{ "metadata": { "gist_id": "ee14af5220bc3512131f", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" }, "name": "", "signature": "sha256:ca9e8a1ad4990322c6d9c464fcd313d87f0ad02d1e688a11487ffc1f0d07f9b1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "name = '2015-10-12-cython_nogil'\n", "title = \"Update on optimizing code for iso-surfaces using cython\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import warnings\n", "warnings.simplefilter(\"ignore\")\n", "\n", "import os\n", "from datetime import datetime\n", "from IPython.core.display import HTML\n", "\n", "with open('creative_commons.txt', 'r') as f:\n", " html = f.read()\n", "\n", "\n", "hour = datetime.utcnow().strftime('%H:%M')\n", "comments=\"true\"\n", "\n", "\n", "date = '-'.join(name.split('-')[:3])\n", "slug = '-'.join(name.split('-')[3:])\n", "\n", "metadata = dict(title=title,\n", " date=date,\n", " hour=hour,\n", " comments=comments,\n", " slug=slug,\n", " name=name)\n", "\n", "markdown = \"\"\"Title: {title}\n", "date: {date} {hour}\n", "comments: {comments}\n", "slug: {slug}\n", "\n", "{{% notebook {name}.ipynb cells[2:] %}}\n", "\"\"\".format(**metadata)\n", "\n", "content = os.path.abspath(os.path.join(os.getcwd(),\n", " os.pardir,\n", " os.pardir, \n", " '{}.md'.format(name)))\n", "with open('{}'.format(content), 'w') as f:\n", " f.writelines(markdown)\n", " \n", "html = '''\n", "\n", "

This post was written as an IPython notebook.\n", " It is available for download\n", " or as a static html.

\n", "

