get_nearest_water.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "[TARDIS](https://github.com/pyoceans/tardis) is a hackish wrapper around iris\nthat expands some of the iris methods to work with 2D coords, and facilitate\nsome of the methos calls.  (Virtually everything that iris does could be\naccomplish with the raw iris API.)"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "from tardis import quick_load_cubes\n\n# Variable.  (CF-names standard_names list.)\nname_list = ['sea_surface_height',\n             'sea_surface_elevation',\n             'sea_surface_height_above_geoid',\n             'sea_surface_height_above_sea_level',\n             'water_surface_height_above_reference_datum',\n             'sea_surface_height_above_reference_ellipsoid']\n\nurl = (\"http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/his_Best/\"\n       \"ESPRESSO_Real-Time_v2_History_Best_Available_best.ncd\")\n\ncube = quick_load_cubes(url, name_list, callback=None, strict=True)\n\nprint(cube)",
     "prompt_number": 1,
     "outputs": [
      {
       "output_type": "stream",
       "text": "/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/cf.py:1032: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'v' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_v', u'xi_v')\n  warnings.warn(msg)\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/cf.py:1032: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'v' via variable u's_rho': Dimensions (u'time', u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_v', u'xi_v')\n  warnings.warn(msg)\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/cf.py:1032: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'u' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_u', u'xi_u')\n  warnings.warn(msg)\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/cf.py:1032: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'u' via variable u's_rho': Dimensions (u'time', u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_u', u'xi_u')\n  warnings.warn(msg)\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1395: UserWarning: Gracefully filling 'time' dimension coordinate masked points\n  warnings.warn(msg.format(str(cf_coord_var.cf_name)))\n",
       "stream": "stderr"
      },
      {
       "output_type": "stream",
       "text": "sea_surface_height / (meter)        (time: 18300; -- : 82; -- : 130)\n     Dimension coordinates:\n          time                           x           -        -\n     Auxiliary coordinates:\n          forecast_reference_time        x           -        -\n          latitude                       -           x        x\n          longitude                      -           x        x\n     Attributes:\n          CPP_options: MyCPP, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, AVERAGES,...\n          Conventions: CF-1.4, _Coordinates\n          DODS_EXTRA.Unlimited_Dimension: ocean_time\n          _ChunkSize: [  1  82 130]\n          _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention\n          ana_file: ROMS/Functionals/ana_btflux.h, /home/julia/ROMS/espresso/RealTime/Comp...\n          avg_base: espresso_avg_3450\n          bry_file: ../Data/espresso_bdry_new.nc\n          cdm_data_type: GRID\n          clm_file: ../Data/espresso_clm_new.nc\n          code_dir: /home/julia/ROMS/espresso/svn1409\n          compiler_command: /opt/pgisoft/openmpi/bin/mpif90\n          compiler_flags:  -O3 -Mfree\n          compiler_system: pgi\n          cpu: x86_64\n          featureType: GRID\n          field: free-surface, scalar, series\n          file: espresso_his_3450_0005.nc\n          flt_file: espresso_flt_3450.nc\n          format: netCDF-4/HDF5 file\n          fpos_file: /home/om/roms/espresso/Data/espresso_floats_glider.in\n          frc_file_01: /home/om/roms/espresso/Data/espresso_tide_c05_20060101.nc\n          frc_file_02: ../Data/espresso_river.nc\n          frc_file_03: ../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_04: ../Data/swrad_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_05: ../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_06: ../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_07: ../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_08: ../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_09: ../Data/Uwind_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_10: ../Data/Vwind_ncepnam_3hourly_MAB_and_GoM.nc\n          grd_file: /home/om/roms/espresso/Data/espresso_grid_c05.nc\n          header_dir: /home/julia/ROMS/espresso/RealTime/Compile/fwd\n          header_file: espresso.h\n          his_base: espresso_his_3450\n          history: ROMS/TOMS, Version 3.5, Tuesday - June 16, 2015 -  6:22:08 AM ;\nFMRC Best...\n          ini_file: /home/julia/ROMS/espresso/RealTime/Storage/run04/espresso_ini_3450.nc\n          location: Proto fmrc:espresso_2013_da_his_best\n          os: Linux\n          rst_file: espresso_rst_3450.nc\n          script_file: nl_ocean_espresso.in\n          summary: Operational nowcast/forecast system version 2 for MARACOOS project (http://maracoos.org)....\n          svn_rev: exported\n          svn_url: https://www.myroms.org/svn/src/trunk\n          tiling: 004x002\n          time: ocean_time\n          title: ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW)...\n          type: ROMS/TOMS history file\n",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/netcdf.py:443: UserWarning: Unable to find coordinate for variable u'zeta'\n  '{!r}'.format(name))\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/netcdf.py:443: UserWarning: Unable to find coordinate for variable u'h'\n  '{!r}'.format(name))\n/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/netcdf.py:560: UserWarning: Unable to construct Ocean s-coordinate, generic form 1 factory due to insufficient source coordinates.\n  warnings.warn('{}'.format(e))\n",
       "stream": "stderr"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Note the warnings?  That makes dealing with all variables as a single dataset\nvery complicated."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import iris\nimport pytz\nfrom datetime import datetime, timedelta\n\nfrom tardis import proc_cube\n\n\n# Time.\nstop = datetime(2014, 7, 7, 12)\nstop = stop.replace(tzinfo=pytz.utc)\nstart = stop - timedelta(days=1)\n\n# Space.\nbbox = [-87.40, 24.25, -74.70, 36.70]\n\n# Units.\nunits = iris.unit.Unit('meters')\n\n# Subet the cube.\ncube = proc_cube(cube, bbox=bbox, time=(start, stop), units=units)\n\nprint(cube)",
     "prompt_number": 2,
     "outputs": [
      {
       "output_type": "stream",
       "text": "sea_surface_height / (meter)        (time: 24; -- : 75; -- : 38)\n     Dimension coordinates:\n          time                           x        -        -\n     Auxiliary coordinates:\n          forecast_reference_time        x        -        -\n          latitude                       -        x        x\n          longitude                      -        x        x\n     Attributes:\n          CPP_options: MyCPP, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, AVERAGES,...\n          Conventions: CF-1.4, _Coordinates\n          DODS_EXTRA.Unlimited_Dimension: ocean_time\n          _ChunkSize: [  1  82 130]\n          _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention\n          ana_file: ROMS/Functionals/ana_btflux.h, /home/julia/ROMS/espresso/RealTime/Comp...\n          avg_base: espresso_avg_3450\n          bry_file: ../Data/espresso_bdry_new.nc\n          cdm_data_type: GRID\n          clm_file: ../Data/espresso_clm_new.nc\n          code_dir: /home/julia/ROMS/espresso/svn1409\n          compiler_command: /opt/pgisoft/openmpi/bin/mpif90\n          compiler_flags:  -O3 -Mfree\n          compiler_system: pgi\n          cpu: x86_64\n          featureType: GRID\n          field: free-surface, scalar, series\n          file: espresso_his_3450_0005.nc\n          flt_file: espresso_flt_3450.nc\n          format: netCDF-4/HDF5 file\n          fpos_file: /home/om/roms/espresso/Data/espresso_floats_glider.in\n          