From 28add95269c42f7cfca52371dbbbd7e4eef0409b Mon Sep 17 00:00:00 2001 From: Annette Stellema <40450353+stellema@users.noreply.github.com> Date: Mon, 16 Oct 2023 15:38:43 +1000 Subject: [PATCH] Add option to fit data to a time varying GEV (#52) * Update gev_fit to include non-stationary distribution parameters * Update calc_moments and return_curve for compatability with fit_gev * Fix bug in rx5day calculation * Add missing fileio.py package dependencies * Test only latest python * Import all modules when testing * Fix deliberate bugs * Add missing package * Add more missing packages * Add another missing package * Fix linting errors and ignore W503 --------- Co-authored-by: Damien Irving --- .github/workflows/tests.yml | 4 +- .pre-commit-config.yaml | 2 +- ci/environment-3.11.yml | 16 - ci/environment-3.9.yml | 16 - ci/{environment-3.10.yml => environment.yml} | 11 +- docs/user_guide/distribution.png | Bin 93113 -> 112810 bytes docs/user_guide/independence.png | Bin 36617 -> 36563 bytes docs/user_guide/moments.png | Bin 246397 -> 238221 bytes docs/user_guide/return_curve.png | Bin 96877 -> 97768 bytes docs/user_guide/stability.png | Bin 907203 -> 972023 bytes .../worked_example-HadGEM3-GC31-MM.ipynb | 2332 ++++++++--------- docs/user_guide/worked_example.rst | 24 +- unseen/general_utils.py | 197 +- unseen/moments.py | 6 +- unseen/stability.py | 65 +- unseen/tests/conftest.py | 32 +- 16 files changed, 1373 insertions(+), 1332 deletions(-) delete mode 100644 ci/environment-3.11.yml delete mode 100644 ci/environment-3.9.yml rename ci/{environment-3.10.yml => environment.yml} (57%) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index e6f6866..13305fb 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -9,7 +9,6 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest", "windows-latest", "macos-latest"] - python-version: ["3.9", "3.10", "3.11"] steps: - name: Checkout source @@ -19,8 +18,7 @@ jobs: uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" - python-version: ${{ matrix.python-version }} - environment-file: ci/environment-${{ matrix.python-version }}.yml + environment-file: ci/environment.yml activate-environment: unseen-test auto-activate-base: false diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 08b331e..fa5b17f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,4 +18,4 @@ repos: # these are errors that will be ignored by flake8 # check out their meaning here # https://flake8.pycqa.org/en/latest/user/error-codes.html - - "--ignore=E203,C901" + - "--ignore=E203,C901,W503" diff --git a/ci/environment-3.11.yml b/ci/environment-3.11.yml deleted file mode 100644 index 70c888e..0000000 --- a/ci/environment-3.11.yml +++ /dev/null @@ -1,16 +0,0 @@ -name: unseen-test -channels: - - conda-forge -dependencies: - - python=3.11 - - geopandas - - regionmask - - xarray - - dask-core - - numpy - - pytest - - cftime - - pip - - pip: - - codecov - - pytest-cov diff --git a/ci/environment-3.9.yml b/ci/environment-3.9.yml deleted file mode 100644 index b33e628..0000000 --- a/ci/environment-3.9.yml +++ /dev/null @@ -1,16 +0,0 @@ -name: unseen-test -channels: - - conda-forge -dependencies: - - python=3.9 - - geopandas - - regionmask - - xarray - - dask-core - - numpy - - pytest - - cftime - - pip - - pip: - - codecov - - pytest-cov diff --git a/ci/environment-3.10.yml b/ci/environment.yml similarity index 57% rename from ci/environment-3.10.yml rename to ci/environment.yml index d6f06e1..3056f43 100644 --- a/ci/environment-3.10.yml +++ b/ci/environment.yml @@ -2,7 +2,7 @@ name: unseen-test channels: - conda-forge dependencies: - - python=3.10 + - python - geopandas - regionmask - xarray @@ -10,7 +10,16 @@ dependencies: - numpy - pytest - cftime + - gitpython + - cmdline_provenance + - xclim + - xskillscore + - seaborn + - zarr + - netcdf4 + - dask-jobqueue - pip - pip: - codecov - pytest-cov + - xstatstests diff 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  • " ], "text/plain": [ "\n", @@ -902,7 +871,7 @@ " * time (time) object 1900-12-31 00:00:00 ... 2022-12-31 00:00:00" ] }, - "execution_count": 9, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -913,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 20, "id": "0f9a6da7", "metadata": {}, "outputs": [], @@ -926,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "id": "308c5f67", "metadata": {}, "outputs": [ @@ -958,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "id": "ffce4086", "metadata": {}, "outputs": [ @@ -996,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "id": "f8b403d4", "metadata": {}, "outputs": [ @@ -1025,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "id": "55c7cf55", "metadata": {}, "outputs": [ @@ -1052,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "id": "6cb0a095", "metadata": {}, "outputs": [], @@ -1062,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 26, "id": "49cfe7db", "metadata": {}, "outputs": [ @@ -1087,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "id": "86ff8bc7", "metadata": {}, "outputs": [ @@ -1187,658 +1156,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 4, "id": "0e9cc32b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dimensions: (ensemble: 10, init_date: 59, lead_time: 12)\n", - "Coordinates:\n", - " * ensemble (ensemble) int64 0 1 2 3 4 5 6 7 8 9\n", - " * init_date (init_date) object 1960-11-01 00:00:00 ... 2018-11-01 00:00:00\n", - " * lead_time (lead_time) int64 0 1 2 3 4 5 6 7 8 9 10 11\n", - " time (lead_time, init_date) object 1960-11-01 12:00:00 ... 2029-11-...\n", - "Data variables:\n", - " pr (init_date, ensemble, lead_time) float32 nan 41.2 ... 14.04 nan\n", - "Attributes: (12/43)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: DCPP\n", - " branch_method: no parent\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 0.0\n", - " cmor_version: 3.4.0\n", - " ... ...\n", - " table_info: Creation Date:(13 December 2018) MD5:f0588f7f55b5...\n", - " title: HadGEM3-GC31-MM output prepared for CMIP6\n", - " tracking_id: hdl:21.14100/3163965c-a593-4abd-9b2a-9ee755aef228\n", - " variable_id: pr\n", - " variable_name: pr\n", - " variant_label: r1i1p1f2\n" - ] - } - ], - "source": [ - "print(model_ds)" - ] - }, - { - "cell_type": "markdown", - "id": "e7cd216b", - "metadata": {}, - "source": [ - "## Stability and stationarity" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "915b0b30", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stability.create_plot(\n", - " model_ds['pr'],\n", - " 'Rx5day',\n", - " [1960, 1970, 1980, 1990, 2000, 2010],\n", - " outfile='stability.png',\n", - " uncertainty=True,\n", - " return_method='gev',\n", - " units='Rx5day (mm)',\n", - " ylim=(0, 250),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d8ed1c5a", - "metadata": {}, - "source": [ - "## Independence testing" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6fc37e4d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", - "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", - " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" - ] - } - ], - "source": [ - "mean_correlations, null_correlation_bounds = independence.run_tests(model_ds['pr'])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "eb3ddc27", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{11: \n", - "dask.array\n", - "Coordinates:\n", - " * lead_time (lead_time) int64 0 1 2 3 4 5 6 7 8 9 10 11}\n" - ] - } - ], - "source": [ - "print(mean_correlations)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a84c90b1", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/g/data/xv83/dbi599/miniconda3/envs/unseen2/lib/python3.10/site-packages/xskillscore/core/np_deterministic.py:309: RuntimeWarning: invalid value encountered in divide\n", - " r = r_num / r_den\n", - "/g/data/xv83/dbi599/miniconda3/envs/unseen2/lib/python3.10/site-packages/xskillscore/core/np_deterministic.py:309: RuntimeWarning: invalid value encountered in divide\n", - " r = r_num / r_den\n" - ] - }, - { - "data": { - "image/png": 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Lr732mt58802NGjXK2/2gBnKUGNqVdUa5+QWKbBCmbnGNZA0iYAMAAoNHYamoqEg9evTwdi+ogdIPZmvmukPKthc4x2JsYUodEq+BCTE+7AwAgMrxaIH3Qw89pHfffdfbvaCGST+YrXHL97gEJUnKsRdo3PI9Sj+Y7aPOAACoPI9mlgoKCrRo0SJ9/PHH6tixo0JCQlxunzdvnleaQ+BylBiaue6QjApuMyRZJM1cd0j94qM5JQfAb7BsABXxKCzt379fnTt3liQdPHjQ5TYWe0OSdmWdKTej9EuGpGx7gXZlnVFy68bV1xgAuMGyAbjjUVjavHmzt/tADZOb7z4oeVIHAFWpbNnAr2fDy5YNLByZSGCqxS77opTff/+9Tpw44Y1eUINENgjzah0AVJWLLRuQSpcNOEoqqkBt4FFYKikp0axZs2Sz2dSyZUu1aNFCDRs21LPPPlvuApWonbrFNVKMLUzuTspaVDq93S2uUXW2BQDlXMqyAdROHp2Ge+aZZ7RkyRI9//zz6tmzpwzD0LZt2zRjxgwVFBToueee83afCDDWIItSh8Rr3PI9skgu/2IrC1CpQ+JZOAnA51g2gIvxKCy99dZbeuONN3Tbbbc5xzp16qQrr7xSjz76KGEJkqSBCTFaODKx3ILJaBZMAvAjLBvAxXgUls6cOaN27dqVG2/Xrp3OnGGaEv81MCFG/eKj+SguAL9Vtmwgx15Q4boli0r/kceygdrLozVLnTp10oIFC8qNL1iwQJ06dbrsplCzWIMsSm7dWEM7X6nk1o0JSgD8StmyAUnl1lmybACShzNLc+fO1S233KKPP/5YycnJslgs2r59u44fP67169d7u0cAAKoUywZgxmIYhkefhfzhhx/0yiuv6F//+pcMw1B8fLweffRRxcbGertHv5eXlyebzSa73a6IiAhftwMA8BBX8K5dKvv+7XFYwn8RlgAACDyVff+u9Gm4/fv3KyEhQUFBQdq/f79pbceOHSvfKQAAgB+rdFjq3LmzcnJyFBkZqc6dO8tisaiiSSmLxSKHw+HVJgEAAHyl0mEpKytLTZs2df4ZAACgNqh0WGrZsqXzz0ePHlWPHj0UHOy6e3FxsbZv3+5SCwAAEMg8us7SjTfeWOHFJ+12u2688cbLbgoALsZRYmjHkdP6YN8J7Thymi85BVBlPLrOkmEYsljKf5Ty9OnTqlev3mU3BfgjPlLsP9IPZpe7Hk4M18MBUEUuKSzdfvvtkkoXcY8ePVqhoaHO2xwOh/bv368ePXp4t0PAD/Dm7D/SD2Zr3PI95b6WIsdeoHHL92jhyESeEwBedUmn4Ww2m2w2mwzDUIMGDZzbNptN0dHRevjhh7V8+fKq6hXwibI3518GJem/b87pB7N91Fnt4ygxNHPdoQq/v6tsbOa6Q5ySA+BVlzSztGzZMklSq1at9NRTT3HKDTXexd6cLSp9c+4XH80puWqwK+tMudD6S4akbHuBdmWdUXLrxtXXGIAazaMF3qmpqQQl1AqX8uaMqpeb7/658KQOACrDowXekvT+++/rb3/7m44dO6aioiKX2/bs2XPZjQH+gDdn/xLZIMyrdQBQGR7NLL300ku6//77FRkZqb1796pbt25q3Lix/v3vf2vQoEHe7hHwGd6c/Uu3uEaKsYXJ3QlPi0oX3neLa1SdbQGo4TwKS6+++qoWLVqkBQsWqE6dOpo0aZI2btyo8ePHy263e7tHwGd4c/Yv1iCLUofES1K556RsO3VIPOvHAHiVR2Hp2LFjzksEhIeHKz8/X5I0atQorVixwnvdAT7Gm7P/GZgQo4UjExVtc53Ni7aFcdkAAFXCozVL0dHROn36tFq2bKmWLVsqIyNDnTp1UlZWVoVfrgsEsrI3519fZyma6yz5zMCEGPWLj+YioQCqhUdh6aabbtK6deuUmJioBx98UBMmTND777+vL774wnnhSqAm4c3Z/1iDLFweAEC1sBgeTAWVlJSopKTE+UW6f/vb3/TZZ5/p6quv1tixY1WnTh2vN+rP8vLyZLPZZLfbFRER4et2AABAJVT2/dujsARXhCUAAAJPZd+/K30abv/+/ZV+8I4dO1a6FgAAwJ9VOix17txZFovlogu4LRaLHA7HZTcGAADgDyodlrKysqqyDwAAAL9U6bDUsmXLquwDAADAL3l0UUpJevvtt9WzZ0/Fxsbq6NGjkqT58+frgw8+8FpzAAAAvuZRWFq4cKEmTpyowYMH68cff3SuUWrYsKHmz5/vzf4AAAB8yqOw9PLLL2vx4sV65plnZLVaneNdu3bVgQMHvNYcAACAr3kUlrKystSlS5dy46GhoTp37txlN2Xm1VdfVVxcnMLCwpSUlKStW7ea1m/ZskVJSUkKCwvTVVddpddee61czapVqxQfH6/Q0FDFx8drzZo1VdU+AAAIMB6Fpbi4OO3bt6/c+EcffaT4+PjL7cmt9957TykpKXrmmWe0d+9e9erVS4MGDdKxY8cqrM/KytLgwYPVq1cv7d27V08//bTGjx+vVatWOWt27Nihu+++W6NGjdKXX36pUaNG6a677tLOnTur7DgAAEDg8OgK3suWLdP06dP14osv6sEHH9Qbb7yhI0eOKC0tTW+88YbuueeequhV3bt3V2JiohYuXOgca9++vYYNG6a0tLRy9ZMnT9batWuVmZnpHBs7dqy+/PJL7dixQ5J09913Ky8vTx999JGzZuDAgbriiiu0YsWKSvXFFbwBAAg8Xr+C9y/df//9Ki4u1qRJk3T+/HkNHz5cV155pf76179WWVAqKirS7t27NWXKFJfx/v37a/v27RXus2PHDvXv399lbMCAAVqyZIkuXLigkJAQ7dixQxMmTChXw0J1AAAgeRCWiouL9c4772jIkCEaM2aMTp06pZKSEkVGRlZFf06nTp2Sw+FQVFSUy3hUVJRycnIq3CcnJ6fC+uLiYp06dUoxMTFua9zdpyQVFhaqsLDQuZ2Xl3ephwMAAALEJa9ZCg4O1rhx45xhoUmTJlUelH7JYrG4bBuGUW7sYvW/Hr/U+0xLS5PNZnP+NG/evNL9AwCAwOLRAu/u3btr79693u7FVJMmTWS1WsvN+OTm5pabGSoTHR1dYX1wcLAaN25sWuPuPiVp6tSpstvtzp/jx497ckgAACAAeLRm6dFHH9WTTz6p77//XklJSapXr57L7R07dvRKc79Up04dJSUlaePGjfrtb3/rHN+4caOGDh1a4T7Jyclat26dy9iGDRvUtWtXhYSEOGs2btzosm5pw4YN6tGjh9teQkNDFRoaejmHAwAAAoXhAYvFUu4nKCjI+d+qsnLlSiMkJMRYsmSJcejQISMlJcWoV6+e8d133xmGYRhTpkwxRo0a5az/97//bdStW9eYMGGCcejQIWPJkiVGSEiI8f777ztrtm3bZlitVuP55583MjMzjeeff94IDg42MjIyKt2X3W43JBl2u917BwsAAKpUZd+/PZpZysrK8m5iq6S7775bp0+f1qxZs5Sdna2EhAStX7/e+SW/2dnZLtdciouL0/r16zVhwgS98sorio2N1UsvvaQ77rjDWdOjRw+tXLlS06ZN0/Tp09W6dWu999576t69e7UfHwAA8D+XfJ2lCxcuqG3btvrwww+r9AKUgYTrLAEAEHgq+/59yQu8Q0JCVFhYaPppMQAAgJrCo0/D/f73v9ecOXNUXFzs7X4AAAD8ikdrlnbu3KlNmzZpw4YN6tChQ7lPw61evdorzQEAAPiaR2GpYcOGLoukAQAAaiqPwtKyZcu83QcAAIBf8igslTl58qQOHz4si8WiNm3aqGnTpt7qCwAAwC94tMD73LlzeuCBBxQTE6PevXurV69eio2N1YMPPqjz5897u0cAAACf8SgsTZw4UVu2bNG6dev0448/6scff9QHH3ygLVu26Mknn/R2jwAAAD5zyRellEq/1Pb999/XDTfc4DK+efNm3XXXXTp58qS3+gsIXJQSAIDAU2UXpZSk8+fPKyoqqtx4ZGQkp+EAAECN4lFYSk5OVmpqqgoKCpxjP//8s2bOnKnk5GSvNQcAAOBrHn0abv78+Ro0aJCaNWumTp06yWKxaN++fQoNDdWGDRu83SMAAIDPeLRmSSqdSVq+fLn+9a9/yTAMxcfHa8SIEQoPD/d2j36PNUsAAASeyr5/ezSzlJaWpqioKI0ZM8ZlfOnSpTp58qQmT57syd0CqAaOEkO7ss4oN79AkQ3C1C2ukaxBfDE2ALjjUVh6/fXX9e6775Ybv/baa3XPPfcQlgA/lX4wWzPXHVK2/b/rDWNsYUodEq+BCTE+7AwA/JdHC7xzcnIUE1P+hbVp06bKzs6+7KYAeF/6wWyNW77HJShJUo69QOOW71H6Qf7uAkBFPApLzZs317Zt28qNb9u2TbGxsZfdFADvcpQYmrnukCpaoFg2NnPdITlKPFrCCAA1mken4R566CGlpKTowoULuummmyRJmzZt0qRJk7iCN+CHdmWdKTej9EuGpGx7gXZlnVFy68bV1xgABACPwtKkSZN05swZPfrooyoqKpIkhYWFafLkyZo6dapXGwRw+XLz3QclT+oAoDbxKCxZLBbNmTNH06dPV2ZmpsLDw3XNNdcoNDTU2/0B8ILIBmFerQOA2sSjsFSmfv36+s1vfuOtXgBUkW5xjRRjC1OOvaDCdUsWSdG20ssIAABcebTAG0BgsQZZlDokXlJpMPqlsu3UIfFcbwkAKkBYAmqJgQkxWjgyUdE211Nt0bYwLRyZyHWWAMCNyzoNByCwDEyIUb/4aK7gDQCXgLAE1DLWIAuXBwCAS8BpOAAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABPBvm4AAACgIo4SQ7uyzig3v0CRDcLULa6RrEGWau+DsAQAAPxO+sFszVx3SNn2AudYjC1MqUPiNTAhplp74TQcAADwK+kHszVu+R6XoCRJOfYCjVu+R+kHs6u1H8ISAADwG44SQzPXHZJRwW1lYzPXHZKjpKKKqhEwYens2bMaNWqUbDabbDabRo0apR9//NF0H8MwNGPGDMXGxio8PFw33HCDvvrqK+ftZ86c0e9//3u1bdtWdevWVYsWLTR+/HjZ7fYqPhoAAFCRXVlnys0o/ZIhKdteoF1ZZ6qtp4AJS8OHD9e+ffuUnp6u9PR07du3T6NGjTLdZ+7cuZo3b54WLFigzz//XNHR0erXr5/y8/MlST/88IN++OEH/fnPf9aBAwf05ptvKj09XQ8++GB1HBIAAPiV3Hz3QcmTOm+wGIZRffNYHsrMzFR8fLwyMjLUvXt3SVJGRoaSk5P1r3/9S23bti23j2EYio2NVUpKiiZPnixJKiwsVFRUlObMmaNHHnmkwsf63//9X40cOVLnzp1TcHDl1r/n5eXJZrPJbrcrIiLCw6MEAAA7jpzWvYszLlq3Ysx1Sm7d+LIeq7Lv3wExs7Rjxw7ZbDZnUJKk6667TjabTdu3b69wn6ysLOXk5Kh///7OsdDQUPXp08ftPpKcv7DKBiUAAOA93eIaKcYWJncXCLCo9FNx3eIaVVtPARGWcnJyFBkZWW48MjJSOTk5bveRpKioKJfxqKgot/ucPn1azz77rNtZpzKFhYXKy8tz+QEAAJfPGmRR6pB4SSoXmMq2U4fEV+v1lnwalmbMmCGLxWL688UXX0iSLJbyvxTDMCoc/6Vf3+5un7y8PN1yyy2Kj49Xamqq6X2mpaU5F5rbbDY1b978YocKAAAqaWBCjBaOTFS0LcxlPNoWpoUjE6v9Oks+Pdf0+OOP65577jGtadWqlfbv36///Oc/5W47efJkuZmjMtHR0ZJKZ5hiYv77S83NzS23T35+vgYOHKj69etrzZo1CgkJMe1p6tSpmjhxonM7Ly+PwAQAgBcNTIhRv/horuDdpEkTNWnS5KJ1ycnJstvt2rVrl7p16yZJ2rlzp+x2u3r06FHhPnFxcYqOjtbGjRvVpUsXSVJRUZG2bNmiOXPmOOvy8vI0YMAAhYaGau3atQoLC6vw/n4pNDRUoaGhlTlEAADgIWuQ5bIXcXtDQKxZat++vQYOHKgxY8YoIyNDGRkZGjNmjG699VaXT8K1a9dOa9askVR6+i0lJUWzZ8/WmjVrdPDgQY0ePVp169bV8OHDJZXOKPXv31/nzp3TkiVLlJeXp5ycHOXk5MjhcPjkWAEAgH8JmI98vfPOOxo/frzz02233XabFixY4FJz+PBhlwtKTpo0ST///LMeffRRnT17Vt27d9eGDRvUoEEDSdLu3bu1c+dOSdLVV1/tcl9ZWVlq1apVFR4RAAAIBAFxnSV/x3WWAAAIPDXqOksAAAC+QlgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwEezrBgAAgHc5Sgztyjqj3PwCRTYIU7e4RrIGWXzdVsAiLAEAUIOkH8zWzHWHlG0vcI7F2MKUOiReAxNifNhZ4OI0HAAANUT6wWyNW77HJShJUo69QOOW71H6wWwfdRbYCEsAANQAjhJDM9cdklHBbWVjM9cdkqOkogqYISwBAFAD7Mo6U25G6ZcMSdn2Au3KOlN9TdUQhCUAAGqA3Hz3QcmTOvwXYQkAgBogskGYV+vwX4QlAABqgG5xjRRjC5O7CwRYVPqpuG5xjaqzrRqBsAQAQA1gDbIodUi8JJULTGXbqUPiud6SBwhLAADUEAMTYrRwZKKiba6n2qJtYVo4MpHrLHmIi1ICAFCDDEyIUb/4aK7g7UWEJQAAahhrkEXJrRv7uo0ag9NwAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJoJ93QDMnS8qdntbkMWisBBrldf+XOSQIaPCWossCq/jWW3BBYdKjIprJalunWCf14aHWGWxWCRJhcUOOUq8UxsWbFVQUGltUXGJiktKvFIbGmyV1YPaC44SXXC4r61jDVKwNeiSa4sdJSoyqQ2xBinEg1pHiaHCYofb2uCgINUJvvTakhJDBV6qtQZZFBpc+v+7YRj6+YJ3aqvr7z2vEZWr5TWiVHW8RvgSYcnPxf/xH25vu7FtUy27v5tzO+nZj92+yHaPa6T3Hkl2bl8/Z7POnCuqsLZjM5vWPn69c7vvvC068ePPFdZeE1lfGyf2cW7ftuAzfZP7U4W1VzYM17YpNzm373p9h/Z/b6+wtlG9OtozvZ9z+3dLd2ln1pkKa8NDrMp8dqBze9zy3dp8+GSFtZL03fO3OP888W/7tP5AjtvaQ7MGOF84n159UKv2fO+2dve0vmpcP1SS9KcPM/V2xlG3tVsn3ajmjepKkv684bAWffpvt7UbJvRWm6gGkqRXNn+rv276xm3tB4/1VKfmDSVJy7ZlKe2jf7mtXTHmOiW3blz6513H9McPvnJbu3R0V93ULkqS9Pe9J/SH9/e7rX1leKJu6RgjSfrHV//RY+/ucVv7wv901J1dm0uSPv3mpB548wu3tbOGXqv7kltJknZlndG9izPc1k4d1E6P9GktSTp4wq6hr2xzW/vEzddoQr82kqRvT/6k/n/51G3tw72v0tOD20uSTvz4s3rN3ey2dtR1LfXssARJ0plzRUr608dua+9IbKYX7+okSfr5gsP07/3gDtF6dUSSc5vXiFK8RtT81whf8n1cAwAA8GMWwzCZX0Sl5OXlyWazyW63KyIiwqv3zRS7b2uZYi/FabhLr+U0XCleIzyr5TWiVFWfhqvs+zdhyQuqMiwBAICqUdn3b9YsVRPDMFRcXCyHw/2/GBF4rFargoODnf9aBADUPAETls6ePavx48dr7dq1kqTbbrtNL7/8sho2bOh2H8MwNHPmTC1atEhnz55V9+7d9corr+jaa6+tsHbw4MFKT0/XmjVrNGzYMK/1XlRUpOzsbJ0/f95r9wn/UbduXcXExKhOnTq+bgUAUAUCJiwNHz5c33//vdLT0yVJDz/8sEaNGqV169a53Wfu3LmaN2+e3nzzTbVp00Z/+tOf1K9fPx0+fFgNGjRwqZ0/f36VzA6UlJQoKytLVqtVsbGxqlOnDrMQNYRhGCoqKtLJkyeVlZWla665RkFBfGYCAGqagAhLmZmZSk9PV0ZGhrp37y5JWrx4sZKTk3X48GG1bdu23D6GYWj+/Pl65plndPvtt0uS3nrrLUVFRendd9/VI4884qz98ssvNW/ePH3++eeKiYnxau9FRUUqKSlR8+bNVbduXa/eN3wvPDxcISEhOnr0qIqKihQWFubrlgAAXhYQ/wzesWOHbDabMyhJ0nXXXSebzabt27dXuE9WVpZycnLUv39/51hoaKj69Onjss/58+d17733asGCBYqOjq6yY2DGoebiuQWAmi0gZpZycnIUGRlZbjwyMlI5ORVfLKxsPCoqymU8KipKR4/+90JgEyZMUI8ePTR06NBK91NYWKjCwkLndl5eXqX3BQAAgcWn/ySeMWOGLBaL6c8XX5Re0beidT6GYVx0/c+vb//lPmvXrtU///lPzZ8//5L6TktLk81mc/40b+77q4sCAICq4dOw9PjjjyszM9P0JyEhQdHR0frPf/5Tbv+TJ0+WmzkqU3ZK7dczT7m5uc59/vnPf+rIkSNq2LChgoODFRxcOtF2xx136IYbbnDb99SpU2W3250/x48f9+TwL4mjxNCOI6f1wb4T2nHktOnFzLxh9OjRslgsev75513G//73v/t0gfp3330ni8Wiffv2eeX+PvnkE1ksFv34449euT8AQM3j09NwTZo0UZMmTS5al5ycLLvdrl27dqlbt9LvOdq5c6fsdrt69OhR4T5xcXGKjo7Wxo0b1aVLF0mli623bNmiOXPmSJKmTJmihx56yGW/Dh066C9/+YuGDBnitp/Q0FCFhoZW6hi9If1gtmauO6Rse4FzLMYWptQh8RqY4N0F6b8UFhamOXPm6JFHHtEVV1xRZY/jKxcuXPB1CwCAABAQK1Pbt2+vgQMHasyYMcrIyFBGRobGjBmjW2+91eWTcO3atdOaNWsklZ5+S0lJ0ezZs7VmzRodPHhQo0ePVt26dTV8+HBJpbNPCQkJLj+S1KJFC8XFxVX/gVYg/WC2xi3f4xKUJCnHXqBxy/co/WB2lT123759FR0drbS0NNO6VatW6dprr1VoaKhatWqlF1980Xnb1KlTdd1115Xbp2PHjkpNTa3w/s6ePasRI0aoadOmCg8P1zXXXKNly5ZJkvN56dKliywWi3MG8PPPP1e/fv3UpEkT2Ww29enTR3v2uH45o8Vi0WuvvaahQ4eqXr16euihh3TjjTdKkq644gpZLBaNHj1akvT++++rQ4cOCg8PV+PGjdW3b1+dO3fu4r80AECNExBhSZLeeecddejQQf3791f//v3VsWNHvf322y41hw8flt3+32+onjRpklJSUvToo4+qa9euOnHihDZs2FDuGkv+ylFiaOa6QxV+i1LZ2Mx1h6rslJzVatXs2bP18ssv6/vvK/4m7d27d+uuu+7SPffcowMHDmjGjBmaPn263nzzTUnSiBEjtHPnTh05csS5z1dffaUDBw5oxIgRFd7n9OnTdejQIX300UfKzMzUwoULnTOQu3btkiR9/PHHys7O1urVqyVJ+fn5+t3vfqetW7cqIyND11xzjQYPHqz8/HyX+05NTdXQoUN14MABzZo1S6tWrZJU+v9Odna2/vrXvyo7O1v33nuvHnjgAWVmZuqTTz7R7bffLr4ZCABqp4D4NJwkNWrUSMuXLzet+fWbmcVi0YwZMzRjxoxKP44/vSHuyjpTbkbplwxJ2fYC7co6o+TWjaukh9/+9rfq3LmzUlNTtWTJknK3z5s3TzfffLOmT58uSWrTpo0OHTqkF154QaNHj1ZCQoI6duyod99911nzzjvv6De/+Y3atGlT4WMeO3ZMXbp0UdeuXSVJrVq1ct7WtGlTSVLjxo1dLvVw0003udzH66+/riuuuEJbtmzRrbfe6hwfPny4HnjgAed2VlaWpNJPVpZdDf7IkSMqLi7W7bffrpYtW0oqPT0LwD1HiaFdWWeUm1+gyAZh6hbXyPklrECgC5iZpdooN999UPKkzlNz5szRW2+9pUOHDpW7LTMzUz179nQZ69mzp7755hvn9+CNGDFC77zzjqTSMLpixQq3s0qSNG7cOK1cuVKdO3fWpEmT3F5L65dyc3M1duxYtWnTxvkpxZ9++knHjh1zqSsLYGY6deqkm2++WR06dNCdd96pxYsX6+zZsxfdD6it0g9m6/o5/9S9izP0xMp9undxhq6f888qXSYAVCfCkh+LbFC5q0FXts5TvXv31oABA/T000+Xu62iyzf8enZu+PDh+vrrr7Vnzx5t375dx48f1z333OP28QYNGqSjR48qJSVFP/zwg26++WY99dRTpj2OHj1au3fv1vz587V9+3bt27dPjRs3VlFRkUtdvXr1Lna4slqt2rhxoz766CPFx8fr5ZdfVtu2bZ2zUAD+y5frKoHqQljyY93iGinGFiZ3E9kWlX4qrltcoyrv5fnnn9e6devKzfLEx8frs88+cxnbvn272rRpI6vVKklq1qyZevfurXfeeUfvvPOO+vbt6/aSD2WaNm2q0aNHa/ny5Zo/f74WLVokSc4vqy2btSqzdetWjR8/XoMHD3YuNj916tRFj8vd/VksFvXs2VMzZ87U3r17VadOHeeHBwCU8vW6SqC6EJb8mDXIotQh8ZJULjCVbacOia+WdQEdOnTQiBEj9PLLL7uMP/nkk9q0aZOeffZZff3113rrrbe0YMGCcjNBI0aM0MqVK/W///u/GjlypOlj/fGPf9QHH3ygb7/9Vl999ZU+/PBDtW/fXlLp2qLw8HClp6frP//5j3NB/9VXX623335bmZmZ2rlzp0aMGKHw8PCLHlfLli1lsVj04Ycf6uTJk/rpp5+0c+dOzZ49W1988YWOHTum1atX6+TJk84eAJS6lHWVQCAjLPm5gQkxWjgyUdE211Nt0bYwLRyZWKXXWfq1Z599ttwptsTERP3tb3/TypUrlZCQoD/+8Y+aNWuW8yP4Ze68806dPn1a58+f17Bhw0wfp06dOpo6dao6duyo3r17y2q1auXKlZKk4OBgvfTSS3r99dcVGxvr/JqapUuX6uzZs+rSpYtGjRql8ePHV/gVOb925ZVXaubMmZoyZYqioqL0+OOPKyIiQp9++qkGDx6sNm3aaNq0aXrxxRc1aNCgyv+ygFrAX9ZVAlXNYvjTx78CVF5enmw2m+x2uyIiIlxuKygoUFZWluLi4i7rG+n5pIn/8tZzDASaHUdO697FGRetWzHmuir7xC5wOczev38pYC4dUNtZgyy82ADwK2XrKnPsBRWuW7KodBa8OtZVAlWJ03AAAI/407pKoCoRlgAAHvOndZVAVeE0HADgsgxMiFG/+GjWVaLGIiwBAC4b6ypRk3EarprwocOai+cWAGo2wlIVCwkJkSSdP3/ex52gqpQ9t2XPNQCgZuE0XBWzWq1q2LChcnNzJUl169Yt911qCEyGYej8+fPKzc1Vw4YNnV/vAgCoWQhL1SA6OlqSnIEJNUvDhg2dzzEAoOYhLFUDi8WimJgYRUZG6sKFC75uB14UEhLCjBIA1HCEpWpktVp5YwUAIMCwwBsAAMAEYQkAAMAEYQkAAMAEa5a8oOyihHl5eT7uBAAAVFbZ+/bFLi5MWPKC/Px8SVLz5s193AkAALhU+fn5stlsbm+3GHxXw2UrKSnRDz/8oAYNGnDBSTfy8vLUvHlzHT9+XBEREb5up9bj+fAvPB/+hefDv1Tl82EYhvLz8xUbG6ugIPcrk5hZ8oKgoCA1a9bM120EhIiICF58/AjPh3/h+fAvPB/+paqeD7MZpTIs8AYAADBBWAIAADBBWEK1CA0NVWpqqkJDQ33dCsTz4W94PvwLz4d/8YfngwXeAAAAJphZAgAAMEFYAgAAMEFYAgAAMEFYQpVJS0vTb37zGzVo0ECRkZEaNmyYDh8+7Ou28H/S0tJksViUkpLi61ZqtRMnTmjkyJFq3Lix6tatq86dO2v37t2+bqtWKi4u1rRp0xQXF6fw8HBdddVVmjVrlkpKSnzdWq3w6aefasiQIYqNjZXFYtHf//53l9sNw9CMGTMUGxur8PBw3XDDDfrqq6+qpTfCEqrMli1b9NhjjykjI0MbN25UcXGx+vfvr3Pnzvm6tVrv888/16JFi9SxY0dft1KrnT17Vj179lRISIg++ugjHTp0SC+++KIaNmzo69ZqpTlz5ui1117TggULlJmZqblz5+qFF17Qyy+/7OvWaoVz586pU6dOWrBgQYW3z507V/PmzdOCBQv0+eefKzo6Wv369XN+5VhV4tNwqDYnT55UZGSktmzZot69e/u6nVrrp59+UmJiol599VX96U9/UufOnTV//nxft1UrTZkyRdu2bdPWrVt93Qok3XrrrYqKitKSJUucY3fccYfq1q2rt99+24ed1T4Wi0Vr1qzRsGHDJJXOKsXGxiolJUWTJ0+WJBUWFioqKkpz5szRI488UqX9MLOEamO32yVJjRo18nEntdtjjz2mW265RX379vV1K7Xe2rVr1bVrV915552KjIxUly5dtHjxYl+3VWtdf/312rRpk77++mtJ0pdffqnPPvtMgwcP9nFnyMrKUk5Ojvr37+8cCw0NVZ8+fbR9+/Yqf3y+Gw7VwjAMTZw4Uddff70SEhJ83U6ttXLlSu3Zs0eff/65r1uBpH//+99auHChJk6cqKefflq7du3S+PHjFRoaqvvuu8/X7dU6kydPlt1uV7t27WS1WuVwOPTcc8/p3nvv9XVrtV5OTo4kKSoqymU8KipKR48erfLHJyyhWjz++OPav3+/PvvsM1+3UmsdP35cTzzxhDZs2KCwsDBftwNJJSUl6tq1q2bPni1J6tKli7766istXLiQsOQD7733npYvX653331X1157rfbt26eUlBTFxsbqd7/7na/bg0pPz/2SYRjlxqoCYQlV7ve//73Wrl2rTz/9VM2aNfN1O7XW7t27lZubq6SkJOeYw+HQp59+qgULFqiwsFBWq9WHHdY+MTExio+Pdxlr3769Vq1a5aOOarc//OEPmjJliu655x5JUocOHXT06FGlpaURlnwsOjpaUukMU0xMjHM8Nze33GxTVWDNEqqMYRh6/PHHtXr1av3zn/9UXFycr1uq1W6++WYdOHBA+/btc/507dpVI0aM0L59+whKPtCzZ89yl9P4+uuv1bJlSx91VLudP39eQUGub4tWq5VLB/iBuLg4RUdHa+PGjc6xoqIibdmyRT169Kjyx2dmCVXmscce07vvvqsPPvhADRo0cJ5zttlsCg8P93F3tU+DBg3KrRerV6+eGjduzDoyH5kwYYJ69Oih2bNn66677tKuXbu0aNEiLVq0yNet1UpDhgzRc889pxYtWujaa6/V3r17NW/ePD3wwAO+bq1W+Omnn/Ttt986t7OysrRv3z41atRILVq0UEpKimbPnq1rrrlG11xzjWbPnq26detq+PDhVd+cAVQRSRX+LFu2zNet4f/06dPHeOKJJ3zdRq22bt06IyEhwQgNDTXatWtnLFq0yNct1Vp5eXnGE088YbRo0cIICwszrrrqKuOZZ54xCgsLfd1arbB58+YK3zN+97vfGYZhGCUlJUZqaqoRHR1thIaGGr179zYOHDhQLb1xnSUAAAATrFkCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgCAAAwQVgC4NduuOEGpaSk+PVjvPnmm2rYsKHX+gHgXwhLAHAJWrVqpfnz57uM3X333fr666990xCAKscX6QLAZQoPD+fLoYEajJklAAGjqKhIkyZN0pVXXql69eqpe/fu+uSTT5y3nz59Wvfee6+aNWumunXrqkOHDlqxYoXLfZw7d0733Xef6tevr5iYGL344ouVfvwbbrhBR48e1YQJE2SxWGSxWCSVPw03Y8YMde7cWUuXLlWLFi1Uv359jRs3Tg6HQ3PnzlV0dLQiIyP13HPPudy/3W7Xww8/rMjISEVEROimm27Sl19+eem/KABeRVgCEDDuv/9+bdu2TStXrtT+/ft15513auDAgfrmm28kSQUFBUpKStKHH36ogwcP6uGHH9aoUaO0c+dO53384Q9/0ObNm7VmzRpt2LBBn3zyiXbv3l2px1+9erWaNWumWbNmKTs7W9nZ2W5rjxw5oo8++kjp6elasWKFli5