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koldunovn/netCDF_to_ASCII.ipynb

Last active December 13, 2015 19:39
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netCDF_to_ASCII.ipynb
{
"metadata": {
"name": "netCDF_to_ASCII_with_Python"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u041a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u0443\u0435\u043c netCDF \u0432 ASCII \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 Python \u0432 Windows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \u041d\u0438\u043a\u043e\u043b\u0430\u0439 \u041a\u043e\u043b\u0434\u0443\u043d\u043e\u0432\n",
" koldunovn@gmail.com\n",
"[koldunov.net](http://koldunov.net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**\u0417\u0430\u0434\u0430\u0447\u0430**: \u041f\u043e\u043c\u043e\u0447\u044c \u0434\u0440\u0443\u0437\u044c\u044f\u043c \u0432\u0438\u043d\u0434\u0443\u0437\u044f\u0442\u043d\u0438\u043a\u0430\u043c \u0441\u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c *netCDF* \u0432 *ASCII*, \u043f\u043e\u043f\u0443\u0442\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0432 \u043d\u0430 \u0438\u0445 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u044b *Python*, \u0432 \u043d\u0430\u0434\u0435\u0436\u0434\u0435, \u0447\u0442\u043e \u043e\u043d\u0438 \u0442\u0430\u043a\u0438 \u043f\u043e\u0441\u0442\u0435\u043f\u0435\u043d\u043d\u043e \u0437\u0430\u0431\u0443\u0434\u0443\u0442 \u043f\u0440\u043e \u0434\u0435\u043b\u044c\u0444\u0438, \u0444\u043e\u0440\u0442\u0440\u0430\u043d \u0438 \u043f\u0440\u043e\u0447\u0438\u0435 \u0433\u0430\u0434\u043e\u0441\u0442\u0438. \u0417\u0430\u043e\u0434\u043d\u043e \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c \u0443\u0434\u043e\u0431\u043d\u043e \u043b\u0438 \u0432 *ipython notebook* \u043f\u0438\u0441\u0430\u0442\u044c \u043f\u043e\u0441\u0442\u044b.\n",
"\n",
"**\u0418\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b**: *cdo*, *Pyhton(x,y)*, *ipython notebook*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041b\u044e\u0434\u0438 \u0440\u0430\u0431\u043e\u0442\u0430\u044e\u0449\u0438\u0435 \u043f\u043e\u0434 \u0432\u0438\u043d\u0434\u043e\u0443\u0437 \u043b\u044e\u0431\u044f\u0442 ASCII, \u044f \u0437\u043d\u0430\u044e, \u044f \u0441\u0430\u043c \u0431\u044b\u043b \u0442\u0430\u043a\u043e\u0439. \u041e\u043d\u0438 \u0433\u043e\u0442\u043e\u0432\u044b \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u0442\u044c \u0432 ASCII \u0432\u0441\u0451 \u043d\u0430 \u0441\u0432\u0435\u0442\u0435, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u0434\u0430\u043d\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 IPCC, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0437\u0430\u043d\u0438\u043c\u0430\u044f \u0432 \u0431\u0438\u043d\u0430\u0440\u043d\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435 \u0441\u043e\u0442\u043d\u0438 \u0433\u0438\u0433\u0430\u0431\u0430\u0439\u0442, \u0431\u0443\u0434\u0443\u0447\u0438 \u043f\u0435\u0440\u0435\u0432\u0435\u0434\u0435\u043d\u044b \u0432 ASCII \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u044e\u0442\u0441\u044f \u0432 \u043c\u043e\u043d\u0441\u0442\u0440\u043e\u0432, \u0441\u0436\u0438\u0440\u0430\u044e\u0449\u0438\u0445 \u0432\u0441\u0451 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e\u0435 \u0434\u0438\u0441\u043a\u043e\u0432\u043e\u0435 \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u043e \u0432 \u0440\u0430\u0434\u0438\u0443\u0441\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u043a\u0438\u043b\u043e\u043c\u0435\u0442\u0440\u043e\u0432. \u041d\u043e \u0442\u0430\u043a\u043e\u0432\u044b \u0440\u0435\u0430\u043b\u0438\u0438 \u0432\u0438\u043d\u0434\u0443\u0437\u043e\u0432\u043e\u0439 \u0436\u0438\u0437\u043d\u0438, \u043c\u043d\u043e\u0433\u0438\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0442\u0430\u043c, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0437\u0430\u043d\u0438\u043c\u0430\u044e\u0449\u0438\u0435\u0441\u044f \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u043e\u0439 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u043e\u043c \u0434\u0430\u043d\u043d\u044b\u0445, \u0445\u043e\u0442\u044f\u0442, \u0447\u0442\u043e\u0431\u044b \u0438\u043c \u0441\u043a\u0430\u0440\u043c\u043b\u0438\u0432\u0430\u043b\u0438 \u0442\u0435\u043a\u0441\u0442 \u0438 \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0435\u043a\u0441\u0442. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0423 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0430 \u0438\u0437 netCDF \u0432 ASCII \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u0435\u0442 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u0439. \u041c\u043e\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0434\u0430\u043c\u043f \u0432\u0441\u0435\u0433\u043e \u0444\u0430\u0439\u043b\u0430, \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430, \u0438\u043b\u0438 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043a\u0438 *ncdump.exe*. \u041d\u0435\u0431\u043e\u043b\u044c\u0448\u0443\u044e \u0438\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u044e \u043a\u0430\u043a \u044d\u0442\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0438 \u0433\u0434\u0435 \u0432\u0437\u044f\u0442\u044c \u044d\u0442\u0443 \u043d\u0435\u0443\u043b\u043e\u0432\u0438\u043c\u0443\u044e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043a\u0443 \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u0447\u0438\u0442\u0430\u0442\u044c [\u0437\u0434\u0435\u0441\u044c](http://nsidc.org/data/hdfeos/hdf_to_ascii.html) (\u0442\u0430\u043c \u043f\u0438\u0448\u0443\u0442 \u043f\u0440\u043e HDF, \u043d\u043e \u0434\u043b\u044f netCDF \u044d\u0442\u0430 \u0438\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0438\u044f \u0442\u0430\u043a\u0436\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u0438\u0442). \u041f\u0440\u0430\u0432\u0434\u0430 \u044d\u0442\u043e\u0442 \u0434\u0430\u043c\u043f \u0432\u0430\u043c \u043f\u0440\u0438\u0434\u0451\u0442\u0441\u044f \u043f\u043e\u0442\u043e\u043c \u0435\u0449\u0451 \u0434\u043e\u043b\u0433\u043e \u0438 \u043f\u0435\u0447\u0430\u043b\u044c\u043d\u043e \u0440\u0430\u0437\u0431\u0438\u0440\u0430\u0442\u044c, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0442\u043e, \u0447\u0442\u043e \u0432\u044b \u0443\u0432\u0438\u0434\u0438\u0442\u0435, \u0431\u0443\u0434\u0435\u0442 \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0441\u0438\u043b\u044c\u043d\u043e \u043e\u0442\u043b\u0438\u0447\u0430\u0442\u044c\u0441\u044f \u043e\u0442 \u0436\u0435\u043b\u0430\u0435\u043c\u043e\u0439 \u0442\u0430\u0431\u043b\u0438\u0447\u043a\u0438 *\u0432\u0440\u0435\u043c\u044f/\u0448\u0438\u0440\u043e\u0442\u0430/\u0434\u043e\u043b\u0433\u043e\u0442\u0430/\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435* (\u044f \u0437\u043d\u0430\u044e \u043c\u0435\u0447\u0442\u0430\u0435\u0442\u0435 \u0432\u044b \u0438\u043c\u0435\u043d\u043d\u043e \u043e\u0431 \u044d\u0442\u043e\u043c :)). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043c \u043a\u0442\u043e \u0437\u043d\u0430\u043a\u043e\u043c \u0441 Matlab \u0441 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043f\u043e\u0440 \u0432\u043e\u043e\u0431\u0449\u0435 \u0441\u0442\u0430\u043b\u043e \u0445\u043e\u0440\u043e\u0448\u043e, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0442\u0430\u043c \u043f\u043e\u044f\u0432\u0438\u043b\u0430\u0441\u044c \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 netCDF \u0444\u0430\u0439\u043b\u043e\u0432 \u0438\u0437 \u043a\u043e\u0440\u043e\u0431\u043a\u0438 \u0438 \u043e \u0442\u043e\u043c, \u043a\u0430\u043a \u0438\u0445 \u0442\u0430\u043c \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0442\u044c \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u0447\u0438\u0442\u0430\u0442\u044c [\u0442\u0443\u0442](http://koldunov.net/?p=540). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0417\u0434\u0435\u0441\u044c \u044f \u0440\u0430\u0441\u0441\u043a\u0430\u0436\u0443 \u043a\u0430\u043a \u0441\u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c netCDF \u0432 ASCII \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 Python, \u043f\u0440\u0438 \u044d\u0442\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442 \u0432\u044b\u0432\u043e\u0434\u0430 \u0432\u044b \u0441\u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u0430\u0434\u0430\u0432\u0430\u0442\u044c \u043a\u0430\u043a\u043e\u0439 \u043f\u043e\u0436\u0435\u043b\u0430\u0435\u0442\u0435. \u0423\u043f\u0440\u0430\u0436\u043d\u044f\u0442\u044c\u0441\u044f \u0431\u0443\u0434\u0435\u043c, \u043a\u0430\u043a \u043e\u0431\u044b\u0447\u043d\u043e, \u043d\u0430 \u0444\u0430\u0439\u043b\u0430\u0445 NCEP \u0440\u0435\u0430\u043d\u0430\u043b\u0438\u0437\u0430."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 Pyhton(x,y) \u0438 cdo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0423\u0447\u0451\u043d\u043e\u043c\u0443, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0445\u043e\u0447\u0435\u0442 \u043f\u0438\u0441\u0430\u0442\u044c \u0441\u043a\u0440\u0438\u043f\u0442\u044b \u043d\u0430 \u043f\u0438\u0442\u043e\u043d \u043f\u043e\u0434 Windows, \u043d\u0443\u0436\u043d\u043e \u043a\u0440\u043e\u043c\u0435 \u0441\u0430\u043c\u043e\u0433\u043e \u043f\u0438\u0442\u043e\u043d\u0430 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u0435\u0449\u0451 \u043c\u043d\u043e\u0433\u043e \u043f\u043e\u043b\u0435\u0437\u043d\u044b\u0445 \u0432 \u0445\u043e\u0437\u044f\u0439\u0441\u0442\u0432\u0435 \u043c\u043e\u0434\u0443\u043b\u0435\u0439, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a numpy, scipy, ipyhton. \u0414\u0435\u043b\u043e \u044d\u0442\u043e \u0437\u0430\u0447\u0430\u0441\u0442\u0443\u044e \u043c\u0443\u0442\u043e\u0440\u043d\u043e\u0435, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0434\u043b\u044f \u0442\u0430\u043a\u043e\u0433\u043e \u043d\u043e\u0432\u0438\u0447\u043a\u0430 \u0432 \u0432\u0438\u043d\u0434\u043e\u0443\u0437 \u0430\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u043a\u0430\u043a \u044f. \u041e\u0434\u043d\u0430\u043a\u043e \u043d\u0430\u0448\u043b\u0438\u0441\u044c \u0434\u043e\u0431\u0440\u044b\u0435 \u043b\u044e\u0434\u0438, \u0441\u043e\u0431\u0440\u0430\u043b\u0438 \u0432\u0441\u0451 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0435 \u0432 \u043e\u0434\u0438\u043d \u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u043f\u0430\u043a\u0435\u0442 \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u044e\u0442 \u0435\u0433\u043e \u043f\u043e\u0434 \u0438\u043c\u0435\u043d\u0435\u043c [Pyhton(x,y)](http://code.google.com/p/pythonxy/). \u041f\u0440\u0435\u0438\u043c\u0443\u0449\u0435\u0441\u0442\u0432\u043e \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u043d\u0435 \u043d\u0443\u0436\u043d\u043e \u0437\u0430\u043c\u043e\u0440\u0430\u0447\u0438\u0432\u0430\u0442\u044c\u0441\u044f \u0441 \u0441\u0430\u043c\u043e\u0441\u0442\u043e\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u043e\u0439 \u043f\u0430\u043a\u0435\u0442\u043e\u0432, \u0430 \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u0442\u043e\u043a, \u043d\u0430 \u043c\u043e\u0439 \u0432\u0437\u0433\u043b\u044f\u0434, \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u043f\u043e\u043b\u0443\u0447\u0438\u0432\u0448\u0438\u0439\u0441\u044f \u044d\u043a\u0437\u0435\u0448\u043d\u0438\u043a \u0432\u0435\u0441\u0438\u0442 \u0431\u043e\u043b\u044c\u0448\u0435 500 \u043c\u0435\u0433\u0430\u0431\u0430\u0439\u0442, \u0447\u0442\u043e, \u043a \u0441\u043e\u0436\u0430\u043b\u0435\u043d\u0438\u044e, \u043f\u043e\u043a\u0430 \u0435\u0449\u0451 \u043d\u0435 \u0442\u043e\u0442 \u043e\u0431\u044a\u0451\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u043e\u0436\u043d\u043e \u0431\u0435\u0437\u0431\u043e\u043b\u0435\u0437\u043d\u0435\u043d\u043d\u043e \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u044c \u0432 \u043a\u0430\u0436\u0434\u043e\u043c \u041d\u0438\u0433\u0435\u0440\u0438\u0439\u0441\u043a\u043e\u043c \u0441\u0435\u043b\u0435."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u0440\u043e\u0446\u0435\u0441\u0441 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438 \u043f\u043e\u0441\u043b\u0435 \u0441\u043a\u0430\u0447\u0438\u0432\u0430\u043d\u0438\u044f \u043f\u0440\u043e\u0441\u0442 \u0438 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u0435\u043d. \u041d\u0430 \u043a\u0430\u043a\u043e\u043c-\u0442\u043e \u044d\u0442\u0430\u043f\u0435 \u0432\u0430\u043c \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043e \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u043d\u0443\u0436\u043d\u044b\u0435 \u043f\u0430\u043a\u0435\u0442\u044b - \u0441\u0442\u0430\u0432\u044c\u0442\u0435 \u0432\u0441\u0451, \u0447\u0442\u043e \u043f\u043e\u043c\u0435\u0447\u0435\u043d\u043e. \u041f\u0440\u0438 \u0442\u043e\u043c, \u0447\u0442\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u0443 \u0432\u0430\u0441 \u043a\u0443\u0447\u0430 \u0432\u0441\u0435\u0433\u043e, \u043d\u0430\u043c \u044d\u0442\u043e\u0433\u043e \u043c\u0430\u043b\u043e :) \u0427\u0442\u043e\u0431\u044b \u0440\u0438\u0441\u043e\u0432\u0430\u0442\u044c \u043a\u0440\u0430\u0441\u0438\u0432\u044b\u0435 \u043a\u0430\u0440\u0442\u044b, \u043d\u0430\u043c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c \u043f\u0430\u043a\u0435\u0442 Basemap, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0443\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u043c\u043e\u0434\u0443\u043b\u044c \u043a Pyhton(x,y), \u0438 \u043b\u0435\u0436\u0438\u0442 [\u0442\u0443\u0442](http://code.google.com/p/pythonxy/wiki/AdditionalPlugins) (\u043f\u0435\u0440\u0432\u044b\u0439 \u0432 \u0441\u043f\u0438\u0441\u043a\u0435). \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0432 \u0435\u0433\u043e \u0432\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 \u043d\u0430 \u0432\u0430\u0448\u0435\u0439 Win \u043c\u0430\u0448\u0438\u043d\u0435 \u043f\u043e\u043b\u043d\u043e\u0446\u0435\u043d\u043d\u0443\u044e \u0441\u0440\u0435\u0434\u0443 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u043f\u043e\u0434 \u043f\u0438\u0442\u043e\u043d. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041d\u0430 \u0441\u0430\u043c\u043e\u043c \u0434\u0435\u043b\u0435 \u044f \u0441 \u0442\u0440\u0443\u0434\u043e\u043c \u0434\u043e\u043f\u0443\u0441\u043a\u0430\u044e \u043c\u044b\u0441\u043b\u044c, \u0447\u0442\u043e \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u0442\u044c \u0432 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442, \u0441\u043a\u0430\u0436\u0435\u043c \u0432\u0435\u0441\u044c \u0444\u0430\u0439\u043b \u0441 \u0440\u0435\u0430\u043d\u0430\u043b\u0438\u0437\u043e\u043c \u0435\u0436\u0435\u0434\u043d\u0435\u0432\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043f\u0440\u0438\u0437\u0435\u043c\u043d\u043e\u0439 \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u044b \u0437\u0430 \u0433\u043e\u0434. \u0411\u044b\u043b\u043e \u0431\u044b \u043d\u0435\u043f\u043b\u043e\u0445\u043e \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0440\u043e\u0434\u0435\u043b\u0430\u0442\u044c \u043d\u0430\u0434 \u043d\u0438\u043c \u043a\u0430\u043a\u0438\u0435-\u0442\u043e \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 - \u043e\u0441\u0440\u0435\u0434\u043d\u0438\u0442\u044c \u043f\u043e \u043c\u0435\u0441\u044f\u0446\u0430\u043c, \u0438\u043b\u0438 \u0441\u0435\u0437\u043e\u043d\u0430\u043c, \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435 \u043d\u0430\u043a\u043e\u043d\u0435\u0446. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u043e\u043a\u0430 \u043d\u0435 \u043f\u0440\u0438\u0434\u0443\u043c\u0430\u043b\u043e \u043d\u0438\u0447\u0435\u0433\u043e \u043b\u0443\u0447\u0448\u0435, \u0447\u0435\u043c climate data operators, \u0438\u043b\u0438 cdo. \u041d\u0435\u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u0440\u0430\u0441\u0441\u043a\u0430\u0437 \u043e \u0442\u043e\u043c, \u0437\u0430\u0447\u0435\u043c \u043e\u043d\u0438 \u043d\u0443\u0436\u043d\u044b \u0438 \u043a\u0430\u043a \u0438\u043c\u0438 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043c\u043e\u0436\u043d\u043e, \u043a\u0430\u043a \u043e\u0431\u044b\u0447\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u043d\u0430 [koldunov.net](http://koldunov.net/?p=401). \u0421 \u043d\u0435\u0434\u0430\u0432\u043d\u0438\u0445 \u043f\u043e\u0440 \u043e\u043d\u0438 \u043f\u043e\u044f\u0432\u0438\u043b\u0438\u0441\u044c \u0438 \u043f\u043e\u0434 Win, \u0442\u0430\u043a \u0447\u0442\u043e \u0441\u043a\u0430\u0447\u0438\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u044e\u044e \u0432\u0435\u0440\u0441\u0438\u044e [\u043e\u0442\u0441\u044e\u0434\u0430](https://code.zmaw.de/projects/cdo/files) \u0438 \u0440\u0430\u0441\u043f\u0430\u043a\u043e\u0432\u044b\u0432\u0430\u0439\u0442\u0435 \u0435\u0451 \u0432 \u0442\u0443 \u043f\u0430\u043f\u043a\u0443, \u0433\u0434\u0435 \u0432\u044b \u0431\u0443\u0434\u0435\u0442\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 netCDF. \u0412 \u043c\u043e\u0451\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u044d\u0442\u043e \u043f\u0430\u043f\u043a\u0430 *C:/notebook/* (\u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u0432\u0430\u043c \u0435\u0451 \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u0442\u044c). "
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u0417\u0430\u043f\u0443\u0441\u043a IPython notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0415\u0449\u0451 \u043e\u0434\u0438\u043d \u043c\u0430\u043b\u0435\u043d\u044c\u043a\u0438\u0439 \u0448\u0430\u0436\u043e\u043a \u0438 \u043c\u044b \u0441\u043c\u043e\u0436\u0435\u043c \u043f\u0435\u0440\u0435\u0439\u0442\u0438 \u043a \u0440\u0430\u0431\u043e\u0442\u0435 \u043d\u0430\u0434 netCDF. \u0420\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u043d\u0435 \u0438\u0437 \u043f\u0438\u0442\u043e\u043d\u043e\u0432\u0441\u043a\u043e\u0439 \u043a\u043e\u043d\u0441\u043e\u043b\u0438, \u0438 \u043d\u0435 \u0441\u0442\u0430\u043d\u0435\u043c \u043f\u0438\u0441\u0430\u0442\u044c \u0441\u043a\u0440\u0438\u043f\u0442\u044b \u0432 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0445 \u0444\u0430\u0439\u043b\u0430\u0445, \u0430 \u043f\u043e\u0442\u043e\u043c \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0442\u044c \u0438\u0445. \u041c\u044b \u0431\u0443\u0434\u0435\u043c \u0434\u0435\u043b\u0430\u0442\u044c \u0432\u0441\u0451 \u0432 IPython notebook, \u0443\u0434\u0438\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u043c \u0438\u0437\u043e\u0431\u0440\u0435\u0442\u0435\u043d\u0438\u0438 \u0440\u0435\u0431\u044f\u0442 \u0438\u0437 IPython, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u044f \u043f\u0438\u0448\u0443 \u044d\u0442\u043e\u0442 \u043f\u043e\u0441\u0442, \u0438 \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442 \u0432\u0430\u043c \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0442\u044c \u043a\u043e\u0434 \u0438 \u0447\u0438\u0442\u0430\u0442\u044c \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u044f\u0449\u0435\u0433\u043e. \u0421\u043f\u0435\u0440\u0432\u0430 \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u0435 \u043d\u043e\u0443\u0442\u0431\u0443\u043a \u0438\u0437 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0441\u0434\u0435\u043b\u0430\u043d \u044d\u0442\u043e\u0442 \u043f\u043e\u0441\u0442 [\u043e\u0442\u0441\u044e\u0434\u0430](http://nbviewer.ipython.org/4964582/) \u0432 \u0432\u0430\u0448\u0443 \u0440\u0430\u0431\u043e\u0447\u0443\u044e \u043f\u0430\u043f\u043a\u0443 (*C:/notebook/*). \u0417\u0430\u0442\u0435\u043c \u043e\u0442\u043a\u0440\u043e\u0439\u0442\u0435 \u043a\u043e\u043d\u0441\u043e\u043b\u044c (\u0435\u0441\u043b\u0438 \u0432\u044b \u043d\u0430 XP \u0442\u043e \u044d\u0442\u043e *\u041f\u0443\u0441\u043a >> \u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b >> \u0421\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0435 >> \u041a\u043e\u043c\u0430\u043d\u0434\u043d\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430*) \u0438 \u043f\u0435\u0440\u0435\u0439\u0434\u0438\u0442\u0435 \u0432 \u0441\u0432\u043e\u044e \u0440\u0430\u0431\u043e\u0447\u0443\u044e \u043f\u0430\u043f\u043a\u0443 (\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043a\u043e\u043c\u0430\u043d\u0434\u0443 cd). \u0415\u0441\u043b\u0438 \u0432\u0430\u0448\u0430 \u043f\u0430\u043f\u043a\u0430, \u0442\u0430\u043a\u0436\u0435 \u043a\u0430\u043a \u0443 \u043c\u0435\u043d\u044f, \u0440\u0430\u0441\u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0430 \u043f\u043e \u0430\u0434\u0440\u0435\u0441\u0443 *C:/notebook/* \u0442\u043e \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u0432\u0432\u0435\u0441\u0442\u0438:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" cd \\\n",
" cd notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418 \u043d\u0430\u043a\u043e\u043d\u0435\u0446 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c IPyhton notebook, \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0432 \u043d\u0430\u0445\u043e\u0434\u044f\u0441\u044c \u0432 \u0432\u0430\u0448\u0435\u043c \u0440\u0430\u0431\u043e\u0447\u0435\u043c \u043a\u0430\u0442\u0430\u043b\u043e\u0433\u0435 \u043a\u043e\u043c\u0430\u043d\u0434\u0443"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ipython notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412 \u0432\u0430\u0448\u0435\u043c \u0431\u0440\u043e\u0443\u0437\u0435\u0440\u0435 \u0434\u043e\u043b\u0436\u043d\u0430 \u043e\u0442\u043a\u0440\u044b\u0442\u044c\u0441\u044f \u0432\u043a\u043b\u0430\u0434\u043a\u0430 \u0441\u043e \u0441\u043f\u0438\u0441\u043a\u043e\u043c \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u043e\u0432, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c, \u0441\u043a\u043e\u0440\u0435\u0435 \u0432\u0441\u0435\u0433\u043e, \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u043e\u0443\u0442\u0431\u0443\u043a, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u043b\u0438, \u0438, \u043d\u0430\u0434\u0435\u044e\u0441\u044c, \u0443\u0436\u0435 \u0441\u0435\u0439\u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u0435\u0442\u0435. \u0422\u0435\u043f\u0435\u0440\u044c, \u043a\u043e\u0433\u0434\u0430 \u0432\u0441\u0435 \u043f\u0440\u0438\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u0437\u0430\u043a\u043e\u043d\u0447\u0435\u043d\u044b, \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u0435\u0440\u0435\u0439\u0434\u0451\u043c \u043a \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u043d\u0430\u0448\u0438\u043c\u0438 \u043b\u044e\u0431\u0438\u043c\u044b\u043c\u0438 netCDF \u0444\u0430\u0439\u043b\u0430\u043c\u0438."