Repository: AI4S2S/s2spy (language: python)

Issue:
correlating across both intra-seasonal and inter-seasonal variability ('subseasonal mode')  
The current implementation is forcing the RGDR().fit method to correlate across the anchor_year dimension (1 datapoint per year). I would like to flatten the anchor_year and i_interval year in order to correlate once using all datapoints (instead of looping over each target). 

To solve this issue, I propose to add the 'corr_dim' argument to RGDR().fit. Such that people can also use it more flexibly (even without creating a calendar). 

This is what I want to do: 
'''
target = target_resampled['t2m'].sel(i_interval=(slice(1,6))).stack(time=['anchor_year', 'i_interval']) 
field = field_resampled.sel(i_interval=slice(-1, 5)).stack(time=['anchor_year', 'i_interval'])

RGDR().fit(field, target, corr_dim='time')
'''

Any other comments suggestions? I know this is already supported by https://github.com/AI4S2S/s2spy/blob/825d359e9bc02313a97c222f72699993b611a3fb/s2spy/rgdr/rgdr.py#L274-L276 so should be a very minor change. 
Supporting multiple target intervals in `map_correlation`.
Currently `rgdr`'s `map_correlation` implementation will calculate the correlation coefficient and p-value between precursor data and target data. It will calculate this over the `anchor_year` dim,  for every dimension in the input data (e.g. `latitude`, `longitude`, `i_interval`). 

However, it does not take into account the Calendar's `n_targets`, more specifically the case where multiple intervals are labelled as target. 
A wrapper function is required to handle this. In the case of multiple target intervals, the precursor data and field data for those intervals are flattened over the `i_interval` dimension. 

The following image serves as a visualization for this concept:
[![image](https://user-images.githubusercontent.com/12114825/181215172-86c33687-51a9-479f-9a54-aa5d556d0bc4.png)](https://nlesc.sharepoint.com/:p:/s/team-beta/EZHgy9Agmd5EieXKWuAUhLgB2eCOxRZaIRfDuStLq8b23w?e=53nCDX)



Discussion / hints:
Hi Sem, I tried out code similar to your example, and I was indeed able to get the right correlation out using `rgdr.correlation`

Modified from `rgdr_tutorial.ipynb`:
```py
target_data = target_resampled.sel(cluster=3).ts.sel(i_interval=(slice(1,6))).stack(anch_int=['anchor_year', 'i_interval'])
field_data = field_resampled.sst.sel(i_interval=slice(-1, 5)).stack(anch_int=['anchor_year', 'i_interval'])

field_data["anch_int"] = range(field_data["anch_int"].size)
target_data["anch_int"] = range(target_data["anch_int"].size)

corr, p_val = correlation(field_data, target_data, corr_dim="anch_int")
```

However, it is essential that:
- the number of intervals in both the target and the field are the same
- the target and field data is properly stacked
- users select the right intervals for both field and target

Perhaps it could be better to modify RGDR, rather than the `.fit` method. As `.fit` should not really take any input other than data. We then can put the interval handling and dim stacking inside RGDR. How about the following syntax:
```py
rgdr = RGDR(
    target_intervals=[1, 2, 3],  #int or list
    lag=2  # cross correlation lag. Would make precursor_intervals=[-2, -1, 1]
)
```
Yes definitely agree with all suggestions! Would love that feature! 
By multiple target periods you mean "a target period consisting of multiple data points/intervals", right?
> a target period consisting of multiple data points/intervals

