Handlers are special Python modules that convert between a given data format and the data model used by Pydap (defined in the
pydap.model module). They are necessary in order to Pydap be able to actually serve a dataset. There are handlers for NetCDF, HDF 4 & 5, Matlab, relational databases, Grib 1 & 2, CSV, Seabird CTD files, and a few more.
Installing data handlers¶
$ pip install Pydap[handlers.netcdf]
This will take care of the necessary dependencies. You don’t even need to have to NetCDF libraries installed, since the handler will use a pure Python NetCDF library from scipy.io.netcdf.
The NetCDF handler uses a buffered reader that access the data in contiguous blocks from disk, avoiding reading everything into memory at once. You can configure the size of the buffer by specifying a key in the
[app:main] use = egg:pydap#server root = %(here)s/data templates = %(here)s/templates x-wsgiorg.throw_errors = 0 pydap.handlers.netcdf.buf_size = 10000
In this example, the handler will read 10 thousand values at a time, converting the data and sending to the client before reading more blocks.
pydap.handlers.nca is a simple handler for NetCDF aggregation (hence the name). The configuration is extremely simple. As an example, to aggregate model output in different files (say,
output2.nc, etc.) along a new axis “ensemble” just create an INI file with the extension
; output.nca [dataset] match = /path/to/output*.nc axis = ensemble ; below optional metadata: history = Test for NetCDF aggregator [ensemble] values = 1, 2, ... long_name = Ensemble members
This will assign the values 1, 2, and so on to each ensemble member. The new, aggregated dataset, will be accessed at the location of the INI file:
Another example: suppose we have monthly data in files
data12.nc, and we want to aggregate along the time axis:
[dataset] match = /path/to/data*.nc axis = TIME # existing axis
The handler only works with NetCDF files for now, but in the future it should be changed to work with any other Pydap-supported data format. As all handlers, it can be installed using pip:
$ pip install pydap.handlers.nca
This is a handler that uses the
cdms2.open function from CDAT/CdatLite to read files in any of the self-describing formats netCDF, HDF, GrADS/GRIB (GRIB with a GrADS control file), or PCMDI DRS. It can be installed using pip:
$ pip install pydap.handlers.cdms
The handler will automatically install
CdatLite, which requires the NetCDF libraries to be installed on the system.
The SQL handler reads data from a relation database, as the name suggests. It works by reading a file with the extension
.sql, defining the connection to the database and other metadata using either YAML or INI syntax. Below is an example that reads data from a SQLite database:
# please read http://groups.google.com/group/pydap/browse_thread/thread/c7f5c569d661f7f9 before # setting your password on the DSN database: dsn: 'sqlite://simple.db' table: test dataset: NC_GLOBAL: history: Created by the Pydap SQL handler dataType: Station Conventions: GrADS contact: email@example.com name: test_dataset owner: Roberto De Almeida version: 1.0 last_modified: !Query 'SELECT time FROM test ORDER BY time DESC LIMIT 1;' sequence: name: simple items: !Query 'SELECT COUNT(id) FROM test' _id: col: id long_name: sequence id missing_value: -9999 lon: col: lon axis: X grads_dim: x long_name: longitude units: degrees_east missing_value: -9999 type: Float32 global_range: [-180, 180] valid_range: !Query 'SELECT min(lon), max(lon) FROM test' lat: col: lat axis: Y grads_dim: y long_name: latitude units: degrees_north missing_value: -9999 type: Float32 global_range: [-90, 90] valid_range: !Query 'SELECT min(lat), max(lat) FROM test' time: col: time axis: T grads_dim: t long_name: time missing_value: -9999 type: String depth: axis: Z col: depth long_name: depth missing_value: -9999 type: Float32 units: m temp: col: temp long_name: temperature missing_value: -9999 type: Float32 units: degc
The handler works with SQLite, MySQL, PostgreSQL, Oracle, MSSQL and ODBC databases. To install the handler use pip; you should also install the dependencies according to the database used:
$ pip install pydap.handlers.sql $ pip install "pydap.handlers.sql[oracle]" $ pip install "pydap.handlers.sql[postgresql]" $ pip install "pydap.handlers.sql[mysql]" $ pip install "pydap.handlers.sql[mssql]"
This is a simple handler intended to serve remote datasets locally. For example, suppose you want to serve this dataset on your Pydap server. The URL of the dataset is:
So we create an INI file called, say,
[dataset] url = http://test.opendap.org:8080/dods/dts/D1 pass = dds, das, dods
The file specifies that requests for the DDS, DAS and DODS responses should be passed directly to the server (so that the data is downloaded directly from the remote server). Other requests, like for the HTML form or a WMS image are built by Pydap; in this case Pydap acts as an Opendap client, connecting to the remote server and downloading data to fulfill the request.
This is a handler for files with comma separated values. The first column should contain the variable names, and subsequent lines the data. Metadata is not supported. The handler is used mostly as a reference for building handlers for sequential data. You can install it with:
$ pip install pydap.handlers.csv
A handler for HDF5 files, based on h5py. In order to install it:
$ pip install pydap.handlers.hdf5
This is a handler very similar to the SQL handler. The major difference is that data and metadata are all contained in a single
.db SQLite file. Metadata is stored as JSON in a table called
attributes, while data goes into a table
The handler was created as a way to move sequential data from one server to another. It comes with a script called
freeze which will take an Opendap dataset with sequential data and create a
.db file that can be served using this handler. For example:
$ freeze http://opendap.ccst.inpe.br/Observations/PIRATA/pirata_stations.sql
This will creata file called
pirata_stations.db that can be served using the SQLite handler.