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aaronspring/quick-start-big_data-ICON.ipynb

Last active April 9, 2022 14:09
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`bitinformation_pipeline` for ICONO output: compression factor 5
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quick-start-big_data-ICON.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "3f2c9098",
"metadata": {},
"source": [
"# Example to retrieve bitinformation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0f7e319d-8e1c-4116-8ea3-59349e26fdad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.core.options.set_options at 0x7ffff0482020>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import bitinformation_pipeline as bp\n",
"\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import shutil\n",
"import os\n",
"\n",
"xr.set_options(display_style=\"text\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "895c2c7a-240d-4e7a-81dd-4f05befbf7a6",
"metadata": {},
"outputs": [],
"source": [
"label = 'ICONO_R2B8'\n",
"path = \"/work/mh0727/m300524/test_output/exp.ocean_era51h_r2b8_hel20218-ERA_20000401T000000Z.nc\"\n",
"v = \"to\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d11eef48-7a74-490d-9e56-2b67ee3c644c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre>&lt;xarray.Dataset&gt;\n",
"Dimensions: (ncells: 3729001, vertices: 3,\n",
" ncells_2: 5612762, vertices_2: 4,\n",
" depth: 128, depth_2: 129, lev: 1, time: 1)\n",
"Coordinates:\n",
" clon (ncells) float32 ...\n",
" clon_bnds (ncells, vertices) float32 ...\n",
" clat (ncells) float32 ...\n",
" clat_bnds (ncells, vertices) float32 ...\n",
" elon (ncells_2) float32 ...\n",
" elon_bnds (ncells_2, vertices_2) float32 ...\n",
" elat (ncells_2) float32 ...\n",
" elat_bnds (ncells_2, vertices_2) float32 ...\n",
" * depth (depth) float64 5.5 15.5 ... 6.262e+03\n",
" * depth_2 (depth_2) float64 0.0 11.0 ... 6.362e+03\n",
" * lev (lev) float64 0.0\n",
" * time (time) datetime64[ns] 2000-04-01\n",
"Dimensions without coordinates: ncells, vertices, ncells_2, vertices_2\n",
"Data variables: (12/36)\n",
" zos (ncells, time) float32 ...\n",
" to (ncells, time, depth) float32 ...\n",
" so (ncells, time, depth) float32 ...\n",
" w (ncells, time, depth_2) float32 ...\n",
" wet_c (ncells, depth) float32 ...\n",
" wet_e (ncells_2, depth) float32 ...\n",
" ... ...\n",
" velocity_windMixing (ncells_2, time, depth_2) float32 ...\n",
" tracer_windMixing (ncells, time, depth_2) float32 ...\n",
" K_tracer_h_to (ncells_2, time, depth) float32 ...\n",
" A_tracer_v_to (ncells, time, depth_2) float32 ...\n",
" K_tracer_h_so (ncells_2, time, depth) float32 ...\n",
" A_tracer_v_so (ncells, time, depth_2) float32 ...\n",
"Attributes:\n",
" CDI: Climate Data Interface version 1.8.3rc (http://mpim...\n",
" Conventions: CF-1.6\n",
" number_of_grid_used: 42\n",
" uuidOfHGrid: 66c2eb2c-9bd9-11e8-97bc-e1d6091d8653\n",
" institution: Max Planck Institute for Meteorology/Deutscher Wett...\n",
" title: ICON simulation\n",
" source: git@gitlab.dkrz.de:icon/icon-oes.git@80c1ffd53ef5dd...\n",
" history: /work/mh0033/m211054/projects/icon/icon-oes-1.3.01/...\n",
" references: see MPIM/DWD publications\n",
" comment: Helmuth Haak (m211054) on m20000 (Linux 2.6.32-754....</pre>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (ncells: 3729001, vertices: 3,\n",
" ncells_2: 5612762, vertices_2: 4,\n",
" depth: 128, depth_2: 129, lev: 1, time: 1)\n",
"Coordinates:\n",
" clon (ncells) float32 ...\n",
" clon_bnds (ncells, vertices) float32 ...\n",
" clat (ncells) float32 ...\n",
" clat_bnds (ncells, vertices) float32 ...\n",
" elon (ncells_2) float32 ...\n",
" elon_bnds (ncells_2, vertices_2) float32 ...\n",
" elat (ncells_2) float32 ...\n",
" elat_bnds (ncells_2, vertices_2) float32 ...\n",
" * depth (depth) float64 5.5 15.5 ... 6.262e+03\n",
" * depth_2 (depth_2) float64 0.0 11.0 ... 6.362e+03\n",
" * lev (lev) float64 0.0\n",