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u043a \u044d\u043a\u0441\u043f\u043e\u0440\u0442\u0443"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0414\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u0437\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u0444\u0430\u0439\u043b NCEP \u0440\u0435\u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0432 \u0440\u0430\u0431\u043e\u0447\u0443\u044e \u043f\u0430\u043f\u043a\u0443. \u042f \u0431\u0443\u0434\u0443 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0430\u0439\u043b [\u0435\u0436\u0435\u0434\u043d\u0435\u0432\u043d\u043e\u0439 \u043f\u0440\u0438\u0437\u0435\u043c\u043d\u043e\u0439 \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u044b \u0432\u043e\u0437\u0434\u0443\u0445\u0430 \u0437\u0430 2012 \u0433\u043e\u0434](ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.dailyavgs/surface/air.sig995.2012.nc). IPython \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0435 \u0432\u044b\u0437\u043e\u0432\u044b (\u043f\u0435\u0440\u0435\u0434 \u043d\u0438\u043c\u0438 \u0434\u043e\u043b\u0436\u0435\u043d \u0441\u0442\u043e\u044f\u0442\u044c \u0432\u043e\u0441\u043a\u043b\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0437\u043d\u0430\u043a), \u0442\u0430\u043a \u0447\u0442\u043e \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u0447\u0442\u043e \u0443 \u043d\u0430\u0441 \u0432 \u043f\u0430\u043f\u043a\u0435:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!dir"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \u2019\u00ae\u00ac \u045e \u0433\u0431\u0432\u0430\u00ae\u00a9\u0431\u0432\u045e\u0490 C \u00ad\u0490 \u0401\u00ac\u0490\u0490\u0432 \u00ac\u0490\u0432\u0404\u0401.\n",
" \u2018\u0490\u0430\u0401\u00a9\u00ad\u043b\u00a9 \u00ad\u00ae\u00ac\u0490\u0430 \u0432\u00ae\u00ac\u00a0: 9CDD-02A9\n",
"\n",
" \u2018\u00ae\u00a4\u0490\u0430\u00a6\u0401\u00ac\u00ae\u0490 \u0407\u00a0\u0407\u0404\u0401 C:\\notebook\n",
"\n",
"15.02.2013 21:08 <DIR> .\n",
"15.02.2013 21:08 <DIR> ..\n",
"15.02.2013 21:08 7\u044f700\u044f344 air.sig995.2012.nc\n",
"07.01.2013 11:26 10\u044f192\u044f583 cdo.exe\n",
"15.02.2013 21:07 29\u044f927 netCDF_to_ASCII_with_Python.ipynb\n",
"06.08.2012 13:40 127\u044f720 pthreadGC2.dll\n",
" 4 \u0434\u00a0\u00a9\u00ab\u00ae\u045e 18\u044f050\u044f574 \u040e\u00a0\u00a9\u0432\n",
" 2 \u0407\u00a0\u0407\u00ae\u0404 2\u044f759\u044f348\u044f224 \u040e\u00a0\u00a9\u0432 \u0431\u045e\u00ae\u040e\u00ae\u00a4\u00ad\u00ae\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u043e\u043d\u044f\u0442\u043d\u043e, \u0447\u0442\u043e \u0443 \u043d\u0430\u0441 \u0432 \u043f\u0430\u043f\u043a\u0435 \u043d\u0430\u0445\u043e\u0434\u0438\u0442\u0441\u044f \u044d\u043a\u0437\u0435\u0448\u043d\u0438\u043a cdo, \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u043d\u0435\u043e\u0445\u043e\u0434\u0438\u043c\u0430\u044f \u0435\u043c\u0443 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b, \u0444\u0430\u0439\u043b \u0441 \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u043e\u043c \u0438, \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043d\u0430\u0448 netCDF \u0444\u0430\u0439\u043b. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u0447\u0442\u043e \u043d\u0430\u043c \u0440\u0430\u0441\u0441\u043a\u0430\u0436\u0443\u0442 \u043e\u0431 \u044d\u0442\u043e\u043c \u0444\u0430\u0439\u043b\u0435 cdo "
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"!cdo pardes air.sig995.2012.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -1 air mean Daily Air temperature at sigma level 995 [degK]\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo pardes: Processed 1 variable\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cdo showyear air.sig995.2012.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2012\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo showyear: Processed 1 variable over 366 timesteps\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cdo showmon air.sig995.2012.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1 2 3 4 5 6 7 8 9 10 11 12\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo showmon: Processed 1 variable over 366 timesteps\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041c\u044b \u0443\u0437\u043d\u0430\u043b\u0438 \u0438\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043a\u0430\u043a\u0438\u0435 \u0432 \u0444\u0430\u0439\u043b\u0435 \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u0441\u044f \u0433\u043e\u0434\u044b \u0438 \u043c\u0435\u0441\u044f\u0446\u044b. \u0422\u0435\u043f\u0435\u0440\u044c \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0435\u043c \u0441\u0440\u0435\u0434\u043d\u044e\u044e \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u0443 \u0437\u0430 \u0441\u0435\u0437\u043e\u043d:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cdo seasmean air.sig995.2012.nc air.sig995.2012_sm.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo seasmean: Processed 3847392 values from 1 variable over 366 timesteps\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cdo sinfo air.sig995.2012_sm.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" File format: netCDF\n",
" -1 : Institut Source Ttype Levels Num Gridsize Num Dtype : Parameter ID\n",
" 1 : unknown unknown instant 1 1 10512 1 I16 : -1 \n",
" Grid coordinates :\n",
" 1 : lonlat > size : dim = 10512 nlon = 144 nlat = 73\n",
" lon : first = 0 last = 357.5 inc = 2.5 degrees_east circular\n",
" lat : first = 90 last = -90 inc = -2.5 degrees_north\n",
" Vertical coordinates :\n",
" 1 : surface : 0 \n",
" Time coordinate : 5 steps\n",
" RefTime = 0001-01-01 00:00:00 Units = hours Calendar = STANDARD\n",
" YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss\n",
" 2012-02-29 00:00:00 2012-05-31 00:00:00 2012-08-31 00:00:00 2012-11-30 00:00:00\n",
" 2012-12-31 00:00:00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo sinfo: Processed 1 variable over 5 timesteps\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041a\u0430\u043a \u0432\u0438\u0434\u0438\u0442\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u043e\u0441\u044c \u0443 \u043d\u0430\u0441 \u043d\u0435 \u0447\u0435\u0442\u044b\u0440\u0435, \u0430 \u043f\u044f\u0442\u044c \u043f\u043e\u043b\u0435\u0439, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0437\u0438\u043c \u0437\u0430 2012 \u0433\u043e\u0434 \u0431\u044b\u043b\u043e \u0430\u0436 \u0434\u0432\u0435 \u0448\u0442\u0443\u043a\u0438. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0414\u043e\u043f\u0443\u0441\u0442\u0438\u043c, \u0432\u0435\u0441\u044c \u043c\u0438\u0440 \u043d\u0430\u043c \u043d\u0435 \u043d\u0443\u0436\u0435\u043d, \u0430 \u043c\u044b \u0445\u043e\u0442\u0438\u043c \u0432\u044b\u0440\u0435\u0437\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0415\u0432\u0440\u043e\u043f\u0443:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cdo sellonlatbox,0,90,30,70 air.sig995.2012_sm.nc air.sig995.2012_sm_europe.nc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"cdo sellonlatbox: Processed 52560 values from 1 variable over 5 timesteps\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u042d\u0442\u043e\u0442 \u0444\u0430\u0439\u043b \u0438 \u0431\u0443\u0434\u0435\u043c \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0432 ASCII."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u041e\u0442\u043a\u0440\u044b\u0432\u0430\u0435\u043c netCDF \u0444\u0430\u0439\u043b \u0432 Pyhton"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0427\u0442\u043e\u0431\u044b \u0438\u043c\u0435\u0442\u044c \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0442\u044c netCDF, \u043d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u0430\u0440\u0443 \u043c\u043e\u0434\u0443\u043b\u0435\u0439:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.io import netcdf\n",
