{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Handling CTM datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial shows how to load and save datasets, and also how it can be manipulated.\n", "\n", "The `pygchem.datasets` module provides functions to load and save CTM datasets: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pygchem import datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE: In the PyGChem development version, this module was previously named `datafields` and has been recently renamed to `datasets` (4-12-2014).**\n", "\n", "if\n", "\n", " from pygchem import datasets\n", "\n", "doesn't work, try\n", "\n", " import pygchem.datafields as datasets\n", "\n", "to use this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Iris backend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyGChem uses Iris (http://scitools.org.uk/iris/) for handling CTM datasets. Iris provides a data abstraction layer that is based on the Climate and Forecast (CF) data model (http://cfconventions.org/) and which allows to isolate analysis and visualisation code from data format specifics. It natively supports read/write access to the (CF)netCDF format. PyGChem currently adds support to the BPCH format and the netCDF files created with the GAMAP routines BPCH2NC and BPCH2COARDS (read access only).\n", "\n", "With Iris, a data field (data + metadata) relative to a particular phenomenon is represented by a `iris.cube.Cube` object (more info on Iris data strutures [here](http://scitools.org.uk/iris/docs/latest/userguide/iris_cubes.html)). Iris also supports basic data manipulation operations, such as arithmetic, interpolation, and statistics. This tutorial shows only a few examples. For a more detailled description of these features, see the [Iris user guide](http://scitools.org.uk/iris/docs/latest/userguide/index.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datasets used in this tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial uses the datasets created by the 1-year GEOS-Chem benchmark simulations. These are available for download via anonymous FTP from .\n", "\n", "For running the code cells below, you must specify the root folder of your local copy of these benchmarks: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bmk_root = '/home/bovy/geoschem'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `pygchem.datasets.load` allows to load the content of a BPCH file or a netCDF file into a list of `iris.cube.Cube` objects. This function is just an alias of the `iris.load` function. Note that it doesn't load all the data into memory but enough information to select, manipulate and retreive the data.\n", "\n", "See the Iris documentation: [loading iris cubes](http://scitools.org.uk/iris/docs/latest/userguide/loading_iris_cubes.html) for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "cd into the 1-year benchmark (v10-01c Run1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/bovy/geoschem/1yr_benchmarks/v10-01/v10-01c/Run1\n" ] } ], "source": [ "%cd {bmk_root}/1yr_benchmarks/v10-01/v10-01c/Run1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple (unconstrained) loading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The line below loads the BPCH file corresponding to the first month of the simulation:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = 'bpch/ctm.bpch.v10-01c-geosfp-Run1.20120801'\n", "dataset = datasets.load(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The line below print the list of the 20 lasts data fields of the list (name, units, dimensions and coordinates). Note that the field name may be reformatted during the loading so that it is consistent with the name set by the GAMAP's netCDF writing routines. Note also that the units may also be reformatted as required by the udunits2 library (used by Iris). " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: WETDLS_S__IEPOX / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "1: WETDLS_S__ISOPN / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "2: WETDLS_S__MAP / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "3: WETDLS_S__MMN / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "4: WETDLS_S__MOBA / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "5: WETDLS_S__MP / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "6: WETDLS_S__MSA / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "7: WETDLS_S__NH3 / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "8: WETDLS_S__NH4 / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "9: WETDLS_S__NIT / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "10: WETDLS_S__NITs / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "11: WETDLS_S__OCPI / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "12: WETDLS_S__OCPO / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "13: WETDLS_S__PROPNN / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "14: WETDLS_S__RIP / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "15: WETDLS_S__SALA / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "16: WETDLS_S__SALC / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "17: WETDLS_S__SO2 / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "18: WETDLS_S__SO4 / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n", "19: WETDLS_S__SO4s / (kg/s) (longitude: 72; latitude: 46; model_level_number: 72)\n" ] } ], "source": [ "print dataset[-20:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same function can be used for loading the content of the netCDF files. The netCDF files available in this simulation were created by the 'BPCH2COARDS' GAMAP routine. As there are differences between the COARDS Conventions and the CF Conventions, we have to use a callback function 'gamap_bpch2coards' to properly load the content of the netCDF file into Iris cubes: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/python/PythonEnvs/pygchem_gitmaster_py27/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1358: UserWarning: Failed to create 'lon' dimension coordinate: The points array must be strictly monotonic.\n", "Gracefully creating 'lon' auxiliary coordinate instead.