Tutorial about fluopy - two Cy5 in dSTORM simulation¶
Here we outline a simulation procedure for a 2-fluorophore Cy5 system under dSTORM conditions.
from pprint import pprint
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import fluopy
import fluopy.analysis as an
import fluopy.blinking as bl
import fluopy.emissions as em
import fluopy.fcs as fcs_p
import fluopy.figure as fi
import fluopy.fluorophores as fl
import fluopy.formulas as fo
import fluopy.miscellaneous as mi
import fluopy.prediction as pr
import fluopy.simulation as si
import fluopy.transitions as tr
fluopy.__version__
'0.4.0.dev4+gc3c2cb30d'
rng = np.random.default_rng(seed=1)
Define the fluorophore system¶
fluorophores = fl.construct_fluorophores(
name="cy5_dna", distance=10, count=2, shape=None
)
fluorophore_system = fl.FluorophoreSystem(fluorophores=fluorophores)
pprint(vars(fluorophore_system))
{'count': 2,
'distances': {(0, 1): np.float64(10.0), (1, 0): np.float64(10.0)},
'fluorophores': [Fluorophore(identity=0,
name='cy5_dna',
position=array([0, 0]),
constants=FluorophoreData(data_files='cy5_data',
QUANTUM_YIELD=0.27,
FLUORESCENCE_LIFETIME=1.7e-09,
S1_QUENCH_RATE=0,
ISC_ST_RATE=830000.0,
ISC_TS_RATE=5000.0,
RISC_RATE=0,
STA_EFFICIENCY=0,
PHOTOBLEACH_T1_RATE=10.0,
PHOTOBLEACH_T2_RATE=0,
DSTORM_PET_T_RATE_MOL=100000000.0,
DSTORM_PET_S_RATE_MOL=1000000000.0,
DSTORM_PET_SUCCESS_RATE=0.001,
DSTORM_TH_EL_RATE_1=0.01,
DSTORM_TH_EL_RATE_2=0,
DSTORM_P_EL_CROSS_SECTION=6e-24,
RAD_ESCAPE_EFFICIENCY=0.01,
RAD_RELAX_RATE=1300.0,
OFRET_EFFICIENCY=0.001,
ISO_RATE=4000000.0,
BISO_CROSS_SECTION=6e-18,
BISO_THERMAL_RATE=5000.0,
BISO_EFFICIENCY=0.04,
H2O_ATTACK_S=0,
H2O_ATTACK_T=0,
BACK_REACTION=0)),
Fluorophore(identity=1,
name='cy5_dna',
position=array([10, 0]),
constants=FluorophoreData(data_files='cy5_data',
QUANTUM_YIELD=0.27,
FLUORESCENCE_LIFETIME=1.7e-09,
S1_QUENCH_RATE=0,
ISC_ST_RATE=830000.0,
ISC_TS_RATE=5000.0,
RISC_RATE=0,
STA_EFFICIENCY=0,
PHOTOBLEACH_T1_RATE=10.0,
PHOTOBLEACH_T2_RATE=0,
DSTORM_PET_T_RATE_MOL=100000000.0,
DSTORM_PET_S_RATE_MOL=1000000000.0,
DSTORM_PET_SUCCESS_RATE=0.001,
DSTORM_TH_EL_RATE_1=0.01,
DSTORM_TH_EL_RATE_2=0,
DSTORM_P_EL_CROSS_SECTION=6e-24,
RAD_ESCAPE_EFFICIENCY=0.01,
RAD_RELAX_RATE=1300.0,
OFRET_EFFICIENCY=0.001,
ISO_RATE=4000000.0,
BISO_CROSS_SECTION=6e-18,
BISO_THERMAL_RATE=5000.0,
BISO_EFFICIENCY=0.04,
H2O_ATTACK_S=0,
H2O_ATTACK_T=0,
BACK_REACTION=0))],
'multi_type': False}
Define the transition set¶
transitions = fluorophore_system.load_transitions(
summarize=True,
irradiance=2,
wavelength=640,
bleaching=False,
energy_transfer=True,
dstorm=True,
)
transition_set = tr.TransitionSet(transitions, fluorophore_system)
transition_set.finalize()
<fluopy.transitions.TransitionSet at 0x6ffdf0628d70>
transition_set.plot(graph_type="shell", colors=None, scale=1);
transition_set.transition_df
| transition_type | abbreviation | initial_state | final_state | rate | photon | fluorophore_ids | absorbing | ||
|---|---|---|---|---|---|---|---|---|---|
| Fluorophore | identity | ||||||||
| cy5_dna | 0 | TransitionType.EXCITATION | EXC | SingleState.S0 | SingleState.S1 | 5.815700e+06 | False | [0, 1] | False |
