A SEJITS Specializer for DC Removal
DC Removal is the process of making a list of values zero-mean. For example, if
our input dataset was an array like [1, 2, 3, 4, 5], which has an average of
3, after performing DC Removal, our output array would look like [-2, -1, 0, 1, 2],
where the mean, 3 is subtracted out from each element of the original dataset. Notice
how the output dataset has a mean of 0, or in other words, the dataset is zero-mean.
Prerequisites
In order to use this specializer, certain prerequisite libraries must be installed. Here
we use pip for our installation process. In your shell, outside your project directory,
run the following commands:
$ pip install ctree # installs the ctree library
$ git clone https://github.com/ucb-sejits/cstructures.git # clones the cstructures library
$ cd cstructures
$ pip install -e . # installs the development version of cstructures
$ cd ..
$ pip install scipy # install scipy
$ pip install nose # install nose
$ pip install mako # install mako
Library Installation
Once we have installed the prerequiste libraries, we need to install the development version of this specializer. You can do this with the following commands from the same directory as above.
$ git clone https://github.com/ucb-sejits/dc-rem-specializer.git # clones the DC Removal Specializer
$ cd dc-rem-specializer
$ pip install -e . # installs the DC Removal Specializer
$ cd ..
Congratulations, installation is complete!
To check for successful installation, run the following:
$ python -m nose # running nose tests
All tests should pass.
To use the specializer we first do the following importations.
from dc_removal.dc_rem_specializer import dcRemoval
from cstructures.array import Array
Next, we construct our input dataset using cstructures.array (of type Array), which is a subclass of numpy.array. Let's call this input data set input_data. Now, we can just make the following call:
output_data = dcRemoval(input_data, segmentation_length, stride_height)