\n", " error=e_msg))\n" ] } ], "source": [ "filename = 'netcdf/v10-01c-geosfp-Run1.20120801.nc'\n", "clb = datasets.load_callbacks['gamap_bpch2coards']\n", "dataset = datasets.load(filename, callback=clb)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: OD_MAP_S__SBC / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "1: OD_MAP_S__SD / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "2: OD_MAP_S__SOC / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "3: OD_MAP_S__SSO4 / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "4: OD_MAP_S__SSSa / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "5: OD_MAP_S__SSSc / (cm2/cm3) (-- : 59; latitude: 46; longitude: 72)\n", "6: PEDGE_S__PSURF / (mb) (Eta Edges: 73; latitude: 46; longitude: 72)\n", "7: PL_BC_S__BLKC / (kg) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "8: PL_OC_S__ORGC / (kg) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "9: SALTSRCE__SALA / (kg) (latitude: 46; longitude: 72)\n", "10: SALTSRCE__SALC / (kg) (latitude: 46; longitude: 72)\n", "11: SO2_AC_S__SO2 / (kg S) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "12: SO2_AN_S__SO2 / (kg S) (-- : 2; latitude: 46; longitude: 72)\n", "13: SO2_BIOB__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "14: SO2_BIOF__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "15: SO2_EV_S__SO2 / (kg S) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "16: SO2_NV_S__SO2 / (kg S) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "17: SO2_SHIP__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "18: SO4_AN_S__SO4 / (kg S) (-- : 2; latitude: 46; longitude: 72)\n", "19: SO4_BIOF__SO4 / (kg S) (latitude: 46; longitude: 72)\n" ] } ], "source": [ "print dataset[-20:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyGChem also provides a callback for loading netCDF files created by the GAMAP routine 'BPCH2NC':" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/bovy/geoschem/1yr_benchmarks/v9-02/v9-02r/geos5/Run0\n" ] } ], "source": [ "% cd {bmk_root}/1yr_benchmarks/v9-02/v9-02r/geos5/Run0" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = 'netcdf/v9-02r-geos5-Run0.20050101.nc'\n", "clb = datasets.load_callbacks['gamap_bpch2nc']\n", "dataset = datasets.load(filename, callback=clb)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: OD-MAP-S__OPSSc / (1) (-- : 38; latitude: 46; longitude: 72)\n", "1: OD-MAP-S__OPTD / (1) (-- : 38; latitude: 46; longitude: 72)\n", "2: OD-MAP-S__SBC / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "3: OD-MAP-S__SD / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "4: OD-MAP-S__SOC / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "5: OD-MAP-S__SSO4 / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "6: OD-MAP-S__SSSa / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "7: OD-MAP-S__SSSc / (cm2/cm3) (-- : 38; latitude: 46; longitude: 72)\n", "8: PEDGE-S__PSURF / (mb) (-- : 48; latitude: 46; longitude: 72)\n", "9: SALTSRCE__SALA / (kg) (latitude: 46; longitude: 72)\n", "10: SALTSRCE__SALC / (kg) (latitude: 46; longitude: 72)\n", "11: SO2-AC-S__SO2 / (kg S) (model_level_number: 47; latitude: 46; longitude: 72)\n", "12: SO2-AN-S__SO2 / (kg S) (-- : 2; latitude: 46; longitude: 72)\n", "13: SO2-BIOB__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "14: SO2-BIOF__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "15: SO2-EV-S__SO2 / (kg S) (model_level_number: 47; latitude: 46; longitude: 72)\n", "16: SO2-NV-S__SO2 / (kg S) (model_level_number: 47; latitude: 46; longitude: 72)\n", "17: SO2-SHIP__SO2 / (kg S) (latitude: 46; longitude: 72)\n", "18: SO4-AN-S__SO4 / (kg S) (-- : 2; latitude: 46; longitude: 72)\n", "19: SO4-BIOF__SO4 / (kg S) (latitude: 46; longitude: 72)\n" ] } ], "source": [ "print dataset[-20:]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/bovy/geoschem/1yr_benchmarks/v10-01/v10-01c/Run1\n" ] } ], "source": [ "%cd {bmk_root}/1yr_benchmarks/v10-01/v10-01c/Run1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constrained loading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For large datasets, it is possible to constrain the load to match specific metadata or to load only a subset of the data. It is recommended as the loading is faster with given constraints." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example loads only the \"IJ_AVG_S__O3\" variable: " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = 'netcdf/v10-01c-geosfp-Run1.20120801.nc'\n", "clb = datasets.load_callbacks['gamap_bpch2coards']\n", "dataset = datasets.load(filename, \"IJ_AVG_S__O3\",\n", " callback=clb)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: IJ_AVG_S__O3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n" ] } ], "source": [ "print dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can specify multiple variables:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataset = datasets.load(filename, [\"IJ_AVG_S__O3\", \"IJ_AVG_S__NO\"],\n", " callback=clb)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: IJ_AVG_S__O3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "1: IJ_AVG_S__NO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n" ] } ], "source": [ "print dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to define more advanced constraints. For example, to load all \"IJ-AVG-$\" diagnostics:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import iris\n", "\n", "check_ij_avg = lambda cube: cube.name().startswith(\"IJ_AVG_S\")\n", "ij_avg = iris.Constraint(cube_func=check_ij_avg)\n", "\n", "dataset = datasets.load(filename, ij_avg,\n", " callback=clb)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: IJ_AVG_S__ACET / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "1: IJ_AVG_S__ALD2 / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "2: IJ_AVG_S__ALK4 / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "3: IJ_AVG_S__BCPI / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "4: IJ_AVG_S__BCPO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "5: IJ_AVG_S__Br / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "6: IJ_AVG_S__Br2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "7: IJ_AVG_S__BrCl / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "8: IJ_AVG_S__BrNO2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "9: IJ_AVG_S__BrNO3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "10: IJ_AVG_S__BrO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "11: IJ_AVG_S__C2H6 / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "12: IJ_AVG_S__C3H8 / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "13: IJ_AVG_S__CCl4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "14: IJ_AVG_S__CFC11 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "15: IJ_AVG_S__CFC12 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "16: IJ_AVG_S__CFCX / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "17: IJ_AVG_S__CH2Br2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "18: IJ_AVG_S__CH2O / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "19: IJ_AVG_S__CH3Br / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "20: IJ_AVG_S__CH3CCl3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "21: IJ_AVG_S__CH3Cl / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "22: IJ_AVG_S__CH4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "23: IJ_AVG_S__CHBr3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "24: IJ_AVG_S__CO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "25: IJ_AVG_S__Cl / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "26: IJ_AVG_S__Cl2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "27: IJ_AVG_S__Cl2O2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "28: IJ_AVG_S__ClNO2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "29: IJ_AVG_S__ClNO3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "30: IJ_AVG_S__ClO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "31: IJ_AVG_S__ClOO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "32: IJ_AVG_S__DMS / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "33: IJ_AVG_S__DST1 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "34: IJ_AVG_S__DST2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "35: IJ_AVG_S__DST3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "36: IJ_AVG_S__DST4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "37: IJ_AVG_S__GLYC / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "38: IJ_AVG_S__H1211 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "39: IJ_AVG_S__H1301 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "40: IJ_AVG_S__H2402 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "41: IJ_AVG_S__H2O / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "42: IJ_AVG_S__H2O2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "43: IJ_AVG_S__HAC / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "44: IJ_AVG_S__HBr / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "45: IJ_AVG_S__HCFC22 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "46: IJ_AVG_S__HCFCX / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "47: IJ_AVG_S__HCl / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "48: IJ_AVG_S__HNO2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "49: IJ_AVG_S__HNO3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "50: IJ_AVG_S__HNO4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "51: IJ_AVG_S__HOBr / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "52: IJ_AVG_S__HOCl / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "53: IJ_AVG_S__IEPOX / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "54: IJ_AVG_S__ISOP / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "55: IJ_AVG_S__ISOPN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "56: IJ_AVG_S__MACR / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "57: IJ_AVG_S__MAP / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "58: IJ_AVG_S__MEK / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "59: IJ_AVG_S__MMN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "60: IJ_AVG_S__MOBA / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "61: IJ_AVG_S__MP / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "62: IJ_AVG_S__MPN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "63: IJ_AVG_S__MSA / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "64: IJ_AVG_S__MVK / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "65: IJ_AVG_S__N2O / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "66: IJ_AVG_S__N2O5 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "67: IJ_AVG_S__NH3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "68: IJ_AVG_S__NH4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "69: IJ_AVG_S__NIT / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "70: IJ_AVG_S__NITs / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "71: IJ_AVG_S__NO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "72: IJ_AVG_S__NO2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "73: IJ_AVG_S__NO3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "74: IJ_AVG_S__O3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "75: IJ_AVG_S__OCPI / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "76: IJ_AVG_S__OCPO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "77: IJ_AVG_S__OCS / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "78: IJ_AVG_S__OClO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "79: IJ_AVG_S__PAN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "80: IJ_AVG_S__PMN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "81: IJ_AVG_S__PPN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "82: IJ_AVG_S__PROPNN / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "83: IJ_AVG_S__PRPE / (ppb) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "84: IJ_AVG_S__R4N2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "85: IJ_AVG_S__RCHO / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "86: IJ_AVG_S__RIP / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "87: IJ_AVG_S__SALA / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "88: IJ_AVG_S__SALC / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "89: IJ_AVG_S__SO2 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "90: IJ_AVG_S__SO4 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n", "91: IJ_AVG_S__SO4s / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 72)\n" ] } ], "source": [ "print dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more advanced example, combining constraints and extracting data subsets:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def lon_subset(cell):\n", " \"\"\"\n", " return True or False as to whether the cell\n", " center in question should be kept\n", " \"\"\"\n", " return cell > 0. and cell < 20.\n", "\n", "lon_cst = iris.Constraint(longitude=lon_subset)\n", "\n", "dataset = datasets.load(filename,\n", " \"IJ_AVG_S__O3\" & lon_cst,\n", " callback=clb)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: IJ_AVG_S__O3 / (ppbv) (atmosphere_hybrid_sigma_pressure_coordinate: 72; latitude: 46; longitude: 3)\n" ] } ], "source": [ "print dataset\n", "# note the reduced grid-size for the longitude" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading multiple files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to load multiple files at once. The `pygchem.datafields.load` function will try to merge the fields when it is possible, i.e., when the multiple fields describing the same phenomenom overlap in space and/or time, so that it reduces at the minimum the number of loaded fields." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example load the \"IJ_AVG_S__O3\" diagnostic for the entire 1-year simulation:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# note the wildcard character in the filename\n", "# (UNIX expressions are supported)\n", "filename = 'bpch/ctm.bpch.v10-01c-geosfp-Run1.