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Commit 5b4be0c3 authored by Lars Hubatsch's avatar Lars Hubatsch
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Adopt multi drop FEM for better accuracy, fix bug in z distance.

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Plots_Droplet_FRAP.ipynb
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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from fem_sol import frap_solver from fem_sol import frap_solver
from matplotlib import cm, rc, rcParams from matplotlib import cm, rc, rcParams
from matplotlib.ticker import MaxNLocator from matplotlib.ticker import MaxNLocator
import fem_utils import fem_utils
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import seaborn as sns import seaborn as sns
from scipy.interpolate import interp1d from scipy.interpolate import interp1d
fol = '/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/' fol = '/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/'
# sns.set_style("ticks") # sns.set_style("ticks")
rcParams['axes.linewidth'] = 0.75 rcParams['axes.linewidth'] = 0.75
rcParams['xtick.major.width'] = 0.75 rcParams['xtick.major.width'] = 0.75
rcParams['ytick.major.width'] = 0.75 rcParams['ytick.major.width'] = 0.75
# rcParams['text.usetex']=True # rcParams['text.usetex']=True
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Define colors # Define colors
pa = sns.color_palette("Set2") pa = sns.color_palette("Set2")
sns.set_palette(pa) sns.set_palette(pa)
grey = (0.6, 0.6, 0.6) grey = (0.6, 0.6, 0.6)
dark_grey = (0.2, 0.2, 0.2) dark_grey = (0.2, 0.2, 0.2)
green = pa[0] green = pa[0]
blue = pa[2] blue = pa[2]
red = pa[1] red = pa[1]
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import pylab as pl import pylab as pl
params = {'legend.fontsize': 9, params = {'legend.fontsize': 9,
'legend.handlelength': 1} 'legend.handlelength': 1}
pl.rcParams.update(params) pl.rcParams.update(params)
def nice_fig(xla, yla, xli, yli, size, fs=12): def nice_fig(xla, yla, xli, yli, size, fs=12):
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
# rc('font',**{'family':'serif','serif':['Palatino']}) # rc('font',**{'family':'serif','serif':['Palatino']})
plt.gcf().set_size_inches(size[0], size[1]) plt.gcf().set_size_inches(size[0], size[1])
plt.xlabel(xla,fontsize=fs) plt.xlabel(xla,fontsize=fs)
plt.ylabel(yla,fontsize=fs) plt.ylabel(yla,fontsize=fs)
plt.xlim(xli) plt.xlim(xli)
plt.ylim(yli) plt.ylim(yli)
plt.tick_params(axis='both', which='major', labelsize=fs) plt.tick_params(axis='both', which='major', labelsize=fs)
def save_nice_fig(name, form='pdf'): def save_nice_fig(name, form='pdf'):
plt.savefig(name, format=form, dpi=300, bbox_inches='tight', plt.savefig(name, format=form, dpi=300, bbox_inches='tight',
transparent=True) transparent=True)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### FRAP geometries ### FRAP geometries
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# For radial average: define angles and radial spacing # For radial average: define angles and radial spacing
alphas = np.linspace(0,2*np.pi, 20) alphas = np.linspace(0,2*np.pi, 20)
ns = np.c_[np.cos(alphas), np.sin(alphas), np.zeros(len(alphas))] ns = np.c_[np.cos(alphas), np.sin(alphas), np.zeros(len(alphas))]
eps = np.linspace(0, 0.23, 100) eps = np.linspace(0, 0.23, 100)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
#### Multi Drop #### Multi Drop
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
me = ['Meshes/multi_drop_gauss.xml', 'Meshes/multi_drop_gauss_med.xml', me = ['Meshes/multi_drop_gauss.xml', 'Meshes/multi_drop_gauss_med.xml',
'Meshes/multi_drop_gauss_far.xml', 'Meshes/multi_drop_gauss.xml', 'Meshes/multi_drop_gauss_far.xml', 'Meshes/multi_drop_gauss.xml',
'Meshes/multi_drop_gauss_med.xml', 'Meshes/multi_drop_gauss_far.xml'] 'Meshes/multi_drop_gauss_med.xml', 'Meshes/multi_drop_gauss_far.xml']
point_lists = [[[4, 4.5, 0.5], [4, 3.5, 0.5], [3.5, 4, 0.5], [4.5, 4, 0.5]], point_lists = [[[4, 4.5, 0.25], [4, 3.5, 0.25], [3.5, 4, 0.25], [4.5, 4, 0.25]],
[[4, 5, 0.5], [4, 3, 0.5], [3, 4, 0.5], [5, 4, 0.5]], [[4, 5, 0.25], [4, 3, 0.25], [3, 4, 0.25], [5, 4, 0.25]],
[[4, 5.5, 0.5], [4, 2.5, 0.5], [2.5, 4, 0.5], [5.5, 4, 0.5]], [[4, 5.5, 0.25], [4, 2.5, 0.25], [2.5, 4, 0.25], [5.5, 4, 0.25]],
[[4, 4.5, 0.5], [4, 3.5, 0.5], [3.5, 4, 0.5], [4.5, 4, 0.5]], [[4, 4.5, 0.25], [4, 3.5, 0.25], [3.5, 4, 0.25], [4.5, 4, 0.25]],
[[4, 5, 0.5], [4, 3, 0.5], [3, 4, 0.5], [5, 4, 0.5]], [[4, 5, 0.25], [4, 3, 0.25], [3, 4, 0.25], [5, 4, 0.25]],
[[4, 5.5, 0.5], [4, 2.5, 0.5], [2.5, 4, 0.5], [5.5, 4, 0.5]]] [[4, 5.5, 0.25], [4, 2.5, 0.25], [2.5, 4, 0.25], [5.5, 4, 0.25]]]
phi_tot_int = [.99, .99, .99, .9, .9, .9] phi_tot_int = [.99, .99, .99, .9, .9, .9]
phi_tot_ext = [.01, .01, .01, .1, .1, .1] phi_tot_ext = [.01, .01, .01, .1, .1, .1]
G_in = [1, 1, 1, .1, .1, .1] G_in = [1, 1, 1, .1, .1, .1]
G_out = [1, 1, 1, 0.99/0.9, 0.99/0.9, 0.99/0.9] G_out = [1, 1, 1, 0.99/0.9, 0.99/0.9, 0.99/0.9]
f_i = [] f_i = []
for p, m, p_i, p_e, G_i, G_o in zip(point_lists, me, phi_tot_int, for p, m, p_i, p_e, G_i, G_o in zip(point_lists, me, phi_tot_int,
phi_tot_ext, G_in, G_out): phi_tot_ext, G_in, G_out):
f = frap_solver([4, 4, 0.5], m, name='FRAP_multi_'+m[:-4]+str(G_i), point_list=p, f = frap_solver([4, 4, 0.25], m, name='FRAP_multi/FRAP_multi_'+m[:-4]+str(G_i), point_list=p,
T=50, phi_tot_int=p_i, phi_tot_ext=p_e, G_in=G_i, G_out=G_o) T=240, dt=0.02, phi_tot_int=p_i, phi_tot_ext=p_e, G_in=G_i, G_out=G_o)
f.solve_frap() f.solve_frap()
f_i.append(f) f_i.append(f)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
profs = [] profs = []
for i in range(len(f_i)): for i in range(len(f_i)):
# if i>2: # if i>2:
profs.append([]) profs.append([])
for j in range(50): for j in range(50):
values=[] values=[]
fs = fem_utils.load_time_point(f_i[i].name+'t_p_'+str(j)+'.h5', fs = fem_utils.load_time_point(f_i[i].name+'t_p_'+str(j)+'.h5',
f_i[i].mesh) f_i[i].mesh)
for n in ns: for n in ns:
values.append([fs([4, 4, 0.5]+e*n) for e in eps]) values.append([fs([4, 4, 0.5]+e*n) for e in eps])
profs[i].append(np.mean(np.transpose(values), 1)) profs[i].append(np.mean(np.transpose(values), 1))
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
np.savetxt('t_p.csv', profs, delimiter=',') np.savetxt('t_p.csv', profs, delimiter=',')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
ft = f_i[1] ft = f_i[1]
meta_data = np.r_[ft.dt, ft.T, eps] meta_data = np.r_[ft.dt, ft.T, eps]
np.savetxt('meta_data.csv', meta_data, delimiter=',') np.savetxt('meta_data.csv', meta_data, delimiter=',')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
plt.plot(eps,np.transpose(profs)[:,:]) plt.plot(eps,np.transpose(profs)[:,:])
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', 'intensity (a.u)', [0,50], [0,1.1], [1.5,2]) nice_fig('time $t$ [s]', 'intensity (a.u)', [0,50], [0,1.1], [1.5,2])
plt.plot([np.mean(x)/f_i[0].phi_tot_int for x in profs[0]], plt.plot([np.mean(x)/f_i[0].phi_tot_int for x in profs[0]],
lw=2, label='d=0.5', ls='-') lw=2, label='d=0.5', ls='-')
plt.plot([np.mean(x)/f_i[1].phi_tot_int for x in profs[1]], plt.plot([np.mean(x)/f_i[1].phi_tot_int for x in profs[1]],
lw=2, label='d=1', ls='--') lw=2, label='d=1', ls='--')
plt.plot([np.mean(x)/f_i[2].phi_tot_int for x in profs[2]], plt.plot([np.mean(x)/f_i[2].phi_tot_int for x in profs[2]],
