python_p4_0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Satellite Oceanography. Pylab. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вводный урок\n",
    "#### (А. Мизюк)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.Начнем с вопросов:\n",
    "\n",
    "* У кого получилось установить? Что было самым сложным? У кого не получилось?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Почему Python?\n",
    "### [Введение из предыдущего курса](https://github.com/amizyuk/pyMHI/blob/master/Meeting_00/Meeting_00.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Самые востребованные навыки в сфере data science](https://habr.com/ru/company/everydaytools/blog/426557/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Диаграмма из статьи\n",
    "\n",
    "![Из статьи](https://habrastorage.org/webt/dl/nb/zx/dlnbzxkz2382uuhnzfl9cx74foo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Python as a glue](https://docs.scipy.org/doc/numpy/user/c-info.python-as-glue.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Python in Ocean Modelling](http://img.youtube.com/vi/lorRZWM-dSc/0.jpg)](http://www.youtube.com/watch?v=lorRZWM-dSc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Как быстро начать? - Jupyter и Pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter - система работы с \"тетрадками\" (\"блокнотами\"), использующая web-технологии и возможность обмена памятью с другими ЯП.\n",
    "#### Jupyter-lab - развитие проекта notebook с еще большим уклоном к технологиям web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pylab - адаптированное под Matlab научное окружение для python\n",
    "#### [Введение в Pylab из предыдущего курса](https://github.com/amizyuk/pyMHI/blob/master/Meeting_01/Lesson_01_02_pylab.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Ячейки\n",
    "#### отдельные блоки исполнения кода"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ячейки можно копировать Esc+c, вырезать Esc+x, вставлять Esc+v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создать ячейку перед... Esc+a, после... Esc+b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание**: вырежьте эту ячейку и вставте на 3 ячейки выше, затем скопируйте и вставьте несколько раз\n",
    "\n",
    "*Подсказка*: вместо сочетания клавиш можно использовать пункт меню Edit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для ввыполнения кода в ячейке используйте Ctrl+Enter, Alt+Enter или Shift+Enter\n",
    "**Задание**: Попробуйте понять, в чем разница"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "для включения режима ~~godmode~~ pylab выполните код в следующей ячейке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем какой-нибудь простенький! код из matlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe187ab1a90>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = linspace(-3.,3.,100)\n",
    "y = sin(x)\n",