frc_file_01: /home/om/roms/espresso/Data/espresso_tide_c05_20060101.nc\n          frc_file_02: ../Data/espresso_river.nc\n          frc_file_03: ../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_04: ../Data/swrad_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_05: ../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_06: ../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_07: ../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_08: ../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_09: ../Data/Uwind_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_10: ../Data/Vwind_ncepnam_3hourly_MAB_and_GoM.nc\n          grd_file: /home/om/roms/espresso/Data/espresso_grid_c05.nc\n          header_dir: /home/julia/ROMS/espresso/RealTime/Compile/fwd\n          header_file: espresso.h\n          his_base: espresso_his_3450\n          history: ROMS/TOMS, Version 3.5, Tuesday - June 16, 2015 -  6:22:08 AM ;\nFMRC Best...\n          ini_file: /home/julia/ROMS/espresso/RealTime/Storage/run04/espresso_ini_3450.nc\n          location: Proto fmrc:espresso_2013_da_his_best\n          os: Linux\n          rst_file: espresso_rst_3450.nc\n          script_file: nl_ocean_espresso.in\n          summary: Operational nowcast/forecast system version 2 for MARACOOS project (http://maracoos.org)....\n          svn_rev: exported\n          svn_url: https://www.myroms.org/svn/src/trunk\n          tiling: 004x002\n          time: ocean_time\n          title: ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW)...\n          type: ROMS/TOMS history file\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Round trip"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "iris.save(cube, \"subset.nc\")",
     "prompt_number": 3,
     "outputs": [
      {
       "output_type": "stream",
       "text": "/home/filipe/.virtualenvs/blog/lib/python2.7/site-packages/iris/fileformats/netcdf.py:1785: UserWarning: NetCDF default saving behaviour currently assigns the outermost dimensions to unlimited. This behaviour is to be deprecated, in favour of no automatic assignment. To switch to the new behaviour, set iris.FUTURE.netcdf_no_unlimited to True.\n  warnings.warn(msg)\n",
       "stream": "stderr"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "cube = iris.load_cube(\"subset.nc\")",
     "prompt_number": 4,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "print(cube)",
     "prompt_number": 5,
     "outputs": [
      {
       "output_type": "stream",
       "text": "sea_surface_height / (meter)        (time: 24; -- : 75; -- : 38)\n     Dimension coordinates:\n          time                           x        -        -\n     Auxiliary coordinates:\n          forecast_reference_time        x        -        -\n          latitude                       -        x        x\n          longitude                      -        x        x\n     Attributes:\n          CPP_options: MyCPP, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, AVERAGES,...\n          Conventions: CF-1.5\n          DODS_EXTRA.Unlimited_Dimension: ocean_time\n          _ChunkSize: [  1  82 130]\n          _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention\n          ana_file: ROMS/Functionals/ana_btflux.h, /home/julia/ROMS/espresso/RealTime/Comp...\n          avg_base: espresso_avg_3450\n          bry_file: ../Data/espresso_bdry_new.nc\n          cdm_data_type: GRID\n          clm_file: ../Data/espresso_clm_new.nc\n          code_dir: /home/julia/ROMS/espresso/svn1409\n          compiler_command: /opt/pgisoft/openmpi/bin/mpif90\n          compiler_flags:  -O3 -Mfree\n          compiler_system: pgi\n          cpu: x86_64\n          featureType: GRID\n          field: free-surface, scalar, series\n          file: espresso_his_3450_0005.nc\n          flt_file: espresso_flt_3450.nc\n          format: netCDF-4/HDF5 file\n          fpos_file: /home/om/roms/espresso/Data/espresso_floats_glider.in\n          frc_file_01: /home/om/roms/espresso/Data/espresso_tide_c05_20060101.nc\n          