netCDF4-enhancement 0.1.1

Extends the default NetCDF4 driver by providing helpful functionality like reading and writing to variable in some chunks or dealing with variables regardless of its dimension order. Principally directly extends netCDF4 Dataset class with new functionality. Covers most of the functionality that is often the only reason why developers choose to use stogy libraries like xarray.

Enhancement of the netCDF4 Python driver

Author: David Salac http://www.github.com/david-salac

Extends the default NetCDF4 driver (package netCDF4) by providing helpful functionality like reading and writing to variable in some chunks (previously pre-stored in memory - Dask like style of chunking) or dealing with variables regardless of its dimension order. Principally directly extends netCDF4 Dataset class with new functionality. Covers most of the functionality that is often the only reason why developers choose to use stogy libraries like xarray or Dask.

Purpose of the streamer (use cases)

The typical task that occurs during data processing is to swap computed data to the disk (due to shortage of memory). This can be done by using Dask and similar tools that are notoriously known for the inefficiency (the developer has to struggle with the finding of the proper chunk size etc.). Another way is to stream directly to the NetCDF4 file. Unfortunately, default NetCDF4 driver available in Python does not support any simple way to do so (and neither does GDAL or xarray).

The same rationale is valid also for the reading of the data from the large NetCDF4 file where the size of the variable can exceed the size of the available memory.

The presented utility offers the simplest way how to treat this issue. It allows the developer directly stream to the desired variable inside the NetCDF4 file (by selected dimension). It also allows to read (yield) from the NetCDF4 variable the chunks of data. The main class simply extends (inherits) from the netCDF4.Dataset class and add the possibility to create a variable for streaming using method createStreamerVariable.

Another quite typical situation occurs when a user does not exactly know the order of dimensions in the variable before you write into it (or read from it). Or similarly, when the application is in development and order can be changed at some point. This issue is efficiently solved in xarray library. But installing xarray involves many drawbacks like having a massive library in a system (that is not useful otherwise) or risking memory leaks (often related even to the correct usage of xarray). This library directly extends the native netCDF4 drive. Accordingly, it is minimalistic and solves exactly this typical problem.

How to use the script for streaming to the variable?

Suppose that user wants to create a variable called 'var' with dimensions called (d1, d2, d3) with sizes (500, 20, 600) and swapped (stream) to the variable using the dimension 'd2' as a pivotal one.

Following example shows the way how to deal with this issue. There are two options what to stream: stream line by line (entity by entity) or stream the whole blob of data.

Once the job is finished, variable must be closed using the flush method.

from netCDF4_streamer import NetCDF4Streamer

# Create a file handler:
fh = NetCDF4Streamer("PATH_TO_NC.nc", "w")
# Define dimensions with names: ("d1", "d2", "d3"), sizes (500, 20, 600)
fh.createDimension("d1", 500)
fh.createDimension("d2", 20)
fh.createDimension("d3", 600)

# Suppose that user wants to have dimensions (d1, d2, d3) and stream
# to the variable using the dimension "d2" and the in-memory "swap" (chunk)
# of size 3 MB.
# Create a streamed variable called 'var' (following expected API):
variable = fh.createStreamerVariable("var", "f8", ("d1", "d2", "d3")),
                                     chunk_dimension="d2",
                                     chunk_size_mb=3)

# Writing the data to the variable:

# A) Write the single value (single entity):
data_set = np.random.random((500, 600))
variable.streamNumpyData(data_set, single_entity=True)
variable.flush()  # MUST BE CALLED EXPLICITELY WHEN FINISHED

# B) Writing the chunk of values:
data_set = np.random.random((500, 19, 600))
variable.streamNumpyData(data_set, single_entity=False)
variable.flush()  # MUST BE CALLED EXPLICITELY WHEN FINISHED

# ...

# Best practice is to close file at the end (not required)
fh.close()

How to use the script for reading in chunks from the variable?

The purpose of this part is to read from the NetCDF4 variable in some chunks of the defined size. Logic is the opposite of the streaming logic described above.

Suppose that we have a variable described above and we want to read chunks by slicing the dimension d2:

from netCDF4_streamer import NetCDF4Streamer

fh = NetCDF4Streamer("PATH_TO_NC.nc", "r")

variable = fh.openStreamerVariable("var", 
                                   chunk_dimension="d2",
                                   chunk_size_mb=3)

# ...
# For reading the file "line by line"
for line in variable.yieldNumpyData(True):
    # Work with the line

# ...
# For reading the file by blobs (chunks)
for blob in variable.yieldNumpyData(False):
    # Work with the blob
# ...

