwXgqBjPrWuHbgmZWRJIHtJtZVyrqbUFHVLOyk+gbN75qqp9dB0m6U9Ldkk4cYf/xkm6XtETSlZK2bks9zKx0hm4LVkud4saVmUFff/VUg6R+4EzgrcCOwLsl7TjssJuBORGxE3AR8KU21MDMSipCVVOn+LagWdlJaHz9Hqoq9gDujoh7slPpAmAecPvQARFxVcXx1wFHNXsxM7NK4TFXZlZMqtdDNV3Swor38yNifnq9JfBAxb5lwJ41znUscFlTxTQzG0Ene6iqcePKrOQkodpjq1ZExJwWXOcoYA7wxrznMjPLiAFPxWBmhSPqjq2q4UFgVsX7mWnbupeQDgBOAt4YEc83ezEzs0pFfVrQjSuz0qvbc1XLjcB2krYha1QdCbxnnbNLuwDfBg6KiEfzlNTMbB0BA25cmVnhqA/GTWgqa0SslfQx4HKgHzgrIm6TdCqwMCIWAF8G1gculARwf0TMbU3hzazMwrcFzayQBMqxtmBEXApcOmzbyRWvD2i+cGZmtUXkyy/pIOBfyb4gfjciTh/hmMOBU8juRN4SEe8ZfkwlN67Myk6C5m8Lmpl1TAQM5ui5qpir70Cyp51vlLQgIm6vOGY74HPA3hHxpKSX1TuvG1dmpSfU/IB2M7OOyjmgve5cfcCHgDMj4kmARsaOunFlVnb1p2IwMyuswcFcjatG5urbHkDSb8luHZ4SEb+oddK2jwKT1C/pZkmXpPfbSLo+rUP2Q0nNjaQ1sxZR08vf9DrHL7NiC6ovfZMmF50uaWFFOq6Jy4wDtgP2A94NfEfShrUyjMUQ+48Dd1S8PwP4WkRsCzxJNmOzmXWI0vI31VLJOX6ZFVlktwWrJdIkyBVp/rAzNDJX3zJgQUSsiYh7gT+RNbaqamvjStJM4G3Ad9N7AfuTLd4KcA5waDvLYGZ1SNlUDNVSSTl+mXWHGFTV1IAX5upLPdFHAguGHfNTsl4rJE0nu014T62TtnvM1deBzwBT0/tNgJURsTa9X0Z2v9PMOkh9xZsnpgC+juOXWeHlmYqhwbn6LgfeIul2YAA4ISIer3XehhpXkrYHvgVsFhGvkbQTMDci/qlGnkOARyNikaT9GrnOsPzHAccBbDZhAjf+84WjPcULZuy6RdN5p+2Sb0m1vqebn5B6m/VW57r2Nhvn+w/zHbs0f/3nDt4r17Wvue+ppvNuveHkXNf+9b01/2bq+sxPbm0671WfeEOuazdFgv7evv032hjWyvg1ifWY8Kubmi57TJ1a/6Aa/rpP3afGq3rw5tm5rj159l+azrv6fU/kuvbt+zcf9wHuPeTnTefd7dSP5Lr2k3s0H3u3++7a+gfVsM3/XZwrP5/Pl320IiByTiLawFx9ARyfUkMaLdF3yOZ4WJMutISs66yWvYG5kpYCF5B1p/8rsKGkoUbdiOuQpWvMH7pHuqGfZDJrI2WztFdLvWG0Maxl8Ws8E1tTAzMbUUT11CmNRs71IuKGYdtqNo8j4nMRMTMiZpMFsV9HxHuBq4DD0mFHAz8bRXnNrNUE0TeuauoRo4phjl9m3aL6eKsGx1y1RaONqxWSXkE27TuSDgOWN3nNzwLHS7qbbAzD95o8j5m1hLJbg9VSb2hVDHP8MiuaqJE6pNGvpf8TmA/sIOlB4F7gqEYvEhFXA1en1/eQzYhqZgUQQPT3TA9VNU3HMMcvswILOtpDVU1DETUFlAMkTQH6IuLp9hbLzMaMBL1z+29EjmFmPSzf8jdtUTOiShpxZLzSrYKI+GobymRmY0q9NHB9HY5hZiXQwdt/1dT7ujr0DPArgd15cWKttwPDB4eaWZfqoYHrwzmGmfWybrwtGBFfAJB0LbDrUFe6pFOA5icBMbPi6K2B6+twDDMrgS7suRqyGVA5q9nqtM3MekAP91wNcQwz61Hqtp6rCt8HbpB0cXp/KNm6WmbW9QS9v/yNY5hZL+rwlAvVNPq04GmSLgP2TZv+LiJubl+xzGzMlONpQccws54k6NaeK0lbASuAiyu3RcT97SqYmY2dXr8t6Bhm1sMGO12Al2o0ov6cFzveJgPbAHcCr25HocxsDCnfVAySDiJbd68f+G5EnD5s/0Sy23K7AY8DR0TE0qYv2BzHMLNeFHTfPFdDIuK1le8l7Qp8tC0lMrMxJujrby6n1A+cCRwILANulLQgIm6vOOxY4MmI2FbSkcAZwBE5Cz0qjmFmvUsF7Llq6utqRNwE7NnisphZh4T6qqY69gDujoh7ImI1cAEwb9gx83hx8PhFwJulzs794BhmZu3U6JirylmO+4BdgYfaUiIzG1MhEbV7rqZLWljxfn5EzE+vtwQeqNi3jJc2Wl44JiLWSnqKbNHjFbkKPgqOYWa9q5unYpha8Xot2fiFH7e+OGY25gKi9qPMKyJizhiVpl0cw8x6UTdPxQDcHhEXVm6Q9C7gwirHm1nXCAbqtK5qeBCYVfF+Zto20jHLJI0DNiAb2D6WHMPMelQ3j7n6XIPbzKzLBDAwGFVTHTcC20naRtIE4EheXL9vyALg6PT6MODXEc235prkGGbWqwZrpA6p2XMl6a3AwcCWkr5RsWsaWde6mXW5AOq3oarkzcZQfQy4nGwqhrMi4jZJpwILI2IB8D3gXEl3A0+QNcDGhGOYWW9TZKlo6t0WfAhYCMwFFlVsfxr4ZLsKZWZjKGAgR3CKiEuBS4dtO7ni9XPAu5q/Qi6OYWa9rtsGtEfELcAtks6PCH/LM+tRY3+Xbmw4hpn1vq7ruZL0o4g4HLhZemnxI2KntpXMzMZEkK/nqsgcw8x6XBRzQHu924IfT/8e0u6CmFnnNDvmqgs4hpn1ugLGr5pPC0bE8vTyoxFxX2XCS0eY9YQIGIiomrqZY5hZ79Ng9dQpjU7FcOAI297ayoKYWWfknIqhWziGmfWqqJEaIOkgSXdKulvSiTWOe6ekkFR3UuV6Y64+Qvbt7uWSllTsmgr8trFim1nR9UwTahjHMLMel3MqhgYXn0fSVLJhBtc3ct56Y67+E7gM+GegsjX3dEQ80WDZzazgBgo4ILRFHMPMel2++PXC4vMAkoYWn7992HFfBM4ATmjkpPXGXD0VEUsj4t1pjMJfyb7kri9pq1p5JU2SdIOkWyTdJukLafvZku6VtDilnRspqJm1RxAM1kjdrNkY5vhl1h3EixOJjpQaMNLi81uucw1pV2BWRPy80XI1tLagpLcDXwW2AB4FtgbuAF5dI9vzwP4RsUrSeOA3ki5L+06IiIsaLaSZtVH0dM8V0FQMc/wy6wb1p2KYLmlhxfv5ETG/0dNL6iOLHceMpliNLtz8T8BewK8iYhdJbwKOqpUhrR22Kr0dn1J3fw0260FB9sRgjxtVDHP8Musitf8yV0RErQHo9Rafnwq8BrhaEsDmwAJJcyOistG2jkafFlwTEY8DfZL6IuIqoO5oeUn9khaTfVO8IiKGBoKdJmmJpK9JmthgGcysTXp1KoYKo45hjl9m3SHnVAw1F59PQwumR8TsiJgNXAfUbFhB4z1XKyWtD1wLnC/pUeCZepkiYgDYWdKGwMWSXkO2Ev3DwARgPvBZ4NTheSUdBxwHMHOjqbzqyD0bLOpLqb/RNuRL/enb5zedF+DJe1bmyp/HFrvPzJV/5iEHNJ13opr/mQPsv+XLm867Ym1/rmuvei7fKikzNp7cdN5X/sNPc127GRGwplenaH/RqGNYq+LXpHHT6N9m6+ZLrnzrpm1yweKm8278/dW5rp1H3wbTcuWf/MaNcuV/+fhjm857xee/nOvaX354pJlDGnPfM7NzXbtvcvPxC+DA/iNy5R+1INeA9gYXnx+1Rv8HnEc2EPSTwC+APwNvb/QiEbESuAo4KCKWR+Z54D/IRuqPlGd+RMyJiDmbTMn3yzaz6gIYjKiaekTTMSxv/JrQ7/hl1k45B7QTEZdGxPYR8YqIOC1tO3mkhlVE7Fev1woa7LmKiMpveOc0kkfSpmRd8SslTSabQ+IMSTMiYrmym5eHAn9o5Hxm1h5BsGawt0e0jzaGOX6ZdY+uW1tQ0tOMPFRMZGM+a/XbzgDOSRN09QE/iohLJP06BS4Bi4EPN1VyM2uNHn5aMEcMc/wy6xYF7GCv2biKiKnNnjgilgC7jLB9/2bPaWatF9CzPVfNxjDHL7PuMJrbf2Op0QHtZtajsjFXnS6FmVmTChi/3LgyK7mIYE2v3hc0s57XdWOuzKz3ZbcFC/jVz8ysnvoztHeEG1dmZRcw4MaVmXWrAoavfDM9mlnXG+q5qpbykLSxpCsk3ZX+fcnMjpJ2lvT7tEDyEkljPAuhmXWznDO0t4UbV2Yl1+ZJRE8EroyI7YAr0/vhngXeHxGvBg4Cvp5mRTczqyvvJKLt4NuCZiWXDWhvWxSaB+yXXp8DXE22ZEzl9f9U8fqhtDTNpsDKdhXKzHpEzuVv2sWNKzOr10M1XVLlcg/zI2J+g6feLCKWp9cPA5vVOljSHmTr9v25wfObWYkJz3NlZgWULdxc86vfioiYU22npF8Bm4+w66R1rxMhVQ+DkmYA5wJHR0QBv4uaWRGpgA/kuHFlVnJ5p2KIiAOq7ZP0SMV6fDOAR6scNw34OXBSRFzXdGHMrFwKOhWDB7SblVwQDET1lNMC4Oj0+mjgZ8MPkDQBuBj4fkRclPeCZlYyUSN1iBtXZiUXAavXDlZNOZ0OHCjpLuCA9B5JcyR9Nx1zOPAG4BhJi1PaOe+FzawcijgVg28LmpVctHES0Yh4HHjzCNsXAh9Mr88DzmtLAcyst3nhZjMroiBa0UNlZjbmRDHHXLlxZVZ2Xv7GzLpZ/rGhLefGlVnJDaYxV2ZmXaegTwu6cWVWcr4taGbdTAOdLsFLuXFlVnIRsNa3Bc2sS3lAu5kVTvi2oJl1q/AM7WZWUC2YLNTMrDMKGL7cuDIrucHwmCsz606KcM+VmRWTp2Iws25VxDFXXv7GrOQigtVrB6omM7Miy7v8jaSDJN0p6W5JJ46w/3hJt0taIulKSVvXO6cbV2YlNxjw/NrBqsnMrLACGIjqqQ5J/cCZwFuBHYF3S9px2GE3A3MiYifgIuBL9c7btsaVpEmSbpB0i6TbJH0hbd9G0vWphfhDSRPaVQYzqy/IbgtWS2XlGGbWHRTVUwP2AO6OiHsiYjVwATCv8oCIuCoink1vrwNm1jtpO3uungf2j4jXATsDB0naCzgD+FpEbAs8CRzbxjKYWR2RBrRXSyXmGGbWBTQYVVMDtgQeqHi/LG2r5ljgsnonbVvjKjKr0tvxKQWwP1m3GsA5wKHtKoOZNcY9Vy/lGGbWBaJOgumSFlak45q9lKSjgDnAl+sd29anBdO9zEXAtmT3NP8MrIyItemQqi3E9AM4DmDW9A2ZssXLmi/HlGlN593xLYc2nRdAEyY3nXdw4pRc1x6YNiNX/uf6Jjad966/rM517Ucebj7/9fc/muvaN933ZK78f/rjilz5x1oErC13D1VVzcawyvg1aeIGrJmxQdNlWL3B+KbzAjz4oeZj58AG+R5o2HjLp5rO+8Odzsp17bwO/PnxTec97Csn5Lr2jKueaDqvHlye69oDT/0lV/6xJkC1x1atiIg5NfY/CMyqeD8zbVv3OtIBwEnAGyPi+XrlauuA9ogYiIidyQq7B7DDKPLOj4g5ETFn+rR8jQwzqy4CBgejaiqzZmNYZfwaP97xy6ydFFE1NeBGYLs0lnICcCSwYJ3zS7sA3wbmRkRD397HZJ6riFgp6Srg9cCGksalb34jthDNbCwFgwPuuarFMcysoCKyR56bzh5rJX0MuBzoB86KiNsknQosjIgFZLcB1wculARwf0TMrXXetjWuJG0KrElBaTJwINlA0KuAw8hG5B8N/KxdZTCz+iJgYG25e6hG4hhm1h3yztAeEZcClw7bdnLF6wNGe8529lzNAM5JYxb6gB9FxCWSbgcukPRPZHNHfK+NZTCzBoTXFhyJY5hZ0UXjk4WOpbY1riJiCbDLCNvvIRu7YGZFEMFAmwa0S9oY+CEwG1gKHB4RIz4xIGkacDvw04j4WFsKNAqOYWZdooBjQz1Du1nJBRCDUTXldCJwZURsB1yZ3lfzReDavBc0s3LJOaC9Ldy4Miu7gIGBwaopp3lkc0FBjTmhJO0GbAb8Mu8FzaxEci5/0y5uXJlZO3uuNouIoYl3HiZrQK1DUh/wL8Cn817MzMpFVO+16mTP1ZhMxWBmxRUR9XqopktaWPF+fkTMH3oj6VfA5iPkO2nYdUIacbWvjwKXRsSy9JizmVnjBos3ot2NKzMjasemmjMc13pMWdIjkmZExHJJM4CRJuB7PbCvpI+SzSUzQdKqiKg1PsvMLLstWLy2lRtXZmWXzXPVtui0gGwuqNOpMidURLx36LWkY4A5bliZWaNUwJ4rj7kyK7t0W7BNA9pPBw6UdBdwQHqPpDmSvpv35GZWctn6XdVTh7jnyqzkhqZiaMu5Ix4H3jzC9oXAB0fYfjZwdlsKY2a9qXgdV25cmZVee28Lmpm1VRFvC7pxZWZe/sbMulNQyBna3bgyK7lo4/I3ZmbtFZ6KwcyKKQYHOl0EM7PmFLDn3Y0rs5KLGGRw7epOF8PMbPQiYKB4Xw7duDIruwgG17hxZWZdKID8U8a0nBtXZmUX4duCZta9fFvQzIomCN8WNLMu5QHtZlZE4QHtZtalAjeuzKyAYpAB91yZWbdy48rMiiZb/sY9V2bWjcKTiJpZAflpQTPrVgHhqRjMrHA8z5WZdSvPc2VmRZTdFizemAUzs4Z4KgYzK5zwVAxm1q2ikLcF+9p1YkmzJF0l6XZJt0n6eNp+iqQHJS1O6eB2lcHMGhDB4OBA1VRGjl9mXSLIBrRXSx3Szp6rtcCnIuImSVOBRZKuSPu+FhFfaeO1zaxB4QHtI3H8MusCQckGtEfEcmB5ev20pDuALdt1PTNrlpe/Gc7xy6xLREAUb8xo224LVpI0G9gFuD5t+pikJZLOkrTRWJTBzKpIY66qpbJz/DIrthgYqJo6RdHmUfaS1geuAU6LiJ9I2gxYQdab90VgRkR8YIR8xwHHpbevBO7MUYzp6Zpl43qXzysjYupoMkj6BdnPrJoVEXFQvmJ1J8evjiprvaG8de+Z+NXWxpWk8cAlwOUR8dUR9s8GLomI17StENl1FkbEnHZeo4hc7/Ipc91bzfGrs8pabyhv3Xup3u18WlDA94A7KgOTpBkVh70D+EO7ymBm1gzHLzPLo51PC+4NvA+4VdLitO3zwLsl7UzWrb4U+Ps2lsHMrBmOX2bWtHY+LfgbQCPsurRd16xhfgeuWQSud/mUue4t4/hVCGWtN5S37j1T77YPaDczMzMrkzGZisHMzMysLLq+cZXmmnlU0h8qtu0s6bq0PMVCSXuk7ZL0DUl3p3lqdu1cyfOpsTzHxpKukHRX+nejtL0Mdf+ypD+m+l0sacOKPJ9Ldb9T0t92rPA5VKt3xf5PSQpJ09P7nvmd9zLHsHLFsLLGLyhZDIuIrk7AG4BdgT9UbPsl8Nb0+mDg6orXl5GNpdgLuL7T5c9R7xnArun1VOBPwI7Al4AT0/YTgTNKVPe3AOPS9jMq6r4jcAswEdgG+DPQ3+l6tKre6f0s4HLgPmB6r/3Oezk5hpUrhpU1ftWqe3rfUzGs63uuIuJa4Inhm4Fp6fUGwEPp9Tzg+5G5DthQ6z5a3TUiYnlE3JRePw0MLc8xDzgnHXYOcGh63fN1j4hfRsTadNh1wMz0eh5wQUQ8HxH3AncDe4x1ufOq8TsH+BrwGbLP/pCe+Z33MsewcsWwssYvKFcMa+dUDJ30CeBySV8hu/X5N2n7lsADFcctS9uWj2npWkzrLs+xWWTrogE8DGyWXpeh7pU+APwwvd6SLFgNGap716qst6R5wIMRcYu0zgNuPfk7L4lP4BgGPR7Dyhq/oPdjWNf3XFXxEeCTETEL+CTZZIA9SdnyHD8GPhERf6ncF1m/as8+Dlqt7pJOAtYC53eqbO1UWW+yen4eOLmTZbKWcwyjt2NYWeMXlCOG9Wrj6mjgJ+n1hbzYhfog2X3dITPTtq6kbHmOHwPnR8RQfR8Z6jZN/z6atpeh7kg6BjgEeG8KzNBDdR+h3q8gG4dxi6SlZHW7SdLm9FC9S8gxjN6NYWWNX1CeGNarjauHgDem1/sDd6XXC4D3pycQ9gKequh+7irSyMtzkNXx6PT6aOBnFdt7uu6SDiK7Zz83Ip6tyLIAOFLSREnbANsBN4xlmVthpHpHxK0R8bKImB0Rs8m6zXeNiIfpod95CTmGZXouhpU1fkHJYli7RsqPVQJ+QHb/dQ3ZL+VYYB9gEdkTFtcDu6VjBZxJ9rTFrcCcTpc/R733IesuXwIsTulgYBPgSrJg/Ctg4xLV/W6y+/ND2/69Is9Jqe53kp7C6rZUrd7DjlnKi0/a9MzvvJeTY1i5YlhZ41etug87pidimGdoNzMzM2uhXr0taGZmZtYRblyZmZmZtZAbV2ZmZmYt5MaVmZmZWQu5cWVmZmbWQm5c9ThJq9pwzrmSTkyvD5W0YxPnuFrSnFaXzcx6h+OXdSs3rmzUImJBRJye3h5Ktmq7mVnhOX7ZWHDjqiTSDLdflvQHSbdKOiJt3y99C7tI0h8lnZ9m0UXSwWnbIknfkHRJ2n6MpG9K+htgLvBlSYslvaLyG52k6Wk5AyRNlnSBpDskXQxMrijbWyT9XtJNki5M606ZmQGOX9Z9xnW6ADZm/gewM/A6YDpwo6Rr075dgFeTLbnxW2BvSQuBbwNviIh7Jf1g+Akj4neSFgCXRMRFAFp3RfNKHwGejYhXSdoJuCkdPx34R+CAiHhG0meB44FTW1BnM+sNjl/WVdy4Ko99gB9ExADZwqjXALsDfwFuiIhlAJIWA7OBVcA9EXFvyv8D4Lgc138D8A2AiFgiaUnavhdZt/xvU2CbAPw+x3XMrPc4fllXcePKAJ6veD1Avs/FWl683TypgeMFXBER785xTTMrL8cvKxyPuSqP/waOkNQvaVOyb2K1Vla/E3i5pNnp/RFVjnsamFrxfimwW3p9WMX2a4H3AEh6DbBT2n4dWTf+tmnfFEnbN1IhMysNxy/rKm5clcfFZCuR3wL8GvhMRDxc7eCI+CvwUeAXkhaRBaGnRjj0AuAESTdLegXwFeAjkm4mGxsx5FvA+pLuIBuPsChd5zHgGOAHqav998AOeSpqZj3H8cu6iiKi02WwgpK0fkSsSk/fnAncFRFf63S5zMzqcfyyTnLPldXyoTRA9DZgA7Knb8zMuoHjl3WMe67MzMzMWsg9V2ZmZmYt5MaVmZmZWQu5cWVmZmbWQm5cmZmZmbWQG1dmZmZmLeTGlZmZmVkL/X8FHWZpDajbbwAAAABJRU5ErkJggg==",
+                                    "text/plain": [
+                                          "<Figure size 720x216 with 4 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, (ax1, ax2) = plt.subplots(figsize=(10, 3), ncols=2)\n",
+                        "\n",
+                        "# Visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
+                        "rgdr.corr_map.plot(ax=ax1)\n",
+                        "\n",
+                        "# Visualize p-values map\n",
+                        "rgdr.pval_map.plot(ax=ax2)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "The cluster maps can also be visualized:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 13,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "<matplotlib.collections.QuadMesh at 0x2602868b7c0>"
+                                    ]
+                              },
+                              "execution_count": 13,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        },
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 432x288 with 2 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "rgdr.cluster_map.plot(cmap='RdYlBu', vmin=-2, vmax=2)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "## Multiple target intervals\n",
+                        "\n",
+                        "As mentioned earlier, with RGDR you can also analyse the correlation of multiple intervals at once.\n",
+                        "\n",
+                        "Instead of a simple correlation, this is more akin to a cross-correlation operation.\n",
+                        "\n",
+                        "When you select multiple target intervals, these will be correlated with the same number of precursor intervals, shifted by the `lag` value.\n",
+                        "\n",
+                        "As an illustration, see the image below:\n",
+                        "\n",
+                        "![Multiple Targets](../notebooks/multiple_targets_RGDR.svg)\n",
+                        "\n",
+                        "Now let's see it in action:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 14,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "text/plain": [
+                                          "[-4, -3, -2, -1]"
+                                    ]
+                              },
+                              "execution_count": 14,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        }
+                  ],
+                  "source": [
+                        "target_intervals = [1, 2, 3, 4]\n",
+                        "lag = 4\n",
+                        "rgdr = RGDR(\n",
+                        "    target_intervals=target_intervals,\n",
+                        "    lag=lag,\n",
+                        "    eps_km=600,\n",
+                        "    alpha=0.05,\n",
+                        "    min_area_km2=0\n",
+                        ")\n",
+                        "rgdr.precursor_intervals"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 15,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
+                                    "image/png": 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",
+                                    "text/plain": [
+                                          "<Figure size 864x144 with 4 Axes>"
+                                    ]
+                              },
+                              "metadata": {
+                                    "needs_background": "light"
+                              },
+                              "output_type": "display_data"
+                        }
+                  ],
+                  "source": [
+                        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
+                        "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
+                        "rgdr.fit(precursor_field, target_timeseries)"
+                  ]
+            },
+            {
+                  "attachments": {},
+                  "cell_type": "markdown",
+                  "metadata": {},
+                  "source": [
+                        "Note that the transformed data now has all four precursor intervals:"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": 16,
+                  "metadata": {},
+                  "outputs": [
+                        {
+                              "data": {
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+                                          "  grid-column: 1;\n",
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+                                          "\n",
+                                          ".xr-attrs dt:hover span {\n",
+                                          "  display: inline-block;\n",
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+                                          "  padding-right: 10px;\n",
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+                                          "\n",
+                                          ".xr-attrs dd {\n",
+                                          "  grid-column: 2;\n",
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+                                          "\n",
+                                          ".xr-icon-database,\n",
+                                          ".xr-icon-file-text2 {\n",
+                                          "  display: inline-block;\n",
+                                          "  vertical-align: middle;\n",
+                                          "  width: 1em;\n",
+                                          "  height: 1.5em !important;\n",
+                                          "  stroke-width: 0;\n",
+                                          "  stroke: currentColor;\n",
+                                          "  fill: currentColor;\n",
+                                          "}\n",
+                                          "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 40, i_interval: 4, cluster_labels: 2)&gt;\n",
+                                          "289.6 298.0 289.9 297.8 290.6 297.9 ... 290.1 297.4 290.7 297.6 291.1 298.2\n",
+                                          "Coordinates:\n",
+                                          "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
+                                          "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
+                                          "    target          (i_interval) bool False False False False\n",
