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@ocefpaf
Created October 9, 2015 12:47
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "A URL abaixo contém os mesmo dados `L3/mapped/V4` que está espalhado em diversos arquivos no servidor da NASA.\n\nEsse servidor OPeNDAP é um \"OPeNDAP\" de verdade e não um \"dump\" de arquivos.\n\nO exemplo abaixo mostra como tirar vantagem disso para pegar apenas os dados de interesse sem muito esforço."
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "import iris\n\n\nurl = (\"http://thredds.axiomdatascience.com/\"\n \"thredds/dodsC/Aquarius_V4_SSS_Daily.nc\")\n\ncube = iris.load_cube(url)",
"execution_count": 1,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "O `print` abaixo é apenas para olharmos os metadados."
},
{
"metadata": {
"scrolled": false,
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "print(cube)",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": "sea_surface_salinity / (1e-3) (time: 1351; latitude: 180; longitude: 360)\n Dimension coordinates:\n time x - -\n latitude - x -\n longitude - - x\n Attributes:\n Data_Bins: 9855\n Data_Center: NASA/GSFC OBPG\n Data_Maximum: 38.932\n Data_Minimum: 26.2357\n Easternmost_Longitude: 180.0\n End_Day: 158\n End_Millisec: 4723957\n End_Orbit: 21445\n End_Time: 2015158011843957\n End_Year: 2015\n Input_Files: Q2015157.L3b_DAY_SCI_V4.0.main\n Input_Parameters: ifile = Q2015157.L3b_DAY_SCI_V4.0.main|ofile = Q2015157.L3m_DAY_SCI_V4.0_SSS_1deg|prod...\n Intercept: 0.0\n L2_Flag_Names: POINTING,NAV,LANDRED,ICERED,REFL_1STOKESMOONRED,REFL_1STOKESGAL,TFTADI...\n Latitude_Step: 1.0\n Latitude_Units: degrees North\n Longitude_Step: 1.0\n Longitude_Units: degrees East\n Map_Projection: Equidistant Cylindrical\n Measure: Mean\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n Metadata_Link: http://podaac.jpl.nasa.gov/dataset/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V3\n Mission: SAC-D Aquarius\n Mission_Characteristics: Nominal orbit: inclination=98.0 (Sun-synchronous); node=6PM (ascending);...\n Northernmost_Latitude: 90.0\n Number_of_Columns: 360\n Number_of_Lines: 180\n Parameter: Sea Surface Salinity\n Period_End_Day: 158\n Period_End_Year: 2015\n Period_Start_Day: 157\n Period_Start_Year: 2015\n Processing_Control: smigen par=Q2015157.L3m_DAY_SCI_V4.0_SSS_1deg.param\n Processing_Time: 2015184115809000\n Processing_Version: V4.0\n Product_Name: Q2015157.L3m_DAY_SCI_V4.0_SSS_1deg\n Product_Type: DAY\n SW_Point_Latitude: -89.5\n SW_Point_Longitude: -179.5\n Scaling: linear\n Scaling_Equation: (Slope*l3m_data) + Intercept = Parameter value\n Sensor: Aquarius\n Sensor_Characteristics: Number of beams=3; channels per receiver=4; frequency 1.413 GHz; bits per...\n Sensor_Name: Aquarius\n Slope: 1.0\n Software_Name: smigen\n Software_Version: 5.04\n Southernmost_Latitude: -90.0\n Start_Day: 157\n Start_Millisec: 3858494\n Start_Orbit: 21431\n Start_Time: 2015157010418495\n Start_Year: 2015\n Suggested_Image_Scaling_Applied: No\n Suggested_Image_Scaling_Maximum: 70.0\n Suggested_Image_Scaling_Minimum: 0.0\n Suggested_Image_Scaling_Type: ATAN\n Title: Aquarius Level-3 Standard Mapped Image\n Units: psu\n Westernmost_Longitude: -180.0\n _lastModified: 2015184115809000\n cdm_data_type: grid\n contributor_name: Frank Wentz, Simon Yueh, Gary Lagerloef\n contributor_role: Data Providers\n creator_email: [email protected]\n creator_name: NASA JPL, PODAAC\n creator_url: http://podaac.jpl.nasa.gov\n date_issued: 20140530\n institution: NASA JPL PODAAC\n keywords: Oceans > Salinity/Density > Salinity\n keywords_vocabulary: Olsen, L.M., G. Major, K. Shein, J. Scialdone, S. Ritz, T. Stevens, M....\n license: Please provide acknowledgement of the use of PO.DAAC data products, images,...\n processing_level: L3 V3.0 data processing is described in ftp://podaac-ftp.jpl.nasa.gov/...\n project: Aquarius/SAC-D Mission\n publisher_email: [email protected]\n publisher_name: NASA Jet Propulsion Laboratory, Physical Oceanography Distributed Active...\n publisher_url: podaac.jpl.nasa.gov\n standard_name_vocabulary: CF Standard Name Table V26\n summary: Aquarius Level 3 sea surface salinity (SSS) standard mapped image data...\n title: Aquarius Level 3 Sea Surface Salinity Standard Mapped Image Daily Data...\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Vamos restringir a uma região menor."
