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2.0 KiB
2.0 KiB
Sensospot Data Parser
Parsing the numerical output from Sensovation Sensospot image analysis.
Example:
import sensospot_data
# read the raw data of a folder
raw_data = sensospot_data.parse_folder(<path to results directory>)
sorted(raw_data.columns) == [
'Bkg.Area', 'Bkg.Mean', 'Bkg.Median', 'Bkg.StdDev', 'Bkg.Sum',
'Exposure.Id',
'Parameters.Channel', 'Parameters.Time',
'Pos.Id', 'Pos.Nom.X', 'Pos.Nom.Y', 'Pos.X', 'Pos.Y',
'Spot.Area', 'Spot.Diameter', 'Spot.Found', 'Spot.Mean', 'Spot.Median', 'Spot.Saturation', 'Spot.StdDev', 'Spot.Sum',
'Well.Column', 'Well.Name', 'Well.Row'
]
Avaliable functions:
from .parser import parse_file, parse_folder # noqa: F401
- parse_folder(path_to_folder) Searches the folder for parsable .csv files, parses them into one big pandas data frame and will add additional meta data from parameters folder, if it is present.
- parse_file(path_to_csv_file)
Parses the csv file into a pandas data frame and will add additional some
meta data from the file name. Is internally also used by
parse_folder()
CLI
For the (propably) most important function, there is even a cli command
Usage: parse_sensospot_data [OPTIONS] SOURCE
Arguments:
SOURCE: Folder with Sensospot measurement
Options:
-o, --outfile TEXT Output file name, use a dash '-' for stdout, default:
'collected_data.csv'
-q, --quiet Ignore Sanity Check
-r, --recurse Recurse into folders one level down
--help Show this message and exit.
Development
To install the development version of Sensovation Data Parser:
git clone https://git.cpi.imtek.uni-freiburg.de/holgi/sensospot_data.git
# create a virtual environment and install all required dev dependencies
cd sensospot_data
make devenv
To run the tests, use make tests
(failing on first error) or make coverage
for a complete report.