Yes, exactly. I'll clarify this in the issue

Fix patch:
diff --git a/notebooks/example_label_alignment_RGDR.ipynb b/notebooks/example_label_alignment_RGDR.ipynb
index b0c4fd4..efd6eaf 100644
--- a/notebooks/example_label_alignment_RGDR.ipynb
+++ b/notebooks/example_label_alignment_RGDR.ipynb
@@ -41,12 +41,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As precursor we select the 'sst' DataArray, at `i_interval=-5` (5th interval before the anchor).\n",
-    "\n",
-    "The target is the (preclustered) 'st' DataArray, at the first target interval."
+    "As precursor we select the 'sst' DataArray. The target is the (preclustered) 'st' DataArray."
    ]
   },
   {
@@ -55,8 +54,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "precursor = field_resampled['sst'].sel(i_interval=-5)\n",
-    "target = target_resampled['ts'].sel(i_interval=1)"
+    "precursor = field_resampled['sst']\n",
+    "target = target_resampled['ts']"
    ]
   },
   {
@@ -80,7 +79,7 @@
     "shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state = seed)\n",
     "cv = s2spy.traintest.TrainTestSplit(shufflesplit)\n",
     "\n",
-    "rgdrs = [RGDR(eps_km=800, alpha=0.10, min_area_km2=0) for _ in range(n_splits)]"
+    "rgdrs = [RGDR(target_intervals=1, lag=5, eps_km=800, alpha=0.10, min_area_km2=0) for _ in range(n_splits)]"
    ]
   },
   {
@@ -119,7 +118,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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kbwJPmmPWu21f3H0RvA74ry3j64GeK/fkOSZWl3luUpaHVsEDjwFH235U0hLgXyRdCrwN+Kjt1ZI+CZwEfGKI5YhYWAZt7u6Unu0XD7II5b2yc/X5/tQeN5k8x2TqMs9NyvLQKviy95tHy9El5WDgaOAN5fTzgPeSL4QYMzXeBz+j9QhjZ+A1wO69bix5jklWc557zvIwj+CRtJjitN3TgI9T3I7zkO2ZNol3A/vMs+7JwMkAi5cvH2YxJ1qj+lEfE+rhCL7t9qRXAh8D9gS+KukG2y9tt47tn28z6SxJayk6nem1HD3luTXLuzypck+XPRt2f/MLZVz6tR/175hB5nmhszzUCt72FPAsSbsBFwFP72LdVRT3/LHTU/ar/XAoojIbTQ2uf0nbF1HkpzJJh7WMLqI4Cugr773muTXLK357RbIco2WAeV7oLA+1gp9h+yFJVwD/nqJP2x3KX/37AvcsRBkiFoxh0QCP4Hv0ty2vN1N0KPPaQWw4eY6JUn+ee87yMFvR70nxzOuHJD2OokecM4ErgFdTtLw9Eei6VWFE0w3yCL4Xtl84yO0lzzHJ6sxzP1ke5hH83sB55XW7RcAXbX9F0jpgtaT3A9cD5w6xDBELTjbaXM8XgqS3tZtv+yM9bjp5jolUV54HkeVhtqK/CXj2HNPvAA4f1vtG1M5ATRU80O7hVD2fZ0yeY2LVl+e+s7wg1+AjJovR1AJ3BD/zzvb7ACSdB5w60+OUpOVsfS0vIiqpJ8+DyHIq+IhBM7Wdom9xaGt3krZ/IWm7I/CI6KD+PPec5ardxUZEVTZs2rxlqMei8pc+AJJ2Jz/oI7pXf557znICHzFoNmyurWKf8bfAVZIuLMdfA3ygxvJEjKb689xzllPBRwyagc31XIOfLYL92bLHq6PLSa8qO62IiG7UnOd+spwKPmLQ6v/FXxbD64BU6hH9aECee81yKviIgXPtR/ARMSijm+dU8BGDZnADjuAjYgBGOM9pRR8xaDbetGl26JekD0n6vqSbJF1UdvYSEQthgHle6Cyngo8YtJlrdjND/y4Hnmn7UOBfgTMGsdGIqGCweV7QLKeCjxgw20xv2jw7DGB732jpc/1qil7bImIBDDLPC51l2bV3a9mRpJ8Bd3WxygrggSEVZyFlP5rlYNvtng8NgKTLKPZ5xs7AhpbxVWUf6V2T9H+AL9j+XC/r162HLMP4/P/JfjRLrXleiCyPRCM723t2s7yka22vHFZ5Fkr2o1nKe1E7sv2yHrb9TeBJc8x6t+2Ly2XeTdEf9Pndbr8pus0yjNf/n+xHcwwrz03K8khU8BHjzvaL282X9GbgFcCLPAqn3SImVJOynAo+ouEkvQx4B3CU7V/XXZ6I6M1CZ3lcG9n1dH2zgbIfzVLXfvw9Rd/Ql0u6QdInaypHXfL/p1myH71b0CyPRCO7iIiI6M64HsFHRERMtFTwERERY2gkK3hJn5Z0v6RbWqY9S9LV5XWNayUdXk6XpLMl/bB8POBh9ZV8a5L2k3SFpHWSbpV0ajl9d0mXS7q9/Lu8nN64fWmzD/M+klHSGeU+/EDSS2srfIv59qNl/n+TZEkryvHGfRajKFlu1r4kz836PPpme+QG4PnAYcAtLdO+Aby8fH0McGXL60sBAUcA3627/C1l3hs4rHy9jOLRhYcAfwOcXk4/HTizqfvSZh9eAuxQTj+zZR8OAW4EdgIOAH4ELG7qfpTj+wFfp3hAy4qmfhajOCTLzdqX5LlZn0e/w0gewdteAzy47WRg1/L1E4B7y9fHAZ914WpgN0l7L0xJ27N9n+3rytePALcB+1CU+bxysfOA48vXjduX+fbB8z+S8Thgte3HbK8HfggcvtDl3labzwLgoxS3trS2SG3cZzGKkuVm7Uvy3KzPo1/jdB/8acDXJX2Y4tLD75fT9wF+0rLc3eW0+xa0dB1I2h94NvBdYC/bM+X7KbBX+brR+7LNPrT6M+AL5et9KL4gZszsQ2O07oek44B7bN8oqXWxRn8WI+40kuXaJc/N+jx6MZJH8PM4BXir7f2AtwLn1lyeyiQtBb4MnGb74dZ5Ls4fNf5exvn2QSP2eNXW/aAo97uAv6qzTBMoWa5Z8jwexqmCPxH4p/L1hWw5TXQPxTWXGfuW0xpB0hKK/4Dn254p/7/NnB4q/95fTm/kvsyzD62PZHxj+eUGDd0HmHM/DqS4rnijpDspynqdpCfR4P0YA8lyjZJnoEH70Y9xquDvBY4qXx8N3F6+vgT4k7KV5BHAL1tOmdVKxTmic4HbbH+kZdYlFF9ylH8vbpneqH2Zbx+05ZGMx3rrRzJeArxe0k6SDgAOAq5ZyDLPZa79sH2z7Sfa3t/2/hSn7Q6z/VMa+FmMkWS5Jslzsz6Pvg2r9d4wB+ACimsjmyg+pJOAI4G1FC06vws8p1xWwMcpWnfeDKysu/wt+3EkxSm7m4AbyuEYYA/gnym+2L4J7N7UfWmzDz+kuKY1M+2TLeu8u9yHH1C2lq57mG8/tlnmTra0um3cZzGKQ7LcrH1Jnpv1efQ75FG1ERERY2icTtFHREREKRV8RETEGEoFHxERMYZSwUdERIyhVPARERFjKBV8g0h6dAjbPFbS6eXr4yUd0sM2rpS0ctBlixhnyXPULRX8mLN9ie0PlqPHU/T+FBEjKHmObqSCb6DyaUofknSLpJslva6c/oLy1/eXVPTNfH75xCYkHVNOW6uiX+OvlNPfLOnvJf0+cCzwIRX9bB/Y+kte0ory8Y1Iepyk1ZJuk3QR8LiWsr1E0lWSrpN0Yfms54iYR/IcdRmn3uTGyauAZwG/C6wAvidpTTnv2cAzKB7n+R3geZKuBc4Bnm97vaQLtt2g7f8n6RLgK7a/BKCte1NqdQrwa9u/LelQ4Lpy+RXAe4AX2/6VpHcCbwP++wD2OWJcJc9Ri1TwzXQkcIHtKYrOKr4FPBd4GLjG9t0Akm4A9gceBe5w0R8zFI//PLmP938+cDaA7Zsk3VROP4LilOB3yi+THYGr+nifiEmQPEctUsGPnsdaXk/R32e4mS2XaXausLyAy22f0Md7RsQWyXMMTa7BN9O3gddJWixpT4pf4O16aPoB8FRJ+5fjr5tnuUeAZS3jdwLPKV+/umX6GuANAJKeCRxaTr+a4hTi08p5u0j6rSo7FDHBkueoRSr4ZrqIohekG4H/C7zDRZeGc7L9G+AtwGWS1lIE/5dzLLoa+EtJ10s6EPgwcIqk6ymuDc74BLBU0m0U1+PWlu/zM+DNwAXlab6rgKf3s6MREyB5jlqkN7kxIWmp7UfLVrgfB263/dG6yxUR3UueYxByBD8+/rxspHMr8ASKVrgRMZqS5+hbjuAjIiLGUI7gIyIixlAq+IiIiDGUCj4iImIMpYKPiIgYQ6ngIyIixlAq+IiIiDGUCj4iImIMpYKPiIgYQ6ngIyIixlAq+IiIiDGUCj4iImIMpYKPiIgYQ6ngIyIixlAq+IiIiDE01Ape0p2SbpZ0g6Rry2m7S7pc0u3l3+XDLENE00naT9IVktZJulXSqXWXaS7Jc0RnTcrzUPuDl3QnsNL2Ay3T/gZ40PYHJZ0OLLf9zqEVIqLhJO0N7G37OknLgLXA8bbX1Vy0rSTPEZ01Kc91nKI/DjivfH0ecHwNZYhoDNv32b6ufP0IcBuwT72lqix5jmjRpDwP+wh+PfALwMA5tldJesj2buV8Ab+YGd9m3ZOBkwG0447PWbLXE4dWzogqNv7k7gds79lpuZe+8PF+4MHp2fHrbnrsVmBDyyKrbK+aa11J+wNrgGfafri/Eg9Wr3kehSwveaS75TctG045YuFMQp53GPL2j7R9j6QnApdL+n7rTNuWNOcvjPIfbBXATk/Zz/u8/bQhFzWivfWnvv2uKsv97MEpvn3Zk2bHlz75xxtsr+y0nqSlwJeB05pWuZd6yvMoZPnJ3+ruQOfeozSkksRCmYQ8D/UUve17yr/3AxcBhwP/Vl6jmLlWcf8wyxCx0KYxGzw1O1QhaQnFl8H5tv9pqAXsUfIck2iU8zy0Cl7SLmUDAyTtArwEuAW4BDixXOxE4OJhlSGiDgYe8/Ts0El5avtc4DbbHxl2+XqRPMekGuU8D/MU/V7ARcW+sgPweduXSfoe8EVJJwF3Aa8dYhkiFty0zYbu2rY8D3gTcLOkG8pp77L9tUGXrQ/Jc0ykUc7z0Cp423cAvzvH9J8DLxrW+0bUzYgNrn5yzPa/AI2+qJs8x6Qa5TwPu5FdxMSZBjZ4cd3FiIgBGOU8p4KPGLBpxAYnWhHjYJTzPJqljmiw4pTekrqLEREDMMp5TgUfMWDTHt0vhIjY2ijnORV8xIAZsWG6OV8IkhYBSxv68JyIRmtSnrvNcrqLjRiw4prdjrNDHSR9XtKu5T3rtwDrJP1lLYWJGGF157mfLKeCjxiwaRe/+GeGmhxS/so/HrgUOIDi3tyI6EID8txzllPBRwzYTKOcmaEmS8rHZR4PXGJ7E8VDuSKiCw3Ic89ZTgUfMWDTiMeml8wONTkHuBPYBVgj6d8BuQYf0aUG5LnnLKeRXcSAuQH3zdo+Gzi7ZdJdkl5YV3kiRlXdee4ny6ngIwZs2qrtyF3S2zos0sjObCKaqq48DyLLqeDHTLf9WjfVKPe3Pc0ifj1VT+t5YFldbxyDdeBpV9ddhIH40VlH1F2EvtSY576znAo+YsBseGy6u2hJ+jTwCuB+28/s/b39vl7XjYjtdZvnJmU5jewiBqxolLPD7FDRZ4CXDaoMkn5L0j9LuqUcP1TSewa1/YhJ0UOeP0NDspwKPmLA7O4reNtrgAcHWIz/BZwBbCq3fxPw+gFuP2IidJvnJmU5p+gjBsyIjVt/EayQdG3L+Crbq4ZcjMfbvkbaqi3D5iG/Z8TYaUCee85yKviIAbPFxqmtovWA7ZULXIwHJB1I+UAMSa8G7lvgMkSMvAbkuecsp4KPGLBpYOP04rqL8Z+BVcDTJd0DrAfeWG+RIkZPA/Lcc5ZTwUcMWHFKr94K3vYdwIvLDioW2X6k1gJFjKi689xPltPILmLAbNg4tXh2qELSBcBVwMGS7pZ0Uj9lkLSHpLOBbwNXSvo7SXv0s82ISdRtnpuU5UoVfG65iajOiE3Ti2eHSuvYJ9je2/YS2/vaPrfPYqwGfgb8B+DV5esvQPIc0Y1u87yQWe6k6hF8brmJqMgWm6YWzw412dv2/7C9vhzeD+xVzkueIypqQJ7bZbmtqhX8421fs8203HITMQcbNk8tmh1q8g1Jr5e0qBxeC3y9nJc8R1TUgDy3y3JbVRvZ5ZabiIpmTunVQdIjFDkVcBrwuXLWIuBR4O0kzxGV1ZXnilluq2oFP1cz/f9YsZCLgWuBe2y/QtIBFNcU9gDWAm+yvbFiOSIaz4apmo7cbVfpoKKnPCfLMYnqynPFLLdVqYLv85abU4HbgF3L8TOBj9peLemTwEnAJ7rYXkTj1Xhqfpak5cBBwM4z02yv6SPPyXJMpLrzPF+WO63XtoKfrz/amUfm2W7bH62kfYE/Aj4AvE3FikcDbygXOQ94L/lSiDFii6np2r8Q/hNFhbwvcANwBHC3pO2yViXPyXJMqrrzPE+Wr6LIX1udSr2sHFYCpwD7lMNfAIdVKNtZwDsoHgYExam8h2zPNOi5u9xexNgwMLV50exQk1OB5wJ32X4h8GyKVvO95vkskuWYQA3I81xZfqjKim2P4Gf6o5W0Bjhs5lSepPcCX223rqSZ/nDXSnpBlcJss/7JwMkAi5cv73b1Rnryt1x3EWIhGDylzssN1wbbGyQhaSfb35dk2+/rNs+TkOV7j6r989rOj846out1Djzt6iGUZMLVn+e5snxwlRWrNrLbC2htPLORzvfhPQ84VtIxFNcNdgX+DthN0g7lL/99gXvmWrnsnWcVwE5P2S81Y4wOi+n6r8HfLWk34H8Dl0v6BXBXOa/bPCfLMbnqz3O7LLdVtYL/LHCNpIvK8eMprrnNy/YZFA/ToPzV/3bbb5R0IcXTeFYDJwIXVyxDxOiYrveI0PYry5fvlXQF8ATgsnJaV3lOlmPi1ZjnDlluq2or+g9IuhT4g3LSn9q+vuuSFt4JrJb0fuB6oN/H+EU0S42n9CTtPsfkm8u/S4EHB5jnZDnGX015rpLlTtuoVMFLegrwAHBR6zTbP66yvu0rgSvL13cAh1dZL2JUqb5rdmvZ8nCMGTPjBp7aT56T5ZhENeW5Y5Y7baDqKfqvlhsEeBxwAPAD4BlVSxoxMSzo8gtB0ssormsvBj5l+4M9vbV9QIXFvgrsCDxG8hzRXk15rphlJD3D9q1zzavUcsD279g+tBwOovjVflX1okZMEFN8IUxV+2IonxD3ceDlwCHACZIOGVrx7N8BfpU8R1TQ8DwD/zjfjJ6aBtq+Dvi9nosTMeY0vWWo4HDgh7bvKB/1uho4bpjlo+W0X/Ic0V7D8zzvr46q1+Bbn2i3iOKhGPf2WaiI8eTtrtmtkHRty/iq8taxGfsAP2kZv5shVrhlnvcs/ybPEe00PM9suXy+narX4Fsfer+Z4hrel/spUcQ409RWow/YXllTUeayjKJiX0byHNFRw/M8r6oV/DrbF7ZOkPQa4MJ5lo+YWNr+F38n9wD7tYzP+9CYSu9fPCd+X9s/mWeRdcCPZ55UWa6TPEfMoc48V8gybP3Qqq1UvQZ/RsVpEUHxi39mqOB7wEGSDpC0I/B64JJe39u2ga+1WeQM29s+BzV5jphHXXmukGXmyPKsTr3JvRw4BthH0tkts3alOLUXEdty5S+CYnF7s6T/Anyd4raaT89320sXrpP0XNvfm5mQPEf0oP48b5flqjqdor8XuBY4luKm+xmPAG/t9s0iJkXF1razbH+NDr/Uu/R7wBsl3QX8iqKl7U4U3b0mzxFdqDnPc2XZtg/ttGKn3uRuBG6UdH5Lt5AR0U6Xv/iH5KVzTbR9V/Ic0YX68zxnlqvodIr+i7ZfC1wvabum+FV+QURMGhkW1Vx9lhX5kcBBtv9B0p7AZ4A/InmOqKzuPM+T5aVV1u10iv7U8u8r+ilgFJrY53RT+6jvtlxN+7et+whe0l8DK4GDgX8AlgB7lrOT5wDS53xVdeZ5nix/jqIb57batqK3fV/58i2272odgLf0V+yIMeWuW90OwysprrX/CsD2vRR9uUPyHFFd/XmeK8vL2q5Rqnqb3B/OMe3lFdeNmCz1fyEAbCxvsTGApF1a5iXPEVXVn+d2WW6r0zX4Uyh+2T9V0k0ts5YB3+mhoBFjT8CiLlvdDsEXJZ0D7Cbpz4E/A26TdDPJc0RlDcjzXFn+VJUVO12D/zxwKfA/gdNbpj9iu2Nn8xETqf5Wt9j+sKQ/BB6muHb3V8A1wHKS54jqas7zXFm2fXmVdTvdJvdL4JfACQCSnkhxHW+ppKW2f9xXySPGVN0VvKQzbb8TuHyOaclzRBdqbmTXLsttVboGL+mPJd0OrAe+BdxJcWQfEduq/5odtLnOnjxHdKH+PPfcZqZqI7v3A0cA/2r7AOBFwOTdKxFRgYBFU54dFvS9pVPK6+wHS7qpZVgPzFx3T54jKqorzxWz3FbV3uQ22f65pEWSFtm+QtJZvRY8YqzVe82uSruZ5Dmiqvry3HcbuKoV/EOSlgJrgPMl3U95T15EbMOwqKYKfqbdjKT3AD+1/ZikFwCHSvqs7YdIniOqqynPFbPcVtVT9McBv6HokOIy4EfAH/dS6IhxJ0BTnh363p70Gkm3SpqWtLLial8GpiQ9DVhF0T/158t5yXNERYPM8xCy3FalI3jbrb/uz6tYqIjJNPhnV98CvAo4p4t1pstuK18FfMz2xyRdD8lzRFcGm+eBZrmTTg+6eYTy6TnbzqLorm7XLgoZMRnMQI7cZzdn3wYgdfW8/U2STgD+hC1H54dKeniOZZPniPkMMM8DzPKSKit2ug++0vNuI2KLotXtVpNWSLq2ZXyV7VVDLsafAn8BfMD2ekkHAO+yfeaQ3zdirDQgz3Nl+R+rrFi1kV3XJO1M0Yhnp/J9vmT7r8vCrQb2ANYCb7K9cVjliFhw3u5a3QO2215vk/RN4ElzzHq37Yu7L4LXAf+1ZXw90HPlnjzHxOoyz03K8tAqeOAx4Gjbj0paAvyLpEuBtwEftb1a0ieBk4BPDLEcEQvLoM3dndKz/eJBFqG8V3auPt+f2uMmk+eYTF3muUlZHloFX/Z+82g5uqQcDBwNvKGcfh7wXvKFEOPEsKjLCn4IWo8wdgZeA+ze68aS55hY9ee55ywP8wgeSYspTts9Dfg4xe04D9meaZN4N7DPPOueDJwMsHj58mEWc6Lde1RXjT2iAgGLNg+u+ylJrwQ+BuwJfFXSDbZf2m4d2z/fZtJZktZSdDrTazl6ynOyvHB+dNYRY/EeTTLIPC90lodawdueAp4laTfgIuDpXay7iuKeP3Z6yn61Hw5FVLb9Nbs+N+eLKPJTmaTDWkYXURwF9JX3XvOcLMdIG2CeFzrLQ63gZ9h+SNIVwL+n6NN2h/JX/77APQtRhogFY9AAj+B79LctrzdTdCjz2kFsOHmOiVJ/nnvO8jBb0e9J8czrhyQ9jqJHnDOBK4BXU7S8PRHoulVhRNNpqt4K3vYLB7m95DkmWZ157ifLwzyC3xs4r7xutwj4ou2vSFoHrJb0fuB64NwhliFiwcmu7Re/pLe1m2/7Iz1uOnmOiVRXngeR5WG2or8JePYc0+8ADh/W+0bUrt5Teu0eTtXzhcTkOSZWfXnuO8sLcg0+YqLYMFVPd3K23wcg6Tzg1JkepyQtZ+treRFRRU15HkSWU8FHDEEDGtkd2tqdpO1fSNruCDwiOqs5zz1nuWp3sRFRlQ2bp7YM9VhU/tIHQNLu5Ad9RPfqz3PPWU7gIwbNhs2D7S+2B38LXCXpwnL8NcAHaixPxGiqP889ZzkVfMSgmTqP3Isi2J8te7w6upz0qrLTiojoRs157ifLqeAjBq3+X/xlMbwOSKUe0Y8G5LnXLKeCjxg4134EHxGDMrp5TgUfMWgGN+AIPiIGYITznFb0EYNm402bZod+SfqQpO9LuknSRWVnLxGxEAaY54XOcir4iAGzjTdumh0G4HLgmbYPBf4VOGMQG42Izgac5wXNcir4iEGz8eZNs0P/m/M3Wvpcv5qi17aIWAgDzPNCZ1l287tnlvQz4K4uVlkBPDCk4iyk7EezHGy73fOhAZB0GcU+z9gZ2NAyvqrsI71rkv4P8AXbn+tl/br1kGUYn/8/2Y9mqTXPC5HlkWhkZ3vPbpaXdK3tlcMqz0LJfjRLeS9qR7Zf1sO2vwk8aY5Z77Z9cbnMuyn6gz6/2+03RbdZhvH6/5P9aI5h5blJWR6JCj5i3Nl+cbv5kt4MvAJ4kUfhtFvEhGpSllPBRzScpJcB7wCOsv3russTEb1Z6CyPayO7nq5vNlD2o1nq2o+/p+gb+nJJN0j6ZE3lqEv+/zRL9qN3C5rlkWhkFxEREd0Z1yP4iIiIiZYKPiIiYgyNZAUv6dOS7pd0S8u0Z0m6uryuca2kw8vpknS2pB+Wjwc8rL6Sb03SfpKukLRO0q2STi2n7y7pckm3l3+Xl9Mbty9t9mHeRzJKOqPchx9IemlthW8x3360zP9vkixpRTneuM9iFCXLzdqX5LlZn0ffbI/cADwfOAy4pWXaN4CXl6+PAa5seX0pIOAI4Lt1l7+lzHsDh5Wvl1E8uvAQ4G+A08vppwNnNnVf2uzDS4AdyulntuzDIcCNwE7AAcCPgMVN3Y9yfD/g6xQPaFnR1M9iFIdkuVn7kjw36/PodxjJI3jba4AHt50M7Fq+fgJwb/n6OOCzLlwN7CZp74UpaXu277N9Xfn6EeA2YB+KMp9XLnYecHz5unH7Mt8+eP5HMh4HrLb9mO31wA+Bwxe63Ntq81kAfJTi1pbWFqmN+yxGUbLcrH1Jnpv1efRrnO6DPw34uqQPU1x6+P1y+j7AT1qWu7ucdt+Clq4DSfsDzwa+C+xle6Z8PwX2Kl83el+22YdWfwZ8oXy9D8UXxIyZfWiM1v2QdBxwj+0bJbUu1ujPYsSdRrJcu+S5WZ9HL0byCH4epwBvtb0f8Fbg3JrLU5mkpcCXgdNsP9w6z8X5o8bfyzjfPmjEHq/auh8U5X4X8Fd1lmkCJcs1S57HwzhV8CcC/1S+vpAtp4nuobjmMmPfclojSFpC8R/wfNsz5f+3mdND5d/7y+mN3Jd59qH1kYxvLL/coKH7AHPux4EU1xVvlHQnRVmvk/QkGrwfYyBZrlHyDDRoP/oxThX8vcBR5eujgdvL15cAf1K2kjwC+GXLKbNaqThHdC5wm+2PtMy6hOJLjvLvxS3TG7Uv8+2DtjyS8Vhv/UjGS4DXS9pJ0gHAQcA1C1nmucy1H7Zvtv1E2/vb3p/itN1htn9KAz+LMZIs1yR5btbn0bdhtd4b5gBcQHFtZBPFh3QScCSwlqJF53eB55TLCvg4RevOm4GVdZe/ZT+OpDhldxNwQzkcA+wB/DPFF9s3gd2bui9t9uGHFNe0ZqZ9smWdd5f78APK1tJ1D/PtxzbL3MmWVreN+yxGcUiWm7UvyXOzPo9+hzyqNiIiYgyN0yn6iIiIKKWCj4iIGEOp4CMiIsZQKviIiIgxlAo+IiJiDKWCbxBJjw5hm8dKOr18fbykQ3rYxpWSVg66bBHjLHmOuqWCH3O2L7H9wXL0eIrenyJiBCXP0Y1U8A1UPk3pQ5JukXSzpNeV019Q/vr+koq+mc8vn9iEpGPKaWtV9Gv8lXL6myX9vaTfB44FPqSin+0DW3/JS1pRPr4RSY+TtFrSbZIuAh7XUraXSLpK0nWSLiyf9RwR80ieoy7j1JvcOHkV8Czgd4EVwPckrSnnPRt4BsXjPL8DPE/StcA5wPNtr5d0wbYbtP3/JF0CfMX2lwC0dW9KrU4Bfm37tyUdClxXLr8CeA/wYtu/kvRO4G3Afx/APkeMq+Q5apEKvpmOBC6wPUXRWcW3gOcCDwPX2L4bQNINwP7Ao8AdLvpjhuLxnyf38f7PB84GsH2TpJvK6UdQnBL8TvllsiNwVR/vEzEJkueoRSr40fNYy+sp+vsMN7PlMs3OFZYXcLntE/p4z4jYInmOock1+Gb6NvA6SYsl7UnxC7xdD00/AJ4qaf9y/HXzLPcIsKxl/E7gOeXrV7dMXwO8AUDSM4FDy+lXU5xCfFo5bxdJv1VlhyImWPIctUgF30wXUfSCdCPwf4F3uOjScE62fwO8BbhM0lqK4P9yjkVXA38p6XpJBwIfBk6RdD3FtcEZnwCWSrqN4nrc2vJ9fga8GbigPM13FfD0fnY0YgIkz1GL9CY3JiQttf1o2Qr348Dttj9ad7kionvJcwxCjuDHx5+XjXRuBZ5A0Qo3IkZT8hx9yxF8RETEGMoRfERExBhKBR8RETGGUsFHRESMoVTwERERYygVfERExBj6/3SKbW2ZzBkwAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 576x288 with 8 Axes>"