" * time (time) datetime64[ns] 2000-04-01\n",
"Dimensions without coordinates: ncells, vertices, ncells_2, vertices_2\n",
"Data variables: (12/36)\n",
" zos (ncells, time) float32 ...\n",
" to (ncells, time, depth) float32 ...\n",
" so (ncells, time, depth) float32 ...\n",
" w (ncells, time, depth_2) float32 ...\n",
" wet_c (ncells, depth) float32 ...\n",
" wet_e (ncells_2, depth) float32 ...\n",
" ... ...\n",
" velocity_windMixing (ncells_2, time, depth_2) float32 ...\n",
" tracer_windMixing (ncells, time, depth_2) float32 ...\n",
" K_tracer_h_to (ncells_2, time, depth) float32 ...\n",
" A_tracer_v_to (ncells, time, depth_2) float32 ...\n",
" K_tracer_h_so (ncells_2, time, depth) float32 ...\n",
" A_tracer_v_so (ncells, time, depth_2) float32 ...\n",
"Attributes:\n",
" CDI: Climate Data Interface version 1.8.3rc (http://mpim...\n",
" Conventions: CF-1.6\n",
" number_of_grid_used: 42\n",
" uuidOfHGrid: 66c2eb2c-9bd9-11e8-97bc-e1d6091d8653\n",
" institution: Max Planck Institute for Meteorology/Deutscher Wett...\n",
" title: ICON simulation\n",
" source: git@gitlab.dkrz.de:icon/icon-oes.git@80c1ffd53ef5dd...\n",
" history: /work/mh0033/m211054/projects/icon/icon-oes-1.3.01/...\n",
" references: see MPIM/DWD publications\n",
" comment: Helmuth Haak (m211054) on m20000 (Linux 2.6.32-754...."
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.open_dataset(path)\n",
"# bnds shouldnt be data_vars\n",
"ds = ds.set_coords([\"clon_bnds\",\"clat_bnds\",\"elon_bnds\",\"elat_bnds\"])\n",
"dsa = ds.transpose(\"ncells\",...).transpose(\"ncells_2\",...)\n",
"dsa"
]
},
{
"cell_type": "markdown",
"id": "822d7b08",
"metadata": {},
"source": [
"## Get information content per bit\n",
"\n",
"using `bp.get_bitinformation(ds)`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4fd451f-4853-498a-a029-364a62a9c2b0",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# make triangles first dim with transpose\n",
"info_per_bit = bp.get_bitinformation(dsa, dim=1, masked_value=\"convert(Float32,0)\", label=label)\n",
"\n",
"info_per_bit[v]"
]
},
{
"cell_type": "markdown",
"id": "bdfd9186",
"metadata": {},
"source": [
"## Visualize information content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05d43ae8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9min 11s, sys: 37.4 s, total: 9min 48s\n",
"Wall time: 9min 26s\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x662.4 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%time fig = bp.plot_bitinformation(ds, info_per_bit)\n",
"plt.savefig(f\"bitinformation_{label}.png\")"
]
},
{
"cell_type": "markdown",
"id": "c14645a5",
"metadata": {},
"source": [
"## Get keepbits\n",
"\n",
"using `bp.get_bitinformation(ds, info_per_bit)`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5385105d",
"metadata": {},
"outputs": [],
"source": [
"%time keepbits = bp.get_keepbits(info_per_bit, 0.99)\n",
"keepbits[v]"
]
},
{
"cell_type": "markdown",
"id": "ec3dd057-b634-48b8-b9f4-47b09b2f4599",
"metadata": {},
"source": [
"## Apply bitrounding\n",
"\n",
"using `bp.xr_bitround(ds, keepbits)`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b7a24e0e-fd58-40b6-a217-751f45038bf5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'zos': 4,\n",
" 'to': 7,\n",
" 'so': 14,\n",
" 'w': 0,\n",
" 'wet_c': 23,\n",
" 'wet_e': 23,\n",
" 'rho': 18,\n",
" 'rhopot': 18,\n",
" 'mass_flux': 0,\n",
" 'u': 1,\n",
" 'v': 1,\n",
" 'u_vint': 0,\n",
" 'v_vint': 23,\n",
" 'mld': 3,\n",
" 'mlotst': 3,\n",
" 'mlotstsq': 2,\n",
" 'swsum': 0,\n",
" 'heatabs': 6,\n",
" 'swrab': 0,\n",
" 'rsdoabsorb': 7,\n",
" 'condep': 5,\n",
" 'zos_square': 3,\n",
" 'hi': 3,\n",
" 'hs': 2,\n",
" 'conc': 7,\n",
" 'ice_u': 0,\n",
" 'ice_v': 0,\n",
" 'BiharmonicViscosity_BasisCoeff': 23,\n",
" 'BiharmonicViscosity_coeff': 0,\n",
" 'A_veloc_v': 6,\n",
" 'velocity_windMixing': 23,\n",
" 'tracer_windMixing': 23,\n",
" 'K_tracer_h_to': 23,\n",
" 'A_tracer_v_to': 5,\n",
" 'K_tracer_h_so': 23,\n",