lw=2, label='d=1.5', ls=':') lw=2, label='d=1.5', ls=':')
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
plt.title('$P=99}$', size=12) plt.title('$P=99}$', size=12)
plt.gca().get_yaxis().set_visible(False) plt.gca().get_yaxis().set_visible(False)
save_nice_fig(fol+'Fig3/tot_recov_neighbours_bad.pdf') save_nice_fig(fol+'Fig3/tot_recov_neighbours_bad.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', r'av. volume fraction $\bar{\phi}_\mathrm{u}$', [0,30], [0,1.1], [1.5,2]) nice_fig('time $t$ [s]', r'av. volume fraction $\bar{\phi}_\mathrm{u}$', [0,30], [0,1.1], [1.5,2])
plt.plot([np.mean(x)/f_i[3].phi_tot_int for x in profs[3]], plt.plot([np.mean(x)/f_i[3].phi_tot_int for x in profs[3]],
lw=2, label='$d=0.5 \,\mathrm{\mu m}$', ls='-') lw=2, label='$d=0.5 \,\mathrm{\mu m}$', ls='-')
plt.plot([np.mean(x)/f_i[4].phi_tot_int for x in profs[4]], plt.plot([np.mean(x)/f_i[4].phi_tot_int for x in profs[4]],
lw=2, label='$d=1 \,\mathrm{\mu m}$', ls='--') lw=2, label='$d=1 \,\mathrm{\mu m}$', ls='--')
plt.plot([np.mean(x)/f_i[5].phi_tot_int for x in profs[5]], plt.plot([np.mean(x)/f_i[5].phi_tot_int for x in profs[5]],
lw=2, label='$d=1.5 \,\mathrm{\mu m}$', ls=':') lw=2, label='$d=1.5 \,\mathrm{\mu m}$', ls=':')
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
plt.title('$P=9$', size=12) plt.title('$P=9$', size=12)
plt.legend(prop={'size': 9}, frameon=False, loc=(0.22, 0.025), plt.legend(prop={'size': 9}, frameon=False, loc=(0.22, 0.025),
handletextpad=0.4, labelspacing=0.2) handletextpad=0.4, labelspacing=0.2)
save_nice_fig(fol+'Fig3/tot_recov_neighbours_good.pdf') save_nice_fig(fol+'Fig3/tot_recov_neighbours_good.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
ml = np.loadtxt('/Users/hubatsch/Desktop/DropletFRAP/matlab_fit.csv', ml = np.loadtxt('/Users/hubatsch/Desktop/DropletFRAP/matlab_fit.csv',
delimiter=',') delimiter=',')
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'volume fraction $\phi_\mathrm{u}$', [0,0.25], [0,1.1], [3.8,2]) nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'volume fraction $\phi_\mathrm{u}$', [0,0.25], [0,1.1], [3.8,2])
l_sim = plt.plot(eps, np.transpose(profs[0])[:,::8]/f_i[0].phi_tot_int, c=green, l_sim = plt.plot(eps, np.transpose(profs[0])[:,::8]/f_i[0].phi_tot_int, c=green,
lw=4.5, alpha=0.7) lw=4.5, alpha=0.7)
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
l_fit = plt.plot(np.linspace(0, 0.23, 100), np.transpose(ml)[:,::8], c='k', lw=1.5) l_fit = plt.plot(np.linspace(0, 0.23, 100), np.transpose(ml)[:,::8], c='k', lw=1.5)
plt.legend([l_sim[0], l_fit[0]], ['Model, eq. (6)', 'Fit, eq. (1)'], prop={'size': 9}, frameon=False) plt.legend([l_sim[0], l_fit[0]], ['Model, eq. (6)', 'Fit, eq. (1)'], prop={'size': 9}, frameon=False)
save_nice_fig(fol+'Fig3/spat_recov_neighbours.pdf') save_nice_fig(fol+'Fig3/spat_recov_neighbours.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
#### Coverslip #### Coverslip
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Define parameters for all simulations # Define parameters for all simulations
point_list = [[4, 4, 0.25], [4, 4, 1.5], [4, 4, 4], point_list = [[4, 4, 0.25], [4, 4, 1.5], [4, 4, 4],
[4, 4, 0.25], [4, 4, 1.5], [4, 4, 4]] [4, 4, 0.25], [4, 4, 1.5], [4, 4, 4]]
me = ['coverslip.xml', '1_5.xml', 'symmetric.xml', me = ['coverslip.xml', '1_5.xml', 'symmetric.xml',
'coverslip.xml', '1_5.xml', 'symmetric.xml'] 'coverslip.xml', '1_5.xml', 'symmetric.xml']
phi_tot_int = [.99, .99, .99, .9, .9, .9] phi_tot_int = [.99, .99, .99, .9, .9, .9]
phi_tot_ext = [.01, .01, .01, .1, .1, .1] phi_tot_ext = [.01, .01, .01, .1, .1, .1]
G_in = [1, 1, 1, .1, .1, .1] G_in = [1, 1, 1, .1, .1, .1]
G_out = [1, 1, 1, 0.99/0.9, 0.99/0.9, 0.99/0.9] G_out = [1, 1, 1, 0.99/0.9, 0.99/0.9, 0.99/0.9]
f_cs = [] f_cs = []
# Zip all parameters, iterate # Zip all parameters, iterate
for p, m, p_i, p_e, G_i, G_o, i in zip(point_list, me, phi_tot_int, for p, m, p_i, p_e, G_i, G_o, i in zip(point_list, me, phi_tot_int,
phi_tot_ext, G_in, G_out, range(len(me))): phi_tot_ext, G_in, G_out, range(len(me))):
if i in [0, 1, 2, 3, 4, 5]: if i in [0, 1, 2, 3, 4, 5]:
f_cs.append(frap_solver(p, 'Meshes/single_drop_'+m, f_cs.append(frap_solver(p, 'Meshes/single_drop_'+m,
name='FRAP_coverslip/FRAP_'+m[:-4]+str(G_i), T=240, name='FRAP_coverslip/FRAP_'+m[:-4]+str(G_i), T=240,
phi_tot_int=p_i, dt=0.02, phi_tot_ext=p_e, G_in=G_i, phi_tot_int=p_i, dt=0.02, phi_tot_ext=p_e, G_in=G_i,
G_out=G_o)) G_out=G_o))
# f_cs[-1].solve_frap() # f_cs[-1].solve_frap()
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
z = [0.25, 1.5, 4, 0.25, 1.5, 4] z = [0.25, 1.5, 4, 0.25, 1.5, 4]
# eps = np.linspace(0, 3.5, 100) # for full profile # eps = np.linspace(0, 3.5, 100) # for full profile
eps = np.r_[np.linspace(0, 0.4, 100), np.linspace(0.41, 2, 20)] eps = np.r_[np.linspace(0, 0.4, 100), np.linspace(0.41, 2, 20)]
profs_cs = [] profs_cs = []
for i, z_i in enumerate(z): for i, z_i in enumerate(z):
profs_cs.append([]) profs_cs.append([])
for j in range(240): for j in range(240):
values=[] values=[]
fs = fem_utils.load_time_point(f_cs[i].name+ fs = fem_utils.load_time_point(f_cs[i].name+
't_p_'+str(j)+'.h5', f_cs[i].mesh) 't_p_'+str(j)+'.h5', f_cs[i].mesh)
for n in ns: for n in ns:
values.append([fs([4, 4, z_i]+e*n) for e in eps]) values.append([fs([4, 4, z_i]+e*n) for e in eps])
profs_cs[i].append(np.mean(np.transpose(values), 1)) profs_cs[i].append(np.mean(np.transpose(values), 1))
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Write out profiles to .csv # Write out profiles to .csv
for i in [0, 1, 2, 3, 4, 5]: for i in [0, 1, 2, 3, 4, 5]:
z = str(f_cs[i].cent_poin[-1]) z = str(f_cs[i].cent_poin[-1])
P = str(f_cs[i].phi_tot_int/f_cs[i].phi_tot_ext) P = str(f_cs[i].phi_tot_int/f_cs[i].phi_tot_ext)
np.savetxt('eps_'+str(i)+'.csv', eps, delimiter=',') np.savetxt('eps_'+str(i)+'.csv', eps, delimiter=',')
np.savetxt('t_p_long_coverslip_' + P + '_' + z +'.csv', np.savetxt('t_p_long_coverslip_' + P + '_' + z +'.csv',
profs_cs[i][1:], delimiter=',') profs_cs[i][1:], delimiter=',')
meta_data = np.r_[f_cs[i].dt, f_cs[i].T-1, eps, 0.25] # last param: droplet radius meta_data = np.r_[f_cs[i].dt, f_cs[i].T-1, eps, 0.25] # last param: droplet radius
np.savetxt('meta_data_long_coverslip_' + P + '_' + z + '.csv', meta_data, delimiter=',') np.savetxt('meta_data_long_coverslip_' + P + '_' + z + '.csv', meta_data, delimiter=',')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', '', [0,50], [0,1.1], [1.5,2]) nice_fig('time $t$ [s]', '', [0,50], [0,1.1], [1.5,2])
ls = ['-', '--', '-.'] ls = ['-', '--', '-.']
for i, f in enumerate(f_cs[0:3]): for i, f in enumerate(f_cs[0:3]):
plt.plot([np.mean(x)/f.phi_tot_int for x in profs_cs[i]], plt.plot([np.mean(x)/f.phi_tot_int for x in profs_cs[i]],
label='d='+str(z[i]), ls=ls[i], lw=2) label='d='+str(z[i]), ls=ls[i], lw=2)
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
plt.title('$P=99$', size=12) plt.title('$P=99$', size=12)
plt.gca().get_yaxis().set_visible(False) plt.gca().get_yaxis().set_visible(False)
save_nice_fig(fol+'Fig3/tot_recov_cs_bad.pdf') save_nice_fig(fol+'Fig3/tot_recov_cs_bad.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', r'av. volume fraction $\bar{\phi}_\mathrm{u}$', [0,30], [0,1.1], [1.5,2]) nice_fig('time $t$ [s]', r'av. volume fraction $\bar{\phi}_\mathrm{u}$', [0,30], [0,1.1], [1.5,2])
ls = ['-', '--', '-.'] ls = ['-', '--', '-.']
