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bradyrx/pangeo_example_faster.ipynb

Created February 27, 2020 19:11
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pangeo_example_faster.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bootstrap and Compute Metric\n",
"\n",
"---\n",
"\n",
"Here we bootstrap with replacement members from a historical ensemble and compare the new ensemble mean to observations (e.g. through a pearson r correlation)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"%load_ext line_profiler\n",
"%matplotlib inline\n",
"\n",
"import xskillscore as xs\n",
"\n",
"from climpred.prediction import compute_hindcast\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# from dask.distributed import Client\n",
"# from ncar_jobqueue import NCARCluster\n",
"# cluster = NCARCluster(cores=36, memory=\"100GB\", walltime=\"00:45:00\", processes=36) # Change this to SLURMCluster if on DAV/Casper\n",
"# cluster.scale(36*1)\n",
"# client = Client(cluster)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create Dummy Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"nlead = 10\n",
"nmember = 10\n",
"\n",
"init = np.arange(1950, 2000)\n",
"lead = np.arange(nlead) + 1\n",
"member = np.arange(nmember) + 1\n",
"time = np.arange(1900, 2020)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"hindcast = xr.DataArray(np.random.rand(len(init), nlead, nmember),\n",
" dims=['init', 'lead', 'member'],\n",
" coords=[init, lead, member]).rename('SST').to_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"historical = xr.DataArray(np.random.rand(len(time), nmember),\n",
" dims=['time', 'member'],\n",
" coords=[time, member]).rename('SST').to_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"observations = xr.DataArray(np.random.rand(30), dims=['time'], coords=[np.arange(1980, 2010)]).rename('SST').to_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"hindcast = hindcast.chunk()\n",
"historical = historical.chunk()\n",
"observations = observations.chunk()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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"text/plain": [
"dask.array<xarray-SST, shape=(50, 10, 10), dtype=float64, chunksize=(50, 10, 10), chunktype=numpy.ndarray>"
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},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hindcast.SST.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Old Way"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def resample(hind, hist):\n",
" \"\"\"\n",
" Resample with a replacement a historical simulation along the ensemble member axis.\n",
" \n",
" Args:\n",
" hind (xarray object): Initialized hindcast simulation.\n",
" hist (xarray object): Historical simulation with multiple ensemble members.\n",
" \"\"\"\n",
" nlead = hind.lead.size\n",
" nmember = hind.member.size\n",
"\n",
" # Resample members with replacement.\n",
" random_members = np.random.choice(hist.member.values, hind.member.size)\n",
" hist = hist.sel(member=random_members)\n",
" \n",
" resampled_hist = xr.concat(\n",
" [hist.sel(time=hind['init'] + lead) for lead in hind.lead.values], \n",
" 'lead',\n",
" coords='minimal', compat='override', data_vars='minimal')\n",
" del resampled_hist['time']\n",
" resampled_hist['member'] = hind['member']\n",
" resampled_hist['lead'] = hind['lead']\n",
" return resampled_hist"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def bootstrap(hind, hist, verif, N):\n",
" uninit_skill = []\n",
" for i in range(N):\n",
" uninit_rs = resample(hind, hist)\n",
" skill = compute_hindcast(uninit_rs, verif, metric='acc')\n",
" uninit_skill.append(skill)\n",
