diff --git a/Plots_Droplet_FRAP.ipynb b/Plots_Droplet_FRAP.ipynb
index 5f1b226a7790d77381cbb68b2c3d9167ddedb48e..15f06f243d6f662cb7201285466464d3983b1184 100644
--- a/Plots_Droplet_FRAP.ipynb
+++ b/Plots_Droplet_FRAP.ipynb
@@ -60,8 +60,8 @@
" plt.ylim(yli)\n",
" plt.tick_params(axis='both', which='major', labelsize=fs)\n",
"\n",
- "def save_nice_fig(name, form='pdf'):\n",
- " plt.savefig(name, format=form, dpi=300, bbox_inches='tight',\n",
+ "def save_nice_fig(name, form='pdf', dpi=300):\n",
+ " plt.savefig(name, format=form, dpi=dpi, bbox_inches='tight',\n",
" transparent=True)"
]
},
@@ -184,7 +184,7 @@
"metadata": {},
"outputs": [],
"source": [
- "nice_fig('time $t$ [s]', 'intensity (a.u)', [0,4], [0,1.05], [1.5,2])\n",
+ "nice_fig('time $t$ [s]', 'intensity [a.u.]', [0,4], [0,1.05], [1.5,2])\n",
"plt.plot(np.linspace(0, (f_i[0].T-1)*f_i[0].dt, f_i[0].T), \n",
" [np.mean(x)/f_i[0].phi_tot_int for x in profs[0]],\n",
" lw=2, label='d=0.5', ls='-')\n",
@@ -422,6 +422,7 @@
"fig, ax1 = plt.subplots()\n",
"ax2 = ax1.twinx()\n",
"plt.sca(ax1)\n",
+ "nice_fig('c_\\mathrm{salt} [\\mathrm{mM}]', '$\\eta^{-1} \\;[Pa\\cdot s]^{-1}$', [40,190], [0,7.24], [2.3,2])\n",
"sns.lineplot(x=\"conc\", y=\"D\", data=lars, color=sns.color_palette()[1])\n",
"sns.scatterplot(x=\"conc\", y=\"D\", data=lars, color=sns.color_palette()[1], alpha=0.8)\n",
"plt.xlabel('$c_\\mathrm{salt}\\; [\\mathrm{mM}]$')\n",
@@ -432,11 +433,10 @@
"ax1.patch.set_visible(False)\n",
"plt.sca(ax2)\n",
"sns.lineplot(x=\"conc\", y=\"vis\", data=louise, color=grey, label='data from Jawerth \\net al. 2018')\n",
- "nice_fig('c_\\mathrm{salt} [\\mathrm{mM}]', '$\\eta^{-1} \\;[Pa\\cdot s]^{-1}$', [40,190], [0,7.24], [2.3,2])\n",
- "plt.yticks(color = grey)\n",
- "plt.ylabel('$\\eta^{-1} \\;[\\mathrm{Pa\\cdot s}]^{-1}$ ', color = grey)\n",
+ "plt.yticks(color = grey, fontsize=12)\n",
+ "plt.ylabel('$\\eta^{-1} \\;[\\mathrm{Pa\\cdot s}]^{-1}$ ', color = grey, fontsize=12)\n",
"plt.legend(frameon=False, fontsize=9)\n",
- "save_nice_fig(fol+'Fig1/Lars_vs_Louise.pdf')"
+ "# save_nice_fig(fol+'Fig1/Lars_vs_Louise.pdf')"
]
},
{
@@ -493,13 +493,13 @@
"metadata": {},
"outputs": [],
"source": [
- "CMD = np.loadtxt(fol+'/Fig1/CMD_timecourse.csv', delimiter=',')\n",
- "CMD_fit = np.loadtxt(fol+'/Fig1/CMD_fit_timecourse.csv', delimiter=',')\n",
- "l_sim = plt.plot(CMD[:, 0], CMD[:, 1::2], '.', c=green)\n",
- "l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1::2], '-', lw=1, c='k')\n",
+ "CMD = np.loadtxt(fol+'/Fig1/CMD_timecourse_D.csv', delimiter=',')\n",
+ "CMD_fit = np.loadtxt(fol+'/Fig1/CMD_fit_timecourse_D.csv', delimiter=',')\n",
+ "l_sim = plt.plot(CMD[:, 0], CMD[:, 1::40], '.', c=green)\n",
+ "l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1::40], '-', lw=1, c='k')\n",
"plt.plot(range(0, 10), np.ones(10)*np.min(CMD_fit[:, 1]), linestyle='--', color=grey, lw=1.5)\n",
"plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0, 0.85), frameon=False)\n",
- "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity (a.u)', [0,np.max(CMD_fit[:, 0])], [0,0.65], [2.3,2])\n",
+ "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]', [0,np.max(CMD_fit[:, 0])], [0,0.65], [2.3,2])\n",
"save_nice_fig(fol+'Fig1/CMD_spat_recov.pdf')"
]
},
@@ -516,13 +516,13 @@
"metadata": {},
"outputs": [],
"source": [
- "PGL = np.loadtxt(fol+'/Fig1/PGL_timecourse.csv', delimiter=',')\n",
- "PGL_fit = np.loadtxt(fol+'/Fig1/PGL_fit_timecourse.csv', delimiter=',')\n",
- "l_sim = plt.plot(PGL[:, 0], PGL[:, 1::2], '.', c=red)\n",
- "l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 1::2], '-', lw=1, c='k')\n",
+ "PGL = np.loadtxt(fol+'/Fig1/PGL_timecourse_D.csv', delimiter=',')\n",
+ "PGL_fit = np.loadtxt(fol+'/Fig1/PGL_fit_timecourse_D.csv', delimiter=',')\n",
+ "l_sim = plt.plot(PGL[:, 0], PGL[:, 1::130], '.', c=red)\n",
+ "l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 1::130], '-', lw=1, c='k')\n",
"plt.plot(range(0, 10), np.ones(10)*np.min(PGL_fit[:, 1]), linestyle='--', color=grey, lw=1.5)\n",
"plt.legend([l_sim[0], l_fit[0]], ['data', 'fit'], ncol=2, loc=(0.015, 0.865), frameon=False)\n",
- "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity (a.u)', [0,np.max(PGL_fit[:, 0])], [0, 0.7], [2.3,2])\n",
+ "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]', [0,np.max(PGL_fit[:, 0])], [0, 0.65], [2.3,2])\n",
"save_nice_fig(fol+'Fig1/PGL_spat_recov.pdf')"
]
},
@@ -546,7 +546,7 @@
"metadata": {},
"outputs": [],
"source": [
- "l_sim = plt.plot(PGL[:, 0], PGL[:, 4], '-', c=red)\n",
+ "l_sim = plt.plot(PGL[:, 0], PGL[:, 4*80], '-', c=red)\n",
"nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5)\n",
"plt.yticks([0, 0.5], ['', ''])\n",
"plt.xticks([0, 3], ['', ''])\n",
@@ -560,7 +560,7 @@
"metadata": {},
"outputs": [],
"source": [
- "l_sim = plt.plot(PGL[:, 0], PGL[:, 7], '-', c=red)\n",
+ "l_sim = plt.plot(PGL[:, 0], PGL[:, 7*80], '-', c=red)\n",
"nice_fig('', '', [0,np.max(PGL_fit[:, 0])], [0, 0.55], [0.7,0.7], fs=5)\n",
"plt.yticks([0, 0.5], ['', ''])\n",
"plt.xticks([0, 3], ['', ''])\n",
@@ -568,6 +568,51 @@
"save_nice_fig(fol+'Fig1/PGL_spat_510.pdf')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Supplement: movie PLYS**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "PLYS = np.loadtxt(fol+'/Fig1/PLYS_timecourse_D.csv', delimiter=',')\n",
+ "PLYS_fit = np.loadtxt(fol+'/Fig1/PLYS_fit_timecourse_D.csv', delimiter=',')\n",
+ "CMD = np.loadtxt(fol+'/Fig1/CMD_timecourse_D.csv', delimiter=',')\n",
+ "CMD_fit = np.loadtxt(fol+'/Fig1/CMD_fit_timecourse_D.csv', delimiter=',')\n",
+ "PGL = np.loadtxt(fol+'/Fig1/PGL_timecourse_D.csv', delimiter=',')\n",
+ "PGL_fit = np.loadtxt(fol+'/Fig1/PGL_fit_timecourse_D.csv', delimiter=',')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "zipped = zip([PLYS, CMD, PGL], [PLYS_fit, CMD_fit, PGL_fit],\n",
+ " [blue, green, red], ['PLYS/ATP', 'CMD/PLYS', 'PGL-3'],\n",
+ " [0.9, 0.7, 0.8])\n",
+ "for mov, mov_fit, c, l, yl in zipped:\n",
+ " for i in range(np.shape(mov)[1]-1):\n",
+ " l_sim = plt.plot(mov[:, 0], mov[:, 1+i], '-', c=c)\n",
+ " l_fit = plt.plot(mov_fit[:, 0], mov_fit[:, 1+i], '-', lw=1, c='k')\n",
+ " plt.plot(range(0, 10), np.ones(10)*np.min(mov_fit[:, 1]), linestyle='--', color=grey, lw=1.5)\n",
+ " plt.legend([l_sim[0], l_fit[0]], [l, 'Fit to Eq. (1)'], ncol=2, loc=(0, 0.85), frameon=False,\n",
+ " columnspacing=0.8, handletextpad=0.5, handlelength=0.65)\n",
+ " nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]', [0,np.max(mov_fit[:, 0])], [0, yl], [2.3,2])\n",
+ " plt.yticks([0.2, 0.4, 0.6])\n",
+ " if l=='PLYS/ATP':\n",
+ " plt.yticks([0.2, 0.4, 0.6, 0.8])\n",
+ " save_nice_fig(fol+'Fig4/Movies/'+l[:3]+'_spat_recov_mov_'+str(i)+'.png', form='png', dpi=149.5)\n",
+ " plt.show();"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -587,7 +632,7 @@
"# fig, ax1 = plt.subplots()\n",
"# ax2 = ax1.twiny()\n",
"# plt.sca(ax1)\n",
- "nice_fig('$t/T_\\mathrm{max}$', 'intensity (a.u)', [0,200], [0,0.62], [2.3,2])\n",
+ "nice_fig('$t/T_\\mathrm{max}$', 'intensity [a.u.]', [0,200], [0,0.62], [2.3,2])\n",
"# plt.sca(ax2)\n",
"# ax2.tick_params(axis=\"x\",direction=\"in\")\n",
"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)\n",
@@ -619,7 +664,7 @@
"metadata": {},
"outputs": [],
"source": [
- "nice_fig('time $t$ [s]', 'intensity (a.u)', [0,140], [0,0.82], [1,2])\n",
+ "nice_fig('time $t$ [s]', 'boundary intensity', [0,140], [0,0.82], [1,2])\n",
"temp = sns.color_palette()\n",
"sns.set_palette(sns.color_palette(\"rocket\", 9))\n",
"# plt.sca(ax2)\n",
@@ -642,7 +687,7 @@
"metadata": {},
"outputs": [],
"source": [
- "nice_fig('time $t$ [s]', 'intensity (a.u)', [0,10], [0,0.72], [1,2])\n",
+ "nice_fig('time $t$ [s]', 'intensity [a.u.]', [0,10], [0,0.72], [1,2])\n",
"plt.plot(ATP[::1, 0], ATP[::1,1], '-', label='PLYS/ATP', c=blue, markersize=3, alpha=0.7, lw=1.5)\n",
"plt.plot(CMD[::5, 0], CMD[::5,1], '-', label='CMD/PLYS', c=green, markersize=3, alpha=0.7, lw=1.5)\n",
"plt.legend(frameon=False, fontsize=7, loc=(0.1, 0), handletextpad=0.5)\n",
@@ -773,12 +818,15 @@
"source": [
"CMD = np.loadtxt(fol+'Fig4/CMD_timecourse.csv', delimiter=',')\n",
"CMD_fit = np.loadtxt(fol+'Fig4/CMD_fit_timecourse.csv', delimiter=',')\n",
- "l_data = plt.plot(CMD[:, 0], CMD[:, 1:], c=green, lw=2,\n",
+ "CMD_t = np.loadtxt(fol+'Fig4/CMD_fit_time.csv', delimiter=',')\n",
+ "l_data = plt.plot(CMD[:, 0], CMD[:, 1::30], c=green, lw=2,\n",
" label='Experiment')\n",
- "l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1:], '-', lw=1,\n",
+ "l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 2::30], '-', lw=1,\n",
" c=dark_grey, label='Simulation')\n",
- "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u]',\n",
- " [0, 2.4*np.max(CMD[:, 0])], [0,0.5], [2.3,2])\n",
+ "l_fit = plt.plot(CMD_fit[:, 0], CMD_fit[:, 1], '-', lw=1,\n",
+ " c=dark_grey) # Initial condition\n",
+ "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]',\n",
+ " [0, 2.4*np.max(CMD[:, 0])], [0,0.7], [2.3,2])\n",
"plt.legend([l_data[0], l_fit[0]], ['CMD/PLYS', 'Full model'], frameon=False,\n",
" fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))\n",
"save_nice_fig(fol+'Fig4/CMD_spat_recov_new.pdf')"
@@ -798,11 +846,11 @@
" c=dark_grey, label='Simulation') # time course\n",
"l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 1], '-', lw=1,\n",
" c=dark_grey) # Initial condition\n",
- "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u]',\n",
+ "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]',\n",
" [0, 2.4*np.max(PLYS[:, 0])], [0,0.75], [2.3,2])\n",
- "plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False,\n",
+ "plt.legend([l_data[0], l_fit[0]], ['PLYS/ATP', 'Full model'], frameon=False,\n",
" fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))\n",
- "# save_nice_fig(fol+'Fig4/PLYS_spat_recov_new.pdf')"
+ "save_nice_fig(fol+'Fig4/PLYS_spat_recov_new.pdf')"
]
},
{
@@ -811,18 +859,20 @@
"metadata": {},
"outputs": [],
"source": [
- "for i in range(np.shape(PLYS)[1]):\n",
- " l_data = plt.plot(PLYS[:, 0], PLYS[:, 1+i], c=blue, lw=2)\n",
- " l_fit = plt.plot(PLYS_fit[:, 0], PLYS_fit[:, 2+i], '-', lw=1,\n",
- " c=dark_grey, label='Simulation') # time course\n",
- " nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u]',\n",
- " [0, 2.4*np.max(PLYS[:, 0])], [0,0.9], [2.3,2.005])\n",
- " plt.legend([l_data[0], l_fit[0]], ['ATP/PLYS', 'Full model'], frameon=False,\n",
- " fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.52, 0.7))\n",
- " t = str(np.round(PLYS_t[i+1], 2))\n",
- " plt.text(0.5, 0.785, t.ljust(4, '0') + ' s')\n",
- " save_nice_fig(fol+'Fig4/PLYSATP_mov/PLYS_spat_recov_mov_'+str(i)+'.png', form='png')\n",
- " plt.show();"
+ "PGL = np.loadtxt(fol+'Fig4/PGL_timecourse.csv', delimiter=',')\n",
+ "PGL_fit = np.loadtxt(fol+'Fig4/PGL_fit_timecourse.csv', delimiter=',')\n",
+ "PGL_t = np.loadtxt(fol+'Fig4/PGL_fit_time.csv', delimiter=',')\n",
+ "l_data = plt.plot(PGL[:, 0], PGL[:, 1::140], c=red, lw=2,\n",
+ " label='Experiment')\n",
+ "l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 2::140], '-', lw=1,\n",
+ " c=dark_grey, label='Simulation')\n",
+ "plt.plot(PGL_fit[:, 0], PGL_fit[:, 1], '-', lw=1, c=dark_grey)\n",
+ "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u.]',\n",
+ " [0, 2.7*np.max(PGL[:, 0])], [0,0.7], [2.3,2])\n",
+ "plt.legend([l_data[0], l_fit[0]], ['PGL-3', 'Full model'],\n",
+ " frameon=False, fontsize=9, handletextpad=0.4,\n",
+ " handlelength=0.8, loc=(0.54, 0.7))\n",
+ "save_nice_fig(fol+'Fig4/PGL_spat_recov.pdf')"
]
},
{
@@ -831,19 +881,26 @@
"metadata": {},
"outputs": [],
"source": [
- "PGL = np.loadtxt(fol+'Fig4/PGL_timecourse.csv', delimiter=',')\n",
- "PGL_fit = np.loadtxt(fol+'Fig4/PGL_fit_timecourse.csv', delimiter=',')\n",
- "l_data = plt.plot(PGL[:, 0], PGL[:, 1::4], c=red, lw=2,\n",
- " label='Experiment')\n",
- "l_fit = plt.plot(PGL_fit[:, 0], PGL_fit[:, 2::4], '-', lw=1,\n",
- " c=dark_grey, label='Simulation')\n",
- "plt.plot(PGL_fit[:, 0], PGL_fit[: 1], '-', lw=1, c=dark_grey)\n",
- "nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity (a.u)',\n",
- " [0, 2.5*np.max(PGL[:, 0])], [0,0.65], [2.3,2])\n",
- "plt.legend([l_data[0], l_fit[0]], ['PGL', 'Full model'],\n",
- " frameon=False, fontsize=9, handletextpad=0.4,\n",
- " handlelength=0.8, loc=(0.54, 0.7))\n",
- "save_nice_fig(fol+'Fig4/PGL_spat_recov.pdf')"
+ "zipped = zip([PLYS, CMD, PGL], [PLYS_fit, CMD_fit, PGL_fit],\n",
+ " [PLYS_t, CMD_t, PGL_t], [blue, green, red],\n",
+ " ['PLYS/ATP', 'CMD/PLYS', 'PGL-3'], [0.9, 0.7, 0.8])\n",
+ "for (mov, mov_fit, mov_t, c, l, yl) in zipped:\n",
+ " for i in range(np.shape(mov)[1]-1):\n",
+ " l_data = plt.plot(mov[:, 0], mov[:, 1+i], c=c, lw=2)\n",
+ " l_fit = plt.plot(mov_fit[:, 0], mov_fit[:, 2+i], '-', lw=1,\n",
+ " c=dark_grey) # time course\n",
+ " nice_fig('radial distance $r$ [$\\mathrm{\\mu m}$]', 'intensity [a.u]',\n",
+ " [0, 2.7*np.max(mov[:, 0])], [0, yl], [2.35,2.005])\n",
+ " plt.legend([l_data[0], l_fit[0]], [l, 'Fit to Eq. (6)'], frameon=False,\n",
+ " fontsize=9, handletextpad=0.4, handlelength=0.8, loc=(0.48, 0.7))\n",
+ " t = str(np.round(mov_t[i+1], 2))\n",
+ " plt.text(0.5, yl-0.11/0.8*yl, t.ljust(4, '0') + ' s')\n",
+ " plt.yticks([0.2, 0.4, 0.6])\n",
+ " if l=='PLYS/ATP':\n",
+ " plt.yticks([0.2, 0.4, 0.6, 0.8])\n",
+ " save_nice_fig(fol+'Fig4/Movies/'+l[:3]+'_spat_recov_model_'+str(i)+'.png',\n",
+ " form='png', dpi=149)\n",
+ " plt.show();"
]
},
{
@@ -879,7 +936,7 @@
"ax1.set_xscale('log')\n",
"nice_fig('Partition coefficient $P$', '$D_\\mathrm{out} \\;[\\mathrm{\\mu m^2s^{-1}}]$',\n",
" [1,340], [0.08,450], [2.3,2])\n",
- "plt.legend(['CMD/PLYS', 'ATP/PLYS'], frameon=False, fontsize=9, loc=(0.44,0.05))\n",
+ "plt.legend(['CMD/PLYS', 'PLYS/ATP'], frameon=False, fontsize=9, loc=(0.44,0.05))\n",
"plt.text(1.1, 2, '$D_\\mathrm{in, P/A}$', color=blue)\n",
"plt.text(1.1, 7, '$D_\\mathrm{in, C/P}$', color=green)\n",
"# plt.plot([1, 9], [1.7, 1.7], '--', color=blue)\n",
@@ -932,7 +989,8 @@
" color=sns.color_palette()[4], ci=None)\n",
"l100 = sns.lineplot(x=\"P\", y=\"D_out\", data=PGL_100,\n",
" color=sns.color_palette()[5], ci=None)\n",
- "leg1 = plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM'],\n",
+ "ls = plt.gca().get_lines()\n",
+ "leg1 = plt.legend([ls[4], ls[3], ls[2], ls[1], ls[0]], ['100 mM', '90 mM', '75 mM', '60 mM', '50 mM'],\n",
" labelspacing=0.3, loc=(0.63, 0.0),\n",
" handletextpad=0.4, handlelength=0.5, frameon=0)\n",
"\n",
@@ -952,14 +1010,10 @@
" [1,20000], [0.0003,600], [2.3,2])\n",
"plt.xticks([1, 10, 100, 1000, 10000]);\n",
"plt.legend(loc=1)\n",
- "plt.legend(['50 mM', '60 mM', '75 mM', '90 mM', '100 mM',\n",
- " '120 mM', '150 mM', '180 mM'], labelspacing=0.3,\n",
+ "ls = plt.gca().get_lines()\n",
+ "plt.legend([ls[7], ls[6],ls[5]], ['180 mM', '150 mM', '120 mM'], labelspacing=0.3,\n",
" loc=(0, 0.66), handletextpad=0.4, handlelength=0.5, frameon=0)\n",
"plt.gca().add_artist(leg1)\n",
- "# plt.plot([700, 10000], [30, 30], color=(1, 0, 0))\n",
- "# plt.plot([700, 10000], [70, 70], color=(1, 0, 0))\n",
- "# plt.plot([700, 700], [30, 70], color=(1, 0, 0))\n",
- "# plt.plot([10000, 10000], [30, 70], color=(1, 0, 0))\n",
"save_nice_fig(fol+'Fig4/PGL-3.pdf')\n",
"sns.set_palette(temp)"
]
@@ -999,7 +1053,7 @@
"salts = [50, 60, 75, 90, 100, 120, 150, 180]\n",
"nice_fig('$c_\\mathrm{salt} \\; [\\mathrm{mM}]$', 'Partition coefficient $P$',\n",
" [50, 180], [12,20000], [2.3,2])\n",
- "plt.plot(salts, Ps_1, color=red, label='$D_{\\mathrm{out}}=1$')\n",
+ "plt.plot(salts, Ps_2, color=red, label='$D_{\\mathrm{out}}=2$')\n",
"plt.plot(salts, Ps_10, color=green, label='$D_{\\mathrm{out}}=10$')\n",
"plt.plot(salts, Ps_50, color=blue, label='$D_{\\mathrm{out}}=50$')\n",
"# plt.plot(salt_anatol, P_anatol)\n",
@@ -1045,15 +1099,22 @@
"nice_fig('Partition coefficient $P$', '$D_\\mathrm{out}$ [$\\mathrm{\\mu m^2/s}$]', [0.9,320], [0.000001,340], [2.3,2])\n",
"lines = plt.loglog(P_Do[0, :], P_Do[1:, :].transpose())\n",
"plt.plot(P_Do[0, :], P_Do[0, :], '--', c='grey')\n",
- "plt.legend([lines[2], lines[0], lines[3], lines[1]],\n",
+ "# plt.legend([lines[2], lines[0], lines[3], lines[1]],\n",
+ "# ['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False,\n",
+ "# title=r'$D_\\mathrm{out}$/P [$\\mathrm{\\mu m^2/s}$]:', columnspacing=0.5, labelspacing=0.3,\n",
+ "# loc=(0.4, 0), handletextpad=0.4, handlelength=0.5)\n",
+ "plt.gca().set_prop_cycle(None)\n",
+ "lines[0] = plt.plot(P[0], D_o[0], 'o', mfc='none', markersize=8)\n",
+ "lines[1] = plt.plot(P[1], D_o[1], 'o', mfc='none', markersize=8)\n",
+ "lines[2] = plt.plot(P[2], D_o[2], 'o', mfc='none', markersize=8)\n",
+ "lines[3] = plt.plot(P[3], D_o[3], 'o', mfc='none', markersize=8)\n",
+ "def circle(i):\n",
+ " return plt.Line2D(range(1), range(1), color=sns.color_palette()[i],\n",
+ " marker='o', markersize=5, markerfacecolor=\"white\")\n",
+ "plt.legend([circle(2), circle(0), circle(3), circle(1)],\n",
" ['0.2', '0.02', '0.0067', '0.00067'], ncol=2, frameon=False,\n",
" title=r'$D_\\mathrm{out}$/P [$\\mathrm{\\mu m^2/s}$]:', columnspacing=0.5, labelspacing=0.3,\n",
" loc=(0.4, 0), handletextpad=0.4, handlelength=0.5)\n",
- "plt.gca().set_prop_cycle(None)\n",
- "plt.plot(P[0], D_o[0], 'd')\n",
- "plt.plot(P[1], D_o[1], 'd')\n",
- "plt.plot(P[2], D_o[2], 'd')\n",
- "plt.plot(P[3], D_o[3], 'd')\n",
"plt.annotate('$D_{out}$/P = 1 $\\mathrm{\\mu m^2/s}$', [1,40], c='grey')\n",
"plt.xticks([1, 10, 100]);\n",
"save_nice_fig(fol+'Fig4/D_vs_P.pdf')"
@@ -1080,7 +1141,7 @@
"# title=r'$D_\\mathrm{out}$/P set to:', columnspacing=0.5, labelspacing=0.3,\n",
"# loc=(0.081, 0), handletextpad=0.4, handlelength=0.5)\n",
"plt.xticks([1, 10, 100]);\n",
- "save_nice_fig(fol+'Fig4/D_vs_Cost.pdf')"
+ "# save_nice_fig(fol+'Fig4/D_vs_Cost.pdf')"
]
},
{
@@ -1094,7 +1155,7 @@
"lines = plt.plot(P_Cost[0], P_Cost[1:].transpose()/[x[0] for x in P_Cost[1:]])\n",
"plt.gca().set_xscale('log')\n",
"plt.plot([150, 150], [0, 1], '--', lw=2, c=grey)\n",
- "save_nice_fig(fol+'Fig4/D_vs_Cost_single.pdf')"
+ "# save_nice_fig(fol+'Fig4/D_vs_Cost_single.pdf')"
]
},
{
@@ -1114,8 +1175,8 @@
"levels = MaxNLocator(nbins=100).tick_values(np.log10(con[:, 2:].min()), np.log10(con[:, 2:].max()))\n",
"nice_fig('Partition coefficient $P$', '$D_\\mathrm{out} \\;[\\mathrm{\\mu m^2 s^{-1}}]$', [1, 3], [-2,1], [2.3,2])\n",
"CS = plt.contourf(np.log10(con[:, 0]), np.log10(con[:, 1]), np.log10(con[:, 2:]), levels=levels, cmap=cm.coolwarm)\n",
- "plt.plot(np.log10(150), np.log10(10**-1), 'd', c=sns.color_palette()[1], label='Initial Simul.', markersize=6)\n",
- "le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.4)\n",
+ "plt.plot(np.log10(150), np.log10(10**-1), 'o', c=sns.color_palette()[1], label='Reference Simulation', mfc='none', markersize=8)\n",
+ "le = plt.legend(loc=(0, 0.83), frameon=False, handletextpad=0.2)\n",
"# plt.plot(np.log10(P_Do[0, :]), np.log10(P_Do[2, :].transpose()), '--', c=green, lw = 1)\n",
"le.get_texts()[0].set_color('white')\n",
"plt.xticks([1, 2, 3], ['$10^1$', '$10^2$', '$10^3$'])\n",