Titration datasets¶
Import with Calkulate¶
If your titration dataset contains all the necessary information described in Input data formatting in correctly labelled columns, then using Calkulate is straightforward:
import calkulate as calk
# If your titration table is in a file, just provide the path and name:
tdata = calk.Dataset("path/to/titration_table.csv")
# If you have already imported your titration table as a pandas DataFrame
# or anything that can be converted into one, just provide the table itself:
tdata = calk.Dataset(titration_table)
# The only Calkulate command you may ever need:
tdata.calibrate_and_solve()
The result tdata
consists of three components:
- The titration table is now stored as a standard pandas DataFrame in
tdata.table
. - All of the individual titration data files have also been imported and are stored in
tdata.titrations
(see Individual titrations for more details). - A second DataFrame containing metadata about each analysis batch is stored in
tdata.batches
.
Read on for more detail on the calibration and alkalinity-solving step.
Calibrate the titrant molinities¶
Assuming that the titration table contains some rows with a certified alkalinity value (reference material) and others without (samples), we can now
- Calibrate the titrant molinity for each indiviual reference,
- Calculate the average titrant molinity across each analysis batch, and
- Add the appropriate batch-average titrant molinities into a
titrant_molinity
column in the titration table
with the command:
tdata.calibrate()
Solve for alkalinity¶
To then solve every titration in the table for its total alkalinity using these calibrated titrant molinities and store the result in a alkalinity
column in the titration table:
tdata.solve()