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@ocefpaf
Last active August 29, 2015 14:17
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{
"cells": [
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "import iris\n\n\nurl = ('http://tds.marine.rutgers.edu:8080/thredds/dodsC/'\n 'cool/glider/mab/Gridded/20130911T000000_20130920T000000_gp2013_modena.nc')\n\nglider = iris.load(url)\n\nlon = glider.extract_strict('Longitude').data\nlat = glider.extract_strict('Latitude').data\nglider = glider.extract_strict('Temperature')\ndepth = glider.coord('depth').points",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "/home/filipe/.virtualenvs/iris/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1301: UserWarning: Ignoring netCDF variable 'salinity' invalid units 'psu'\n warnings.warn(msg.format(msg_name, msg_units))\n",
"name": "stderr"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "import numpy as np\nimport numpy.ma as ma\nimport seawater as sw\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import interp1d\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\n\nfrom utilities import time_coord\n\n%matplotlib inline\n\n\ndef plot_glider(cube, mask_topo=False, track_inset=False, **kw):\n \"\"\"Plot glider cube.\"\"\"\n cmap = kw.pop('cmap', plt.cm.rainbow)\n \n data = ma.masked_invalid(cube.data.squeeze())\n t = time_coord(cube)\n t = t.units.num2date(t.points.squeeze())\n \n dist, pha = sw.dist(lat, lon, units='km')\n dist = np.r_[0, np.cumsum(dist)]\n \n dist, z = np.broadcast_arrays(dist[..., None], depth)\n\n try:\n z_range = cube.coord(axis='Z').attributes['actual_range']\n except KeyError:\n z_range = z.min(), z.max()\n try:\n data_range = cube.attributes['actual_range']\n except KeyError: \n data_range = data.min(), data.max()\n \n condition = np.logical_and(data >= data_range[0], data <= data_range[1])\n data = ma.masked_where(~condition, data)\n \n condition = np.logical_and(z >= z_range[0], z <= z_range[1])\n z = ma.masked_where(~condition, z)\n\n fig, ax = plt.subplots(figsize=(9, 3.75))\n cs = ax.pcolor(dist, z, data, cmap=cmap, snap=True, **kw)\n if mask_topo:\n h = z.max(axis=1)\n x = dist[:, 0]\n ax.plot(x, h, color='black', linewidth='0.5', zorder=3)\n ax.fill_between(x, h, y2=h.max(), color='0.9', zorder=3)\n ax.set_title('Glider track from {} to {}'.format(t[0], t[-1]))\n fig.tight_layout()\n \n if track_inset:\n axin = inset_axes(ax, width=\"25%\", height=\"30%\", loc=4)\n axin.plot(lon, lat, 'k.')\n start, end = (lon[0], lat[0]), (lon[-1], lat[-1])\n kw = dict(marker='o', linestyle='none')\n axin.plot(*start, color='g', **kw)\n axin.plot(*end, color='r', **kw)\n axin.axis('off')\n return fig, ax, cs",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "fig, ax, cs = plot_glider(glider, mask_topo=False, track_inset=True)",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
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G8qSeeQ4lTZtXGrbhKUI+C4SIF9IHtURzCjXt3YQahO9CvSxxlfbzGRZ4CQT1VtkR1FOk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\nQBEREZEtRgNAERERkS3m/weAVyTDoF9jZQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f8ad2aa82d0>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Model"
},
{
"metadata": {
"scrolled": true,
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from utilities import CF_names, quick_load_cubes\n\nmodels = dict(useast=('http://ecowatch.ncddc.noaa.gov/thredds/dodsC/'\n 'ncom_us_east_agg/US_East_Apr_05_2013_to_Current_best.ncd'),\n hycom=('http://ecowatch.ncddc.noaa.gov/thredds/dodsC/'\n 'hycom/hycom_reg1_agg/HYCOM_Region_1_Aggregation_best.ncd'),\n sabgom=('http://omgsrv1.meas.ncsu.edu:8080/thredds/dodsC/'\n 'fmrc/sabgom/SABGOM_Forecast_Model_Run_Collection_best.ncd'))\n \nsos_name = 'sea_water_temperature'\nname_list = CF_names[sos_name]\n\ncube = quick_load_cubes(models['useast'], name_list, strict=True)",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": "/home/filipe/.virtualenvs/iris/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1396: UserWarning: Gracefully filling 'time' dimension coordinate masked points\n warnings.warn(msg.format(str(cf_coord_var.cf_name)))\n/home/filipe/.virtualenvs/iris/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1301: UserWarning: Ignoring netCDF variable 'surf_salt_flux' invalid units 'psu-m/s'\n warnings.warn(msg.format(msg_name, msg_units))\n",
"name": "stderr"
}
]
},
{
"metadata": {
"scrolled": true,
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from datetime import datetime\n\nfrom utilities import proc_cube\n\n\nstart = glider.coord(axis='T').attributes['minimum']\nstop = glider.coord(axis='T').attributes['maximum']\n\nstart = datetime.strptime(start, '%Y-%m-%d %H:%M:%S') \nstop = datetime.strptime(stop, '%Y-%m-%d %H:%M:%S')\n\nbbox = lon.min(), lat.min(), lon.max(), lat.max()\n\ncube = proc_cube(cube, bbox=bbox, time=(start, stop), units=glider.units)\n\nprint(cube)",
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": "sea_water_temperature / (degC) (time: 68; depth: 40; latitude: 12; longitude: 14)\n Dimension coordinates:\n time x - - -\n depth - x - -\n latitude - - x -\n longitude - - - x\n Auxiliary coordinates:\n forecast_reference_time x - - -\n Attributes:\n Conventions: CF-1.4, _Coordinates\n NAVO_code: 15\n _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention\n cdm_data_type: GRID\n classification_authority: not applicable\n classification_level: not applicable\n contact: NRL Code 7323\n distribution_statement: Approved for public release; distribution is unlimited.\n downgrade_date: not applicable\n featureType: GRID\n generating_model: NCOM NOKI\n history: not applicable ;\nFMRC Best Dataset\n initial_time: 2000010100\n input_data_source: not_applicable\n institution: Naval Research Laboratory\n location: Proto fmrc:US_East_Apr_05_2013_to_Current\n model_type: NCOM_2.3\n operational_status: development\n time_origin: 2015-03-05 00:00:00\n title: NAVO NCOM Relocatable Model: American Seas Regional Forecast\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from iris.analysis import trajectory\n\nsample_points = [('latitude', lat),\n ('longitude', lon),\n ('time', glider.coord(axis='T').points)]\n\ninterpolated_cube = trajectory.interpolate(cube, sample_points)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "interpolated_cube.data = ma.masked_invalid(interpolated_cube.data)",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "import iris.quickplot as qplt\n\nfig, ax = plt.subplots(figsize=(9, 3.75))\nqplt.pcolormesh(interpolated_cube)\nax.set_ylim(120, 0)",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "(120, 0)"
},
"metadata": {},
"execution_count": 18
},
{
"output_type": "display_data",
"data": {
"image/png": 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iIrmnKKdIO6KcprMQUU7N0tcsyqdZlFMSMVFqiNiZLqIolkbIpNFOjWM6NY7t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\nyNIyX3/b1deLiIiI5J4yNCI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"text/plain": "<matplotlib.figure.Figure at 0x7f8abedcb2d0>"
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "iris_python2",
"display_name": "Iris (Python 2)",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8",
"file_extension": ".py",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
},
"gist_id": "9255e5cea03f7705a5fe"
},
"nbformat": 4,
"nbformat_minor": 0
}
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