for i, f in enumerate(f_cs[3:]): for i, f in enumerate(f_cs[3:]):
plt.plot([np.mean(x)/f.phi_tot_int for x in profs_cs[i+3]], plt.plot([np.mean(x)/f.phi_tot_int for x in profs_cs[i+3]],
label='$h=$'+str(z[i])+'$\,\mathrm{\mu m}$', lw=2, ls=ls[i]) label='$h=$'+str(z[i])+'$\,\mathrm{\mu m}$', lw=2, ls=ls[i])
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
plt.title('$P=9$', size=12) plt.title('$P=9$', size=12)
plt.legend(prop={'size': 9}, frameon=False, loc=(0.22, 0.025), plt.legend(prop={'size': 9}, frameon=False, loc=(0.22, 0.025),
handletextpad=0.4, labelspacing=0.2) handletextpad=0.4, labelspacing=0.2)
save_nice_fig(fol+'Fig3/tot_recov_cs_good.pdf') save_nice_fig(fol+'Fig3/tot_recov_cs_good.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
ml_neigh = np.loadtxt('/Users/hubatsch/Desktop/DropletFRAP/matlab_fit_neigh.csv', ml_neigh = np.loadtxt('/Users/hubatsch/Desktop/DropletFRAP/matlab_fit_neigh.csv',
delimiter=',') delimiter=',')
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'volume fraction $\phi_\mathrm{u}$', [0,0.25], [0,1.1], [3.8,2]) nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'volume fraction $\phi_\mathrm{u}$', [0,0.25], [0,1.1], [3.8,2])
l_sim = plt.plot(eps, np.transpose(profs_cs[0])[:,::8]/f_cs[0].phi_tot_int, c=green, l_sim = plt.plot(eps, np.transpose(profs_cs[0])[:,::8]/f_cs[0].phi_tot_int, c=green,
lw=4.5, alpha=0.7) lw=4.5, alpha=0.7)
plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k') plt.plot(range(0, 100), np.ones(100), linestyle='--', color='k')
l_fit = plt.plot(np.linspace(0, 0.23, 100), np.transpose(ml_neigh)[:,::8], l_fit = plt.plot(np.linspace(0, 0.23, 100), np.transpose(ml_neigh)[:,::8],
ls='-', c='k', lw=1) ls='-', c='k', lw=1)
plt.legend([l_sim[0], l_fit[0]], ['Model, eq. (6)', 'Fit, eq. (1)'], frameon=False, loc=(0.63, 0.0)) plt.legend([l_sim[0], l_fit[0]], ['Model, eq. (6)', 'Fit, eq. (1)'], frameon=False, loc=(0.63, 0.0))
save_nice_fig(fol+'Fig3/spat_recov_coverslip.pdf') save_nice_fig(fol+'Fig3/spat_recov_coverslip.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Figure 1: Fitting $D_{in}$ and data analysis. ### Figure 1: Fitting $D_{in}$ and data analysis.
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: comparison PGL-3 diffusivity with Louise's viscosity** **Panel: comparison PGL-3 diffusivity with Louise's viscosity**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
louise = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Louise.csv') louise = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Louise.csv')
lars = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Lars.csv') lars = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Lars.csv')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Calculate rough molecular radius, based on Stokes-Einstein and Louise's # Calculate rough molecular radius, based on Stokes-Einstein and Louise's
# viscosity data from the Science paper supplement (Fig. S5G), email from Louise: # viscosity data from the Science paper supplement (Fig. S5G), email from Louise:
# earliest point is 10.4 Pa*s . # earliest point is 10.4 Pa*s .
# Einstein kinetic theory: D=kB*T/(6*pi*eta*r) # Einstein kinetic theory: D=kB*T/(6*pi*eta*r)
D = lars.D[lars.conc==75].mean() D = lars.D[lars.conc==75].mean()
eta = 10.4 eta = 10.4
kBT = 4.11*10**-21 kBT = 4.11*10**-21
r = kBT/(D*6*np.pi*eta) r = kBT/(D*6*np.pi*eta)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax1 = plt.subplots() fig, ax1 = plt.subplots()
ax2 = ax1.twinx() ax2 = ax1.twinx()
plt.sca(ax1) plt.sca(ax1)
sns.lineplot(x="conc", y="D", data=lars, color=sns.color_palette()[1]) sns.lineplot(x="conc", y="D", data=lars, color=sns.color_palette()[1])
sns.scatterplot(x="conc", y="D", data=lars, color=sns.color_palette()[1], alpha=0.8) sns.scatterplot(x="conc", y="D", data=lars, color=sns.color_palette()[1], alpha=0.8)
plt.xlabel('$c_\mathrm{salt}\; [\mathrm{mM}]$') plt.xlabel('$c_\mathrm{salt}\; [\mathrm{mM}]$')
plt.ylabel('$D_{\mathrm{in}} \;[\mathrm{\mu m^2\cdot s^{-1}}]$', color=red) plt.ylabel('$D_{\mathrm{in}} \;[\mathrm{\mu m^2\cdot s^{-1}}]$', color=red)
plt.yticks([0, 0.05, 0.1], rotation=90, color = pa[1]) plt.yticks([0, 0.05, 0.1], rotation=90, color = pa[1])
plt.ylim(0, 0.1) plt.ylim(0, 0.1)
ax1.set_zorder(1) ax1.set_zorder(1)
ax1.patch.set_visible(False) ax1.patch.set_visible(False)
plt.sca(ax2) plt.sca(ax2)
sns.lineplot(x="conc", y="vis", data=louise, color=grey, label='data from Jawerth \net al. 2018') sns.lineplot(x="conc", y="vis", data=louise, color=grey, label='data from Jawerth \net al. 2018')
nice_fig('c_\mathrm{salt} [\mathrm{mM}]', '$\eta^{-1} \;[Pa\cdot s]^{-1}$', [40,190], [0,7.24], [2.3,2]) nice_fig('c_\mathrm{salt} [\mathrm{mM}]', '$\eta^{-1} \;[Pa\cdot s]^{-1}$', [40,190], [0,7.24], [2.3,2])
plt.yticks(color = grey) plt.yticks(color = grey)
plt.ylabel('$\eta^{-1} \;[\mathrm{Pa\cdot s}]^{-1}$ ', color = grey) plt.ylabel('$\eta^{-1} \;[\mathrm{Pa\cdot s}]^{-1}$ ', color = grey)
plt.legend(frameon=False, fontsize=9) plt.legend(frameon=False, fontsize=9)
save_nice_fig(fol+'Fig1/Lars_vs_Louise.pdf') save_nice_fig(fol+'Fig1/Lars_vs_Louise.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Ratio between $D_{out}$ for maximum and minimum salt concentrations for PGL-3** **Ratio between $D_{out}$ for maximum and minimum salt concentrations for PGL-3**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
lars[lars.conc==180].mean()/lars[lars.conc==50].mean() lars[lars.conc==180].mean()/lars[lars.conc==50].mean()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel:coacervates PLYS/ATP, CMD/PLYS**m **Panel:coacervates PLYS/ATP, CMD/PLYS**m
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
coacervates = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Coacervates.csv') coacervates = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig1/Coacervates.csv')
sns.stripplot(data=coacervates, palette=[green, blue], jitter=0.35,**{'marker': '.', 'size': 10}) sns.stripplot(data=coacervates, palette=[green, blue], jitter=0.35,**{'marker': '.', 'size': 10})
ax = sns.barplot(data=coacervates, palette=pa, facecolor=(1, 1, 1, 0), edgecolor=[pa[0], pa[2]], capsize=.15, ci='sd', errwidth=1.5) ax = sns.barplot(data=coacervates, palette=pa, facecolor=(1, 1, 1, 0), edgecolor=[pa[0], pa[2]], capsize=.15, ci='sd', errwidth=1.5)
plt.setp(ax.lines, zorder=100) plt.setp(ax.lines, zorder=100)
nice_fig(None, '$D_\mathrm{in} \;[\mathrm{\mu m^2\cdot s^{-1}}]$', [None, None], [0,7], [2.3,2]) nice_fig(None, '$D_\mathrm{in} \;[\mathrm{\mu m^2\cdot s^{-1}}]$', [None, None], [0,7], [2.3,2])
plt.xticks([0,1], ('CMD/PLYS', 'PLYS/ATP'), rotation=20) plt.xticks([0,1], ('CMD/PLYS', 'PLYS/ATP'), rotation=20)
ax.get_xticklabels()[0].set_color(green) ax.get_xticklabels()[0].set_color(green)
ax.get_xticklabels()[1].set_color(blue) ax.get_xticklabels()[1].set_color(blue)
plt.xlim(-0.7, 1.7) plt.xlim(-0.7, 1.7)
# save_nice_fig(fol+'Fig1/Coacervates.pdf') # save_nice_fig(fol+'Fig1/Coacervates.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: time course CMD** **Panel: time course CMD**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
CMD = np.loadtxt(fol+'/Fig1/CMD_timecourse.csv', delimiter=',') CMD = np.loadtxt(fol+'/Fig1/CMD_timecourse.csv', delimiter=',')
CMD_fit = np.loadtxt(fol+'/Fig1/CMD_fit_timecourse.csv', delimiter=',') CMD_fit = np.loadtxt(fol+'/Fig1/CMD_fit_timecourse.csv', delimiter=',')
