11.1. List of possible input types¶

Each variable to be entered for the use of ADAO can be represented by means of particular “pseudo-types”, which help to logically fill it in and validate it computationaly. These pseudo-types explicitly represent mathematical forms (Pseudo-types of mathematical representation of data) or simple computer forms (Pseudo-types of digital data description), which are detailed here. Notations and common conventions are also used, together with Information on the names required for file entries.

The explicit Checking algorithm “InputValuesTest” test is designed so that the user can specifically check some entries, with the same analysis and criteria as during the usual setup of a study.

11.1.1. Pseudo-types of mathematical representation of data¶

The inputs are described according to the simplest possible logic, in mathematical representation, for algorithms or calculation tools.

Vector: List of real values. This indicates a variable that has to be filled as a vector. It is a simple ordered collection of real numbers. The vectors can be described in row or column version without making any difference. The details of the Requirements for observation or experimental measurements description help to illustrate different possible ways of writing this entry.

VectorSerie: List of list of real values. This indicates a variable that has to be filled in as a list of vectors. It is an ordered collection of vectors. The details of the Requirements for observation or experimental measurements description help to illustrate different possible ways of writing this entry.

Matrix: Matrix. This indicates a variable that has to be filled by a matrix. The matrices are special entries described by the Requirements to describe covariance matrices.

Function: Function. This indicates a variable that must be given as a function in the Python sense.. The functions are special entries described by the Requirements for functions describing an operator.

Dict: Dictionary. This indicates a variable that has to be filled by a Python dictionary {"key":"value...}.

The variables to which these pseudo-types apply can themselves be given using the following computer descriptions.

11.1.2. Pseudo-types of digital data description¶

Three pseudo-types, purely computer-based, are used to specify the way in which input variables are provided.

Script: File name. This indicates a script given as an external file. It can be described by a full absolute path name or only by the file name without path. If the file is given only by a file name without path, and if a study directory is also indicated, the file is searched in the given study directory.

String: String. This indicates a string giving a literal representation of a matrix, a vector or a vector series, such as “1 2 ; 3 4” or “[[1,2],[3,4]]” for a square 2x2 matrix.

DataFile, ColNames, ColMajor

This indicates data given in an external file. It can be described by a full absolute path name or only by the file name without path. If the file is given only by a file name without path, and if a study directory is also indicated, the file is searched in the given study directory. The content of the file can be:

in text format (simple text in a file with a “.txt” or “.dat” extension, text with comma delimiter in a file with a “.csv” extension, text with tabulation delimiter in a file with a “.tsv” extension), named here TXT, CSV, TSV or DAT.
in binary format (single-variable in a Numpy file with a “.npy” extension, multi-variables in a NumpyZ file with a “.npz” extension), named here NPY or NPZ.

By default, the values of a variable has to be ordered in rows to be acquired record line by record line (“ColMajor=False”), but they can also be ordered in lines to be acquired record row by record row (“ColMajor=True”).

Without information or with a void list, all the values of all the variables are used, but one can also select only the ones of the variables that are indicated in the name list “ColNames”. the variable names are always as header of columns.

Example of CSV file for “Observation” variable in “DataFile”

# CSV file with delimiter ";" or ","
# ==================================
# Comment line beginning with #
# The next line is dedicated to variable naming
Alpha1;Observation;Alpha2
0.1234;5.6789;9.0123
1.2345;2.3456;3.
2.3456;3.4567;4.56
3.;4.;5.
4;5;6
5.123;6.789;7.8

Example of TXT file for “Observation” variable in “DataFile”

# Fichier TXT à séparateur espace
# TXT file with space delimiter
# =============================
# Ligne de commentaires quelconques débutant par #
# Ligne suivante réservée au nommage des variables
Alpha1 Observation Alpha2
0.1234 5.6789 9.0123
1.2345 2.3456 3.
2.3456 3.4567 4.56
3.     4.     5.
4      5      6
5.123  6.789  7.8

Warning

It is recommended, before using them as an input of this type, to carefully check text or binary files . Various checks are carried out on loading, but the variety of potential errors is great. In practice, by respecting the requirements for naming variables and comments, text files from programs or spreadsheets are (most of the time) compatible.

11.1.3. Information on the names required for file entries¶

When a command or keyword can be entered using a script file identified by the pseudo-type “Script”, this script must contain a variable or method that has the same name than the variable to be completed. In other words, when importing such a script into a Python command or Python node, it must create a variable of the correct name in the current namespace of the node. For example, a Python script making available the draft variable, named “Background”, must have the following form:

...
...
Background =...
...
...

Its import thus makes it possible to create the variable “Background” in the current namespace. The dots”…” symbolize any code around this particular line beginning.

Similarly, when a particular vector can be filled in using a data file designated by the pseudo-type “DataFile”, the information in the file “DataFile” must be named after the vector to be loaded.