Utility to convert Excel tables to a sqlite database and access the data
Project description
SASA Database
I don't think this is usable by anyone lese but it's a dependency to sasa_stacker
and I wanted to package it separately. An explanation to the whole project can be found here.
Usage
exl_to_sql.py -h
:
exl_to_sql.py [-h] [-n SHEET_NUMBER] [-v] [-s] exl db
positional arguments:
exl path to excel-file
db path to sqlite3-db
optional arguments:
-h, --help show this help message and exit
-n SHEET_NUMBER, --sheet-number SHEET_NUMBER
which excel-sheet to convert
-v, --verbose verbose output
-s, --skip-existing skipping rows already contained in the db
Writes the excel file exl
into the sqlite database db
. Every row in the Excel sheet represents one simulation run of metasurfaces. The problem is with our current setup they are saved as one big .mat
file but the sasa_stacker
needs to access them and their parameters individually. This script assigns each single metasurface an address and saves its parameters in the db separately. Examples for the formating of the excel sheet can be found in data/NN_smats.xlsx
.
Crawler
The Crawler class allows access to the db and loads the simulation data. The main functions are:
find_smat
Crawler.find_smat(name, adress=None)
Loads the simulation data to name
. If an adress is provided it only loads this single S-matrix.
Arguments
- name: string, name of the simulation in the database
- adress: list, for example
[1,4,5,3]
the adress can also be found in the database
find_smat_by_id
Crawler.find_smat_by_id(id)
Same as above but takes the simulation id
Arguments
- id: int, simulation id found in the database
extract_params
Crawler.extract_params(id)
Queries meta_materials.db for all the parameters to the given ID.
Arguments
- id: int, simulation id found in the database
Returns
- param_dict: dict, contains the combined data from the simulations and geometry tables with coresponding names
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