" 'A_tracer_v_so': 5}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# limit before fixing bitround.py to using mantissa bits only: https://github.com/observingClouds/bitinformation_pipeline/issues/27\n",
"# keepbits = {k:max(0,min(v-9,23)) for k,v in keepbits.items()}\n",
"keepbits"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1e9789eb-c1cb-4b59-8ac7-429470ed49fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 44s, sys: 37 s, total: 2min 21s\n",
"Wall time: 2min 22s\n"
]
}
],
"source": [
"%time ds_bitrounded = bp.xr_bitround(ds, keepbits)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "37c8e27b-fa81-4fac-86f9-f45af399d2f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2min 6s, sys: 21.3 s, total: 2min 27s\n",
"Wall time: 2min 31s\n"
]
}
],
"source": [
"%time ds_bitrounded = bp.jl_bitround(ds, keepbits)"
]
},
{
"cell_type": "markdown",
"id": "c0b507c7-4261-4ff0-8d8e-617d874c6832",
"metadata": {},
"source": [
"## Save compressed\n",
"\n",
"using `ds_bitrounded.to_compressed_netcdf(path)`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e1f153a5-df6b-4646-a744-806d0b84612c",
"metadata": {},
"outputs": [],
"source": [
"_ = shutil.copyfile(path, f\"{label}_original.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fd9be7d7-3e36-457f-9714-0afd94f85625",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOStream.flush timed out\n",
"IOStream.flush timed out\n",
"IOStream.flush timed out\n",
"IOStream.flush timed out\n",
"IOStream.flush timed out\n",
"IOStream.flush timed out\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 56min 55s, sys: 35.4 s, total: 57min 31s\n",
"Wall time: 57min 31s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOStream.flush timed out\n"
]
}
],
"source": [
"%time ds.to_compressed_netcdf(f\"{label}_compressed.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "41604e42-f6ee-482c-b43f-fcf1252d6b5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3min 56s, sys: 22.2 s, total: 4min 18s\n",
"Wall time: 4min 19s\n"
]
}
],
"source": [
"%time ds_bitrounded.to_compressed_netcdf(f\"{label}_bitrounded_compressed_l2.nc\", complevel=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf8a791a-002d-46e8-b5e2-6d4c5538b7fd",
"metadata": {},
"outputs": [],
"source": [
"%time ds_bitrounded.to_compressed_netcdf(f\"{label}_bitrounded_compressed_l4.nc\", complevel=4)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5ea0ccf9-2db8-4741-b2e1-370546fde4f8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOStream.flush timed out\n",
"IOStream.flush timed out\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 26min 30s, sys: 23.9 s, total: 26min 54s\n",
"Wall time: 26min 55s\n"
]
}
],
"source": [
"%time ds_bitrounded.to_compressed_netcdf(f\"{label}_bitrounded_compressed_l7.nc\", complevel=7) # fails for large ICON output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c502fe6c-1d66-4399-a0d6-dbb259698406",
"metadata": {},
"outputs": [],
"source": [
"# takes long\n",
"%time ds_bitrounded.to_compressed_netcdf(f\"{label}_bitrounded_compressed_l9.nc\", complevel=9) # fails for large ICON output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbb5b20a-3bb5-4b86-a2ea-4445870f0a03",
"metadata": {},
"outputs": [],
"source": [
"# takes long # for_cdo=True enforces chunksizes time = 1\n",
"%time ds_bitrounded.to_compressed_netcdf(f\"{label}_bitrounded_compressed_l9_for_cdo.nc\", for_cdo=True)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "93f97c30-f76f-4eaf-a590-e5bb433f8626",
"metadata": {},
"outputs": [],
"source": [
"files = ['original','compressed','bitrounded_compressed_l2','bitrounded_compressed_l4','bitrounded_compressed_l7','bitrounded_compressed_l7_for_cdo','bitrounded_compressed_l9']\n",
"for f in files:\n",
" try:\n",
" print(f, xr.open_dataset(f\"{label}_{f}.nc\")[v].encoding.get(\"chunksizes\",None))\n",
" except:\n",
" pass"
]
},
{
"cell_type": "markdown",
"id": "e427874d-5471-4eb6-a991-f8b3678a464d",