       ]
@@ -183,10 +182,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['i_interval:-5_cluster:-1']\n",
-      "['i_interval:-5_cluster:-1' 'i_interval:-5_cluster:1']\n",
-      "['i_interval:-5_cluster:-1']\n",
-      "['i_interval:-5_cluster:-1' 'i_interval:-5_cluster:-2']\n"
+      "[-1]\n",
+      "[-1  1]\n",
+      "[-1]\n",
+      "[-2 -1]\n"
      ]
     }
    ],
@@ -216,7 +215,7 @@
       "['A']\n",
       "['A' 'C']\n",
       "['A']\n",
-      "['B' 'A']\n"
+      "['A' 'B']\n"
      ]
     }
    ],
@@ -241,7 +240,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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        "<Figure size 576x288 with 8 Axes>"
       ]
diff --git a/notebooks/multiple_targets_rgdr.svg b/notebooks/multiple_targets_rgdr.svg
new file mode 100644
index 0000000..584321c
--- /dev/null
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+       id="text939">0</text>
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+       font-weight="400"
+       font-size="15px"
+       transform="translate(814.151,615)"
+       id="text943">1</text>
+    <text
+       font-family="Calibri, Calibri_MSFontService, sans-serif"
+       font-weight="400"
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+       transform="translate(483.714,582)"
+       id="text945">X =</text>
+    <text
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+       font-weight="400"
+       font-size="24px"
+       transform="translate(483.714,616)"
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+       font-weight="400"
+       font-size="24px"
+       transform="translate(495.954,667)"
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+       font-weight="400"
+       font-size="24px"
+       transform="translate(495.954,696)"
+       id="text951">i.e.,</text>
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+       font-size="24px"
+       transform="translate(536.954,696)"
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+       transform="translate(539.54,579)"
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+       transform="translate(734.95,547)"
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diff --git a/notebooks/single_target_RGDR.svg b/notebooks/single_target_RGDR.svg
new file mode 100644
index 0000000..11a921a
--- /dev/null
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+    <text
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+    <text
+       font-family="Consolas, Consolas_MSFontService, sans-serif"
+       font-weight="400"
+       font-size="12px"
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+       id="text287">2016</text>
+    <text
+       font-family="Consolas, Consolas_MSFontService, sans-serif"
+       font-weight="400"
+       font-size="12px"
+       transform="translate(555.206,204)"
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+    <text
+       font-family="Consolas, Consolas_MSFontService, sans-serif"
+       font-weight="400"
+       font-size="12px"
+       transform="translate(607.304,204)"
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+    <text
+       font-family="Consolas, Consolas_MSFontService, sans-serif"
+       font-weight="400"
+       font-size="12px"
+       transform="translate(657.289,204)"
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+    <text
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+       font-weight="700"
+       font-size="24px"
+       id="text295"
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+       y="51.636269">Single target</text>
+  </g>
+</svg>
diff --git a/notebooks/tutorial_RGDR.ipynb b/notebooks/tutorial_RGDR.ipynb
index d5e54cd..b57122d 100644
--- a/notebooks/tutorial_RGDR.ipynb
+++ b/notebooks/tutorial_RGDR.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,15 +22,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we will load in some example data, and resample them using the `AdventCalendar`."
+    "First we will load in some example data, and resample them using `s2spy`'s `Calendar`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,22 +49,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
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AH8CARe5eWsNuIiKyd0hSwrDvCD/rKk9DRbl7YhZwObDC3ecpYRARkYZSVFSUnJmZmZ2ZmZndqVOnAZ07d+4fe79161arz7ZWr16dfN9996VVtf3www/PrKmOu+++u3NJSUnCr/0YOXJkzyeffLJDotupKMrpiXOBHwOfmtlM4EngbXdX5ikisq+ZaAPrtb7rfVZ1m7t27bp94cKFCwCuu+66bikpKdvvvvvub2uqtrS0lObNm9cqlDVr1iQ//vjjnW+55ZZVlW2fM2fOwprqeOSRR7qMGTNmbWpqannUdsvKymjWLMrXceOrMRty9yXufhuQAfwReAJYZmZ3mVnHRAcoIiISb+LEiZ369euX1adPn+whQ4YcEvtlP3LkyJ4XXHDBQf3798+84ooresyfP7/lgAEDMjMyMrKvueaabq1btz48Vsftt9/epV+/flkZGRnZ1157bTeA66+/vsc333zTMjMzM3vs2LE9KrYb2/+1115LHTRoUJ+hQ4em9+rVq++ZZ57Zq7y8nF/+8pedV65c2fyEE07IGDx4cAbAtGnT2ubk5GRmZ2dnDRs2LH3Dhg1JAN27dz/siiuu6J6dnZ11xx13dD3ssMOyYu0sWrSoRUZGRjbADTfccEC/fv2yevfu3ff8888/uLw8ci6SEJGGUMysPzAR+BXwEnAOUAy8l7jQREREdnXhhReumzdvXv6iRYsW9OnTZ8ukSZM6xbYVFha2mD179sLHHnvsX1ddddWBV1555covvvhiQY8ePXacWp82bVrbJUuWtJo7d25+fn7+gry8vNZvvPFGysSJE/914IEHblu4cOGCRx555F/VxZCfn7/f73//+2+WLFky/+uvv245ffr0lF/84hcrO3fuXPrBBx98MWPGjC8KCwubTZgw4YAPP/zwiwULFuQfccQRm++5554usTr233//sgULFuRPmDChqLS01BYuXNgCYMqUKR2HDx++DuDGG29cOW/evPzFixfP37JlS9Lzzz/frv6PaHQ1joeE1zSsBx4HbnH3beGmGWb2vQTGJiIisotZs2btd8cdd3QvKSlJ3rRpU/IJJ5yw4zEAI0aMWBcb6p8zZ07K22+/vQTgsssuWzN+/PgeAG+++WbbDz/8sG12dnY2wObNm5MWLlzYKj09/buoMRx22GGbDjnkkFKAvn37bv7yyy9bVCzz/vvvt/nyyy9bDRo0KBOgtLTUBg4cuDG2ffTo0etiy8OHD187ZcqUjhMmTCj685//3OGFF14oAHjjjTdSH3jgga5bt25NWr9+fbPs7OwtNOJjD6KcRDnH3Qsq2+DuI+o5HhERkWpdfvnlvV588cUlRx999JZJkybt/8EHH6TGtqWkpNQ4fu/ujBs3rvDGG29cHb9+0aJFu3zxV6Vly5Y7rutLTk6mrKxsl4sy3Z1jjz22+NVXX11aWR3x1z2MGjVq3TnnnJN+3nnnrTMzDjvssG2bN2+266+//uAZM2YsOPTQQ0uvu+66blu3bm3UB2xFuaah0oRBRESkMWzevDnpoIMOKt22bZs9//zzVV5bl5OTs/Gpp57qAPDEE0/sKDds2LDiZ555plPs+oKlS5c2X758ebN27dpt37RpU52+lNu0abM9Vu+JJ564aebMmSnz5s1rCVBcXJw0d+7clpXt17dv321JSUnccccd3X74wx+ujfUToGvXrmUbNmxIevXVVxv8bomK9EhQERHZo9xyyy0rBg0alJWbm5vZu3fvrVWVe/DBB7958MEHu2RkZGQvWbKkVUpKynaAESNGFJ9zzjlrjzzyyMyMjIzsH/7wh4esX78+uWvXrtsHDhy4sXfv3n0ruxAyiosvvnj10KFDMwYPHpzRrVu3skceeeSr8847Lz0jIyM7Nzc38/PPP29V1b4jRoxY+5e//KXjqFGj1gF06tRp+4UXXrgqKyur70knnZQxYMCATbsTU32q9vSEmSUBR7n7PxooHhERacpquEUykR544IEVseWbb755l9siX3rppa/i3/fs2bM0Ly9vYVJSEo8++miHxYsX7/iVf/vtt6+8/fbbd5lfoapTCQCbN2+eA3D66aeXnH766SWx9VOmTPk6tnzbbbetvO2223bUe+aZZ5aceeaZ+RXrWr58+ecV1919993fVryddNKkSSsmTZq0omLZin1tKNWONLh7OfD73anYzA40s7+Z2QIzm29mPwvXdzSz6Wa2OPzbIVyfaWYfm9k2M7shrp4+ZpYX9yo2s3G7E5OIiOw7/v73v7fOysrKzsjIyH700Uc7/+53v6v2jgipWZQLId81s5HAtFo+0KkMuN7dZ5tZKjDLzKYDlwDvuvt9ZnYLcAtwM7AWuAYYHl+Juy8CcgDMLBlYDvy5FnGIiMg+aOjQoRsXLVq0oLHj2JtEuaZhLDAV+C78lV9iZsU17eTuhe4+O1wuAfKB7sBZwNNhsacJkwR3X+nunwLVPab6FOBLd18WIW4RERGpR1EmrEqtqUxNzKwncDgwA+ji7oXhpiKgS1X7VeI84H+raedygnkyOOigg3YrVhEREalc1CdCnmlmvw5fp9emATNLIXiK5Dh332mEIjzdEemUh5m1IJjfe2pVZdz9UXfPdffctLQq5xwRERGR3RBllsv7gJ8BC8LXz8zs3iiVm1lzgoThOXefFq7+1swOCLcfAOxy9WoVhgGz3b3GiUpERESk/kUZafgP4DR3f8LdnwCGAj+oaSczM4JHT+e7+wNxm14BLg6XLwb+EjHW86nm1ISIiOydkpOTB2ZmZmb37t2777Bhw9IbYurpRBg3bly3l19+udpT/q+99lrq9OnT2yQ6ltdeey31pJNOOrS2+0Wdi7M9wd0NAFEny/geMAr43MzywnU/B+4D/mRmPwGWAT8CMLOuwEygLVAe3laZ7e7FZtYGOI3gokwREWkkncZ/WK9TY68ef3yNz31o2bJleWx67DPPPLPXxIkT08aPH79j1Hl3psGOoq71Vpzy+re//e0uz1uo6L333ktNSUnZftppp0V+kFOi+l+ZKNnavcAcM3vKzJ4GZgH/VdNO7v6Ru5u793f3nPD1V3df4+6nuHtvdz/V3deG5YvcvYe7t3X39uFycbhtk7vv7+6NNkmHiIg0vmOPPXbjkiVLWr722mupAwcO7HPyyScf2rt3735lZWWMHTu2R2y661/96lc7Zr687bbbumZkZGT36dMn+8orr+wOMGjQoD4ffvhha4DCwsJm3bt3Pwxg0qRJ+5988smHHnXUURnHHHNMn2XLljXPzc3tExvpePPNN1MAHnnkkY4ZGRnZvXv37nvFFVd0j7XVunXrw8eMGdOjT58+2e+++25KfOwjR47s+eSTT3aAYGrsa6+9tlt2dnZWRkZG9pw5c1otWrSoxZQpU9IefvjhLpmZmdlvvvlmyooVK5oNGTLkkH79+mX169cv6+23324DcN1113UbPnx4ryOOOCJzxIgRvQYMGJA5c+bMHU+bjPXvb3/7W+ucnJzMrKys7MMPPzzzs88+q/Qx1lFFuXvif83sfeDIcNXN7l5Ul0ZFRERqq7S0lLfeeqvt97///WKABQsWtJ4zZ878zMzM73796193ateu3fZ58+blb9myxY488sjMM844o3ju3Lmt/vrXv7afNWvWwtTU1PJvv/02uaZ25s+f33ru3Lnzu3Tpsv3OO+/scsopp2y4//77i8rKyigpKUn66quvmo8fP777rFmz8tPS0sqOO+64jGeeeab9qFGj1m/ZsiVp8ODBmyZPnlzjg6Q6depUtmDBgvz77rsv7b777uvywgsvLBs9evSqlJSU7bEnQ55xxhm9rrvuum+HDBmycfHixS2GDBnSu6CgYD7A4sWLW82YMWNhSkqK33XXXZ2fe+65jrm5uSuWLVvWfOXKlc2PP/74zWvXrk369NNPFzZv3pyXX3459aabburx1ltvfbm7n0HU0xNJwOqwfIaZZbj7h7vbqIiISFTbtm1LyszMzAYYPHhwyc9+9rPV77zzTkr//v03ZWZmfgfwzjvvtF24cGHrV155pQNASUlJ8oIFC1pNnz697UUXXbQ6NqNkly5dttfU3nHHHVccK3fUUUdtGjt2bM/S0tKks88+e90xxxyz5fXXX2971FFHlXTr1q0M4Nxzz137wQcfpIwaNWp9cnIyl1xyybrqWwhccMEF6wAGDRq0ORZ3RX//+9/bLl68eL/Y+40bNybHJsQaOnTo+pSUFIdgmu3TTjst4ze/+c2KKVOmdDjjjDPWAaxduzb53HPP7fXVV1+1MjMvLS3dZTbO2qgxaTCz+4FzgflAbBpPB5Q0iIhIwsVf0xCvdevWO6aWdnebOHHi1yNHjtzp1v433nijbWV1NmvWzLdvD/KHzZs37/RFGl/vsGHDNn744YeLXnrppXaXXnppr6uuuurb9u3bV5l4tGjRojz+OobqtGrVymOxVDa1dtgvZs+end+6detdHk/Qpk2bHXH26tWrtH379mUzZszYb9q0aR0ffvjhZQA333xz9xNOOKFk+vTpXy5atKjFySef3CdScFWIck3DcKCPu//A3c8IX2fWpVEREZH6dNppp234wx/+kLZt2zYDmDt3bsvi4uKkIUOGFD/77LOdYndcxE5PHHjggds++eSTNgDPPfdclVNOf/HFFy169OhRev31168ePXr0qtmzZ7c+7rjjNs2YMSO1sLCwWVlZGVOnTu144oknbqyPfqSmpm4vKSnZcQrl2GOPLb733ns7x97/4x//2K/yPWHkyJFrJ0yY0LWkpCR58ODBWwCKi4uTe/To8R3AI4880qmqfaOKkjQUAA1zWaaIiMhuuPbaa1dnZmZuPeyww7J69+7dd8yYMQeXlpba2WefXTxs2LD1OTk5WZmZmdn33HNPV4Bbbrnl28cffzwtKysre/Xq1VUODbz11lupWVlZfbOysrJfeumljjfddNO3Bx98cOmdd965/IQTTsjIysrqO2DAgE0XXXTR+vrox8iRI9e//vrr7WMXQj766KPfzJ49u01GRkb2IYcc0vehhx6q8smFF1100brXX3+941lnnRW725Gbb765aPz48T2ysrKyy8rK6hxflQfKzB4kOA2xGcgzs3eBbbHt7n5NnVsXEZE9SpRbJOtbbErqeBWnp05OTuahhx5aTjCp4U4mTJhQNGHChJ0u4D/88MO3fvHFFztOecSmn77mmmvWAGti66+++uo1V1999RoqGDt27NqxY8eurbi+slhj4qezjp8a+/jjj9/8ySefLALo37//tvi4AF5//fWCinXFTxMec+CBB5aVlZXt9Pmceuqpm7766qt5FftZ8fhFVd2Jl5nh31kED2QSERGRfViVSYO7Pw0QPlhpq7tvD98nA3W6z1NERET2PFGuaXgXiL/wYj/gncSEIyIiIk1VlKShlbvvuCo0XG6duJBERKQJKS8vL6/Tvf2y5wg/6/KqtkdJGjaZ2RGxN2Y2ENhSD7GJiEjTN2/VqlXtlDjs/crLy23VqlXtgHlVlYnyBIpxwFQzWwEY0JXgYU8iIrKXKysru6yoqOixoqKifkT7oSl7rnJgXllZ2WVVFYgy98SnZpYJxJ4itcjdS+spQBERacIGDhy4EtAD/QSIPvfEkUDPsPwRZoa7T0lYVCIiItLkRJl74hngECAPiD1v2wElDSIiIvuQKCMNuUC2u+8yWYaIiIjsO6Jc1DKP4OJHERER2YdFGWnoBCwws0/Yee4JXRgjIiKyD4mSNIxPdBAiIiLS9EW55fKD+PdmdixwPvBB5XuIiIjI3ijSLZdmdjhwAXAOsBR4KZFBiYiISNNTZdJgZhkEIwrnA6uBFwBz95MaKDYRERFpQqobaVgI/B9wursvATCzaxskqgZQUrSMmU/clfB2vttYjNlK8p7/bULbKd1cAmsK1afdtLf1qaH6A+pTXXy3sZjlXsatU/MT2g7A0tVb6JXwVmRvV13SMAI4D/ibmb0JPE8w98QeLz09vcEmz8jbkA5ATt+0xLZTGjzlO6dXq4S2A+pTndppoD41VH9AfapTOxvScdKhf05C2wHoRfD/PpG6sJqe2WRmbYCzCE5TnEzwJMg/u/vbiQ9v9+Xm5vrMmTMbOwwRkT2Gmc1y99zGjkOarhof7uTum9z9j+5+BtADmAPcnPDIREREpEmp1TSn7r7O3R9191MSFZCIiIg0TZobXURERCJR0iAiIiKRKGkQERGRSJQ0iIiISCRKGkRERCQSJQ0iIiISiZIGERERiURJg4iIiESipEFEREQiUdIgIiIikVQ3y+Vea/LkyRQUFDRIW3l5eQDk5OTsFe00ZFvqU9NvpyHbUp/qLj09nTFjxjRIW7J32ieThoKCAl74eBGpXQ9OeFvL5hdgqZ1Y1nxVQttZPncRzbscwoqlWxPaDqhPddFQfWqo/oD6VBfL5hdwTJsVkLQ4oe0ALF29BRid8HZk77ZPJg0AqV0PJvfSOxPeTuHcj0hq35mc88YltJ2V+Z/Scv8D1KfdtLf1qaH6A+pTXRTO/Yju7VZy7zlZCW0H4Nap+QlvQ/Z+uqZBREREIlHSICIiIpEoaRAREZFIlDSIiIhIJEoaREREJBIlDSIiIhKJkgYRERGJREmDiIiIRKKkQURERCJR0iAiIiKRKGkQERGRSJQ0iIiISCRKGkRERCQSJQ0iIiISiZIGERERiURJg4iIiESipEFEREQiUdIgIiIikShpEBERkUiUNIiIiEgkShpEREQkEiUNIiIiEomSBhEREYkkYUmDmR1oZn8zswVmNt/Mfhau72hm081scfi3Q7g+08w+NrNtZnZDhbram9mLZrbQzPLN7OhExS0iIiKVS+RIQxlwvbtnA0cBPzWzbOAW4F137w28G74HWAtcA/y6krp+B7zp7pnAACA/gXGLiIhIJRKWNLh7obvPDpdLCL7ouwNnAU+HxZ4GhodlVrr7p0BpfD1m1g44Hng8LPedu69PVNwiIiJSuQa5psHMegKHAzOALu5eGG4qArrUsHsvYBXwpJnNMbPHzKxNwoIVERGRSiU8aTCzFOAlYJy7F8dvc3cHvIYqmgFHAH9w98OBTfz7lEbFti43s5lmNnPVqlV1D15ERER2SGjSYGbNCRKG59x9Wrj6WzM7INx+ALCyhmr+BfzL3WeE718kSCJ24e6Punuuu+empaXVvQMiIiKyQyLvnjCC6xDy3f2BuE2vABeHyxcDf6muHncvAr4xsz7hqlOABfUcroiIiNSgWQLr/h4wCvjczPLCdT8H7gP+ZGY/AZYBPwIws67ATKAtUG5m44Ds8JTG1cBzZtYCKAB+nMC4RUREpBIJSxrc/SPAqth8SiXli4AeVdSVB+TWW3AiIiJSa3oipIiIiESipEFEREQiUdIgIiIikShpEBERkUiUNIiIiEgkShpEREQkEiUNIiIiEomSBhEREYlESYOIiIhEoqRBREREIlHSICIiIpEoaRAREZFIlDSIiIhIJEoaREREJBIlDSIiIhKJkgYRERGJREmDiIiIRKKkQURERCJR0iAiIiKRKGkQERGRSJQ0iIiISCRKGkRERCQSJQ0iIiISSbPGDqCxlBQtY+YTdyW8ne82FmO2krznf5vQdko3l8CaQvVpN+1tfWqo/oD6VBffbSxmuZdx69T8hLYDsHT1FnolvBXZ2+2TSUN6ejrnNlBbeRvSAcjpm5bYdkr7BO30apXQdkB9qlM7DdSnhuoPqE91amdDOk469M9JaDsAvQj+3ydSF/tk0jBmzJjGDkFERGSPo2saREREJBIlDSIiIhKJkgYRERGJREmDiIiIRKKkQURERCIxd2/sGBLCzFYByxo7jlAnYHVjB1ELijexFG9iKd7dd7C7J/6eVtlj7bVJQ1NiZjPdPbex44hK8SaW4k0sxSuSODo9ISIiIpEoaRAREZFIlDQ0jEcbO4BaUryJpXgTS/GKJIiuaRAREZFINNIgIiIikShpEBERkUiUNNQjM7vHzOaaWZ6ZvW1m3cL1ZmaTzGxJuP2IuH0uNrPF4eviBo73V2a2MIzpz2bWPlzf08y2hP3IM7OH4/YZaGafh32ZZGbW2PGG224NY1pkZkPi1g8N1y0xs1saKtaw7XPMbL6ZlZtZbtz6Jnl8q4s53NbkjnGF+Mab2fK44/ofcdsqjb2xNZVjJxKZu+tVTy+gbdzyNcDD4fJ/AG8ABhwFzAjXdwQKwr8dwuUODRjv94Fm4fL9wP3hck9gXhX7fBL2wcI+DWsC8WYDnwEtgV7Al0By+PoSSAdahGWyGzDeLKAP8D6QG7e+SR7fGmJukse4QuzjgRsqWV9p7I0RY4W4msyx00uvqC+NNNQjdy+Oe9sGiF1lehYwxQP/BNqb2QHAEGC6u69193XAdGBoA8b7truXhW//CfSornwYc1t3/6e7OzAFGJ7YKP+tmnjPAp53923uvhRYAgwKX0vcvcDdvwOeD8s2VLz57r4oavnGPr5QbcxN8hhHVFXsjW1POHYiO1HSUM/M7L/M7BvgQuCOcHV34Ju4Yv8K11W1vjFcSvDLNqaXmc0xsw/M7LhwXXeCGGOaSrx7wvGtqKkf34r2lGN8VXj66gkz6xCua2oxxjTVuESq1KyxA9jTmNk7QNdKNt3m7n9x99uA28zsVuAq4M4GDbCCmuINy9wGlAHPhdsKgYPcfY2ZDQReNrO+TTjeRhMl3ko02vGF3Y65SaguduAPwD0EI3z3ABMJkksRqSdKGmrJ3U+NWPQ54K8EScNy4MC4bT3CdcuBEyusf7/OQcapKV4zuwQ4HTglHBLH3bcB28LlWWb2JZARxht/CiPWj0aNl6qPL9Wsrxe1+PcQv0+jHd+wzVrHTCMe43hRYzezycBr4dvqYm9MTTUukSrp9EQ9MrPecW/PAhaGy68Ao8O7KI4CNrh7IfAW8H0z6xAOpX4/XNdQ8Q4FbgLOdPfNcevTzCw5XE4HegMFYczFZnZUeFX/aKDBfplWFS/B8T3PzFqaWa8w3k+AT4HeZtbLzFoA54VlG1VTPb41aPLHOLwmJOaHwLxwuarYG1uTOXYiUWmkoX7dZ2Z9gHKCabn/M1z/V4I7KJYAm4EfA7j7WjO7h+B/HgB3u/vaBoz3IYIryqeHd/b9093/EzgeuNvMSgn68p9xcV0JPAXsR3BNwRsVK23oeN19vpn9CVhAcNrip+6+HcDMriJIxJKBJ9x9fkMFa2Y/BB4E0oDXzSzP3YfQdI9vlTE31WNcwX+bWQ7B6YmvgLEA1cXemNy9rAkdO5FI9BhpERERiUSnJ0RERCQSJQ0iIiISiZIGERERiURJg4iIiESipEFEREQiUdIgjcrMtoczEs43s8/M7HozS9i/SzPbWMvyaWY2I3zk83E179HwzOwrM+uUoLqfMrOzayhziYUzuorI3k3PaZDGtsXdcwDMrDPwR6Atjfz47TinAJ+7+2VRdzCz5KbwHIAozKxZ3CRgu+sSggcprah7RCLSlGmkQZoMd18JXE4w6ZCZWU8z+z8zmx2+jgEwsylmNjy2n5k9Z2ZnmVlfM/skHLmYW+EJncSV/004svGumaWF6w4xszfNbFbYZmb4oKD/Bs4K69zPzM43s8/NbJ6Z3R9X50Yzm2hmnwFHm9lFcbE8EnsCZIU47jCzT8O6Hg2fAomZvW9m94f7fxEb4TCzZDP7dVh+rpldHVfd1eEx+tzMMsPyHc3s5bDsP82sf7h+vJk9Y2Z/B56pEJOZ2UNmtsiCeR46VxdvOAqRCzwXd4wGWjAR1ywze8t2flKjiOzJGntubr327RewsZJ164EuQGugVbiuNzAzXD4BeDlcbgcsJRg1exC4MFzfAtivkro9rswdwEPh8rtA73B5MPBeuHxJXJluwNcET0tsBrwHDI+r90fhchbwKtA8fP8/wOhKYukYt/wMcEa4/D4wMVz+D+CdcPkK4EWgWfz+BE8/vDpcvhJ4LFx+ELgzXD4ZyAuXxwOzqjg+IwimaE8O+7seODtCvLnhcnPgH0Ba+P5cgicdNvq/Nb300qvuL52ekKasOfBQ+It/O8GkTrj7B2b2P+EowUjgJQ8eyfsxwQyjPYBp7r64kjrLgRfC5WeBaWaWAhwDTA1/7EPwuOqKjgTed/dVEIxwEDwS+uUwvpfCcqcAA4FPw/r2A1ZWUt9JZnYTQXLUEZhPkGwATAv/zgJ6hsunAg97eDrBd37keHz5EeHysQTHB3d/z8z2N7O24bZX3H1LJTEdD/yvB6dXVpjZexHjjekD9OPfj/pOJpjVU0T2AkoapEmxYAKn7QRfsncC3wIDCE6lbY0rOgW4iGCSn9hcHn80sxnAD4C/mtlYd4//0quMh3Wv9/Dait201f99HYMBT7v7rVUVNrNWBCMQue7+jZmNB1rFFdkW/t1OtP9Oa1t+U4QyO0SId0dRYL67H12b+kVkz6BrGqTJCEcOHiY4HeAEpx4K3b0cGEXwqzXmKWAcgLsvCPdPJ5gtchLB7JD9K2kmCYjdDXAB8JG7FwNLzeycsB4zswGV7PsJcIKZdQqvUTgf+KCScu8CZ1twYWfs2oKDK5SJfeGuDkc6qr1DITQdGGtmzWL11lD+/4ALw7InAqvDvlbnQ+Dc8PqJA4CTIsRbAqSGy4uANDM7Omy3uZn1raljIrJn0EiDNLb9zCyP4FREGcG58gfCbf8DvGRmo4E3ift17O7fmlk+wamBmB8BoyyYPbIImFBJe5uAQWb2C4LRjHPD9RcCfwjXNweeBz6L39HdC83sFuBvBL+oX3f3XaaudvcFYT1vW3D7aCnwU4KZT2Nl1pvZZIK7Dor490yn1XmM4BTN3LCPkwlm/qzKeOAJM5tLMLvqxRHa+DPB9Q8LCK7f+DhCvE8BD5vZFuBogoRikpm1I/h/zG8JTmWIyB5Os1zKHsnMWgOfA0e4+4bGjkdEZF+g0xOyxzGzU4F84EElDCIiDUcjDSIiIhKJRhpEREQkEiUNIiIiEomSBhEREYlESYOIiIhEoqRBREREIvl/0nfEaVUVfE0AAAAASUVORK5CYII=",
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",
       "text/plain": [
-       "<Figure size 700x400 with 1 Axes>"
+       "<Figure size 504x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "cal = s2spy.time.AdventCalendar(\"8-2\", freq=\"30d\")\n",
+    "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\")"
@@ -78,72 +84,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "field_resampled = s2spy.time.resample(cal, field)\n",
-    "target_resampled = s2spy.time.resample(cal, target)"
+    "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."
+    "### 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": 28,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-4]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "target_timeseries = target_resampled.sel(cluster=3).ts.sel(i_interval=1)\n",
-    "precursor_field = field_resampled.sst.sel(i_interval=slice(-4,-1)) # Multiple i_intervals: -1 through -4\n",
+    "target_timeseries = target_resampled.sel(cluster=3)[\"ts\"]\n",
+    "precursor_field = field_resampled[\"sst\"]\n",
     "\n",
-    "rgdr = RGDR(eps_km=600, alpha=0.10, min_area_km2=0)"
+    "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, for a single i_interval.\n",
+    "Using `.preview_correlation` we can visualize the correlation and p-value map of the target timeseries and precursor field.\n",
     "\n",
-    "In this case we use i_interval `-1`."
+    "The red hashing shows where p-value `<` alpha"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "[<matplotlib.collections.QuadMesh at 0x1429e2cd3f0>,\n",
-       " <matplotlib.collections.QuadMesh at 0x1429e347d90>]"
+       "<Figure size 864x144 with 4 Axes>"
       ]
      },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x200 with 4 Axes>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
-    "rgdr.preview_correlation(precursor_field, target_timeseries, i_interval=-1, ax1=ax1, ax2=ax2)"
+    "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
+    "rgdr.fit(precursor_field, target_timeseries)"
    ]
   },
   {
@@ -157,56 +188,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x142b3f82ef0>"
+       "<matplotlib.collections.QuadMesh at 0x2602845d6c0>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x200 with 4 Axes>"
+       "<Figure size 864x144 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
     "\n",
-    "RGDR(eps_km=600, alpha=0.10, min_area_km2=500**2).preview_clusters(\n",
-    "    precursor_field,\n",
-    "    target_timeseries,\n",
-    "    i_interval=-3, ax=ax1)\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(eps_km=600, alpha=0.10, min_area_km2=None).preview_clusters(\n",
-    "    precursor_field,\n",
-    "    target_timeseries,\n",
-    "    i_interval=-3, ax=ax2)"
+    "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 paramters, the `.fit` method can be used to fit the RGDR clustering to a precursor field.\n",
+    "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",
-    "`.transform` can then be used to return the data reduced to clusters:"
+    "Note that only the data for the specified precursor interval index (`-4`) is returned:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -473,11 +524,6 @@
        "  grid-column: 4;\n",
        "}\n",
        "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
        ".xr-var-name,\n",
        ".xr-var-dims,\n",
        ".xr-var-dtype,\n",
@@ -499,16 +545,14 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\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",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -518,16 +562,13 @@
        "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
        ".xr-attrs {\n",
        "  padding-left: 25px !important;\n",
        "}\n",
        "\n",
        ".xr-attrs,\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
+       ".xr-var-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -565,8 +606,7 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
+       ".xr-icon-file-text2 {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -575,129 +615,134 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 39, cluster_labels: 8)&gt;\n",
-       "287.8 295.4 286.4 291.3 277.1 278.3 ... 285.5 291.1 276.2 277.2 282.9 282.6\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 ... 1982 1981 1980\n",
-       "  * cluster_labels  (cluster_labels) &lt;U32 &#x27;i_interval:-1_cluster:-1&#x27; ... &#x27;i_i...\n",
-       "    latitude        (cluster_labels) float64 38.64 31.13 38.88 ... 42.53 43.03\n",
-       "    longitude       (cluster_labels) float64 218.1 186.8 219.2 ... 219.2 217.5\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>: 39</li><li><span class='xr-has-index'>cluster_labels</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-17cb0257-dc1c-42df-a1f6-2a2d489ee7df' class='xr-array-in' type='checkbox' ><label for='section-17cb0257-dc1c-42df-a1f6-2a2d489ee7df' 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>291.4 298.2 288.4 296.6 286.9 285.1 ... 298.9 287.8 296.6 286.0 284.9</span></div><div class='xr-array-data'><pre>array([[291.37066195, 298.21053747, 288.43352188, 296.60848274,\n",
-       "        286.90046576, 285.06801918],\n",
-       "       [291.45966391, 299.19568083, 288.59732323, 296.99780806,\n",
-       "        286.61949986, 284.98540926],\n",
-       "       [291.91085874, 298.89195415, 289.25948746, 296.46499542,\n",
-       "        287.04384111, 285.45310605],\n",
-       "       [293.32741257, 298.91044985, 289.77576952, 296.28250604,\n",
-       "        287.36028116, 286.22768626],\n",
-       "       [292.3259726 , 299.48610574, 289.2110373 , 296.53067801,\n",
-       "        287.49833021, 286.25535463],\n",
-       "       [291.99974208, 299.04325189, 288.62988464, 297.33760943,\n",
-       "        287.24689238, 285.63134742],\n",
-       "       [290.34711093, 299.320106  , 287.57482541, 297.90456269,\n",
-       "        286.10235052, 284.79830112],\n",
-       "       [290.48853941, 299.65241405, 287.49144993, 298.35400031,\n",
-       "        286.18180894, 284.64238114],\n",
-       "       [290.70922506, 299.69018872, 287.76165755, 297.16978009,\n",
-       "        286.06129633, 284.49058091],\n",
-       "       [291.91882282, 299.30649003, 289.03807993, 296.61841539,\n",
-       "        286.86319553, 285.41171316],\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",
-       "       [290.99960169, 298.35675492, 288.43398779, 296.56159793,\n",
-       "        286.77823224, 285.46594319],\n",
-       "       [290.80443321, 299.61375876, 287.73813437, 297.72830858,\n",
-       "        285.91317401, 284.66681325],\n",
-       "       [290.26277142, 298.495829  , 287.68074275, 294.90712055,\n",
-       "        286.11305079, 285.06475995],\n",
-       "       [291.11538436, 298.51701671, 288.42280444, 295.87495752,\n",
-       "        286.59086822, 285.47541953],\n",
-       "       [291.31511491, 298.06875363, 288.30114902, 296.60273768,\n",
-       "        286.36983148, 285.23982798],\n",
-       "       [291.08960917, 299.35080249, 288.1596998 , 297.99213155,\n",
-       "        286.72862973, 285.2133661 ],\n",
-       "       [290.0762239 , 298.98291705, 287.73584298, 296.41157889,\n",
-       "        285.86096977, 284.26479301],\n",
-       "       [290.71731703, 297.71694295, 287.67598112, 294.73800866,\n",
-       "        285.88040068, 284.63316991],\n",
-       "       [290.970545  , 298.92119198, 288.55306112, 296.90258837,\n",
-       "        286.41065088, 285.21587978],\n",
-       "       [290.79588914, 298.94548042, 287.83356654, 296.5913755 ,\n",
-       "        286.03044129, 284.87776786]])</pre></div></div></li><li class='xr-section-item'><input id='section-a9951cc5-0486-436e-b53b-cf9a56244778' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9951cc5-0486-436e-b53b-cf9a56244778' class='xr-section-summary' >Coordinates: <span>(4)</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 ... 1982 1981 1980</div><input id='attrs-3a10bea3-a945-4c75-9a9c-c1bf569c1842' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a10bea3-a945-4c75-9a9c-c1bf569c1842' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64784064-90ab-4e5d-bb25-d68edf6ce76c' class='xr-var-data-in' type='checkbox'><label for='data-64784064-90ab-4e5d-bb25-d68edf6ce76c' 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",
+       "       [[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], dtype=int64)</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'>&lt;U32</div><div class='xr-var-preview xr-preview'>&#x27;i_interval:-1_cluster:-2&#x27; ... &#x27;...</div><input id='attrs-9a10e615-e0db-4969-be1e-ee46133d7a27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a10e615-e0db-4969-be1e-ee46133d7a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc0fe756-1bf4-4a1f-bd0b-174601772d29' class='xr-var-data-in' type='checkbox'><label for='data-fc0fe756-1bf4-4a1f-bd0b-174601772d29' 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;i_interval:-1_cluster:-2&#x27;, &#x27;i_interval:-1_cluster:1&#x27;,\n",
-       "       &#x27;i_interval:-2_cluster:-1&#x27;, &#x27;i_interval:-2_cluster:1&#x27;,\n",
-       "       &#x27;i_interval:-3_cluster:-1&#x27;, &#x27;i_interval:-4_cluster:-2&#x27;], dtype=&#x27;&lt;U32&#x27;)</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'>36.05 29.44 37.33 29.58 38.14 39.78</div><input id='attrs-9115d808-4d5c-423e-a8c2-20f7857287df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9115d808-4d5c-423e-a8c2-20f7857287df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f2deb0-905a-4a6f-a2f0-5337086321dd' class='xr-var-data-in' type='checkbox'><label for='data-b6f2deb0-905a-4a6f-a2f0-5337086321dd' 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([36.0508552 , 29.4398051 , 37.33257702, 29.58134561, 38.13773082,\n",
-       "       39.78162825])</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'>223.9 185.4 221.8 190.2 217.8 219.3</div><input id='attrs-ba1e2f3b-8e0e-4020-8c40-bd54172e602a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba1e2f3b-8e0e-4020-8c40-bd54172e602a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1099093b-2c6a-48fa-b835-092a034d2fe6' class='xr-var-data-in' type='checkbox'><label for='data-1099093b-2c6a-48fa-b835-092a034d2fe6' 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([223.86658208, 185.40970765, 221.82516648, 190.20336403,\n",
