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Graphing and Statistics.py
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Graphing and Statistics.py
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# -*- coding: utf-8 -*-
"""
Created on Fri November 15 10:01:12 2023
@author: holtj
This script replicates the figures present in the paper using the provided datasheet.
The figures in the paper were generated directly from .mat files or .tiff files,
which were in turn generated using BiofilmQ.
"""
import glob
from itertools import product
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import ternary
import seaborn as sns
import matplotlib as mpl
from ast import literal_eval
from palettable.colorbrewer.sequential import Greys_6_r
from palettable.matplotlib import Inferno_20
#%%
"""
Figure 1A
Planktonic Sock Plot
"""
df_og = pd.read_excel('SI_Data.xlsx', sheet_name='Fig1A')
df = df_og.groupby('time').mean().reset_index()
t = np.arange(0,len(df['time'].unique()))
width=0.9
plt.bar(t, df.pa, width=width, color='#E2E604', label=r'${\itP. aeruginosa}$', alpha=0.75)
plt.bar(t, df.ef, width=width, color='#008F8F', bottom=np.array(df.pa), label=r'${\itE. faecalis}$', alpha=0.75)
plt.bar(t, df.ec, width=width, color='#9813DE', bottom=np.array(df.pa)+np.array(df.ef), label=r'${\itE. coli}$', alpha=0.75)
plt.scatter(np.ones(4)*0,np.ones(4)*0.333, facecolors='none', edgecolors='black', alpha=1)
plt.scatter(df_og['time']/24, df_og['pa'], facecolors='none', edgecolors='black', alpha=1)
plt.ylim(0,1)
plt.xticks(ticks=t, labels=np.array(df['time'].unique()), fontsize=20)
plt.yticks(fontsize=20)
plt.grid(False)
plt.ylabel(r'R.A.')
plt.xlabel('Time (d)')
plt.title('Liquid Shaking Culture (24 hr Back Dilutions)')
# plt.savefig('Fig1A.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 1B
Biofilm Sock Plot
"""
df_og = pd.read_excel('SI_Data.xlsx', sheet_name='Fig1B')
df = df_og.groupby('time').mean().reset_index()
t = np.arange(0,len(df['time'].unique()))
width=0.9
plt.bar(t, df.pa, width=width, color='#E2E604', label=r'${\itP. aeruginosa}$', alpha=0.75)
plt.bar(t, df.ef, width=width, color='#008F8F', bottom=np.array(df.pa), label=r'${\itE. faecalis}$', alpha=0.75)
plt.bar(t, df.ec, width=width, color='#9813DE', bottom=np.array(df.pa)+np.array(df.ef), label=r'${\itE. coli}$', alpha=0.75)
plt.scatter(np.ones(4)*0,np.ones(4)*0.333, facecolors='none', edgecolors='black', alpha=1)
plt.scatter(df_og['time'], df_og['pa'], facecolors='none', edgecolors='black', alpha=1)
plt.xticks(ticks=t, labels=np.array(df['time'].unique()), fontsize=20)
plt.ylim(0,1)
plt.yticks(fontsize=20)
plt.grid(False)
plt.ylabel(r'R.A.')
plt.xlabel('Time (d)')
plt.title('Biofilm Culture')
# plt.savefig('Fig1B.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',|
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 1C , SI Figure S2B
Tri-Culture vs Monoculture Lines
"""
ecoli_co = [0, np.mean([327.31799833195225, 74739.97154885676, 30178.86602171233, 2272,10348]),
np.mean([337654.3018299466, 16377.00412162256, 102800.81758497875, 65958.14699239816, 17644.44592143285,
60281.3781279211, 22315.762891329276, 226175.7079400265, 87119.63391914888, 83563]),
np.mean([174980.53583302326, 131468.29809900734, 2172.6553144305585, 291359.03185848374, 293299.52973667695,
199684.7876095191, 233148.2572287676, 90213.35726961477, 34802.62569790094, 22346.36296689037, 367732.49514154455, 188472]),
np.mean([30012.369571568208, 136101.86383347574, 12751.512534025518, 109377.53775453306, 140409.82671136397, 355895.2917991453, 387598.1942840074, 373512.4762440534, 23562])]
ecoli_co_std = [0, stats.sem([327.31799833195225, 74739.97154885676, 30178.86602171233, 2272, 10348]),
stats.sem([337654.3018299466, 16377.00412162256, 102800.81758497875, 65958.14699239816, 17644.44592143285,
60281.3781279211, 22315.762891329276, 226175.7079400265, 87119.63391914888,83563]),
stats.sem([174980.53583302326, 131468.29809900734, 2172.6553144305585, 291359.03185848374, 293299.52973667695,
199684.7876095191, 233148.2572287676, 90213.35726961477, 34802.62569790094, 22346.36296689037, 367732.49514154455,188472]),
stats.sem([30012.369571568208, 136101.86383347574, 12751.512534025518, 109377.53775453306, 140409.82671136397, 355895.2917991453, 387598.1942840074, 373512.4762440534, 23562])]