+                                          "  * cluster_labels  (cluster_labels) int16 -1 1\n",
+                                          "    latitude        (cluster_labels) float64 38.05 29.58\n",
+                                          "    longitude       (cluster_labels) float64 221.1 188.1\n",
+                                          "Attributes:\n",
+                                          "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
+                                          "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 40</li><li><span class='xr-has-index'>i_interval</span>: 4</li><li><span class='xr-has-index'>cluster_labels</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8a118852-8385-43d8-ad4b-8095acf06cdb' class='xr-array-in' type='checkbox' ><label for='section-8a118852-8385-43d8-ad4b-8095acf06cdb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>289.6 298.0 289.9 297.8 290.6 297.9 ... 297.4 290.7 297.6 291.1 298.2</span></div><div class='xr-array-data'><pre>array([[[289.5783292 , 297.97253854],\n",
+                                          "        [289.9437494 , 297.84123593],\n",
+                                          "        [290.59375437, 297.87556143],\n",
+                                          "        [291.14586621, 298.14905515]],\n",
+                                          "\n",
+                                          "       [[289.58515932, 298.52527274],\n",
+                                          "        [290.05585833, 298.87564332],\n",
+                                          "        [290.58959074, 298.95332217],\n",
+                                          "        [291.0557513 , 299.2138487 ]],\n",
+                                          "\n",
+                                          "       [[290.15395151, 297.94216929],\n",
+                                          "        [290.51146981, 298.33819147],\n",
+                                          "        [290.94559058, 298.60726164],\n",
+                                          "        [291.58073576, 298.61821901]],\n",
+                                          "\n",
+                                          "       [[291.69750902, 297.84277249],\n",
+                                          "        [291.80184887, 298.48058434],\n",
+                                          "        [291.99959682, 299.09760852],\n",
+                                          "        [292.36201799, 298.96715692]],\n",
+                                          "\n",
+                                          "...\n",
+                                          "\n",
+                                          "       [[288.75208738, 296.54021412],\n",
+                                          "        [289.36292891, 297.22635236],\n",
+                                          "        [289.59244458, 297.71173429],\n",
+                                          "        [290.34015463, 298.25471266]],\n",
+                                          "\n",
+                                          "       [[289.30640172, 298.24357205],\n",
+                                          "        [289.51077876, 299.19472633],\n",
+                                          "        [289.96666198, 298.38401708],\n",
+                                          "        [290.32748515, 299.00638299]],\n",
+                                          "\n",
+                                          "       [[288.69719671, 298.00652225],\n",
+                                          "        [289.64060397, 298.52628344],\n",
+                                          "        [290.04893905, 298.8792455 ],\n",
+                                          "        [290.52763796, 299.06176483]],\n",
+                                          "\n",
+                                          "       [[289.46465551, 297.27006951],\n",
+                                          "        [290.14110504, 297.406225  ],\n",
+                                          "        [290.72314837, 297.64015827],\n",
+                                          "        [291.09759615, 298.20393399]]])</pre></div></div></li><li class='xr-section-item'><input id='section-08c13ac5-16d0-490b-84f9-1ecaecf335ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08c13ac5-16d0-490b-84f9-1ecaecf335ea' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2018 2017 2016 ... 1981 1980 1979</div><input id='attrs-7b94777b-fdfa-4a82-83a5-a92b7b434440' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b94777b-fdfa-4a82-83a5-a92b7b434440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-080d3f09-77c0-4597-8b08-112b239a2636' class='xr-var-data-in' type='checkbox'><label for='data-080d3f09-77c0-4597-8b08-112b239a2636' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007,\n",
+                                          "       2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995,\n",
+                                          "       1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983,\n",
+                                          "       1982, 1981, 1980, 1979], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1</div><input id='attrs-ac4a2564-720a-433e-948d-fca1624a3298' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac4a2564-720a-433e-948d-fca1624a3298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03019e5f-0351-42d7-ad37-453f04af9836' class='xr-var-data-in' type='checkbox'><label for='data-03019e5f-0351-42d7-ad37-453f04af9836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4, -3, -2, -1], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False False</div><input id='attrs-e1d3babf-0dfa-4089-94aa-7eb0bc6cb418' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1d3babf-0dfa-4089-94aa-7eb0bc6cb418' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a862fc75-0a69-4ffb-b3f7-f8d46fb2ca33' class='xr-var-data-in' type='checkbox'><label for='data-a862fc75-0a69-4ffb-b3f7-f8d46fb2ca33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-1 1</div><input id='attrs-7326887e-1985-4080-9fa9-6c0d27ff8807' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7326887e-1985-4080-9fa9-6c0d27ff8807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d94f87-c069-4d25-a7bb-7a06b75cdf65' class='xr-var-data-in' type='checkbox'><label for='data-74d94f87-c069-4d25-a7bb-7a06b75cdf65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1,  1], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.05 29.58</div><input id='attrs-27c1034b-6ee2-4899-95bf-579b5843e244' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27c1034b-6ee2-4899-95bf-579b5843e244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33e5b00a-86e0-469e-aedc-589454fa5b37' class='xr-var-data-in' type='checkbox'><label for='data-33e5b00a-86e0-469e-aedc-589454fa5b37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([38.04510914, 29.58134561])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>221.1 188.1</div><input id='attrs-98a77891-383d-4b25-be74-07faad3f6751' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98a77891-383d-4b25-be74-07faad3f6751' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a9cfb2-553b-4d56-a138-3670d0a9a291' class='xr-var-data-in' type='checkbox'><label for='data-72a9cfb2-553b-4d56-a138-3670d0a9a291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([221.11227597, 188.12201842])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3ebff15-f332-4b44-9854-055b081525cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3ebff15-f332-4b44-9854-055b081525cd' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
+                                    ],
+                                    "text/plain": [
+                                          "<xarray.DataArray 'sst' (anchor_year: 40, i_interval: 4, cluster_labels: 2)>\n",
+                                          "289.6 298.0 289.9 297.8 290.6 297.9 ... 290.1 297.4 290.7 297.6 291.1 298.2\n",
+                                          "Coordinates:\n",
+                                          "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
+                                          "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
+                                          "    target          (i_interval) bool False False False False\n",
+                                          "  * cluster_labels  (cluster_labels) int16 -1 1\n",
+                                          "    latitude        (cluster_labels) float64 38.05 29.58\n",
+                                          "    longitude       (cluster_labels) float64 221.1 188.1\n",
+                                          "Attributes:\n",
+                                          "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
+                                          "    coordinates:  Latitudes and longitudes are geographical centers associate..."
+                                    ]
+                              },
+                              "execution_count": 16,
+                              "metadata": {},
+                              "output_type": "execute_result"
+                        }
+                  ],
+                  "source": [
+                        "clustered_data = rgdr.transform(precursor_field)\n",
+                        "clustered_data"
+                  ]
+            },
+            {
+                  "cell_type": "code",
+                  "execution_count": null,
+                  "metadata": {},
+                  "outputs": [],
+                  "source": []
+            }
       ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (anchor_year: 40, i_interval: 1, cluster_labels: 5)>\n",
-       "292.6 288.8 285.7 292.2 295.8 293.0 ... 296.1 292.2 289.7 286.0 292.0 296.1\n",
-       "Coordinates:\n",
-       "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
-       "  * i_interval      (i_interval) int64 -4\n",
-       "    target          (i_interval) bool False\n",
-       "  * cluster_labels  (cluster_labels) int16 -3 -2 -1 1 2\n",
-       "    latitude        (cluster_labels) float64 28.7 37.5 45.58 37.5 32.5\n",
-       "    longitude       (cluster_labels) float64 233.7 232.5 215.4 180.0 195.0\n",
-       "Attributes:\n",
-       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
-       "    coordinates:  Latitudes and longitudes are geographical centers associate..."
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clustered_data = rgdr.transform(precursor_field)\n",
-    "xr.set_options(display_expand_data=False) # Hide the full data repr\n",
-    "clustered_data"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot the data for each cluster (blue for positively correlated lcusters, red for negatively correlated ones):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x26028538430>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "clustered_data = clustered_data  - clustered_data.mean(dim=\"anchor_year\")\n",
-    "kwargs = {'x': 'anchor_year', 'add_legend': False}\n",
-    "clustered_data.sel(cluster_labels=1).plot.line(c='tab:blue', **kwargs)\n",
-    "clustered_data.sel(cluster_labels=2).plot.line(c='tab:blue', **kwargs)\n",
-    "clustered_data.sel(cluster_labels=-1).plot.line(c='tab:red', **kwargs)\n",
-    "clustered_data.sel(cluster_labels=-2).plot.line(c='tab:red', **kwargs)\n",
-    "clustered_data.sel(cluster_labels=-3).plot.line(c='tab:red', **kwargs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `RGDR` object will also contain the correlation maps, p-values, and cluster maps.\n",
-    "\n",
-    "These are all `xr.DataArray` objects, and can easily be plot using the builtin methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<defs>\n",
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
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-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 5, longitude: 13)&gt;\n",
-       "0.2697 0.2123 0.0529 -0.1931 -0.3189 ... -0.1533 -0.246 -0.3174 -0.4126 -0.4341\n",
-       "Coordinates:\n",
-       "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
-       "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
-       "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
-       "    cluster     int64 3\n",
-       "    tfreq       int64 5\n",
-       "    n_clusters  int64 6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14114774-aab0-40e6-aa62-d77f68145afb' class='xr-array-in' type='checkbox' ><label for='section-14114774-aab0-40e6-aa62-d77f68145afb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.2697 0.2123 0.0529 -0.1931 ... -0.246 -0.3174 -0.4126 -0.4341</span></div><div class='xr-array-data'><pre>array([[ 0.26973879,  0.21227671,  0.05289627, -0.19309815, -0.31889021,\n",
-       "        -0.47274522, -0.55472119, -0.53583813, -0.56565047, -0.53421202,\n",
-       "        -0.41056264, -0.2310342 ,         nan],\n",
-       "       [ 0.2037804 ,  0.26253778,  0.19418173,  0.06666334, -0.08516656,\n",
-       "        -0.18050655, -0.27848657, -0.35489399, -0.41916154, -0.47261077,\n",
-       "        -0.31655439, -0.12650094,         nan],\n",
-       "       [ 0.40535219,  0.32321764,  0.16911906,  0.11556145,  0.08321155,\n",
-       "         0.07836849,  0.10415046, -0.0085853 , -0.16056509, -0.1003596 ,\n",
-       "        -0.22329525, -0.3465616 ,         nan],\n",
-       "       [ 0.2630065 ,  0.26032401,  0.35231884,  0.40363035,  0.40450999,\n",
-       "         0.31832834,  0.18927728,  0.09921395,  0.01973299, -0.05579345,\n",
-       "        -0.24113899, -0.30901333, -0.43695491],\n",
-       "       [ 0.21990857,  0.19720435,  0.12089699,  0.10531533,  0.07945109,\n",
-       "         0.01934902, -0.0193646 , -0.10691568, -0.15330192, -0.24602795,\n",
-       "        -0.31735925, -0.41262283, -0.43411288]])</pre></div></div></li><li class='xr-section-item'><input id='section-e3a869ff-d54c-4ac9-a9e6-f5d9b52bff02' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3a869ff-d54c-4ac9-a9e6-f5d9b52bff02' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.5 42.5 37.5 32.5 27.5</div><input id='attrs-573c8629-c58c-4535-9d78-3a87f9ffd9b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-573c8629-c58c-4535-9d78-3a87f9ffd9b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d0ea2a0-ec6b-4cd3-a6d3-34789c2826d7' class='xr-var-data-in' type='checkbox'><label for='data-1d0ea2a0-ec6b-4cd3-a6d3-34789c2826d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([47.5, 42.5, 37.5, 32.5, 27.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>177.5 182.5 187.5 ... 232.5 237.5</div><input id='attrs-e99ecefd-7768-4d79-879c-fcf7154e577a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e99ecefd-7768-4d79-879c-fcf7154e577a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b134f8-544e-45a5-91a4-1cdf5e7bef52' class='xr-var-data-in' type='checkbox'><label for='data-70b134f8-544e-45a5-91a4-1cdf5e7bef52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
-       "       227.5, 232.5, 237.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.088e+05 2.278e+05 ... 2.741e+05</div><input id='attrs-e9d32b8c-7b02-48b7-ae63-4c636bebf5c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9d32b8c-7b02-48b7-ae63-4c636bebf5c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d228b03e-f29b-41a1-8bca-e0407ddfd06d' class='xr-var-data-in' type='checkbox'><label for='data-d228b03e-f29b-41a1-8bca-e0407ddfd06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([208767.02150832, 227829.22531495, 245157.51114987, 260620.0002948 ,\n",