"metadata": {},
"source": [
"## size analysis on a whole netcdf file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5c4a0f91-c276-4b75-a854-4490d023ed60",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.2G\tICONO_R2B8_bitrounded_compressed_l1.nc\n",
"3.1G\tICONO_R2B8_bitrounded_compressed_l2.nc\n",
"2.8G\tICONO_R2B8_bitrounded_compressed_l4.nc\n",
"2.7G\tICONO_R2B8_bitrounded_compressed_l7_for_cdo.nc\n",
"2.7G\tICONO_R2B8_bitrounded_compressed_l7.nc\n",
"2.4G\tICONO_R2B8_bitrounded_compressed_l9.nc\n",
"13G\tICONO_R2B8_compressed.nc\n",
"13G\tICONO_R2B8_original.nc\n"
]
}
],
"source": [
"!du -hs ICON*.nc"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2f98b724-58c2-4756-97e7-080f4997bd43",
"metadata": {},
"outputs": [],
"source": [
"original_size = os.path.getsize(f\"{label}_original.nc\")\n",
"\n",
"new_sizes = xr.DataArray(np.array([os.path.getsize(f\"{label}_{l}.nc\") for l in files]),dims='file',coords={'file':files})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ebbb01f-b1a8-454e-8503-4152d6afdc22",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>compression factor ICONO_R2B8</th>\n",
" </tr>\n",
" <tr>\n",
" <th>file</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>original</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>compressed</th>\n",
" <td>1.032433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bitrounded_compressed_l2</th>\n",
" <td>4.194372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bitrounded_compressed_l4</th>\n",
" <td>4.633989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bitrounded_compressed_l7</th>\n",
" <td>4.718895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bitrounded_compressed_l7_for_cdo</th>\n",
" <td>4.718894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bitrounded_compressed_l9</th>\n",
" <td>5.289242</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" compression factor ICONO_R2B8\n",
"file \n",
"original 1.000000\n",
"compressed 1.032433\n",
"bitrounded_compressed_l2 4.194372\n",
"bitrounded_compressed_l4 4.633989\n",
"bitrounded_compressed_l7 4.718895\n",
"bitrounded_compressed_l7_for_cdo 4.718894\n",
"bitrounded_compressed_l9 5.289242"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# compression factors on disk\n",
"(original_size/new_sizes).to_dataframe(name=f'compression factor {label}')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d964fabd-0b16-4432-9708-151c9ac19217",
"metadata": {},
"outputs": [],
"source": [
"assert xr.open_dataset(f\"{label}_original.nc\",chunks={}).nbytes == xr.open_dataset(f\"{label}_bitrounded_compressed_l7.nc\",chunks={}).nbytes"
]
},
{
"cell_type": "markdown",
"id": "da737027-6895-4c01-83b2-87e9c0b4b2e2",
"metadata": {},
"source": [
"## analysis with `cdo`"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f902638e-99a6-4444-b0a4-8ce829c586c3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 10.2 ms, sys: 148 ms, total: 159 ms\n",
"Wall time: 3.23 s\n"
]
}
],
"source": [
"%time !cdo -s -fldmean -timmean ICONO_R2B8_original.nc out.nc"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8e1a1e82-5ffb-4c4b-9814-a74d6b336cf4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning (cdfScanVarAttr): NetCDF: Variable not found - clon_bnds\n",
"Warning (cdfScanVarAttr): NetCDF: Variable not found - clat_bnds\n",
"Warning (cdfScanVarAttr): NetCDF: Variable not found - elon_bnds\n",
"Warning (cdfScanVarAttr): NetCDF: Variable not found - elat_bnds\n",
"CPU times: user 611 ms, sys: 212 ms, total: 823 ms\n",
"Wall time: 2.57 s\n"
]
}
],
"source": [
"%time !cdo -s -fldmean -timmean ICONO_R2B8_bitrounded_compressed_l2.nc out.nc"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4f68621c-81fa-469f-a3a8-86f59f0b4f2b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning (cdf_set_var): Inconsistent variable definition for clon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for clat_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elat_bnds!\n",