ecoli = [0, np.mean([429326.5281541101, 20830.831426016266, 26769.516920138325,26992.17734452068]),
np.mean([111347.8469921274, 183315.5309570613, 145620.88152866834, 409437.6682564925, 134370.40829945993, 277304.7236815254]),
np.mean([387877.2050713496, 312281.15418290574, 486196.3041085362, 286454.46008238854]),
np.mean([337383.11888024263, 348827.4483178621, 148572.00581428283, 269781.17495603, 461549.52062919])]
ecoli_std = [0, stats.sem([429326.5281541101, 20830.831426016266, 26769.516920138325,26992.17734452068]),
stats.sem([111347.8469921274, 183315.5309570613, 145620.88152866834, 409437.6682564925, 134370.40829945993, 277304.7236815254]),
stats.sem([387877.2050713496, 312281.15418290574, 486196.3041085362, 286454.46008238854]),
stats.sem([128992.59076753035, 92435.67970619304, 182715.3318089329, 89181.74673619,116083.68712655])]
ent_co = [0, np.mean([45944.71951992576, 42819.65134236022, 14419.980738746319, 63980, 132762]),
np.mean([109925.465309905, 79612.13354098213, 46202.46510145668, 55182.472283692354, 179308.4799678017, 72140.62085399251, 59930.49838893081, 81622.88932344121, 125376]),
np.mean([133616.5332796591, 23329.239782307275, 38164.97239072235, 3566.42683158818, 49841.93215699958, 33499.418502635635, 32309.77994890224, 23665.44896543966, 33135.575647364, 22937.923767081345, 115016.10230890731, 187618]),
np.mean([55616.538048072034, 10079.947927941344, 34003.70827562903, 48037.69756361926, 57930.14145661801, 26650.75047392173, 18062.719116312706, 42176.42001744546, 14012])]
ent_co_std= [0, stats.sem([45944.71951992576, 42819.65134236022, 14419.980738746319, 63980, 132762]),
stats.sem([109925.465309905, 79612.13354098213, 46202.46510145668, 55182.472283692354, 179308.4799678017,
72140.62085399251, 59930.49838893081, 81622.88932344121, 125376]),
stats.sem([133616.5332796591, 23329.239782307275, 38164.97239072235, 35166.42683158818, 49841.93215699958, 33499.418502635635,
32309.77994890224, 23665.44896543966, 33135.575647364, 22937.923767081345, 115016.10230890731, 187618]),
stats.sem([55616.538048072034, 10079.947927941344, 34003.70827562903, 48037.69756361926, 57930.14145661801, 26650.75047392173, 18062.719116312706, 42176.42001744546, 14012])]
ent = [0, np.mean([145719.7700434657, 132853.42173086293, 49593.08968900539, 42897.058102400624, 132762]),
np.mean([191166.61280572205, 96122.08029061732, 133520.86963127236, 172674.3443290808, 127962.97477912421, 105634.37227330725]),
np.mean([190393.93985167015, 82472.47297014121, 103497.2411231636, 83590.30802445539]),
np.mean([128992.59076753035, 92435.67970619304, 182715.3318089329, 89181.74673619,116083.68712655])]
ent_std = [0, stats.sem([145719.7700434657, 132853.42173086293, 49593.08968900539, 42897.058102400624]),
stats.sem([191166.61280572205, 96122.08029061732, 133520.86963127236, 172674.3443290808, 127962.97477912421, 105634.37227330725]),
stats.sem([190393.93985167015, 82472.47297014121, 103497.2411231636, 83590.30802445539]),
stats.sem([128992.59076753035, 92435.67970619304, 182715.3318089329, 89181.74673619,116083.68712655])]
pa_co = [0, np.mean([564.8839733898792, 59753.966369545655, 76084.78222377267, 1218, 1887]),
np.mean([304214.7366569871, 314624.12837939983, 256.68741611089274, 677.4296636374746, 216.95526415455606, 495.2815851177541, 8132.448033845329, 77540.33970772212, 298198.37500867364, 1114.08660477438,1083]),
np.mean([510447.32744726987, 232140.6416575893, 97072.44822831418, 152974.1378831626, 544.3205115672279, 359884.46961609664, 207820.33371656644, 232250.19955164785, 109811.70338191417, 49315.8829610963, 353645]),
np.mean([282075.78929444845, 326177.65918198874, 128033.73931436759, 398119.9328066944, 103348.1002644917, 1776.8635350431903, 26057.464556832147, 365102.55372229044, 196572])]
pa_co_std = [0, stats.sem([564.8839733898792, 59753.966369545655, 76084.78222377267, 1218, 1887]),
stats.sem([304214.7366569871, 314624.12837939983, 256.68741611089274, 677.4296636374746, 216.95526415455606,
495.2815851177541, 8132.448033845329, 77540.33970772212, 298198.37500867364, 1114.08660477438, 1083]),
stats.sem([510447.32744726987, 232140.6416575893, 97072.44822831418, 152974.1378831626, 544.3205115672279,