Accessing the NetCDF4 variable inside streamed variable

It can be helpful to access the netCDF4.Variable object inside the streamed variable (e. g. for defining of some attributes). To do so follow the logic (example define the attribute description):

# ...
wrapped_variable = fh.createStreamerVariable("var", "f8", ("d1", "d2", "d3")),
                                             chunk_dimension="d2",
                                             chunk_size_mb=3)

# ...
# Access the netCDF4.Variable object:
netCDF4_variable_object: netCDF4.Variable = wraped_variable.netcdf4_variable
# Define the description attribute (in writing/append mode only):
netCDF4_variable_object.description = "Streamed variable!"
# ...

Reading / writing data in the not-matching dimension order

If the dimension order of data set to be stream to the file or the expected dimension order for file to be read from the file do not match to the actual order of dimension in the file, you can use the axes_order property to treat this issue.

For example:

variable.axes_order = ("d2", "d3", "d1")

After calling this command, you can stream to the variable using proposed dimension order or read in this order.

If you want to reset to the default dimension order, just write:

variable.axes_order = None

For reading/writing data afterwards, you can use standard approach:

# Using slices
variable[:, :, :, :] = new_value_to_be_written  # <- for writing
value_inside = variable[:, :, :, :]  # <- for reading

# Slices and concrete indices:
variable[:, :, 34, :] = new_value_to_be_written  # <- for writing
value_inside = variable[:, :, 34, :]  # <- for reading

This overloaded operator for accessing the values does always return either numpy.ndarray object or the float value (never masked array).

API documentation

Documentation of the basic functionality.

NetCDF4Streamer class

Class NetCDF4Streamer is the child of netCDF4.Dataset class. Accordingly inherits all the methods.

There are following new methods:

• createStreamerVariable: has the same interface as netCDF4.Dataset.createVariable. Creates new variable inside NetCDF4 file. Returns NetCDF4StreamerVariable object. Method adds two new optional (keyword type) parameters:

  • chunk_dimension (Optional[str]): define dimension by that data are streamed to the variable, if None the first dimension is chosen. Default value is None. It is pertinent only if you want to use streaming to the variable.
  • chunk_size_mb (float): Size of the memory chunk to that data are streamed first (before they are streamed to the variable on the disk). Unit is megabyte. Default size is 512 MB. It is pertinent only if you want to use streaming to the variable.

• openStreamerVariable: has the same logic as netCDF4.Dataset['variable_name']'. Opens an existing variable inside NetCDF4 file. Returns NetCDF4StreamerVariable object. Method adds two new optional (keyword type) parameters:

  • chunk_dimension (Optional[str]): define dimension by that data are streamed to the variable, if None the first dimension is chosen. Default value is None. It is pertinent only if you want to use streaming to the variable.
  • chunk_size_mb (float): Size of the memory chunk to that data are streamed first (before they are streamed to the variable on the disk). Unit is megabyte. Default size is 512 MB. It is pertinent only if you want to use streaming to the variable.

NetCDF4StreamerVariable class

Represent the crucial class in the logic.

There are following methods and properties:

• streamNumpyData: Method that stream the data to the NetCDF4 variable. With parameters:
  • data (np.ndarray): The line or the blob of the data to be streamed to the variable.
  • single_entity (bool): If True data are streamed as a single "line" (one entity), if False the whole blob is streamed. Number of dimensions is of -1 smaller for True value.
• yieldNumpyData: Method that yields the data from the NetCDF4 variable. With parameters:
  • single_entity (bool): If True data are streamed as a single "line" (one entity), if False the whole blob is streamed. Number of dimensions is of -1 smaller for True value.
• flush: Important method that store temporal results of streaming into the physical variable.
• axes_order: Property that gets or sets dimension order that is required. Default order can be re-seted using obj.axes_order = None. Returns/accepts the tuple of dimension names (string).
• dimensions: Return the physical dimension order (tuple of dimension names as string values).
• __getitem__: Operator for getting value in the position reflecting order of dimensions defined by the axes_order property (similar logic to the normal netCDF4 variable). Always return either float value or np.ndarray value (never the mask array).
• __setitem__: Operator for setting value in the position reflecting order of dimensions defined by the axes_order property (similar logic to the normal netCDF4 variable).
• netcdf4_variable: Return the variable as a netCDF4.Variable object.

Software User Manual (SUM), how to use it?

Installation

To install the most actual package, use the command:

git clone https://github.com/david-salac/NetCDF4-variable-streamer
cd NetCDF4-variable-streamer/
python setup.py install

or simply install using PIP:

pip install netCDF4-enhancement

Running of the unit-tests

For running package unit-tests, use command:

python setup.py test

In order to run package unit-tests you need to clone package first.