l_sim = plt.plot(CMD[:, 0], CMD[:, 1::2], '.', c=green) l_sim = plt.plot(CMD[:, 0], CMD[:, 1::2], '.', c=green)
l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1::2], '-', lw=1, c='k') l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1::2], '-', lw=1, c='k')
plt.plot(range(0, 10), np.ones(10)*np.min(CMD_fit[:, 1]), linestyle='--', color=grey, lw=1.5) plt.plot(range(0, 10), np.ones(10)*np.min(CMD_fit[:, 1]), linestyle='--', color=grey, lw=1.5)
plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0, 0.85), frameon=False) plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0, 0.85), frameon=False)
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)', [0,np.max(CMD_fit[:, 0])], [0,0.65], [2.3,2]) nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)', [0,np.max(CMD_fit[:, 0])], [0,0.65], [2.3,2])
save_nice_fig(fol+'Fig1/CMD_spat_recov.pdf') save_nice_fig(fol+'Fig1/CMD_spat_recov.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: time course PGL-3** **Panel: time course PGL-3**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PGL = np.loadtxt(fol+'/Fig1/PGL_timecourse.csv', delimiter=',') PGL = np.loadtxt(fol+'/Fig1/PGL_timecourse.csv', delimiter=',')
PGL_fit = np.loadtxt(fol+'/Fig1/PGL_fit_timecourse.csv', delimiter=',') PGL_fit = np.loadtxt(fol+'/Fig1/PGL_fit_timecourse.csv', delimiter=',')
l_sim = plt.plot(PGL[:, 0], PGL[:, 1::2], '.', c=red) l_sim = plt.plot(PGL[:, 0], PGL[:, 1::2], '.', c=red)
l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 1::2], '-', lw=1, c='k') l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 1::2], '-', lw=1, c='k')
plt.plot(range(0, 10), np.ones(10)*np.min(PGL_fit[:, 1]), linestyle='--', color=grey, lw=1.5) plt.plot(range(0, 10), np.ones(10)*np.min(PGL_fit[:, 1]), linestyle='--', color=grey, lw=1.5)
plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0.015, 0.865), frameon=False) plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0.015, 0.865), frameon=False)
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)', [0,np.max(PGL_fit[:, 0])], [0, 0.7], [2.3,2]) nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)', [0,np.max(PGL_fit[:, 0])], [0, 0.7], [2.3,2])
save_nice_fig(fol+'Fig1/PGL_spat_recov.pdf') save_nice_fig(fol+'Fig1/PGL_spat_recov.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_sim = plt.plot(PGL[:, 0], PGL[:, 1], '-', c=red) l_sim = plt.plot(PGL[:, 0], PGL[:, 1], '-', c=red)
nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5) nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5)
plt.yticks([0, 0.5], ['', '']) plt.yticks([0, 0.5], ['', ''])
plt.xticks([0, 3], ['', '']) plt.xticks([0, 3], ['', ''])
plt.gca().tick_params(direction='in', length=3, width=1) plt.gca().tick_params(direction='in', length=3, width=1)
save_nice_fig(fol+'Fig1/PGL_spat_90.pdf') save_nice_fig(fol+'Fig1/PGL_spat_90.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_sim = plt.plot(PGL[:, 0], PGL[:, 4], '-', c=red) l_sim = plt.plot(PGL[:, 0], PGL[:, 4], '-', c=red)
nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5) nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5)
plt.yticks([0, 0.5], ['', '']) plt.yticks([0, 0.5], ['', ''])
plt.xticks([0, 3], ['', '']) plt.xticks([0, 3], ['', ''])
plt.gca().tick_params(direction='in', length=3, width=1) plt.gca().tick_params(direction='in', length=3, width=1)
save_nice_fig(fol+'Fig1/PGL_spat_300.pdf') save_nice_fig(fol+'Fig1/PGL_spat_300.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_sim = plt.plot(PGL[:, 0], PGL[:, 7], '-', c=red) l_sim = plt.plot(PGL[:, 0], PGL[:, 7], '-', c=red)
nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5) nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5)
plt.yticks([0, 0.5], ['', '']) plt.yticks([0, 0.5], ['', ''])
plt.xticks([0, 3], ['', '']) plt.xticks([0, 3], ['', ''])
plt.gca().tick_params(direction='in', length=3, width=1) plt.gca().tick_params(direction='in', length=3, width=1)
save_nice_fig(fol+'Fig1/PGL_spat_510.pdf') save_nice_fig(fol+'Fig1/PGL_spat_510.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: time course total intensity** **Panel: time course total intensity**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PGL = np.loadtxt(fol+'/Fig1/PGL_bc.csv', delimiter=',') PGL = np.loadtxt(fol+'/Fig1/PGL_bc.csv', delimiter=',')
ATP = np.loadtxt(fol+'/Fig1/ATP_bc.csv', delimiter=',') ATP = np.loadtxt(fol+'/Fig1/ATP_bc.csv', delimiter=',')
CMD = np.loadtxt(fol+'/Fig1/CMD_bc.csv', delimiter=',') CMD = np.loadtxt(fol+'/Fig1/CMD_bc.csv', delimiter=',')
# fig, ax1 = plt.subplots() # fig, ax1 = plt.subplots()
# ax2 = ax1.twiny() # ax2 = ax1.twiny()
# plt.sca(ax1) # plt.sca(ax1)
nice_fig('$t/T_\mathrm{max}$', 'intensity (a.u)', [0,200], [0,0.62], [2.3,2]) nice_fig('$t/T_\mathrm{max}$', 'intensity (a.u)', [0,200], [0,0.62], [2.3,2])
# plt.sca(ax2) # plt.sca(ax2)
# ax2.tick_params(axis="x",direction="in") # ax2.tick_params(axis="x",direction="in")
plt.plot(PGL[::10, 0]/np.max(PGL[1:-1:2, 0]), PGL[::10,1], '.', label='PGL-3', c='#CC406E', markersize=3, alpha=0.7, lw=2) plt.plot(PGL[::10, 0]/np.max(PGL[1:-1:2, 0]), PGL[::10,1], '.', label='PGL-3', c='#CC406E', markersize=3, alpha=0.7, lw=2)
plt.plot(ATP[::1, 0]/np.max(ATP[:, 0]), ATP[::1,1], '.', label='PLYS/ATP', c='#FF508A', markersize=3, alpha=0.7, lw=2) plt.plot(ATP[::1, 0]/np.max(ATP[:, 0]), ATP[::1,1], '.', label='PLYS/ATP', c='#FF508A', markersize=3, alpha=0.7, lw=2)
plt.plot(CMD[::5, 0]/np.max(CMD[:, 0]), CMD[::5,1], '.', label='CMD/PLYS', c='#7F2845', markersize=3, alpha=0.7, lw=2) plt.plot(CMD[::5, 0]/np.max(CMD[:, 0]), CMD[::5,1], '.', label='CMD/PLYS', c='#7F2845', markersize=3, alpha=0.7, lw=2)
plt.legend(frameon=False, fontsize=9) plt.legend(frameon=False, fontsize=9)
plt.xlim(0, 1) plt.xlim(0, 1)
# save_nice_fig(fol+'Fig1/tot_recov.pdf') # save_nice_fig(fol+'Fig1/tot_recov.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PGL = [] PGL = []
for i in range(8): for i in range(8):
PGL.append(np.loadtxt(fol+'/Fig1/PGL_bc'+str(i+1)+'.csv', delimiter=',')) PGL.append(np.loadtxt(fol+'/Fig1/PGL_bc'+str(i+1)+'.csv', delimiter=','))
conc = np.loadtxt(fol+'/Fig1/PGL_conc.csv', delimiter=',') conc = np.loadtxt(fol+'/Fig1/PGL_conc.csv', delimiter=',')
rads = [25, 24, 29, 26, 25, 53, 33, 26] rads = [25, 24, 29, 26, 25, 53, 33, 26]
# PGL = [PGL[i] for i in [0, 1, 2, 3, 4, 5, 7]] # PGL = [PGL[i] for i in [0, 1, 2, 3, 4, 5, 7]]
# rads = # rads =
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', 'intensity (a.u)', [0,140], [0,0.82], [1,2]) nice_fig('time $t$ [s]', 'intensity (a.u)', [0,140], [0,0.82], [1,2])
temp = sns.color_palette() temp = sns.color_palette()
sns.set_palette(sns.color_palette("rocket", 9)) sns.set_palette(sns.color_palette("rocket", 9))
# plt.sca(ax2) # plt.sca(ax2)
# ax2.tick_params(axis="x",direction="in") # ax2.tick_params(axis="x",direction="in")
# plt.plot(PGL[::10, 0], PGL[::10,1], '.', label='PGL-3', c='#CC406E', markersize=3, alpha=0.7, lw=2) # plt.plot(PGL[::10, 0], PGL[::10,1], '.', label='PGL-3', c='#CC406E', markersize=3, alpha=0.7, lw=2)
for jj, i in enumerate(PGL): for jj, i in enumerate(PGL):
if jj != 10: if jj != 10:
plt.plot(i[::, 0]/(0.136*rads[jj])**2, i[::,1], '-', color=sns.color_palette()[jj], lw=1.5) plt.plot(i[::, 0]/(0.136*rads[jj])**2, i[::,1], '-', color=sns.color_palette()[jj], lw=1.5)