359884.46961609664, 207820.33371656644, 232250.19955164785, 109811.70338191417, 49315.8829610963, 353645]),
stats.sem([282075.78929444845, 326177.65918198874, 128033.73931436759, 398119.9328066944, 103348.1002644917, 1776.8635350431903, 26057.464556832147, 365102.55372229044, 196572])]
pa = [0, np.mean([362289.6023790988, 550.316753017643, 118188.07173208568]),
np.mean([126345.30028869871, 80597.25082750342, 21905.3075725621, 485315.62176974496, 3045.379556830059, 2071.840862834238, 27850.31514594865, 8115.406266916172]),
np.mean([337295.43424540985, 398535.4141324807, 242220.39670517063, 23597.645090594877, 164344.37751006486]),
np.mean([385489.7102260408, 439545.5021568775, 227691.2968063428, 5.02094258e+05, 452658.56209157, 520242.070775, 429652.64031865])]
pa_std = [0, stats.sem([362289.6023790988, 550.316753017643, 118188.07173208568]),
stats.sem([126345.30028869871, 80597.25082750342, 21905.3075725621, 485315.62176974496, 3045.379556830059, 2071.840862834238, 27850.31514594865, 8115.406266916172]),
stats.sem([337295.43424540985, 398535.4141324807, 242220.39670517063, 23597.645090594877, 164344.37751006486]),
stats.sem([385489.7102260408, 439545.5021568775, 227691.2968063428, 5.02094258e+05, 452658.56209157, 520242.070775, 429652.64031865])]
time = [0, 24, 48, 72, 96]
plt.figure(figsize=(3.5,4))
plt.plot(time, ecoli_co, marker='o', color='#9813DE', alpha=0.75)
plt.errorbar(time, ecoli_co, yerr=ecoli_co_std, color='#9813DE', alpha=0.75)
plt.plot(time, ecoli, linestyle='--', marker='p', color='#9813DE', alpha=0.75)
plt.errorbar(time, ecoli, yerr=ecoli_std, linestyle='--', color='#9813DE', alpha=0.75)
plt.plot(time, ent_co, marker='o', color='#008F8F', alpha=0.75)
plt.errorbar(time, ent_co, yerr=ent_co_std, color='#008F8F', alpha=0.75)
plt.plot(time, ent, linestyle='--', marker='p', color='#008F8F', alpha=0.75)
plt.errorbar(time, ent, yerr=ent_std, linestyle='--', color='#008F8F', alpha=0.75)
plt.plot(time, pa_co, marker='o', color='#E2E604', alpha=0.75)
plt.errorbar(time, pa_co, yerr=pa_co_std, color='#E2E604', alpha=0.75)
plt.plot(time, pa, linestyle='--', marker='p', color='#E2E604', alpha=0.75)
plt.errorbar(time, pa, yerr=pa_std, linestyle='--', color='#E2E604', alpha=0.75)
plt.ylabel('Volume $\mu$m$^3$', fontsize=20)
plt.xlabel('Time (hr)', fontsize = 20)
plt.grid(False)
plt.xticks(fontsize=20)
plt.yticks(ticks=np.arange(0,600000,100000), labels=np.arange(0,6,1), fontsize=20)
plt.ylim(-10000,600000)
# plt.savefig('Fig1C.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 1D
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig1D')
data_list = [[df['x0'].dropna().tolist(), df['y0'].dropna().tolist()], [df['x1'].dropna().tolist(), df['y1'].dropna().tolist()], [df['x2'].dropna().tolist(), df['y2'].dropna().tolist()]]
figure, tax = ternary.figure(scale=1.0)
figure.set_size_inches(5, 5)
color_dictionary = {r"$E. coli$":'#9813DE', r'$E. faecalis$':'#008F8F',r"$P. aeruginosa$":'#E2E604'}
tax.boundary()
tax.gridlines(multiple=0.2, color="black")
tax.left_axis_label(" $E. faecalis$", fontsize=20, offset=0.2, color='#008F8F')
tax.right_axis_label("$E. coli$", fontsize=20, offset=0.2, color='#9813DE')
tax.bottom_axis_label("$P. aeruginosa$", fontsize=20, offset=0.2, color='#E2E604')
sax = tax.get_axes()
cmap = mpl.cm.inferno
norm = mpl.colors.Normalize(vmin=0, vmax=360)
for to_plot in data_list:
to_plot[0] = [literal_eval(x) for x in to_plot[0]]
print(to_plot[0])
for point in np.arange(0,len(to_plot[0])-1):
pstart = ternary.project_point((to_plot[0][point]))
print(pstart)
pend = ternary.project_point((to_plot[0][point+1]))
pstart_m = ternary.project_point((to_plot[0][point]))
pend_m = ternary.project_point((to_plot[0][point+1]))
mag = 1
dxy = (pend - pstart) * 0.99
lw=1.75
print(to_plot[1][point])
color = cmap(norm(to_plot[1][point]))
sax.arrow(*pstart, *dxy, linewidth=lw, color=color, head_width=0.02, alpha=0.75)
if point == 0:
sax.scatter(*pstart, marker=(5, 1), color=color)
tax.ticks(axis='lbr', multiple=0.2, linewidth=1, tick_formats="%.1f", offset=0.02)
tax.get_axes().axis('off')
tax.clear_matplotlib_ticks()
# tax.savefig('Fig1D.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
tax.show()
fig, ax = plt.subplots(figsize=(8,0.5))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.inferno
