![binder badge](\"https://mybinder.org/badge_logo.svg\"){kind=icon}
](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/examples/decadal/Significance.ipynb) or view it [on Github](https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/decadal/Significance.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Significance Testing\n",
"\n",
"This demo shows how to handle significance testing with {py:meth}`.HindcastEnsemble.bootstrap` and {py:meth}`.PerfectModelEnsemble.bootstrap`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 1;\n",
" var nbb_unformatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n",
" var nbb_formatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
" nbb_cells[i].set_text(nbb_formatted_code);\n",
" }\n",
" break;\n",
" }\n",
" }\n",
" }, 500);\n",
" "
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (skill: 4, results: 4, lead: 10)\n",
"Coordinates:\n",
" * lead (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
" * results (results) <U12 'verify skill' 'p' 'low_ci' 'high_ci'\n",
" * skill (skill) <U13 'initialized' 'uninitialized' ... 'climatology'\n",
"Data variables:\n",
" SST (skill, results, lead) float64 0.6387 0.4999 ... 2.534e-17\n",
"Attributes: (12/13)\n",
" prediction_skill_software: climpred https://climpred.readthedocs.io/\n",
" skill_calculated_by_function: HindcastEnsemble.bootstrap()\n",
" number_of_initializations: 64\n",
" number_of_members: 10\n",
" alignment: same_verif\n",
" metric: pearson_r\n",
" ... ...\n",
" dim: init\n",
" reference: ['uninitialized', 'persistence', 'climatol...\n",
" resample_dim: member\n",
" sig: 95\n",
" iterations: 500\n",
" confidence_interval_levels: 0.975-0.025array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=int32)
array(['verify skill', 'p', 'low_ci', 'high_ci'], dtype='<U12')
array(['initialized', 'uninitialized', 'persistence', 'climatology'],\n",
" dtype='<U13')array([[[ 6.38720606e-01, 4.99886306e-01, 4.00974544e-01,\n",
" 4.64753850e-01, 3.94859390e-01, 4.22195091e-01,\n",
" 3.75768806e-01, 4.04019362e-01, 2.21970733e-01,\n",
" 1.15090868e-01],\n",
" [ nan, nan, nan,\n",
" nan, nan, nan,\n",
" nan, nan, nan,\n",
" nan],\n",
" [ 6.05607808e-01, 4.42839478e-01, 3.04958391e-01,\n",
" 2.94752795e-01, 2.38798104e-01, 2.78128696e-01,\n",
" 1.78472999e-01, 2.25385057e-01, 3.44836690e-02,\n",
" 1.06077717e-02],\n",
" [ 6.58540248e-01, 5.16541572e-01, 4.60744203e-01,\n",
" 5.17384425e-01, 4.82651967e-01, 4.59664714e-01,\n",
" 4.72143290e-01, 4.69375421e-01, 3.37799962e-01,\n",
" 1.94808990e-01]],\n",
"\n",
" [[ 2.00206230e-01, 2.00206230e-01, 2.00206230e-01,\n",
" 2.00206230e-01, 2.00206230e-01, 2.00206230e-01,\n",
" 2.00206230e-01, 2.00206230e-01, 2.00206230e-01,\n",
"...\n",
" 1.35736767e-02, -2.15695852e-01, -1.63570706e-01,\n",
" 4.21097401e-02, 1.33615735e-03, -1.12685502e-01,\n",
" 1.35590826e-01]],\n",
"\n",
" [[ 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17],\n",
" [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 6.00000000e-03,\n",
" 1.40000000e-02],\n",
" [ 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17],\n",
" [ 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17, 2.53443994e-17, 2.53443994e-17,\n",
" 2.53443994e-17]]])