-       "       274099.01387082])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-ecf81775-cda5-434f-8c7a-36eaebcefd6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecf81775-cda5-434f-8c7a-36eaebcefd6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eff327f-c005-41ca-8b65-741de3454c50' class='xr-var-data-in' type='checkbox'><label for='data-9eff327f-c005-41ca-8b65-741de3454c50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tfreq</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5d2ef2c-39db-4f0d-b39a-a72e28803262' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5d2ef2c-39db-4f0d-b39a-a72e28803262' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98838557-31ab-4b2b-9b28-abad9eb77c4c' class='xr-var-data-in' type='checkbox'><label for='data-98838557-31ab-4b2b-9b28-abad9eb77c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(5, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_clusters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52592435-3ae0-4243-b411-9a0d91357019' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52592435-3ae0-4243-b411-9a0d91357019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e2c7eb-6e36-4511-b92a-52a820d7ec7c' class='xr-var-data-in' type='checkbox'><label for='data-51e2c7eb-6e36-4511-b92a-52a820d7ec7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(6, dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa1963e0-4c33-4751-8373-182fb245b730' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa1963e0-4c33-4751-8373-182fb245b730' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (latitude: 5, longitude: 13)>\n",
-       "0.2697 0.2123 0.0529 -0.1931 -0.3189 ... -0.1533 -0.246 -0.3174 -0.4126 -0.4341\n",
-       "Coordinates:\n",
-       "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
-       "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
-       "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
-       "    cluster     int64 3\n",
-       "    tfreq       int64 ...\n",
-       "    n_clusters  int64 ..."
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# View the contents of the corr_map DataArray:\n",
-    "rgdr.corr_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x260285b71c0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax1, ax2) = plt.subplots(figsize=(10, 3), ncols=2)\n",
-    "\n",
-    "# Visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
-    "rgdr.corr_map.plot(ax=ax1)\n",
-    "\n",
-    "# Visualize p-values map\n",
-    "rgdr.pval_map.plot(ax=ax2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The cluster maps can also be visualized:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x2602868b7c0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rgdr.cluster_map.plot(cmap='RdYlBu', vmin=-2, vmax=2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multiple target intervals\n",
-    "\n",
-    "As mentioned earlier, with RGDR you can also analyse the correlation of multiple intervals at once.\n",
-    "\n",
-    "Instead of a simple correlation, this is more akin to a cross-correlation operation.\n",
-    "\n",
-    "When you select multiple target intervals, these will be correlated with the same number of precursor intervals, shifted by the `lag` value.\n",
-    "\n",
-    "As an illustration, see the image below:\n",
-    "\n",
-    "![Multiple Targets](../notebooks/multiple_targets_RGDR.svg)\n",
-    "\n",
-    "Now let's see it in action:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-4, -3, -2, -1]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target_intervals = [1, 2, 3, 4]\n",
-    "lag = 4\n",
-    "rgdr = RGDR(\n",
-    "    target_intervals=target_intervals,\n",
-    "    lag=lag,\n",
-    "    eps_km=600,\n",
-    "    alpha=0.05,\n",
-    "    min_area_km2=0\n",
-    ")\n",
-    "rgdr.precursor_intervals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x144 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
-    "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
-    "rgdr.fit(precursor_field, target_timeseries)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the transformed data now has all four precursor intervals:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 40, i_interval: 4, cluster_labels: 2)&gt;\n",
-       "289.6 298.0 289.9 297.8 290.6 297.9 ... 290.1 297.4 290.7 297.6 291.1 298.2\n",
-       "Coordinates:\n",
-       "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
-       "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
-       "    target          (i_interval) bool False False False False\n",
-       "  * cluster_labels  (cluster_labels) int16 -1 1\n",
-       "    latitude        (cluster_labels) float64 38.05 29.58\n",
-       "    longitude       (cluster_labels) float64 221.1 188.1\n",
-       "Attributes:\n",
-       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
-       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 40</li><li><span class='xr-has-index'>i_interval</span>: 4</li><li><span class='xr-has-index'>cluster_labels</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8a118852-8385-43d8-ad4b-8095acf06cdb' class='xr-array-in' type='checkbox' ><label for='section-8a118852-8385-43d8-ad4b-8095acf06cdb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>289.6 298.0 289.9 297.8 290.6 297.9 ... 297.4 290.7 297.6 291.1 298.2</span></div><div class='xr-array-data'><pre>array([[[289.5783292 , 297.97253854],\n",
-       "        [289.9437494 , 297.84123593],\n",
-       "        [290.59375437, 297.87556143],\n",
-       "        [291.14586621, 298.14905515]],\n",
-       "\n",
-       "       [[289.58515932, 298.52527274],\n",
-       "        [290.05585833, 298.87564332],\n",
-       "        [290.58959074, 298.95332217],\n",
-       "        [291.0557513 , 299.2138487 ]],\n",
-       "\n",
-       "       [[290.15395151, 297.94216929],\n",
-       "        [290.51146981, 298.33819147],\n",
-       "        [290.94559058, 298.60726164],\n",
-       "        [291.58073576, 298.61821901]],\n",
-       "\n",
-       "       [[291.69750902, 297.84277249],\n",
-       "        [291.80184887, 298.48058434],\n",
-       "        [291.99959682, 299.09760852],\n",
-       "        [292.36201799, 298.96715692]],\n",
-       "\n",
-       "...\n",
-       "\n",
-       "       [[288.75208738, 296.54021412],\n",
-       "        [289.36292891, 297.22635236],\n",
-       "        [289.59244458, 297.71173429],\n",
-       "        [290.34015463, 298.25471266]],\n",
-       "\n",
-       "       [[289.30640172, 298.24357205],\n",
-       "        [289.51077876, 299.19472633],\n",
-       "        [289.96666198, 298.38401708],\n",
-       "        [290.32748515, 299.00638299]],\n",
-       "\n",
-       "       [[288.69719671, 298.00652225],\n",
-       "        [289.64060397, 298.52628344],\n",
-       "        [290.04893905, 298.8792455 ],\n",
-       "        [290.52763796, 299.06176483]],\n",
-       "\n",
-       "       [[289.46465551, 297.27006951],\n",
-       "        [290.14110504, 297.406225  ],\n",
-       "        [290.72314837, 297.64015827],\n",
-       "        [291.09759615, 298.20393399]]])</pre></div></div></li><li class='xr-section-item'><input id='section-08c13ac5-16d0-490b-84f9-1ecaecf335ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08c13ac5-16d0-490b-84f9-1ecaecf335ea' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2018 2017 2016 ... 1981 1980 1979</div><input id='attrs-7b94777b-fdfa-4a82-83a5-a92b7b434440' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b94777b-fdfa-4a82-83a5-a92b7b434440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-080d3f09-77c0-4597-8b08-112b239a2636' class='xr-var-data-in' type='checkbox'><label for='data-080d3f09-77c0-4597-8b08-112b239a2636' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007,\n",
-       "       2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995,\n",
-       "       1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983,\n",
-       "       1982, 1981, 1980, 1979], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1</div><input id='attrs-ac4a2564-720a-433e-948d-fca1624a3298' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac4a2564-720a-433e-948d-fca1624a3298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03019e5f-0351-42d7-ad37-453f04af9836' class='xr-var-data-in' type='checkbox'><label for='data-03019e5f-0351-42d7-ad37-453f04af9836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4, -3, -2, -1], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False False</div><input id='attrs-e1d3babf-0dfa-4089-94aa-7eb0bc6cb418' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1d3babf-0dfa-4089-94aa-7eb0bc6cb418' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a862fc75-0a69-4ffb-b3f7-f8d46fb2ca33' class='xr-var-data-in' type='checkbox'><label for='data-a862fc75-0a69-4ffb-b3f7-f8d46fb2ca33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-1 1</div><input id='attrs-7326887e-1985-4080-9fa9-6c0d27ff8807' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7326887e-1985-4080-9fa9-6c0d27ff8807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d94f87-c069-4d25-a7bb-7a06b75cdf65' class='xr-var-data-in' type='checkbox'><label for='data-74d94f87-c069-4d25-a7bb-7a06b75cdf65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1,  1], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.05 29.58</div><input id='attrs-27c1034b-6ee2-4899-95bf-579b5843e244' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27c1034b-6ee2-4899-95bf-579b5843e244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33e5b00a-86e0-469e-aedc-589454fa5b37' class='xr-var-data-in' type='checkbox'><label for='data-33e5b00a-86e0-469e-aedc-589454fa5b37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([38.04510914, 29.58134561])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>221.1 188.1</div><input id='attrs-98a77891-383d-4b25-be74-07faad3f6751' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98a77891-383d-4b25-be74-07faad3f6751' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a9cfb2-553b-4d56-a138-3670d0a9a291' class='xr-var-data-in' type='checkbox'><label for='data-72a9cfb2-553b-4d56-a138-3670d0a9a291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([221.11227597, 188.12201842])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3ebff15-f332-4b44-9854-055b081525cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3ebff15-f332-4b44-9854-055b081525cd' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (anchor_year: 40, i_interval: 4, cluster_labels: 2)>\n",
-       "289.6 298.0 289.9 297.8 290.6 297.9 ... 290.1 297.4 290.7 297.6 291.1 298.2\n",
-       "Coordinates:\n",
-       "  * anchor_year     (anchor_year) int64 2018 2017 2016 2015 ... 1981 1980 1979\n",
-       "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
-       "    target          (i_interval) bool False False False False\n",
-       "  * cluster_labels  (cluster_labels) int16 -1 1\n",
-       "    latitude        (cluster_labels) float64 38.05 29.58\n",
-       "    longitude       (cluster_labels) float64 221.1 188.1\n",
-       "Attributes:\n",
-       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
-       "    coordinates:  Latitudes and longitudes are geographical centers associate..."
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clustered_data = rgdr.transform(precursor_field)\n",
-    "clustered_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 ('ai4s2s')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "a87ac8a6238ac8fffcd070769ea0ffc634a0ba702c52b706c3554dda7777b3fd"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+      "metadata": {
+            "kernelspec": {
+                  "display_name": "Python 3.10.5 ('ai4s2s')",
+                  "language": "python",
+                  "name": "python3"
+            },
+            "language_info": {
+                  "codemirror_mode": {
+                        "name": "ipython",
+                        "version": 3
+                  },
+                  "file_extension": ".py",
+                  "mimetype": "text/x-python",
+                  "name": "python",
+                  "nbconvert_exporter": "python",
+                  "pygments_lexer": "ipython3",
+                  "version": "3.10.5"
+            },
+            "orig_nbformat": 4,
+            "vscode": {
+                  "interpreter": {
+                        "hash": "a87ac8a6238ac8fffcd070769ea0ffc634a0ba702c52b706c3554dda7777b3fd"
+                  }
+            }
+      },
+      "nbformat": 4,
+      "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/notebooks/tutorial_calendar_shifter.ipynb b/notebooks/tutorial_calendar_shifter.ipynb
new file mode 100644
index 0000000..78940e3
--- /dev/null
+++ b/notebooks/tutorial_calendar_shifter.ipynb
@@ -0,0 +1,1101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shifted calendars \n",
+    "In s2s weather forecasting modelling we often use a calendar type that 'rolls' forward \n",
+    "over time.\n",
+    "\n",
+    "For example, we wish to forecast temperature for every week of June and July with a two \n",
+    "week leadtime, keeping that leadtime constant. \n",
+    "\n",
+    "This imitates an Numerical Weather Prediction (NWP) model that is initiated every week.\n",
+    "\n",
+    "Another example could be that we wish to investigate the influence of intraseasonal \n",
+    "variability of one of our precursors over time.\n",
+    "\n",
+    "With `s2spy.calendar_shifter`, the user can easily create calendars that suit these needs.\n",
+    "This notebook shows how to:\n",
+    "  * Shift a calendar n number of times and return a list of those calendars\n",
+    "  * Resample a dataset to those calendars resulting in an `xarray.Dataset`\n",
+    "    where the data is concatenated along a new dimension with default name 'step'\n",
+    "\n",
+    "Note that the anchor year for every target period is kept the same relative to the first\n",
+    "calendar. Even if a target period is shifted to the next year, the anchor year of the \n",
+    "target period stays the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import s2spy.time\n",
+    "import s2spy.calendar_shifter\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's initiate a calendar with the following characteristics:\n",
+    "  * starting on July 1st\n",
+    "  * 1 target interval with 7 days length\n",
+    "  * 4 precursor intervals of 7 days length\n",
+    "  * 14 days leadtime between the target interval and first precusor interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting relative_dates=True. To see absolute dates, first call calendar.map_years or calendar.map_data\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 504x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cal = s2spy.time.Calendar(anchor='07-01')\n",
+    "cal.add_intervals(\"target\", \"7d\")\n",
+    "cal.add_intervals(\"precursor\", \"7d\", gap=\"14d\")\n",
+    "cal.add_intervals(\"precursor\", \"7d\", n=3)\n",
+    "cal.visualize(show_length=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we build up a list of calendars. We shift (using `s2spy.calendar_shifter.staggered_calendar`) the initial calendar by 7 days three times such that we get four calendars:\n",
+    "\n",
+    "(note that the anchor date does not change, only the gaps relative to the anchor date change)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal_list =  s2spy.calendar_shifter.staggered_calendar(cal, shift='7d', n_shifts=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We map all the calendars to some year (2021) before visualizing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for cal in cal_list:\n",
+    "    cal.map_years(2021,2021)\n",
+    "    \n",
+    "fig, axes = plt.subplots(nrows=len(cal_list), sharex=True, figsize=(8,6))\n",
+    "for i, cal in enumerate(cal_list):\n",
+    "    cal.visualize(n_years=1, relative_dates=False, show_length=True, ax=axes[i])\n",
+    "axes[-1].set_xlim([np.datetime64(\"2021-05-12\"), np.datetime64(\"2021-08-01\")])\n",
+    "fig.autofmt_xdate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before resampling, we make a dummy dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
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+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