" return xr.concat(uninit_skill, 'bootstrap')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timer unit: 1e-06 s\n",
"\n",
"Total time: 0.215581 s\n",
"File: <ipython-input-8-3a536d8ea0b7>\n",
"Function: resample at line 1\n",
"\n",
"Line # Hits Time Per Hit % Time Line Contents\n",
"==============================================================\n",
" 1 def resample(hind, hist):\n",
" 2 \"\"\"\n",
" 3 Resample with a replacement a historical simulation along the ensemble member axis.\n",
" 4 \n",
" 5 Args:\n",
" 6 hind (xarray object): Initialized hindcast simulation.\n",
" 7 hist (xarray object): Historical simulation with multiple ensemble members.\n",
" 8 \"\"\"\n",
" 9 5 1762.0 352.4 0.8 nlead = hind.lead.size\n",
" 10 5 1573.0 314.6 0.7 nmember = hind.member.size\n",
" 11 \n",
" 12 # Resample members with replacement.\n",
" 13 5 22306.0 4461.2 10.3 random_members = np.random.choice(hist.member.values, hind.member.size)\n",
" 14 5 13278.0 2655.6 6.2 hist = hist.sel(member=random_members)\n",
" 15 \n",
" 16 5 17.0 3.4 0.0 resampled_hist = xr.concat(\n",
" 17 5 133815.0 26763.0 62.1 [hist.sel(time=hind['init'] + lead) for lead in hind.lead.values], \n",
" 18 5 13.0 2.6 0.0 'lead',\n",
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" 20 5 148.0 29.6 0.1 del resampled_hist['time']\n",
" 21 5 5065.0 1013.0 2.3 resampled_hist['member'] = hind['member']\n",
" 22 5 5957.0 1191.4 2.8 resampled_hist['lead'] = hind['lead']\n",
" 23 5 8.0 1.6 0.0 return resampled_hist"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%lprun -f resample bootstrap(hindcast, historical, observations, 5).compute()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.92 s, sys: 61.9 ms, total: 1.99 s\n",
"Wall time: 1.98 s\n"
]
}
],
"source": [
"%time graph = bootstrap(hindcast, historical, observations, 10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"source": [
"graph.SST.data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 95.1 ms, sys: 11.9 ms, total: 107 ms\n",
"Wall time: 97.3 ms\n"
]
},
{
"data": {
"text/html": [
"<pre>&lt;xarray.Dataset&gt;\n",
"Dimensions: (bootstrap: 10, lead: 10)\n",
"Coordinates:\n",
" * lead (lead) int64 1 2 3 4 5 6 7 8 9 10\n",
"Dimensions without coordinates: bootstrap\n",
"Data variables:\n",
" SST (bootstrap, lead) float64 0.07966 0.222 0.203 ... -0.1767 0.1989\n",
"Attributes:\n",
" prediction_skill: calculated by climpred https://climpred.re...\n",
" skill_calculated_by_function: compute_hindcast\n",
" number_of_initializations: 50\n",
" number_of_members: 10\n",
" metric: pearson_r\n",
" comparison: e2o\n",
" dim: time\n",
" units: None\n",
" created: 2020-02-27 12:10:35</pre>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (bootstrap: 10, lead: 10)\n",
"Coordinates:\n",
" * lead (lead) int64 1 2 3 4 5 6 7 8 9 10\n",
"Dimensions without coordinates: bootstrap\n",
"Data variables:\n",
" SST (bootstrap, lead) float64 0.07966 0.222 0.203 ... -0.1767 0.1989\n",
"Attributes:\n",
" prediction_skill: calculated by climpred https://climpred.re...\n",
" skill_calculated_by_function: compute_hindcast\n",
" number_of_initializations: 50\n",
" number_of_members: 10\n",
" metric: pearson_r\n",
" comparison: e2o\n",
" dim: time\n",
" units: None\n",
" created: 2020-02-27 12:10:35"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time graph.compute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# New Way"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def process(hind, hist, obs, lead):\n",
" verif_window = hind['init'] + lead\n",
" hist_verif = hist.sel(time=verif_window.values)\n",
" obs_verif = obs.sel(time=verif_window.values)\n",
" return xs.pearson_r(hist_verif, obs_verif, 'time')\n",