frc_file_02: ../Data/espresso_river.nc\n          frc_file_03: ../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_04: ../Data/swrad_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_05: ../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_06: ../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_07: ../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_08: ../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_09: ../Data/Uwind_ncepnam_3hourly_MAB_and_GoM.nc\n          frc_file_10: ../Data/Vwind_ncepnam_3hourly_MAB_and_GoM.nc\n          grd_file: /home/om/roms/espresso/Data/espresso_grid_c05.nc\n          header_dir: /home/julia/ROMS/espresso/RealTime/Compile/fwd\n          header_file: espresso.h\n          his_base: espresso_his_3450\n          history: ROMS/TOMS, Version 3.5, Tuesday - June 16, 2015 -  6:22:08 AM ;\nFMRC Best...\n          ini_file: /home/julia/ROMS/espresso/RealTime/Storage/run04/espresso_ini_3450.nc\n          location: Proto fmrc:espresso_2013_da_his_best\n          os: Linux\n          rst_file: espresso_rst_3450.nc\n          script_file: nl_ocean_espresso.in\n          summary: Operational nowcast/forecast system version 2 for MARACOOS project (http://maracoos.org)....\n          svn_rev: exported\n          svn_url: https://www.myroms.org/svn/src/trunk\n          tiling: 004x002\n          time: ocean_time\n          title: ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW)...\n          type: ROMS/TOMS history file\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# or"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "!ncdump -h subset.nc",
     "prompt_number": 6,
     "outputs": [
      {
       "output_type": "stream",
       "text": "netcdf subset {\r\ndimensions:\r\n\ttime = UNLIMITED ; // (24 currently)\r\n\tdim1 = 75 ;\r\n\tdim2 = 38 ;\r\nvariables:\r\n\tfloat zeta(time, dim1, dim2) ;\r\n\t\tzeta:_FillValue = 1.e+37f ;\r\n\t\tstring zeta:standard_name = \"sea_surface_height\" ;\r\n\t\tstring zeta:long_name = \"free-surface\" ;\r\n\t\tzeta:units = \"meter\" ;\r\n\t\tzeta:coordinates = \"lat_rho lon_rho time_run\" ;\r\n\tdouble time(time) ;\r\n\t\ttime:axis = \"T\" ;\r\n\t\ttime:units = \"hours since 2013-05-18T00:00:00Z\" ;\r\n\t\tstring time:standard_name = \"time\" ;\r\n\t\tstring time:long_name = \"Forecast time for ForecastModelRunCollection\" ;\r\n\t\ttime:calendar = \"gregorian\" ;\r\n\t\ttime:_CoordinateAxisType = \"Time\" ;\r\n\tdouble time_run(time) ;\r\n\t\ttime_run:units = \"hours since 2013-05-18T00:00:00Z\" ;\r\n\t\tstring time_run:standard_name = \"forecast_reference_time\" ;\r\n\t\tstring time_run:long_name = \"run times for coordinate = time\" ;\r\n\t\ttime_run:calendar = \"gregorian\" ;\r\n\t\ttime_run:_CoordinateAxisType = \"RunTime\" ;\r\n\tdouble lat_rho(dim1, dim2) ;\r\n\t\tlat_rho:units = \"degrees\" ;\r\n\t\tstring lat_rho:standard_name = \"latitude\" ;\r\n\t\tstring lat_rho:long_name = \"latitude of RHO-points\" ;\r\n\t\tlat_rho:_ChunkSize = 82, 130 ;\r\n\t\tlat_rho:_CoordinateAxisType = \"Lat\" ;\r\n\t\tlat_rho:field = \"lat_rho, scalar\" ;\r\n\tdouble lon_rho(dim1, dim2) ;\r\n\t\tlon_rho:units = \"degrees\" ;\r\n\t\tstring lon_rho:standard_name = \"longitude\" ;\r\n\t\tstring lon_rho:long_name = \"longitude of RHO-points\" ;\r\n\t\tlon_rho:_ChunkSize = 82, 130 ;\r\n\t\tlon_rho:_CoordinateAxisType = \"Lon\" ;\r\n\t\tlon_rho:field = \"lon_rho, scalar\" ;\r\n\r\n// global attributes:\r\n\t\t:CPP_options = \"MyCPP, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE_OUT, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, SSH_TIDES, TCLIMATOLOGY, TCLM_NUDGING, TS_A4HADVECTION, TS_A4VADVECTION, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_PSOURCE, UV_TIDES, UV_VIS2, VAR_RHO_2D, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION\" ;\r\n\t\t:DODS_EXTRA.Unlimited_Dimension = \"ocean_time\" ;\r\n\t\t:_ChunkSize = 1, 82, 130 ;\r\n\t\t:_CoordSysBuilder = \"ucar.nc2.dataset.conv.CF1Convention\" ;\r\n\t\t:ana_file = \"ROMS/Functionals/ana_btflux.h, /home/julia/ROMS/espresso/RealTime/Compile/fwd/ana_nudgcoef.h\" ;\r\n\t\t:avg_base = \"espresso_avg_3450\" ;\r\n\t\t:bry_file = \"../Data/espresso_bdry_new.nc\" ;\r\n\t\t:cdm_data_type = \"GRID\" ;\r\n\t\t:clm_file = \"../Data/espresso_clm_new.nc\" ;\r\n\t\t:code_dir = \"/home/julia/ROMS/espresso/svn1409\" ;\r\n\t\t:compiler_command = \"/opt/pgisoft/openmpi/bin/mpif90\" ;\r\n\t\t:compiler_flags = \" -O3 -Mfree\" ;\r\n\t\t:compiler_system = \"pgi\" ;\r\n\t\t:cpu = \"x86_64\" ;\r\n\t\t:featureType = \"GRID\" ;\r\n\t\t:field = \"free-surface, scalar, series\" ;\r\n\t\t:file = \"espresso_his_3450_0005.nc\" ;\r\n\t\t:flt_file = \"espresso_flt_3450.nc\" ;\r\n\t\t:format = \"netCDF-4/HDF5 file\" ;\r\n\t\t:fpos_file = \"/home/om/roms/espresso/Data/espresso_floats_glider.in\" ;\r\n\t\t:frc_file_01 = \"/home/om/roms/espresso/Data/espresso_tide_c05_20060101.nc\" ;\r\n\t\t:frc_file_02 = \"../Data/espresso_river.nc\" ;\r\n\t\t:frc_file_03 = \"../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_04 = \"../Data/swrad_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_05 = \"../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_06 = \"../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_07 = \"../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_08 = \"../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_09 = \"../Data/Uwind_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:frc_file_10 = \"../Data/Vwind_ncepnam_3hourly_MAB_and_GoM.nc\" ;\r\n\t\t:grd_file = \"/home/om/roms/espresso/Data/espresso_grid_c05.nc\" ;\r\n\t\t:header_dir = \"/home/julia/ROMS/espresso/RealTime/Compile/fwd\" ;\r\n\t\t:header_file = \"espresso.h\" ;\r\n\t\t:his_base = \"espresso_his_3450\" ;\r\n\t\t:history = \"ROMS/TOMS, Version 3.5, Tuesday - June 16, 2015 -  6:22:08 AM ;\\nFMRC Best Dataset\" ;\r\n\t\t:ini_file = \"/home/julia/ROMS/espresso/RealTime/Storage/run04/espresso_ini_3450.nc\" ;\r\n\t\t:location = \"Proto fmrc:espresso_2013_da_his_best\" ;\r\n\t\t:os = \"Linux\" ;\r\n\t\t:rst_file = \"espresso_rst_3450.nc\" ;\r\n\t\t:script_file = \"nl_ocean_espresso.in\" ;\r\n\t\t:summary = \"Operational nowcast/forecast system version 2 for MARACOOS project (http://maracoos.org). Grid configuration and assimilation system the same as for the ~6 km resolution ROMS Espresso Experiment. These simulations are forced by NCEP NAM operational atmospheric forcing, boundary conditions are from HYCOM-NCODA 1/12 deg model; tides are from ADCIRC model. Assimilated data: SST (AVHRR and blended MW+IR), HF Radar, along track SSH from Jason2.\" ;\r\n\t\t:svn_rev = \"exported\" ;\r\n\t\t:svn_url = \"https://www.myroms.org/svn/src/trunk\" ;\r\n\t\t:tiling = \"004x002\" ;\r\n\t\t:time = \"ocean_time\" ;\r\n\t\t:title = \"ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW) 2013-present FMRC History (Best)\" ;\r\n\t\t:type = \"ROMS/TOMS history file\" ;\r\n\t\t:Conventions = \"CF-1.5\" ;\r\n}\r\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "from tardis import get_nearest_water, make_tree\n\n\nobs = dict(lon=-76.67, lat=34.72)\n\n# I am re-writting this to use rtree.  Right now it uses scipy KDTree.\ntree, lon, lat = make_tree(cube)\nkw = dict(k=10, max_dist=0.04, min_var=0.01)\nseries, dist, idx = get_nearest_water(cube, tree, obs['lon'], obs['lat'], **kw)",
     "prompt_number": 7,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Some plots"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "%matplotlib inline\n\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nfrom cartopy.feature import NaturalEarthFeature\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n\nstates = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n                             name='admin_1_states_provinces_shp')\n\ndef make_map(projection=ccrs.PlateCarree()):\n    fig, ax = plt.subplots(figsize=(8, 6),\n                           subplot_kw=dict(projection=projection))\n    ax.coastlines(resolution='50m')\n    gl = ax.gridlines(draw_labels=True)\n    gl.xlabels_top = gl.ylabels_right = False\n    gl.xformatter = LONGITUDE_FORMATTER\n    gl.yformatter = LATITUDE_FORMATTER\n    return fig, ax",