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+       ".xr-section-item input + label {\n",
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+       "}\n",
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+       "}\n",
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+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "  display: none;\n",
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+       "\n",
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+       "  padding-top: 4px;\n",
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+       "  margin-bottom: 5px;\n",
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+       "\n",
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+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
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+       "  padding-right: 5px;\n",
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+       "\n",
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+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
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+       "  background-color: var(--xr-background-color-row-even);\n",
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+       "  padding-right: 5px;\n",
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+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
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+       ".xr-var-dims,\n",
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+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (time: 2192)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2016-01-01 2016-01-02 ... 2021-12-31\n",
+       "Data variables:\n",
+       "    data     (time) float64 0.7542 0.6469 0.9521 0.8415 ... 0.3956 0.8287 0.1396</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7489ab65-5aaf-4fba-9ec1-0de7dfb47bfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7489ab65-5aaf-4fba-9ec1-0de7dfb47bfe' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-992c4d61-373e-4a35-a257-720fb4f85c17' class='xr-section-summary-in' type='checkbox'  checked><label for='section-992c4d61-373e-4a35-a257-720fb4f85c17' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-01-01 ... 2021-12-31</div><input id='attrs-0ca2c3c0-96fe-46cf-af34-b44da3011532' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca2c3c0-96fe-46cf-af34-b44da3011532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fba8a1c-96a0-459b-8df0-821d03e1f978' class='xr-var-data-in' type='checkbox'><label for='data-3fba8a1c-96a0-459b-8df0-821d03e1f978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-03T00:00:00.000000000&#x27;, ..., &#x27;2021-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-12-30T00:00:00.000000000&#x27;, &#x27;2021-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcfebdc8-4832-4e3b-b998-c502b0714fbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcfebdc8-4832-4e3b-b998-c502b0714fbd' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7542 0.6469 ... 0.8287 0.1396</div><input id='attrs-31cb04e8-e027-48b3-9f44-ca46d0d75712' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31cb04e8-e027-48b3-9f44-ca46d0d75712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fecd1cf-829d-4f57-b95a-0934fca08f18' class='xr-var-data-in' type='checkbox'><label for='data-2fecd1cf-829d-4f57-b95a-0934fca08f18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.75424114, 0.64687377, 0.95213303, ..., 0.39556738, 0.82868476,\n",
+       "       0.13957358])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90bea330-e1aa-440c-8ec5-43f11854a079' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-90bea330-e1aa-440c-8ec5-43f11854a079' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (time: 2192)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2016-01-01 2016-01-02 ... 2021-12-31\n",
+       "Data variables:\n",
+       "    data     (time) float64 0.7542 0.6469 0.9521 0.8415 ... 0.3956 0.8287 0.1396"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_index = pd.date_range('20160101', '20211231', freq='1d')\n",
+    "ds = xr.Dataset(\n",
+    "    data_vars=dict(data=('time', np.random.random(len(time_index)))),\n",
+    "    coords=dict(time=time_index)\n",
+    ")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resample by using the `s2spy.calendar_shifter.calendar_list_resampler` function.\n",
+    "\n",
+    "Before we resample, it is important to map our calendar to the desired years. Before, we mapped it to the year\n",
+    "it to 2021. Now, we can set it to other years using:\n",
+    "\n",
+    "```py\n",
+    "for cal in cal_list:\n",
+    "    cal.map_years(2018,2021)\n",
+    "```\n",
+    "\n",
+    "or we use the cal.map_to_data function to map our calendar to the dummy dataset:\n",
+    "\n",
+    "```py\n",
+    "for cal in cal_list:\n",
+    "    cal.map_to_data(ds)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Map to data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for cal in cal_list:\n",
+    "    cal.map_to_data(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we we have to resample `ds` to all the calendars in `cal_list` using \n",
+    "`s2spy.time.resample`.\n",
+    "\n",
+    "We can do that with a simple list comprehension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_r_list = [s2spy.time.resample(cal, ds) for cal in cal_list]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wish to also concatenate all resampled data to one `xr.Dataset`, we can use the \n",
+    "built-in `s2spy.calendar_shifter.calendar_list_resampler` function which resamples the \n",
+    "data to all the calendars in cal_list and concatenates these datasets along a new \n",
+    "dimension (default name is 'step')."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:      (anchor_year: 6, i_interval: 5, step: 4, bounds: 2)\n",
+       "Coordinates:\n",
+       "  * anchor_year  (anchor_year) int64 2021 2020 2019 2018 2017 2016\n",
+       "  * i_interval   (i_interval) int64 -4 -3 -2 -1 1\n",
+       "    interval     (step, anchor_year, i_interval, bounds) datetime64[ns] 2021-...\n",
+       "    target       (i_interval) bool False False False False True\n",
+       "  * step         (step) int64 0 1 2 3\n",
+       "Dimensions without coordinates: bounds\n",
+       "Data variables:\n",
+       "    data         (step, anchor_year, i_interval) float64 0.4237 ... 0.6039</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4d191859-6f6d-473c-9c73-fee2f330af89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d191859-6f6d-473c-9c73-fee2f330af89' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 6</li><li><span class='xr-has-index'>i_interval</span>: 5</li><li><span class='xr-has-index'>step</span>: 4</li><li><span>bounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-671ed559-f3a8-46c7-bb05-fe05139787ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-671ed559-f3a8-46c7-bb05-fe05139787ea' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2021 2020 2019 2018 2017 2016</div><input id='attrs-5690cb13-3f12-4eef-972a-811c88cb33c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5690cb13-3f12-4eef-972a-811c88cb33c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-008a126c-1ac7-47ae-9425-affa50e7329b' class='xr-var-data-in' type='checkbox'><label for='data-008a126c-1ac7-47ae-9425-affa50e7329b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2021, 2020, 2019, 2018, 2017, 2016])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1 1</div><input id='attrs-3cc0962e-1b51-4e55-804f-0c38a09f905e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3cc0962e-1b51-4e55-804f-0c38a09f905e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-376e81bb-e927-483a-9733-c6395b42886d' class='xr-var-data-in' type='checkbox'><label for='data-376e81bb-e927-483a-9733-c6395b42886d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4, -3, -2, -1,  1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interval</span></div><div class='xr-var-dims'>(step, anchor_year, i_interval, bounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-05-20 ... 2016-07-29</div><input id='attrs-e127e182-b603-41b7-a254-3647b896ed33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e127e182-b603-41b7-a254-3647b896ed33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2625fe87-e072-498f-9b36-744998949876' class='xr-var-data-in' type='checkbox'><label for='data-2625fe87-e072-498f-9b36-744998949876' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[&#x27;2021-05-20T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2021-05-27T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2021-05-27T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2021-06-03T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2021-06-03T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2021-06-10T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2021-06-10T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2021-06-17T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2021-07-08T00:00:00.000000000&#x27;]],\n",
+       "\n",
+       "        [[&#x27;2020-05-20T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2020-05-27T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2020-05-27T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2020-06-03T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2020-06-03T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2020-06-10T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2020-06-10T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2020-06-17T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2020-07-01T00:00:00.000000000&#x27;,\n",
+       "...\n",
+       "          &#x27;2017-06-17T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2017-06-17T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2017-06-24T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2017-06-24T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2017-07-01T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2017-07-01T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2017-07-08T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2017-07-22T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2017-07-29T00:00:00.000000000&#x27;]],\n",
+       "\n",
+       "        [[&#x27;2016-06-10T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2016-06-17T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2016-06-17T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2016-06-24T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2016-06-24T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2016-07-01T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2016-07-08T00:00:00.000000000&#x27;],\n",
+       "         [&#x27;2016-07-22T00:00:00.000000000&#x27;,\n",
+       "          &#x27;2016-07-29T00:00:00.000000000&#x27;]]]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False False True</div><input id='attrs-90edf2eb-c371-47bd-85ef-937feab32dd9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90edf2eb-c371-47bd-85ef-937feab32dd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9a2f394-bd60-4b39-93d8-50b63fe5f3b2' class='xr-var-data-in' type='checkbox'><label for='data-e9a2f394-bd60-4b39-93d8-50b63fe5f3b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, False,  True])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f6367709-3728-41fe-82f6-df771116cf6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6367709-3728-41fe-82f6-df771116cf6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff87b276-3b00-484a-81f8-fc5eb09fa156' class='xr-var-data-in' type='checkbox'><label for='data-ff87b276-3b00-484a-81f8-fc5eb09fa156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8b0ccb1-69ec-4a3e-927e-ecb32314e793' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8b0ccb1-69ec-4a3e-927e-ecb32314e793' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(step, anchor_year, i_interval)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4237 0.3431 ... 0.4446 0.6039</div><input id='attrs-5779094e-1cb2-4315-a053-e1d963b5d787' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5779094e-1cb2-4315-a053-e1d963b5d787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c903eab-92b3-48ba-aa1e-cd851ecadaf3' class='xr-var-data-in' type='checkbox'><label for='data-4c903eab-92b3-48ba-aa1e-cd851ecadaf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.42365156, 0.34306837, 0.42904283, 0.58852129, 0.52402581],\n",
+       "        [0.41897254, 0.65964202, 0.31095992, 0.54099223, 0.58206516],\n",
+       "        [0.49859236, 0.59489748, 0.63605561, 0.64856008, 0.41001469],\n",
+       "        [0.55794433, 0.66770382, 0.50583655, 0.47905868, 0.47766496],\n",
+       "        [0.56227384, 0.375732  , 0.42606768, 0.40379915, 0.46642891],\n",
+       "        [0.50315838, 0.29877681, 0.57990039, 0.48767529, 0.44463891]],\n",
+       "\n",
+       "       [[0.34306837, 0.42904283, 0.58852129, 0.37766496, 0.36422361],\n",
+       "        [0.65964202, 0.31095992, 0.54099223, 0.44601228, 0.37291832],\n",
+       "        [0.59489748, 0.63605561, 0.64856008, 0.42432033, 0.26293964],\n",
+       "        [0.66770382, 0.50583655, 0.47905868, 0.48001552, 0.33995612],\n",
+       "        [0.375732  , 0.42606768, 0.40379915, 0.58442322, 0.57304802],\n",
+       "        [0.29877681, 0.57990039, 0.48767529, 0.58889171, 0.52396668]],\n",
+       "\n",
+       "       [[0.42904283, 0.58852129, 0.37766496, 0.45599642, 0.32003422],\n",
+       "        [0.31095992, 0.54099223, 0.44601228, 0.53416751, 0.36748769],\n",
+       "        [0.63605561, 0.64856008, 0.42432033, 0.24367335, 0.23863823],\n",
+       "        [0.50583655, 0.47905868, 0.48001552, 0.39328855, 0.43531123],\n",
+       "        [0.42606768, 0.40379915, 0.58442322, 0.47009231, 0.38566868],\n",
+       "        [0.57990039, 0.48767529, 0.58889171, 0.41778352, 0.25932276]],\n",
+       "\n",
+       "       [[0.58852129, 0.37766496, 0.45599642, 0.52402581, 0.36730117],\n",
+       "        [0.54099223, 0.44601228, 0.53416751, 0.58206516, 0.60873999],\n",
+       "        [0.64856008, 0.42432033, 0.24367335, 0.41001469, 0.51347322],\n",
+       "        [0.47905868, 0.48001552, 0.39328855, 0.47766496, 0.48205436],\n",
+       "        [0.40379915, 0.58442322, 0.47009231, 0.46642891, 0.40611171],\n",
+       "        [0.48767529, 0.58889171, 0.41778352, 0.44463891, 0.60387725]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-279bf3ff-682a-4a73-90ec-aca429e2a807' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-279bf3ff-682a-4a73-90ec-aca429e2a807' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:      (anchor_year: 6, i_interval: 5, step: 4, bounds: 2)\n",
+       "Coordinates:\n",
+       "  * anchor_year  (anchor_year) int64 2021 2020 2019 2018 2017 2016\n",
+       "  * i_interval   (i_interval) int64 -4 -3 -2 -1 1\n",
+       "    interval     (step, anchor_year, i_interval, bounds) datetime64[ns] 2021-...\n",
+       "    target       (i_interval) bool False False False False True\n",
+       "  * step         (step) int64 0 1 2 3\n",
+       "Dimensions without coordinates: bounds\n",
+       "Data variables:\n",
+       "    data         (step, anchor_year, i_interval) float64 0.4237 ... 0.6039"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_r = s2spy.calendar_shifter.calendar_list_resampler(cal_list, ds)\n",
+    "ds_r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('s2spy')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "4c8901add0872cf59113c57929fb99881a7f0f46837d8b99712d7db552e5b9e5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/s2spy/__init__.py b/s2spy/__init__.py
index e596d5b..268563b 100644
--- a/s2spy/__init__.py
+++ b/s2spy/__init__.py
@@ -5,6 +5,7 @@
 and artificial intelligence to boost (sub) seasonal forecasting.
 """
 import logging
+from . import calendar_shifter
 from . import time
 from . import traintest
 from .rgdr.rgdr import RGDR
@@ -16,4 +17,4 @@
 __email__ = "y.liu@esciencecenter.nl"
 __version__ = "0.2.1"
 
