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"description": "bpch\r\n====\r\n\r\nGEOS-Chem Binary Punch File Reader/Plotter/ncdumper\r\n\r\nThe python-based library for Binary Punch files is designed to provide NetCDF-like access to data in Binary Punch files. In addition, it can be used to produce plots.\r\n\r\nPrerequisites\r\n-------------\r\n\r\n * Linux`*`\r\n * Python >= 2.7\r\n * numpy >= 1.5\r\n * matplotlib >= 1.0`**`\r\n * mpl_toolkits.basemap >= 1.0`***`\r\n\r\n`*`Linux is not actually required; Windows and Mac versions have been used. They are not supported.\r\n\r\n`**`matplotlib is only required for plotting\r\n\r\n`***`basemap is used only if available to add geopolitical boundaries to lat-lon plots.\r\n\r\n### Prerequisites via EPD (Mac OS X/Linux/Unix/Windows)\r\n\r\nThis is the easiest way to get going. All of these prerequisites are available via the Enthought Python Distribution (EPD). Download their installer and run it. It's that easy. EPD is free for academics, but the edX version is not sufficient (unless combined with virtualenv instructions below). \r\n\r\n\r\n### Prerequisites via virtualenv on Linux/Unix/Mac OS X\r\n\r\nIf you have a working version of Python 2.7 (or you can get edX from EPD for free) but you don't have root access to install, then I recommend you use virtualenv by downloading virtualenv.py. The commands below setting up virtualenv and installing prerequisites:\r\n\r\n```\r\ncd ~\r\ncurl -LO https://raw.github.com/pypa/virtualenv/master/virtualenv.py\r\npython virtualenv.py aqc\r\nsource aqc/bin/activate\r\npip install numpy\r\npip install matplotlib\r\npip install basemap\r\n```\r\n\r\nAny time you want to use bpch, you'll need to activate virtualenv by running `source bpch/bin/activate`. You'll need to `source bpch/bin/activate`.\r\n\r\nInstallation\r\n------------\r\n\r\n## First release\r\n\r\n```\r\npip install bpch\r\n```\r\n\r\nor \r\n\r\n```\r\npip install http://github.com/barronh/bpch/archive/1.0.zip\r\n```\r\n\r\n## Latest version\r\n\r\n```\r\npip install https://github.com/barronh/bpch/archive/master.zip\r\n```\r\n\r\nExamples\r\n--------\r\n\r\n### Example Plotting\r\n\r\n1. This example produce a Lat-Lon, time average, mean layer with a log color-scale.\r\n\r\n ```\r\n $ python bpch.py --oldplot -g IJ-AVG-$ -v O3 -t mean -l mean --log ctm.bpch\r\n Successfully created ctm.bpch_IJ-AVG_O3_timemean_layermean_rowall_colall.png\r\n ```\r\n\r\n2. This example produces a zonal mean, time average plot stopping at 20km (if BOXHEIGHT available) or the 21st layer.\r\n\r\n ```\r\n $ python bpch.py --oldplot -g IJ-AVG-$ -v O3 -t mean -c mean --ymax 20 ctm.bpch\r\n Successfully created ctm.bpch_IJ-AVG_O3_timemean_layerall_rowall_colmean.png\r\n ```\r\n\r\n3. This example produces a 1st layer Latitude-Time Hovmoller Diagram. \r\n\r\n ```\r\n $ python bpch.py --oldplot -g IJ-AVG-$ -v O3 -l 0 -c mean ctm.bpch\r\n Successfully created ctm.bpch_IJ-AVG_O3_timeall_layer0_rowall_colmean.png\r\n ```\r\n\r\n4. This example produces a 1st layer Longitude-Time Hovmoller Diagram. \r\n\r\n ```\r\n $ python bpch.py --oldplot -g IJ-AVG-$ -v O3 -l 0 -r mean ctm.bpch\r\n Successfully created ctm.bpch_IJ-AVG_O3_timeall_layer0_rowmean_colall.png\r\n ```\r\n\r\n5. This example would produce two Ox figures. Both from time 1 (default), but the first file from layer 1 and the second file from layer 2. The first figure has a minimum (max) value of 20 (60) and the second has a minimum (maximum) of 25 (65).\r\n\r\n ```\r\n $ python bpch.py --oldplot -g IJ-AVG-$ -v O3 -n 20 -x 60 -t 0 -l 0 -n 25 -x 65 -t 0 -l 1 ctm.bpch ctm.bpch2\r\n Successfully created ctm.bpch_IJ-AVG_O3_time0_layer0_rowall_colall.png\r\n Successfully created ctm.bpch2_IJ-AVG_O3_time0_layer1_rowall_colall.png\r\n ```\r\n \r\n6. This example would produce one Ox difference figure with a minimum of -2 and a maximum of 2.\r\n \r\n ```\r\n $ python bpch.py --oldplot -d -g IJ-AVG-$ -v O3 -n -2 -x 2 -t 0 -l 0 ctm.bpch ctm.bpch2\r\n Successfully created ctm.bpch-ctm.bpch2-diff_IJ-AVG_O3_time0_layer0_rowall_colall.png\r\n ```\r\n\r\n### Example Python\r\n```\r\nfrom bpch import bpch\r\n\r\nbcfile = bpch('ctm.bpch')\r\nprint gcfile.groups.keys()\r\ngroup = bcfile.groups['IJ-AVG-$']\r\nprint group.variables.keys()\r\nozone = group.variables['O3']\r\nprint ozone.dimensions\r\nprint ozone.units\r\nprint ozone.mean(0).reshape(ozone.shape[1], -1).mean(1)\r\n...\r\n\r\n```",
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