In this notebook we show how to read a ns-ALEX smFRET measurement stored in Photon-HDF5 format using python and a few common scientific libraries (numpy, pytables, matplotlib). Specifically, we show how to load timestamps, detectors and nanotimes arrays and how to plot a TCSPC histogram.
For a µs-ALEX example see Reading µs-ALEX data from Photon-HDF5.
In [1]:
from __future__ import division, print_function # only needed on py2
%matplotlib inline
import numpy as np
import tables
import matplotlib.pyplot as plt
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def print_children(group):
"""Print all the sub-groups in `group` and leaf-nodes children of `group`.
Parameters:
group (pytables group): the group to be printed.
"""
for name, value in group._v_children.items():
if isinstance(value, tables.Group):
content = '(Group)'
else:
content = value.read()
print(name)
print(' Content: %s' % content)
print(' Description: %s\n' % value._v_title.decode())
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filename = '../data/Pre.hdf5'
We can open the file, as a normal HDF5 file
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h5file = tables.open_file(filename)
The object h5file
is a pytables file reference. The root group is accessed with h5file.root
.
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print_children(h5file.root)
We see the typical Photon-HDF5 structure. In particular the field description
provides a short description of the measurement and acquisition_duration
tells that the acquisition lasted 900 seconds.
As an example, let's take a look at the content of the sample
group:
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print_children(h5file.root.sample)
Let's define a shortcut to the photon_data group to save some typing later:
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photon_data = h5file.root.photon_data
First, we make sure the file contains the right type of measurement:
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photon_data.measurement_specs.measurement_type.read().decode()
Out[8]:
Ok, tha's what we espect.
Now we can load all the photon_data
arrays an their specs:
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timestamps = photon_data.timestamps.read()
timestamps_unit = photon_data.timestamps_specs.timestamps_unit.read()
detectors = photon_data.detectors.read()
nanotimes = photon_data.nanotimes.read()
tcspc_num_bins = photon_data.nanotimes_specs.tcspc_num_bins.read()
tcspc_unit = photon_data.nanotimes_specs.tcspc_unit.read()
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print('Number of photons: %d' % timestamps.size)
print('Timestamps unit: %.2e seconds' % timestamps_unit)
print('TCSPC unit: %.2e seconds' % tcspc_unit)
print('TCSPC number of bins: %d' % tcspc_num_bins)
print('Detectors: %s' % np.unique(detectors))
We may want to check the excitation wavelengths used in the measurement. This information is found in the setup group:
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h5file.root.setup.excitation_wavelengths.read()
Out[11]:
Now, let's load the definitions of donor/acceptor channel and excitation periods:
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donor_ch = photon_data.measurement_specs.detectors_specs.spectral_ch1.read()
acceptor_ch = photon_data.measurement_specs.detectors_specs.spectral_ch2.read()
print('Donor CH: %d Acceptor CH: %d' % (donor_ch, acceptor_ch))
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laser_rep_rate = photon_data.measurement_specs.laser_repetition_rate.read()
donor_period = photon_data.measurement_specs.alex_excitation_period1.read()
acceptor_period = photon_data.measurement_specs.alex_excitation_period2.read()
print('Laser repetion rate: %5.1f MHz \nDonor period: %s \nAcceptor period: %s' % \
(laser_rep_rate*1e-6, donor_period, acceptor_period))
These numbers define the donor and acceptor excitation periods as shown below:
$$150 < \widetilde{t} < 1500 \qquad \textrm{donor period}$$$$1540 < \widetilde{t} < 3050 \qquad \textrm{acceptor period}$$where $\widetilde{t}$ represent the nanotimes
array.
For more information please refer to the measurements_specs section of the Reference Documentation.
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nanotimes_donor = nanotimes[detectors == donor_ch]
nanotimes_acceptor = nanotimes[detectors == acceptor_ch]
Next, we compute the histograms:
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bins = np.arange(0, tcspc_num_bins + 1)
hist_d, _ = np.histogram(nanotimes_donor, bins=bins)
hist_a, _ = np.histogram(nanotimes_acceptor, bins=bins)
And finally we plot the TCSPC histogram using matplotlib:
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fig, ax = plt.subplots(figsize=(10, 4.5))
scale = tcspc_unit*1e9
ax.plot(bins[:-1]*scale, hist_d, color='green', label='donor')
ax.plot(bins[:-1]*scale, hist_a, color='red', label='acceptor')
ax.axvspan(donor_period[0]*scale, donor_period[1]*scale, alpha=0.3, color='green')
ax.axvspan(acceptor_period[0]*scale, acceptor_period[1]*scale, alpha=0.3, color='red')
ax.set_xlabel('TCSPC Nanotime (ns) ')
ax.set_title('TCSPC Histogram')
ax.set_yscale('log')
ax.set_ylim(10)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));
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#plt.close('all')
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