-       "       217.7810629 , 219.30300121])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ad3b7b3-e410-40ce-8094-9c511aeb58ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ad3b7b3-e410-40ce-8094-9c511aeb58ce' 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>"
+       "       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: 39, cluster_labels: 8)>\n",
-       "287.8 295.4 286.4 291.3 277.1 278.3 ... 285.5 291.1 276.2 277.2 282.9 282.6\n",
+       "<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 ... 1982 1981 1980\n",
-       "  * cluster_labels  (cluster_labels) <U32 'i_interval:-1_cluster:-1' ... 'i_i...\n",
-       "    latitude        (cluster_labels) float64 38.64 31.13 38.88 ... 42.53 43.03\n",
-       "    longitude       (cluster_labels) float64 218.1 186.8 219.2 ... 219.2 217.5\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": 31,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rgdr.fit(precursor_field, target_timeseries)\n",
     "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:"
+    "Now we can plot the data for each cluster (blue for positively correlated lcusters, red for negatively correlated ones):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x142b08f5810>"
+       "[<matplotlib.lines.Line2D at 0x26028538430>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "clustered_data -= clustered_data.mean(dim='anchor_year')\n",
-    "clustered_data.sel(cluster_labels=\"i_interval:-1_cluster:1\").plot.line(x='anchor_year', label='pos. corr')\n",
-    "clustered_data.sel(cluster_labels=\"i_interval:-1_cluster:-1\").plot.line(x='anchor_year', label='neg. corr')\n",
-    "plt.legend()"
+    "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)"
    ]
   },
   {
@@ -711,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -978,11 +1023,6 @@
        "  grid-column: 4;\n",
        "}\n",
        "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
        ".xr-var-name,\n",
        ".xr-var-dims,\n",
        ".xr-var-dtype,\n",
@@ -1004,16 +1044,14 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\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",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -1023,16 +1061,13 @@
        "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
        ".xr-attrs {\n",
        "  padding-left: 25px !important;\n",
        "}\n",
        "\n",
        ".xr-attrs,\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
+       ".xr-var-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -1070,8 +1105,7 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
+       ".xr-icon-file-text2 {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -1080,73 +1114,45 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (i_interval: 4, latitude: 5, longitude: 13)&gt;\n",
-       "-0.1352 -0.1545 -0.2249 -0.208 -0.1531 ... -0.3452 -0.3065 -0.3574 -0.4362\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",
-       "  * i_interval  (i_interval) int64 -4 -3 -2 -1\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 3.34e+06 3.645e+06 ... 4.169e+06 4.385e+06\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\n",
-       "    cluster     int64 3</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><li><span class='xr-has-index'>i_interval</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fa45b6e9-cf75-4240-8308-26bb56909d3f' class='xr-array-in' type='checkbox' ><label for='section-fa45b6e9-cf75-4240-8308-26bb56909d3f' 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>-0.2663 -0.2194 -0.2187 0.01618 ... -0.1126 -0.3063 -0.38 -0.3984</span></div><div class='xr-array-data'><pre>array([[[-0.26630719, -0.21941745, -0.21868534,  0.01617516],\n",
-       "        [-0.18191947, -0.21464621, -0.12315571,  0.02414245],\n",
-       "        [-0.25372747, -0.4007449 , -0.23253951, -0.1105048 ],\n",
-       "        [-0.35772739, -0.46371192, -0.35037825, -0.24520611],\n",
-       "        [-0.3473753 , -0.46975605, -0.4534247 , -0.32824171],\n",
-       "        [-0.29461981, -0.41748415, -0.43326205, -0.323078  ],\n",
-       "        [-0.28702518, -0.34457092, -0.34313136, -0.30660624],\n",
-       "        [-0.37721962, -0.37666874, -0.34666184, -0.38616188],\n",
-       "        [-0.32951966, -0.30240485, -0.33402403, -0.3822271 ],\n",
-       "        [-0.33065629, -0.28493204, -0.41554246, -0.36827501],\n",
-       "        [-0.27627975, -0.19205284, -0.27858467, -0.28047128],\n",
-       "        [-0.12823108, -0.15956299, -0.2545805 , -0.10757776],\n",
-       "        [        nan,         nan,         nan,         nan]],\n",
-       "\n",
-       "       [[-0.19425886, -0.1379688 , -0.12801465, -0.02842247],\n",
-       "        [-0.10557213, -0.0967947 , -0.0124359 ,  0.00598683],\n",
-       "        [-0.1265711 , -0.13544553, -0.09888618,  0.00479765],\n",
-       "        [-0.2325628 , -0.16586661, -0.06881219,  0.01930277],\n",
-       "        [-0.25983436, -0.22187587, -0.1865748 , -0.01405595],\n",
-       "        [-0.29141342, -0.28542316, -0.26110484,  0.01204043],\n",
-       "...\n",
-       "        [-0.29769152, -0.12912278, -0.12178649, -0.18490648],\n",
-       "        [-0.3604698 , -0.25308925, -0.27218421, -0.24043321],\n",
-       "        [-0.39152915, -0.34714351, -0.38322784, -0.29061517],\n",
-       "        [-0.3338985 , -0.46870146, -0.54341488, -0.35707131],\n",
-       "        [-0.155657  , -0.36081808, -0.48241304, -0.26357851],\n",
-       "        [-0.10772449, -0.26949376, -0.32115578, -0.27768413]],\n",
-       "\n",
-       "       [[ 0.01770287,  0.20242345,  0.50036829,  0.42487447],\n",
-       "        [ 0.00806344,  0.2784725 ,  0.4839066 ,  0.34211447],\n",
-       "        [ 0.14065339,  0.3199787 ,  0.43694834,  0.3433933 ],\n",
-       "        [ 0.13369711,  0.33449607,  0.35050515,  0.18648733],\n",
-       "        [ 0.1233588 ,  0.29487164,  0.25401412,  0.04340644],\n",
-       "        [ 0.0517264 ,  0.26352384,  0.09830805, -0.10334886],\n",
-       "        [-0.01373979,  0.16013368, -0.08694884, -0.17111999],\n",
-       "        [-0.06523411, -0.08067427, -0.25987983, -0.24750864],\n",
-       "        [-0.1921587 , -0.31606223, -0.39384288, -0.35825522],\n",
-       "        [-0.11634853, -0.34898835, -0.4202639 , -0.41354082],\n",
-       "        [-0.1791453 , -0.35672934, -0.43487533, -0.37029449],\n",
-       "        [-0.21013388, -0.34349675, -0.42768064, -0.40753351],\n",
-       "        [-0.11258559, -0.3062787 , -0.37996126, -0.39836497]]])</pre></div></div></li><li class='xr-section-item'><input id='section-d06045e8-3005-4c23-ab82-e290c9446f5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d06045e8-3005-4c23-ab82-e290c9446f5e' class='xr-section-summary' >Coordinates: <span>(7)</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'>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-471585e4-f049-4260-8677-55835d20a817' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-471585e4-f049-4260-8677-55835d20a817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9e9b499-ca71-42dc-a483-e563f93dc911' class='xr-var-data-in' type='checkbox'><label for='data-b9e9b499-ca71-42dc-a483-e563f93dc911' 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 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-9473b146-36c8-4625-b2e1-591f8aaa974a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9473b146-36c8-4625-b2e1-591f8aaa974a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6a3a0a-79e1-443e-a6f2-c4a87e164c70' class='xr-var-data-in' type='checkbox'><label for='data-cd6a3a0a-79e1-443e-a6f2-c4a87e164c70' 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-adf341d9-183c-48d8-a0c9-b0b9f41dfc73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adf341d9-183c-48d8-a0c9-b0b9f41dfc73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a40df185-f83c-4960-8345-e4780e7ca0df' class='xr-var-data-in' type='checkbox'><label for='data-a40df185-f83c-4960-8345-e4780e7ca0df' 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'>3.34e+06 3.645e+06 ... 4.385e+06</div><input id='attrs-74543780-07e8-4100-a0b5-915271fd3e54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74543780-07e8-4100-a0b5-915271fd3e54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74a0b22-1a27-4820-9357-1a448c3aa184' class='xr-var-data-in' type='checkbox'><label for='data-d74a0b22-1a27-4820-9357-1a448c3aa184' 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([3339800.84283772, 3644753.05166713, 3921966.48901128,\n",
-       "       4169331.39322594, 4384965.16802703])</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'>5</div><input id='attrs-bc584487-8b3e-49d8-98de-45ccf7fec656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc584487-8b3e-49d8-98de-45ccf7fec656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c02e7dc-67f5-4a67-8f17-9e98f38fac0f' class='xr-var-data-in' type='checkbox'><label for='data-4c02e7dc-67f5-4a67-8f17-9e98f38fac0f' 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'>6</div><input id='attrs-1f9a8de0-45c6-43b5-9d24-ffd3e2fdcddb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f9a8de0-45c6-43b5-9d24-ffd3e2fdcddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a8f9552-aa30-42df-9d51-05c6c6b1e8ab' class='xr-var-data-in' type='checkbox'><label for='data-3a8f9552-aa30-42df-9d51-05c6c6b1e8ab' 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><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-50abc10b-0c4f-47a1-8575-ec4c3a4017f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50abc10b-0c4f-47a1-8575-ec4c3a4017f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70f820bc-b408-4c83-b006-476eb2bf9d33' class='xr-var-data-in' type='checkbox'><label for='data-70f820bc-b408-4c83-b006-476eb2bf9d33' 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></ul></div></li><li class='xr-section-item'><input id='section-eb5c026b-afc3-497e-88a0-b25754150310' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eb5c026b-afc3-497e-88a0-b25754150310' 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>"
+       "    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 (i_interval: 4, latitude: 5, longitude: 13)>\n",
-       "-0.1352 -0.1545 -0.2249 -0.208 -0.1531 ... -0.3452 -0.3065 -0.3574 -0.4362\n",
+       "<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",
-       "  * i_interval  (i_interval) int64 -4 -3 -2 -1\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 3.34e+06 3.645e+06 ... 4.169e+06 4.385e+06\n",
-       "    tfreq       int64 5\n",
-       "    n_clusters  int64 6\n",
-       "    cluster     int64 3"
+       "    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": 34,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1158,27 +1164,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x142b417fd90>"
+       "<matplotlib.collections.QuadMesh at 0x260285b71c0>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x300 with 4 Axes>"
+       "<Figure size 720x216 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1186,44 +1194,572 @@
     "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.sel(i_interval=-1).plot(ax=ax1)\n",
+    "rgdr.corr_map.plot(ax=ax1)\n",
     "\n",
     "# Visualize p-values map\n",
-    "rgdr.pval_map.sel(i_interval=-1).plot(ax=ax2)"
+    "rgdr.pval_map.plot(ax=ax2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The cluster maps can also be visualized. Note that clusters can differ between i_intervals."
+    "The cluster maps can also be visualized:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1800x200 with 8 Axes>"
+       "<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": [
-    "from s2spy.rgdr.utils import cluster_labels_to_ints\n",
-    "cluster_map = cluster_labels_to_ints(rgdr.cluster_map.copy())\n",
+    "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",
-    "fig, axes = plt.subplots(figsize=(18, 2), ncols=4)\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",
-    "# Loop through i_interval -1 -> -4\n",
-    "for i, ax in zip((-1, -2, -3, -4), axes):\n",
-    "    cluster_map.cluster_labels.sel(i_interval=i).plot(ax=ax, cmap='RdYlBu', vmin=-2, vmax=2)"
+    "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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+       "\n",
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+       "  grid-column: 1;\n",
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+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
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+       "\n",
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+       "  grid-column: 2;\n",
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+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
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+       "  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"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index 7bcebb3..34d5704 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,14 @@ branch = true
 source = ["s2spy"]
 command_line = "-m pytest"
 