"import numpy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0435\u043c \u0444\u0430\u0439\u043b:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = netcdf.netcdf_file('air.sig995.2012_sm_europe.nc')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0421\u043c\u043e\u0442\u0440\u0438\u043c, \u0447\u0442\u043e \u0442\u0430\u043c \u0443 \u043d\u0430\u0441 \u0432\u043d\u0443\u0442\u0440\u0438:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.variables"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 22,
"text": [
"{'air': <scipy.io.netcdf.netcdf_variable at 0x2c43bb0>,\n",
" 'lat': <scipy.io.netcdf.netcdf_variable at 0x2c436b0>,\n",
" 'lon': <scipy.io.netcdf.netcdf_variable at 0x2c43690>,\n",
" 'time': <scipy.io.netcdf.netcdf_variable at 0x2c43870>}"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041d\u0430\u043c \u043f\u043e\u043d\u0430\u0434\u043e\u0431\u044f\u0442\u0441\u044f \u0432\u0441\u0435 \u044d\u0442\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435, \u0442\u0430\u043a \u0447\u0442\u043e \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u0438\u0445 \u0432 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b \u043f\u0438\u0442\u043e\u043d\u0430:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"air = f.variables['air']\n",
"lat = f.variables['lat']\n",
"lon = f.variables['lon']\n",
"time = f.variables['time']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0415\u0441\u043b\u0438 \u0432\u044b \u0442\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u0442\u0430\u0432\u0438\u0442\u0435 \u0442\u043e\u0447\u043a\u0443 \u043f\u043e\u0441\u043b\u0435 \u0438\u043c\u0435\u043d\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 (\u043b\u044e\u0431\u043e\u0439, \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e air) \u0438 \u043d\u0430\u0436\u043c\u0451\u0442\u0435 TAB, \u0442\u043e \u0443\u0432\u0438\u0434\u0438\u0442\u0435 \u0441\u043f\u0438\u0441\u043e\u043a \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u0442\u0440\u0438\u0431\u0443\u0442\u043e\u0432 \u044d\u0442\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439, \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u044e \u0432\u0430\u043c \u0438\u0445 \u043f\u043e\u0438\u0437\u0443\u0447\u0430\u0442\u044c:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"air."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0414\u0432\u0430 \u0438\u0437 \u043d\u0438\u0445 \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0432\u0430\u0436\u043d\u044b, \u044d\u0442\u043e add_offset \u0438 scale_factor. \u041e\u0442\u043a\u0443\u0434\u0430 \u043e\u043d\u0438 \u0432\u0437\u044f\u043b\u0438\u0441\u044c \u0438 \u0437\u0430\u0447\u0435\u043c \u043d\u0443\u0436\u043d\u044b \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u0447\u0438\u0442\u0430\u0442\u044c [\u0437\u0434\u0435\u0441\u044c](http://www.oceanographers.ru/forum/viewtopic.php?f=7&t=835). \u0415\u0441\u043b\u0438 \u0447\u0438\u0442\u0430\u0442\u044c \u043b\u0435\u043d\u044c, \u0442\u043e \u043f\u0440\u043e\u0441\u0442\u043e \u0437\u043d\u0430\u0439\u0442\u0435: \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 \u041a\u0435\u043b\u044c\u0432\u0438\u043d\u0430\u0445 \u043d\u0443\u0436\u043d\u043e \u0443\u043c\u043d\u043e\u0436\u0438\u0442\u044c air \u043d\u0430 scale_factor \u0438 \u043f\u0440\u0438\u0431\u0430\u0432\u0438\u0442\u044c add_offset. \u0422\u0430\u043a \u043d\u0430\u0434\u043e. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412 \u0434\u0430\u043d\u043d\u044b\u0439 \u043c\u043e\u043c\u0435\u043d\u0442 \u0432 \u043d\u0430\u0448\u0438\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u0441\u044f \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u0430\u043d\u043d\u044b\u0435, \u043d\u043e \u0438 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f, \u0432\u0440\u043e\u0434\u0435 \u0430\u0442\u0440\u0438\u0431\u0443\u0442\u043e\u0432. \u0414\u043b\u044f \u0442\u043e\u0433\u043e, \u0447\u0442\u043e\u0431\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0441 \u0434\u0430\u043d\u043d\u044b\u043c, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0445 \u0441\u043a\u043e\u043f\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0432 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u0443\u044e \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e, \u043f\u043e\u043f\u0443\u0442\u043d\u043e \u0441\u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0432 \u0438\u0445 \u0432 \u041a\u0435\u043b\u044c\u0432\u0438\u043d\u044b:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"airK = (air.data*air.scale_factor)+air.add_offset"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0415\u0449\u0451 \u0432\u0430\u0440\u0438\u0430\u043d\u0442"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"airK = (air[:]*air.scale_factor)+air.add_offset"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0435, \u0435\u0441\u043b\u0438 \u0431\u044b \u043c\u044b \u043d\u0435 \u0445\u043e\u0442\u0435\u043b\u0438 \u043f\u043e\u043b\u0443\u0447\u0430\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e\u0431 \u0430\u0442\u0440\u0438\u0431\u0443\u0442\u0430\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445, \u0442\u043e \u043c\u043e\u0433\u043b\u0438 \u0431\u044b \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u0430\u043d\u043d\u044b\u0435 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n",
" \n",
" air = f.variables['air'][:]\n",
" lat = f.variables['lat'][:]\n",
" lon = f.variables['lon'][:]\n",
" time = f.variables['time'][:]\n",
"\n",
"\u043d\u043e \u0432 \u044d\u0442\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043e scale_factor \u0438 add_offset \u043f\u0440\u0438\u0448\u043b\u043e\u0441\u044c \u0431\u044b \u0434\u043e\u0431\u044b\u0432\u0430\u0442\u044c \u0438\u0437 \u0434\u0440\u0443\u0433\u0438\u0445 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u0435\u0440\u0435\u0432\u0435\u0434\u0451\u043c \u0434\u0430\u043d\u043d\u044b\u0435 \u0438\u0437 \u041a\u0435\u043b\u044c\u0432\u0438\u043d\u043e\u0432 \u0432 \u0426\u0435\u043b\u044c\u0441\u0438\u0438"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"airC = airK-273.15"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412\u044b \u0443\u0436\u0435, \u043d\u0430\u0432\u0435\u0440\u043d\u043e\u0435, \u043f\u043e\u043d\u044f\u043b\u0438, \u0447\u0442\u043e \u043d\u0438\u043a\u0430\u043a\u0438\u0445 \u0446\u0438\u043a\u043b\u043e\u0432 \u0441 \u043f\u0435\u0440\u0435\u0431\u0438\u0440\u0430\u043d\u0438\u0435\u043c \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0438 \u0432\u044b\u0447\u0438\u0442\u0430\u043d\u0438\u0435\u043c \u043c\u0430\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0446\u0438\u0444\u0440\u044b \u0442\u0443\u0442 \u043d\u0435 \u043d\u0443\u0436\u043d\u043e, \u0432\u0441\u0451 \u043a\u0430\u043a \u0432 \u043c\u0430\u0442\u043b\u0430\u0431\u0435, \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u044f \u0432\u044b\u0447\u0438\u0442\u0430\u043d\u0438\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0441\u044f \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432 \u043c\u0430\u0441\u0441\u0438\u0432\u0430. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u0432\u0437\u0433\u043b\u044f\u043d\u0438\u043c \u043d\u0430 \u043d\u0430\u0448\u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lat.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 42,
"text": [
"(17,)"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lon.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 43,
"text": [