plt.legend([str(x)[:-2] for x in conc], columnspacing=0.2, frameon=False, plt.legend([str(x)[:-2] for x in conc], columnspacing=0.2, frameon=False,
fontsize=7, handletextpad=0.4, handlelength=0.5, labelspacing=0.1, fontsize=7, handletextpad=0.4, handlelength=0.5, labelspacing=0.1,
loc=(0.3, 0), ncol=2) loc=(0.3, 0), ncol=2)
# plt.xticks([0, 500]) # plt.xticks([0, 500])
save_nice_fig(fol+'Fig1/tot_recov_PGL.pdf') save_nice_fig(fol+'Fig1/tot_recov_PGL.pdf')
sns.set_palette(temp) sns.set_palette(temp)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
nice_fig('time $t$ [s]', 'intensity (a.u)', [0,10], [0,0.72], [1,2]) nice_fig('time $t$ [s]', 'intensity (a.u)', [0,10], [0,0.72], [1,2])
plt.plot(ATP[::1, 0], ATP[::1,1], '-', label='PLYS/ATP', c=blue, markersize=3, alpha=0.7, lw=1.5) plt.plot(ATP[::1, 0], ATP[::1,1], '-', label='PLYS/ATP', c=blue, markersize=3, alpha=0.7, lw=1.5)
plt.plot(CMD[::5, 0], CMD[::5,1], '-', label='CMD/PLYS', c=green, markersize=3, alpha=0.7, lw=1.5) plt.plot(CMD[::5, 0], CMD[::5,1], '-', label='CMD/PLYS', c=green, markersize=3, alpha=0.7, lw=1.5)
plt.legend(frameon=False, fontsize=7, loc=(0.1, 0), handletextpad=0.5) plt.legend(frameon=False, fontsize=7, loc=(0.1, 0), handletextpad=0.5)
plt.yticks([]); plt.ylabel('') plt.yticks([]); plt.ylabel('')
save_nice_fig(fol+'Fig1/tot_recov_coac.pdf') save_nice_fig(fol+'Fig1/tot_recov_coac.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Figure 2: model sketches ### Figure 2: model sketches
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
model = np.loadtxt(fol+'Fig2/model_timecourse.csv', delimiter=',') model = np.loadtxt(fol+'Fig2/model_timecourse.csv', delimiter=',')
l_fit = plt.plot(model[:, 0], model[:, 1:], '-', lw=1, l_fit = plt.plot(model[:, 0], model[:, 1:], '-', lw=1,
c=green, label='Simulation') c=green, label='Simulation')
nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2], nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2],
[0,1], [2.3,2]) [0,1], [2.3,2])
plt.plot(model[:, 0], model[:, -1], c=green, lw=2) plt.plot(model[:, 0], model[:, -1], c=green, lw=2)
plt.plot(model[:, 0], model[:, -1], 'k', lw=2) plt.plot(model[:, 0], model[:, -1], 'k', lw=2)
# plt.annotate('$t \longrightarrow \infty$', (0.97, 0.89), (1.3,0.85), fontsize=12) # plt.annotate('$t \longrightarrow \infty$', (0.97, 0.89), (1.3,0.85), fontsize=12)
# plt.annotate('$t \longrightarrow \infty$', (0.97, 0.89), (1.3,0.85), fontsize=12) # plt.annotate('$t \longrightarrow \infty$', (0.97, 0.89), (1.3,0.85), fontsize=12)
plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10) plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10)
save_nice_fig(fol+'Fig2/full_time_course.pdf') save_nice_fig(fol+'Fig2/full_time_course.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_fit = plt.plot(model[:, 0], model[:, 2], '-', lw=1, l_fit = plt.plot(model[:, 0], model[:, 2], '-', lw=1,
c=dark_grey, label='Simulation') c=dark_grey, label='Simulation')
plt.plot(model[:, 0], model[:, -1], 'k', lw=2) plt.plot(model[:, 0], model[:, -1], 'k', lw=2)
nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2], nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2],
[0,1], [2.3,2]) [0,1], [2.3,2])
plt.title('$t=0.22 \;R^2/D_\mathrm{in}$') plt.title('$t=0.22 \;R^2/D_\mathrm{in}$')
plt.text(0.75, 0.18, '$\phi_\mathrm{u}$', fontsize=10) plt.text(0.75, 0.18, '$\phi_\mathrm{u}$', fontsize=10)
plt.gca().fill_between(model[:, 0], 0, model[:, 2], color=green) plt.gca().fill_between(model[:, 0], 0, model[:, 2], color=green)
plt.gca().fill_between(model[:, 0], model[:, -1], model[:, 2], color=grey) plt.gca().fill_between(model[:, 0], model[:, -1], model[:, 2], color=grey)
plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10) plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10)
save_nice_fig(fol+'Fig2/snap_shot.pdf') save_nice_fig(fol+'Fig2/snap_shot.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_fit = plt.plot(model[:, 0], model[:, 1], '-', lw=1, l_fit = plt.plot(model[:, 0], model[:, 1], '-', lw=1,
c=dark_grey, label='Simulation') c=dark_grey, label='Simulation')
plt.plot(model[:, 0], model[:, -1], 'k', lw=2) plt.plot(model[:, 0], model[:, -1], 'k', lw=2)
nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2], nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2],
[0,1], [2.3,2]) [0,1], [2.3,2])
plt.title('$t=0.22 \;R^2/D_\mathrm{in}$') plt.title('$t=0.22 \;R^2/D_\mathrm{in}$')
plt.text(1.45, 0.02, '$\phi_\mathrm{u}$', fontsize=10) plt.text(1.45, 0.02, '$\phi_\mathrm{u}$', fontsize=10)
plt.gca().fill_between(model[:, 0], 0, model[:, -1], color=green) plt.gca().fill_between(model[:, 0], 0, model[:, -1], color=green)
plt.gca().fill_between(model[:, 0], model[:, -1], model[:, 1], color=grey) plt.gca().fill_between(model[:, 0], model[:, -1], model[:, 1], color=grey)
plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10) plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10)
save_nice_fig(fol+'Fig2/snap_shot_bleach.pdf') save_nice_fig(fol+'Fig2/snap_shot_bleach.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
l_fit = plt.plot(model[:, 0], model[:, -2], '-', lw=1, l_fit = plt.plot(model[:, 0], model[:, -2], '-', lw=1,
c=dark_grey, label='Simulation') c=dark_grey, label='Simulation')
plt.plot(model[:, 0], model[:, -1], 'k', lw=2) plt.plot(model[:, 0], model[:, -1], 'k', lw=2)
nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2], nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2],
[0,1], [2.3,2]) [0,1], [2.3,2])
plt.title('$t=0.22 \;R^2/D_\mathrm{in}$') plt.title('$t=0.22 \;R^2/D_\mathrm{in}$')
plt.text(0.47, 0.4, '$\phi_\mathrm{u}$', fontsize=10) plt.text(0.47, 0.4, '$\phi_\mathrm{u}$', fontsize=10)
plt.gca().fill_between(model[:, 0], 0, model[:, -2], color=green) plt.gca().fill_between(model[:, 0], 0, model[:, -2], color=green)
plt.gca().fill_between(model[:, 0], model[:, -2], model[:, -1], color=grey) plt.gca().fill_between(model[:, 0], model[:, -2], model[:, -1], color=grey)
plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10) plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10)
save_nice_fig(fol+'Fig2/snap_shot_late.pdf') save_nice_fig(fol+'Fig2/snap_shot_late.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# l_fit = plt.plot(model[:, 0], model[:, -2], '-', lw=1, # l_fit = plt.plot(model[:, 0], model[:, -2], '-', lw=1,
# c=dark_grey, label='Simulation') # c=dark_grey, label='Simulation')
plt.plot(model[:, 0], model[:, -1], 'k', lw=2) plt.plot(model[:, 0], model[:, -1], 'k', lw=2)
nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2], nice_fig('radial distance $r/R$', 'volume fraction $\phi_\mathrm{u}$', [0, 2],
[0,1], [2.3,2]) [0,1], [2.3,2])
plt.title('$t=0.22 \;R^2/D_\mathrm{in}$') plt.title('$t=0.22 \;R^2/D_\mathrm{in}$')
plt.text(0.47, 0.4, '$\phi_\mathrm{u}$', fontsize=10) plt.text(0.47, 0.4, '$\phi_\mathrm{u}$', fontsize=10)
plt.gca().fill_between(model[:, 0], 0, model[:, -1], color=green) plt.gca().fill_between(model[:, 0], 0, model[:, -1], color=green)
plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10) plt.annotate('$\phi_\mathrm{tot}$', (1, 0.5), (1.5,0.5), fontsize=10)
save_nice_fig(fol+'Fig2/snap_shot_before.pdf') save_nice_fig(fol+'Fig2/snap_shot_before.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Figure 4: Obtaining info about outside: experiments. ### Figure 4: Obtaining info about outside: experiments.
