08 - Other modules for geoscientists.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other modules for geoscientists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "============="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the things will not work on ZMAW computers (Iris, Cartopy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pydap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pydap](http://www.pydap.org/) is a pure Python library implementing the Data Access Protocol, also known as DODS or OPeNDAP. You can use Pydap as a client to access hundreds of scientific datasets in a transparent and efficient way through the internet; or as a server to easily distribute your data from a variety of formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydap.client import open_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to access [sea ice data](http://icdc.zmaw.de/seaiceconcentration_asi_amsre.html?&L=1) from [CliSAP-Integrated Climate Data Center (ICDC)](http://icdc.zmaw.de/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = open_url(u\"http://icdc.cen.uni-hamburg.de/thredds/dodsC/ssmi_asi_icecon_arc2011\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<DatasetType with children 'latitude', 'longitude', 'time', 'sea_ice_area_fraction', 'land'>\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ice = dataset['sea_ice_area_fraction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 896, 608)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_FillValue': 103,\n",
       " 'add_offset': 0.0,\n",
       " 'coordinates': 'latitude longitude time',\n",
       " 'long_name': 'sea-ice concentration',\n",
       " 'missing_value': 101,\n",
       " 'scale_factor': 1.0,\n",
       " 'standard_name': 'sea_ice_area_fraction',\n",
       " 'units': 'percent'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice.attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11b12bef0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPEAAAD8CAYAAACmVULXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d67d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.squeeze(ice[0,:,:]))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F2PY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [F2PY](http://www.f2py.com/) project is created to unify the efforts of supporting easy connection between Fortran and Python languages. Example below is from [Using Python and FORTRAN with F2py](http://www.f2py.com/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create FORTRAN file (use %%file instead of %%writefile if you on IPython < 1.0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing FIB1.F\n"
     ]
    }
   ],
   "source": [
    "%%writefile FIB1.F\n",
    "C FILE: FIB1.F\n",
    "      SUBROUTINE FIB(A,N)\n",
    "C\n",
    "C     CALCULATE FIRST N FIBONACCI NUMBERS\n",
    "C\n",
    "      INTEGER N\n",
    "      REAL*8 A(N)\n",
    "      DO I=1,N\n",
    "         IF (I.EQ.1) THEN\n",
    "            A(I) = 0.0D0\n",
    "         ELSEIF (I.EQ.2) THEN\n",
    "            A(I) = 1.0D0\n",
    "         ELSE \n",
    "            A(I) = A(I-1) + A(I-2)\n",
    "         ENDIF\n",
    "      ENDDO\n",
    "      END\n",
    "C END FILE FIB1.F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile it with f2py:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39mrunning build\u001b[0m\n",
      "\u001b[39mrunning config_cc\u001b[0m\n",
      "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\n",
      "\u001b[39mrunning config_fc\u001b[0m\n",
      "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\n",
      "\u001b[39mrunning build_src\u001b[0m\n",
      "\u001b[39mbuild_src\u001b[0m\n",
      "\u001b[39mbuilding extension \"fib1\" sources\u001b[0m\n",
      "\u001b[39mf2py options: []\u001b[0m\n",
      "\u001b[39mf2py:> /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.c\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "Reading fortran codes...\n",
      "\tReading file 'FIB1.F' (format:fix,strict)\n",
      "Post-processing...\n",
      "\tBlock: fib1\n",
      "\t\t\tBlock: fib\n",
      "Post-processing (stage 2)...\n",
      "Building modules...\n",
      "\tBuilding module \"fib1\"...\n",
      "\t\tConstructing wrapper function \"fib\"...\n",
      "\t\t  fib(a,[n])\n",
      "\tWrote C/API module \"fib1\" to file \"/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.c\"\n",
      "\u001b[39m  adding '/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.c' to sources.\u001b[0m\n",
      "\u001b[39m  adding '/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6' to include_dirs.\u001b[0m\n",
      "\u001b[39mcopying /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.c -> /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39mcopying /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.h -> /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\n",
      "\u001b[39mrunning build_ext\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n",
      "\u001b[39mFound executable /usr/local/bin/gfortran\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\n",
