Calculations
Net transmission window
The net transmission window of two filters is approximated by
$$T_{final} = T_{1} * T_{2}$$
Optical density
$$OD = -log10(T)$$
Total optical density
$$OD_{total} = OD_{1} + OD_{2}$$
Please consider using own calculations for more precise values.
How to provide input data?
CASE 1
The function expects that all filter values are either of type matrix
or data.frame
with two columns. The first columns contains the wavelength, the second the relative transmission
(but not in percentage, i.e. the maximum transmission can be only become 1).
In this case only the transmission window is show as provided. Changes in filter thickness and
reflection factor are not considered.
CASE 2
The filter data itself are provided as list element containing a matrix
or
data.frame
and additional information on the thickness of the filter, e.g.,
list(filter1 = list(filter_matrix, d = 2))
.
The given filter data are always considered as standard input and the filter thickness value
is taken into account by
$$Transmission = Transmission^(d)$$
with d given in the same dimension as the original filter data.
CASE 3
Same as CASE 2 but additionally a reflection factor P is provided, e.g.,
list(filter1 = list(filter_matrix, d = 2, P = 0.9))
.
The final transmission becomes:
$$Transmission = Transmission^(d) * P$$
Advanced plotting parameters
The following further non-common plotting parameters can be passed to the function:
Argument | Datatype | Description |
legend | logical | enable/disable legend |
legend.pos | character | change legend position (graphics::legend) |
legend.text | character | same as the argument legend in (graphics::legend) |
net_transmission.col | col | colour of net transmission window polygon |
net_transmission.col_lines | col | colour of net transmission window polygon lines |
net_transmission.density | numeric | specify line density in the transmission polygon |
grid | list | full list of arguments that can be passed to the function graphics::grid |
For further modifications standard additional R plot functions are recommend, e.g., the legend
can be fully customised by disabling the standard legend and use the function graphics::legend
instead.