dqltuuUXff/+9tmzZojlz5mjatGnKyMiQJBmGoVtuuUU5OTlav369du/ercTERN188806c+bMZfzWAFw2AwD8WJ8+fYwnnnjC+Pbbbw2LxWKcOHHC5fabb77ZmDp1qtv9Bw8ebDz55JOGYRhGfn6+UadOHWPlypXO20+fPm2Eh4cbTzzxRKX6admypfGXv/zFZWzZsmWGzWZzbqemphp169Y18vLynGMDBgwwWrVqZTgcDudY27ZtjbS0NMMwDGPTpk1GRESEUVBQ4HLfrVu3Nl5//fVK9QagarBmCUBA2LNnjwzDUJs2bVzGCwsL1bhxY0mSw+HQ888/r/fee08nTpxQYWGhCgsLVa9ePUmlsz1FRUVKTk527t+oUSO1bdvW6/22atVKDRo0cG5HRUXJarUqKCjIZSw3N1eStHv3bv3000/OYynz888/68iRI17vD0DlEZYABISSkhJZrVbt3r1bVqvV5bb69etLkl588UX95S9/0fz589WhQwfVq1dPKSkpKioqklR6qqu6hISEuGxbLJYKx0pKSiSVHl9MTIzLGqwyXJYA8C3CEoCA0KVLFzkcDuXm5qpXr14V1mzdulVDhw7VyJEjJZUGkG+++Ubt27eXJF199dUKCQlRRkaGWrRoIUk6e/asvv76a/Xp06dSfdSpU0cOh8MLR+QqMTFROTk5Cg4OVqtWrbx+/wA8xwJvAAGhTZs2GjFihO677z6tXr1aWVlZ+vzzzzVnzhytX79eUmkY2rhxo7Zv367MzEw98sgjysnJcd5H/fr19eCDD+oPf/iDNm3apIMHD2r06NEup8YuplWrVvr000914sQJnTp1ymvH17dvXyUnJ2vYsGH6xz/+oe+++07bt2/XtGnT9MUXX3jtcQBcOsISgICxbNky3XfffXryySfVtm1b3Xbbbdq5c6eaN28uSZo+fboSExM1YMAA3XDDDYqOjtawYcNc7uOFF15Q7969ddttt6lv3766/vrrlZSUVOkeZs2ape+++06tW7dW06ZNvXZsFotF69evV+/evfXAAw+oTZs2uueee/Tdd98pKirKa48D4NJZjOo8iQ8AABBgmFkCAAAwQVgCgP+zdetW1a9f3+0PgNqJ03AA8H9+/vlnnThxwu3tV199dTV2A8BfEJYAAABMcBoOAADABGEJAADABGEJAADABGEJAADABGEJAADABGEJAADABGEJAADABGEJAADAxP8HXM+L82k+SqYAAAAASUVORK5CYII=", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "independence.create_plot(\n", - " mean_correlations,\n", - " null_correlation_bounds,\n", - " 'independence.png'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d65e755f", - "metadata": {}, - "source": [ - "So we should drop the first two lead times (TODO: We need a function where we can drop a different number of lead times for each init date)." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "2b6314e8", - "metadata": {}, - "outputs": [], - "source": [ - "#cafe_da_indep = cafe_ds['pr'].dropna('lead_time').sel({'lead_time': slice(3, None)})\n", - "model_da_indep = model_ds['pr'].where(model_ds['lead_time'] > 0)\n", - "model_da_indep = model_da_indep.dropna('lead_time')" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "ec9a0bb7", - "metadata": {}, "outputs": [ { "data": { @@ -2206,96 +1526,29 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray 'pr' (init_date: 59, ensemble: 10, lead_time: 10)>\n",
    -       "array([[[ 41.199123,  40.186687,  22.814072, ...,  57.60288 ,\n",
    -       "          41.32931 ,  34.600357],\n",
    -       "        [ 49.84055 ,  30.568817,  51.367992, ...,  32.480885,\n",
    -       "          21.5431  ,  32.533993],\n",
    -       "        [ 19.802956,  73.28052 ,  46.354927, ...,  34.632904,\n",
    -       "          40.449646,  41.214836],\n",
    -       "        ...,\n",
    -       "        [ 33.763847,  88.322   ,  39.87821 , ...,  40.472565,\n",
    -       "          29.641193,  54.27389 ],\n",
    -       "        [ 30.851843,  44.261265,  22.47876 , ...,  32.31197 ,\n",
    -       "          63.645695,  45.358185],\n",
    -       "        [ 29.998043,  23.077745,  64.67597 , ...,  48.22903 ,\n",
    -       "          87.894135,  31.217402]],\n",
    -       "\n",
    -       "       [[ 35.952183,  37.173084,  45.09424 , ...,  25.966082,\n",
    -       "          55.67482 ,  52.313187],\n",
    -       "        [100.861885,  22.75312 ,  35.821037, ...,  42.128433,\n",
    -       "          33.976517,  38.801132],\n",
    -       "        [ 88.57587 ,  28.236967,  37.589485, ...,  29.574917,\n",
    -       "          48.696068,  54.606216],\n",
    -       "...\n",
    -       "        [ 36.109245,  38.47985 ,  40.838726, ...,  27.531712,\n",
    -       "          36.88378 ,  26.897398],\n",
    -       "        [ 47.674904,  21.990286,  37.557766, ...,  29.134203,\n",
    -       "          35.37774 ,  35.718452],\n",
    -       "        [ 37.643116,  33.45197 ,  21.30828 , ..., 110.490944,\n",
    -       "          37.69071 ,  46.277946]],\n",
    -       "\n",
    -       "       [[ 70.42627 ,  50.774883,  29.232243, ...,  32.145668,\n",
    -       "          40.57894 ,  30.883896],\n",
    -       "        [ 36.89426 ,  26.009771,  32.998264, ...,  38.336956,\n",
    -       "          24.077715,  30.150114],\n",
    -       "        [ 16.728746,  35.554142,  36.486023, ...,  32.034267,\n",
    -       "          27.76408 ,  28.515844],\n",
    -       "        ...,\n",
    -       "        [ 32.79029 ,  40.95202 ,  27.295418, ...,  38.58903 ,\n",
    -       "          21.752804,  67.68842 ],\n",
    -       "        [ 33.86381 ,  30.406788,  31.090996, ...,  69.38824 ,\n",
    -       "          37.479424,  35.757618],\n",
    -       "        [ 37.543518,  80.15443 ,  57.797176, ...,  33.73056 ,\n",
    -       "          63.42307 ,  14.038993]]], dtype=float32)\n",
    +       "
    <xarray.Dataset>\n",
    +       "Dimensions:    (ensemble: 10, init_date: 59, lead_time: 12)\n",
            "Coordinates:\n",
            "  * ensemble   (ensemble) int64 0 1 2 3 4 5 6 7 8 9\n",
            "  * init_date  (init_date) object 1960-11-01 00:00:00 ... 2018-11-01 00:00:00\n",
    -       "  * lead_time  (lead_time) int64 1 2 3 4 5 6 7 8 9 10\n",
    -       "    time       (lead_time, init_date) object 1961-11-01 12:00:00 ... 2028-11-...\n",
    -       "Attributes:\n",
    -       "    standard_name:  lwe_precipitation_rate\n",
    -       "    units:          mm d-1
  • lead_time
    PandasIndex
    PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype='int64', name='lead_time'))
  • Conventions :
    CF-1.7 CMIP-6.2
    activity_id :
    DCPP
    branch_method :
    no parent
    branch_time_in_child :
    0.0
    branch_time_in_parent :
    0.0
    cmor_version :
    3.4.0
    creation_date :
    2020-05-27T14:51:42Z
    cv_version :
    6.2.37.5
    data_specs_version :
    01.00.29
    experiment :
    hindcast initialized based on observations and using historical forcing
    experiment_id :
    dcppA-hindcast
    external_variables :
    areacella
    forcing_index :
    2
    frequency :
    day
    further_info_url :
    https://furtherinfo.es-doc.org/CMIP6.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1960.r1i1p1f2
    grid :
    N216
    grid_label :
    gn
    history :
    Wed Oct 11 18:51:13 2023: /g/data/xv83/dbi599/miniconda3/envs/unseen2/bin/python3.10 /g/data/xv83/dbi599/miniconda3/envs/unseen2/bin/fileio /home/599/dbi599/east-coast-rain/file_lists/HadGEM3-GC31-MM_dcppA-hindcast_pr_files.txt /g/data/xv83/dbi599/unseen/Rx5day_HadGEM3-GC31-MM_dcppA-hindcast_s1960-2018_gn_hobart.zarr.zip --n_ensemble_files 10 --n_time_files 12 --variables pr --rolling_sum_window 5 --time_freq A-DEC --time_agg max --input_freq D --point_selection -42.9 147.3 --reset_times --complete_time_agg_periods --units pr=mm day-1 --forecast -v
    initialization_index :
    1
    institution :
    Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
    institution_id :
    MOHC
    license :
    CMIP6 model data produced by Met Office Hadley Centre is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https://ukesm.ac.uk/cmip6. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
    mip_era :
    CMIP6
    mo_runid :
    u-av640
    nominal_resolution :
    100 km
    parent_mip_era :
    CMIP6
    physics_index :
    1
    product :
    model-output
    realization_index :
    1
    realm :
    atmos
    references :
    Williams, K., et al: The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 & GC3.1) configurations. JAMES, 10, 357-380 (2017).