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from iris import Constraint\n\nlon = Constraint(longitude=lambda lon: -60 <= lon <= -40)\nlat = Constraint(latitude=lambda lat: 0 <= lat <= 12)\n\nc = cube.extract(lon & lat)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Note que o \"cubo\" de dados é menor."
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "cube.shape, c.shape",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "((1351, 180, 360), (1351, 13, 21))"
},
"metadata": {},
"execution_count": 4
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Vamos fazer um plot global rápido só para examinar os dados."
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "import numpy.ma as ma\nlast = cube[-1, ...] # Pega os dados mais recentes.\n\nlast.data = ma.masked_invalid(last.data)",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "%matplotlib inline\n\nimport iris.quickplot as qplt\n\ncs = qplt.pcolormesh(last)",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": "/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/coords.py:779: UserWarning: Coordinate 'longitude' is not bounded, guessing contiguous bounds.\n 'contiguous bounds.'.format(self.name()))\n/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/coords.py:779: UserWarning: Coordinate 'latitude' is not bounded, guessing contiguous bounds.\n 'contiguous bounds.'.format(self.name()))\n/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/cartopy/mpl/geoaxes.py:1263: RuntimeWarning: invalid value encountered in greater\n to_mask = ((np.abs(dx_horizontal) > np.pi / 2) |\n",
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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7YEp9cmsVucQG5slwihRH08/HKJU4vPX5zXtF/NiBzZCdQHoEzDjhJFT5sePl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\nzTxYmd/Bw5mTQ36+xzV8XNQ+V3c/WVHZoODqWO1jX0+w1I2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"text/plain": "<matplotlib.figure.Figure at 0x7f17e04ab590>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Note no gráfico acima que esses dados são muito esparsos!\n\nPara não precisar fazer um novo download dos dados no futuro vamos salvar essa região no disco local."
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "iris.save(c, \"equator_sss.nc\")",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": "/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/fileformats/netcdf.py:1783: 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",
"name": "stderr"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Com os dados salvos podemos explorar e modificar o nosso programa a partir desse ponto.\nNão há necessidade de rodar a parte acima a não ser para salvar um região diferente."
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "import iris\n\n\nc = iris.load_cube('equator_sss.nc') # Carrega os dados do salvos.\n\nlon = c.coord(axis='X').points # Pega apenas os valores (pontos) das coordenadas.\nlat = c.coord(axis='Y').points\ntime = c.coord(axis='T').points\ntime = c.coord(axis='T')\ntime = time.units.num2date(time.points) # A coordenada tempo precisa ser convertida para \"datetimes.\"\nc.data = ma.masked_invalid(c.data) # Aplica a máscara de dados.",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "import matplotlib.pyplot as plt\nfrom ipywidgets import widgets\n\nwidgets.interact\n\n\ndef choose_time(time_k):\n fig, ax = plt.subplots()\n cs = ax.pcolormesh(lon, lat, c.data[time_k, ...], cmap=plt.cm.Greens)\n fig.colorbar(cs, shrink=0.7)\n title = ax.set_title(time[time_k].strftime('%Y-%m-%d'))\n\nw = widgets.interact(choose_time,\n time_k=widgets.IntSlider(min=0, max=c.shape[0]-1, step=1))",
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7f17dbe9a090>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "A ferramenta acima ajuda apenas a explorar os dados ao long do tempo. Abaixo vamos fazer uma média dessa área e checar a série temporal."
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "iris.analysis.MEAN\n\ntime_series = c.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)",
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": "/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/cube.py:2866: UserWarning: Collapsing spatial coordinate u'latitude' without weighting\n warnings.warn(msg.format(coord.name()))\n/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/coords.py:963: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for u'latitude'.\n warnings.warn(msg.format(self.name()))\n/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/iris/coords.py:963: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for u'longitude'.\n warnings.warn(msg.format(self.name()))\n",
"name": "stderr"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "from iris.pandas import as_series\n\ntime_series = as_series(time_series)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "import seaborn\nseaborn.set(style='ticks')\n\nfig, ax = plt.subplots(figsize=(11, 2.75))\nax = time_series.plot(ax=ax)",
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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8KyJC/KHTGzC4ZwIeuKmnwNlmpj69HiolgzWvcSNYLYmAP3v+ejz17nacvlCJ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\nk5MTX3zxBd26daN169a0bt2ac+fOkZ+fbzqy8/b25tdff2X9+vWkpqa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"text/plain": "<matplotlib.figure.Figure at 0x7f17fa5d3b90>"
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python2",
"display_name": "Python 2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10",
"file_extension": ".py",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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