\n", "%s''' % (name, name, html)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This post is an update on of the previous iso-surface [post](https://ocefpaf.github.io/python4oceanographers/blog/2015/10/05/isosurfaces/).\n", "\n", "After reading a little bit more on cython I found out that,\n", "when using memory views,\n", "we can release the [GIL](https://wiki.python.org/moin/GlobalInterpreterLock) using the [nogil](http://docs.cython.org/src/userguide/memoryviews.html) annotation.\n", "I wonder if releasing the GIL can get the cython performance closer to numba's.\n", "\n", "First let's set the same data as before up to run some tests." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "\n", "p = np.linspace(-100, 0, 30)[:, None, None] * np.ones((50, 70))\n", "x, y = np.mgrid[0:20:50j, 0:20:70j]\n", "\n", "q = np.sin(x) + p\n", "p0 = -50." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two cells below will compiled two versions of the `zslice` function.\n", "The first with the GIL and the second releasing during the loop." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext Cython" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "cimport cython\n", "import numpy as np\n", "cimport numpy as np\n", "\n", "NaN = np.NaN\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def gil_zslice(double[:, :, ::1] q,\n", " double[:, :, ::1] p,\n", " double p0,\n", " mask_val=NaN):\n", " cdef int L = q.shape[2]\n", " cdef int M = q.shape[1]\n", " cdef int N = q.shape[0]\n", " cdef double dp, dq, dq0\n", " cdef int i, j, k\n", " \n", " cdef np.ndarray[double, ndim=2, mode='c'] q_iso = np.empty((M, L), dtype=np.float64)\n", " \n", " for i in range(L):\n", " for j in range(M):\n", " q_iso[j, i] = mask_val\n", " for k in range(N-1):\n", " if (((p[k, j, i] < p0) and (p[k+1, j, i] > p0)) or\n", " ((p[k, j, i] > p0) and (p[k+1, j, i] < p0))):\n", " dp = p[k+1, j, i] - p[k, j, i]\n", " dp0 = p0 - p[k, j, i]\n", " dq = q[k+1, j, i] - q[k, j, i]\n", " q_iso[j, i] = q[k, j, i] + dq*dp0/dp\n", " return q_iso" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "cimport cython\n", "\n", "import numpy as np\n", "cimport numpy as np\n", "\n", "NaN = np.NaN\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def nogil_zslice_2D(double[:, ::1] q,\n", " double[:, ::1] p,\n", " double p0,\n", " double mask_val=NaN):\n", "\n", " cdef int IJ = q.shape[1]\n", " cdef int K = q.shape[0]\n", " cdef int ij, k\n", "\n", " cdef np.ndarray[double, ndim=1, mode='c'] q_iso = np.empty(IJ, dtype=np.float64)\n", "\n", " with nogil:\n", " for ij in range(IJ):\n", " q_iso[ij] = mask_val\n", " for k in range(K-1):\n", " if (((p[k, ij] < p0) and (p[k+1, ij] > p0)) or\n", " ((p[k, ij] > p0) and (p[k+1, ij] < p0))):\n", " q_iso[ij] = (q[k, ij] +\n", " (q[k+1, ij] - q[k, ij]) * # dq\n", " (p0 - p[k, ij]) / # dp0\n", " (p[k+1, ij] - p[k, ij])) # dp\n", " return q_iso" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that cython won't allow any conversion to Python objects without GIL.\n", "That is why we had to eliminate the temporary variables (`dq`, `dp0`, and `dp`).\n", "Hopefully we are sacrificing readability to gain some speed.\n", "\n", "The second difference has nothing to do with the GIL!\n", "I modified the code to work with 2D arrays (depth, y_x_space) instead of 3D\n", "arrays (depth, y, x).\n", "The reason is to make this code more\n", "[UGRID](http://ocefpaf.github.io/ugrid-conventions/) friendly.\n", "\n", "Again we will use our poor man's benchmarking." ] }, { "cell_type": "code", "collapsed": false, "input": [ "K, J, I = q.shape\n", "\n", "qr = q.reshape(K, -1)\n", "pr = p.reshape(K, -1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "gil = %timeit -n1000 -o gil_zslice(q, p, p0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 477 \u00b5s per loop\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "nogil = %timeit -n1000 -o nogil_zslice_2D(qr, pr, p0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 151 \u00b5s per loop\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "gil.best / nogil.best" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "3.1497008061938865" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that, under the same test conditions, we got a slightly better performance\n", "than numba (175 \u00b5s).\n", "\n", "\n", "Due to the the input shape change and the fact that the cython code expects only `np.float64` we need to wrap the cython function to prepare the input.\n", "That is a small price to pay to have a single code that can deal with any ocean model grid.\n", "\n", "The two functions below should take care of the input for us." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def _save_typecast(arr):\n", " if arr.dtype is not 'float64' and np.can_cast(arr, np.float64):\n", " arr = arr.astype(np.float64)\n", " return arr\n", "\n", "\n", "def zslice(q, p, p0):\n", " q = _save_typecast(q)\n", " p = _save_typecast(p)\n", " p0 = -abs(p0)\n", "\n", " if q.ndim == 3:\n", " K, J, I = q.shape\n", " iso = nogil_zslice_2D(q.reshape(K, -1), p.reshape(K, -1), p0)\n", " return iso.reshape(J, I)\n", " elif q.ndim == 2:\n", " return nogil_zslice_2D(q, p, p0)\n", " else:\n", " msg = \"Expected 2 (ugrid) or 3 (r-/s-grid) dimensions. Got {}.