+[tool.coverage.report]
+exclude_lines = [
+  "pragma: no cover",
+  "@overload",
+  "if TYPE_CHECKING:",
+  "if typing.TYPE_CHECKING:"
+]
+
 [tool.tox]
 legacy_tox_ini = """
 
diff --git a/s2spy/_resample.py b/s2spy/_resample.py
index 866cdb2..d64463a 100644
--- a/s2spy/_resample.py
+++ b/s2spy/_resample.py
@@ -1,10 +1,20 @@
+from typing import TYPE_CHECKING
 from typing import Union
+from typing import overload
 import numpy as np
 import pandas as pd
 import xarray as xr
 from . import utils
 
 
+if TYPE_CHECKING:
+    from s2spy.time import AdventCalendar
+    from s2spy.time import Calendar
+    from s2spy.time import MonthlyCalendar
+    from s2spy.time import WeeklyCalendar
+    Calendars = Union[Calendar, AdventCalendar, WeeklyCalendar, MonthlyCalendar]
+
+
 PandasData = (pd.Series, pd.DataFrame)
 XArrayData = (xr.DataArray, xr.Dataset)
 
@@ -209,8 +219,24 @@ def resample_dataset(calendar, input_data: xr.Dataset) -> xr.Dataset:
     return data.transpose("anchor_year", "i_interval", ...)
 
 
+@overload
+def resample(
+    mapped_calendar: "Calendars",
+    input_data: Union[xr.DataArray, xr.Dataset]
+) -> xr.Dataset:
+    ...
+
+
+@overload
+def resample(
+    mapped_calendar: "Calendars",
+    input_data: Union[pd.Series, pd.DataFrame]
+) -> pd.DataFrame:
+    ...
+
+
 def resample(
-    mapped_calendar,
+    mapped_calendar: "Calendars",
     input_data: Union[pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset],
 ) -> Union[pd.DataFrame, xr.Dataset]:
     """Resample input data to the calendar frequency.
diff --git a/s2spy/rgdr/label_alignment.py b/s2spy/rgdr/label_alignment.py
index e3ba3d2..63b0784 100644
--- a/s2spy/rgdr/label_alignment.py
+++ b/s2spy/rgdr/label_alignment.py
@@ -10,7 +10,6 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from s2spy.rgdr import utils
 
 
 if TYPE_CHECKING:
@@ -240,8 +239,8 @@ def remove_overlapping_clusters(clusters: Set) -> Set:
 def name_clusters(clusters: Set) -> Dict:
     """Gives each cluster a unique name.
 
-    Note: the first 52 names will be from A - Z, and a - z. If more than 52 clusters are
-    present, these will get names with two uppercase letters.
+    Note: the first 26 names will be from A - Z. If more than 26 clusters are present,
+    these will get names with two uppercase letters (AA - ZZ).
 
     Args:
         clusters: A set of different clusters. Each element is a list of clusters and
@@ -358,15 +357,14 @@ def _rename_datasets(
         A list of the input clustered data, with the labels renamed.
     """
     renamed = []
-    for split, _rgdr in enumerate(rgdr_list):
+    for split, _ in enumerate(rgdr_list):
         # A copy is required to not modify labels of the input data
         data = copy(clustered_data[split])
-        labels = data["cluster_labels"].values
-        i_interval = str(_rgdr.cluster_map["i_interval"].values)  # type: ignore
+        labels = data["cluster_labels"].values.astype("U2")
         for cl in renaming_dict[split]:
-            if f"i_interval:{i_interval}_cluster:{cl[0]}" not in labels:
+            if f"{cl[0]}" not in labels:
                 break
-            labels[labels == f"i_interval:{i_interval}_cluster:{cl[0]}"] = cl[1]
+            labels[labels == f"{cl[0]}"] = cl[1]
         data["cluster_labels"] = labels
         renamed.append(data)
     return renamed
@@ -375,7 +373,7 @@ def _rename_datasets(
 def rename_labels(
     rgdr_list: List["RGDR"], clustered_data: List[xr.DataArray]
 ) -> List[xr.DataArray]:
-    """Renames labels of clustered data over different splits to have similar names.
+    """Returns a new object with renamed cluster labels aligned over different splits.
 
     To aid in users comparing the clustering over different splits, this function tries
     to match the clusters over different splits, and give clusters that are in the same
@@ -392,11 +390,10 @@ def rename_labels(
     """
     n_splits = len(rgdr_list)
     cluster_maps = [
-        utils.cluster_labels_to_ints(
-            rgdr_list[split].cluster_map.reset_coords()  # type: ignore
-        )
+        rgdr_list[split].cluster_map.reset_coords()  # type: ignore
         for split in range(n_splits)
     ]
+
     cluster_map_ds = xr.concat(cluster_maps, dim="split")
 
     clusters = get_overlapping_clusters(cluster_map_ds["cluster_labels"])
diff --git a/s2spy/rgdr/rgdr.py b/s2spy/rgdr/rgdr.py
index 5e85c22..ee676bb 100644
--- a/s2spy/rgdr/rgdr.py
+++ b/s2spy/rgdr/rgdr.py
@@ -1,14 +1,15 @@
 """Response Guided Dimensionality Reduction."""
 import warnings
+from os import linesep
 from typing import List
 from typing import Optional
 from typing import Tuple
-from typing import Type
 from typing import TypeVar
-import matplotlib as mpl
+from typing import Union
 import matplotlib.pyplot as plt
 import numpy as np
 import xarray as xr
+from matplotlib.collections import QuadMesh
 from scipy.stats import pearsonr as _pearsonr
 from sklearn.cluster import DBSCAN
 from . import utils
@@ -80,7 +81,7 @@ def remove_small_area_clusters(ds: XrType, min_area_km2: float) -> XrType:
     valid_clusters = np.array([c for c, a in zip(clusters, areas) if a > min_area_km2])
 
     ds["cluster_labels"] = ds["cluster_labels"].where(
-        np.isin(ds["cluster_labels"], valid_clusters), "0"
+        np.isin(ds["cluster_labels"], valid_clusters), 0
     )
 
     return ds
@@ -106,42 +107,29 @@ def add_gridcell_area(data: xr.DataArray):
 def assert_clusters_present(data: xr.DataArray) -> None:
     """Asserts that any (non-'0') clusters are present in the data."""
 
-    if "i_interval" in data.dims:
-        n_clusters = np.zeros(data["i_interval"].size)
-        for i, _ in enumerate(n_clusters):
-            n_clusters[i] = np.unique(data.sel(i_interval=data["i_interval"][i]).cluster_labels).size
-
-        if np.any(n_clusters == 1):  # A single cluster is the '0' (leftovers) cluster.
-            empty_lags = data["i_interval"].values[n_clusters == 1]
-            warnings.warn(
-                f"No significant clusters found in i_interval(s): i_interval={empty_lags}."
-            )
-
-    elif np.unique(data.cluster_labels).size == 1:
+    if np.unique(data.cluster_labels).size == 1:
         warnings.warn("No significant clusters found in the input DataArray")
 
 
 def _get_dbscan_clusters(
-    data: xr.Dataset, coords: np.ndarray, i_interval: int, dbscan_params: dict
+    data: xr.Dataset, coords: np.ndarray, dbscan_params: dict
 ) -> np.ndarray:
     """Generates the DBSCAN cluster labels based on the correlation and p-value.
 
         Args:
-        data (xr.DataArray): DataArray of the precursor field, of only a single
+        data: DataArray of the precursor field, of only a single
              i_interval. Requires the 'latitude' and 'longitude' dimensions to be stacked
              into a "coords" dimension.
-        coords (np.ndarray): 2-D array containing the coordinates of each (lat, lon) grid
+        coords: 2-D array containing the coordinates of each (lat, lon) grid
             point, in radians.
-        i_interval (int): The i_interval value of the input data.
-        dbscan_params (dict): Dictionary containing the elements 'alpha', 'eps',
+        dbscan_params: Dictionary containing the elements 'alpha', 'eps',
             'min_area_km2'. See the documentation of RGDR for more information.
 
     Returns:
         np.ndarray: 1-D array of the same length as `coords`, containing cluster labels
             for every coordinate."""
 
-    labels = np.zeros(len(coords), dtype="<U32")
-    labels[:] = "0"
+    labels = np.zeros(len(coords), dtype=int)
 
     for sign, sign_mask in zip([1, -1], [data["corr"] >= 0, data["corr"] < 0]):
         mask = np.logical_and(data["p_val"] < dbscan_params["alpha"], sign_mask)
@@ -154,8 +142,7 @@ def _get_dbscan_clusters(
                 metric="haversine",
             ).fit(coords[mask])
 
-            cluster_labels = sign * (db.labels_ + 1)
-            labels[mask] = [f"i_interval:{i_interval}_cluster:{int(lbl)}" for lbl in cluster_labels]
+            labels[mask] = sign * (db.labels_ + 1)
 
     return labels
 
@@ -187,27 +174,15 @@ def _find_clusters(
     data = precursor.to_dataset()
     data["corr"], data["p_val"] = corr, p_val  # Will require less tracking of indices
 
-    if "i_interval" not in data.dims:
-        data = data.expand_dims("i_interval")
-    i_intervals = data["i_interval"].values
-
     data = data.stack(coord=["latitude", "longitude"])
     coords = np.asarray(data["coord"].values.tolist())
     coords = np.radians(coords)
 
-    # Prepare labels, default value is 0 (not in cluster)
-    labels = np.zeros((len(i_intervals), len(coords)), dtype="<U32")
-
-    for i, i_interval in enumerate(i_intervals):
-        labels[i] = _get_dbscan_clusters(
-            data.sel(i_interval=i_interval), coords, i_interval, dbscan_params
-        )
+    labels = _get_dbscan_clusters(data, coords, dbscan_params)
 
     precursor = precursor.stack(coord=["latitude", "longitude"])
-    if "i_interval" not in precursor.dims:
-        precursor["cluster_labels"] = ("coord", labels[0])
-    else:
-        precursor["cluster_labels"] = (("i_interval", "coord"), labels)
+    precursor["cluster_labels"] = ("coord", labels)
+    precursor["cluster_labels"] = precursor["cluster_labels"].astype("int16")
     precursor = precursor.unstack(("coord"))
 
     return precursor
@@ -240,13 +215,11 @@ def masked_spherical_dbscan(
         xr.DataArray: Precursor data grouped by the DBSCAN clusters.
     """
     precursor = add_gridcell_area(precursor)
-
     precursor = _find_clusters(precursor, corr, p_val, dbscan_params)
 
     if dbscan_params["min_area"] is not None:
         precursor = remove_small_area_clusters(precursor, dbscan_params["min_area"])
 
-    # Make sure a cluster is present in each i_interval
     assert_clusters_present(precursor)
 
     return precursor
@@ -323,11 +296,31 @@ def regression(field, target):
     raise NotImplementedError
 
 
+def stack_input_data(precursor, target, precursor_intervals, target_intervals):
+    target = target.sel(i_interval=target_intervals).stack(
+        anch_int=["anchor_year", "i_interval"]
+    )
+    precursor = precursor.sel(i_interval=precursor_intervals).stack(
+        anch_int=["anchor_year", "i_interval"]
+    )
+
+    precursor = precursor.drop_vars({"anchor_year", "anch_int", "i_interval"})
+    target = target.drop_vars({"anchor_year", "anch_int", "i_interval"})
+
+    precursor["anch_int"] = range(precursor["anch_int"].size)
+    target["anch_int"] = range(target["anch_int"].size)
+
+    return precursor, target
+
+
 class RGDR:
     """Response Guided Dimensionality Reduction."""
 
+    # pylint: disable=too-many-arguments
     def __init__(
         self,
+        target_intervals: Union[int, List[int]],
+        lag: int,
         eps_km: float,
         alpha: float,
         min_area_km2: Optional[float] = None,
@@ -338,6 +331,13 @@ def __init__(
         target timeseries.
 
         Args:
+            target_intervals: The target interval indices which should be correlated
+                with the precursors. Input in the form of "[1, 2, 3]" or "1"
+                The precursor intervals will be determined based on the `lag` kwarg.
+            lag: The lag between the precursor and target intervals to compute the
+                correlation, akin to lag in cross-correlation. E.g. if the target
+                intervals are [1, 2], and lag is 2, the precursor intervals will be
+                [-2, -1]
             alpha (float): p-value below which the correlation is considered significant
                 enough for a location to be included in a cluster.
             eps_km (float): The maximum distance (in km) between two grid cells for them
@@ -355,28 +355,72 @@ def __init__(
                 smaller than this minimum area will be discarded.
 
         Attributes:
-            corr_map (float): correlation coefficient map of given precursor field and
+            corr_map: correlation coefficient map of given precursor field and
                 target series.
-            pval_map (float): p-values map of correlation
-            cluster_map (U32): cluster labels for precursor field masked by p-values
+            pval_map: p-values map of correlation
+            cluster_map: cluster labels for precursor field masked by p-values
         """
-        self.corr_map: xr.DataArray
-        self.pval_map: xr.DataArray
-        self.cluster_map = None
-        self._area = None
+        self._lag = lag
         self._dbscan_params = {"eps": eps_km, "alpha": alpha, "min_area": min_area_km2}
 
+        self._target_intervals = (
+            [target_intervals]
+            if isinstance(target_intervals, int)
+            else target_intervals
+        )
+        self._precursor_intervals = utils.intervals_subtract(
+            self._target_intervals, lag
+        )
+
+        self._corr_map: Union[None, xr.DataArray] = None
+        self._pval_map: Union[None, xr.DataArray] = None
+        self._cluster_map: Union[None, xr.DataArray] = None
+
+        self._area = None
+
+    @property
+    def target_intervals(self) -> List[int]:
+        return self._target_intervals
+
+    @property
+    def precursor_intervals(self) -> List[int]:
+        return self._precursor_intervals
+
+    @property
+    def cluster_map(self) -> xr.DataArray:
+        if self._cluster_map is None:
+            raise ValueError(
+                "No cluster map exists yet, .fit() has to be called first."
+            )
+        return self._cluster_map
+
+    @property
+    def pval_map(self) -> xr.DataArray:
+        if self._pval_map is None:
+            raise ValueError(
+                "No p-value map exists yet, .fit() has to be called first."
+            )
+        return self._pval_map
+
+    @property
+    def corr_map(self) -> xr.DataArray:
+        if self._corr_map is None:
+            raise ValueError(
+                "No correlation map exists yet, .fit() has to be called first."
+            )
+        return self._corr_map
+
     def get_correlation(
         self,
         precursor: xr.DataArray,
-        timeseries: xr.DataArray,
+        target: xr.DataArray,
     ) -> Tuple[xr.DataArray, xr.DataArray]:
-        """Calculates the correlation and p-value between input precursor and timeseries.
+        """Calculates the correlation and p-value between input precursor and target.
 