"(37,)"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041e\u043d\u0438 \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0435. \u041d\u0438\u0447\u0435\u0433\u043e \u043f\u043b\u043e\u0445\u043e\u0433\u043e \u0432 \u044d\u0442\u043e\u043c \u043d\u0435\u0442, \u043d\u043e \u043c\u044b \u0445\u043e\u0442\u0438\u043c \u043d\u0430\u0440\u0438\u0441\u043e\u0432\u0430\u0442\u044c \u043d\u0430\u0448\u0438 \u0434\u0430\u043d\u043d\u044b\u0435, \u043f\u0440\u0435\u0436\u0434\u0435 \u0447\u0435\u043c \u044d\u043a\u0441\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u0430 \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e, \u043d\u0443\u0436\u043d\u043e \u043f\u0435\u0440\u0435\u0432\u0441\u0442\u0438 \u0448\u0438\u0440\u043e\u0442\u044b \u0438 \u0434\u043e\u043b\u0433\u043e\u0442\u044b \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 (\u0442\u043e \u0435\u0441\u0442\u044c \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u043e\u043c\u0443 \u043c\u0430\u0441\u0441\u0438\u0432\u0443, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442\u0441\u044f \u043f\u043e\u043b\u0435 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438, \u0431\u0443\u0434\u0443\u0442 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c \u0434\u0432\u0430 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0445 \u043c\u0430\u0441\u0441\u0438\u0432\u0430 \u0432 \u0448\u0438\u0440\u043e\u0442\u0430\u043c\u0438 \u0438 \u0434\u043e\u043b\u0433\u043e\u0442\u0430\u043c\u0438)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lons, lats = numpy.meshgrid(lon[:],lat[:])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u043e\u0433\u043b\u044f\u0434\u0438\u043c, \u0432 \u043a\u0430\u043a\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435 \u0443 \u043d\u0430\u0441 \u0432\u0440\u0435\u043c\u044f"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time.units"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 70,
"text": [
"'hours since 1-01-01 00:00:00'"
]
}
],
"prompt_number": 70
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412\u0440\u0435\u043c\u044f \u0443 \u043d\u0430\u0441 \u0432 \u0447\u0430\u0441\u0430\u0445 \u043e\u0442 \u043d\u0430\u0447\u0430\u043b\u0430 \u043d\u0430\u0448\u0435\u0439 \u044d\u0440\u044b. \u0412\u044b\u0433\u043b\u044f\u0434\u044f\u0442 \u043e\u043d\u0438 \u0432\u043e\u0442 \u0442\u0430\u043a:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time[:][0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 71,
"text": [
"17629512.0"
]
}
],
"prompt_number": 71
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \u0427\u0442\u043e\u0431\u044b \u043f\u0435\u0440\u0435\u0432\u0435\u0441\u0442\u0438 \u044d\u0442\u0438 \u0447\u0430\u0441\u044b \u0432 \u0443\u0434\u043e\u0431\u043e\u0432\u0430\u0440\u0438\u043c\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442, \u043d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043c\u043e\u0434\u0443\u043b\u0435\u043c datetime"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import datetime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0421\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u043e\u0431\u044a\u0435\u043a\u0442 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0442\u0438\u043f\u0430 datetime, \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0443\u0434\u043e\u0431\u043d\u043e \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c\u0441\u044f \u0441\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0435\u043c, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0431\u0443\u0434\u0435\u0442 \u0434\u0430\u0442\u0430 \u0441 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0441\u0447\u0438\u0442\u0430\u044e\u0442\u0441\u044f \u0447\u0430\u0441\u044b \u0432 \u043d\u0430\u0448\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u0438:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"first_date = datetime.datetime(1,1,1,0,0,0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 154
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(first_date)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 155,
"text": [
"datetime.datetime"
]
}
],
"prompt_number": 155
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043c \u043d\u0430\u0448\u0443 \u043f\u0435\u0440\u0432\u0443\u044e \u0434\u0430\u0442\u0443 \u0432 \u0432\u0438\u0434\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u0440\u0430\u0437\u043d\u0438\u0446\u044b"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"difference1 = datetime.timedelta(hours=(time[:][0]-48))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u043e \u043f\u0440\u0438\u0447\u0438\u043d\u0435, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u044f \u0435\u0449\u0451 \u043d\u0435 \u0440\u0430\u0437\u043e\u0431\u0440\u0430\u043b\u0441\u044f, \u044d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u043e\u0448\u0438\u0431\u0430\u0435\u0442\u0441\u044f \u043d\u0430 \u0434\u0432\u0430 \u0434\u043d\u044f, \u0442\u0430\u043a \u0447\u0442\u043e \u043c\u044b \u0438\u0445 (\u0442\u043e \u0435\u0441\u0442\u044c 48 \u0447\u0430\u0441\u043e\u0432) \u0432\u044b\u0447\u0438\u0442\u0430\u0435\u043c."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u0440\u0438\u0431\u0430\u0432\u0438\u043c \u043a \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0439 \u0434\u0430\u0442\u0435 \u0440\u0430\u0437\u043d\u0438\u0446\u0443, \u0438 \u043f\u043e\u043b\u0443\u0447\u0438\u043c datetime \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u044c\u0441\u044f \u043d\u0430\u0448\u0430 \u0434\u0430\u0442\u0430."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"present = first_date+difference1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 169
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0423 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 present \u043c\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u044b\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0442 \u0443\u0437\u043d\u0430\u0442\u044c \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u0430\u0442\u0443, \u043d\u043e \u0438, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u0435\u043d\u044c \u043d\u0435\u0434\u0435\u043b\u0438. \u041d\u043e \u043d\u0430\u0441 \u0431\u0443\u0434\u0435\u0442 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u0432\u0430\u0442\u044c \u043f\u043e\u043b\u043d\u0430\u044f \u0437\u0430\u043f\u0438\u0441\u044c \u0434\u0430\u0442\u044b"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"present.ctime()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 170,
"text": [
"'Wed Feb 29 00:00:00 2012'"
]
}
],
"prompt_number": 170
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041a\u0430\u043a \u0432\u0438\u0434\u0438\u0442\u0435 \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0434\u0430\u0442\u044b \u0434\u0430\u0451\u0442\u0441\u044f \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0439 \u0434\u0435\u043d\u044c \u0441\u0435\u0437\u043e\u043d\u0430. \u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u043c\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u043f\u043e\u0437\u0436\u0435 \u043f\u0440\u0438 \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u044d\u043a\u0441\u043f\u043e\u0440\u0442\u0435 \u0432 netCDF."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0435\u043c\u043d\u043e\u0436\u043d\u043e \u043c\u0430\u0433\u0438\u0438, \u0447\u0442\u043e\u0431\u044b \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0442\u044c \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0438 \u0432\u043d\u0443\u0442\u0440\u0438 \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u0430:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u043c\u043e\u0434\u0443\u043b\u044c \u0434\u043b\u044f \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u0438 \u043a\u0430\u0440\u0442:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpl_toolkits.basemap import Basemap"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0413\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u043c \u043a\u0430\u0440\u0442\u0443"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = Basemap(llcrnrlon=-10,llcrnrlat=25,urcrnrlon=95,urcrnrlat=75,projection='mill', resolution='l')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u043c \u0448\u0438\u0440\u043e\u0442\u044b \u0438 \u0434\u043e\u043b\u0433\u043e\u0442\u044b \u0432 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u043a\u0430\u0440\u0442\u044b"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x, y = m(lons, lats)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418 \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u044b\u0432\u0430\u0435\u043c \u043d\u0430\u0448\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u0440\u0438\u0441\u0443\u043d\u043e\u043a \u0438 \u0437\u0430\u0434\u0430\u0451\u043c \u0435\u0433\u043e \u0440\u0430\u0437\u043c\u0435\u0440\u044b\n",