| 1 | TransitionType.FLUORESCENT_EMISSION | FLU | SingleState.S1 | SingleState.S0 | 1.588235e+08 | True | [0, 1] | False | |
| 2 | TransitionType.INTERSYSTEM_CROSSING_ST | ISC_ST | SingleState.S1 | SingleState.T1 | 8.300000e+05 | False | [0, 1] | False | |
| 3 | TransitionType.ISOMERIZATION | ISO | SingleState.S1 | SingleState.cis | 4.000000e+06 | False | [0, 1] | False | |
| 4 | TransitionType.ADDUCT_T | PET_TO | SingleState.T1 | SingleState.OFF | 4.383440e+02 | False | [0, 1] | False | |
| 5 | TransitionType.ADDUCT_S | PET_SO | SingleState.S1 | SingleState.OFF | 4.383440e+03 | False | [0, 1] | False | |
| 6 | TransitionType.RAD_ESCAPE | PET_TR | SingleState.T1 | SingleState.R | 4.383440e+03 | False | [0, 1] | False | |
| 7 | TransitionType.RAD_RELAX | OXI | SingleState.R | SingleState.S0 | 1.300000e+03 | False | [0, 1] | False | |
| 8 | TransitionType.S1_S0_TRANSITIONS | S1S0SUM | SingleState.S1 | SingleState.S0 | 4.289652e+08 | False | [0, 1] | False | |
| 9 | TransitionType.T1_S0_TRANSITIONS | T1S0SUM | SingleState.T1 | SingleState.S0 | 4.433440e+05 | False | [0, 1] | False | |
| 10 | TransitionType.CIS_S0_TRANSITIONS | cisS0SUM | SingleState.cis | SingleState.S0 | 4.366202e+04 | False | [0, 1] | False | |
| 11 | TransitionType.OFF_S0_TRANSITIONS | OFFS0SUM | SingleState.OFF | SingleState.S0 | 4.866202e-02 | False | [0, 1] | False | |
| D: cy5_dna, A: cy5_dna, dist: 10.0 | 12 | TransitionType.CIS_FRET_1 | CET_1 | PairedState.S1_Cis | PairedState.S0_Cis | 8.581421e+07 | False | [(0, 1), (1, 0)] | False |
| 13 | TransitionType.CIS_FRET_2 | CET_2 | PairedState.S1_Cis | PairedState.S0_S0 | 3.575592e+06 | False | [(0, 1), (1, 0)] | False | |
| 14 | TransitionType.OFF_FRET_1 | OET_1 | PairedState.S1_OFF | PairedState.S0_OFF | 3.649102e+06 | False | [(0, 1), (1, 0)] | False | |
| 15 | TransitionType.OFF_FRET_2 | OET_2 | PairedState.S1_OFF | PairedState.S0_S0 | 3.652755e+03 | False | [(0, 1), (1, 0)] | False | |
| 16 | TransitionType.FRET | FRET | PairedState.S1_S0 | PairedState.S0_S1 | 4.664706e+07 | False | [(0, 1), (1, 0)] | False | |
| 17 | TransitionType.S_S_ANNIHILATION | SSA | PairedState.S1_S1 | PairedState.S0_S1 | 8.938980e+07 | False | [(0, 1), (1, 0)] | False | |
| 18 | TransitionType.S_T_ANNIHILATION | STA | PairedState.S1_T1 | PairedState.S0_T1 | 2.634792e+07 | False | [(0, 1), (1, 0)] | False |
Make a prediction¶
%%time
prediction = pr.Prediction(transition_set)
prediction
prediction accuracy of energy transfers more difficult to tune. Only frequencies available, lifetimes and occupations not available.
CPU times: user 19.3 ms, sys: 0 ns, total: 19.3 ms
Wall time: 10.7 ms
<fluopy.prediction.Prediction at 0x6ffdb0be2e40>
prediction.plot_frequency_transitions(scale=1)
prediction.plot_frequency_states(scale=1)
array([[<Axes: ylabel='Prob. occurrence'>]], dtype=object)
Run a simulation¶
simulation = si.Simulation(transition_set)
simulation
<fluopy.simulation.Simulation at 0x6ffdb0d67620>
%%time
# simulate until it reaches given end_time
simulation.run(start_at=None, size=1e6, end_time=500, seed=rng, use_memmap=None)
mi.print_class(simulation)
Floating point precision error warning:
The smallest safe increment is 5.68e-14.
Everything drawn below this number might be rounded to zero
when approaching the time limit of this simulation.
Using the highest possible rate which occurs for example in state combination [1, 1]
gives a probability of 7.75e-05 for a smaller increment to be drawn.
Attributes of <fluopy.simulation.Simulation object at 0x6ffdb0d67620>:
.................................................................
transition_set = <fluopy.transitions.TransitionSet object at 0x6ffdf0628d70>
_________________________________________________________________
time_series = array([0.00000000e+00, 5.75837004e-08, 6.0586144...81150266e+02, 5.00000000e+02], shape=(22687295,))
_________________________________________________________________
transition_series = array([ 0, 96, 1, ..., 106, 10, 70], shape=(22687293,), dtype=uint32)
_________________________________________________________________
state_series = array([[0, 0, 0, ..., 7, 7, 7],
[0, 1, 0, ..., 0, 1, 7]], shape=(2, 22687294), dtype=int8)
_________________________________________________________________
memmap_path = None
_________________________________________________________________
CPU times: user 1min 7s, sys: 181 ms, total: 1min 7s
Wall time: 1min 7s
Analyze the simulation¶
analysis = an.Analysis(simulation=simulation)
mi.print_class(analysis)
Attributes of <fluopy.analysis.Analysis object at 0x6ffdb0d66270>:
.................................................................
simulation = <fluopy.simulation.Simulation object at 0x6ffdb0d67620>
_________________________________________________________________
frequency_transitions = array([4.93169767e-01, 1.29110335e-01, 6.7883815... 1.00874088e-02, 1.63792128e-04, 1.00144164e-04])
_________________________________________________________________
frequency_states = {'cy5_dna': array([4.98053379e-01, 4.98053379e-01, 6.7181880...3435e-03,
3.44611944e-06, 6.54326476e-06])}
_________________________________________________________________
transition_time_distributions = [array([1.60771192e-07, 5.14620749e-08, 2.7001028...86329713e-08, 9.42114298e-08], shape=(11188687,)), array([2.46836785e-09, 5.99342571e-12, 1.9523953....70265604e-10, 3.42532758e-09], shape=(2929164,)), array([3.32554442e-09, 1.21016084e-09, 1.7050239... 3.57761110e-09, 1.22037136e-09], shape=(15401,)), array([2.00663296e-09, 3.07114014e-10, 9.0759629... 2.08842721e-10, 1.45507784e-09], shape=(73620,)), array([1.76919881e-06, 8.02568351e-07, 1.1900283... 2.70325421e-06, 3.45086215e-06, 6.86523890e-07]), array([9.53683447e-10, 1.21147536e-12, 4.4465124... 4.28940439e-10, 4.95276709e-10, 1.00789066e-09]), ...]
_________________________________________________________________
lifetime_distributions = {'cy5_dna': [array([1.60771192e-07, 5.14620749e-08, 2.7001028...86329713e-08, 9.42114298e-08], shape=(11417543,)), array([2.93141396e-10, 4.38715019e-10, 2.4683678...77180937e-10, 1.00789066e-09], shape=(11417543,)), array([9.28066744e-07, 5.20142133e-07, 2.7614122... 4.88162186e-08, 3.24744178e-08], shape=(15401,)), array([9.28299589e-07, 1.33557993e-05, 5.6726824... 5.51811695e-07, 1.05429953e-05], shape=(73620,)), array([2.48218820e-02, 2.94804611e+01, 5.3698889...8137e-02, 3.59128428e+01,
1.97457573e+01]), array([7.42170840e-04, 5.72825337e-04, 1.4633080...6747e-03,
1.40326991e-03, 4.16235275e-04])]}
_________________________________________________________________
mean_transition_times = array([1.68868321e-07, 1.61986326e-09, 1.6101849... 1.56650020e-09, 1.48670950e-09, 1.62599836e-09])
_________________________________________________________________
mean_lifetimes = {'cy5_dna': array([1.68461594e-07, 1.61689203e-09, 2.2119280...4157e-05,
1.23085768e+01, 7.95602338e-04])}
_________________________________________________________________
state_occupations = {'cy5_dna': array([1.97075215e-03, 1.89152516e-05, 3.4904254...0361e-03,
9.96307447e-01, 1.22277276e-04])}
_________________________________________________________________
analysis.get_fluorescence_lifetimes(fluorophore="cy5_dna")
analysis.get_emitting_transition_lifetimes(fluorophore="cy5_dna")
analysis.plot_frequency_transitions(scale=0.5, prediction=prediction)
analysis.plot_frequency_states(scale=0.5, prediction=prediction)
# analysis.plot_mean_transition_times(scale=0.5, prediction=prediction)
# analysis.plot_mean_lifetimes(scale=0.5, prediction=prediction)
# analysis.plot_state_occupations(scale=0.5, prediction=prediction)
# analysis.plot_lifetime_distributions(
# scale=0.5, prediction=prediction, fluorophore="cy5_dna", state_identity=1
# )
# analysis.plot_transition_time_distributions(
# scale=0.5, prediction=prediction, fluorophore="cy5_dna", transition_id=0
# )
array([[<Axes: ylabel='Prob. occurrence'>]], dtype=object)
Simulation of experimentally observable (photons per frames) only¶
Extract photon emission events from simulation¶
%%time
emissions = em.Emissions(frame_time="5ms", seed=rng, bandpass=(600, 800))
emissions.extract(simulation=simulation)
emissions
CPU times: user 947 ms, sys: 11 ms, total: 958 ms
Wall time: 957 ms
<fluopy.emissions.Emissions at 0x6ffdb0d66ba0>
Simulate photon emission events¶
Correct for detection efficiency and noise contributions:
emissions.add_photon_collection_objective(p=0.1, seed=rng) # 1.
emissions.add_quantum_efficiency(p=0.9, seed=rng) # 3.1.
emissions.add_poisson_noise(
rate=0.05, seed=rng
) # 3.2. (dark noise), note the frame time
emissions.add_emccd_gain(emccd_gain=10, seed=rng) # 4.
emissions.add_gaussian_noise(mean=10, std=1, seed=rng) # 5. (readout noise)
emissions
<fluopy.emissions.Emissions at 0x6ffdb0d66ba0>
emissions = em.Emissions(frame_time=”5ms”, seed=rng, bandpass=(660, 700)) emissions.extract(simulation=approximation)
# 2.
# at this point, the bandpass filter was applied
# yet, the effect of photon collection by the objective is missing
# the order is not relevant for two consecutive binomial distributions
# it is more convenient to apply the bandpass first because it needs the
# information about the emitting fluorophore whereas all the other effects are
# roughly wavelength independent
p_photon_collection = fo.calculate_photon_collection_rate(NA=1.45, n1=1.51)
emissions.add_photon_collection_objective(p=p_photon_collection) # 1.
emissions.add_quantum_efficiency(p=0.9) # 4.1.
emissions.add_transmittance(p=0.99) # 3 (depending on number of components of optical
# path, may be applied multiple times)
emissions.add_poisson_noise(rate=0.05) # 4.2. (dark noise), note the frame time
emissions.add_emccd_gain(emccd_gain=10) # 5. (+ multiplicative noise)
emissions.add_gaussian_noise(mean=10, std=1, seed=rng) # 6. (readout noise)
# CIC (spurious noise) neglected since low probability to happen in the pixels of
# interest
emissions.apply_threshold(threshold=100) # 7 (thresholding)
emissions.plot_cumulative_events(scale=1)
emissions.plot_histogram(scale=1)
emissions.plot_time_series(scale=1)
# to save the time_series and time_points
# emissions.save(path='', name_extension='test')
# to load time_series and time_points
# emissions.load(path='', name_extension='test')
array([[<Axes: xlabel='Time (s)', ylabel='$\\frac{photons}{frame}$'>]],
dtype=object)
Simulation of pulsed excitation¶
%%time
emissions_tcspc = em.Emissions(frame_time="1ms", seed=rng, bandpass=None)
lifetimes_DA, lifetimes_D, lifetimes_all, simulation_object = emissions_tcspc.tcspc(
transition_set=transition_set,
number_pulses=5e5,
pulse_duration=1e-11,
time_between_pulses=1e-7,
excitation_rates={"cy5_dna": 1e11},
size=1e5,
store_time_points=True,
# details = True
)
the last frame (of index 0.05) has 1.00e+00 times the pulses of other frames.
CPU times: user 1min 20s, sys: 216 ms, total: 1min 21s
Wall time: 1min 20s
emissions_tcspc.plot_time_series()
array([[<Axes: xlabel='Time (s)', ylabel='$\\frac{photons}{frame}$'>]],
dtype=object)
fi.universal_figure(
type_="hist", data=lifetimes_all, ylabel="PD", density=True, xlabel="Lifetime (s)"
)
array([[<Axes: xlabel='Lifetime (s)', ylabel='PD'>]], dtype=object)
Fluorescence correlation spectroscopy¶
Observed fluorescence emission events can be analyzed by a correlation analysis.
fcs = fcs_p.FCS(emissions)
list(vars(fcs).keys())
['emissions', 'autocorrelation', 'tau']
Autocorrelation of time points¶
%%time
fcs.autocorrelate_time_points(
exp_min=-20, exp_max=-6, points_per_base=4, base=4, normalize=True
)
CPU times: user 4.35 s, sys: 337 ms, total: 4.69 s
Wall time: 4.69 s
<fluopy.fcs.FCS at 0x6ffdb0d67b60>
mi.print_class(fcs)
fcs.plot(normalize_to=None, unit="s", scale=1);
Attributes of <fluopy.fcs.FCS object at 0x6ffdb0d67b60>:
.................................................................
emissions = <fluopy.emissions.Emissions object at 0x6ffdb0d66ba0>
_________________________________________________________________
autocorrelation = array([ 0. , 105.25773385, 74.42845738,...37465, 195.96795256, 195.71451995, 194.70409197])
_________________________________________________________________
tau = array([1.09785722e-12, 1.55260457e-12, 2.1957144... 1.04193529e-04, 1.47351902e-04, 2.08387058e-04])
_________________________________________________________________
Autocorrelation of time series¶
fcs.autocorrelate_time_series(log=True, m=4, normalize=True)
<fluopy.fcs.FCS at 0x6ffdb0d67b60>
fcs.plot(normalize_to=None, unit="s", scale=1);
# some fcs fits are available:
# fcs_predict = fcs_p.fit_dark(tau, dark_lifetime, dark_occupation)
# fcs_predict = fcs_p.fit_antibunching(tau, excitation_rate, s1_lifetime)
# fcs_predict = fcs_p.fit_triplet_cis(tau, k_isc, k_T, k_01, k_10, k_iso, k_biso_eff)
Antibunching¶
Alternatively, you can focus on fast time scales in a linear scale and observe antibunching.
# sensible to tau_max and bin_width, see coincidence notebook
hist, bins = fcs_p.coincidence(
emissions.event_time_points[: int(2e5)], tau_max=1e-8, bin_width=1e-10, seed=rng
)
fi.universal_figure(
type_="line",
data=[bins, hist],
xlabel=r"$\tau$ (s)",
ylabel=r"$g^{(2)}(\tau)$",
scale=1,
);
Blinking¶
Emissions from a short simulation¶
We limit the dataset to 2000 frames for illustration purposes.
%%time
emissions = em.Emissions(frame_time="10ms", seed=rng, bandpass=None)
emissions.simulate(
transition_set=transition_set, store_time_points=False, frames=10_000
)
emissions
CPU times: user 10.7 s, sys: 3.99 ms, total: 10.7 s
Wall time: 10.7 s
<fluopy.emissions.Emissions at 0x6ffdad0e2850>
threshold: int = 1000
emissions.plot_time_series(scale=1)
plt.hlines(threshold, xmin=0, xmax=100)
<matplotlib.collections.LineCollection at 0x6ffdac16ef90>
blinks = bl.Blinking(emissions, threshold=threshold)
blinks
<fluopy.blinking.Blinking at 0x6ffdac16f0e0>
mi.print_class(blinks)
Attributes of <fluopy.blinking.Blinking object at 0x6ffdac16f0e0>:
.................................................................
emissions = <fluopy.emissions.Emissions object at 0x6ffdad0e2850>
_________________________________________________________________
on_periods = array([ 3, 2, 6, 1, 7, 7, 1, 8, 4, 3, 1, 15])
_________________________________________________________________
off_periods = array([ 896, 197, 95, 261, 592, 1092, 115, 3555, 957, 944, 570])
_________________________________________________________________
on_periods_frames = array([ 1, 900, 1099, 1200, 1462, 2061, 3160, 3276, 6839, 7800, 8747,
9318])
_________________________________________________________________
off_periods_frames = array([ 4, 902, 1105, 1201, 1469, 2068, 3161, 3284, 6843, 7803, 8748])
_________________________________________________________________
# plot a histogram of OFF times
blinks.plot(
mode="off_histogram", density=True, display_mean=True, as_time="s", scale=0.5
)
# plot a histogram of ON times
blinks.plot(
mode="on_histogram", density=True, display_mean=True, as_time="ms", scale=0.5
)
# plot a time series of OFF times
blinks.plot(mode="off_frame_series", scale=0.5)
# plot a time series of ON times
blinks.plot(mode="on_frame_series", scale=0.5)
array([[<Axes: xlabel='identity', ylabel='consecutive ON frames'>]],
dtype=object)
# to get information of the photophysical (not analytical) OFF of each fluorophore, use
on_off_times_photophys, on_off_values_photophys = bl.get_off_statistics(
simulation=simulation, index=0
)
# to get the analytical OFF statistics as the same view, use
on_off_times_analytic, on_off_values_analytic = bl.get_analytical_off_statistics(
off_frames=blinks.off_periods_frames,
off_periods=blinks.off_periods,
on_frames=blinks.on_periods_frames,
frame_time=blinks.emissions.parameters["frame_time"],
)
# plot the photophysical OFF statistics
bl.plot_off_statistics(
on_off_times_photophys,
on_off_values_photophys,
scale=1,
title="photophysical OFF",
)
# plot the analytical OFF statistics (no differentiation between fluorophores)
bl.plot_off_statistics(
on_off_times_analytic, on_off_values_analytic, scale=1, title="analytical OFF"
)
array([[<Axes: title={'center': 'analytical OFF'}, xlabel='Time (s)'>]],
dtype=object)
Emissions from the long simulation¶
Get more detailed information from a complete simulation:
%%time
emissions = em.Emissions(frame_time="10ms", seed=rng, bandpass=None)
emissions.extract(simulation=simulation)
emissions
CPU times: user 975 ms, sys: 9 ms, total: 984 ms
Wall time: 983 ms
<fluopy.emissions.Emissions at 0x6ffd9f54d350>
blinks = bl.Blinking(emissions, threshold=threshold)
blinks
<fluopy.blinking.Blinking at 0x6ffd9f0fcb90>
mi.print_class(blinks)
Attributes of <fluopy.blinking.Blinking object at 0x6ffd9f0fcb90>:
.................................................................
emissions = <fluopy.emissions.Emissions object at 0x6ffd9f54d350>
_________________________________________________________________
on_periods = array([ 6, 12, 4, 22, 2, 1, 4, 5, 4, 7, ..., 21, 9, 2, 2, 3,
10, 1, 7, 6, 6])
_________________________________________________________________
off_periods = array([2945, 351, 991, 755, 948, 2349, 2504,... 293, 2152,
1705, 713, 2872, 881, 1087])
_________________________________________________________________
on_periods_frames = array([ 1, 2952, 3315, 4310, 5087, 6037,...40825, 42533, 43256,
46129, 47017, 48110])
_________________________________________________________________
off_periods_frames = array([ 7, 2964, 3319, 4332, 5089, 6038,...38673, 40828, 42543, 43257,
46136, 47023])
_________________________________________________________________
# plot a histogram of OFF times
blinks.plot(
mode="off_histogram", density=True, display_mean=True, as_time="s", scale=0.5
)
# plot a histogram of ON times
blinks.plot(
mode="on_histogram", density=True, display_mean=True, as_time="ms", scale=0.5
)
# plot a time series of OFF times
blinks.plot(mode="off_frame_series", scale=0.5)
# plot a time series of ON times
blinks.plot(mode="on_frame_series", scale=0.5)
array([[<Axes: xlabel='identity', ylabel='consecutive ON frames'>]],
dtype=object)
# to get information of the photophysical (not analytical) OFF of each fluorophore, use
on_off_times_photophys, on_off_values_photophys = bl.get_off_statistics(
simulation=simulation, index=0
)
# to get the analytical OFF statistics as the same view, use
on_off_times_analytic, on_off_values_analytic = bl.get_analytical_off_statistics(
off_frames=blinks.off_periods_frames,
off_periods=blinks.off_periods,
on_frames=blinks.on_periods_frames,
frame_time=blinks.emissions.parameters["frame_time"],
)
# plot the photophysical OFF statistics
bl.plot_off_statistics(
on_off_times_photophys,
on_off_values_photophys,
scale=1,
title="photophysical OFF",
)
# plot the analytical OFF statistics (no differentiation between fluorophores)
bl.plot_off_statistics(
on_off_times_analytic, on_off_values_analytic, scale=0.5, title="analytical OFF"
)
array([[<Axes: title={'center': 'analytical OFF'}, xlabel='Time (s)'>]],
dtype=object)