         Args:
             precursor: Precursor field data with the dimensions
                 'latitude', 'longitude', and 'anchor_year'
-            timeseries: Timeseries data with only the dimension 'anchor_year'
+            target: Timeseries data with only the dimension 'anchor_year'
 
         Returns:
             (correlation, p_value): DataArrays containing the correlation and p-value.
@@ -384,62 +428,59 @@ def get_correlation(
         if not isinstance(precursor, xr.DataArray):
             raise ValueError("Please provide an xr.DataArray, not a dataset")
 
-        return correlation(precursor, timeseries, corr_dim="anchor_year")
+        p, t = stack_input_data(
+            precursor, target, self._precursor_intervals, self._target_intervals
+        )
+
+        return correlation(p, t, corr_dim="anch_int")
 
     def get_clusters(
         self,
         precursor: xr.DataArray,
-        timeseries: xr.DataArray,
+        target: xr.DataArray,
     ) -> xr.DataArray:
         """Generates clusters for the precursor data.
 
         Args:
             precursor: Precursor field data with the dimensions
-                'latitude', 'longitude', and 'anchor_year'
-            timeseries: Timeseries data with only the dimension 'anchor_year'
+                'latitude', 'longitude', 'anchor_year', and 'i_interval'
+            target: Target timeseries data with only the dimensions 'anchor_year' and
+                'i_interval'
 
         Returns:
             DataArray containing the clusters as masks.
         """
-        corr, p_val = self.get_correlation(precursor, timeseries)
+        corr, p_val = self.get_correlation(precursor, target)
         return masked_spherical_dbscan(precursor, corr, p_val, self._dbscan_params)
 
-    def preview_correlation(  # pylint: disable=too-many-arguments
+    def preview_correlation(
         self,
         precursor: xr.DataArray,
-        timeseries: xr.DataArray,
-        i_interval: Optional[int] = None,
+        target: xr.DataArray,
+        add_alpha_hatch: bool = True,
         ax1: Optional[plt.Axes] = None,
         ax2: Optional[plt.Axes] = None,
-    ) -> List[Type[mpl.collections.QuadMesh]]:
+    ) -> List[QuadMesh]:
         """Generates a figure showing the correlation and p-value results with the
         initiated RGDR class and input precursor field.
 
         Args:
             precursor: Precursor field data with the dimensions
-                'latitude', 'longitude', and 'anchor_year'
-            timeseries: Timeseries data with only the dimension 'anchor_year'
-            i_interval: The i_interval which should be plotted. Required if the precursor
-                has the dimension "i_interval".
-            ax1: a matplotlib axis handle to plot
-                the correlation values into. If None, an axis handle will be created
-                instead.
-            ax2: a matplotlib axis handle to plot
-                the p-values into. If None, an axis handle will be created instead.
+                'latitude', 'longitude', 'anchor_year', and 'i_interval'
+            target: Target timeseries data with only the dimensions 'anchor_year' and
+                'i_interval'
+            add_alpha_hatch: Adds a red hatching when the p-value is lower than the
+                RGDR's 'alpha' value.
+            ax1: a matplotlib axis handle to plot the correlation values into.
+                If None, an axis handle will be created instead.
+            ax2: a matplotlib axis handle to plot the p-values into. If None, an axis
+                handle will be created instead.
 
         Returns:
-            List[mpl.collections.QuadMesh]: List of matplotlib artists.
+            List of matplotlib QuadMesh artists.
         """
 
-        if "i_interval" in precursor.dims:
-            if i_interval is None:
-                raise ValueError(
-                    "Precursor contains multiple intervals, please provide"
-                    " the i_interval which should be plotted."
-                )
-            precursor = precursor.sel(i_interval=i_interval)
-
-        corr, p_val = self.get_correlation(precursor, timeseries)
+        corr, p_val = self.get_correlation(precursor, target)
 
         if (ax1 is None) and (ax2 is None):
             _, (ax1, ax2) = plt.subplots(ncols=2)
@@ -448,8 +489,19 @@ def preview_correlation(  # pylint: disable=too-many-arguments
                 "Either pass axis handles for both ax1 and ax2, or pass neither."
             )
 
-        plot1 = corr.plot.pcolormesh(ax=ax1, cmap="viridis")  # type: ignore
-        plot2 = p_val.plot.pcolormesh(ax=ax2, cmap="viridis")  # type: ignore
+        plot1 = corr.plot.pcolormesh(ax=ax1, cmap="coolwarm")  # type: ignore
+        plot2 = p_val.plot.pcolormesh(ax=ax2, cmap="viridis_r", vmin=0, vmax=1)  # type: ignore
+
+        if add_alpha_hatch:
+            coords = plot2.get_coordinates()
+            plt.rcParams["hatch.color"] = "r"
+            plt.pcolor(
+                coords[:, :, 0],
+                coords[:, :, 1],
+                p_val.where(p_val < self._dbscan_params["alpha"]).values,
+                hatch="x",
+                alpha=0.0,
+            )
 
         ax1.set_title("correlation")
         ax2.set_title("p-value")
@@ -459,44 +511,34 @@ def preview_correlation(  # pylint: disable=too-many-arguments
     def preview_clusters(
         self,
         precursor: xr.DataArray,
-        timeseries: xr.DataArray,
-        i_interval: Optional[int] = None,
+        target: xr.DataArray,
         ax: Optional[plt.Axes] = None,
-        **kwargs
-    ) -> Type[mpl.collections.QuadMesh]:
+        **kwargs,
+    ) -> QuadMesh:
         """Generates a figure showing the clusters resulting from the initiated RGDR
         class and input precursor field.
 
         Args:
             precursor: Precursor field data with the dimensions
-                'latitude', 'longitude', and 'anchor_year'
-            timeseries: Timeseries data with only the dimension 'anchor_year'
-            i_interval: The i_interval which should be plotted. Required if the precursor
-                has the dimension "i_interval".
+                'latitude', 'longitude', 'anchor_year', and 'i_interval'
+            target: Target timeseries data with only the dimensions 'anchor_year' and
+                'i_interval'
             ax (plt.Axes, optional): a matplotlib axis handle to plot the clusters
                 into. If None, an axis handle will be created instead.
 
         Returns:
-            matplotlib.collections.QuadMesh: Matplotlib artist.
+            Matplotlib QuadMesh artist.
         """
         if ax is None:
             _, ax = plt.subplots()
 
-        clusters = self.get_clusters(precursor, timeseries)
+        clusters = self.get_clusters(precursor, target)
 
-        if "i_interval" in precursor.dims:
-            if i_interval is None:
-                raise ValueError(
-                    "Precursor contains multiple intervals, please provide"
-                    " the i_interval which should be plotted."
-                )
-            clusters = clusters.sel(i_interval=i_interval)
+        return clusters["cluster_labels"].plot(
+            cmap="viridis", ax=ax, **kwargs
+        )  # type: ignore
 
-        clusters = utils.cluster_labels_to_ints(clusters)
-
-        return clusters["cluster_labels"].plot(cmap="viridis", ax=ax, **kwargs)
-
-    def fit(self, precursor: xr.DataArray, timeseries: xr.DataArray):
+    def fit(self, precursor: xr.DataArray, target: xr.DataArray):
         """Fits RGDR clusters to precursor data.
 
         Performs DBSCAN clustering on a prepared DataArray, and then groups the data by
@@ -513,28 +555,26 @@ def fit(self, precursor: xr.DataArray, timeseries: xr.DataArray):
         the label '0'.
 
         Args:
-            precursor: Precursor field data with the dimensions 'latitude', 'longitude',
-                and 'anchor_year'
-            timeseries: Timeseries data with only the dimension 'anchor_year', which
-                will be correlated with the precursor field.
+            precursor: Precursor field data with the dimensions
+                'latitude', 'longitude', 'anchor_year', and 'i_interval'
+            target: Target timeseries data with only the dimensions 'anchor_year' and
+                'i_interval', which will be correlated with the precursor field.
 
         Returns:
             xr.DataArray: The precursor data, with the latitute and longitude dimensions
                 reduced to clusters.
         """
 
-        corr, p_val = correlation(precursor, timeseries, corr_dim="anchor_year")
+        corr, p_val = self.get_correlation(precursor, target)
 
         masked_data = masked_spherical_dbscan(
             precursor, corr, p_val, self._dbscan_params
         )
-        self.corr_map = corr
-        self.pval_map = p_val
-        self.cluster_map = masked_data.cluster_labels
+        self._corr_map = corr
+        self._pval_map = p_val
+        self._cluster_map = masked_data.cluster_labels
         self._area = masked_data.area
 
-        return self
-
     def transform(self, data: xr.DataArray) -> xr.DataArray:
         """Apply RGDR on the input data, based on the previous fit.
 
@@ -546,6 +586,7 @@ def transform(self, data: xr.DataArray) -> xr.DataArray:
             raise ValueError(
                 "Transform requires the model to be fit on other data first"
             )
+        data = data.sel(i_interval=self._precursor_intervals)
         data["cluster_labels"] = self.cluster_map
         data["area"] = self._area
 
@@ -556,11 +597,15 @@ def transform(self, data: xr.DataArray) -> xr.DataArray:
         # Add the geographical centers for later alignment between, e.g., splits
         reduced_data = utils.geographical_cluster_center(data, reduced_data)
         # Include explanations about geographical centers as attributes
-        reduced_data.attrs['data'] = "Clustered data with Response Guided Dimensionality Reduction."
-        reduced_data.attrs['coordinates'] = "Latitudes and longitudes are geographical centers associated with clusters."
+        reduced_data.attrs[
+            "data"
+        ] = "Clustered data with Response Guided Dimensionality Reduction."
+        reduced_data.attrs[
+            "coordinates"
+        ] = "Latitudes and longitudes are geographical centers associated with clusters."
 
         # Remove the '0' cluster
-        reduced_data = reduced_data.where(reduced_data["cluster_labels"] != "0").dropna(
+        reduced_data = reduced_data.where(reduced_data["cluster_labels"] != 0).dropna(
             dim="cluster_labels"
         )
 
@@ -582,3 +627,18 @@ def fit_transform(self, precursor: xr.DataArray, timeseries: xr.DataArray):
 
         self.fit(precursor, timeseries)
         return self.transform(precursor)
+
+    def __repr__(self) -> str:
+        """String representation of the RGDR transformer."""
+        props = [
+            ("target_intervals", repr(self.target_intervals)),
+            ("lag", repr(self._lag)),
+            ("eps_km", repr(self._dbscan_params["eps"])),
+            ("alpha", repr(self._dbscan_params["alpha"])),
+            ("min_area_km2", repr(self._dbscan_params["min_area"])),
+        ]
+
+        propstr = f"{linesep}\t" + f",{linesep}\t".join(
+            [f"{k}={v}" for k, v in props]
+        )  # sourcery skip: use-fstring-for-concatenation
+        return f"{self.__class__.__name__}({propstr}{linesep})".replace("\t", "    ")
diff --git a/s2spy/rgdr/utils.py b/s2spy/rgdr/utils.py
index b06dd61..b7c7ee8 100644
--- a/s2spy/rgdr/utils.py
+++ b/s2spy/rgdr/utils.py
@@ -1,3 +1,4 @@
+from typing import List
 from typing import TypeVar
 import numpy as np
 import xarray as xr
@@ -14,12 +15,13 @@ def weighted_groupby(
     (https://github.com/pydata/xarray/pull/5480), but this branch was never merged.
 
     Args:
-        ds (xr.DataArray or xr.Dataset): Data containing the coordinates or variables
+        ds: Data containing the coordinates or variables
             specified in the `groupby` and `weight` kwargs.
-        groupby (str): Coordinate which should be used to make the groups.
-        weight (str): Variable in the Dataset containing the weights that should be used.
-        method (str): Method that should be used to reduce the dataset, by default
-            'mean'. Supports any of xarray's builtin methods, e.g. median, min, max.
+        groupby: Coordinate which should be used to make the groups.
+        weight: Variable in the Dataset containing the weights that should be used.
+        method: Method that should be used to reduce the dataset, by default
+            'mean'. Supports any of xarray's builtin methods, e.g. 'median', 'min',
+            'max'.
 
     Returns:
         Same as input: Dataset reduced using the `groupby` coordinate, using weights =
@@ -30,15 +32,13 @@ def weighted_groupby(
     # find stacked dim name
     group0 = list(groups)[0][1]
     dims = list(group0.dims)
-    stacked_dims = [
-        dim for dim in dims if "stacked_" in str(dim)
-    ]  # str() is just for mypy
+    stacked_dims = [dim for dim in dims if "stacked_" in str(dim)]
 
     reduced_groups = [
         getattr(g.weighted(g[weight]), method)(dim=stacked_dims) for _, g in groups
     ]
-    reduced_data: XrType
-    reduced_data = xr.concat(reduced_groups, dim=groupby)
+
+    reduced_data: XrType = xr.concat(reduced_groups, dim=groupby)
 
     if isinstance(reduced_data, xr.DataArray):  # Add back the labels of the groupby dim
         reduced_data[groupby] = np.unique(ds[groupby])
@@ -73,16 +73,12 @@ def geographical_cluster_center(
         )
 
         if "i_interval" in cluster_area.dims:
-            cluster_area = cluster_area.dropna('i_interval', how='all')
+            cluster_area = cluster_area.dropna("i_interval", how="all")
         cluster_area = cluster_area.dropna("coords")
 
         # Area weighted mean to get the geographical center of the cluster
-        cluster_lats[i] = (
-            cluster_area["latitude"].weighted(cluster_area).mean().item()
-        )
-        cluster_lons[i] = (
-            cluster_area["longitude"].weighted(cluster_area).mean().item()
-        )
+        cluster_lats[i] = cluster_area["latitude"].weighted(cluster_area).mean().item()
+        cluster_lons[i] = cluster_area["longitude"].weighted(cluster_area).mean().item()
 
     reduced_data["latitude"] = ("cluster_labels", cluster_lats)
     reduced_data["longitude"] = ("cluster_labels", cluster_lons)
@@ -90,23 +86,13 @@ def geographical_cluster_center(
     return reduced_data
 
 
-def cluster_labels_to_ints(clustered_data: xr.DataArray) -> xr.DataArray:
-    """Converts the labels of already clustered data to integers.
-
-    Args:
-        clustered_data: Data already clustered and grouped by cluster.
-
-    Returns:
-        Same as input, but with the labels converted to integers
-    """
-    un_labels = np.unique(clustered_data.cluster_labels)
-    label_vals = [int(lb[-2:].replace(":","")) for lb in un_labels]
-    label_lookup = dict(zip(un_labels, label_vals))
+def intervals_subtract(intervals: List[int], n: int) -> List[int]:
+    """Subtracts n from the interval indices, skipping 0."""
+    if n < 0:
+        raise ValueError("Lag values below 0 are not supported")
 
-    clustered_data['cluster_labels'] = xr.apply_ufunc(
-        lambda val: label_lookup[val],
-        clustered_data['cluster_labels'],
-        vectorize=True
-    )
-
-    return clustered_data
+    lag_intervals = [i - n for i in intervals]
+    # pylint: disable=chained-comparison
+    return [
+        i - 1 if (i <= 0 and j > 0) else i for i, j in zip(lag_intervals, intervals)
+    ]


Test patch:
diff --git a/tests/test_rgdr/test_alignment.py b/tests/test_rgdr/test_alignment.py
index 8cff79a..69bd451 100644
--- a/tests/test_rgdr/test_alignment.py
+++ b/tests/test_rgdr/test_alignment.py
@@ -1,5 +1,4 @@
 import numpy as np
-import pandas as pd
 import pytest
 import xarray as xr
 from sklearn.model_selection import ShuffleSplit
@@ -138,14 +137,13 @@ def raw_field():
 @pytest.fixture(autouse=True, scope="class")
 def example_field(raw_field, dummy_calendar):
     cal = dummy_calendar.map_to_data(raw_field)
-    return resample(cal, raw_field).sst.sel(i_interval=-5)
+    return resample(cal, raw_field).sst
 