"fig = plt.figure(figsize=(10,10))\n",
"# \u0412\u044b\u0431\u0438\u0440\u0430\u0435\u043c \u0441\u0435\u0437\u043e\u043d\n",
"season=2\n",
"# \u0420\u0438\u0441\u0443\u0435\u043c \u0431\u0435\u0440\u0435\u0433\u043e\u0432\u0443\u044e \u043b\u0438\u043d\u0438\u044e\n",
"m.drawcoastlines(linewidth=1)\n",
"# \u0420\u0438\u0441\u0443\u0435\u043c \u0433\u0440\u0430\u043d\u0438\u0446\u044b \u0441\u0442\u0440\u0430\u043d\n",
"m.drawcountries(linewidth=0.5)\n",
"# \u0420\u0438\u0441\u0443\u0435\u043c \u0433\u0440\u0430\u043d\u0438\u0446\u0443 \u043a\u0430\u0440\u0442\u044b\n",
"m.drawmapboundary\n",
"# \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u043a\u0430\u0440\u0442\u0443 \u0441 \u0437\u0430\u043b\u0438\u0442\u044b\u043c\u0438 \u0438\u0437\u043e\u043b\u0438\u043d\u0438\u0438\u044f\u043c\u0438, 20 \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u043e\u0432\n",
"cs = m.contourf(x,y,airC[season,:,:],20)\n",
"# \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u043b\u0435\u0433\u0435\u043d\u0434\u0443\n",
"cbar = m.colorbar(cs,location='bottom',pad=\"5%\")\n",
"# \u041f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u043b\u0435\u0433\u0435\u043d\u0434\u0443\n",
"cbar.set_label(r'$^{\\circ}\\mathrm{C}$')\n",
"# \u041f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u043c \u0432\u0440\u0435\u043c\u044f \u0432 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u043e\u0435\n",
"difference1 = datetime.timedelta(hours=(time[:][season]-48))\n",
"present = first_date+difference1\n",
"# \u041e\u0442\u0440\u0438\u0441\u043e\u0432\u044b\u0432\u0430\u0435\u043c \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043e\u043a\n",
"plt.title(present.ctime())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 171,
"text": [
"<matplotlib.text.Text at 0x3b31b70>"
]
},
{
"output_type": "display_data",
"png": 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7dlC9evUoKCioyHHCw8OpVq1aFBUVRUTM1VS7dm1av349paamymWupeHTp09k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HYexm+e+szWDxJMTtFFYlxPAYAjqVXUAwf7sVBJ3bi3YSsHiIwcdlqAx1aZlyHyzr+qysGJ6y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\nxhhjjGk9LngYY4wxpvW44GGMMcaY1vv/+2mky5ZHphkAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 171
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0421 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435\u043c \u0415\u0432\u0440\u043e\u043f\u044b \u043c\u044b \u044f\u0432\u043d\u043e \u043f\u0440\u043e\u043c\u0430\u0445\u043d\u0443\u043b\u0438\u0441\u044c :) \u0427\u0442\u043e\u0431\u044b \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u044d\u0442\u0443 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044e \u043c\u043e\u0436\u043d\u043e \u0432\u0435\u0440\u043d\u0443\u0442\u044c\u0441\u044f \u043a \u0448\u0430\u0433\u0443, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043c\u044b \u0432\u044b\u0440\u0435\u0437\u0430\u0435\u043c \u0440\u0435\u0433\u0438\u043e\u043d \u0438 \u043f\u043e\u0434\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u0435\u0440\u0435\u0434\u0430\u044e\u0442\u0441\u044f \u0432 cdo."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0435, \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0432 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 \u043f\u0438\u0442\u043e\u043d (\u043d\u0430\u0437\u043e\u0432\u0451\u043c \u044d\u0442\u043e \u0442\u0430\u043a) \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0434\u0435\u043b\u0430\u0442\u044c \u0441 \u043d\u0438\u043c\u0438 \u0432\u0441\u0451, \u0447\u0442\u043e \u0443\u0433\u043e\u0434\u043d\u043e. \u0412 \u044d\u0442\u043e\u043c \u0441\u043c\u044b\u0441\u043b\u0435 \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u043d\u0435 \u0441\u0438\u043b\u044c\u043d\u043e \u043e\u0442\u043b\u0438\u0447\u0430\u0435\u0442\u0441\u044f \u043e\u0442 \u0440\u0430\u0431\u043e\u0442\u044b, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0432 Matlab. \u041d\u043e \u043c\u044b \u0441 \u0432\u0430\u043c\u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u043b\u0438\u0441\u044c \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 ASCII, \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u044d\u0442\u0438\u043c \u0438 \u0437\u0430\u0439\u043c\u0451\u043c\u0441\u044f."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u042d\u043a\u0441\u043f\u043e\u0440\u0442 \u0432 ASCII"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0441\u0442: \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0435\u043c \u0444\u0430\u0439\u043b, \u043f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0437\u0430\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u0432 \u043d\u0435\u0433\u043e \u0441\u0442\u0440\u043e\u0447\u043a\u0438 \u0432 \u043d\u0443\u0436\u043d\u043e\u043c \u043d\u0430\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435, \u0437\u0430\u043a\u0440\u044b\u0432\u0430\u0435\u043c \u0444\u0430\u0439\u043b. \u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043a\u0430\u043a \u044d\u0442\u043e \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435. \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0440\u0438\u043c\u0435\u0440, \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043a\u0430\u0436\u0434\u043e\u0435 \u043f\u043e\u043b\u0435 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u0432 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0439 \u0444\u0430\u0439\u043b:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u0426\u0438\u043a\u043b \u043f\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438\n",
"for one_field in range(airC.shape[0]):\n",
" \n",
" #\u041e\u0442\u043a\u0440\u044b\u0432\u0430\u0435\u043c \u0444\u0430\u0439\u043b, \u0434\u043e\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u043a \u0438\u043c\u0435\u043d\u0438 \u043d\u043e\u043c\u0435\u0440 \u043f\u043e\u043b\u044f \u0438 \u043d\u0443\u043b\u0438, \n",
" #\u0447\u0442\u043e\u0431\u044b \u043f\u0440\u0438 \u0431\u043e\u043b\u044c\u0448\u043e\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435 \u0444\u0430\u0439\u043b\u043e\u0432 \u043e\u043d\u0438 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e \u0441\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043b\u0438\u0441\u044c\n",
" ofile = open(\"netcdf_to_ASCII_field_\"+str(one_field).zfill(5)+\".txt\",\"w\")\n",
" \n",
" #\u041f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u043c \u0432\u0440\u0435\u043c\u044f \u0432 \u0447\u0435\u043b\u043e\u0432\u0435\u0441\u0435\u0441\u043a\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442\n",
" difference1 = datetime.timedelta(hours=(time[:][one_field]-48))\n",
" present = first_date+difference1\n",
" \n",
" #\u041f\u0435\u0447\u0430\u0442\u0430\u0435\u043c, \u0447\u0442\u043e\u0431\u044b \u0432\u0438\u0434\u0435\u0442\u044c \u043f\u0440\u043e\u0433\u0440\u0435\u0441\u0441\n",
