Development now here: https://gitlab.e4.physik.tu-dortmund.de/fmentzel/gcpy
University project: glow curve analysis software for exponential heating. For questions and support please contact the maintainer Florian Mentzel ([email protected]).
Attention: Tested with Ubuntu 16.04 and CentOS7, might fail on other platforms, especially fails probably with Windows.
You can download this repository and install it using
git clone https://github.com/flome/gcpy
pip install gcpy
If you use a python installation that is hosted by an administrator and not by you, you can install the package locally just for our user account:
pip install --user gcpy
You can use gcpy in your project by importing it with
import gcpy
If you want to edit the gcpy source code, install it with
pip install -e .
which will keep the files locally and changes on the source will be active immediately. If you want to contribute, please contact one of the current contributors.
Simple examples to get started are provided in the examples folder.
To load data from a directory, you can call
path2data = PUT_YOUR_DIRECTORY_PATH_HERE
measurement_db = gcpy.gcdb.readDir(path2data)
To append new data to an existing data base, you can pass the object in the form
measurement_db = gcpy.gcdb.readDir('ANOTHER_DIRECTORY_YOU_WANT_TO_IMPORT', db = measurement_db)
You can access a list of all documents (measurements) with
list_of_measurements = measurement_db.all()
which you can use to create e.g. a pandas DataFrame
import pandas as pd
measurements_dataframe = pd.DataFrame(list_of_all_meas)
to perform further analysis tasks.
There are predefined functions to compute glow curve parameters like the netto photon counts
measurement_db.update(gcpy.gcana.calcGCparams('time_sec', 'PhCount'))
to perform the temperature reconstruction (please note that you need to specify the number of peaks here!)
measurement_db.update(gcpy.gcana.calcTreco('time_sec', 'PhCount', peaks=3))
and the glow curve deconvolution (note the difference in the x-Axis!)
measurement_db.update(gcpy.gcana.gcFit('Treco_T', 'PhCount'))
They update the documents within the database automatically.
You can create and save glow curve plots:
import matplotlib.pyplot as plt
for measurement in measurement_db:
plt.plot(measurement['time_sec'], measurement['PhCount'], label='Measurement')
plt.xlabel('$t$ in s')
plt.ylabel('$N$ in $\\mathrm{\\frac{1}{5\\,ms}}$)
plt.legend(loc='upper right')
plt.savefig('gc_plot_file_%s.pdf'%measurement['filename'])
# this saves each plot with its individual filename
plt.clf()
In version 0.1.1, TXT files from the Prototype II can be converted to json. The Prototype II is data in the format
KEY VALUE
KEY VALUE
[BEGIN TABLENAME]
table content
[END TABLENAME]
or
KEY=VALUE
KEY=VALUE
[BEGIN TABLENAME]
table content
[END TABLENAME]
If a line does not contain a key-value pair but only one value, it will be named UNKNOWN_i where i is a increasing index for values without label. You may replace them later on in your analysis.
To import a folder containing measurements in legacy format, you can call
measurement_db = gcpy.gcdb.newDB()
measurement_db.insert_multiple(gcpy.gcdb.prototypeIItoJson(dirPath))
From here you can continue as described above.