-__all__ = ["time", "traintest", "RGDR"]
+__all__ = ["time", "traintest", "RGDR", "calendar_shifter"]
diff --git a/s2spy/_base_calendar.py b/s2spy/_base_calendar.py
index 083437b..97930b7 100644
--- a/s2spy/_base_calendar.py
+++ b/s2spy/_base_calendar.py
@@ -67,8 +67,10 @@ def allow_overlap(self, value: bool):
         if isinstance(value, bool):
             self._allow_overlap = value
         else:
-            raise ValueError(f"allow_overlap should be either True or False, not {value}"
-                             f"of type {type(value)}")
+            raise ValueError(
+                f"allow_overlap should be either True or False, not {value}"
+                f"of type {type(value)}"
+            )
 
     def _get_anchor(self, year: int) -> pd.Timestamp:
         """Method to generate an anchor timestamp for your specific calendar.
@@ -304,8 +306,10 @@ def _set_mapping(self, mapping):
             self._first_timestamp = mapping[1]
             self._last_timestamp = mapping[2]
         else:
-            raise ValueError("Unknown mapping passed to calendar. Valid options are"
-                             "either 'years' or 'data'.")
+            raise ValueError(
+                "Unknown mapping passed to calendar. Valid options are"
+                "either 'years' or 'data'."
+            )
 