    source :
    HadGEM3-GC31-MM (2016): \n", + "aerosol: UKCA-GLOMAP-mode\n", + "atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km)\n", + "atmosChem: none\n", + "land: JULES-HadGEM3-GL7.1\n", + "landIce: none\n", + "ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m)\n", + "ocnBgchem: none\n", + "seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude)
    source_id :
    HadGEM3-GC31-MM
    source_type :
    AOGCM AER
    sub_experiment :
    initialized near end of year 1960
    sub_experiment_id :
    s1960
    table_id :
    day
    table_info :
    Creation Date:(13 December 2018) MD5:f0588f7f55b5732b17302f8d9d0d7b8c
    title :
    HadGEM3-GC31-MM output prepared for CMIP6
    tracking_id :
    hdl:21.14100/3163965c-a593-4abd-9b2a-9ee755aef228
    variable_id :
    pr
    variable_name :
    pr
    variant_label :
    r1i1p1f2
  • " ], "text/plain": [ - "\n", - "array([[[ 41.199123, 40.186687, 22.814072, ..., 57.60288 ,\n", - " 41.32931 , 34.600357],\n", - " [ 49.84055 , 30.568817, 51.367992, ..., 32.480885,\n", - " 21.5431 , 32.533993],\n", - " [ 19.802956, 73.28052 , 46.354927, ..., 34.632904,\n", - " 40.449646, 41.214836],\n", - " ...,\n", - " [ 33.763847, 88.322 , 39.87821 , ..., 40.472565,\n", - " 29.641193, 54.27389 ],\n", - " [ 30.851843, 44.261265, 22.47876 , ..., 32.31197 ,\n", - " 63.645695, 45.358185],\n", - " [ 29.998043, 23.077745, 64.67597 , ..., 48.22903 ,\n", - " 87.894135, 31.217402]],\n", - "\n", - " [[ 35.952183, 37.173084, 45.09424 , ..., 25.966082,\n", - " 55.67482 , 52.313187],\n", - " [100.861885, 22.75312 , 35.821037, ..., 42.128433,\n", - " 33.976517, 38.801132],\n", - " [ 88.57587 , 28.236967, 37.589485, ..., 29.574917,\n", - " 48.696068, 54.606216],\n", - "...\n", - " [ 36.109245, 38.47985 , 40.838726, ..., 27.531712,\n", - " 36.88378 , 26.897398],\n", - " [ 47.674904, 21.990286, 37.557766, ..., 29.134203,\n", - " 35.37774 , 35.718452],\n", - " [ 37.643116, 33.45197 , 21.30828 , ..., 110.490944,\n", - " 37.69071 , 46.277946]],\n", - "\n", - " [[ 70.42627 , 50.774883, 29.232243, ..., 32.145668,\n", - " 40.57894 , 30.883896],\n", - " [ 36.89426 , 26.009771, 32.998264, ..., 38.336956,\n", - " 24.077715, 30.150114],\n", - " [ 16.728746, 35.554142, 36.486023, ..., 32.034267,\n", - " 27.76408 , 28.515844],\n", - " ...,\n", - " [ 32.79029 , 40.95202 , 27.295418, ..., 38.58903 ,\n", - " 21.752804, 67.68842 ],\n", - " [ 33.86381 , 30.406788, 31.090996, ..., 69.38824 ,\n", - " 37.479424, 35.757618],\n", - " [ 37.543518, 80.15443 , 57.797176, ..., 33.73056 ,\n", - " 63.42307 , 14.038993]]], dtype=float32)\n", + "\n", + "Dimensions: (ensemble: 10, init_date: 59, lead_time: 12)\n", "Coordinates:\n", " * ensemble (ensemble) int64 0 1 2 3 4 5 6 7 8 9\n", " * init_date (init_date) object 1960-11-01 00:00:00 ... 2018-11-01 00:00:00\n", - " * lead_time (lead_time) int64 1 2 3 4 5 6 7 8 9 10\n", - " time (lead_time, init_date) object 1961-11-01 12:00:00 ... 2028-11-...\n", - "Attributes:\n", - " standard_name: lwe_precipitation_rate\n", - " units: mm d-1" + " * lead_time (lead_time) int64 0 1 2 3 4 5 6 7 8 9 10 11\n", + " time (lead_time, init_date) object 1960-11-01 12:00:00 ... 2029-11-...\n", + "Data variables:\n", + " pr (init_date, ensemble, lead_time) float32 nan 100.9 ... 28.0 nan\n", + "Attributes: (12/43)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: DCPP\n", + " branch_method: no parent\n", + " branch_time_in_child: 0.0\n", + " branch_time_in_parent: 0.0\n", + " cmor_version: 3.4.0\n", + " ... ...\n", + " table_info: Creation Date:(13 December 2018) MD5:f0588f7f55b5...\n", + " title: HadGEM3-GC31-MM output prepared for CMIP6\n", + " tracking_id: hdl:21.14100/3163965c-a593-4abd-9b2a-9ee755aef228\n", + " variable_id: pr\n", + " variable_name: pr\n", + " variant_label: r1i1p1f2" ] }, - "execution_count": 59, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model_da_indep" + "model_ds" ] }, { "cell_type": "markdown", - "id": "10bdac97", + "id": "e7cd216b", "metadata": {}, "source": [ - "## Bias correction" + "## Stability and stationarity" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "1c575d96", + "execution_count": 7, + "id": "915b0b30", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stability.create_plot(\n", + " model_ds['pr'],\n", + " 'Rx5day',\n", + " [1960, 1970, 1980, 1990, 2000, 2010],\n", + " outfile='stability.png',\n", + " uncertainty=True,\n", + " return_method='gev',\n", + " units='Rx5day (mm)',\n", + " ylim=(0, 450),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d8ed1c5a", "metadata": {}, - "outputs": [], "source": [ - "correction_method = 'multiplicative'\n", - "baseline_period = ['1970-01-01', '2018-12-30']" + "## Independence testing" ] }, { "cell_type": "code", - "execution_count": 45, - "id": "8bf25d36", + "execution_count": 8, + "id": "6fc37e4d", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", "output_type": "stream", "text": [ - "\n", - "array([0.49017265], dtype=float32)\n", - "Coordinates:\n", - " * month (month) int64 11\n", - "Attributes:\n", - " standard_name: lwe_precipitation_rate\n", - " units: mm d-1\n", - " climatological_period: ['1970-01-01', '2018-12-30']\n", - " bias_correction_method: multiplicative\n", - " bias_correction_period: 1970-01-01-2018-12-30\n" + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sample' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n", + "/home/599/dbi599/unseen/unseen/independence.py:261: FutureWarning: updating coordinate 'sample' with a PandasMultiIndex would leave the multi-index level coordinates ['init_date', 'lead_time', 'ensemble'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['sample', 'init_date', 'lead_time', 'ensemble'])` before assigning new coordinate values.\n", + " ds_random_sample = ds_random_sample.assign_coords({\"sample\": index}).unstack()\n" ] } ], "source": [ - "bias = bias_correction.get_bias(\n", - " model_da_indep,\n", - " agcd_ds['pr'],\n", - " correction_method,\n", - " time_rounding='A',\n", - " time_period=baseline_period\n", - ")\n", - "print(bias)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "c02f4466", - "metadata": {}, - "outputs": [], - "source": [ - "model_da_bc = bias_correction.remove_bias(model_da_indep, bias, correction_method)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "456ef9c0", - "metadata": {}, - "outputs": [], - "source": [ - "model_da_bc = model_da_bc.compute()" - ] - }, - { - "cell_type": "markdown", - "id": "8dd4c55f", - "metadata": {}, - "source": [ - "## Similarity testing" + "mean_correlations, null_correlation_bounds = independence.run_tests(model_ds['pr'])" ] }, { "cell_type": "code", - "execution_count": 48, - "id": "4b191023", - "metadata": {}, + "execution_count": 9, + "id": "eb3ddc27", + "metadata": { + "scrolled": true + }, "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model_da_indep.plot.hist(bins=50, density=True, alpha=0.7, facecolor='tab:blue')\n", - "model_raw_shape, model_raw_loc, model_raw_scale = general_utils.fit_gev(model_da_indep.values, generate_estimates=True)\n", - "model_raw_pdf = gev.pdf(xvals, model_raw_shape, model_raw_loc, model_raw_scale)\n", - "plt.plot(xvals, model_raw_pdf, color='tab:blue', linewidth=4.0, label='model')\n", - "\n", - "model_da_bc.plot.hist(bins=50, density=True, alpha=0.7, facecolor='tab:orange')\n", - "model_bc_shape, model_bc_loc, model_bc_scale = general_utils.fit_gev(model_da_bc.values, generate_estimates=True)\n", - "model_bc_pdf = gev.pdf(xvals, model_bc_shape, model_bc_loc, model_bc_scale)\n", - "plt.plot(xvals, model_bc_pdf, color='tab:orange', linewidth=4.0, label='model (corrected)')\n", - "\n", - "agcd_ds['pr'].plot.hist(ax=ax, bins=50, density=True, facecolor='tab:gray', alpha=0.7)\n", - "plt.plot(xvals, agcd_pdf, color='tab:gray', linewidth=4.0, label='observations')\n", - "\n", - "plt.xlabel('Rx5day (mm)')\n", - "plt.ylabel('probability')\n", - "plt.title('Hobart')\n", - "plt.xlim(0, 250)\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.savefig(\n", - " 'distribution.png',\n", - " bbox_inches='tight',\n", - " facecolor='white',\n", - " dpi=200\n", - ")\n", - "plt.show()" + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{11: \n", + "dask.array\n", + "Coordinates:\n", + " * lead_time (lead_time) int64 0 1 2 3 4 5 6 7 8 9 10 11}\n" + ] + } + ], + "source": [ + "print(mean_correlations)" ] }, { "cell_type": "code", - "execution_count": 49, - "id": "5c230f45", - "metadata": {}, + "execution_count": 10, + "id": "a84c90b1", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:mean: Obs = 83.49502563476562, Model 95% CI =36.75685091018677 to 43.17814531326294\n", - "INFO:root:standard deviation: Obs = 34.267822265625, Model 95% CI =13.141557312011718 to 24.036773061752317\n", - "INFO:root:skew: Obs = 1.4069650511166405, Model 95% CI =0.6416781516215879 to 4.687919945020748\n", - "INFO:root:kurtosis: Obs = 3.4493238193282894, Model 95% CI =-0.0837022379302009 to 31.097994934829973\n", - "INFO:root:GEV shape: Obs = -0.05232628138994108, Model 95% CI =-0.2267801249821537 to 0.057305507141358736\n", - "INFO:root:GEV location: Obs = 67.7079983493445, Model 95% CI =29.532443302309993 to 34.49916551947474\n", - "INFO:root:GEV scale: Obs = 25.03453176147036, Model 95% CI =9.83219636328737 to 13.311995518599122\n", - "INFO:root:mean: Obs = 83.49502563476562, Bias corrected model 95% CI =75.20064277648926 to 87.51631908416748\n", - "INFO:root:standard deviation: Obs = 34.267822265625, Bias corrected model 95% CI =27.199085092544557 to 53.103369140625\n", - "INFO:root:skew: Obs = 1.4069650511166405, Bias corrected model 95% CI =0.6620468497364028 to 5.620837596080243\n", - "INFO:root:kurtosis: Obs = 3.4493238193282894, Bias corrected model 95% CI =-0.03760303280409018 to 44.08717656720828\n", - "INFO:root:GEV shape: Obs = -0.05232628138994108, Bias corrected model 95% CI =-0.24620749505635764 to 0.054590351138043725\n", - "INFO:root:GEV location: Obs = 67.7079983493445, Bias corrected model 95% CI =60.36206889964265 to 70.0552498151841\n", - "INFO:root:GEV scale: Obs = 25.03453176147036, Bias corrected model 95% CI =20.027647688449953 to 27.118281848260168\n" + "/g/data/xv83/dbi599/miniconda3/envs/unseen2/lib/python3.10/site-packages/xskillscore/core/np_deterministic.py:309: RuntimeWarning: invalid value encountered in divide\n", + " r = r_num / r_den\n", + "/g/data/xv83/dbi599/miniconda3/envs/unseen2/lib/python3.10/site-packages/xskillscore/core/np_deterministic.py:309: RuntimeWarning: invalid value encountered in divide\n", + " r = r_num / r_den\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -2643,18 +2330,37 @@ } ], "source": [ - "moments.create_plot(\n", - " model_da_indep,\n", - " agcd_ds['pr'],\n", - " da_bc_fcst=model_da_bc,\n", - " outfile='moments.png',\n", + "independence.create_plot(\n", + " mean_correlations,\n", + " null_correlation_bounds,\n", + " 'independence.png'\n", ")" ] }, + { + "cell_type": "markdown", + "id": "d65e755f", + "metadata": {}, + "source": [ + "So we should drop the first two lead times (TODO: We need a function where we can drop a different number of lead times for each init date)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2b6314e8", + "metadata": {}, + "outputs": [], + "source": [ + "#cafe_da_indep = cafe_ds['pr'].dropna('lead_time').sel({'lead_time': slice(3, None)})\n", + "model_da_indep = model_ds['pr'].where(model_ds['lead_time'] > 0)\n", + "model_da_indep = model_da_indep.dropna('lead_time')" + ] + }, { "cell_type": "code", - "execution_count": 50, - "id": "f54273ed", + "execution_count": 12, + "id": "ec9a0bb7", "metadata": {}, "outputs": [ { @@ -3023,250 +2729,454 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset>\n",
