def plot(iter_info, min_max_concns):
axis = figure.add_subplot('', 111, frameon=options.frameon)
toolbox_idynomics.color_cells_by_species(iter_info.agent_output,
species_color_dict)
toolbox_idynomics.plot_cells_2d(axis, iter_info.agent_output)
if options.substratum:
axis.fill_between([0, nJ*res], [0]*2, y2=[-res]*2, color='k', zorder=-1)
figure.subplots_adjust(left=0.01, bottom=0.01, right=0.9, top=0.9)
figure.inset_axes()
if not options.solute_name == "none":
#print min_max_concns[options.solute_name]
solute_output = toolbox_results.SoluteOutput(iter_info.env_output,
name=options.solute_name)
cs = toolbox_idynomics.solute_contour(axis, solute_output,
concn_range=min_max_concns[options.solute_name],
interpolation='bicubic')
if options.color_bar:
toolbox_plotting.make_colorbar(axis, cs)
if options.titleon:
axis.set_title(r'Biofilm (%s g L$^{-1}$)'%(options.solute_name))
if options.frameon:
axis.set_xlabel('x')
axis.set_ylabel('y')
if options.timeon:
axis.text(0.0, nI*res, 'Time: %d h'%(int(iter_info.time)),
va='top', ha='left')
axis.set_xlim(0, nJ * res)
axis.set_ylim(-res, nI * res)
save_num = str(iter_info.number)
#save_num = str(counter)
#counter += 1
save_num = (num_digits - len(save_num))*'0' + save_num
save_path = os.path.join(sim.figures_dir,
save_name+'_'+save_num+options.file_ext)
figure.save(save_path, dpi=options.dpi)
def plot(iter_info, min_max_concns):
axis = figure.add_subplot('', 111, frameon=options.frameon)
for species in iter_info.agent_output.species_outputs:
species_name = species.name
for cell in species.members:
spStatus = cell.vars['sporeStatus']
mbStatus = cell.vars['metabolismStatus']
if spStatus == 'spore':
cell.color = species_color_dict[species_name+"_spore"]
continue
if spStatus == 'sporulating':
cell.color = species_color_dict[species_name+"_sporulating"]
continue
if mbStatus == 'glycolysis':
cell.color = species_color_dict[species_name+"_glycolysis"]
continue
if mbStatus == 'solventogenesis':
cell.color = species_color_dict[species_name+"_solventogenesis"]
continue
toolbox_idynomics.plot_cells_2d(axis, iter_info.agent_output)
if options.substratum:
axis.fill_between([0, nJ*res], [0]*2, y2=[-res]*2, color='k', zorder=-1)
lb = 0.01
if options.frameon:
lb = 0.12
figure.process_lines()
figure.subplots_adjust(left=lb, bottom=lb, right=0.9, top=0.9)
figure.inset_axes()
if not options.solute_name == "none":
#print min_max_concns[options.solute_name]
solute_output = toolbox_results.SoluteOutput(iter_info.env_output,
name=options.solute_name)
cs = toolbox_idynomics.solute_contour(axis, solute_output,
concn_range=min_max_concns[options.solute_name],
interpolation='bicubic')
if options.color_bar:
toolbox_plotting.make_colorbar(axis, cs)
if options.titleon:
axis.set_title(r'Biofilm (%s g L$^{-1}$)'%(options.solute_name))
if options.frameon:
axis.set_xlabel(r'x ($\mu$m)')
axis.set_ylabel(r'y ($\mu$m)')
if options.timeon:
axis.text(0.1*res, (nI+0.1)*res, 'Time: %.2f h'%(iter_info.time),
va='bottom', ha='left', color='0.5')
axis.set_xlim(0, nJ * res)
axis.set_ylim(-res, nI * res)
save_num = str(iter_info.number)
#save_num = str(counter)
#counter += 1
save_num = (num_digits - len(save_num))*'0' + save_num
save_path = os.path.join(sim.figures_dir,
save_name+'_'+save_num+options.file_ext)
figure.save(save_path, dpi=options.dpi)
def plot_rate_array(self, axis, detail_level, sif_name):
array = self.get_rate_array(detail_level, sif_name)
extent = [0.0, self.side_length]*2
max_val = numpy.max(abs(array))
cdict = {'red': ((0, 0, 0), (0.5, 1, 1), (1, 1, 1)),
'green': ((0, 0, 0), (0.5, 1, 1), (1, 0, 0)),
'blue': ((0, 1, 1), (0.5, 1, 1), (1, 0, 0))}
cmap = matplotlib.colors.LinearSegmentedColormap('cmap', cdict, 255)
cs = axis.imshow(array, interpolation='nearest', extent=extent,
origin='lower', cmap=cmap)
cs.set_clim(-max_val, max_val)
axis.set_xlim(0.0, self.side_length)
axis.set_ylim(0.0, self.side_length)
toolbox_plotting.make_colorbar(axis, cs)
def plot_concn_array(self, axis, detail_level, sif_name, set_as_white=None, plot_cs=True):
array = self.get_concn_array(detail_level, sif_name)
extent = [-0.5/detail_level, self.side_length + 0.5/detail_level]*2
bottom_red, top_red = 0.1, 0.7
bottom_green, top_green = 0.6, 0.0
bottom_blue, top_blue = 0.1, 0.5
mid_point = 0.5
if not set_as_white == None:
max_val, min_val = numpy.max(array), numpy.min(array)
up_diff, down_diff = max_val - set_as_white, set_as_white - min_val