norm = mpl.colors.Normalize(vmin=0, vmax=360)
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
cax=ax, orientation='horizontal', label='Time (h)')
# plt.savefig('Fig1D_colorbar.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2A, SI Figure 2A
! -> Time is in steps
"""
df = pd.read_csv('Stochastic_Spatial_Model/nullbiased_dispersal.csv')
time_array = df['Time']
spc_1 = df['Species1']
spc_2 = df['Species2']
spc_3 = df['Species3']
num_iter = len(df)
plt.plot(time_array, spc_1, color='#9813DE', label=r'$\it{E. coli}$')
plt.plot(time_array, spc_2, color='#E2E604', label=r'$\it{P. aeruginosa}$')
plt.plot(time_array, spc_3, color='#008F8F', label=r'$\it{E. faecalis}$')
plt.ylim(-5,2100)
plt.xlim(-5,num_iter)
plt.xticks(fontsize=20, rotation=-45)
plt.yticks(fontsize=20)
plt.xlabel('Time',fontsize=20)
plt.ylabel('Individuals',fontsize=20)
# plt.savefig('Fig2A.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2B, SI Figure 2B
! -> Time is in steps
"""
df = pd.read_csv('Stochastic_Spatial_Model/unbiased_dispersal.csv')
time_array = df['Time']
spc_1 = df['Species1']
spc_2 = df['Species2']
spc_3 = df['Species3']
num_iter = len(df)
plt.plot(time_array, spc_1, color='#9813DE', label=r'$\it{E. coli}$')
plt.plot(time_array, spc_2, color='#E2E604', label=r'$\it{P. aeruginosa}$')
plt.plot(time_array, spc_3, color='#008F8F', label=r'$\it{E. faecalis}$')
plt.ylim(-5,2100)
plt.xlim(-5,num_iter)
plt.xticks(fontsize=20, rotation=-45)
plt.yticks(fontsize=20)
plt.xlabel('Time',fontsize=20)
plt.ylabel('Individuals',fontsize=20)
# plt.savefig('Fig2B.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2C, SI Figure 2C
! -> Time is in steps
"""
df = pd.read_csv('Stochastic_Spatial_Model/biased_dispersal.csv')
time_array = df['Time']
spc_1 = df['Species1']
spc_2 = df['Species2']
spc_3 = df['Species3']
num_iter = len(df)
plt.plot(time_array, spc_1, color='#9813DE', label=r'$\it{E. coli}$')
plt.plot(time_array, spc_2, color='#E2E604', label=r'$\it{P. aeruginosa}$')
plt.plot(time_array, spc_3, color='#008F8F', label=r'$\it{E. faecalis}$')
plt.ylim(-5,2100)
plt.xlim(-5,num_iter)
plt.xticks(fontsize=20, rotation=-45)
plt.yticks(fontsize=20)
plt.xlabel('Time',fontsize=20)
plt.ylabel('Individuals',fontsize=20)
plt.grid(False)
# plt.savefig('Fig2C.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2D
Representastive trace of three-species community dynamics in the biofilm
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig2D')
plt.plot(df.Time, df.Ec_mean, color='#9813DE', label=r'$\it{E. coli}$')
plt.fill_between(df.Time, df.Ec_mean-df.Ec_std, df.Ec_mean+df.Ec_std, color='#9813DE', label=r'$\it{E. coli}$', alpha=0.25)
plt.plot(df.Time, df.Pa_mean, color='#E2E604', label=r'$\it{P. aeruginosa}$')
plt.fill_between(df.Time, df.Pa_mean-df.Pa_std, df.Pa_mean+df.Pa_std, color='#E2E604', label=r'$\it{P. aeruginosa}$', alpha=0.25)
plt.plot(df.Time, df.Ef_mean, color='#008F8F', label=r'$\it{E. faecalis}$')
plt.fill_between(df.Time, df.Ef_mean-df.Ef_std, df.Ef_mean+df.Ef_std, color='#008F8F', label=r'$\it{E. faecalis}$', alpha=0.25)
plt.xticks(fontsize=20, rotation=-45)
plt.yticks(fontsize=20)
plt.xlabel('Time',fontsize=20)
plt.ylabel('Biovolume (um^3)',fontsize=20)
plt.grid(False)
# plt.savefig('Fig2D.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2E
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig2E')
x = df['v']
y = df['dv']
slope3, intercept3, r_value3, p_value3, std_err3 = stats.linregress(x, y)
sns.regplot(x=x, y=y, color = 'black', line_kws={'label':"y={0:.1f}x+{1:.1f}, r={2:.1f}, p={3:.4f}".format(slope3,intercept3, r_value3, p_value3)})
plt.ylabel(r'dv_pa14/dt', fontsize=20)
plt.xlabel(r'v_pa14', fontsize=20)
plt.xticks(fontsize=20, rotation=-90)
plt.yticks(fontsize=20)
plt.hlines(0, xmin=-10000, xmax=630000, color='red', linestyles='--')
plt.xlim(-10000,630000)
plt.ylim(-22000,22000)
plt.legend()
# plt.savefig('Fig2E.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2F
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig2F')
Pa = df['Pa']
Ec = df['Ec']
Ef = df['Ef']
slope3, intercept3, r_value3, p_value3, std_err3 = stats.linregress(Pa, Pa)
slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(Pa, Ef)
slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(Pa, Ec)
sns.regplot(x=Pa, y=Pa, color = '#E2E604', line_kws={'label':"y={0:.1f}x+{1:.1f}, r={2:.1f}, p={3:.4f}".format(slope3,intercept3, r_value3, p_value3)})
sns.regplot(x=Pa, y=Ef, color = '#008F8F', line_kws={'label':"y={0:.1f}x+{1:.1f}, r={2:.1f}, p={3:.4f}".format(slope2,intercept2, r_value2, p_value2)})
sns.regplot(x=Pa, y=Ec, color = '#9813DE', line_kws={'label':"y={0:.1f}x+{1:.1f}, r={2:.1f}, p={3:.4f}".format(slope1,intercept1, r_value1, p_value1)})
plt.ylabel(r'dv_species/dt', fontsize=20)
plt.xlabel(r'dv_pa14/dt', fontsize=20)
plt.grid(False)
plt.xticks(fontsize=20, rotation=-90)
plt.yticks(fontsize=20)
plt.legend()
# plt.savefig('Fig2F.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2G
"""
plt.rcParams["figure.figsize"] = (5,6)
data=pd.read_excel('SI_Data.xlsx', sheet_name='Fig2G')
plt.plot(data.X, data.PA14/np.sum(data.PA14), color='#E2E604')
plt.plot(data.X, data.Ent/np.sum(data.Ent), color='#008F8F')
plt.plot(data.X, data.Ecoli/np.sum(data.Ecoli), color='purple')
plt.scatter(data.X, data.PA14/np.sum(data.PA14), color='#E2E604')
plt.scatter(data.X, data.Ent/np.sum(data.Ent), color='#008F8F')
plt.scatter(data.X, data.Ecoli/np.sum(data.Ecoli), color='#9813DE')
plt.ylim(0,0.06)
plt.ylabel('Frequency', fontsize=20)
plt.xlabel('Pseudomonas Cell Packing', fontsize=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.grid(False)
# plt.savefig('Fig2G.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 2H
Use the stochastic spatial model make_heatmap script to generate data prior to running this code.
"""
ratio_list = list(np.flip(np.arange(0,1.1,.1)))
growth_list = list(np.arange(.03,.06003,.003))
experiment_title = 'phase_diagram'
result = []
for ratio in ratio_list:
ratio_cake = []
for growth in growth_list:
to_average = []
for rep in np.arange(0,20):
df = pd.read_excel('Stochastic_Spatial_Model/phase_diagram/'+experiment_title+'_p3_'+str(ratio)+'_'+str(growth)+'_'+str(rep)+'_20000.xlsx')
if df.Species1[len(df)-1] != 0 and df.Species2[len(df)-1] != 0 and df.Species3[len(df)-1] != 0:
to_average.append(1)
else:
to_average.append(0)
ratio_cake.append(np.mean(to_average))
result.append(ratio_cake)
ax = sns.heatmap(result, square=True, cmap=Greys_6_r.mpl_colormap)
for _, spine in ax.spines.items():
spine.set_visible(True)
# plt.savefig('Fig2H.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show
#%%
"""
Figure 3A
Stochastic spatial model rep ternary plot
Use the generate_ternary_plot code in the stochastic_spatial_model directory to generate data prior to running this section
"""
figure, tax = ternary.figure(scale=1.0)
figure.set_size_inches(5, 5)
tax.boundary()
tax.gridlines(multiple=0.2, color="black")
sax = tax.get_axes()
#grab one of the runs with a 0.33,0.33,0.33 starting condition for this
volume_dataframe2 = pd.read_excel('Stochastic_Spatial_Model/biased_dispersal_vector_plot_cont_1000.xlsx')
norm = mpl.colors.Normalize(vmin=0, vmax=1000)
for i in np.arange(0,1000):
cmap= Inferno_20.mpl_colormap
color = cmap(norm(i))
pstart = ternary.project_point((volume_dataframe2['Species2'][i],volume_dataframe2['Species1'][i],volume_dataframe2['Species3'][i]))
pend = ternary.project_point((volume_dataframe2['Species2'][i+1],volume_dataframe2['Species1'][i+1],volume_dataframe2['Species3'][i+1]))
pstart_m = ternary.project_point((volume_dataframe2['Species2'][i],volume_dataframe2['Species1'][i],volume_dataframe2['Species3'][i]))
pend_m = ternary.project_point((volume_dataframe2['Species2'][i+1],volume_dataframe2['Species1'][i+1],volume_dataframe2['Species3'][i+1]))
mag = 1
stepsize = np.sqrt((pend[0]-pstart[0])**2+(pend[1]-pstart[1])**2)
dxy = (pend - pstart)*.95
lw=1
sax.arrow(*pstart, *dxy, linewidth=lw, color=color, head_width=0.02, alpha=.25)
if i == 0:
sax.scatter(*pstart, marker=(5, 1), color='black')
tax.get_axes().axis('off')
tax.clear_matplotlib_ticks()
# tax.savefig('single_run_trace.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
tax.show()
fig, ax = plt.subplots(figsize=(8,0.5))
fig.subplots_adjust(bottom=0.5)
cmap=Inferno_20.mpl_colormap
norm = mpl.colors.Normalize(vmin=0, vmax=1000)
# fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
# cax=ax, orientation='horizontal', label='Time (h)')
# plt.savefig('hot_colorbar.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 3B
Stochastic spatial model ternary plot
Use the generate_ternary_plot_parallel_pool code in the stochastic_spatial_model directory to generate data
"""
file_list = glob.glob('Stochastic_Spatial_Model/ternary_plot/*.xlsx', recursive=False)
#Define the range and increments
start = 0
stop = 1
increment = 0.1
# Calculate the number of steps
num_steps = int((stop - start) / increment) + 1
# Generate all combinations
combinations = list(product(range(num_steps), repeat=3))
# Convert combinations to actual values
result = [[start + c[0] * increment, start + c[1] * increment, start + c[2] * increment] for c in combinations]
# Print the result
for combination in result:
print(combination)
averaged = {'Species1':[], 'Species2':[], 'Species3':[]}
averaged_dispersal = {'Species1':[], 'Species2':[], 'Species3':[]}
result_final = []
for value in result:
if round(np.sum(value), 2) == 1:
result_final.append(value)
for i in np.arange(0,len(result_final)):
if 1 in result_final:
print(result_final)
if 1 not in result_final:
start_to_average_1 = []
end_to_average_1 = []
start_to_average_2 = []
end_to_average_2 = []
start_to_average_3 = []
end_to_average_3 = []
step = increment
for file in file_list:
volume_dataframe= pd.read_excel(file)
quadrant = volume_dataframe.drop(volume_dataframe[(volume_dataframe['Species1'] <= result_final[i][0]-step) |
(volume_dataframe['Species1'] > result_final[i][0]) |
(volume_dataframe['Species3'] <= result_final[i][1]-step) |
(volume_dataframe['Species3'] > result_final[i][1]) |
(volume_dataframe['Species2'] <= result_final[i][2]-step) |
(volume_dataframe['Species2'] > result_final[i][2])| (volume_dataframe['Dispersal'] == 1)].index)
look_length = 90
for item in quadrant.index:
if (item+look_length in volume_dataframe.index) and (quadrant['Iteration'][item] == volume_dataframe['Iteration'][item+look_length]) and (1 not in volume_dataframe['Dispersal'][item:item+look_length] ):
start_to_average_1.append(quadrant['Species1'][item])
end_to_average_1.append(volume_dataframe['Species1'][item+look_length])
start_to_average_2.append(quadrant['Species2'][item])
end_to_average_2.append(volume_dataframe['Species2'][item+look_length])
start_to_average_3.append(quadrant['Species3'][item])
end_to_average_3.append(volume_dataframe['Species3'][item+look_length])
averaged['Species1'].append([np.mean(start_to_average_1), np.mean(end_to_average_1)])
averaged['Species2'].append([np.mean(start_to_average_2), np.mean(end_to_average_2)])
averaged['Species3'].append([np.mean(start_to_average_3), np.mean(end_to_average_3)])
for i in np.arange(0,len(result_final)):
if 1 in result_final:
print(result_final)
if 1 not in result_final:
start_to_average_1 = []
end_to_average_1 = []
start_to_average_2 = []
end_to_average_2 = []
start_to_average_3 = []
end_to_average_3 = []
step = increment
for file in file_list:
volume_dataframe= pd.read_excel(file)
quadrant = volume_dataframe.drop(volume_dataframe[(volume_dataframe['Species1'] <= result_final[i][0]-step) |
(volume_dataframe['Species1'] > result_final[i][0]) |
(volume_dataframe['Species3'] <= result_final[i][1]-step) |
(volume_dataframe['Species3'] > result_final[i][1]) |
(volume_dataframe['Species2'] <= result_final[i][2]-step) |
(volume_dataframe['Species2'] > result_final[i][2]) | (volume_dataframe['Dispersal'] == 0)].index)
look_length = 1
for item in quadrant.index:
if (item+look_length in volume_dataframe.index) and (quadrant['Iteration'][item] == volume_dataframe['Iteration'][item+look_length]):
start_to_average_1.append(quadrant['Species1'][item])
end_to_average_1.append(volume_dataframe['Species1'][item+look_length])
start_to_average_2.append(quadrant['Species2'][item])
end_to_average_2.append(volume_dataframe['Species2'][item+look_length])
start_to_average_3.append(quadrant['Species3'][item])
end_to_average_3.append(volume_dataframe['Species3'][item+look_length])
averaged_dispersal['Species1'].append([np.mean(start_to_average_1), np.mean(end_to_average_1)])
averaged_dispersal['Species2'].append([np.mean(start_to_average_2), np.mean(end_to_average_2)])
averaged_dispersal['Species3'].append([np.mean(start_to_average_3), np.mean(end_to_average_3)])