\"\n", " raise ValueError(msg(q.ndim))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "All that is left is to test the nogil and UGRID friendly version against real\n", "data. First some UGRID (FVCOM) data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import iris\n", "\n", "url = ('http://crow.marine.usf.edu:8080/thredds/dodsC/'\n", " 'FVCOM-Nowcast-Agg.nc')\n", "\n", "cubes = iris.load_raw(url)\n", "\n", "# Last time step.\n", "temp = cubes.extract_strict('sea_water_potential_temperature')[-1, ...]\n", "\n", "p = temp.coord('sea_surface_height_above_reference_ellipsoid').points\n", "\n", "q = temp.data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "(In the future iris will use `pyugrid` and the next cell won't be necessary.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pyugrid\n", "\n", "ugrid = pyugrid.UGrid.from_ncfile(url)\n", "\n", "lon = ugrid.nodes[:, 0]\n", "lat = ugrid.nodes[:, 1]\n", "triangles = ugrid.faces[:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.tri as tri\n", "\n", "triang = tri.Triangulation(lon, lat, triangles=triangles)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "\n", "import cartopy.crs as ccrs\n", "from cartopy.io import shapereader\n", "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", "\n", "cmap = plt.cm.viridis\n", "\n", "def make_map(projection=ccrs.PlateCarree()):\n", " fig, ax = plt.subplots(figsize=(9, 13),\n", " subplot_kw=dict(projection=projection))\n", " gl = ax.gridlines(draw_labels=True)\n", " gl.xlabels_top = gl.ylabels_right = False\n", " gl.xformatter = LONGITUDE_FORMATTER\n", " gl.yformatter = LATITUDE_FORMATTER\n", " ax.coastlines('50m')\n", " return fig, ax" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute a slice of potential temperature at -25 meters.\n", "\n", "I could not figure out how to use masked array and `tricontourf`.\n", "The code was segfaulting!\n", "I ended up filling the data with `-999` and specifying the contour levels instead." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy.ma as ma\n", "\n", "temp_slice = zslice(q, p, -25)\n", "\n", "\n", "mask = temp_slice = ma.masked_invalid(temp_slice)\n", "vmin, vmax = temp_slice.min(), temp_slice.max()\n", "temp_slice = temp_slice.filled(fill_value=-999)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = make_map()\n", "extent = [lon.min(), lon.max(),\n", " lat.min(), lat.max()]\n", "ax.set_extent(extent)\n", "\n", "levels = np.arange(vmin, vmax, 0.5)\n", "\n", "kw = dict(cmap=cmap, alpha=0.9, levels=levels)\n", "cs = ax.tricontourf(triang, temp_slice, **kw)\n", "\n", "kw = dict(shrink=0.5, orientation='vertical')\n", "cbar = fig.colorbar(cs, **kw)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ydGIDh76tHKG1zijDby0aYj8PlRYOvrHvVyZu57WFw5BFv9SNGjUKO3bswOjRo+3yN2tH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\npKBhKSUlJZg3bx4A4ObNmygvL+/U+xLo6QWFSGHzttlaXFwcpk+fDkA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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "UGRID seems to be OK. Let's try some SGRID (ROMS) now." ] }, { "cell_type": "code", "collapsed": false, "input": [ "url = ('http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/avg/'\n", " 'ESPRESSO_Real-Time_v2_Averages_Best')\n", "\n", "cubes = iris.load_raw(url)\n", "\n", "salt = cubes.extract_strict('sea_water_salinity')[-1, ...] # Last time step.\n", "\n", "lon = salt.coord(axis='X').points\n", "lat = salt.coord(axis='Y').points\n", "\n", "p = salt.coord('sea_surface_height_above_reference_ellipsoid').points\n", "q = salt.data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "salt_slice = ma.masked_invalid(zslice(q, p, 300))\n", "\n", "fig, ax = make_map()\n", "extent = [lon.min(), lon.max(),\n", " lat.min(), lat.max()]\n", "ax.set_extent(extent)\n", "\n", "cs = ax.pcolormesh(lon, lat, salt_slice, cmap=cmap)\n", "\n", "kw = dict(shrink=0.5, orientation='vertical', extend='both')\n", "cbar = fig.colorbar(cs, **kw)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Ct27d6j0PiQKCaAWSk5Nx+vRpAEpvg65du9ZJ/zt//nwkhkYQRDtk+/bt4DgO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/zur4eEDmy/+Y+R5M8o3sX8+ULBQL6Dy+hSITxLUAPKggoX4QPA4vOe+fTi+NphFh7It7sVATpr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\nqqp48cUXiUQiqKrKgAED6N+/P6NHjyYct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"text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I known we still have a bug when requesting levels where the data will fall in the top or bottom levels.\n", "I am working to find a proper way to return a valid slice in those places.\n", "However, we are pretty close to a code that works with any ocean model grid!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "HTML(html)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "

This post was written as an IPython notebook.\n", " It is available for download\n", " or as a static html.

\n", "

\n", "
python4oceanographers by Filipe Fernandes is\n", "licensed under a Creative Commons\n", "Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "" ] } ], "prompt_number": 20 } ], "metadata": {} } ] }