*'\n", "diagnostics = [\"BXHGHT_S__BXHEIGHT\",\n", " \"BXHGHT_S__N(AIR)\",\n", " \"IJ_AVG_S__NO2\"]\n", "dataset = datasets.load(filename, diagnostics)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: BXHGHT_S__BXHEIGHT / (m) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", "1: BXHGHT_S__N(AIR) / (count/m3) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", "2: IJ_AVG_S__NO2 / (ppbv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n" ] } ], "source": [ "print dataset\n", "# note the additional time dimension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Merging fields may take a long time. If speed matters, it is still possible to load the fields without any merging: " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "1: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "2: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "3: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "4: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "5: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "6: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "7: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "8: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "9: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "10: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "11: BXHGHT_S__BXHEIGHT / (m) (longitude: 72; latitude: 46; model_level_number: 72)\n", "12: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "13: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "14: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "15: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "16: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "17: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "18: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "19: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "20: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "21: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "22: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "23: BXHGHT_S__N(AIR) / (count/m3) (longitude: 72; latitude: 46; model_level_number: 72)\n", "24: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "25: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "26: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "27: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "28: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "29: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "30: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "31: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "32: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "33: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "34: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n", "35: IJ_AVG_S__NO2 / (ppbv) (longitude: 72; latitude: 46; model_level_number: 72)\n" ] } ], "source": [ "dataset_nomerge = datasets.load_raw(filename, diagnostics)\n", "print dataset_nomerge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply contraints to the field list after loading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can extract data subsets and/or specific fields from a loaded list of fields:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: BXHGHT_S__BXHEIGHT / (m) (time: 12; longitude: 3; latitude: 46; model_level_number: 72)\n", "1: BXHGHT_S__N(AIR) / (count/m3) (time: 12; longitude: 3; latitude: 46; model_level_number: 72)\n", "2: IJ_AVG_S__NO2 / (ppbv) (time: 12; longitude: 3; latitude: 46; model_level_number: 72)\n" ] } ], "source": [ "dataset_lon_subset = dataset.extract(lon_cst)\n", "print dataset_lon_subset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To select only one field (cube), the `extract_strict` method can be used: " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppbv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " time x - - -\n", " longitude - x - -\n", " latitude - - x -\n", " model_level_number - - - x\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "no2_avg = dataset.extract_strict(\"IJ_AVG_S__NO2\")\n", "print no2_avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datasets can be written to a netCDF file (with the CF-conventions) using the function `save` of the `datafields` module (an alias to the `iris.save` function)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "outfile = 'netcdf/test.nc'\n", "datasets.save(dataset, outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A text representation of (the header information of) the written netCDF file using the `ncdump` utility (provided with the netCDF4 package):" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "netcdf test {\r\n", "dimensions:\r\n", "\ttime = UNLIMITED ; // (12 currently)\r\n", "\tlongitude = 72 ;\r\n", "\tlatitude = 46 ;\r\n", "\tmodel_level_number = 72 ;\r\n", "\tbnds = 2 ;\r\n", "variables:\r\n", "\tfloat BXHGHT_S__BXHEIGHT(time, longitude, latitude, model_level_number) ;\r\n", "\t\tBXHGHT_S__BXHEIGHT:standard_name = \"BXHGHT_S__BXHEIGHT\" ;\r\n", "\t\tBXHGHT_S__BXHEIGHT:long_name = \"Grid box height\" ;\r\n", "\t\tBXHGHT_S__BXHEIGHT:units = \"m\" ;\r\n", "\t\tBXHGHT_S__BXHEIGHT:ctm_units = \"m\" ;\r\n", "\t\tBXHGHT_S__BXHEIGHT:grid_mapping = \"latitude_longitude\" ;\r\n", "\tint latitude_longitude ;\r\n", "\t\tlatitude_longitude:grid_mapping_name = \"latitude_longitude\" ;\r\n", "\t\tlatitude_longitude:longitude_of_prime_meridian = 0. ;\r\n", "\t\tlatitude_longitude:earth_radius = 6367470. ;\r\n", "\tdouble time(time) ;\r\n", "\t\ttime:axis = \"T\" ;\r\n", "\t\ttime:bounds = \"time_bnds\" ;\r\n", "\t\ttime:units = \"hours since 1985-01-01 00:00:00\" ;\r\n", "\t\ttime:standard_name = \"time\" ;\r\n", "\t\ttime:calendar = \"standard\" ;\r\n", "\tdouble time_bnds(time, bnds) ;\r\n", "\tdouble longitude(longitude) ;\r\n", "\t\tlongitude:axis = \"X\" ;\r\n", "\t\tlongitude:bounds = \"longitude_bnds\" ;\r\n", "\t\tlongitude:units = \"degrees_east\" ;\r\n", "\t\tlongitude:standard_name = \"longitude\" ;\r\n", "\tdouble longitude_bnds(longitude, bnds) ;\r\n", "\tdouble latitude(latitude) ;\r\n", "\t\tlatitude:axis = \"Y\" ;\r\n", "\t\tlatitude:bounds = \"latitude_bnds\" ;\r\n", "\t\tlatitude:units = \"degrees_north\" ;\r\n", "\t\tlatitude:standard_name = \"latitude\" ;\r\n", "\tdouble latitude_bnds(latitude, bnds) ;\r\n", "\tint64 model_level_number(model_level_number) ;\r\n", "\t\tmodel_level_number:units = \"1\" ;\r\n", "\t\tmodel_level_number:standard_name = \"model_level_number\" ;\r\n", "\tfloat BXHGHT_S__N\\(AIR\\)(time, longitude, latitude, model_level_number) ;\r\n", "\t\tBXHGHT_S__N\\(AIR\\):standard_name = \"BXHGHT_S__N(AIR)\" ;\r\n", "\t\tBXHGHT_S__N\\(AIR\\):long_name = \"Number density of air\" ;\r\n", "\t\tBXHGHT_S__N\\(AIR\\):units = \"count/m3\" ;\r\n", "\t\tBXHGHT_S__N\\(AIR\\):ctm_units = \"molec/m3\" ;\r\n", "\t\tBXHGHT_S__N\\(AIR\\):grid_mapping = \"latitude_longitude\" ;\r\n", "\tfloat IJ_AVG_S__NO2(time, longitude, latitude, model_level_number) ;\r\n", "\t\tIJ_AVG_S__NO2:standard_name = \"IJ_AVG_S__NO2\" ;\r\n", "\t\tIJ_AVG_S__NO2:long_name = \"NO2 tracer\" ;\r\n", "\t\tIJ_AVG_S__NO2:units = \"ppbv\" ;\r\n", "\t\tIJ_AVG_S__NO2:ctm_units = \"ppbv\" ;\r\n", "\t\tIJ_AVG_S__NO2:grid_mapping = \"latitude_longitude\" ;\r\n", "\r\n", "// global attributes:\r\n", "\t\t:model = \"GEOSFP\" ;\r\n", "\t\t:Conventions = \"CF-1.5\" ;\r\n", "}\r\n" ] } ], "source": [ "!ncdump -h netcdf/test.nc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading the written file using the `load` function:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: BXHGHT_S__BXHEIGHT / (m) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", "1: BXHGHT_S__N(AIR) / (count/m3) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", "2: IJ_AVG_S__NO2 / (ppbv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n" ] } ], "source": [ "print datasets.load('netcdf/test.nc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to write the datasets to the BPCH format, using the low-level function `write_bpch` in the module `pygchem.io.bpch` (not yet documented)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing data and metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### String representation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print a string representation of the data field (cube): " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppbv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " time x - - -\n", " longitude - x - -\n", " latitude - - x -\n", " model_level_number - - - x\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "print no2_avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Field name(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get or set the name(s) of the field with the following function and/or attributes:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'IJ_AVG_S__NO2'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.name()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyGChem considers that the GEOS-Chem variable name (category + tracer) is a standard name, although it is not CF-compliant (i.e., not listed in the standard name table of the udunits package)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'IJ_AVG_S__NO2'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.standard_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`long_name` is the full name of the diagnostic" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'NO2 tracer'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.long_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`var_name` is the (netCDF) variable name" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'IJ_AVG_S__NO2'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.var_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any attribute of the data field is stored in the following dictionary" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'ctm_units': 'ppbv', 'model': 'GEOSFP'}" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Field units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the field units:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Unit('ppbv')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no2_avg.units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easy to change the units of the field (data values are re-computed accordingly):" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppmv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " time x - - -\n", " longitude - x - -\n", " latitude - - x -\n", " model_level_number - - - x\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "no2_avg.convert_units('ppmv')\n", "print no2_avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retreiving a coordinate by name:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lat_coord = no2_avg.coord('latitude')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Coordinate data and metadata:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-89., -86., -82., -78., -74., -70., -66., -62., -58., -54., -50.,\n", " -46., -42., -38., -34., -30., -26., -22., -18., -14., -10., -6.,\n", " -2., 2., 6., 10., 14., 18., 22., 26., 30., 34., 38.,\n", " 42., 46., 50., 54., 58., 62., 66., 70., 74., 78., 82.,\n", " 86., 89.])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lat_coord.points" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-90., -88.],\n", " [-88., -84.],\n", " [-84., -80.],\n", " [-80., -76.],\n", " [-76., -72.],\n", " [-72., -68.],\n", " [-68., -64.],\n", " [-64., -60.],\n", " [-60., -56.],\n", " [-56., -52.],\n", " [-52., -48.],\n", " [-48., -44.],\n", " [-44., -40.],\n", " [-40., -36.],\n", " [-36., -32.],\n", " [-32., -28.],\n", " [-28., -24.],\n", " [-24., -20.],\n", " [-20., -16.],\n", " [-16., -12.],\n", " [-12., -8.],\n", " [ -8., -4.],\n", " [ -4., 0.],\n", " [ 0., 4.],\n", " [ 4., 8.],\n", " [ 8., 12.],\n", " [ 12., 16.],\n", " [ 16., 20.],\n", " [ 20., 24.],\n", " [ 24., 28.],\n", " [ 28., 32.],\n", " [ 32., 36.],\n", " [ 36., 40.],\n", " [ 40., 44.],\n", " [ 44., 48.],\n", " [ 48., 52.],\n", " [ 52., 56.],\n", " [ 56., 60.],\n", " [ 60., 64.],\n", " [ 64., 68.],\n", " [ 68., 72.],\n", " [ 72., 76.],\n", " [ 76., 80.],\n", " [ 80., 84.],\n", " [ 84., 88.],\n", " [ 88., 90.]])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lat_coord.bounds" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Unit('degrees_north')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lat_coord.units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The field data can be accessed with the `data` attribute. It returns a Numpy array." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manipulating datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is very easy to manipulate data fields (Iris cubes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indexing a cube return a new cube. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppmv) (time: 12; longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " time x - - -\n", " longitude - x - -\n", " latitude - - x -\n", " model_level_number - - - x\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "print no2_avg" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46)\n", " Dimension coordinates:\n", " longitude x -\n", " latitude - x\n", " Scalar coordinates:\n", " model_level_number: 1\n", " time: 2012-08-01 00:00:00, bound=(2012-08-01 00:00:00, 2012-09-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "# Get the first element of the 1st and last dimensions (time and model level number)\n", "no2_avg_t0_l1 = no2_avg[0, :, :, 0]\n", "print no2_avg_t0_l1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that another way to extract a subset is by applying one or more constraints on the cube (see above)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iterating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `slice` method allow iterating over layers or time slices (return a Python generator). The slices can be 1-dimensional or n-dimensional." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example below generate time slices (returns a 3-d cube for each time slice)." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2012-08-01 00:00:00, bound=(2012-08-01 00:00:00, 2012-09-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2012-09-01 00:00:00, bound=(2012-09-01 00:00:00, 2012-10-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2012-10-01 00:00:00, bound=(2012-10-01 00:00:00, 2012-11-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2012-11-01 00:00:00, bound=(2012-11-01 00:00:00, 2012-12-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2012-12-01 00:00:00, bound=(2012-12-01 00:00:00, 2013-01-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-01-01 00:00:00, bound=(2013-01-01 00:00:00, 2013-02-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-02-01 00:00:00, bound=(2013-02-01 00:00:00, 2013-03-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-03-01 00:00:00, bound=(2013-03-01 00:00:00, 2013-04-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-04-01 00:00:00, bound=(2013-04-01 00:00:00, 2013-05-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-05-01 00:00:00, bound=(2013-05-01 00:00:00, 2013-06-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-06-01 00:00:00, bound=(2013-06-01 00:00:00, 2013-07-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", "IJ_AVG_S__NO2 / (ppmv) (longitude: 72; latitude: 46; model_level_number: 72)\n", " Dimension coordinates:\n", " longitude x - -\n", " latitude - x -\n", " model_level_number - - x\n", " Scalar coordinates:\n", " time: 2013-07-01 00:00:00, bound=(2013-07-01 00:00:00, 2013-08-01 00:00:00)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n" ] } ], "source": [ "no2_avg_time_slices = no2_avg.slices(['longitude', 'latitude', 'model_level_number'])\n", "for s in no2_avg_time_slices:\n", " print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Collapsing data dimensions (statistics)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example below calculate the sum over the vertical levels" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IJ_AVG_S__NO2 / (ppmv) (time: 12; longitude: 72; latitude: 46)\n", " Dimension coordinates:\n", " time x - -\n", " longitude - x -\n", " latitude - - x\n", " Scalar coordinates:\n", " model_level_number: 36, bound=(1, 72)\n", " Attributes:\n", " ctm_units: ppbv\n", " model: GEOSFP\n", " Cell methods:\n", " sum: model_level_number\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/python/PythonEnvs/pygchem_gitmaster_py27/lib/python2.7/site-packages/iris/coords.py:954: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'model_level_number'.\n", " warnings.warn(msg.format(self.name()))\n" ] } ], "source": [ "import iris.analysis\n", "\n", "no2_avg_sum_levels = no2_avg.collapsed('model_level_number', iris.analysis.SUM)\n", "print no2_avg_sum_levels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic cube mathematics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example below calculates the total columns of the tracer for all grid cells and all time slices:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NO2 columns / (count/cm2) (time: 12; longitude: 72; latitude: 46)\n", " Dimension coordinates:\n", " time x - -\n", " longitude - x -\n", " latitude - - x\n", " Scalar coordinates:\n", " model_level_number: 36, bound=(1, 72)\n", " Cell methods:\n", " sum: model_level_number\n" ] } ], "source": [ "# extract the data fields (cubes) needed to compute the tracer columns\n", "box_heights = dataset.extract_strict(\"BXHGHT_S__BXHEIGHT\")\n", "n_air = dataset.extract_strict(\"BXHGHT_S__N(AIR)\")\n", "\n", "# convert units back to ppbv for the NO2 tracer\n", "no2_avg.convert_units('ppbv')\n", "\n", "# calculate the columns\n", "no2_avg_columns = (box_heights * n_air * no2_avg).collapsed('model_level_number',\n", " iris.analysis.SUM)\n", "\n", "# set name convert units to count/cm2 (count is used for #molecules)\n", "no2_avg_columns.rename(\"NO2 columns\")\n", "no2_avg_columns.convert_units('count/cm2')\n", "\n", "# string repr\n", "print no2_avg_columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iris provides some modules for basic dataset plotting. It is built on top of [matplotlib](http://matplotlib.org/), and it uses the [cartopy package](http://scitools.org.uk/cartopy/) for map projections." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cartopy.crs as ccrs\n", "import matplotlib.pyplot as plt\n", "import iris.quickplot as qplt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the NO2 total columns for the first time slice:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ShPalHO2909//7Cuygnd9F83z1aCPtZ/Ucm3NNftNCI8XT3sFf72fU588maLJ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\n08dhy8vAnJmAIIqo/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0uRlizFcdf9lH73A7U/foMYHEWmVF+Wz1dX08g4EjFRq7hTcTia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\nsMrM1lbXL8ePDVNSGBKRIxGYfFXzyW7LqY4xVSWn1nN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 8))\n", "qplt.contourf(no2_avg_columns[0], 15)\n", "plt.gca().coastlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Hovmoller diagram example" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/python/PythonEnvs/pygchem_gitmaster_py27/lib/python2.7/site-packages/iris/cube.py:2750: UserWarning: Collapsing spatial coordinate 'latitude' without weighting\n", " warnings.warn(msg.format(coord.name()))\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SRClEaUSs1ucM0SnKSlG2jRyL3puEr9/gOAT4PdriU8ct2NbpUaMETW4tRykT6v/Wepin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kKTAn+Mywe4fSIilyAUhtmUhtmUQFRWGn5Y0ZR/x1BRZXqmcVdDkAXqxGjeGYv6GYtBm9JMhGCD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6kmvCy+2RMMpxmcPQqQv9aMZjWLPG3KlOYUHk9GIiR889PREcaI8mVKUkM8q9GPC7MX4ZlgVC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.dates as mdates\n", "\n", "no2_hovmoller = no2_avg_columns.collapsed('latitude',\n", " iris.analysis.MEAN)\n", "\n", "fig = plt.figure(figsize=(10, 8))\n", "qplt.contourf(no2_hovmoller, 20)\n", "\n", "# fine control over time axis ticks and labels\n", "plt.gca().yaxis.set_major_locator(mdates.MonthLocator())\n", "plt.gca().yaxis.set_major_formatter(mdates.DateFormatter('%m-%Y'))\n", "plt.gca().set_ylabel(\"Time\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The CTM diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For loading datasets, Pygchem reads the information stored in the `diaginfo.dat` and `tracerinfo.dat` files. The module `pygchem.diagnostics` provides a basic API for reading / writing these files and handling their contents. " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pygchem import diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To load a couple of files:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dinfo = diagnostics.CTMDiagnosticInfo(diaginfo_file='diaginfo.dat',\n", " tracerinfo_file='tracerinfo.dat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `CTMDiagnosticInfo` object contains all information stored in those files. The attributes `categories` and `diagnostics` contains each record (line) in `diaginfo.dat` and `tracerinfo.dat`, respectively " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Record list of 138 CTMCategory:
KeyCTMCategory
0IJ-AVG-$CTMCategory(offset=0, name=IJ-AVG-$, description=Tracer concentration)
1IJ-24H-$CTMCategory(offset=0, name=IJ-24H-$, description=24-hr avg tracer conc.)