"CPU times: user 19.5 ms, sys: 282 ms, total: 302 ms\n",
"Wall time: 3.53 s\n"
]
}
],
"source": [
"%time !cdo -s -fldmean -timmean ICONO_R2B8_bitrounded_compressed_l7.nc out.nc"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b9d342bf-0718-4771-b07f-63eb78e9a6cf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning (cdf_set_var): Inconsistent variable definition for clon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for clat_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elat_bnds!\n",
"CPU times: user 17 ms, sys: 191 ms, total: 208 ms\n",
"Wall time: 3.84 s\n"
]
}
],
"source": [
"%time !cdo -s -fldmean -timmean ICONO_R2B8_bitrounded_compressed_l7_for_cdo.nc out.nc"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "08340604-3c77-4975-b801-8693e6bd611d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning (cdf_set_var): Inconsistent variable definition for clon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for clat_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elon_bnds!\n",
"Warning (cdf_set_var): Inconsistent variable definition for elat_bnds!\n",
"CPU times: user 8.1 ms, sys: 175 ms, total: 183 ms\n",
"Wall time: 3.43 s\n"
]
}
],
"source": [
"%time !cdo -s -fldmean -timmean ICONO_R2B8_bitrounded_compressed_l9.nc out.nc"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "16bbc5ce-d141-4641-95db-e391bbea733b",
"metadata": {},
"outputs": [],
"source": [
"!rm out.nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad4a2d80-d5dc-485a-9c95-1cc79ed57f2c",
"metadata": {},
"outputs": [],
"source": [
"#!cdo diff mpiom_data_2d_mm_bitrounded_compressed_for_cdo.nc mpiom_data_2d_mm_original.nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c709ada7-0c1d-4f75-af9c-5a09360dba09",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ed962dee-45ac-4745-968f-76848a4ae845",
"metadata": {},
"outputs": [],
"source": [
"#!rm *.nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c303b5fc-9b21-4dbf-ae86-43cb674845fa",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "53de2f90-30c6-42fc-9d80-88893a092398",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2eea60f7-7356-4329-b31e-f9f5d60a202e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" total used free shared buff/cache available\n",
"Mem: 251 58 187 0 5 190\n",
"Swap: 0 0 0\n"
]
}
],
"source": [
"!free -g"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "47a75e6f-3c34-4592-8925-fb068dfba15a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Architecture: x86_64\n",
"CPU op-mode(s): 32-bit, 64-bit\n",
"Byte Order: Little Endian\n",
"CPU(s): 256\n",
"On-line CPU(s) list: 0-255\n",
"Thread(s) per core: 2\n",
"Core(s) per socket: 64\n",
"Socket(s): 2\n",
"NUMA node(s): 8\n",
"Vendor ID: AuthenticAMD\n",
"CPU family: 25\n",
"Model: 1\n",
"Model name: AMD EPYC 7763 64-Core Processor\n",
"Stepping: 1\n",
"CPU MHz: 1797.441\n",
"CPU max MHz: 2450.0000\n",
"CPU min MHz: 1500.0000\n",
"BogoMIPS: 4900.14\n",
"Virtualization: AMD-V\n",
"L1d cache: 32K\n",
"L1i cache: 32K\n",
"L2 cache: 512K\n",
"L3 cache: 32768K\n",
"NUMA node0 CPU(s): 0-15,128-143\n",
"NUMA node1 CPU(s): 16-31,144-159\n",
"NUMA node2 CPU(s): 32-47,160-175\n",
"NUMA node3 CPU(s): 48-63,176-191\n",
"NUMA node4 CPU(s): 64-79,192-207\n",
"NUMA node5 CPU(s): 80-95,208-223\n",
"NUMA node6 CPU(s): 96-111,224-239\n",
"NUMA node7 CPU(s): 112-127,240-255\n",
"Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall sev_es fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\n"
]
}
],
"source": [
"!lscpu"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cab20c04-8678-44e4-a95b-817a99c0687d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "bitinfo",
"language": "python",
"name": "bitinfo"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"toc-autonumbering": true
},
"nbformat": 4,
"nbformat_minor": 5
}
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