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: data time course with full model.** **Panel: data time course with full model.**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
CMD = np.loadtxt(fol+'Fig4/CMD_timecourse.csv', delimiter=',') CMD = np.loadtxt(fol+'Fig4/CMD_timecourse.csv', delimiter=',')
CMD_fit = np.loadtxt(fol+'Fig4/CMD_fit_timecourse.csv', delimiter=',') CMD_fit = np.loadtxt(fol+'Fig4/CMD_fit_timecourse.csv', delimiter=',')
l_data = plt.plot(CMD[:, 0], CMD[:, 1:], c=green, lw=2, l_data = plt.plot(CMD[:, 0], CMD[:, 1:], c=green, lw=2,
label='Experiment') label='Experiment')
l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1:], '-', lw=1, l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1:], '-', lw=1,
c=dark_grey, label='Simulation') c=dark_grey, label='Simulation')
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]', nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]',
[0, 2.4*np.max(CMD[:, 0])], [0,0.5], [2.3,2]) [0, 2.4*np.max(CMD[:, 0])], [0,0.5], [2.3,2])
plt.legend([l_data[0], l_fit[0]], ['CMD/PLYS', 'Full model'], frameon=False, plt.legend([l_data[0], l_fit[0]], ['CMD/PLYS', 'Full model'], frameon=False,
fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7)) fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))
save_nice_fig(fol+'Fig4/CMD_spat_recov_new.pdf') save_nice_fig(fol+'Fig4/CMD_spat_recov_new.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PLYS = np.loadtxt(fol+'Fig4/PLYS_timecourse.csv', delimiter=',') PLYS = np.loadtxt(fol+'Fig4/PLYS_timecourse.csv', delimiter=',')
PLYS_fit = np.loadtxt(fol+'Fig4/PLYS_fit_timecourse.csv', delimiter=',') PLYS_fit = np.loadtxt(fol+'Fig4/PLYS_fit_timecourse.csv', delimiter=',')
PLYS_t = np.loadtxt(fol+'Fig4/PLYS_fit_time.csv', delimiter=',') PLYS_t = np.loadtxt(fol+'Fig4/PLYS_fit_time.csv', delimiter=',')
l_data = plt.plot(PLYS[:, 0], PLYS[:, 1::30], c=blue, lw=2) l_data = plt.plot(PLYS[:, 0], PLYS[:, 1::30], c=blue, lw=2)
l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 2::30], '-', lw=1, l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 2::30], '-', lw=1,
c=dark_grey, label='Simulation') # time course c=dark_grey, label='Simulation') # time course
l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 1], '-', lw=1, l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 1], '-', lw=1,
c=dark_grey) # Initial condition c=dark_grey) # Initial condition
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]', nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]',
[0, 2.4*np.max(PLYS[:, 0])], [0,0.75], [2.3,2]) [0, 2.4*np.max(PLYS[:, 0])], [0,0.75], [2.3,2])
plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False, plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False,
fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7)) fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))
# save_nice_fig(fol+'Fig4/PLYS_spat_recov_new.pdf') # save_nice_fig(fol+'Fig4/PLYS_spat_recov_new.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i in range(np.shape(PLYS)[1]): for i in range(np.shape(PLYS)[1]):
l_data = plt.plot(PLYS[:, 0], PLYS[:, 1+i], c=blue, lw=2) l_data = plt.plot(PLYS[:, 0], PLYS[:, 1+i], c=blue, lw=2)
l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 2+i], '-', lw=1, l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 2+i], '-', lw=1,
c=dark_grey, label='Simulation') # time course c=dark_grey, label='Simulation') # time course
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]', nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity [a.u]',
[0, 2.4*np.max(PLYS[:, 0])], [0,0.9], [2.3,2.005]) [0, 2.4*np.max(PLYS[:, 0])], [0,0.9], [2.3,2.005])
plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False, plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False,
fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7)) fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))
t = str(np.round(PLYS_t[i+1], 2)) t = str(np.round(PLYS_t[i+1], 2))
plt.text(0.5, 0.785, t.ljust(4, '0') + ' s') plt.text(0.5, 0.785, t.ljust(4, '0') + ' s')
save_nice_fig(fol+'Fig4/PLYSATP_mov/PLYS_spat_recov_mov_'+str(i)+'.png', form='png') save_nice_fig(fol+'Fig4/PLYSATP_mov/PLYS_spat_recov_mov_'+str(i)+'.png', form='png')
plt.show(); plt.show();
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PGL = np.loadtxt(fol+'Fig4/PGL_timecourse.csv', delimiter=',') PGL = np.loadtxt(fol+'Fig4/PGL_timecourse.csv', delimiter=',')
PGL_fit = np.loadtxt(fol+'Fig4/PGL_fit_timecourse.csv', delimiter=',') PGL_fit = np.loadtxt(fol+'Fig4/PGL_fit_timecourse.csv', delimiter=',')
l_data = plt.plot(PGL[:, 0], PGL[:, 1::4], c=red, lw=2, l_data = plt.plot(PGL[:, 0], PGL[:, 1::4], c=red, lw=2,
label='Experiment') label='Experiment')
l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 2::4], '-', lw=1, l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 2::4], '-', lw=1,
c=dark_grey, label='Simulation') c=dark_grey, label='Simulation')
plt.plot(PGL_fit[:, 0], PGL_fit[: 1], '-', lw=1, c=dark_grey) plt.plot(PGL_fit[:, 0], PGL_fit[: 1], '-', lw=1, c=dark_grey)
nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)', nice_fig('radial distance $r$ [$\mathrm{\mu m}$]', 'intensity (a.u)',
[0, 2.5*np.max(PGL[:, 0])], [0,0.65], [2.3,2]) [0, 2.5*np.max(PGL[:, 0])], [0,0.65], [2.3,2])
plt.legend([l_data[0], l_fit[0]], ['PGL', 'Full model'], plt.legend([l_data[0], l_fit[0]], ['PGL', 'Full model'],
frameon=False, fontsize=9, handletextpad=0.4, frameon=False, fontsize=9, handletextpad=0.4,
handlelength=0.8, loc=(0.54, 0.7)) handlelength=0.8, loc=(0.54, 0.7))
save_nice_fig(fol+'Fig4/PGL_spat_recov.pdf') save_nice_fig(fol+'Fig4/PGL_spat_recov.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Experimental Partition coefficient vs $D_{out}$ for CMD** **Panel: Experimental Partition coefficient vs $D_{out}$ for CMD**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PLYS = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PLYS.csv') PLYS = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PLYS.csv')
CMD = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/CMD.csv') CMD = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/CMD.csv')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax1 = plt.subplots() fig, ax1 = plt.subplots()
plt.sca(ax1) plt.sca(ax1)
sns.lineplot(x="P", y="D_out", data=CMD, color=green, ci='sd') sns.lineplot(x="P", y="D_out", data=CMD, color=green, ci='sd')
sns.lineplot(x="P", y="D_out", data=PLYS, color=blue, ci='sd') sns.lineplot(x="P", y="D_out", data=PLYS, color=blue, ci='sd')
plt.plot(np.logspace(1, 3, 10), 1.5*np.logspace(1, 3, 10), '--', c='grey', alpha=0.5) plt.plot(np.logspace(1, 3, 10), 1.5*np.logspace(1, 3, 10), '--', c='grey', alpha=0.5)
# plt.plot(np.logspace(0, 2, 10), 0.2*np.logspace(0, 2, 10)**2, '--', c='grey') # plt.plot(np.logspace(0, 2, 10), 0.2*np.logspace(0, 2, 10)**2, '--', c='grey')
ax1.set_yscale('log') ax1.set_yscale('log')
ax1.set_xscale('log') ax1.set_xscale('log')
nice_fig('Partition coefficient $P$', '$D_\mathrm{out} \;[\mathrm{\mu m^2s^{-1}}]$', nice_fig('Partition coefficient $P$', '$D_\mathrm{out} \;[\mathrm{\mu m^2s^{-1}}]$',
[1,340], [0.08,450], [2.3,2]) [1,340], [0.08,450], [2.3,2])
plt.legend(['CMD/PLYS', 'ATP/PLYS'], frameon=False, fontsize=9, loc=(0.44,0.05)) plt.legend(['CMD/PLYS', 'ATP/PLYS'], frameon=False, fontsize=9, loc=(0.44,0.05))
plt.text(1.1, 2, '$D_\mathrm{in, P/A}$', color=blue) plt.text(1.1, 2, '$D_\mathrm{in, P/A}$', color=blue)
plt.text(1.1, 7, '$D_\mathrm{in, C/P}$', color=green) plt.text(1.1, 7, '$D_\mathrm{in, C/P}$', color=green)
# plt.plot([1, 9], [1.7, 1.7], '--', color=blue) # plt.plot([1, 9], [1.7, 1.7], '--', color=blue)
# plt.plot([1, 12], [6, 6], '--', color=green) # plt.plot([1, 12], [6, 6], '--', color=green)
plt.xticks([1, 10, 100]); plt.xticks([1, 10, 100]);
plt.yticks([0.1, 1, 10, 100]); plt.yticks([0.1, 1, 10, 100]);
save_nice_fig(fol+'Fig4/PLYS_CMD.pdf') save_nice_fig(fol+'Fig4/PLYS_CMD.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Experimental Partition coefficient vs $D_out$ for PGL-3** **Panel: Experimental Partition coefficient vs $D_out$ for PGL-3**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
PGL_50 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_50.csv') PGL_50 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_50.csv')
PGL_60 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_60.csv') PGL_60 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_60.csv')
PGL_75 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_75.csv') PGL_75 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_75.csv')
