Modelling quantities and qualities of faecal sludge for improved management solutions
This is the source code repository for the analyses reported in the article Modelling quantities and qualities (Q&Q) of faecal sludge in Hanoi and Kampala for improved management solutions
The repository is maintained by Juan Pablo Carbajal firstname.lastname@example.org. Please do not hesitate to contact us with bug reports (you can also use the issue tracker), questions, and comments.
The repository has the following structure:
. ├── data # Data files ├── doc # Some documents used during the development of the project ├── mfiles # Gnu Octave functions and script to reproduce all results └── README.md # This file
data folder includes a filtered version of the whole raw data that can be
obtained from the data archive.
doc folder has working documents that we used during the development of
the project, as well as the Latex sources of the supplementary information
published along the article.
mfiles folder contains all the scripts needed to reproduce the results
reported in the article.
In this folder, files with prefix
s_ are scripts that will generate reports
and figures of the results.
All other files are functions used to make the scripts simpler and easier to read.
In Windows, the statistics package should be included in your GNU Octave distribution. The GPML package for windows can be installed from this link
In Linux you can install the statistics package running the following command in the octave prompt (the io package is a dependency)
pkg install -forge io statistics
The gpml package can be installed with the following command
pkg install https://bitbucket.org/KaKiLa/gpml/downloads/gpml-4.2.0.tar.gz
which downloads the mirror of the official package
Figures are generate by scripts (m-files with prefix
s_). Here is the list:
- Figure 1 and 2:
- Figure 3 and 4:
- Figure 5 and 6:
- Figure 7 and 8:
- Figure 9 and 10:
s_inflow_constrained_correction.m reports results on the effect of
the constrained amplitude of the covariance function.
The covariance function is used as a correction term, hence its amplitude is
constrained to be smaller then the mean function for each input.
You can generate the report with more details running
This will generate an
html folder with the report in html format, which you
can read in your web browser.
You can do the same for all scripts mentioned here.