      "\u001b[39mbuilding 'fib1' extension\u001b[0m\n",
      "\u001b[39mcompiling C sources\u001b[0m\n",
      "\u001b[39mC compiler: gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/koldunovn/miniconda3/include -arch x86_64 -I/Users/koldunovn/miniconda3/include -arch x86_64\n",
      "\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk\u001b[0m\n",
      "\u001b[39mcreating /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39mcompile options: '-I/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6 -I/Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include -I/Users/koldunovn/miniconda3/include/python3.6m -c'\u001b[0m\n",
      "\u001b[39mgcc: /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.c\u001b[0m\n",
      "In file included from /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.c:19:\n",
      "In file included from /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.h:13:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h:18:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:\n",
      "/Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: \"Using deprecated NumPy API, disable it by \"          \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
      "#warning \"Using deprecated NumPy API, disable it by \" \\\n",
      " ^\n",
      "/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.c:112:12: warning: unused function 'f2py_size' [-Wunused-function]\n",
      "static int f2py_size(PyArrayObject* var, ...)\n",
      "           ^\n",
      "2 warnings generated.\n",
      "\u001b[39mgcc: /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.c\u001b[0m\n",
      "In file included from /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.c:2:\n",
      "In file included from /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.h:13:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h:18:\n",
      "In file included from /Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:\n",
      "/Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: \"Using deprecated NumPy API, disable it by \"          \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
      "#warning \"Using deprecated NumPy API, disable it by \" \\\n",
      " ^\n",
      "/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.c:139:18: warning: comparison of integers of different signs: 'Py_ssize_t' (aka 'long') and 'unsigned long' [-Wsign-compare]\n",
      "        if (size < sizeof(notalloc)) {\n",
      "            ~~~~ ^ ~~~~~~~~~~~~~~~~\n",
      "2 warnings generated.\n",
      "\u001b[39mcompiling Fortran sources\u001b[0m\n",
      "\u001b[39mFortran f77 compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\n",
      "Fortran f90 compiler: /usr/local/bin/gfortran -Wall -g -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\n",
      "Fortran fix compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\u001b[0m\n",
      "\u001b[39mcompile options: '-I/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6 -I/Users/koldunovn/miniconda3/lib/python3.6/site-packages/numpy/core/include -I/Users/koldunovn/miniconda3/include/python3.6m -c'\u001b[0m\n",
      "\u001b[39mgfortran:f77: FIB1.F\u001b[0m\n",
      "\u001b[39m/usr/local/bin/gfortran -Wall -g -arch x86_64 -Wall -g -undefined dynamic_lookup -bundle /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fib1module.o /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/src.macosx-10.7-x86_64-3.6/fortranobject.o /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk/FIB1.o -L/usr/local/Cellar/gcc/7.1.0/lib/gcc/7/gcc/x86_64-apple-darwin16.5.0/7.1.0 -L/Users/koldunovn/miniconda3/lib -lgfortran -o ./fib1.cpython-36m-darwin.so\u001b[0m\n",
      "Removing build directory /var/folders/yq/03v059jn4sg2xhnhpnrqkgcc0000gn/T/tmprf2kbptk\n"
     ]
    }
   ],
   "source": [
    "!f2py -c -m --fcompiler=gnu95 fib1 FIB1.F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import resulting **fib1.so** as python library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fib1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read some auto generated documentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This module 'fib1' is auto-generated with f2py (version:2).\n",
      "Functions:\n",
      "  fib(a,n=len(a))\n",
      ".\n",
      "fib(a,[n])\n",
      "\n",
      "Wrapper for ``fib``.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "a : input rank-1 array('d') with bounds (n)\n",
      "\n",
      "Other Parameters\n",
      "----------------\n",
      "n : input int, optional\n",
      "    Default: len(a)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(fib1.__doc__)\n",
    "print(fib1.fib.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use *fib* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.,    1.,    1.,    2.,    3.,    5.,    8.,   13.,   21.,\n",
       "         34.,   55.,   89.,  144.,  233.,  377.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.zeros(15,'d') \n",
    "fib1.fib(a) \n",
    "a "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}