def figs7(self):
""" returns figure with NPQ trace for Arabidopsis WT simulated, measured and simulated npq4 mutant"""
fig = plt.figure()
ax = plt.subplot(111)
v = visualizeexperiment.VisualizeExperiment('Arabidopsis', 900, 15)
v.plotNPQ('r')
light = cpfd.cpfd('Arabidopsis', 900)
Tfls, PFDs = self.flashes(v, light)
# ===================================================== #
# integrate over the time #
# ===================================================== #
model, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
s = simulate.Sim(model)
s.piecewiseConstant(PFDs[0:], Tfls[0:], y0)
res = npqResults.NPQResults(s)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res.fluo()
timesft = 20 # shift the simulation by an interval to see the experimental points
ax.plot(Tm + timesft, (Fm[0] - Fm) / Fm,
c=self.col[6],
marker='s',
linestyle='-',
label='wt')
model2, y0 = loadspecie.loadspecie('Arabidopsis', 'npq4')
s2 = simulate.Sim(model2)
s2.piecewiseConstant(PFDs[0:], Tfls[0:], y0)
res2 = npqResults.NPQResults(s2)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res2.fluo()
timesft = 20 # shift the simulation by an interval to see the experimental points
ax.plot(Tm + timesft, (Fm[0] - Fm) / Fm,
c=self.col[1],
marker='s',
linestyle='-',
label='npq4')
# add light banner
v.setGraphics([-30, 0, 841, 1792, 2147], 1.7, yheight=0.2)
handles, labels = ax.get_legend_handles_labels()
leg = ax.legend(handles,
['simulated wt', 'simulated npq4', 'measured wt'],
bbox_to_anchor=(0., 1.02, 1., .102),
loc=10,
borderaxespad=0.1)
if leg:
leg.draggable()
plt.title('')
self.save_plot(fig, 'FigS7')
def figs6(self,
PFDs=[
0.1, 10, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700,
800, 900, 1000
]):
""" returns the steady state fraction of the reduced PQ pool for a given list of different light intensities
from dim light to high light """
model, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
s = simulate.Sim(model)
# integrate over time
Y = s.steadyStateLightScan(PFDs, y0)
# visualize results
fig = plt.figure()
ax = plt.subplot(111)
plt.plot(PFDs,
Y[:, 0] / model.par.PQtot,
'sr',
linestyle='-',
markersize=10)
plt.xlabel('light intensity', fontsize=16)
plt.ylabel('PQH$_2$/(PQ+PQH$_2$)', fontsize=16)
ax.set_yticks([0, 0.5, 1])
ax.set_yticklabels(['0%', '50%', '100%'])
ax.set_xticks([0, 220, 320, 900])
ax.set_xticklabels(['dim', 100, 300, 900])
self.save_plot(fig, 'FigS6')
You should have received a copy of the GNU General Public License along with this program (license.txt). If not, see <http://www.gnu.org/licenses/>. """ import numpy as np import matplotlib.pyplot as plt import scipy.optimize import parameters import npqModel import npqResults import simulate import misc import dataAnalysis import visualizeexperiment db = dataAnalysis.DB() # =============================================================== # # load parameters and create model object # # =============================================================== # p = parameters.NPQmodelParameterSet() m = npqModel.NPQModel(p) s = simulate.Sim(m) # =============================================================== # # initial concentrations corresponding to a dark adapted organism # # =============================================================== # y0 = np.array([0., p.bH * misc.pHinv(p.pHstroma), 0, 1, 1, 0])
def fig6(self):
"""
:return: figure with fluorescence dynamics for Pothos simulated and measured
and simulated and measured photosynthetic yield
"""
light = cpfd.cpfd('Pothos', 100)
fig = plt.figure(figsize=(7, 3))
ax = plt.subplot(211)
v = visualizeexperiment.VisualizeExperiment('Pothos', 100, 15)
v.plotFluorescence()
Tfls, PFDs = self.flashes(v, light)
# load the Pothos model, automatically sets the gamma2 parameter to higher value
model, y0 = loadspecie.loadspecie('Pothos', 'wt')
s = simulate.Sim(model)
s.piecewiseConstant(PFDs[0:], Tfls[0:], y0)
# load the results into the res object that allow for easier data ansalysis
res = npqResults.NPQResults(s)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res.fluo()
timesft = 20 # shift the simulation by an interval to see the experimental points
ax.plot(T + timesft, F / max(F), 'r')
# collect handles to set the legend with experimental results
T_avg, T_sd = v.getAvgValue('T')
Fm_avg, Fm_sd = v.getAvgValue('Fm')
l1 = plt.errorbar(T_avg,
Fm_avg / Fm_avg[0],
Fm_sd,
color=self.col[5],
linestyle='None',
marker='^',
label='Experiment')
l3 = plt.Line2D([], [], linewidth=3, color='r')
ax.set_ylabel('Fluorescence (a.u.)', fontsize=18)
ax.set_xlabel('')
ax.set_xticks([0, 840, 1800, 2100])
ax.set_xticklabels([0, '14', '30', '35'])
leg = plt.legend([l1, l3], ["Experiment", "Simulation"])
if leg:
leg.draggable()
ax2 = plt.subplot(212)
v.setGraphics([-100, 0, 841, 1792, 2147], 1, yheight=0.1)
ax2.plot(v.getAvgValue('T')[0],
(v.getAvgValue('Fm')[0] - v.getAvgValue('Ft')[0]) /
v.getAvgValue('Fm')[0],
color=self.col[5],
marker='^',
label='experiment')
ax2.plot(Tm, (Fm - Fs) / Fm, color='r', marker='o', label='simulation')
ax2.set_xticks([0, 840, 1800, 2100])
ax2.set_xticklabels([0, '14', '30', '35'])
ax2.set_xlabel('time [min]', fontsize=16)
ax2.set_ylabel('$\Phi$ PSII', fontsize=18)
leg = plt.legend()
if leg:
leg.draggable()
self.save_plot(fig, 'Fig6')
def fig5(self):
"""
:return: figure visualizing pH dependance of quencher and its components.
