Sfeprapy

- Updated code relating simulation output result \*.p and \*res.csv files. This is to fix an issue which output fires do not align with input / output index numbering. The new \*.p and \*res.csv files are sorted by time equivalence. The new output files are significantly larger than previous versions due to more variables are being passed in and out from the grouped_a_b calculation function `sfeprapy.time_equivalence_core.grouped_a_b()`.
- Fire duration `fire_duration` is checked and reassigned if necessary so that the slowest travelling fire is able to travel the entire room `room_depth`. `fire_duration` defined in the input file will be the minimum fire duration.
- Verification procedures are added for part of the project, including parametric fire testing, travelling fire testing and Eurocode protected steel heat transfer.

- Graphical folder select dialog is available;
- Fixed an issue associated with `sfeprapy.time_equivalence.main_args()` where would not ask for new input folder directory when main_args more than once without re-import the module;
- Fixed window opening fraction factor distribution. Previously the mean $\mu$ and standard deviation $\sigma$ are adopted based on $x$, however, the `scipy.stats.lognorm` module takes $\mu$ and $\sigma$ based on $ln(x)$. This has been corrected;

- Renamed the packaged from `sfepy` to `sfeprapy` (Structural Fire Engineering Probabilistic Risk Assessment Python);
- Github repository created;
- Updated progress bar appearance in `sfeprapy.time_equivalence.main_args()`;
- Implemented new window opening fraction distribution `window_open_fraction`, linear distribution is now replaced by inverse truncated log normal distribution;
- Updated plot appearance; and
- Project now can be installed through `pip install sfeprapy`.

- Implemented Latin hypercube sampling function, `pyDOE` external library is no longer required;
- Boundary for `q_fd`, defined as `q_fd_ubound` and `q_fd_lbound` (upper and lower limit);
- Now output plot for peak steel temperature according to input 'protection_thickness';
- Inputs arguments are packed in a pandas `DataFrame` object instead of a list;
- Automatically generate fires inline with selected percentile `select_fires_teq` ±tolerance `select_fires_teq_tol` and save as .png and .csv.