{"id":67861,"date":"2015-06-10T09:00:34","date_gmt":"2015-06-10T09:00:34","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/accessing-multidimensional-scientific-data-using-python\/"},"modified":"2018-03-26T21:05:40","modified_gmt":"2018-03-26T21:05:40","slug":"accessing-multidimensional-scientific-data-using-python","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/accessing-multidimensional-scientific-data-using-python","title":{"rendered":"Accessing Multidimensional Scientific Data using Python"},"author":5141,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,22771],"tags":[26701,22821,24341,23391,26711],"industry":[],"product":[36561],"class_list":["post-67861","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-natural-resources","tag-climate","tag-oceans-and-maritime","tag-python","tag-spatial-analytics","tag-weather","product-arcgis-pro"],"acf":{"short_description":" \r\n\r\nWith the 10.3 release, a new Python library, netCDF4, began shipping as part of the ArcGIS platform.\u00a0 netCDF4 allows you to easi...","flexible_content":[{"acf_fc_layout":"content","content":"

 <\/p>\n

With the 10.3 release, a new Python library, netCDF4, began shipping as part of the ArcGIS platform.\u00a0 netCDF4 allows you to easily inspect, read, aggregate and write netCDF files.\u00a0 NetCDF (Network Common Data Form) is one of the most important formats for storing and sharing scientific data.<\/p>\n

The ArcGIS platform has had geoprocessing tools which read and write netCDF data since the 9.x release.\u00a0 However, there may be times when you may want to access or create netCDF data directly using Python.\u00a0 There are four ways of interacting with netCDF files in ArcGIS; geoprocessing tools, the NetCDFFileProperties ArcPy class, the new netCDF Python module, and the multidimensional mosaic dataset.\u00a0 Which method you use depends on what you are trying to accomplish.\u00a0 A summary of different ways of interacting with netCDF files appears in the table below.\u00a0 This blog post will focus on the new netCDF4 Python library.<\/p>\n

The netCDF4 library makes it easy for Python developers to read and write netCDF files.\u00a0 For example, this code snippet opens a netCDF file, determines its type, and prints the first data value:<\/p>\n

[code language=”python”]
\n>>>import netCDF4\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 # the module name is case sensitive
\n>>>d = netCDF4.Dataset(r’c:datatemperature.nc’, ‘r’)
\n>>> d.data_model
\n>>>print(d.variables[‘tmin’][0][0][0])\u00a0\u00a0 # tmin[year][lat][lon]<\/p>\n

‘NETCDF3_CLASSIC’
\n-23.2861
\n[\/code]<\/p>\n

NetCDF4 stores the data read from a netCDF file in a numPy array.\u00a0 This means you have access to the powerful slicing syntax of numPy arrays.\u00a0 Slicing allows you to extract part of the data by specifying indices.\u00a0 The variable tmin in the example above has three dimensions; year, latitude and longitude.\u00a0 You can specify an index (or a range of indices) to slice the three dimensional data cube into a smaller cube.\u00a0 This code snippet extracts the first five years of data for the variable tmin and prints summary statistics:<\/p>\n

[code language=”python”]
\n>>>A = d.variables[‘tmin’][0:5][:][:]
\n>>>print(“Minimum = %.2f, Maximum = %.2f, Mean = %.2f” % (A.min(), A.max(), A.mean()))<\/p>\n

Minimum = -60.77, Maximum = 27.69, Mean = 0.41
\n[\/code]<\/p>\n

Here are some potential uses of the netCDF module:<\/p>\n