Upper plot: phase plane trajectories
Bottom plots: pH and quencher components for 3 light intensities
"""
color = list(self.col[i]
for i in [1, 3, 5]) # select 3 distinct colours
mark = ['x', 'o', 's']
v = visualizeexperiment.VisualizeExperiment('Arabidopsis', 100, 15)
# set up the figure
fig = plt.figure(figsize=(7, 10))
#make outer gridspec
outer_grid = gridspec.GridSpec(
2, 1,
hspace=0.2) # gridspec with two adjacent horizontal cellstwo rows
ax = plt.subplot(outer_grid[0])
ax.set_ylabel('lumen pH', fontsize=18)
ax.set_xlabel('quencher activity [Q]', fontsize=18)
ax.set_ylim(3, 8)
ax.set_xlim(0.05, 0.4)
ax.set_yticks([3, 4, 5, 6, 7, 8])
ax.set_yticklabels(['', 4, 5, 6, 7, 8], fontsize=14)
ax.set_xticks([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
ax.set_xticklabels(['', '0.1', '', '0.2', '', '0.3', '', '0.4'],
fontsize=14)
# add the steady state
m, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
ss = simulate.Sim(m)
scan = [
10, 50, 100, 120, 150, 200, 220, 300, 320, 330, 340, 350, 400, 500,
600, 700, 800, 900, 1000, 1250, 1500
]
Y = ss.steadyStateLightScan(scan, y0)
ax.plot(m.quencher(Y[:, 3], Y[:, 4]),
misc.pH(Y[:, 1]),
c='r',
linestyle='-')
for i in [6, 8, 17]:
ax.plot(m.quencher(Y[i, 3], Y[i, 4]),
misc.pH(Y[i, 1]),
'o',
linestyle='None',
markersize=15,
mfc='r')
upper_cell = outer_grid[1]
inner_grid = gridspec.GridSpecFromSubplotSpec(2,
1,
upper_cell,
hspace=0.0)
# From here we can plot using inner_grid's SubplotSpecs
ax2 = plt.subplot(inner_grid[0, 0])
# add the three points for the calibrated light intensity
PFDs = [
cpfd.cpfd('Arabidopsis', 100),
cpfd.cpfd('Arabidopsis', 300),
cpfd.cpfd('Arabidopsis', 900)
]
time = [0, 30, 930, 1830, 2130]
nsteps = [31, 901, 901, 301] # nr of steps for each integration phase
v.setGraphics(time, 9, 1)
ax2.set_ylabel('lumen pH', fontsize=14)
ax2.set_ylim(2, 9)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(True)
ax2.set_yticks([2, 3, 4, 5, 6, 7, 8])
ax2.set_yticklabels(['', 3, 4, 5, 6, 7, 8], fontsize=14)
ax3 = plt.subplot(inner_grid[1, 0])
ax3.set_ylabel('Q components', fontsize=16)
ax3.set_xlabel('time [min]', fontsize=18)
ax3.set_xlim([0, time[-1]])
ax3.set_xticks(time[1:])
ax3.set_xticklabels(['30', '14', '30', '35'], fontsize=14)
ax3.set_yticks([0, 0.2, 0.5, 0.8, 1.])
