This executable document (it is called a Jupyter notebook) will guide you through the conversion of a ns-ALEX data file from SPC/SET to Photon-HDF5 format.
If you are reading this notebook online, please refer to this quick-start guide for instructions on how to install the required software and run the notebook on your machine.
If it's the first time you are using a Jupyter notebook, please click on Help -> User Interface Tour for a quick tour of the interface.
In this notebook there are "text cells", such as this paragraph, and "code cells", containing the code to be executed. To execute a code cell, select it and press SHIFT+ENTER. To edit a code cell it must be selected and with a green frame around it.
Your can run this notebook using example data files available on figshare. If you use these example files, please unzip them and put them in a folder named "data" (lower case) inside the folder containing this notebook.
Alternatively, you can use your own SPC/SET files. In this case, you need to paste
the full path of your SPC file in the following cell, instead of the path between
single quotes '
.
filename = r'data/dsdna_d7_d17_50_50_1.spc'
If you also have a SET file it is expected to be in the same folder and have the same name as the SPC file, with extension ".set".
The next cell will check whether the filename
location is correct:
import os
try:
with open(filename): pass
print('Data file found, you can proceed.')
except IOError:
print('ATTENTION: Data file not found, please check the filename.\n'
' (current value "%s")' % filename)
In case of file not found, please double check that have you put the example
data files in the "data" folder, or that the path you have pasted in filename
is correct. Please re-execute the last two cells until the file is found.
Here, we specify the additional metadata that will be stored in the Photon-HDF5 file. If you are using the example file you don't need to edit any of these. If are using your own file, please modify these description accordingly.
author = 'Eitan Lerner'
author_affiliation = 'UCLA'
creator = 'Antonino Ingargiola'
creator_affiliation = 'UCLA'
license = 'Public Domain'
description = 'A demonstrative smFRET-nsALEX measurement.'
sample_name = '50-50 mixture of two FRET samples'
dye_names = 'ATTO550, ATTO647N'
buffer_name = 'TE50'
Please edit the previous cells and execute them (SHIFT+ENTER) to make sure there are no errors. Then proceed to the next section.
Before loading the data we need to load a few library functions:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import phconvert as phc
print('phconvert version: ' + phc.__version__)
Next, we can load the input file and assign the measurement parameters (measurement_specs), necessary to create a complete Photon-HDF5 file.
If using your own file, please review all the parameters in the next cell.
d, meta = phc.loader.nsalex_bh(filename,
donor = 4,
acceptor = 6,
laser_repetition_rate = 40e6,
tcspc_range = 60e-9,
timestamps_unit = 60e-9,
alex_period_donor = (270, 1500),
alex_period_acceptor = (1800, 3300),
excitation_wavelengths = (532e-9, 635e-9),
detection_wavelengths = (580e-9, 680e-9),
time_reversed = False,
allow_missing_set = False)
The next cell plots a nanotimes
histogram for the donor and acceptor channel separately.
The shaded areas marks the donor (green) and acceptor (red) excitation periods.
If the histogram looks wrong in some aspects (no photons, wrong detectors assignment, wrong period selection) please go back to the previous cell and tweak the relevant parameters until the histogram looks correct.
fig, ax = plt.subplots(figsize=(10, 4))
phc.plotter.alternation_hist(d, ax=ax)
You may also find useful to see how many different detectors are present and their number of photons. This information is shown in the next cell.
detectors = d['photon_data']['detectors']
print("Detector Counts")
print("-------- --------")
for det, count in zip(*np.unique(detectors, return_counts=True)):
print("%8d %8d" % (det, count))
Once you finished editing the the previous sections you can proceed with the actual conversion. It is suggested to execute the notebook in one step by clicking on the menu Cells -> Run All.
After that, you should find a new .hdf5
file in the same folder of the input
file. You can check it's content by using HDFView.
The cells below contain the code to convert the input file to Photon-HDF5.
d['description'] = description
d['sample'] = dict(
sample_name=sample_name,
dye_names=dye_names,
buffer_name=buffer_name,
num_dyes = len(dye_names.split(',')))
d['identity'] = dict(
author=author,
author_affiliation=author_affiliation,
creator=creator,
creator_affiliation=creator_affiliation)
Before writing to disk, we assure the file structure follows the Photon-HDF5 format:
phc.hdf5.assert_valid_photon_hdf5(d) # Throws an error if not valid!
This command saves the new file to disk.
phc.hdf5.save_photon_hdf5(d, close=False, overwrite=True)
#d['_data_file'].close()
/user/bh_params
¶Here we save a custom (user) group where we put all the metadata found in the input .SET file. The important metadata from the .SET file is already saved in the standard Photon-HDF5 fields.
Here we save the full content of the file in order to make sure that no information is lost during the conversion.
h5file = d['_data_file']
h5file.create_group('/', 'user')
bh_group = h5file.create_group('/user', 'becker_hickl')
phc.hdf5.dict_to_group(bh_group, meta)
#h5file.close()
Finally we print the file content to see what's inside the newly-created Photon-HDF5. Here we print the content of the roor node:
phc.hdf5.print_children(h5file.root)
And here we retrieve some information from the user group:
phc.hdf5.dict_from_group(h5file.root.user.becker_hickl.identification)
#phc.hdf5.dict_from_group(h5file.root.user.becker_hickl.sys_params)
h5file.close()
Finally we try to reload the file to check that there are no errors.
from pprint import pprint
filename = d['_data_file'].filename
h5data = phc.hdf5.load_photon_hdf5(filename)
phc.hdf5.dict_from_group(h5data.identity)
phc.hdf5.dict_from_group(h5data.setup)
pprint(phc.hdf5.dict_from_group(h5data.photon_data))
If the next cell output shows "OK" then the execution is terminated.
print('OK')