    -       "Dimensions:     (time: 123)\n",
    +       "
    <xarray.DataArray 'pr' (init_date: 59, ensemble: 10, lead_time: 10)>\n",
    +       "array([[[100.882416,  59.101746,  50.513184, ...,  72.814026,\n",
    +       "          47.615986,  60.41703 ],\n",
    +       "        [ 59.334   ,  33.80462 ,  75.82639 , ...,  44.605373,\n",
    +       "          34.917683,  59.61616 ],\n",
    +       "        [ 31.110619, 127.5415  ,  76.78214 , ...,  62.714207,\n",
    +       "          81.598175,  61.392296],\n",
    +       "        ...,\n",
    +       "        [ 54.87059 ,  92.36635 ,  66.44363 , ...,  95.78178 ,\n",
    +       "          46.16651 ,  83.41238 ],\n",
    +       "        [ 58.627533,  66.82831 ,  32.41794 , ...,  67.723145,\n",
    +       "          77.81938 ,  49.485268],\n",
    +       "        [ 54.334064,  45.583908,  67.08059 , ...,  88.85287 ,\n",
    +       "         115.39417 ,  52.16396 ]],\n",
    +       "\n",
    +       "       [[ 53.48728 ,  56.95953 ,  63.92677 , ...,  44.904198,\n",
    +       "          79.179245, 102.832115],\n",
    +       "        [133.40063 ,  34.3559  ,  63.584846, ...,  71.573845,\n",
    +       "          51.077995,  75.63232 ],\n",
    +       "        [104.252754,  65.710106,  55.676964, ...,  46.016098,\n",
    +       "          72.45645 ,  95.49189 ],\n",
    +       "...\n",
    +       "        [ 88.25009 ,  60.59867 ,  80.30067 , ...,  38.133778,\n",
    +       "          43.382965,  43.320934],\n",
    +       "        [ 63.40218 ,  36.315556,  51.913548, ...,  41.960567,\n",
    +       "          59.104553,  68.90168 ],\n",
    +       "        [ 98.260544,  53.62038 ,  32.57572 , ..., 134.02171 ,\n",
    +       "          79.46202 ,  68.18055 ]],\n",
    +       "\n",
    +       "       [[140.56177 ,  54.30856 ,  65.706985, ...,  67.92311 ,\n",
    +       "          99.22986 ,  36.80957 ],\n",
    +       "        [ 46.83064 ,  46.71146 ,  60.646698, ...,  92.87491 ,\n",
    +       "          32.030388,  40.30447 ],\n",
    +       "        [ 30.642147,  48.3465  ,  76.646576, ...,  74.966415,\n",
    +       "          56.302555,  52.613976],\n",
    +       "        ...,\n",
    +       "        [ 49.801647,  51.675907,  42.132774, ...,  86.53947 ,\n",
    +       "          39.62441 , 102.067055],\n",
    +       "        [ 43.092773,  71.18484 ,  60.19852 , ..., 143.83037 ,\n",
    +       "          68.38716 ,  68.01422 ],\n",
    +       "        [ 60.222404,  95.45831 ,  94.1183  , ...,  46.258926,\n",
    +       "          97.96546 ,  28.004541]]], dtype=float32)\n",
            "Coordinates:\n",
    -       "  * time        (time) object 1900-12-31 00:00:00 ... 2022-12-31 00:00:00\n",
    -       "Data variables:\n",
    -       "    pr          (time) float32 67.24 89.49 63.36 75.72 ... 123.1 71.02 131.0\n",
    -       "    event_time  (time) <U28 '1900-04-17' '1901-04-25' ... '2022-05-08'\n",
    -       "Attributes: (12/33)\n",
    -       "    geospatial_lat_min:        -44.525\n",
    -       "    geospatial_lat_max:        -9.975\n",
    -       "    geospatial_lon_min:        111.975\n",
    -       "    geospatial_lon_max:        156.275\n",
    -       "    time_coverage_start:       1899-12-31T09:00:00\n",
    -       "    date_created:              2017-01-17T22:13:51.976225\n",
    -       "    ...                        ...\n",
    -       "    licence:                   Data Licence: The grid data files in this AGCD...\n",
    -       "    description:               This AGCD data is a snapshot of the operationa...\n",
    -       "    date_issued:               2023-05-19 06:19:17\n",
    -       "    attribution:               Data should be cited as : Australian Bureau of...\n",
    -       "    copyright:                 (C) Copyright Commonwealth of Australia 2023, ...\n",
    -       "    history:                    
    " + " * ensemble (ensemble) int64 0 1 2 3 4 5 6 7 8 9\n", + " * init_date (init_date) object 1960-11-01 00:00:00 ... 2018-11-01 00:00:00\n", + " * lead_time (lead_time) int64 1 2 3 4 5 6 7 8 9 10\n", + " time (lead_time, init_date) object 1961-11-01 12:00:00 ... 2028-11-...\n", + "Attributes:\n", + " standard_name: lwe_precipitation_rate\n", + " units: mm d-1
    " ], "text/plain": [ - "\n", - "Dimensions: (time: 123)\n", + "\n", + "array([[[100.882416, 59.101746, 50.513184, ..., 72.814026,\n", + " 47.615986, 60.41703 ],\n", + " [ 59.334 , 33.80462 , 75.82639 , ..., 44.605373,\n", + " 34.917683, 59.61616 ],\n", + " [ 31.110619, 127.5415 , 76.78214 , ..., 62.714207,\n", + " 81.598175, 61.392296],\n", + " ...,\n", + " [ 54.87059 , 92.36635 , 66.44363 , ..., 95.78178 ,\n", + " 46.16651 , 83.41238 ],\n", + " [ 58.627533, 66.82831 , 32.41794 , ..., 67.723145,\n", + " 77.81938 , 49.485268],\n", + " [ 54.334064, 45.583908, 67.08059 , ..., 88.85287 ,\n", + " 115.39417 , 52.16396 ]],\n", + "\n", + " [[ 53.48728 , 56.95953 , 63.92677 , ..., 44.904198,\n", + " 79.179245, 102.832115],\n", + " [133.40063 , 34.3559 , 63.584846, ..., 71.573845,\n", + " 51.077995, 75.63232 ],\n", + " [104.252754, 65.710106, 55.676964, ..., 46.016098,\n", + " 72.45645 , 95.49189 ],\n", + "...\n", + " [ 88.25009 , 60.59867 , 80.30067 , ..., 38.133778,\n", + " 43.382965, 43.320934],\n", + " [ 63.40218 , 36.315556, 51.913548, ..., 41.960567,\n", + " 59.104553, 68.90168 ],\n", + " [ 98.260544, 53.62038 , 32.57572 , ..., 134.02171 ,\n", + " 79.46202 , 68.18055 ]],\n", + "\n", + " [[140.56177 , 54.30856 , 65.706985, ..., 67.92311 ,\n", + " 99.22986 , 36.80957 ],\n", + " [ 46.83064 , 46.71146 , 60.646698, ..., 92.87491 ,\n", + " 32.030388, 40.30447 ],\n", + " [ 30.642147, 48.3465 , 76.646576, ..., 74.966415,\n", + " 56.302555, 52.613976],\n", + " ...,\n", + " [ 49.801647, 51.675907, 42.132774, ..., 86.53947 ,\n", + " 39.62441 , 102.067055],\n", + " [ 43.092773, 71.18484 , 60.19852 , ..., 143.83037 ,\n", + " 68.38716 , 68.01422 ],\n", + " [ 60.222404, 95.45831 , 94.1183 , ..., 46.258926,\n", + " 97.96546 , 28.004541]]], dtype=float32)\n", "Coordinates:\n", - " * time (time) object 1900-12-31 00:00:00 ... 2022-12-31 00:00:00\n", - "Data variables:\n", - " pr (time) float32 67.24 89.49 63.36 75.72 ... 123.1 71.02 131.0\n", - " event_time (time) \n", + "array([0.8080802], dtype=float32)\n", + "Coordinates:\n", + " * month (month) int64 11\n", + "Attributes:\n", + " standard_name: lwe_precipitation_rate\n", + " units: mm d-1\n", + " climatological_period: ['1970-01-01', '2018-12-30']\n", + " bias_correction_method: multiplicative\n", + " bias_correction_period: 1970-01-01-2018-12-30\n" + ] + } + ], + "source": [ + "bias = bias_correction.get_bias(\n", + " model_da_indep,\n", + " agcd_ds['pr'],\n", + " correction_method,\n", + " time_rounding='A',\n", + " time_period=baseline_period\n", + ")\n", + "print(bias)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c02f4466", + "metadata": {}, + "outputs": [], + "source": [ + "model_da_bc = bias_correction.remove_bias(model_da_indep, bias, correction_method)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "456ef9c0", + "metadata": {}, + "outputs": [], + "source": [ + "model_da_bc = model_da_bc.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "8dd4c55f", + "metadata": {}, + "source": [ + "## Similarity testing" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4b191023", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_da_indep.plot.hist(bins=50, density=True, alpha=0.7, facecolor='tab:blue')\n", + "model_raw_shape, model_raw_loc, model_raw_scale = general_utils.fit_gev(model_da_indep.values, generate_estimates=True)\n", + "model_raw_pdf = gev.pdf(xvals, model_raw_shape, model_raw_loc, model_raw_scale)\n", + "plt.plot(xvals, model_raw_pdf, color='tab:blue', linewidth=4.0, label='model')\n", + "\n", + "model_da_bc.plot.hist(bins=50, density=True, alpha=0.7, facecolor='tab:orange')\n", + "model_bc_shape, model_bc_loc, model_bc_scale = general_utils.fit_gev(model_da_bc.values, generate_estimates=True)\n", + "model_bc_pdf = gev.pdf(xvals, model_bc_shape, model_bc_loc, model_bc_scale)\n", + "plt.plot(xvals, model_bc_pdf, color='tab:orange', linewidth=4.0, label='model (corrected)')\n", + "\n", + "agcd_ds['pr'].plot.hist(ax=ax, bins=50, density=True, facecolor='tab:gray', alpha=0.7)\n", + "plt.plot(xvals, agcd_pdf, color='tab:gray', linewidth=4.0, label='observations')\n", + "\n", + "plt.xlabel('Rx5day (mm)')\n", + "plt.ylabel('probability')\n", + "plt.title('Hobart')\n", + "plt.xlim(0, 250)\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.savefig(\n", + " 'distribution.png',\n", + " bbox_inches='tight',\n", + " facecolor='white',\n", + " dpi=200\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5c230f45", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:mean: Obs = 83.49502563476562, Model 95% CI =61.074156665802 to 70.09144229888916\n", + "INFO:root:standard deviation: Obs = 34.267822265625, Model 95% CI =21.441102838516237 to 32.81253042221069\n", + "INFO:root:skew: Obs = 1.4069650511166405, Model 95% CI =0.7192530924631503 to 2.9413828324235896\n", + "INFO:root:kurtosis: Obs = 3.4493238193282894, Model 95% CI =-0.0791896525551157 to 15.018982501742203\n", + "INFO:root:GEV shape: Obs = -0.05232628138994108, Model 95% CI =-0.2494193550817683 to 0.017538621062703454\n", + "INFO:root:GEV location: Obs = 67.7079983493445, Model 95% CI =49.15214029999066 to 56.95791589189183\n", + "INFO:root:GEV scale: Obs = 25.03453176147036, Model 95% CI =15.42330388516541 to 20.79714860722153\n", + "INFO:root:mean: Obs = 83.49502563476562, Bias corrected model 95% CI =75.91402091979981 to 86.94785614013672\n", + "INFO:root:standard deviation: Obs = 34.267822265625, Bias corrected model 95% CI =26.702784490585326 to 40.30490550994873\n", + "INFO:root:skew: Obs = 1.4069650511166405, Bias corrected model 95% CI =0.713920503518016 to 2.680976814333618\n", + "INFO:root:kurtosis: Obs = 3.4493238193282894, Bias corrected model 95% CI =-0.03625950646116078 to 12.247245323530091\n", + "INFO:root:GEV shape: Obs = -0.05232628138994108, Bias corrected model 95% CI =-0.24674260175245813 to 0.032211295575677236\n", + "INFO:root:GEV location: Obs = 67.7079983493445, Bias corrected model 95% CI =61.23131760151553 to 70.3717694560254\n", + "INFO:root:GEV scale: Obs = 25.03453176147036, Bias corrected model 95% CI =19.11389303066017 to 25.725755950694104\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "moments.create_plot(\n", + " model_da_indep,\n", + " agcd_ds['pr'],\n", + " da_bc_fcst=model_da_bc,\n", + " outfile='moments.png',\n", + ")" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 33, "id": "4f191f2c", "metadata": {}, "outputs": [], @@ -3276,7 +3186,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 34, "id": "1fc017fb", "metadata": {}, "outputs": [ @@ -3284,9 +3194,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "KS score: 0.6598098\n", - "KS p-value: 0.0\n", - "AD score: 235.39091\n", + "KS score: 0.2797175\n", + "KS p-value: 7.914835e-09\n", + "AD score: 29.255798\n", "AD p-value: 0.001\n" ] }, @@ -3308,7 +3218,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 35, "id": "92fc4536", "metadata": {}, "outputs": [], @@ -3318,7 +3228,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 36, "id": "f4d6deb0", "metadata": {}, "outputs": [ @@ -3326,9 +3236,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "KS score: 0.0795494\n", - "KS p-value: 0.40908137\n", - "AD score: -0.29753023\n", + "KS score: 0.07429516\n", + "KS p-value: 0.49535686\n", + "AD score: -0.15807883\n", "AD p-value: 0.25\n" ] }, @@ -3358,7 +3268,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 37, "id": "824593a9", "metadata": {}, "outputs": [], @@ -3368,7 +3278,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 38, "id": "a1832325", "metadata": {}, "outputs": [ @@ -3377,8 +3287,8 @@ "output_type": "stream", "text": [ "\n", - "array([ 84.050224, 81.98476 , 46.54293 , ..., 68.81363 , 129.38924 ,\n", - " 28.640915], dtype=float32)\n", + "array([124.84209 , 73.138466, 62.510113, ..., 57.245464, 121.23235 ,\n", + " 34.655647], dtype=float32)\n", "Coordinates:\n", " time (sample) object 1961-11-01 12:00:00 ... 