     def _rename_intervals(self, intervals: pd.DataFrame) -> pd.DataFrame:
         """Adds target labels to the header row of the intervals.
@@ -411,30 +415,27 @@ def visualize(
                     "calendar.map_years or calendar.map_data"
                 )
                 relative_dates = True
-            add_yticklabels=False
+            add_yticklabels = False
         else:
-            add_yticklabels=True
+            add_yticklabels = True
 
         n_years = max(n_years, 1)
         n_years = min(n_years, len(calendar.get_intervals().index))
 
         if interactive:
             utils.assert_bokeh_available()
-            from ._bokeh_plots import bokeh_visualization  # pylint: disable=import-outside-toplevel
+            # pylint: disable=import-outside-toplevel
+            from ._bokeh_plots import bokeh_visualization
 
             if ax is not None:
                 warnings.warn(
                     "ax is only a valid keyword argument for the non-interactive "
                     "matplotlib backend. Bokeh's figure can be controlled by passing "
                     "Bokeh Figure keyword arguments (e.g. width=800).",
-                    UserWarning
+                    UserWarning,
                 )
             bokeh_visualization(
-                calendar,
-                n_years,
-                relative_dates,
-                add_yticklabels,
-                **bokeh_kwargs
+                calendar, n_years, relative_dates, add_yticklabels, **bokeh_kwargs
             )
         else:
             if bokeh_kwargs:
@@ -442,7 +443,7 @@ def visualize(
                     "kwargs for bokeh have been passed to visualize(), but the "
                     "matplotlib backend does not support these. Use the 'ax' kwarg "
                     "instead to control the generated figure.",
-                    UserWarning
+                    UserWarning,
                 )
 
             _plot.matplotlib_visualization(
@@ -469,7 +470,7 @@ def __init__(
         role: Literal["target", "precursor"],
         length: Union[str, dict],
         gap: Union[str, dict] = "0d",
-    )-> None:
+    ) -> None:
         """This is the basic construction element of the calendar.
 