PGL_90 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_90.csv') PGL_90 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_90.csv')
PGL_100 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_100.csv') PGL_100 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_100.csv')
PGL_120 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_120.csv') PGL_120 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_120.csv')
PGL_150 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_150.csv') PGL_150 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_150.csv')
PGL_180 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_180.csv') PGL_180 = pd.read_csv('/Users/hubatsch/Desktop/DropletFRAP/Latex/Figures/Fig4/PGL_180.csv')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax1 = plt.subplots() fig, ax1 = plt.subplots()
plt.sca(ax1) plt.sca(ax1)
temp = sns.color_palette() temp = sns.color_palette()
sns.set_palette(sns.color_palette("rocket", 9)) sns.set_palette(sns.color_palette("rocket", 9))
l50 = sns.lineplot(x="P", y="D_out", data=PGL_50, l50 = sns.lineplot(x="P", y="D_out", data=PGL_50,
color=sns.color_palette()[1], ci=None) color=sns.color_palette()[1], ci=None)
l60 = sns.lineplot(x="P", y="D_out", data=PGL_60, l60 = sns.lineplot(x="P", y="D_out", data=PGL_60,
color=sns.color_palette()[2], ci=None) color=sns.color_palette()[2], ci=None)
l75 = sns.lineplot(x="P", y="D_out", data=PGL_75, l75 = sns.lineplot(x="P", y="D_out", data=PGL_75,
color=sns.color_palette()[3], ci=None) color=sns.color_palette()[3], ci=None)
l90 = sns.lineplot(x="P", y="D_out", data=PGL_90, l90 = sns.lineplot(x="P", y="D_out", data=PGL_90,
color=sns.color_palette()[4], ci=None) color=sns.color_palette()[4], ci=None)
l100 = sns.lineplot(x="P", y="D_out", data=PGL_100, l100 = sns.lineplot(x="P", y="D_out", data=PGL_100,
color=sns.color_palette()[5], ci=None) color=sns.color_palette()[5], ci=None)
leg1 = plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM'], leg1 = plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM'],
labelspacing=0.3, loc=(0.63, 0.0), labelspacing=0.3, loc=(0.63, 0.0),
handletextpad=0.4, handlelength=0.5, frameon=0) handletextpad=0.4, handlelength=0.5, frameon=0)
l120 = sns.lineplot(x="P", y="D_out", data=PGL_120, l120 = sns.lineplot(x="P", y="D_out", data=PGL_120,
color=sns.color_palette()[6], ci=None) color=sns.color_palette()[6], ci=None)
l150 = sns.lineplot(x="P", y="D_out", data=PGL_150, l150 = sns.lineplot(x="P", y="D_out", data=PGL_150,
color=sns.color_palette()[7], ci=None) color=sns.color_palette()[7], ci=None)
l180 = sns.lineplot(x="P", y="D_out", data=PGL_180, l180 = sns.lineplot(x="P", y="D_out", data=PGL_180,
color=sns.color_palette()[8], ci=None) color=sns.color_palette()[8], ci=None)
plt.plot(np.logspace(0, 4, 10), 0.07*np.logspace(0, 4, 10), plt.plot(np.logspace(0, 4, 10), 0.07*np.logspace(0, 4, 10),
'--', c='grey', alpha=0.5) '--', c='grey', alpha=0.5)
# plt.plot(np.logspace(2, 3, 10), 30*np.ones(10), '--', c='m', lw=2) # plt.plot(np.logspace(2, 3, 10), 30*np.ones(10), '--', c='m', lw=2)
ax1.set_yscale('log') ax1.set_yscale('log')
ax1.set_xscale('log') ax1.set_xscale('log')
nice_fig('Partition coefficient $P$', nice_fig('Partition coefficient $P$',
'$D_\mathrm{out} \;[\mathrm{\mu m^2 s^{-1}}]$', '$D_\mathrm{out} \;[\mathrm{\mu m^2 s^{-1}}]$',
[1,10000], [0.0003,600], [2.3,2]) [1,10000], [0.0003,600], [2.3,2])
plt.xticks([1, 10, 100, 1000, 10000]); plt.xticks([1, 10, 100, 1000, 10000]);
plt.legend(loc=1) plt.legend(loc=1)
plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM', plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM',
'120 mM', '150 mM', '180 mM'], labelspacing=0.3, '120 mM', '150 mM', '180 mM'], labelspacing=0.3,
loc=(0, 0.66), handletextpad=0.4, handlelength=0.5, frameon=0) loc=(0, 0.66), handletextpad=0.4, handlelength=0.5, frameon=0)
plt.gca().add_artist(leg1) plt.gca().add_artist(leg1)
# plt.plot([700, 10000], [30, 30], color=(1, 0, 0)) # plt.plot([700, 10000], [30, 30], color=(1, 0, 0))
# plt.plot([700, 10000], [70, 70], color=(1, 0, 0)) # plt.plot([700, 10000], [70, 70], color=(1, 0, 0))
# plt.plot([700, 700], [30, 70], color=(1, 0, 0)) # plt.plot([700, 700], [30, 70], color=(1, 0, 0))
# plt.plot([10000, 10000], [30, 70], color=(1, 0, 0)) # plt.plot([10000, 10000], [30, 70], color=(1, 0, 0))
# save_nice_fig(fol+'Fig4/PGL-3.pdf') # save_nice_fig(fol+'Fig4/PGL-3.pdf')
sns.set_palette(temp) sns.set_palette(temp)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
le_egfp = 239 le_egfp = 239
le_pgl3 = 693 le_pgl3 = 693
ratio = le_egfp/(le_egfp+le_pgl3) ratio = le_egfp/(le_egfp+le_pgl3)
D_factor = np.sqrt(ratio) D_factor = np.sqrt(ratio)
D_GFPw = 87 # micron^2/s, Arrio-Dupont et al. 2000 BJ, GFP in water D_GFPw = 87 # micron^2/s, Arrio-Dupont et al. 2000 BJ, GFP in water
D = D_GFPw*np.sqrt(ratio) D = D_GFPw*np.sqrt(ratio)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
P_anatol = np.array([686, 533, 590, 474])/np.array([1.6, 5.5, 7.5, 19.3]) P_anatol = np.array([686, 533, 590, 474])/np.array([1.6, 5.5, 7.5, 19.3])
salt_anatol = np.array([100, 150, 175, 200]) salt_anatol = np.array([100, 150, 175, 200])
P = PGL_50['P'] P = PGL_50['P']
def i_P(df, Pa): def i_P(df, Pa):
return interp1d(np.array(df.groupby('P').mean()).flatten(), return interp1d(np.array(df.groupby('P').mean()).flatten(),
P.unique(), bounds_error=False, P.unique(), bounds_error=False,
fill_value="extrapolate")(Pa) fill_value="extrapolate")(Pa)
Ps_15 = [i_P(PGL, 2) for PGL in [PGL_50, PGL_60, PGL_75, PGL_90, Ps_15 = [i_P(PGL, 2) for PGL in [PGL_50, PGL_60, PGL_75, PGL_90,
PGL_100, PGL_120, PGL_150, PGL_180]] PGL_100, PGL_120, PGL_150, PGL_180]]
Ps_30 = [i_P(PGL, 2) for PGL in [PGL_50, PGL_60, PGL_75, PGL_90, Ps_30 = [i_P(PGL, 2) for PGL in [PGL_50, PGL_60, PGL_75, PGL_90,
PGL_100, PGL_120, PGL_150, PGL_180]] PGL_100, PGL_120, PGL_150, PGL_180]]
salts = [50, 60, 75, 90, 100, 120, 150, 180] salts = [50, 60, 75, 90, 100, 120, 150, 180]
nice_fig('$c_\mathrm{salt} \; [\mathrm{mM}]$', 'Partition coefficient $P$', nice_fig('$c_\mathrm{salt} \; [\mathrm{mM}]$', 'Partition coefficient $P$',
[50, 180], [50,2000], [2.3,2]) [50, 180], [50,2000], [2.3,2])
plt.plot(salts, Ps_15, color=red) plt.plot(salts, Ps_15, color=red)
plt.plot(salts, Ps_30, color=red) plt.plot(salts, Ps_30, color=red)
plt.plot(salt_anatol, P_anatol) plt.plot(salt_anatol, P_anatol)
plt.gca().fill_between(salts, Ps_15, Ps_30, color=red, alpha=0.2) plt.gca().fill_between(salts, Ps_15, Ps_30, color=red, alpha=0.2)
plt.yticks(rotation=45) plt.yticks(rotation=45)
plt.yscale('log') plt.yscale('log')
# plt.text(110, 6200, '$D_\mathrm{out}=30\;\mathrm{\mu m^2/s}$', # plt.text(110, 6200, '$D_\mathrm{out}=30\;\mathrm{\mu m^2/s}$',
# color=red, size=8) # color=red, size=8)
# plt.text(54, 700, '$D_\mathrm{out}=15\;\mathrm{\mu m^2/s}$', # plt.text(54, 700, '$D_\mathrm{out}=15\;\mathrm{\mu m^2/s}$',
# color=red, size=8) # color=red, size=8)
# save_nice_fig(fol+'Fig4/PGL-3_part.pdf') # save_nice_fig(fol+'Fig4/PGL-3_part.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Figure 5: Obtaining info about outside: theory. ### Figure 5: Obtaining info about outside: theory.
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Partition coefficient vs. $D_{out}$, showcasing four different simulation start cases.** **Panel: Partition coefficient vs. $D_{out}$, showcasing four different simulation start cases.**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
sns.set_palette(sns.color_palette("Set2")) sns.set_palette(sns.color_palette("Set2"))
P_Do = np.loadtxt(fol+'/Fig4/Part_vs_Do_220121.csv', delimiter=',') P_Do = np.loadtxt(fol+'/Fig4/Part_vs_Do_220121.csv', delimiter=',')
sns.set_style("white") sns.set_style("white")
sns.set_palette([sns.color_palette()[i] for i in [3, 0, 1, 2]]) sns.set_palette([sns.color_palette()[i] for i in [3, 0, 1, 2]])
P = [5, 150, 5, 150] P = [5, 150, 5, 150]
D_o = [0.1, 0.1, 1, 1] D_o = [0.1, 0.1, 1, 1]
plt.gca().set_prop_cycle(None) plt.gca().set_prop_cycle(None)
nice_fig('Partition coefficient $P$', '$D_\mathrm{out}$ [$\mathrm{\mu m^2/s}$]', [0.9,320], [0.000001,340], [2.3,2]) nice_fig('Partition coefficient $P$', '$D_\mathrm{out}$ [$\mathrm{\mu m^2/s}$]', [0.9,320], [0.000001,340], [2.3,2])
lines = plt.loglog(P_Do[0, :], P_Do[1:, :].transpose()) lines = plt.loglog(P_Do[0, :], P_Do[1:, :].transpose())
plt.plot(P_Do[0, :], P_Do[0, :], '--', c='grey') plt.plot(P_Do[0, :], P_Do[0, :], '--', c='grey')
plt.legend([lines[2], lines[0], lines[3], lines[1]], plt.legend([lines[2], lines[0], lines[3], lines[1]],
['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False, ['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False,
title=r'$D_\mathrm{out}$/P [$\mathrm{\mu m^2/s}$]:', columnspacing=0.5, labelspacing=0.3, title=r'$D_\mathrm{out}$/P [$\mathrm{\mu m^2/s}$]:', columnspacing=0.5, labelspacing=0.3,
loc=(0.4, 0), handletextpad=0.4, handlelength=0.5) loc=(0.4, 0), handletextpad=0.4, handlelength=0.5)
plt.gca().set_prop_cycle(None) plt.gca().set_prop_cycle(None)
plt.plot(P[0], D_o[0], 'd') plt.plot(P[0], D_o[0], 'd')
plt.plot(P[1], D_o[1], 'd') plt.plot(P[1], D_o[1], 'd')
plt.plot(P[2], D_o[2], 'd') plt.plot(P[2], D_o[2], 'd')
plt.plot(P[3], D_o[3], 'd') plt.plot(P[3], D_o[3], 'd')
plt.annotate('$D_{out}$/P = 1 $\mathrm{\mu m^2/s}$', [1,40], c='grey') plt.annotate('$D_{out}$/P = 1 $\mathrm{\mu m^2/s}$', [1,40], c='grey')
plt.xticks([1, 10, 100]); plt.xticks([1, 10, 100]);
save_nice_fig(fol+'Fig4/D_vs_P.pdf') save_nice_fig(fol+'Fig4/D_vs_P.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Cost function** **Panel: Cost function**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
P_Cost = np.loadtxt(fol+'/Fig4/Part_vs_Cost_220121.csv', delimiter=',') P_Cost = np.loadtxt(fol+'/Fig4/Part_vs_Cost_220121.csv', delimiter=',')
nice_fig('Partition coefficient $P$', '$Cost_\mathrm{min} (P)$ [a.u.]', [0.9,320], [0.000000001,0.01], [2.3,2]) nice_fig('Partition coefficient $P$', '$Cost_\mathrm{min} (P)$ [a.u.]', [0.9,320], [0.000000001,0.01], [2.3,2])
lines = plt.loglog(P_Cost[0, :], P_Cost[1:, :].transpose()) lines = plt.loglog(P_Cost[0, :], P_Cost[1:, :].transpose())
# plt.legend([lines[2], lines[0], lines[3], lines[1]], # plt.legend([lines[2], lines[0], lines[3], lines[1]],
# ['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False, # ['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False,
# title=r'$D_\mathrm{out}$/P set to:', columnspacing=0.5, labelspacing=0.3, # title=r'$D_\mathrm{out}$/P set to:', columnspacing=0.5, labelspacing=0.3,
# loc=(0.081, 0), handletextpad=0.4, handlelength=0.5) # loc=(0.081, 0), handletextpad=0.4, handlelength=0.5)
plt.xticks([1, 10, 100]); plt.xticks([1, 10, 100]);
save_nice_fig(fol+'Fig4/D_vs_Cost.pdf') save_nice_fig(fol+'Fig4/D_vs_Cost.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
P_Cost = np.loadtxt(fol+'/Fig4/Part_vs_Cost_220121.csv', delimiter=',') P_Cost = np.loadtxt(fol+'/Fig4/Part_vs_Cost_220121.csv', delimiter=',')
nice_fig('Partition coefficient $P$', '$Cost_\mathrm{min} (P)$ [a.u.]', [0.9,100], [0,2], [2.3,2]) nice_fig('Partition coefficient $P$', '$Cost_\mathrm{min} (P)$ [a.u.]', [0.9,100], [0,2], [2.3,2])
lines = plt.plot(P_Cost[0], P_Cost[1:].transpose()/[x[0] for x in P_Cost[1:]]) lines = plt.plot(P_Cost[0], P_Cost[1:].transpose()/[x[0] for x in P_Cost[1:]])
plt.gca().set_xscale('log') plt.gca().set_xscale('log')
plt.plot([150, 150], [0, 1], '--', lw=2, c=grey) plt.plot([150, 150], [0, 1], '--', lw=2, c=grey)
save_nice_fig(fol+'Fig4/D_vs_Cost_single.pdf') save_nice_fig(fol+'Fig4/D_vs_Cost_single.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Valley in parameter space** **Panel: Valley in parameter space**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
con = np.loadtxt(fol+'Fig4/Valley_220121.csv', delimiter=',') con = np.loadtxt(fol+'Fig4/Valley_220121.csv', delimiter=',')
levels = MaxNLocator(nbins=100).tick_values(np.log10(con[:, 2:].min()), np.log10(con[:, 2:].max())) levels = MaxNLocator(nbins=100).tick_values(np.log10(con[:, 2:].min()), np.log10(con[:, 2:].max()))
nice_fig('Partition coefficient $P$', '$D_\mathrm{out} \;[\mathrm{\mu m^2 s^{-1}}]$', [1, 3], [-2,1], [2.3,2]) nice_fig('Partition coefficient $P$', '$D_\mathrm{out} \;[\mathrm{\mu m^2 s^{-1}}]$', [1, 3], [-2,1], [2.3,2])
CS = plt.contourf(np.log10(con[:, 0]), np.log10(con[:, 1]), np.log10(con[:, 2:]), levels=levels, cmap=cm.coolwarm) CS = plt.contourf(np.log10(con[:, 0]), np.log10(con[:, 1]), np.log10(con[:, 2:]), levels=levels, cmap=cm.coolwarm)
plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6) plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6)
le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.4) le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.4)
# plt.plot(np.log10(P_Do[0, :]), np.log10(P_Do[2, :].transpose()), '--', c=green, lw = 1) # plt.plot(np.log10(P_Do[0, :]), np.log10(P_Do[2, :].transpose()), '--', c=green, lw = 1)
le.get_texts()[0].set_color('white') le.get_texts()[0].set_color('white')
plt.xticks([1, 2, 3], ['$10^1$', '$10^2$', '$10^3$']) plt.xticks([1, 2, 3], ['$10^1$', '$10^2$', '$10^3$'])
plt.yticks([-2, -1, 0, 1], ['$10^{-2}$', '$10^{-1}$', '$10^0$', '$10^1$']) plt.yticks([-2, -1, 0, 1], ['$10^{-2}$', '$10^{-1}$', '$10^0$', '$10^1$'])
plt.tick_params('x', pad=5) plt.tick_params('x', pad=5)
fig1 = plt.gcf() fig1 = plt.gcf()
clb = fig1.colorbar(CS, ticks=[0, -2, -4, -6]) clb = fig1.colorbar(CS, ticks=[0, -2, -4, -6])
clb.ax.set_title('Cost') clb.ax.set_title('Cost')
save_nice_fig(fol+'Fig4/Sim_D_out_P.pdf') save_nice_fig(fol+'Fig4/Sim_D_out_P.pdf')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
**Panel: Zoom in for valley in parameter space** **Panel: Zoom in for valley in parameter space**
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# levels = MaxNLocator(nbins=15).tick_values(np.log10(con[:, 2:].min()), np.log10(con[:, 2:].max())) # levels = MaxNLocator(nbins=15).tick_values(np.log10(con[:, 2:].min()), np.log10(con[:, 2:].max()))
nice_fig('Partition coefficient $P$', '', [1.9, 2.4], [-1.5,-0.5], [2.3,2]) nice_fig('Partition coefficient $P$', '', [1.9, 2.4], [-1.5,-0.5], [2.3,2])
CS = plt.contourf(np.log10(con[16:-27, 0]), np.log10(con[16:-27, 1]), CS = plt.contourf(np.log10(con[16:-27, 0]), np.log10(con[16:-27, 1]),
np.log10(con[16:-27, 2+16:-27]), levels=levels, np.log10(con[16:-27, 2+16:-27]), levels=levels,
cmap=cm.coolwarm, vmax=-1.5) cmap=cm.coolwarm, vmax=-1.5)
plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6) plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6)
le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.4) le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.4)
le.get_texts()[0].set_color('white') le.get_texts()[0].set_color('white')
plt.xticks([2, 2.25], ['$10^2$', '$10^{2.25}$']) plt.xticks([2, 2.25], ['$10^2$', '$10^{2.25}$'])
plt.yticks([-1.5, -1, -0.5], ['$10^{-1.5}$', '$10^{-1}$', '$10^{-0.5}$']) plt.yticks([-1.5, -1, -0.5], ['$10^{-1.5}$', '$10^{-1}$', '$10^{-0.5}$'])
plt.tick_params('x', pad=5) plt.tick_params('x', pad=5)
fig1 = plt.gcf() fig1 = plt.gcf()
clb = fig1.colorbar(CS, ticks=[-2, -4, -6]) clb = fig1.colorbar(CS, ticks=[-2, -4, -6])
clb.ax.set_title('Cost') clb.ax.set_title('Cost')
# save_nice_fig(fol+'Fig4/Sim_D_out_P_inset.pdf') # save_nice_fig(fol+'Fig4/Sim_D_out_P_inset.pdf')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
con_fine = np.loadtxt(fol+'Fig4/Valley_fine_210121.csv', delimiter=',') con_fine = np.loadtxt(fol+'Fig4/Valley_fine_210121.csv', delimiter=',')
# nice_fig('Partition coefficient $P$', '', [2.172, 2.1795], [-1.005,-0.9955], [2.3,2]) # nice_fig('Partition coefficient $P$', '', [2.172, 2.1795], [-1.005,-0.9955], [2.3,2])
P = np.log10(con_fine[:,0]) P = np.log10(con_fine[:,0])
D_o = np.log10(con_fine[:,1]) D_o = np.log10(con_fine[:,1])
fval = np.log10(con_fine[:,2]) fval = np.log10(con_fine[:,2])
# levels = np.linspace(-fval.max(), -fval.min(), 100) # levels = np.linspace(-fval.max(), -fval.min(), 100)
levels = MaxNLocator(nbins=50).tick_values(fval.min(), fval.max()) levels = MaxNLocator(nbins=50).tick_values(fval.min(), fval.max())
plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6) plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6)
cs = plt.tricontourf(P, D_o, -fval, levels=np.flip(-levels)) cs = plt.tricontourf(P, D_o, -fval, levels=np.flip(-levels))
plt.yticks([-1.005, -1, -0.996], ['$10^{-1.005}$', '$10^{-1}$', '$10^{-0.996}$'], rotation=30) plt.yticks([-1.005, -1, -0.996], ['$10^{-1.005}$', '$10^{-1}$', '$10^{-0.996}$'], rotation=30)
plt.xticks([2.173, 2.178], ['$10^{2.173}$', '$10^{2.178}$']) plt.xticks([2.173, 2.178], ['$10^{2.173}$', '$10^{2.178}$'])
plt.tick_params(axis='x',direction='out', which='minor', length=10) plt.tick_params(axis='x',direction='out', which='minor', length=10)
fig1 = plt.gcf() fig1 = plt.gcf()
clb = fig1.colorbar(cs, ticks=[5, 7, 9, 11]) clb = fig1.colorbar(cs, ticks=[5, 7, 9, 11])
clb.ax.set_title('Cost') clb.ax.set_title('Cost')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
``` ```
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