ax3.set_yticklabels([0, 0.2, 0.5, 0.8, ''], fontsize=14)
for i in range(len(PFDs)):
s = simulate.Sim(m)
s.clearResults()
PFD = PFDs[i]
light = [0, PFD, 0, PFD]
s.piecewiseConstant(light, time[1:], y0, nsteps)
res = npqResults.NPQResults(s)
ax.plot(m.quencher(res.getVar(3), res.getVar(4)),
misc.pH(res.getVar(1)),
c=color[i],
marker=mark[i],
label=str(self.light_intens[i]))
ax2.plot(res.getT(),
misc.pH(res.getVar(1)),
c=color[i],
label=str(self.light_intens[i]))
ax3.plot(res.getT(),
1 - res.getVar(3),
c=color[i],
linestyle='--',
label=str(self.light_intens[i]))
ax3.plot(res.getT(),
1 - res.getVar(4),
c=color[i],
linestyle='-',
label=str(self.light_intens[i]))
handles, labels = ax.get_legend_handles_labels()
leg1 = ax.legend(handles,
labels,
bbox_to_anchor=(0., 1.02, 1., .102),
loc=10,
ncol=1,
borderaxespad=0.1)
if leg1:
leg1.draggable()
handles, labels = ax3.get_legend_handles_labels()
leg = ax3.legend([
handles[0], handles[2], handles[4], handles[1], handles[3],
handles[5]
], [labels[0], labels[2], labels[4], labels[1], labels[3], labels[5]],
bbox_to_anchor=(0., 1.02, 1., .102),
loc=10,
title='PsbS$^P$ Zx',
ncol=2,
borderaxespad=0.1)
if leg:
leg.draggable()
self.save_plot(fig, 'Fig5')
def fig4(self):
"""
:return: three plots with PAM traces simulated and measured experimentally
"""
timesft = 5 # shift the simulation by an interval to see the experimental points
fig = plt.figure(figsize=(17, 5))
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1])
for i in range(len(self.light_intens)):
ax = plt.subplot(gs[i])
v = visualizeexperiment.VisualizeExperiment(
'Arabidopsis', self.light_intens[i], self.dark_dur[i])
v.plotFluorescence()
# ===================================================== #
# read out time of flashes as applied in the experiment #
# ===================================================== #
light = cpfd.cpfd('Arabidopsis', int(self.light_intens[i]))
Tfls, PFDs = self.flashes(v, light)
# ===================================================== #
# integrate over the time #
# ===================================================== #
model, y0 = loadspecie.loadspecie('Arabidopsis', 'wt')
s = simulate.Sim(model)
s.piecewiseConstant(PFDs[0:], Tfls[0:], y0)
res = npqResults.NPQResults(s)
T, F, Fmax, Tm, Fm, Ts, Fs, Bst = res.fluo()
# plot the simulation
plt.plot(T + timesft, F / max(F), 'orchid', label='simulation')
# add the legend for the experiment
ax = plt.gca()
T_avg, T_sd = v.getAvgValue('T')
Fm_avg, Fm_sd = v.getAvgValue('Fm')
l1 = ax.errorbar(T_avg,
Fm_avg / Fm_avg[0],
Fm_sd,
color='k',
linestyle='None',
marker='^',
label='Fm\'')
l3 = plt.Line2D([], [], linewidth=3, color="orchid")
leg = plt.legend([l1, l3], ["Experiment", "Simulation"])
if leg:
leg.draggable()
if i == 0:
ax.annotate(str(self.light_intens[i]) +
' $\mu$Em$^{-2}$s$^{-1}$', (T_avg[9] / 2, 1.05),
color='k',
weight='bold',
fontsize=16,
ha='center',
va='center')
ax.set_ylabel('Fluorescence ($\mathrm{F_M\'}/\mathrm{F_M}$)',
fontsize=18)
else:
ax.annotate(str(self.light_intens[i]), (T_avg[9] / 2, 1.05),
color='k',
weight='bold',
fontsize=16,
ha='center',
va='center')
ax.annotate(str(self.dark_dur[i]) + ' min',
(T_avg[9] + (T_avg[16] - T_avg[9]) / 2, 1.05),
color='w',
weight='bold',
fontsize=16,
ha='center',
va='center')
if i == 0:
ax.set_xticks([0, 840, 1800, 2100])
ax.set_xticklabels([0, '14', '30', '35'], fontsize=16)
elif i == 1:
ax.set_xticks([0, 840, 2700, 3000])
ax.set_xticklabels([0, '14', '45', '50'], fontsize=16)
else:
ax.set_xticks([0, 840, 4500, 4800])
ax.set_xticklabels([0, '14', '75', '80'], fontsize=16)
ax.set_xlabel('time [min]', fontsize=18)
plt.tight_layout()
self.save_plot(fig, 'Fig4')