"\n",
"def resample_metric(hind, hist, obs):\n",
" \"\"\"\n",
" hind : initialized hindcast\n",
" hist : historical run with ensemble members\n",
" obs : verification product\n",
" \"\"\"\n",
" nlags = hind.lead.size\n",
" \n",
" # Common inits\n",
" imin = max(hind.init.min(), obs.time.min())\n",
" verif_dates = obs.time - nlags\n",
" imax = min(hind.init.max(), verif_dates.max())\n",
" \n",
" # Slice down hindcast\n",
" hind = hind.sel(init=slice(imin, imax))\n",
" \n",
" # Choose random members\n",
" random_members = np.random.choice(hist.member.values, hind.member.size)\n",
" hist_rs = hist.sel(member=random_members)\n",
" \n",
" # Assume we're doing deterministic metric.\n",
" hist_rs = hist_rs.mean('member')\n",
"\n",
" uninit_skill = xr.concat(\n",
" [process(hind, hist_rs, obs, lead) for lead in hind.lead.values],\n",
" 'lead',\n",
" coords='minimal', compat='override'\n",
" )\n",
" return uninit_skill"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def bootstrap(hind, hist, obs, N):\n",
" return xr.concat([resample_metric(hind, hist, obs) for _ in range(N)], 'bootstrap',\n",
" coords='minimal', compat='override',)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timer unit: 1e-06 s\n",
"\n",
"Total time: 0.448864 s\n",
"File: <ipython-input-14-c02423364fb7>\n",
"Function: resample_metric at line 7\n",
"\n",
"Line # Hits Time Per Hit % Time Line Contents\n",
"==============================================================\n",
" 7 def resample_metric(hind, hist, obs):\n",
" 8 \"\"\"\n",
" 9 hind : initialized hindcast\n",
" 10 hist : historical run with ensemble members\n",
" 11 obs : verification product\n",
" 12 \"\"\"\n",
" 13 5 1990.0 398.0 0.4 nlags = hind.lead.size\n",
" 14 \n",
" 15 # Common inits\n",
" 16 5 5074.0 1014.8 1.1 imin = max(hind.init.min(), obs.time.min())\n",
" 17 5 1479.0 295.8 0.3 verif_dates = obs.time - nlags\n",
" 18 5 3874.0 774.8 0.9 imax = min(hind.init.max(), verif_dates.max())\n",
" 19 \n",
" 20 # Slice down hindcast\n",
" 21 5 9990.0 1998.0 2.2 hind = hind.sel(init=slice(imin, imax))\n",
" 22 \n",
" 23 # Choose random members\n",
" 24 5 3419.0 683.8 0.8 random_members = np.random.choice(hist.member.values, hind.member.size)\n",
" 25 5 10157.0 2031.4 2.3 hist_rs = hist.sel(member=random_members)\n",
" 26 \n",
" 27 # Assume we're doing deterministic metric.\n",
" 28 5 17126.0 3425.2 3.8 hist_rs = hist_rs.mean('member')\n",
" 29 \n",
" 30 5 18.0 3.6 0.0 uninit_skill = xr.concat(\n",
" 31 5 352401.0 70480.2 78.5 [process(hind, hist_rs, obs, lead) for lead in hind.lead.values],\n",
" 32 5 24.0 4.8 0.0 'lead',\n",
" 33 5 43302.0 8660.4 9.6 coords='minimal', compat='override'\n",
" 34 )\n",
" 35 5 10.0 2.0 0.0 return uninit_skill"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%lprun -f resample_metric bootstrap(hindcast, historical, observations, 5).compute()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 473 ms, sys: 11.3 ms, total: 484 ms\n",
"Wall time: 478 ms\n"
]
}
],
"source": [
"%time graph = bootstrap(hindcast, historical, observations, 10)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
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"dask.array<concatenate, shape=(10, 10), dtype=float64, chunksize=(1, 1), chunktype=numpy.ndarray>"
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{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 92.6 ms, sys: 13.8 ms, total: 106 ms\n",
"Wall time: 101 ms\n"
]
},
{
"data": {
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" SST (bootstrap, lead) float64 0.1173 0.1912 0.2171 ... 0.3657 0.3371</pre>"
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