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@ccarouge
Created July 18, 2019 04:45
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import xarray as xr\n",
"ff = \"/g/data/al33/replicas/CMIP5/combined/IPSL/IPSL-CM5A-LR/historical/day/ocean/day/r1i1p1/v20111010/tos/tos_day_IPSL-CM5A-LR_historical_r1i1p1_18500101-20051231.nc\"\n",
"ds = xr.open_dataset(ff)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (bnds: 2, i: 182, j: 149, time: 56940, vertices: 4)\n",
"Coordinates:\n",
" * time (time) object 1850-01-01 12:00:00 ... 2005-12-31 12:00:00\n",
" * j (j) int32 1 2 3 4 5 6 7 8 ... 142 143 144 145 146 147 148 149\n",
" * i (i) int32 1 2 3 4 5 6 7 8 ... 175 176 177 178 179 180 181 182\n",
" lat (j, i) float32 ...\n",
" lon (j, i) float32 ...\n",
"Dimensions without coordinates: bnds, vertices\n",
"Data variables:\n",
" time_bnds (time, bnds) object ...\n",
" lat_vertices (j, i, vertices) float32 ...\n",
" lon_vertices (j, i, vertices) float32 ...\n",
" tos (time, j, i) float32 ...\n",
"Attributes:\n",
" institution: IPSL (Institut Pierre Simon Laplace, Paris, France)\n",
" institute_id: IPSL\n",
" experiment_id: historical\n",
" source: IPSL-CM5A-LR (2010) : atmos : LMDZ4 (LMDZ4_v5, 96...\n",
" model_id: IPSL-CM5A-LR\n",
" forcing: Nat,Ant,GHG,SA,Oz,LU,SS,Ds,BC,MD,OC,AA\n",
" parent_experiment_id: piControl\n",
" parent_experiment_rip: r1i1p1\n",
" branch_time: 1850.0\n",
" contact: ipsl-cmip5 _at_ ipsl.jussieu.fr Data manager : Se...\n",
" comment: This 20th century simulation include natural and ...\n",
" references: Model documentation and further reference availab...\n",
" initialization_method: 1\n",
" physics_version: 1\n",
" tracking_id: 3253cfc0-f946-4272-a310-9d097b65e4f7\n",
" product: output\n",
" experiment: historical\n",
" frequency: day\n",
" creation_date: 2011-07-03T13:28:50Z\n",
" history: 2011-07-03T13:28:50Z CMOR rewrote data to comply ...\n",
" Conventions: CF-1.4\n",
" project_id: CMIP5\n",
" table_id: Table day (10 February 2011) 80e409bd73611e9d25d0...\n",
" title: IPSL-CM5A-LR model output prepared for CMIP5 hist...\n",
" parent_experiment: pre-industrial control\n",
" modeling_realm: ocean\n",
" realization: 1\n",
" cmor_version: 2.7.1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'tos' (time: 1065, j: 149, i: 182)>\n",
"[28880670 values with dtype=float32]\n",
"Coordinates:\n",
" * time (time) object 1850-01-01 12:00:00 ... 1852-12-01 12:00:00\n",
" * j (j) int32 1 2 3 4 5 6 7 8 9 ... 141 142 143 144 145 146 147 148 149\n",
" * i (i) int32 1 2 3 4 5 6 7 8 9 ... 174 175 176 177 178 179 180 181 182\n",
" lat (j, i) float32 ...\n",
" lon (j, i) float32 ...\n",
"Attributes:\n",
" standard_name: surface_temperature\n",
" long_name: Sea Surface Temperature\n",
" comment: temperature of liquid ocean.\n",
" units: K\n",
" original_name: sosstsst\n",
" original_units: degC\n",
" history: 2011-07-03T13:28:07Z altered by CMOR: Converted units ...\n",
" cell_methods: time: mean (interval: 30 minutes)\n",
" cell_measures: area: areacello\n",
" associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..."
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tos = ds['tos'].sel(time=slice('1850-01-01','1852-12-01'))\n",
"tos"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"def correlate_1d(timeseries, lag):\n",
" return numpy.correlate(timeseries, timeseries[lag:], mode='valid')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"c = numpy.apply_along_axis(correlate_1d, # Function to apply\n",
"axis=0, # Axis to apply along (time is the first axis)\n",
"arr=tos.where(xr.ufuncs.isfinite(tos),drop=True), # Input array\n",
"lag=1 # Extra arguments passed to the correlate_1d function\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 149, 182)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"c_xr=xr.DataArray(c,dims=('stats','j','i'),coords={'stats':[0,1],'j':tos.j.sel(j=slice(2,150)),'i':tos.i})"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7f7671c53e48>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c_xr.sel(stats=1).plot()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray ()>\n",
"array(83678280., dtype=float32)\n",
"Coordinates:\n",
" stats int64 0\n",
" j int32 40\n",
" i int32 50"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_xr.sel(stats=0,i=50,j=40)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"c2 = numpy.apply_along_axis(correlate_1d, # Function to apply\n",
"axis=0, # Axis to apply along (time is the first axis)\n",
"arr=tos, # Input array\n",
"lag=1 # Extra arguments passed to the correlate_1d function\n",
")\n",
"c_xr2=xr.DataArray(c2,dims=('stats','j','i'),\n",
" coords={'stats':[0,1],'j':tos.j,'i':tos.i})"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray ()>\n",
"array(83678280., dtype=float32)\n",
"Coordinates:\n",
" stats int64 0\n",
" j int32 40\n",
" i int32 50"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_xr2.sel(stats=0,i=50,j=40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:analysis3-19.04] *",
"language": "python",
"name": "conda-env-analysis3-19.04-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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