volume_dataframe_average = pd.DataFrame(data=averaged)
volume_dataframe_average_disp = pd.DataFrame(data=averaged_dispersal)
figure, tax = ternary.figure(scale=1.0)
figure.set_size_inches(5, 5)
color_dictionary = {r"$E. coli$":'#9813DE', r'$E. faecalis$':'#008F8F',r"$P. aeruginosa$":'#E2E604'}
tax.boundary()
tax.gridlines(multiple=0.2, color="black")
for i in np.arange(0,len(volume_dataframe_average)):
if 1 not in [volume_dataframe_average['Species1'][i][0],volume_dataframe_average['Species2'][i][0],volume_dataframe_average['Species3'][i][0]]:
pstart = ternary.project_point((volume_dataframe_average['Species2'][i][0],volume_dataframe_average['Species1'][i][0],volume_dataframe_average['Species3'][i][0]))
pend = ternary.project_point((volume_dataframe_average['Species2'][i][1],volume_dataframe_average['Species1'][i][1],volume_dataframe_average['Species3'][i][1]))
pstart_m = ternary.project_point((volume_dataframe_average['Species2'][i][0],volume_dataframe_average['Species1'][i][0],volume_dataframe_average['Species3'][i][0]))
pend_m = ternary.project_point((volume_dataframe_average['Species2'][i][1],volume_dataframe_average['Species1'][i][1],volume_dataframe_average['Species3'][i][1]))
mag = 1
dxy = (pend - pstart)*.25
lw=1
sax.arrow(*pstart, *dxy, linewidth=lw, color='black', head_width=0.025, alpha=.5)
for i in np.arange(0,len(volume_dataframe_average_disp)):
if 1 not in [volume_dataframe_average_disp['Species1'][i][0],volume_dataframe_average_disp['Species2'][i][0],volume_dataframe_average_disp['Species3'][i][0]]:
pstart = ternary.project_point((volume_dataframe_average_disp['Species2'][i][0],volume_dataframe_average_disp['Species1'][i][0],volume_dataframe_average_disp['Species3'][i][0]))
pend = ternary.project_point((volume_dataframe_average_disp['Species2'][i][1],volume_dataframe_average_disp['Species1'][i][1],volume_dataframe_average_disp['Species3'][i][1]))
pstart_m = ternary.project_point((volume_dataframe_average_disp['Species2'][i][0],volume_dataframe_average_disp['Species1'][i][0],volume_dataframe_average_disp['Species3'][i][0]))
pend_m = ternary.project_point((volume_dataframe_average_disp['Species2'][i][1],volume_dataframe_average_disp['Species1'][i][1],volume_dataframe_average_disp['Species3'][i][1]))
mag = 1
stepsize = np.sqrt((pend[0]-pstart[0])**2+(pend[1]-pstart[1])**2)
dxy = (pend - pstart)
lw=1
sax.arrow(*pstart, *dxy, linewidth=lw, color='red', head_width=0.025, alpha=.5)
tax.get_axes().axis('off')
tax.clear_matplotlib_ticks()
# tax.savefig('Figure3B.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
tax.show()
#%%
"""
Figure 4B
Autocorrelation
"""
plot_dict_post = {'PA14':[21.305538941558908, 5.6511008706908745, 5.0137953552405925, 4.398348976868345, 5.20582174512063, 8.403290287099342, 8.661097047023734, 4.173319267223729, 7.478522930807225, 6.939765424541318, 18.384609714330544],
'Ent':[1.6931792126177123, 1.8544360309360752, 1.809556537246724, 1.7076370669118321, 4.562035820904487, 5.496435664188826, 1.5519221187709673, 1.5740291047592825, 4.618614028430873, 1.7598148440561323, 1.8553295822499383],
'Ecoli':[1.5301727733624557, 1.8394492982430997, 1.8163981946988546, 1.6894066337125933, 1.7227547206431313, 1.7121541346500035, 1.5463180841056863, 1.5627974860670502, 1.5494989071190346, 1.6817066776283336, 0.9197616522573512]
}
plt.figure(figsize=(4,3))
plt.boxplot(plot_dict_post.values())
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[0]])), plot_dict_post[list(plot_dict_post.keys())[0]], alpha=0.75, color='#E2E604')
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[1]]))*2, plot_dict_post[list(plot_dict_post.keys())[1]], alpha=0.75, color='#008F8F')
plt.scatter(np.ones(len( plot_dict_post[list(plot_dict_post.keys())[2]]))*3, plot_dict_post[list(plot_dict_post.keys())[2]], alpha=0.75, color='#9813DE')
plt.xticks(ticks=[1,2, 3], labels=plot_dict_post.keys(), fontsize=18)
plt.yticks(fontsize=18)
plt.grid(False)
plt.ylabel('Autocorrelation Length ($\mu$m$^3$)', fontsize=18)
# plt.savefig('Fig4B.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict_post['Ecoli'], plot_dict_post['Ent']))
print(stats.mannwhitneyu(plot_dict_post['PA14'], plot_dict_post['Ent']))
print(stats.mannwhitneyu(plot_dict_post['PA14'], plot_dict_post['Ecoli']))