         The Interval is characterised by its type (either target or precursor), its
@@ -514,7 +515,9 @@ def length(self):
     def length(self, value: Union[str, dict]):
         self._length = value
         if isinstance(value, str):
-            self._length_dateoffset = DateOffset(**self._parse_timestring(value))
+            self._length_dateoffset = DateOffset(
+                **utils.parse_freqstr_to_dateoffset(value)
+            )
         else:
             self._length_dateoffset = DateOffset(**value)
 
@@ -531,7 +534,9 @@ def gap(self):
     def gap(self, value: Union[str, dict]):
         self._gap = value
         if isinstance(value, str):
-            self._gap_dateoffset = DateOffset(**self._parse_timestring(value))
+            self._gap_dateoffset = DateOffset(
+                **utils.parse_freqstr_to_dateoffset(value)
+            )
         else:
             self._gap_dateoffset = DateOffset(**value)
 
@@ -539,26 +544,6 @@ def gap(self, value: Union[str, dict]):
     def gap_dateoffset(self):
         return self._gap_dateoffset
 
-    def _parse_timestring(self, time_str):
-        """Parses the user-input time strings.
-
-        Args:
-            time_str: Time length string in the right formatting.
-
-        Returns:
-            Dictionary as keyword argument for Pandas DateOffset.
-        """
-        if re.fullmatch(r"[+-]?\d*d", time_str):
-            time_dict = {"days": int(time_str[:-1])}
-        elif re.fullmatch(r"[+-]?\d*M", time_str):
-            time_dict = {"months": int(time_str[:-1])}
-        elif re.fullmatch(r"[+-]?\d*W", time_str):
-            time_dict = {"weeks": int(time_str[:-1])}
-        else:
-            raise ValueError("Please input a time string in the correct format.")
-
-        return time_dict
-
     def __repr__(self):
         """String representation of the Interval class."""
         props = [
diff --git a/s2spy/calendar_shifter.py b/s2spy/calendar_shifter.py
new file mode 100644
index 0000000..0ba4b1e
--- /dev/null
+++ b/s2spy/calendar_shifter.py
@@ -0,0 +1,177 @@
+import copy
+from typing import Dict
+from typing import List
+from typing import Union
+import xarray as xr
+import s2spy.time
+from . import utils
+
+
+def _gap_shift(
+    interval: s2spy.time.Interval, shift: Union[str, Dict[str, int]]
+) -> Dict[str, int]:
+    """
+    Shift a calendar interval's gap property by the given amount.
+
+    Args:
+        gap: the pandas DateOffset from a calendar interval's `gap` property.
+        shift: the shift for the gap, in the form of a pandas-like frequency
+            string (e.g. "10d", "2W", or "3M"), or a pandas.DateOffset compatible
+            dictionary such as {days=10}, {weeks=2}, or {months=1, weeks=2}.
+
+    Returns:
+        A Pandas DateOffset compatible dictionary, with the gap offset by shift
+    """
+    if isinstance(interval.gap, str):
+        gap_time_dict = utils.parse_freqstr_to_dateoffset(interval.gap)
+    else:
+        gap_time_dict = interval.gap
+
+    if isinstance(shift, str):
+        shift_time_dict = utils.parse_freqstr_to_dateoffset(shift)
+    else:
+        shift_time_dict = shift
+    # make the shift negative for the precursor to shift forward in time
+    if interval.role == "precursor":
+        shift_time_dict.update(
+            (key, value * -1) for key, value in shift_time_dict.items()
+        )
+    return {
+        k: gap_time_dict.get(k, 0) + shift_time_dict.get(k, 0)
+        for k in set(gap_time_dict) | set(shift_time_dict)
+    }
+
+
+def calendar_shifter(
+    calendar: s2spy.time.Calendar, shift: Union[str, dict]
+) -> s2spy.time.Calendar:
+    """Shift a Calendar instance by a given time offset.
+
+    Instead of shifting the anchor date, this function shifts two things in reference
+    to the anchor date:
+        target period(s): as a gap between the anchor date and the start of the first
+            target period
+        precursor period(s): as a gap between the anchor date and the start of the
+            first precursor period
+    This way, the anchor year from the input calendar is maintained on the returned
+    calendar. This is important for train-test splitting at later stages.
+
+    Args:
+        calendar: a s2spy.time.Calendar instance
+        shift: a pandas-like
+            frequency string (e.g. "10d", "2W", or "3M"), or a pandas.DateOffset
+            compatible dictionary such as {days=10}, {weeks=2}, or {months=1, weeks=2}
+
+    Example:
+        Shift a calendar by a given dateoffset.
+        >>> import s2spy.time
+        >>> cal = s2spy.time.Calendar(anchor='07-01')
+        >>> cal.add_intervals("target", "7d")
+        >>> cal.add_intervals("precursor", "7d", gap="14d")
+        >>> cal.add_intervals("precursor", "7d, n=3)
+        >>> cal_shifted = cal_shifter(cal, '7d')
+        >>> cal_shifted
+        Calendar(
+            anchor='07-01',
+            allow_overlap=False,
+            mapping=None,
+            intervals=[
+                Interval(role='target', length='7d', gap={'days': 7}),
+                Interval(role='precursor', length='7d', gap={'days': 7}),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d'),
+            ]
+        )
+    """
+    calendar_shifted = copy.deepcopy(calendar)
+    calendar_shifted.targets[0].gap = _gap_shift(calendar.targets[0], shift)
+    calendar_shifted.precursors[0].gap = _gap_shift(calendar.precursors[0], shift)
+
+    return calendar_shifted
+
+
+def staggered_calendar(
+    calendar: s2spy.time.Calendar, shift: Union[str, dict], n_shifts: int
+) -> List[s2spy.time.Calendar]:
+    """Shift a Calendar instance by a given time offset n times to create a list of
+    shifted calendars.
+
+    We call this list a staggered calendar.
+
+    Args:
+        calendar: an s2spy.time.Calendar instance
+        shift: a pandas-like
+            frequency string (e.g. "10d", "2W", or "3M"), or a pandas.DateOffset
+            compatible dictionary such as {days=10}, {weeks=2}, or {months=1, weeks=2}
+        n_shifts: strictly positive integer for the number of shifts
+
+    Example:
+        Shift an input calendar n times by a given dateoffset and return a list of these
+        shifted calendars.
+        >>> import s2spy.time
+        >>> cal = s2spy.time.Calendar(anchor="07-01")
+        >>> cal.add_intervals("target", "7d")
+        >>> cal.add_intervals("precursor", "7d", gap="14d")
+        >>> cal.add_intervals("precursor", "7d", n=3)
+        >>> cal_shifted = cal_stagger(cal, "7d", 1)
+        >>> cal_shifted
+        [Calendar(
+            anchor='07-01',
+            allow_overlap=False,
+            mapping=None,
+            intervals=[
+                Interval(role='target', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='14d'),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d')
+            ]
+        ),
+        Calendar(
+            anchor='07-01',
+            allow_overlap=False,
+            mapping=None,
+            intervals=[
+                Interval(role='target', length='7d', gap={'days': 7}),
+                Interval(role='precursor', length='7d', gap={'days': 7}),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d'),
+                Interval(role='precursor', length='7d', gap='0d')
+            ]
+        )]
+    """
+    if not isinstance(n_shifts, int):
+        raise ValueError(
+            "Please input an 'int' type for the 'n' argument."
+            f" Not a {type(n_shifts)}."
+        )
+    if n_shifts <= 0:
+        raise ValueError(
+            "The number of shifts 'n' has to be 1 or greater, " f"not '{n_shifts}'."
+        )
+    cal_staggered = [calendar]
+    for _ in range(n_shifts):
+        cal_shifted = calendar_shifter(cal_staggered[-1], shift)
+        cal_staggered.append(cal_shifted)
+
+    return cal_staggered
+
+
+def calendar_list_resampler(
+    cal_list: list, ds: xr.Dataset, dim_name: str = "step"
+) -> xr.Dataset:
+    """Resample a dataset to every calendar in a list of calendars and concatenate them
+    along dimension 'step' into an xarray Dataset.
+
+    Args:
+        cal_list: list of shifted custom calendars
+        ds: dataset to resample
+
+    Returns:
+        resampled xr.Dataset
+    """
+    ds_r = xr.concat([s2spy.time.resample(cal, ds) for cal in cal_list], dim=dim_name)
+    ds_r = ds_r.assign_coords({dim_name: ds_r[dim_name].values})
+
+    return ds_r
diff --git a/s2spy/utils.py b/s2spy/utils.py
index b6172ef..1e2417a 100644
--- a/s2spy/utils.py
+++ b/s2spy/utils.py
@@ -221,3 +221,24 @@ def check_week_day(week: int, day: int = 1):
         raise ValueError(
             "Incorrect anchor input. Weekday numbers must be between 1 and 7."
         )
+
+
+def parse_freqstr_to_dateoffset(time_str):
+    """Parses the user-input time strings.
+
+    Args:
+        time_str: Time length string in the right formatting.
+
+    Returns:
+        Dictionary as keyword argument for Pandas DateOffset.
+    """
+    if re.fullmatch(r"[+-]?\d*d", time_str):
+        time_dict = {"days": int(time_str[:-1])}
+    elif re.fullmatch(r"[+-]?\d*M", time_str):
+        time_dict = {"months": int(time_str[:-1])}
+    elif re.fullmatch(r"[+-]?\d*W", time_str):
+        time_dict = {"weeks": int(time_str[:-1])}
+    else:
+        raise ValueError("Please input a time string in the correct format.")
+
+    return time_dict