2028-11-01 12:00:00\n", " * sample (sample) object MultiIndex\n", @@ -3399,7 +3309,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 39, "id": "54859739", "metadata": {}, "outputs": [ @@ -3407,13 +3317,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "220 year return period\n", - "95% CI: 177-280 years\n" + "235 year return period\n", + "95% CI: 188-304 years\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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    " ] diff --git a/docs/user_guide/worked_example.rst b/docs/user_guide/worked_example.rst index 1eb3f40..b0bfda5 100644 --- a/docs/user_guide/worked_example.rst +++ b/docs/user_guide/worked_example.rst @@ -272,7 +272,7 @@ you could also run it at the command line and submit to the job queue. .. code-block:: none - $ fileio HadGEM3-GC31-MM_dcppA-hindcast_pr_files.txt Rx5day_HadGEM3-GC31-MM_dcppA-hindcast_s1960-2018_gn_hobart.zarr.zip --n_ensemble_files 10 --variables pr --time_freq A-DEC --time_agg max --input_freq D --point_selection -42.9 147.3 --reset_times --complete_time_agg_periods --units pr=mm day-1 --forecast -v --n_time_files 12 + $ fileio HadGEM3-GC31-MM_dcppA-hindcast_pr_files.txt Rx5day_HadGEM3-GC31-MM_dcppA-hindcast_s1960-2018_gn_hobart.zarr.zip --n_ensemble_files 10 --variables pr --rolling_sum_window 5 --time_freq A-DEC --time_agg max --input_freq D --point_selection -42.9 147.3 --reset_times --complete_time_agg_periods --units pr=mm day-1 --forecast -v --n_time_files 12 Stability and stationarity testing @@ -295,7 +295,7 @@ To do this, we can use the ``stability`` module: uncertainty=True, return_method='gev', units='Rx5day (mm)', - ylim=(0, 250), + ylim=(0, 450), ) @@ -469,9 +469,9 @@ we can use the ``similarity`` module: .. code-block:: none - KS score: 0.6598098 - KS p-value: 0.0 - AD score: 235.39091 + KS score: 0.2797175 + KS p-value: 7.914835e-09 + AD score: 29.255798 AD p-value: 0.001 @@ -486,9 +486,9 @@ we can use the ``similarity`` module: .. code-block:: none - KS score: 0.0795494 - KS p-value: 0.40908137 - AD score: -0.29753023 + KS score: 0.07429516 + KS p-value: 0.49535686 + AD score: -0.15807883 AD p-value: 0.25 @@ -516,8 +516,8 @@ Once we've stacked our model data so it's one dimensional, .. code-block:: none - array([ 84.050224, 81.98476 , 46.54293 , ..., 68.81363 , 129.38924 , - 28.640915], dtype=float32) + array([124.84209 , 73.138466, 62.510113, ..., 57.245464, 121.23235 , + 34.655647], dtype=float32) Coordinates: time (sample) object 1961-11-01 12:00:00 ... 2028-11-01 12:00:00 * sample (sample) object MultiIndex @@ -549,8 +549,8 @@ Once we've stacked our model data so it's one dimensional, .. code-block:: none - 220 year return period - 95% CI: 177-280 years + 235 year return period + 95% CI: 188-304 years .. image:: return_curve.png diff --git a/unseen/general_utils.py b/unseen/general_utils.py index 5215dec..210d9cb 100644 --- a/unseen/general_utils.py +++ b/unseen/general_utils.py @@ -4,8 +4,11 @@ import re import numpy as np +from scipy.optimize import minimize +from scipy.stats import genextreme, rv_continuous +from scipy.stats._constants import _LOGXMAX +from scipy.stats._distn_infrastructure import _sum_finite import xclim -from scipy.stats import genextreme as gev class store_dict(argparse.Action): @@ -138,45 +141,161 @@ def event_in_context(data, threshold, direction): return n_events, n_population, return_period, percentile -def fit_gev(data, user_estimates=[], generate_estimates=False): - """Fit a GEV by providing fit and scale estimates. +class ns_genextreme_gen(rv_continuous): + """Extreme value distributions (stationary or non-stationary).""" - Parameters - ---------- - data : numpy ndarray - user_estimates : list, optional - Estimate of the location and scale parameters - generate_estimates : bool, default False - Fit GEV to data subset first to estimate parameters (useful for large datasets) + def fit_stationary(self, data, user_estimates, generate_estimates): + """Return estimates of shape, location and scale parameters using genextreme.""" + if user_estimates: + shape, loc, scale = user_estimates + shape, loc, scale = genextreme.fit(data, shape, loc=loc, scale=scale) - Returns - ------- - shape : float - Shape parameter - loc : float - Location parameter - scale : float - Scale parameter - """ + elif generate_estimates: + # Generate initial estimates using a data subset (useful for large datasets). + shape, loc, scale = genextreme.fit(data[::2]) + shape, loc, scale = genextreme.fit(data, shape, loc=loc, scale=scale) + else: + shape, loc, scale = genextreme.fit(data) + return shape, loc, scale + + def nllf(self, theta, data, times): + """Penalised negative log-likelihood of GEV probability density function. + + A modified version of scipy.stats.genextremes.fit for fitting extreme value + distribution parameters, in which the location and scale parameters can vary + linearly with a covariate. The non-stationary parameters will be returned only + if the input 'theta' incudes the time-varying location and scale parameters. + A large, finite penalty (rather than infinite negative log-likelihood) + is applied for observations beyond the support of the distribution. + + Parameters + ---------- + theta : tuple of floats + Shape, location and scale parameters (shape, loc0, loc1, scale0, scale1). + data, times : array_like + Data time series and indexes of covariates. + + Returns + ------- + total : float + The sum of the penalised negative likelihood function. + """ + if len(theta) == 5: + # Non-stationary GEV parameters. + shape, loc0, loc1, scale0, scale1 = theta + loc = loc0 + loc1 * times + scale = scale0 + scale1 * times - if user_estimates: - loc_estimate, scale_estimate = user_estimates - shape, loc, scale = gev.fit(data, loc=loc_estimate, scale=scale_estimate) - elif generate_estimates: - shape_estimate, loc_estimate, scale_estimate = gev.fit(data[::2]) - shape, loc, scale = gev.fit(data, loc=loc_estimate, scale=scale_estimate) - else: - shape, loc, scale = gev.fit(data) + else: + # Stationary GEV parameters. + shape, loc, scale = theta - return shape, loc, scale + s = (data - loc) / scale + # Calculate the NLLF (type 1 or types 2-3 extreme value distributions). + if shape == 0: + f = np.log(scale) + s + np.exp(-s) -def return_period(data, event): - """Get return period for given event by fitting a GEV""" + else: + Z = 1 + shape * s + # NLLF at points where the data is supported by the distribution parameters. + # (N.B. the NLLF is not finite when the shape is nonzero and Z is negative + # because the PDF is zero (log(0)=inf) outside of these bounds). + f = np.where( + Z > 0, + np.log(scale) + + (1 + 1 / shape) * np.ma.log(Z) + + np.ma.power(Z, -1 / shape), + np.inf, + ) + + f = np.where(scale > 0, f, np.inf) # Scale parameter must be positive. + + # Sum function along all axes (where finite) & count infinite elements. + total, n_bad = _sum_finite(f) + + # Add large finite penalty instead of infinity (log of the largest useable float). + total = total + n_bad * _LOGXMAX * 100 + return total + + def fit( + self, + data, + user_estimates=[], + loc1=0, + scale1=0, + generate_estimates=False, + stationary=True, + method="Nelder-Mead", + ): + """Return estimates of data distribution parameters and their trend (if applicable). + + For stationary data, estimates the shape, location and scale parameters using + scipy.stats.genextremes.fit(). For non-stationary data, also estimates the linear + location and scale trend parameters using a penalised negative log-likelihood + function with initial estimates based on the stationary fit. + + Parameters + ---------- + data : array_like + Data timeseries. + user estimates: list, optional + Initial estimates of the shape, loc and scale parameters. + loc1, scale1 : float, optional + Initial estimates of the location and scale trend parameters. Defaults to 0. + stationary : bool, optional + Fit as a stationary GEV using scipy.stats.genextremes.fit. Defaults to True. + method : str, optional + Method used for scipy.optimize.minimize. Defaults to 'Nelder-Mead'. + + Returns + ------- + theta : tuple of floats + Shape, location and scale parameters (and loc1 and scale1 if applicable) + + Example + ------- + ''' + ns_genextreme = ns_genextreme_gen() + data = scipy.stats.genextreme.rvs(0.8, loc=3.2, scale=0.5, size=500, random_state=0) + shape, loc, scale = ns_genextreme.fit(data, stationary=True) + shape, loc, loc1, scale, scale1 = ns_genextreme.fit(data, stationary=False) + ''' + """ + + # Use genextremes to get stationary distribution parameters. + shape, loc, scale = self.fit_stationary( + data, user_estimates, generate_estimates + ) + + if stationary: + theta = shape, loc, scale + else: + times = np.arange(data.shape[-1], dtype=int) + theta_i = shape, loc, loc1, scale, scale1 + + # Optimisation bounds (scale parameter must be non-negative). + bounds = [(None, None), (None, None), (None, None), (0, None), (None, None)] + + # Minimise the negative log-likelihood function to get optimal theta. + res = minimize( + self.nllf, theta_i, args=(data, times), method=method, bounds=bounds + ) + theta = res.x + + return theta - shape, loc, scale = fit_gev(data, generate_estimates=True) - probability = gev.sf(event, shape, loc=loc, scale=scale) - return_period = 1.0 / probability + +fit_gev = ns_genextreme_gen().fit + + +def return_period(data, event, **kwargs): + """Get return period for given event by fitting a GEV.""" + + shape, loc, scale = fit_gev(data, **kwargs) + return_period = genextreme.isf( + event, shape, loc=loc, scale=scale + ) # 1.0 / probability return return_period @@ -206,9 +325,9 @@ def gev_return_curve( curve_return_periods = np.logspace(0, max_return_period, num=10000) curve_probabilities = 1.0 / curve_return_periods - curve_values = gev.isf(curve_probabilities, shape, loc, scale) + curve_values = genextreme.isf(curve_probabilities, shape, loc, scale) - event_probability = gev.sf(event_value, shape, loc=loc, scale=scale) + event_probability = genextreme.sf(event_value, shape, loc=loc, scale=scale) event_return_period = 1.0 / event_probability # Bootstrapping for confidence interval @@ -216,15 +335,17 @@ def gev_return_curve( boot_event_return_periods = [] for i in range(n_bootstraps): if bootstrap_method == "parametric": - boot_data = gev.rvs(shape, loc=loc, scale=scale, size=len(data)) + boot_data = genextreme.rvs(shape, loc=loc, scale=scale, size=len(data)) elif bootstrap_method == "non-parametric": boot_data = np.random.choice(data, size=data.shape, replace=True) boot_shape, boot_loc, boot_scale = fit_gev(boot_data, generate_estimates=True) - boot_value = gev.isf(curve_probabilities, boot_shape, boot_loc, boot_scale) + boot_value = genextreme.isf( + curve_probabilities, boot_shape, boot_loc, boot_scale + ) boot_values = np.vstack((boot_values, boot_value)) - boot_event_probability = gev.sf( + boot_event_probability = genextreme.sf( event_value, boot_shape, loc=boot_loc, scale=boot_scale ) boot_event_return_period = 1.0 / boot_event_probability diff --git a/unseen/moments.py b/unseen/moments.py index c153b72..05faa8c 100644 --- a/unseen/moments.py +++ b/unseen/moments.py @@ -23,7 +23,7 @@ def calc_ci(data): return lower_ci, upper_ci -def calc_moments(sample_da, gev_estimates=[]): +def calc_moments(sample_da, **kwargs): """Calculate all the moments for a given sample.""" moments = {} @@ -31,9 +31,7 @@ def calc_moments(sample_da, gev_estimates=[]): moments["standard deviation"] = float(np.std(sample_da)) moments["skew"] = float(scipy.stats.skew(sample_da)) moments["kurtosis"] = float(scipy.stats.kurtosis(sample_da)) - gev_shape, gev_loc, gev_scale = general_utils.fit_gev( - sample_da, user_estimates=gev_estimates - ) + gev_shape, gev_loc, gev_scale = general_utils.fit_gev(sample_da, **kwargs) moments["GEV shape"] = gev_shape moments["GEV location"] = gev_loc moments["GEV scale"] = gev_scale diff --git a/unseen/stability.py b/unseen/stability.py index 634b9e5..b1f0907 100644 --- a/unseen/stability.py +++ b/unseen/stability.py @@ -7,12 +7,20 @@ import seaborn as sns import matplotlib.pyplot as plt from scipy.stats import genextreme as gev +import matplotlib as mpl from . import fileio from . import general_utils from . import time_utils +mpl.rcParams["axes.titlesize"] = "xx-large" +mpl.rcParams["xtick.labelsize"] = "x-large" +mpl.rcParams["ytick.labelsize"] = "x-large" +mpl.rcParams["legend.fontsize"] = "large" +axis_label_size = "large" + + def plot_dist_by_lead(ax, sample_da, metric, units=None, lead_dim="lead_time"): """Plot distribution curves for each lead time. @@ -48,7 +56,7 @@ def plot_dist_by_lead(ax, sample_da, metric, units=None, lead_dim="lead_time"): ax.grid(True) ax.set_title(f"(a) {metric} distribution by lead time") units_label = units if units else sample_da.attrs["units"] - ax.set_xlabel(units_label) + ax.set_xlabel(units_label, fontsize=axis_label_size) ax.legend() @@ -90,11 +98,11 @@ def plot_dist_by_time(ax, sample_da, metric, start_years, units=None): ax.grid(True) ax.set_title(f"(c) {metric} distribution by year") units_label = units if units else sample_da.attrs["units"] - ax.set_xlabel(units_label) + ax.set_xlabel(units_label, fontsize=axis_label_size) ax.legend() -def return_curve(data, method, params=[]): +def return_curve(data, method, params=[], **kwargs): """Return x and y data for a return period curve. Parameters @@ -104,6 +112,8 @@ def return_curve(data, method, params=[]): Fit a GEV or not to data params : list, default None shape, location and scale parameters (calculated if None) + kwargs : dict, optional + kwargs passed to general_utils.fit_gev (N.B. used to use generate_estimates=True) """ if method == "empirical": @@ -115,7 +125,7 @@ def return_curve(data, method, params=[]): if params: shape, loc, scale = params else: - shape, loc, scale = general_utils.fit_gev(data, generate_estimates=True) + shape, loc, scale = general_utils.fit_gev(data, **kwargs) return_values = gev.isf(probabilities, shape, loc, scale) return return_periods, return_values @@ -128,7 +138,7 @@ def plot_return_by_lead( method, uncertainty=False, units=None, - ylim=None, + ymax=None, lead_dim="lead_time", ): """Plot return period curves for each lead time. @@ -147,8 +157,8 @@ def plot_return_by_lead( Plot 95% confidence interval units : str, optional units for plot axis labels - ylim : float, optional - y axis limits for return curve plots [min, max] + ymax : float, optional + ymax for return curve plot lead_dim: str, default 'lead_time' Name of the lead time dimension in sample_da """ @@ -185,16 +195,15 @@ def plot_return_by_lead( ax.grid(True) ax.set_title(f"(b) {metric} return period by lead time") ax.set_xscale("log") - ax.set_xlabel("return period (years)") + ax.set_xlabel("return period (years)", fontsize=axis_label_size) units_label = units if units else sample_da.attrs["units"] - ax.set_ylabel(units_label) - if ylim: - ax.set_ylim(ylim) - ax.legend(loc="upper left") + ax.set_ylabel(units_label, fontsize=axis_label_size) + ax.legend() + ax.set_ylim((50, ymax)) def plot_return_by_time( - ax, sample_da, metric, start_years, method, uncertainty=False, units=None, ylim=None + ax, sample_da, metric, start_years, method, uncertainty=False, units=None, ymax=None ): """Plot return period curves for each time slice (e.g. decade). @@ -214,8 +223,8 @@ def plot_return_by_time( Plot 95% confidence interval units : str, optional units for plot axis labels - ylim : float, optional - ylim for return curve plot + ymax : float, optional + ymax for return curve plot """ step = start_years[1] - start_years[0] - 1 @@ -253,12 +262,11 @@ def plot_return_by_time( ax.grid(True) ax.set_title(f"(d) {metric} return period by year") ax.set_xscale("log") - ax.set_xlabel("return period (years)") + ax.set_xlabel("return period (years)", fontsize=axis_label_size) units_label = units if units else sample_da.attrs["units"] - ax.set_ylabel(units_label) - if ylim: - ax.set_ylim(ylim) - ax.legend(loc="upper left") + ax.set_ylabel(units_label, fontsize=axis_label_size) + ax.set_ylim((50, ymax)) + ax.legend() def create_plot( @@ -267,7 +275,7 @@ def create_plot( start_years, outfile=None, uncertainty=False, - ylim=None, + ymax=None, units=None, return_method="empirical", ensemble_dim="ensemble", @@ -288,8 +296,8 @@ def create_plot( Path for output image file uncertainty: bool, default False Plot the 95% confidence interval - ylim : float, optional - y axis limits for return curve plots [min, max] + ymax : float, optional + ymax for return curve plots units : str, optional units for plot axis labels return_method : {'empirical', 'gev'}, default empirial @@ -318,7 +326,7 @@ def create_plot( metric, return_method, uncertainty=uncertainty, - ylim=ylim, + ymax=ymax, units=units, lead_dim=lead_dim, ) @@ -331,7 +339,7 @@ def create_plot( return_method, units=units, uncertainty=uncertainty, - ylim=ylim, + ymax=ymax, ) if outfile: @@ -367,11 +375,10 @@ def _parse_command_line(): help="Plot the 95 percent confidence interval [default: False]", ) parser.add_argument( - "--ylim", + "--ymax", type=float, - nargs=2, default=None, - help="y axis limits for return curve plots [min, max]", + help="ymax for return curve plots", ) parser.add_argument( "--return_method", @@ -424,7 +431,7 @@ def _main(): outfile=args.outfile, return_method=args.return_method, uncertainty=args.uncertainty, - ylim=args.ylim, + ymax=args.ymax, units=args.units, ensemble_dim=args.ensemble_dim, init_dim=args.init_dim, diff --git a/unseen/tests/conftest.py b/unseen/tests/conftest.py index 38f4fe0..26215da 100644 --- a/unseen/tests/conftest.py +++ b/unseen/tests/conftest.py @@ -2,10 +2,40 @@ import numpy as np import xarray as xr - import dask import dask.array as dsa +# Import all modules to test that dependencies are installed +import unseen.array_handling +import unseen.bias_correction +import unseen.bootstrap +import unseen.dask_setup +import unseen.fileio +import unseen.general_utils +import unseen.independence +import unseen.indices +import unseen.moments +import unseen.similarity +import unseen.spatial_selection +import unseen.stability +import unseen.time_utils + + +# To avoid linting errors need to use all imported modules +unseen.array_handling.__name__ +unseen.bias_correction.__name__ +unseen.bootstrap.__name__ +unseen.dask_setup.__name__ +unseen.fileio.__name__ +unseen.general_utils.__name__ +unseen.independence.__name__ +unseen.indices.__name__ +unseen.moments.__name__ +unseen.similarity.__name__ +unseen.spatial_selection.__name__ +unseen.stability.__name__ +unseen.time_utils.__name__ + def pytest_configure(): pytest.TIME_DIM = "time"
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zAkvdzjZV72I(58$9Y+Y@yB~O-11}zfH7(p8r*9dOvysM}u^87xyz45U6H)U8<(6ht zK_pP4OHKg@P07F!Q9G^afop>$;$d!S^I=zwj%a zSb`>HsvFrpD_ne4EW?-J)e;5wGz?IzMn6OCA2r(aOm_P`^+S%>#UcwKlBBHOYp`p2 zNK*_Z009ke=x|9cMoH(S+pl%hfT4=PkDwi=iLJ$xEw$o9aNScUS3{ja|BvH)%X zt;S;SvHolpo5*iLM}VM?A27{H;M$PR-GCgyg70t$d(Ia9y`fWtRg{Mcfb;KYh$`1D ziw=jXV2itjvG)w_`5y+e6HC`*ii|JsWvpIJ$%hV8A=MsmQL<BY6Q1#@)75dhi}+FiSH8L9P=+bh~DV$um9JFkbJ$6|B}t} p>tFu-{e;o~2hPy{txF#9{Uv$#Z%bCZo1nL0?$Fqtvh~2J{{|0-_9OrR diff --git a/docs/user_guide/worked_example-HadGEM3-GC31-MM.ipynb b/docs/user_guide/worked_example-HadGEM3-GC31-MM.ipynb index d9b228e..464469c 100644 --- a/docs/user_guide/worked_example-HadGEM3-GC31-MM.ipynb +++ b/docs/user_guide/worked_example-HadGEM3-GC31-MM.ipynb @@ -34,37 +34,6 @@ "from unseen import moments" ] }, - { - "cell_type": "code", - "execution_count": 22, - "id": "eca133a5", - "metadata": {}, - "outputs": [], - "source": [ - "from importlib import reload" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "addb60fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reload(stability)" - ] - }, { "cell_type": "markdown", "id": "7bc59b98", @@ -75,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "f53d24b7", "metadata": {}, "outputs": [ @@ -207,7 +176,7 @@ " '/g/data/zv2/agcd/v1-0-1/precip/total/r005/01day/agcd_v1-0-1_precip_total_r005_daily_2022.nc']" ] }, - "execution_count": 5, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "15af0e40", "metadata": {}, "outputs": [ @@ -250,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "f7dbb743", "metadata": {}, "outputs": [ @@ -288,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "id": "7685b8ff", "metadata": {}, "outputs": [], @@ -298,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "id": "565c8610", "metadata": {}, "outputs": [ @@ -701,7 +670,7 @@ " '2016-06-09', '2017-12-05', '2018-05-14', '2019-08-25',\n", " '2020-06-25', '2021-03-28', '2022-05-08'], dtype='<U28')\n", "Coordinates:\n", - " * time (time) object 1900-12-31 00:00:00 ... 2022-12-31 00:00:00