2INST-MAPCTMCategory(offset=0, name=INST-MAP, description=Instantaneous tracer)
3IJ-MAX-$CTMCategory(offset=0, name=IJ-MAX-$, description=Surface peak concentration)
4ANTHSRCECTMCategory(offset=1000, name=ANTHSRCE, description=Anthropogenic emissions)
5BIOFSRCECTMCategory(offset=1000, name=BIOFSRCE, description=Biofuel emissions)
6NO-AC-$CTMCategory(offset=1000, name=NO-AC-$, description=Aircraft NO)
7NO-AN-$CTMCategory(offset=1000, name=NO-AN-$, description=Anthropogenic NO)
8NO-BIOBCTMCategory(offset=1000, name=NO-BIOB, description=Biomass NO)
9NO-BIOFCTMCategory(offset=1000, name=NO-BIOF, description=Biofuel NO)
10NO-LI-$CTMCategory(offset=1000, name=NO-LI-$, description=Lightning NO)
11NO-SOILCTMCategory(offset=1000, name=NO-SOIL, description=Soil NO)
12NO-FERTCTMCategory(offset=1000, name=NO-FERT, description=Fertilizer NO)
13NO-STRTCTMCategory(offset=1000, name=NO-STRT, description=Stratopsheric NO)
14INST_COLCTMCategory(offset=2000, name=INST_COL, description=Instantaneous columns)
15CV-FLX-$CTMCategory(offset=3000, name=CV-FLX-$, description=Convective mass flux)
16TURBMC-$CTMCategory(offset=3000, name=TURBMC-$, description=PBL mixing mass flux)
17EW-FLX-$CTMCategory(offset=3000, name=EW-FLX-$, description=E/W transport flux)
18NS-FLX-$CTMCategory(offset=3000, name=NS-FLX-$, description=N/S transport flux)
19UP-FLX-$CTMCategory(offset=3000, name=UP-FLX-$, description=Up/down transport flux)
20STRT-FLXCTMCategory(offset=3000, name=STRT-FLX, description=Flux from stratosphere)
21RN--SRCECTMCategory(offset=3000, name=RN--SRCE, description=Rn-Pb-Be source)
22RN-DECAYCTMCategory(offset=3000, name=RN-DECAY, description=Rn-Pb-Be loss)
23WETDCV-$CTMCategory(offset=3000, name=WETDCV-$, description=Conv wet scavenging)
24WETDLS-$CTMCategory(offset=3000, name=WETDLS-$, description=Wet deposition)
25MC-FRC-$CTMCategory(offset=3000, name=MC-FRC-$, description=Moist conv fraction)
26DMS-BIOGCTMCategory(offset=4000, name=DMS-BIOG, description=Biogenic DMS)
27DUSTSRCECTMCategory(offset=4000, name=DUSTSRCE, description=Dust emission)
28NVOCSRCECTMCategory(offset=4000, name=NVOCSRCE, description=NVOC emissions)
29SALTSRCECTMCategory(offset=4000, name=SALTSRCE, description=Seasalt emission)
30SO2-AC-$CTMCategory(offset=4000, name=SO2-AC-$, description=Aircraft SO2 emissions)
31SO2-AN-$CTMCategory(offset=4000, name=SO2-AN-$, description=Anthro SO2 emissions)
32SO2-BIOBCTMCategory(offset=4000, name=SO2-BIOB, description=Biomass SO2 emissions)
33SO2-BIOFCTMCategory(offset=4000, name=SO2-BIOF, description=Biofuel SO2 emissions)
34SO2-EV-$CTMCategory(offset=4000, name=SO2-EV-$, description=Erup. Volcano SO2)
35SO2-NV-$CTMCategory(offset=4000, name=SO2-NV-$, description=Non-Erup. Volcano SO2)
36SO2-SHIPCTMCategory(offset=4000, name=SO2-SHIP, description=SO2 from ship exhaust)
37SO4-AN-$CTMCategory(offset=4000, name=SO4-AN-$, description=Anthro SO4 emissions)
38SO4-BIOFCTMCategory(offset=4000, name=SO4-BIOF, description=Biofuel SO4 emissions)
39NH3-ANTHCTMCategory(offset=4000, name=NH3-ANTH, description=Anthro NH3 emissions)
40NH3-NATUCTMCategory(offset=4000, name=NH3-NATU, description=Natural NH3 emissions)
41NH3-BIOBCTMCategory(offset=4000, name=NH3-BIOB, description=Biomass NH3 emissions)
42NH3-BIOFCTMCategory(offset=4000, name=NH3-BIOF, description=Biofuel NH3 emissions)
43TROPO-AVCTMCategory(offset=4000, name=TROPO-AV, description=Trop avg'd tracer)
44TCMASS-$CTMCategory(offset=4000, name=TCMASS-$, description=Tracer mass (kg))
45PEDGE-$CTMCategory(offset=10000, name=PEDGE-$, description=Pressure at level edges)
46PS-PTOPCTMCategory(offset=10000, name=PS-PTOP, description=Pressure level edges)
47DAO-FLDSCTMCategory(offset=11000, name=DAO-FLDS, description=GMAO 2-D met fields)
48DAO-3D-$CTMCategory(offset=12000, name=DAO-3D-$, description=GMAO 3-D met fields)
49JV-MAP-$CTMCategory(offset=13000, name=JV-MAP-$, description=Photolysis rates)
50OD-MAP-$CTMCategory(offset=14000, name=OD-MAP-$, description=Optical Depths)
.........