Test patch:
diff --git a/tests/test_calendar_shifter.py b/tests/test_calendar_shifter.py
new file mode 100644
index 0000000..21fced5
--- /dev/null
+++ b/tests/test_calendar_shifter.py
@@ -0,0 +1,67 @@
+"""Tests for the s2s.nwp_calendar module.
+"""
+import copy
+import numpy as np
+import pytest
+import xarray as xr
+import pandas as pd
+from s2spy.time import Calendar
+from s2spy import calendar_shifter
+
+
+class TestCalendarShifter:
+    """Test the calendar shifter methods."""
+
+    @pytest.fixture(autouse=True)
+    def dummy_calendar(self):
+        cal = Calendar(anchor="12-31")
+        cal.add_intervals("target", "7d")
+        cal.add_intervals("precursor", "7d", "7d")
+        cal.add_intervals("precursor", "7d")
+        return cal
+
+    @pytest.fixture(autouse=True)
+    def dummy_data(self):
+        time_index = pd.date_range("20211001", "20220501", freq="1d")
+        np.random.seed(0)
+        temperature = 15 + 8 * np.random.randn(2, 2, len(time_index))
+        lon = [[-99.83, -99.32], [-99.79, -99.23]]
+        lat = [[42.25, 42.21], [42.63, 42.59]]
+        ds = xr.Dataset(
+            data_vars=dict(
+                temperature=(["x", "y", "time"], temperature),
+            ),
+            coords=dict(
+                lon=(["x", "y"], lon),
+                lat=(["x", "y"], lat),
+                time=time_index,
+            ),
+            attrs=dict(description="Weather related data."),
+        )
+        return ds
+
+    def test_calendar_shifter(self, dummy_calendar):
+        shift = {"days": 7}
+        cal_shifted = calendar_shifter.calendar_shifter(dummy_calendar, shift)
+        cal_expected = copy.deepcopy(dummy_calendar)
+        cal_expected.targets[0].gap = {"days": 7}
+        cal_expected.precursors[0].gap = {"days": 0}
+        assert len(cal_shifted.targets + cal_shifted.precursors) == len(
+            cal_expected.targets + cal_expected.precursors
+        )
+        assert str(cal_shifted.targets) == str(cal_expected.targets)
+        assert str(cal_expected.precursors) == str(cal_shifted.precursors)
+
+    def test_staggered_calendar(self, dummy_calendar):
+        cal_list = calendar_shifter.staggered_calendar(dummy_calendar, "7d", n_shifts=3)
+        assert len(cal_list) == 4
+
+    def test_calendar_list_resampler(self, dummy_calendar, dummy_data):
+        cal_list = calendar_shifter.staggered_calendar(
+            dummy_calendar, "14d", n_shifts=5
+        )
+        for cal in cal_list:
+            cal.map_to_data(dummy_data)
+        ds_resampled = calendar_shifter.calendar_list_resampler(cal_list, dummy_data)
+        expected_coords = np.array([0, 1, 2, 3, 4, 5])
+        assert np.array_equal(ds_resampled.step.values, expected_coords)
diff --git a/tests/test_rgdr/test_alignment.py b/tests/test_rgdr/test_alignment.py
index 69bd451..fa6001c 100644
--- a/tests/test_rgdr/test_alignment.py
+++ b/tests/test_rgdr/test_alignment.py
@@ -14,51 +14,56 @@
 
 
 class TestAlignmentSubfunctions:
-    labels = np.array([
-       [[ 0,  0,  0,  0, -1, -1, -1, -1,  0,  0,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -2,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0, -2, -2, -2,  0,  0],
-        [-3, -3, -3, -3,  0,  0,  0, -2, -2, -2,  0,  0,  0]],
-
-       [[ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0, -1, -1, -1, -1,  0,  0,  0]],
-
-       [[ 0,  0,  0,  0,  0, -1, -1,  0,  0,  0,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -2, -2,  0],
-        [ 1,  1,  0,  0,  0,  0,  0,  0,  0, -2, -2, -2,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0, -2, -2, -2,  0,  0],
-        [-3,  0,  0,  0,  0,  0, -4,  0, -2, -2,  0, -2,  0]],
-
-       [[ 0,  0,  0,  0, -1, -1,  0,  0,  0,  0,  0,  0,  0],
-        [ 0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
-        [ 0,  0,  0,  0,  0,  0,  0,  0,  0, -2,  0,  0,  0],
-        [ 0,  0,  0,  0,  0, -2, -2, -2, -2, -2, -2,  0,  0]]
-       ])
-
-    cluster_labels = xr.DataArray(
-        labels, dims=("split","latitude", "longitude")
+    labels = np.array(
+        [
+            [
+                [0, 0, 0, 0, -1, -1, -1, -1, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, -2, -2, -2, 0, 0],
+                [-3, -3, -3, -3, 0, 0, 0, -2, -2, -2, 0, 0, 0],
+            ],
+            [
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, -1, -1, -1, -1, 0, 0, 0],
+            ],
+            [
+                [0, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, -2, 0],
+                [1, 1, 0, 0, 0, 0, 0, 0, 0, -2, -2, -2, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, -2, -2, -2, 0, 0],
+                [-3, 0, 0, 0, 0, 0, -4, 0, -2, -2, 0, -2, 0],
+            ],
+            [
+                [0, 0, 0, 0, -1, -1, 0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0],
+                [0, 0, 0, 0, 0, -2, -2, -2, -2, -2, -2, 0, 0],
+            ],
+        ]
     )
 
+    cluster_labels = xr.DataArray(labels, dims=("split", "latitude", "longitude"))
+
     expected_set_of_sets = {
-        frozenset({'0_-3', '2_-3'}),
-        frozenset({'0_-1', '2_-1', '3_-1'}),
-        frozenset({'0_-2', '1_-1', '2_-2', '3_-2'}),
-        frozenset({'0_-2', '1_-1', '2_-2', '2_-4', '3_-2'}),
-        frozenset({'1_1', '2_1', '3_1'}),
-        frozenset({'1_-1', '2_-4', '3_-2'})
-        }
+        frozenset({"0_-3", "2_-3"}),
+        frozenset({"0_-1", "2_-1", "3_-1"}),
+        frozenset({"0_-2", "1_-1", "2_-2", "3_-2"}),
+        frozenset({"0_-2", "1_-1", "2_-2", "2_-4", "3_-2"}),
+        frozenset({"1_1", "2_1", "3_1"}),
+        frozenset({"1_-1", "2_-4", "3_-2"}),
+    }
 
     expected_reduced_sets = {
-        frozenset({'0_-3', '2_-3'}),
-        frozenset({'0_-1', '2_-1', '3_-1'}),
-        frozenset({'0_-2', '1_-1', '2_-2', '2_-4', '3_-2'}),
-        frozenset({'1_1', '2_1', '3_1'})
-        }
+        frozenset({"0_-3", "2_-3"}),
+        frozenset({"0_-1", "2_-1", "3_-1"}),
+        frozenset({"0_-2", "1_-1", "2_-2", "2_-4", "3_-2"}),
+        frozenset({"1_1", "2_1", "3_1"}),
+    }
 
     def test_df_to_full_sets(self):
         set_of_sets = label_alignment.get_overlapping_clusters(
@@ -71,49 +76,35 @@ def test_remove_subsets(self):
         assert self.expected_reduced_sets == reduced_set_of_sets
 
     def test_remove_overlapping_clusters(self):
-        dummy_input = {
-            frozenset({"0_-1", "1_-2"}),
-            frozenset({"0_-1", "1_-1", "2_-1"})
-            }
-        expected = {
-            frozenset({"1_-2"}),
-            frozenset({"0_-1", "1_-1", "2_-1"})
-            }
+        dummy_input = {frozenset({"0_-1", "1_-2"}), frozenset({"0_-1", "1_-1", "2_-1"})}
+        expected = {frozenset({"1_-2"}), frozenset({"0_-1", "1_-1", "2_-1"})}
         assert expected == label_alignment.remove_overlapping_clusters(dummy_input)
 
     def test_name_clusters(self):
-        dummy_input = {
-            frozenset({"0_-1", "1_-2"}),
-            frozenset({"0_-1", "1_-1", "2_-1"})
-            }
+        dummy_input = {frozenset({"0_-1", "1_-2"}), frozenset({"0_-1", "1_-1", "2_-1"})}
         expected = {
-            'A': {"0_-1", "1_-1", "2_-1"},
-            'B': {"0_-1", "1_-2"},
-            }
+            "A": {"0_-1", "1_-1", "2_-1"},
+            "B": {"0_-1", "1_-2"},
+        }
         assert expected == label_alignment.name_clusters(dummy_input)
 
     def test_create_renaming_dict(self):
         dummy_cluster_names = {
-            'A': {"0_-1", "1_-1", "2_-1"},
-            'B': {"0_-2", "1_-2"},
-            }
+            "A": {"0_-1", "1_-1", "2_-1"},
+            "B": {"0_-2", "1_-2"},
+        }
         expected_dict = {
-            0: [(-1, 'A'), (-2, 'B')], 1: [(-1, 'A'), (-2, 'B')], 2: [(-1, 'A')]
+            0: [(-1, "A"), (-2, "B")],
+            1: [(-1, "A"), (-2, "B")],
+            2: [(-1, "A")],
         }
         assert expected_dict == label_alignment.create_renaming_dict(
-            dummy_cluster_names)
+            dummy_cluster_names
+        )
 
     def test_ensure_unique_names(self):
-        dummy_names = {
-            0: [(-1, 'B')],
-            1: [(-2, 'B'), (-1, 'B')],
-            2: [(-1, 'B')]
-        }
-        expected = {
-            0: [(-1, 'B1')],
-            1: [(-2, 'B1'), (-1, 'B2')],
-            2: [(-1, 'B1')]
-        }
+        dummy_names = {0: [(-1, "B")], 1: [(-2, "B"), (-1, "B")], 2: [(-1, "B")]}
+        expected = {0: [(-1, "B1")], 1: [(-2, "B1"), (-1, "B2")], 2: [(-1, "B1")]}
         assert expected == label_alignment.ensure_unique_names(dummy_names)
 
 
@@ -145,30 +136,28 @@ def example_target(raw_target, raw_field, dummy_calendar):
     cal = dummy_calendar.map_to_data(raw_field)
     return resample(cal, raw_target).ts
 
+
 def test_alignment_example(example_field, example_target):
-    seed = 1 #same 'randomness'
+    seed = 1  # same 'randomness'
     n_splits = 4
 
-    shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state = seed)
+    shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state=seed)
     cv = s2spy.traintest.TrainTestSplit(shufflesplit)
 
     rgdrs = [
-        RGDR(
-            target_intervals=[1],
-            lag=5,
-            eps_km=800,
-            alpha=0.10,
-            min_area_km2=0
-        ) for _ in range(n_splits)
-        ]
+        RGDR(target_intervals=[1], lag=5, eps_km=800, alpha=0.10, min_area_km2=0)
+        for _ in range(n_splits)
+    ]
 
     target_timeseries_splits = []
     precursor_field_splits = []
     for x_train, _, _, _ in cv.split(example_field, y=example_target):
         target_timeseries_splits.append(
-            example_target.sel(anchor_year=x_train["anchor_year"].values))
+            example_target.sel(anchor_year=x_train["anchor_year"].values)
+        )
         precursor_field_splits.append(
-            example_field.sel(anchor_year=x_train["anchor_year"].values))
+            example_field.sel(anchor_year=x_train["anchor_year"].values)
+        )
 
     clustered_precursors = [
         rgdrs[i].fit_transform(precursor_field_splits[i], target_timeseries_splits[i])
@@ -176,11 +165,10 @@ def test_alignment_example(example_field, example_target):
     ]
 
     aligned_precursors = s2spy.rgdr.label_alignment.rename_labels(
-        rgdrs,
-        clustered_precursors
-        )
+        rgdrs, clustered_precursors
+    )
 
-    expected = [['A'], ['A', 'C'], ['A'], ['A', 'B']]
+    expected = [["A"], ["A", "C"], ["A"], ["A", "B"]]
     clusters_list = [list(el.cluster_labels.values) for el in aligned_precursors]
 
     assert expected == clusters_list