rstoolbox.io.read_SPR(filename)¶Reads Surface Plasmon Resonance data.
The input data should be a comma-separated file with two types of header, one for raw data:
Run 1; Ch 1; Cy 6; RefSub; name; conc=0.0585_X ,...
And one for fitted data:
Run 1; Ch 1; Cy 16; RefSub; 1kx8_d02; conc=60 fitted curve_X ,...
each conc condition will have a _X and _Y value.
This seems to be a pretty standard format as output from the machine.
Tip
The provided DataFrame will actually be casted to SPRFrame.
This class has no other purpose that identification to help other library functions
and, thus, works as a normal DataFrame.
| Parameters: | filename (str) – Input file. |
|---|---|
| Returns: | DataFrame with MultiIndex. |
See also
Example
In [1]: from rstoolbox.io import read_SPR
...: import pandas as pd
...: pd.set_option('display.width', 1000)
...: pd.set_option('display.max_columns', 500)
...: df = read_SPR("../rstoolbox/tests/data/spr_data.csv.gz")
...: df.head(2)
...:
Out[1]:
data raw fitted
concentration 0.0585 0.117 0.234375 0.46875 0.9375 1.875 3.75 7.5 15 30 60 3.75 7.5 15 30 60
axis X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y X Y
0 -37.400002 -0.011209 -37.300003 0.284098 -37.300003 0.190698 -37.200001 0.159199 -37.100002 0.3337 -37.000000 0.2639 -37.000000 0.2056 -36.900002 0.176199 -36.900002 0.330999 -36.900002 0.119499 -36.900002 0.218399 -37.000000 0.0 -36.900002 0.0 -36.900002 0.0 -36.900002 0.0 -36.900002 0.0
1 -37.299999 -0.117500 -37.200001 0.073299 -37.200001 -0.034101 -37.099998 0.013600 -37.000000 -0.0063 -36.899998 0.0813 -36.899998 0.1044 -36.799999 0.013100 -36.799999 0.090100 -36.799999 -0.051800 -36.799999 0.146300 -36.950001 0.0 -36.850002 0.0 -36.850002 0.0 -36.850002 0.0 -36.850002 0.0