 
 @pytest.fixture(autouse=True, scope="class")
 def example_target(raw_target, raw_field, dummy_calendar):
     cal = dummy_calendar.map_to_data(raw_field)
-    return resample(cal, raw_target).ts.sel(i_interval=1)
-
+    return resample(cal, raw_target).ts
 
 def test_alignment_example(example_field, example_target):
     seed = 1 #same 'randomness'
@@ -154,11 +152,19 @@ def test_alignment_example(example_field, example_target):
     shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state = seed)
     cv = s2spy.traintest.TrainTestSplit(shufflesplit)
 
-    rgdrs = [RGDR(eps_km=800, alpha=0.10, min_area_km2=0) for _ in range(n_splits)]
+    rgdrs = [
+        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, x_test, y_train, y_test in cv.split(example_field, y=example_target):
+    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))
         precursor_field_splits.append(
@@ -174,7 +180,7 @@ def test_alignment_example(example_field, example_target):
         clustered_precursors
         )
 
-    expected = [['A'], ['A', 'C'], ['A'], ['B', 'A']]
+    expected = [['A'], ['A', 'C'], ['A'], ['A', 'B']]
     clusters_list = [list(el.cluster_labels.values) for el in aligned_precursors]
-    print(clusters_list)
+
     assert expected == clusters_list
diff --git a/tests/test_rgdr/test_rgdr.py b/tests/test_rgdr/test_rgdr.py
index c396ae7..5a9170e 100644
--- a/tests/test_rgdr/test_rgdr.py
+++ b/tests/test_rgdr/test_rgdr.py
@@ -8,7 +8,7 @@
 from s2spy import RGDR
 from s2spy.rgdr import rgdr
 from s2spy.rgdr import utils
-from s2spy.time import AdventCalendar
+from s2spy.time import Calendar
 from s2spy.time import resample
 
 
@@ -24,7 +24,10 @@
 
 @pytest.fixture(autouse=True, scope="class")
 def dummy_calendar():
-    return AdventCalendar(anchor="8-2", freq="30d")
+    cal = Calendar(anchor="08-01")
+    cal.add_intervals("target", length="1M", n=4)
+    cal.add_intervals("precursor", length="1M", n=4)
+    return cal
 
 
 @pytest.fixture(autouse=True, scope="module")
@@ -40,37 +43,35 @@ def raw_field():
 
 
 @pytest.fixture(autouse=True, scope="class")
-def example_field(raw_field, dummy_calendar):
+def example_precursor_field(raw_field, dummy_calendar):
     cal = dummy_calendar.map_to_data(raw_field)
-    return resample(cal, raw_field).sst.sel(i_interval=-1)
+    return resample(cal, raw_field).sst
 
 
 @pytest.fixture(autouse=True, scope="class")
-def example_field_multiple_lags(raw_field, dummy_calendar):
+def example_target_timeseries(raw_target, raw_field, dummy_calendar):
     cal = dummy_calendar.map_to_data(raw_field)
-    return resample(cal, raw_field).sst.sel(i_interval=slice(-3, -1))
-
-
-@pytest.fixture(autouse=True, scope="class")
-def example_target(raw_target, raw_field, dummy_calendar):
-    cal = dummy_calendar.map_to_data(raw_field)
-    return resample(cal, raw_target).ts.sel(i_interval=1)
+    return resample(cal, raw_target).ts
 
 
 @pytest.fixture(scope="class")
-def example_corr(example_target, example_field):
-    return rgdr.correlation(example_field, example_target, corr_dim="anchor_year")
+def example_corr(example_target_timeseries, example_precursor_field):
+    return rgdr.correlation(
+        example_precursor_field.sel(i_interval=-4),
+        example_target_timeseries.sel(i_interval=1),
+        corr_dim="anchor_year"
+    )
 
 
 @pytest.fixture(autouse=True, scope="class")
 def expected_labels():
     return np.array(
         [
-            [0.0, 0.0, 0.0, 0.0, -1.0, -1.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0],
-            [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, 0.0, 0.0],
-            [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, -2.0, -2.0],
+            [ 0,  0,  0, -1, -1,  0,  0, -2,  0,  0,  0,  0,  0],
+            [ 0,  0,  0,  0,  0,  0, -2, -2, -2, -2, -2,  0,  0],
+            [ 0,  0,  0,  0,  0,  0,  0, -2, -2, -2, -2,  0,  0],
+            [ 0,  0,  0,  0,  0,  0,  0,  0,  0, -2,  0,  0,  0],
+            [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
         ]
     )
 
@@ -78,18 +79,32 @@ def expected_labels():
 class TestUtils:
     """Validates the various utility functions used in RGDR"""
 
-    testdata = [ # lat, dlat, dlon, area
+    testdata_spherical_area = [ # lat, dlat, dlon, area
         (-1, 2, 2, 49500),
         (45, 5, 5, 218500),
         (45, 5, 2.5, 109250)
     ]
 
-    @pytest.mark.parametrize("lat,dlat,dlon,expected", testdata)
+    @pytest.mark.parametrize("lat, dlat, dlon, expected", testdata_spherical_area)
     def test_spherical_area(self, lat, dlat, dlon, expected):
         np.testing.assert_allclose(
             rgdr.spherical_area(lat, dlat, dlon), expected, rtol=0.05
             )
 
+    testdata_intervals = [ # target_intervals, lag, precursor_intervals
+        ([2], 1, [1]),
+        ([1], 1, [-1]),
+        ([-1], 1, [-2]),
+        ([1, 2, 3, 4], 4, [-4, -3, -2, -1]),
+    ]
+    @pytest.mark.parametrize("intervals, lag, expected", testdata_intervals)
+    def test_intervals_subtract(self, intervals, lag, expected):
+        assert utils.intervals_subtract(intervals, lag) == expected
+
+    def test_intervals_subtract_neg(self):
+        with pytest.raises(ValueError):
+            utils.intervals_subtract([1], -1)
+
 
 class TestCorrelation:
     """Validates that the correlation ufunc works, as well as the input checks."""
@@ -113,12 +128,12 @@ def dummy_timeseries(self, dummy_dataarray):
         return dummy_dataarray.isel(lat=0, lon=0).drop_vars(["lat", "lon"])
 
     def test_pearsonr(self):
-        result = rgdr._pearsonr_nan([0, 0, 1], [0, 1, 1])
+        result = rgdr._pearsonr_nan([0, 0, 1], [0, 1, 1])  # type: ignore
         expected = (0.5, 0.66666667)
         np.testing.assert_array_almost_equal(result, expected)
 
     def test_pearsonr_nan(self):
-        result = rgdr._pearsonr_nan([np.nan, 0, 1], [0, 1, 1])
+        result = rgdr._pearsonr_nan([np.nan, 0, 1], [0, 1, 1])  # type: ignore
         expected = (np.nan, np.nan)
         np.testing.assert_array_almost_equal(result, expected)
 
@@ -165,124 +180,130 @@ class TestDBSCAN:
 
     @pytest.fixture(autouse=True)
     def dummy_dbscan_params(self):
-        return {"alpha": 0.05, "eps": 600, "min_area": None}
+        return {"alpha": 0.025, "eps": 600, "min_area": None}
 
     def test_dbscan(
-        self, example_corr, example_field, dummy_dbscan_params, expected_labels
+        self, example_corr, example_precursor_field, dummy_dbscan_params, expected_labels
     ):
+
         corr, p_val = example_corr
 
         clusters = rgdr.masked_spherical_dbscan(
-            example_field, corr, p_val, dummy_dbscan_params
+            example_precursor_field, corr, p_val, dummy_dbscan_params
         )
-        clusters = utils.cluster_labels_to_ints(clusters)
 
         np.testing.assert_array_equal(clusters["cluster_labels"], expected_labels)
 
     def test_dbscan_min_area(
-        self, example_corr, example_field, dummy_dbscan_params, expected_labels
+        self, example_corr, example_precursor_field, dummy_dbscan_params, expected_labels
     ):
         corr, p_val = example_corr
         dbscan_params = dummy_dbscan_params
         dbscan_params["min_area"] = 1000**2
         clusters = rgdr.masked_spherical_dbscan(
-            example_field, corr, p_val, dbscan_params
+            example_precursor_field, corr, p_val, dbscan_params
         )
-        clusters = utils.cluster_labels_to_ints(clusters)
 
         expected_labels[expected_labels == -1] = 0  # Small -1 cluster is missing
 
         np.testing.assert_array_equal(clusters["cluster_labels"], expected_labels)
 
 
+rgdr_test_config = dict(
+    target_intervals=[1],
+    lag=4,
+    eps_km=600,
+    alpha=0.025,
+    min_area_km2=0
+)
+
+
 class TestRGDR:
     """Test RGDR and its methods."""
 
     @pytest.fixture(autouse=True)
     def dummy_rgdr(self):
-        return RGDR(eps_km=600, alpha=0.05, min_area_km2=1000**2)
+        return RGDR(**rgdr_test_config)  # type: ignore
 
     def test_init(self):
-        rgdr = RGDR(eps_km=600, alpha=0.05, min_area_km2=1000**2)
+        rgdr = RGDR(**rgdr_test_config)  # type: ignore
         assert isinstance(rgdr, RGDR)
 
-    def test_transform_before_fit(self, dummy_rgdr, example_field):
+    def test_target_intervals(self, dummy_rgdr):
+        assert dummy_rgdr.target_intervals == [1]
+
+    def test_precursor_intervals(self, dummy_rgdr):
+        assert dummy_rgdr.precursor_intervals == [-4]
+
+    def test_repr(self, dummy_rgdr):
+        eval(repr(dummy_rgdr))  # pylint: disable=eval-used
+
+    @pytest.mark.parametrize("prop", ("cluster_map", "pval_map", "corr_map"))
+    def test_fitted_properties_err(self, dummy_rgdr, prop):
+        with pytest.raises(ValueError):
+            getattr(dummy_rgdr, prop)
+
+    def test_transform_before_fit(self, dummy_rgdr, example_precursor_field):
         "Should fail as RGDR first has to be fit to (training) data."
         with pytest.raises(ValueError):
-            dummy_rgdr.transform(example_field)
+            dummy_rgdr.transform(example_precursor_field)
 
-    def test_fit(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.fit(example_field, example_target)
+    def test_fit(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
+        dummy_rgdr.fit(example_precursor_field, example_target_timeseries)
         assert dummy_rgdr._area is not None
 
-    def test_transform(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.fit(example_field, example_target)
-        clustered_data = dummy_rgdr.transform(example_field)
-        clustered_data = utils.cluster_labels_to_ints(clustered_data)
-        expected_labels = np.array([-2,  1])
+    def test_transform(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
+        dummy_rgdr.fit(example_precursor_field, example_target_timeseries)
+        clustered_data = dummy_rgdr.transform(example_precursor_field)
+
+        expected_labels = np.array([-2,  -1], dtype='int16')
         np.testing.assert_array_equal(clustered_data["cluster_labels"], expected_labels)
 
-    def test_fit_transform_fits(self, example_field, example_target):
+    def test_fit_transform_fits(self, example_precursor_field, example_target_timeseries):
         # Ensures that after fit_transform, the rgdr object is fit.
-        rgdr = RGDR(eps_km=600, alpha=0.05)
-        _ = rgdr.fit_transform(example_field, example_target)
+        rgdr = RGDR(**rgdr_test_config)  # type: ignore
+        _ = rgdr.fit_transform(example_precursor_field, example_target_timeseries)
         assert rgdr._area is not None
 
-    def test_fit_transform(self, example_field, example_target):
-        rgdr = RGDR(eps_km=600, alpha=0.05, min_area_km2=1000**2)
-        clustered_data = rgdr.fit_transform(example_field, example_target)
+    def test_fit_transform(self, example_precursor_field, example_target_timeseries):
+        rgdr = RGDR(**rgdr_test_config)  # type: ignore
+        clustered_data = rgdr.fit_transform(example_precursor_field, example_target_timeseries)
         expected_labels = np.array(
-            ["i_interval:-1_cluster:-2", "i_interval:-1_cluster:1"]
+            [-2, -1], dtype='int16'
         )
         np.testing.assert_array_equal(clustered_data["cluster_labels"], expected_labels)
 
-    def test_fit_transform_multiple_lags(
-        self, example_field_multiple_lags, example_target
-    ):
-        rgdr = RGDR(eps_km=600, alpha=0.05)
-        clustered_data = rgdr.fit_transform(example_field_multiple_lags, example_target)
-        expected_labels = np.array(
-            [
-                "i_interval:-1_cluster:-1", "i_interval:-1_cluster:-2",
-                "i_interval:-1_cluster:1", "i_interval:-2_cluster:-1",
-                "i_interval:-2_cluster:1", "i_interval:-3_cluster:-1",
-                "i_interval:-3_cluster:1"
-            ]
-        )
-        np.testing.assert_array_equal(clustered_data["cluster_labels"], expected_labels)
+    @pytest.mark.parametrize("prop", ("cluster_map", "pval_map", "corr_map"))
+    def test_fitted_properties(self, dummy_rgdr, example_precursor_field, example_target_timeseries, prop):
+        dummy_rgdr.fit(example_precursor_field, example_target_timeseries)
+        getattr(dummy_rgdr, prop)
 
-    def test_corr_preview(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.preview_correlation(example_field, example_target)
-
-    def test_corr_preview_multiple_lags(
-        self, dummy_rgdr, example_field_multiple_lags, example_target
-        ):
-        dummy_rgdr.preview_correlation(example_field_multiple_lags, example_target, i_interval=-1)
-
-    def test_corr_preview_multiple_lags_fail(
-        self, dummy_rgdr, example_field_multiple_lags, example_target
-    ):
-        with pytest.raises(ValueError):
-            dummy_rgdr.preview_correlation(example_field_multiple_lags, example_target)
+    def test_corr_preview(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
+        dummy_rgdr.preview_correlation(example_precursor_field, example_target_timeseries)
 
-    def test_corr_plot_ax(self, dummy_rgdr, example_field, example_target):
+    def test_corr_plot_ax(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
         _, (ax1, ax2) = plt.subplots(ncols=2)
-        dummy_rgdr.preview_correlation(example_field, example_target, ax1=ax1, ax2=ax2)
+        dummy_rgdr.preview_correlation(example_precursor_field, example_target_timeseries, ax1=ax1, ax2=ax2)
 
-    def test_cluster_plot(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.preview_clusters(example_field, example_target)
+    def test_cluster_plot(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
+        dummy_rgdr.preview_clusters(example_precursor_field, example_target_timeseries)
 
-    def test_cluster_preview_multiple_lags(
-        self, dummy_rgdr, example_field_multiple_lags, example_target
-        ):
-        dummy_rgdr.preview_clusters(example_field_multiple_lags, example_target, i_interval=-1)
+    def test_cluster_plot_ax(self, dummy_rgdr, example_precursor_field, example_target_timeseries):
+        _, ax = plt.subplots()
+        dummy_rgdr.preview_clusters(example_precursor_field, example_target_timeseries, ax=ax)
+
+    def test_multiple_targets(self, example_precursor_field, example_target_timeseries):
+        rgdr = RGDR(
+            target_intervals=[1, 2],
+            lag=4,
+            eps_km=600,
+            alpha=0.025,
+            min_area_km2=0
+            )
 
-    def test_cluster_preview_multiple_lags_fail(
-        self, dummy_rgdr, example_field_multiple_lags, example_target
-    ):
-        with pytest.raises(ValueError):
-            dummy_rgdr.preview_clusters(example_field_multiple_lags, example_target)
+        clustered_data = rgdr.fit_transform(example_precursor_field, example_target_timeseries)
 
-    def test_cluster_plot_ax(self, dummy_rgdr, example_field, example_target):
-        _, ax = plt.subplots()
-        dummy_rgdr.preview_clusters(example_field, example_target, ax=ax)
+        expected_labels = np.array(
+            [-2, -1], dtype='int16'
+        )
+        np.testing.assert_array_equal(clustered_data["cluster_labels"], expected_labels)