    "plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравним с кодом matlab/octave (ячейка с \"%%octave\" у вас, вероятно, не запустится! это для демонстрации)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На уроке разберем подробнее!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext oct2py.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%octave\n",
    "x = linspace(-3.,3.,100);\n",
    "y = sin(x);\n",
    "plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "добавим сетку!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = linspace(-3.,3.,100)\n",
    "y = sin(x)\n",
    "plot(x,y)\n",
    "grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "уже есть небольшое различие в коде..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Bn4YLi4ulrdLP6RXCtJ0Ol3cuLyQRqPRaDQqimI4HJ6cnFz3ufP5vMqXTtRgMDg/Pw99FGE494AH8Ps8OgxwDMHPPaCYz71XPG1uMZeuXflLdhsbG69fvy7+96TR8uNHR0fj8bj03QKQp/JBGo1Gp6enBwcHOzs7Dx8+LIpiPB4PBoPNzc3FE0jb29tbW1v1HSpQhmePeFuczyRVumR3fHw8Ho/v37+/eDJpOBwu9unLly8vfxwA3qnqX4wdDodXVue6jwNtMo+4ToQjyVsHARAFQYLOMo+4WWwjSZAAiIIgAeQrqpEkSNBNrteRHEECyFo8I0mQoIPMI1IkSAC5i2QkCRJ0jXlEogQJgChGkiBBp5hHpEuQACiKCEaSIEF3mEckTZAA+F3YkSRI0BHmEakTJAD+EHAkCRJ0gXlEBwgSAFEQJEieeUS9Ql21EyQAoiBIkDbziCYEGUmCBEAUBAmAK7Q/kgQJEuZ6HV0iSABcreWRJEiQKvOIjhEkAK7V5kgSJEiSeUT3CBIAURAkSI95RJtau2onSABEQZAgMeYR7WtnJAkSAFEQJADerYWRJEiQEtfr6DBBAuBWmh5JggTJMI/oNkEC4LYaHUmCBGkwj+g8QQIgCoIECTCPiEdzV+0ECYAoCBIAq2loJAkSxM71OjIhSACsrImRJEgQNfOIfAgSAGXUPpIECeJlHpEVQQIgCoIEkTKPiF+9V+0ECYAoCBIA5dU4kgQJYuR6HRkSJAAqqWskCRJExzwiT4IEQFW1jCRBgriYR2RLkACIgiBBRMwj0lX9qp0gARAFQQKgHhVHkiBBLFyvI3OCBEBtqvyhSpAgCuYRCBIAURAkCM88gkKQAIiEIAEQBUGCwFyvgwVBAiAKggQhmUewJEgAREGQIBjzCC4TJACiIEgAREGQIAzX6+ANggRAFAQJAjCP4G2CBEAUBAnaZh7BlQQJgCgIErTKPILrCBIAURAkAKIgSNAe1+vgBoIEQBQECVpiHsHNmgrSZDKZzWYN3TkA3dNIkLa3t7///vsHDx6Mx+Mm7h+SYx7BO71X+z2enJz0+/0nT55Mp9P9/f3hcFj7lwCge+pfSJPJZGNjoyiKfr9/enpa+/0D0EmNXLLr9/uLG5ubm03cP6Tl4rOnrtfBO9V/ya4oiul0urhxw0Lq9XrL22tra00cRpwGg0HoQwgm33P/7Gm+557z9z2nc7+4uFjeLv2QXn+QNjY2JpNJURTT6XR9ff26/2w+n9f+peM3GAzOz89DH0UY2Z57b/fV2ovHeZ57kfH3vcj43EtnuP4gjUajo6Ojg4OD09PThw8f1n7/AHRSI5fsjo+Px+Px/fv3l08mQZ4Wr/YevAh9HJCCRoJUFIVXewOwEm8dBEAUBAma4t0ZYCWCBEAUBAkaYR7BqgQJgCgIEtTPPIISBAmAKAgSAFEQJKiZ63VQjiABEAVBgjqZR1CaIAEQBUGC2phHUIUgARAFQQIgCoIE9XC9DioSJACiIEhQA/MIqhMkAKIgSFCVeQS1ECQAoiBIAERBkKAS1+ugLoIEQBQECcozj6BGwYLU230V6ksDECELCUoyj6BewYI0P7xnJAGwZCFBGeYR1C5kkIwkAJYsJACiIEiwMtfroAmBg+SqHQALFhKsxjyChoQPkpEEQBFDkCAh5hE0J4ogGUkARBEkABAkuC3X66BRsQTJVTuAzMUSJIiceQRNiyhIRhJAziIKEkTLPIIWxBUkIwkgW3EFCSJkHkE7oguSkQSQp+iCBECeBAlu4nodtCbGILlqB5ChGIMEkTCPoE2RBslIAshNpEGC4MwjaFm8QTKSALISb5AgIPMI2hd1kIwkgHxEHSQA8hF7kIwk2ud6HQQRe5AAyIQgwZ+YRxBKAkFy1Q4gBwkECVpjHkFAaQTJSALovDSCBC0wjyCsZIJkJAF0WzJBgkaZRxBcSkEykgA6LKUgAdBhiQXJSKIJrtdBDBILEgBdJUjkzjyCSKQXJFftADopvSBBjcwjiEeSQTKSALonySBBLcwjiEqqQTKSADom1SBBReYRxCbhIBlJAF2ScJCgNPMIIpR2kIwkgM5IO0hQgnkEcUo+SEYSQDckHyRYiXkE0epCkIwkgA7oQpDglswjiFlHgmQkAaSuI0ECIHXdCZKRxM1cr4PIdSdIACStapAmk8lsNqvlUKozkriOeQTxe6/KJ29vb/f7/bOzs93d3eFwuPz4J598sr6+XhTF+vr63t5e1WMEIAPlg3RyctLv9588eTKdTvf395dBmk6n6+vrz58/r+cAV7QYSf4szGV+JCAJ5YM0mUw2NjaKouj3+6enp8uPT6fTO3fu7O/vv//++48ePbpz504NhwlA11W6ZNfv9xc3Njc3lx+czWYffvjh3bt3z87OdnZ2rptKvV5veXttba3KYbxhrSh6xdO1F49rvM8aDQaD0IcQTJBzv/js6dqLx4MX7X/lP/F9z1M+535xcbG8XfohfeUgjcfjX3755aOPPiqKYjqdLj54eSGNRqPRaFQUxXA4PDk5ue5+5vP5ygd7a73dV+fn583df2mDwSDOA2tBqHOP4YfB9z30UYSR7bmXzvDKr7IbDod7e3tbW1sbGxuvX78u/vek0fI/ODo6Go/H5Y6mLl5ux4JnjyAh5S/ZjUajo6Ojg4OD09PThw8fFkUxHo8fPHjw448/7uzsfPrpp2dnZ1tbW/UdKgBdVuk5pOPj4/F4fP/+/cWTScPhcLFPX758efnjQXi5HX4AIC2VglQUxeW/fnSbjwPAlbr81kGeScqZeQTJ6XKQAEhIx4NkJOXJPIIUdTxIAKSi+0EyknJjHkGiuh8kAJKQRZCMpHyYR5CuLIJEJtQIkpZLkIwkgMjlEiQ6zzyC1GUUJCMJIGYZBYkOM4+gA/IKkpEEEK28gkQnmUfQDdkFyUgCiFN2QaJjzCPojByDZCQBRCjHINEZ5hF0SaZBMpIAYpNpkApNSp95BB2Tb5BImhpB92QdJCMJIB5ZB4lEmUfQSbkHyUgCiETuQSI55hF0lSAZSQBREKSi0KR0mEfQYYJEMtQIuk2QfmckAYQlSKTBPILOE6Q/GEkAAQkSCTCPIAeC9CdGEkAogvQmTYqNeQSZECSipkaQD0G6gpEE0D5BIl7mEWRFkK5mJAG0TJCupUlhmUeQG0EiRmoEGRKkmxhJAK0RpHfQpPaZR5AnQSIuagTZEqR3M5IAWiBIt6JJ7TCPIGeCRCzUCDInSLdlJAE0SpBWoEnNMY8AQSI8NQIKQVqVkQTQEEFamSbVyzwCFgSJkNQIWBKkMowkgNoJUkmaVJ15BFwmSIShRsAbBKk8I6k0NQLeJkiVaBJAXQSpKk1alXkEXEmQaJUaAdcRpBoYSbekRsANBKkemgRQkSDVRpNudvHZU/MIuIEg1UmTrtPbfbX24nHoowCiJkg0zlNHwG0IUs2MpDeoEXBLglQ/TVpSI+D2BKkRmgSwKkFqiiaZR8BKBKlBOTdJjYBVCVKz8mySGgElCFLjcmuSGgHlCFIb8mmSGgGlCVJLcmiSGgFVvBf6ADIyP7zXK56GPoqmqBFQkYXUqrUXjzu5k9QIqE6Q2ta9a3dqBNRCkALoUpPUCKiLIIXRjSapEVAjQQom9SapEVAvQQop0Sb1dl+pEVA7L/sObNmkVB7fpQhoiIUU3vzwXipTSY2A5ghSLCJvkst0QNNcsotItJfvpAhogSDFZfG4H08A4gwk0EmCFKNIplI8XQRyIEiRWk6lIkSWYsghkJvefD5v4n5ns9l///vffr9/5b8dDAbn5+dNfN3I9Xpl/oe3mYfmvla5c+8G5x76KMLI9txLP8I3tZB++OGH//znP3t7ew3df1ZaWEvLF/hZRUAojQTpiy++ODs7++qrr5q482xdzlJRXzlcnQMi0UiQjo+PDw4OmrhnluWoUqbLf+FJioBIeFFDqt4uU4nPBYhHbc+5jcfjX3755aOPPtra2iqKYrGQrnsOaTAY1PJFAYhQ4Bc1DIfD4XB4y/84z5fYAXAD72UHQBQyfZk8ALGxkACIQrAgjcfj2WwW6quHNZlMptNp6KMIYzabZXjuk8kk25/2PL/jRd6/5qUf3v/yj3/8o+6DeYfZbPbll1/O5/Ovv/76gw8++Pjjj1s+gIAW5/7vf//7p59++u233+7evRv6iNr27bff/vrrr1md+Pb29nQ6PTo66vf7172ZVodl+B3P+de86sP7vHXPnj179uzZfD5//fr13/72t/YPIKBnz57985//XNy+d+9e2INp3+eff762trb8P5CDn3/++e9///s8y5/2eZbf8Xnev+YVH94D/MXYhw8fLm5MJpPc/sD46aefLm7keQEnw7fwmEwmGxsbRVH0+/3T09PQh9O2DL/jRd6/5hUf3oM9h3RwcHB4eLj4Xc3H4qLNeDx+8ODBo0ePQh8ObVj+Wm5uboY9Etrh17z0w3t7C+mNt3LY29t79OjRX//618U/dtjb72Hxr3/965tvvslhHb5x7nlaPrOd4ULKVla/5m8r/fDeXpCWb+Wwv78/HA5Ho9GdO3da++oBXX4Pi++//37xYxr2kFqz0vt3dNLGxsZkMimKYjqdrq+vhz4c2pDbr/llFR/eAzyHtLW1tbOzM5lMzs7Octuzi1eCbm9vL/7x+fPnIY+G5o1Go6Ojo4ODg9PT0+Xldbot51/zig/vYd6pYTabnZ2d5fkqWDI0Ho/9tJOJKg/v3joIgCh46yAAoiBIAERBkACIgiABEAVBAiAK/w8YtZVbRhrUAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%octave\n",
    "x = linspace(-3.,3.,100);\n",
    "y = sin(x);\n",
    "plot(x,y)\n",
    "grid('on')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание**: попробуйте выполнить другой код matlab"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SatOcean",
   "language": "python",
   "name": "satocean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}