" print(present.ctime())\n",
" \n",
" #\u0426\u0438\u043a\u043b \u043f\u043e \u0441\u0442\u0440\u043e\u043a\u0430\u043c\n",
" for nn in range(airC.shape[1]):\n",
" \n",
" #\u0426\u0438\u043a\u043b \u043f\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c\n",
" for kk in range(airC.shape[2]):\n",
" \n",
" #\u0417\u0430\u043f\u0438\u0441\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0444\u0430\u0439\u043b. \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0435\u0440\u0435\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u044b,\n",
" #\u0432 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0431\u0443\u0434\u0442 \u0432\u044b\u0432\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435, \u0430 \u043f\u043e\u0442\u043e\u043c \u043f\u0435\u0440\u0435\u0447\u0438\u0441\u043b\u044f\u0435\u043c\n",
" #\u0441\u0430\u043c\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435\n",
" ofile.write('%5.2f %5.2f %5.2f %5.0f %5.0f %5.0f\\n' % \\\n",
" (lons[nn,kk], lats[nn,kk], airC[one_field,nn,kk], present.day, present.month, present.year))\n",
" \n",
" \n",
" ofile.close()\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Wed Feb 29 00:00:00 2012\n",
"Thu May 31 00:00:00 2012\n",
"Fri Aug 31 00:00:00 2012\n",
"Fri Nov 30 00:00:00 2012"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mon Dec 31 00:00:00 2012\n"
]
}
],
"prompt_number": 221
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0421\u0430\u043c\u043e\u0435 \"\u0441\u043b\u043e\u0436\u043d\u043e\u0435\", \u0438\u043b\u0438 \u0441\u043a\u043e\u0440\u0435\u0435 \u043d\u0435\u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e\u0435 \u0442\u0443\u0442 \u044d\u0442\u043e \u0444\u043e\u0440\u043c\u0430\u0442\u044b \u0432\u044b\u0432\u043e\u0434\u0430 \u0434\u0430\u043d\u043d\u044b\u0445. \u041f\u0440\u043e \u043d\u0438\u0445 \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u0447\u0438\u0442\u0430\u0442\u044c [\u0442\u0443\u0442](http://koldunov.net/?p=195)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412\u0430\u0440\u0438\u0430\u043d\u0442 \u0441 \u0432\u044b\u0432\u043e\u0434\u043e\u043c \u0432 \u043e\u0434\u0438\u043d \u0444\u0430\u0439\u043b \u0441 \u0434\u0430\u0442\u0430 \u0441\u0442\u0430\u043c\u043f\u0430\u043c\u0438 \u043c\u0435\u0436\u0434\u0443 \u043f\u043e\u043b\u044f\u043c\u0438:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u041e\u0442\u043a\u0440\u044b\u0432\u0430\u0442\u044c \u0431\u0443\u0434\u0435\u043c \u0442\u043e\u043b\u044c\u043a\u043e \u043e\u0434\u0438\u043d \u0444\u0430\u0439\u043b\n",
"ofile = open(\"netcdf_to_ASCII_all_fields.txt\",\"w\")\n",
"\n",
"#\u0426\u0438\u043a\u043b \u043f\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438\n",
"for one_field in range(airC.shape[0]):\n",
" \n",
" #\u041f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u043c \u0432\u0440\u0435\u043c\u044f \u0432 \u0447\u0435\u043b\u043e\u0432\u0435\u0441\u0435\u0441\u043a\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442\n",
" difference1 = datetime.timedelta(hours=(time[:][one_field]-48))\n",
" present = first_date+difference1\n",
" \n",
" #\u041f\u0435\u0447\u0430\u0442\u0430\u0435\u043c, \u0447\u0442\u043e\u0431\u044b \u0432\u0438\u0434\u0435\u0442\u044c \u043f\u0440\u043e\u0433\u0440\u0435\u0441\u0441\n",
" print(present.ctime())\n",
" \n",
" #\u0417\u0430\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u0432\u0440\u0435\u043c\u044f \u0432 \u043d\u0430\u0447\u0430\u043b\u0435 \u043f\u043e\u043b\u044f,\n",
" #\u0442\u0443\u0442 \u043c\u044b \u0440\u0435\u0448\u0438\u043b\u0438 \u0435\u0449\u0451 \u0438 \u0447\u0430\u0441\u044b \u0441 \u043c\u0438\u043d\u0443\u0442\u0430\u043c\u0438 \u043f\u0440\u0438\u043f\u0438\u0441\u0430\u0442\u044c\n",
" ofile.write('%5.0f %5.0f %5.0f %5.0f %5.0f\\n' % (present.day, present.month, present.year, present.hour, present.minute ))\n",
" \n",
" #\u0426\u0438\u043a\u043b \u043f\u043e \u0441\u0442\u0440\u043e\u043a\u0430\u043c\n",
" for nn in range(airC.shape[1]):\n",
" \n",
" #\u0426\u0438\u043a\u043b \u043f\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c\n",
" for kk in range(airC.shape[2]):\n",
" \n",
" #\u0417\u0430\u043f\u0438\u0441\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0444\u0430\u0439\u043b. \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043f\u0435\u0440\u0435\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u044b,\n",
" #\u0432 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0431\u0443\u0434\u0442 \u0432\u044b\u0432\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435, \u0430 \u043f\u043e\u0442\u043e\u043c \u043f\u0435\u0440\u0435\u0447\u0438\u0441\u043b\u044f\u0435\u043c\n",
" #\u0441\u0430\u043c\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435\n",
" ofile.write('%5.2f %5.2f %5.2f\\n' % \\\n",
" (lons[nn,kk], lats[nn,kk], airC[one_field,nn,kk]))\n",
" \n",
" \n",
"ofile.close()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Wed Feb 29 00:00:00 2012\n",
"Thu May 31 00:00:00 2012\n",
"Fri Aug 31 00:00:00 2012\n",
"Fri Nov 30 00:00:00 2012\n",
"Mon Dec 31 00:00:00 2012"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 224
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041a\u0430\u043a \u0432\u0438\u0434\u0438\u0442\u0435 \u0441\u043b\u0430\u0433\u0430\u0435\u043c\u044b\u0435 \u0431\u0443\u043a\u0432\u0430\u043b\u044c\u043d\u043e \u0447\u0443\u0442\u044c-\u0447\u0443\u0442\u044c \u043f\u043e\u043c\u0435\u043d\u044f\u043b\u0438\u0441\u044c \u043c\u0435\u0441\u0442\u0430\u043c\u0438, \u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0438\u0437\u043c\u0435\u043d\u0438\u043b\u0441\u044f :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u043e\u043b\u044c\u0437\u0443\u044f\u0441\u044c \u0434\u0432\u0443\u043c\u044f \u044d\u0442\u0438\u043c\u0438 \u0448\u0430\u0431\u043b\u043e\u043d\u0430\u043c\u0438, \u043a\u0430\u043a \u043c\u043d\u0435 \u043a\u0430\u0436\u0435\u0442\u0441\u044f, \u043c\u043e\u0436\u043d\u043e \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0431\u043e\u043b\u044c\u0448\u0438\u043d\u0441\u0442\u0432\u043e \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0432\u0430\u043c \u043c\u043e\u0433\u0443\u0442 \u043f\u043e\u043d\u0430\u0434\u043e\u0431\u0438\u0442\u044c\u0441\u044f, \u0432 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u0444\u043e\u0440\u043c\u0430\u0442\u0430\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0443\u0436\u043d\u044b \u0438\u043c\u0435\u043d\u043d\u043e \u0432\u0430\u043c, \u0430 \u043d\u0435 \u043a\u0430\u043a\u0438\u043c-\u0442\u043e \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u043d\u043e \u0447\u0443\u0436\u0438\u043c \u043b\u044e\u0434\u044f\u043c :) \u041e\u0434\u043d\u0430\u043a\u043e \u043d\u0430\u0443\u0447\u0438\u0432\u0448\u0438\u0441\u044c \"\u0437\u0430\u0433\u0440\u0443\u0436\u0430\u0442\u044c\" \u0438 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 \u043f\u0438\u0442\u043e\u043d\u0435, \u044f \u043d\u0430\u0434\u0435\u044e\u0441\u044c \u0432\u0430\u043c \u0437\u0430\u0445\u043e\u0447\u0435\u0442\u0441\u044f \u043f\u0440\u043e\u0432\u0435\u0441\u0442\u0438 \u0432 \u043d\u0451\u043c \u0431\u043e\u043b\u044c\u0448\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u0438, \u0438 \u043c\u043e\u0436\u0435\u0442 \u0432 \u043e\u0434\u0438\u043d \u043f\u0440\u0435\u043a\u0440\u0430\u0441\u043d\u044b\u0439 \u0434\u0435\u043d\u044c \u0432\u044b \u043f\u043e\u0439\u043c\u0451\u0442\u0435, \u0447\u0442\u043e ASCII \u0444\u0430\u0439\u043b\u044b \u0432\u0430\u043c \u0431\u043e\u043b\u044c\u0448\u0435 \u043d\u0435 \u043d\u0443\u0436\u043d\u044b :)"
]
}
],
"metadata": {}
}
]
}
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