     "prompt_number": 8,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import numpy.ma as ma\n\n\nc = cube[-1, ...]  # Latest time stamp.\nc.data = ma.masked_invalid(c.data)\n\nlon = c.coord(axis='X').points\nlat = c.coord(axis='Y').points\n\nextent = (lon.min(), lon.max(),\n          lat.min(), lat.max())",
     "prompt_number": 9,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "from oceans import cm\n\nfrom matplotlib.ticker import MaxNLocator\nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n\nfig, ax = make_map()\nax.set_extent(extent)\ncs = ax.pcolormesh(lon, lat, c.data, cmap=cm.rscolmap, alpha=0.5)\nax.plot(obs['lon'], obs['lat'], 'o', color='darkgreen', label='Beaufort, NC', alpha=0.5)\nax.plot(lon[idx], lat[idx], 'o', color='crimson', label='ESPRESSO', alpha=0.5)\nax.set_title(c.attributes.get('title', None))\nax.add_feature(states, edgecolor='gray')\nleg = ax.legend(numpoints=1, fancybox=True, framealpha=0, loc=\"upper left\")\n\naxins = zoomed_inset_axes(ax, 20, loc=5,\n                         bbox_to_anchor=(1.1, 0.75),\n                          bbox_transform=ax.figure.transFigure)\naxins.pcolormesh(lon, lat, c.data, cmap=cm.rscolmap, alpha=0.5)\naxins.plot(obs['lon'], obs['lat'], 'o', color='darkgreen', label='Beaufort, NC', alpha=0.5)\naxins.plot(lon[idx], lat[idx], 'o', color='crimson', label='ESPRESSO', alpha=0.5)\naxins.axis([-76.7, -76.64,\n             34.67,  34.73])\nmark_inset(ax, axins, loc1=2, loc2=4, ec=\"0.5\")\naxins.axes.get_xaxis().set_ticks([])\naxins.axes.get_yaxis().set_ticks([])",
     "prompt_number": 10,
     "outputs": [
      {
       "text": "[]",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 10
      },
      {
       "png": 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ZVwgRCKwxdjm/jdpotFSoiyE+Q51rakcI0QZ1ZMz/GX4cwzzJzTNvM1gBM4Hd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\nEZkEBWgRERERkUn4f9sDK15pmh9fAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x7f9578165150>",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import iris.quickplot as qplt\n\nfig, ax = plt.subplots(figsize=(9, 2.75))\nl, = qplt.plot(series)",
     "prompt_number": 11,
     "outputs": [
      {
       "png": 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PGQ1SuaZNi3/jHj1iAoE2eS0fhx8eNS+77RalCOusk3ZE2dW7jouZtQL6uvtd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\n",
       "text": "<matplotlib.figure.Figure at 0x7f9578165ad0>",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "from iris.pandas import as_series\n\ns = as_series(series)\n\nprint(type(s))\ns",
     "prompt_number": 12,
     "outputs": [
      {
       "output_type": "stream",
       "text": "<class 'pandas.core.series.Series'>\n",
       "stream": "stdout"
      },
      {
       "text": "2014-07-06 12:00:00.000000   -0.612113\n2014-07-06 13:00:00.000000   -0.587402\n2014-07-06 14:00:00.000013   -0.478360\n2014-07-06 15:00:00.000000   -0.295291\n2014-07-06 16:00:00.000000   -0.091979\n2014-07-06 17:00:00.000013    0.087692\n2014-07-06 18:00:00.000000    0.197953\n2014-07-06 19:00:00.000000    0.221639\n2014-07-06 20:00:00.000013    0.150499\n2014-07-06 21:00:00.000000   -0.004931\n2014-07-06 22:00:00.000000   -0.205011\n2014-07-06 23:00:00.000013   -0.400538\n2014-07-07 00:00:00.000000   -0.550295\n2014-07-07 01:00:00.000000   -0.684369\n2014-07-07 02:00:00.000013   -0.664711\n2014-07-07 03:00:00.000000   -0.569217\n2014-07-07 04:00:00.000000   -0.416755\n2014-07-07 05:00:00.000013   -0.253293\n2014-07-07 06:00:00.000000   -0.139434\n2014-07-07 07:00:00.000000   -0.096578\n2014-07-07 08:00:00.000013   -0.142669\n2014-07-07 09:00:00.000000   -0.264661\n2014-07-07 10:00:00.000000   -0.438460\n2014-07-07 11:00:00.000013   -0.623228\ndtype: float32",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 12
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "name": "",
  "signature": "sha256:4926c074dc67d9e8fe3e86a5349ce0d8eb3e09d74444d612efbf42be34958a34"
 },
 "nbformat": 3
}