max_diff, total_diff = max(up_diff, down_diff), max_val - min_val
up_rel_diff, down_rel_diff = up_diff/max_diff, down_diff/max_diff
mid_point = down_diff/total_diff
cdict = {'red': ((0, bottom_red, bottom_red),
(mid_point, 1, 1),
(1, top_red, top_red)),
'green': ((0, bottom_green, bottom_green),
(mid_point, 1, 1),
(1, top_green, top_green)),
'blue': ((0, bottom_blue, bottom_blue),
(mid_point, 1, 1),
(1, top_blue, top_blue))}
my_cmap = \
matplotlib.colors.LinearSegmentedColormap('my_cmap', cdict, 255)
cs = axis.imshow(array, interpolation='nearest', origin='lower',
extent=extent, cmap=my_cmap)
axis.set_xlim(0.0, self.side_length), axis.set_ylim(0.0, self.side_length)
if plot_cs:
cbar = toolbox_plotting.make_colorbar(axis, cs, fontsize=8)
return cbar
else:
return cs
def plot(iter_info, min_max_concns):
axis = figure.add_subplot('', 111, frameon=options.frameon)
toolbox_idynomics.color_cells_by_species(iter_info.agent_output,
species_color_dict)
toolbox_idynomics.plot_cells_2d(axis, iter_info.agent_output)
if options.substratum:
axis.fill_between([0, nJ * res], [0] * 2,
y2=[-res] * 2,
color='k',
zorder=-1)
lb = 0.01
if options.frameon:
lb = 0.06
figure.process_lines()
figure.subplots_adjust(left=lb, bottom=lb, right=0.9, top=0.9)
figure.inset_axes()
if not options.solute_name == "none":
#print min_max_concns[options.solute_name]
solute_output = toolbox_results.SoluteOutput(iter_info.env_output,
name=options.solute_name)
cs = toolbox_idynomics.solute_contour(
axis,
solute_output,
concn_range=min_max_concns[options.solute_name],
interpolation='bicubic')
if options.color_bar:
toolbox_plotting.make_colorbar(axis, cs)
if options.titleon:
axis.set_title(r'Biofilm (%s g L$^{-1}$)' % (options.solute_name))
if options.frameon:
axis.set_xlabel(r'x ($\mu$m)')
axis.set_ylabel(r'y ($\mu$m)')
if options.timeon:
axis.text(0.1 * res, (nI + 0.1) * res,
'Time: %d h' % (int(iter_info.time)),
va='bottom',
ha='left',
color='0.5')
axis.set_xlim(0, nJ * res)
axis.set_ylim(-res, nI * res)
save_num = str(iter_info.number)
#save_num = str(counter)
#counter += 1
save_num = (num_digits - len(save_num)) * '0' + save_num
save_path = os.path.join(sim.figures_dir,
save_name + '_' + save_num + options.file_ext)
figure.save(save_path, dpi=options.dpi)
def make_run_plot(self, detail_level, sif_name, maxP=None):
fig = toolbox_plotting.ThesisFigure(double_column=True)
axis = fig.add_subplot('A', 221)
#self.plot_rate_array(axis, detail_level, sif_name)
self.plot_population(axis, detail_level, sif_name)
toolbox_plotting.empty_padding_axis(axis, "bottom")
axis = fig.add_subplot('B', 222)
toolbox_plotting.empty_padding_axis(axis, "bottom")
if maxP == None:
maxP = numpy.max(self.get_concn_array(detail_level, sif_name))
maxP = 10**math.ceil(math.log10(maxP))
self.plot_kinetics(axis, detail_level, sif_name, maxP)
axis.text(8, 0.2, r'$q_{A}([H])$', color='r', va='center', ha='center')
axis.text(8, 0.8, r'$-q_{B}([H])$', color='b', va='center', ha='center')
analytic = AnalyticApproach()
analytic.set_parameters(A=1, qmaxA=1, pmax=10, kA=10, qmaxB=5, pmin=0.04, kB=30)
p_equal = analytic.calc_equal_concn()
r_equal = analytic.production(p_equal)
axis.plot([p_equal]*2, [0,r_equal+0.05],
color='0.5', linestyle='-', zorder=-10)
axis.text(p_equal, r_equal+0.05, '%.2f'%(p_equal),
color='0.5', va='bottom', ha='center', fontsize=8)
axis.plot([0, p_equal+0.5], [r_equal]*2,
color='0.5', linestyle='-', zorder=-10)
axis.text(p_equal+0.6, r_equal, '%.2f'%(r_equal),
color='0.5', va='center', ha='left', fontsize=8)
axis = fig.add_subplot('C', 223)
cs = self.plot_concn_array(axis, detail_level, sif_name, plot_cs=False)
cbar = toolbox_plotting.make_colorbar(axis, cs, side="bottom")
#label = r'Product concentration ($\mu$M)'
label = r'Hydrogen concentration ($\mu$M)'
cbar.set_ticks([2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7])
cbar.set_label(label)
axis.set_xticklabels(['']*10)
axis = fig.add_subplot('D', 224)
toolbox_plotting.empty_padding_axis(axis, "bottom")
self.calc_nearest_neighbor_distances(detail_level)
self.scatter_oNN_dist_vs_rate(axis, detail_level, sif_name)
fig.subplots_adjust(left=0.05, right=0.98, bottom=0.08, top=0.96,
wspace=0.3, hspace=0.25)
fig.process_subplots(label_pos=(0.0, 1.1))
axis = fig.find_axis_from_label('C')
axis.tick_params(bottom="off")
fig.save(os.path.join(self.get_run_dir(detail_level, sif_name), 'run_plot.pdf'))