137IJ-PSC-$CTMCategory(offset=55000, name=IJ-PSC-$, description=PSC restart file)
" ], "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dinfo.categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These attributes behave like a Python list, with added key reference and database lookup-like capabilities. Each item of the list coorespond to a record." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CTMCategory(offset=0, name=IJ-AVG-$, description=Tracer concentration)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the 1st category (a Record like object)\n", "cat_ij_avg = dinfo.categories[0]\n", "cat_ij_avg" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'IJ-AVG-$'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat_ij_avg.name" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat_ij_avg.offset" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'description': 'Tracer concentration', 'name': 'IJ-AVG-$', 'offset': 0}" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert the record object to a dict\n", "cat_ij_avg.to_dict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is aslo possible to filter the data (queries):" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CTMCategory(offset=3000, name=NS-FLX-$, description=N/S transport flux)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select a category based on its name (key)\n", "dinfo.categories.select_item(\"NS-FLX-$\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CTMDiagnostic(number=11, name=ALD2, full_name=ALD2 tracer, unit=ppbC, scale=1000000000.0, chemical=True, molecular_weight=0.012, hydrocarbon=True, carbon_weight=2)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select a diagnostic (tracer) based on its number (key)\n", "dinfo.diagnostics.select_item(11)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Record list of 11 selected CTMCategory:
KeyCTMCategory
0CV-FLX-$CTMCategory(offset=3000, name=CV-FLX-$, description=Convective mass flux)
1TURBMC-$CTMCategory(offset=3000, name=TURBMC-$, description=PBL mixing mass flux)
2EW-FLX-$CTMCategory(offset=3000, name=EW-FLX-$, description=E/W transport flux)
3NS-FLX-$CTMCategory(offset=3000, name=NS-FLX-$, description=N/S transport flux)
4UP-FLX-$CTMCategory(offset=3000, name=UP-FLX-$, description=Up/down transport flux)
5STRT-FLXCTMCategory(offset=3000, name=STRT-FLX, description=Flux from stratosphere)
6RN--SRCECTMCategory(offset=3000, name=RN--SRCE, description=Rn-Pb-Be source)
7RN-DECAYCTMCategory(offset=3000, name=RN-DECAY, description=Rn-Pb-Be loss)
8WETDCV-$CTMCategory(offset=3000, name=WETDCV-$, description=Conv wet scavenging)
9WETDLS-$CTMCategory(offset=3000, name=WETDLS-$, description=Wet deposition)
10MC-FRC-$CTMCategory(offset=3000, name=MC-FRC-$, description=Moist conv fraction)
" ], "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select categories based on other attributes\n", "dinfo.categories.select(offset=3000)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Record list of 4 selected CTMDiagnostic:
KeyCTMDiagnostic
011CTMDiagnostic(number=11, name=ALD2, full_name=ALD2 tracer, unit=ppbC, scale=1000000000.0, chemical=True, molecular_weight=0.012, hydrocarbon=True, carbon_weight=2)
118CTMDiagnostic(number=18, name=PRPE, full_name=PRPE tracer, unit=ppbC, scale=1000000000.0, chemical=True, molecular_weight=0.012, hydrocarbon=True, carbon_weight=3)
219CTMDiagnostic(number=19, name=C3H8, full_name=C3H8 tracer, unit=ppbC, scale=1000000000.0, chemical=True, molecular_weight=0.012, hydrocarbon=True, carbon_weight=3)
321CTMDiagnostic(number=21, name=C2H6, full_name=C2H6 tracer, unit=ppbC, scale=1000000000.0, chemical=True, molecular_weight=0.012, hydrocarbon=True, carbon_weight=2)
" ], "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# advanced selection\n", "dinfo.diagnostics.select(lambda d: d.unit == 'ppbC' and d.number > 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add or remove entries:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CTMDiagnostic(number=9999, name=NEW, full_name=a new tracer, unit=unitless, scale=1.0, chemical=True, molecular_weight=0.0, hydrocarbon=False, carbon_weight=0)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_tracer = diagnostics.CTMDiagnostic(9999, 'NEW', full_name='a new tracer')\n", "\n", "dinfo.diagnostics.append(new_tracer)\n", "\n", "dinfo.diagnostics[-1]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CTMDiagnostic(number=24004, name=N(AIR), full_name=Number density of air, unit=molec/m3, scale=1.0, chemical=False, molecular_weight=0.0, hydrocarbon=False, carbon_weight=1)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select the new tracer added to the list \n", "s = dinfo.diagnostics.select(9999)\n", "\n", "# remove the selected entry\n", "s.selection_remove()\n", "\n", "dinfo.diagnostics[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exporting to diaginfo and tracerinfo files:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NotYetImplementedError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNotYetImplementedError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdinfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_diaginfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'diaginfo_test.dat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdinfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_tracerinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tracerinfo_test.dat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/bovy/GitRepos/PyGChem/pygchem/diagnostics.pyc\u001b[0m in \u001b[0;36msave_diaginfo\u001b[1;34m(self, filename)\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[0mTODO\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 128\u001b[0m \"\"\"\n\u001b[1;32m--> 129\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mNotYetImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 131\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msave_tracerinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNotYetImplementedError\u001b[0m: " ] } ], "source": [ "dinfo.save_diaginfo('diaginfo_test.dat')\n", "dinfo.save_tracerinfo('tracerinfo_test.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "PyGChem Git Master (py27)", "language": "python", "name": "pygchemgitmaster_py27" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }