| Title: | Generation of Customizable, Discretized Time Series of Oscillating Species |
|---|---|
| Description: | The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany. |
| Authors: | Arne Schoch [aut, cre] |
| Maintainer: | Arne Schoch <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.0 |
| Built: | 2024-10-29 02:44:26 UTC |
| Source: | https://github.com/cran/OscillatorGenerator |
This function takes in numeric arguments for a customizable, burst shape with exponential rise and decline. Each oscillation cycle is separated into four phases: the growth phase, in which the oscillator rises from the baseline to the peak concentration, a first drop phase, in which the oscillator declines from the peak to the secondary peak concentration, a second drop phase, in which the oscillator declines from the secondary peak to the baseline concentration and an inactive phase, in which the oscillator stays at baseline concentration. A discretized time course is returned.
ExpBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, peak_pos, duration, resolution)ExpBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, peak_pos, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
sec_duty_cycle |
ratio of the primary active phase (time interval from cycle start till reaching of sec_peak) to the total active phase |
sec_peak |
intermediary value reached after the end of the primary active phase |
trend |
percental decrease or increase in the peak and secondary peak values for the successive oscillation cycles; if set to 1, values remain unchanged |
peak_pos |
position of the peak value in the primary active phase (example: |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak and sec_peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak, sec_peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
sec_duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
peak_pos must be larger or equal to 0 and smaller than 1
Returns a matrix with two columns: first column time vector, second column oscillator abundance vector.
# test effect of changes in period m1 = ExpBurst(baseline = 200, peak = 1000, period = 10, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 0.7, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1.3, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.6, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.9, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = ExpBurst(baseline = 200, peak = 1000, period = 10, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 0.7, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1.3, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.6, duration = 500, resolution = 0.1) m3 = ExpBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.9, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, spike shape, in which rise and decline are modelled by means of an exponential function. A discretized time course is returned.
ExpSpike(baseline, peak, period, duty_cycle, peak_pos, trend, duration, resolution)ExpSpike(baseline, peak, period, duty_cycle, peak_pos, trend, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
peak_pos |
position of the peak value in the active phase of an oscillation cycle (example: |
trend |
percental decrease or increase in the peak value for the successive oscillation cycles; if set to 1, peak value remains unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
peak_pos must be larger or equal to 0 and smaller than 1
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
# test effect of changes in period m1 = ExpSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 0.7, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = ExpSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 0.7, duration = 500, resolution = 0.1) m2 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = ExpSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, burst shape with linear rise and decline. Each oscillation cycle is separated into four phases: the growth phase, in which the oscillator rises from the baseline to the peak concentration, a first drop phase, in which the oscillator declines from the peak to the secondary peak concentration, a second drop phase, in which the oscillator declines from the secondary peak to the baseline concentration and an inactive phase, in which the oscillator stays at baseline concentration. A discretized time course is returned.
LinBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, peak_pos, duration, resolution)LinBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, peak_pos, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
sec_duty_cycle |
ratio of the primary active phase (time interval from cycle start till reaching of the secondary peak) to the total active phase |
sec_peak |
intermediary value reached after the end of the primary active phase |
trend |
percental decrease or increase in the peak and secondary peak values for the successive oscillation cycles; if set to 1, values remain unchanged |
peak_pos |
position of the peak value in the primary active phase (example: |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak and sec_peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak, sec_peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
sec_duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
peak_pos must be larger or equal to 0 and smaller than 1
Returns a matrix with two columns: first column time vector, second column oscillator abundance vector.
# test effect of changes in period m1 = LinBurst(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 0.7, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1.3, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.6, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.9, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = LinBurst(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 0.7, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1.3, peak_pos = 0.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.3, duration = 500, resolution = 0.1) m2 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.6, duration = 500, resolution = 0.1) m3 = LinBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 850, trend = 1, peak_pos = 0.9, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, spike shape, in which rise and decline are modelled by means of a linear function. A discretized time course is returned.
LinSpike(baseline, peak, period, duty_cycle, peak_pos, trend, duration, resolution)LinSpike(baseline, peak, period, duty_cycle, peak_pos, trend, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
peak_pos |
position of the peak value in the active phase of an oscillation cycle (example: |
trend |
percental decrease or increase in the peak value for the successive oscillation cycles; if set to 1, peak value remains unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
peak_pos must be larger or equal to 0 and smaller than 1
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
# test effect of changes in period m1 = LinSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 0.7, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = LinSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in peak_pos m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 0.7, duration = 500, resolution = 0.1) m2 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1, duration = 500, resolution = 0.1) m3 = LinSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, peak_pos = 0.3, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, sinusoidal shape. A discretized time course is returned.
Sinusoid(baseline, peak, period, duty_cycle, trend, duration, resolution)Sinusoid(baseline, peak, period, duty_cycle, trend, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillator (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
trend |
percental decrease or increase in the peak value for the successive oscillation cycles; if set to 1, peak value remains unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak must be larger than baseline value
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
# test effect of changes in period m1 = Sinusoid(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 0.7, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = Sinusoid(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 0.7, duration = 500, resolution = 0.1) m2 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = Sinusoid(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, square-wave burst shape. Each oscillation cycle is separated into three phases: a primary active phase, in which the oscillator resides at peak concentration, a secondary active phase, in which the oscillator stays at secondary peak concentration and an inactive phase, in which the oscillator is fixed to baseline concentration. A discretized time course is returned.
SquareBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, duration, resolution)SquareBurst(baseline, peak, period, duty_cycle, sec_duty_cycle, sec_peak, trend, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
sec_duty_cycle |
ratio of the primary active phase (time interval from cycle start till reaching of the secondary peak level) to the total active phase |
sec_peak |
intermediary value reached after the end of the primary active phase |
trend |
percental decrease or increase in the peak and secondary peak values for the successive oscillation cycles; if set to 1, values remain unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak and sec_peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of the resolution
period must be a multiple of resolution
duration, resolution, peak, sec_peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
sec_duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
# test effect of changes in period m1 = SquareBurst(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 0.7, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = SquareBurst(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, sec_duty_cycle = 0.5, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in sec_duty_cycle m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.3, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.9, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 0.7, duration = 500, resolution = 0.1) m2 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1, duration = 500, resolution = 0.1) m3 = SquareBurst(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, sec_duty_cycle = 0.6, sec_peak = 700, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")
This function takes in numeric arguments for a customizable, square-wave spike shape. A discretized time course is returned.
SquareSpike(baseline, peak, period, duty_cycle, trend, duration, resolution)SquareSpike(baseline, peak, period, duty_cycle, trend, duration, resolution)
baseline |
minimal oscillation value |
peak |
maximal oscillation value |
period |
oscillation period of the oscillating species (reciprocal of the frequency) |
duty_cycle |
ratio of the active phase (oscillator above baseline) to the total oscillation period |
trend |
percental decrease or increase in the peak value for the successive oscillation cycles; if set to 1, peak value remains unchanged |
duration |
duration of the generated time course |
resolution |
temporal resolution of the generated time course |
Standards:
peak must be larger than baseline
duration must be larger than resolution
duration must be a multiple of resolution
period must be a multiple of resolution
duration, resolution, peak and period must be larger than 0
baseline must be larger or equal to 0
duty_cycle must be larger than 0 and smaller or equal to 1
trend must be larger than 0
Returns a matrix with two columns: a time vector and an oscillator abundance vector.
# test effect of changes in period m1 = SquareSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 0.7, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")# test effect of changes in period m1 = SquareSpike(baseline = 200, peak = 1000, period = 50, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 200, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in duty_cycle m1 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.3, trend = 1, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.9, trend = 1, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance") # test effect of changes in trend m1 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 0.7, duration = 500, resolution = 0.1) m2 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1, duration = 500, resolution = 0.1) m3 = SquareSpike(baseline = 200, peak = 1000, period = 100, duty_cycle = 0.6, trend = 1.3, duration = 500, resolution = 0.1) par(mfrow = c(3,1)) plot(m1, type = "l", xlab = "time", ylab = "abundance") plot(m2, type = "l", xlab = "time", ylab = "abundance") plot(m3, type = "l", xlab = "time", ylab = "abundance")