#%%
"""
Figure 4C
Distance to Nearest Neighbor
"""
plot_dict_post = {'Pa_Ef':[16.796813975764763, 2.5752512891003794, 1.9350533052442773, 6.14428108504588, 0.8536672812931766, 6.875872974295542, 5.745158642778306, 4.37020926281347, 14.671940442099613, 3.2222448599935145, 3.8845575009545494, 0.3738369610453203],
'Pa_Ec':[56.27290588895764, 26.019403457337226, 21.601771031091314, 18.747747202686117, 31.374000296292976, 18.43771837207643, 18.462670814974928, 32.40908177290209, 21.604464317904053, 3.203322742605807, 2.3516076875469727, 1.1930308873578803],
'Ec_Ef':[10.132951476744786, 1.062532964894646, 0.7455491632806658, 4.270373677541325, 0.9280165893326213, 7.073060621291107, 3.675005094756839, 2.9271194163854073, 15.701905517496282, 2.6575859119010916, 2.846989390016894,0.4504739043519803],}
plt.figure(figsize=(4,3))
plt.boxplot(plot_dict_post.values())
plt.plot(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[0]]))*1, plot_dict_post[list(plot_dict_post.keys())[0]], alpha=0.5, c='#E2E604', markerfacecoloralt='#008F8F', fillstyle='left', marker='.', markeredgecolor='None', linestyle='', markersize=15)
plt.plot(np.ones(len( plot_dict_post[list(plot_dict_post.keys())[1]]))*2, plot_dict_post[list(plot_dict_post.keys())[1]], alpha=0.5, c='#E2E604', markerfacecoloralt='#9813DE', fillstyle='left', marker='.', markeredgecolor='None', linestyle='', markersize=15)
plt.plot(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[2]]))*3, plot_dict_post[list(plot_dict_post.keys())[2]], alpha=0.5, c='#9813DE', markerfacecoloralt='#008F8F', fillstyle='left', marker='.', markeredgecolor='None', linestyle='', markersize=15)
plt.xticks(ticks=[1,2,3], labels=plot_dict_post.keys(), fontsize=18)
plt.yticks(fontsize=18)
plt.ylabel('Length ($\mu$m)', fontsize=18)
plt.grid(False)
# plt.savefig('Fig4C.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict_post['Pa_Ec'], plot_dict_post['Pa_Ef']))
print(stats.mannwhitneyu(plot_dict_post['Ec_Ef'], plot_dict_post['Pa_Ef']))
print(stats.mannwhitneyu(plot_dict_post['Ec_Ef'], plot_dict_post['Pa_Ec']))
#%%
"""
Figure 5B
Box and Whisker Plot, Sock Plot
"""
plt.figure(figsize=(2,3))
plot_dict = {r'WT':[0.732701392,0.855511269,0.605011441,0.8603337031017434,0.674319833],
'$\Delta bqsS$':[0.8604058699519027, 0.9975449725269114, 0.9744276316075082, 0.975276188580307, 0.9935968885138542]}
plt.boxplot(plot_dict.values(), widths=0.5)
plt.scatter(np.ones(len(plot_dict['WT'])), plot_dict['WT'], alpha=0.75, color='black', s=100)
plt.scatter(np.ones(len(plot_dict['$\Delta bqsS$']))*2, plot_dict['$\Delta bqsS$'], marker='D', alpha=0.75, color='green', s=100)
plt.ylim(0.5,1)
plt.grid(False)
plt.xticks(ticks=[1,2,], labels=plot_dict.keys(), fontsize=18)
plt.yticks(ticks = np.arange(0.5, 1.01, 0.1), fontsize=18)
plt.ylabel(r'Relative Abundance', fontsize=18)
# plt.savefig('Fig3B_1.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict['WT'], plot_dict['$\Delta bqsS$']))
plt.figure(figsize=(3,4))
plot_dict = {r'WT':[[0.732701392, 0.000642044, 0.266656565], [0.855511269,0.064441976,0.080046755], [0.605011441, 0.393689205, 0.001299354], [0.674319833, 0.000281737, 0.32539843]],
'$\Delta bqsS$':[[0.8604058699519027, 0.09367418792941473, 0.04591994211868235], [0.9975449725269114, 0.0004435961750302079, 0.0020114312980582445], [0.9744276316075082, 0.02450629927006042, 0.0010660691224313199], [0.975276188580307, 0.016741727878660777, 0.007982083541032315], [0.9935968885138542, 0.00354877813774713, 0.002854333348398737]]}
data = [[],[],[]]
for key in list(plot_dict.keys()):
pa = []
ef = []
ec = []
for i in plot_dict[key]:
pa.append(i[0])
ef.append(i[1])
ec.append(i[2])
pa = np.mean(pa)
ef = np.mean(ef)
ec = np.mean(ec)
total = pa+ef+ec
pa = pa/total
ef = ef/total
ec = ec/total
if key == 'WT':
data[0].append(pa)
data[1].append(ef)
data[2].append(ec)
elif key == '$\Delta bqsS$':
data[0].append(pa)
data[1].append(ef)
data[2].append(ec)
plt.bar([0.25,0.75], data[0][:], color='#E2E604', label=r'${\itP. aeruginosa}$', alpha=0.75, width=0.2)
plt.bar([0.25,0.75], data[1][:], color='#008F8F', bottom=np.array(data[0][:]), label=r'${\itE. faecalis}$', alpha=0.75, width=0.2)
plt.bar([0.25,0.75], data[2][:], color='#9813DE', bottom=np.array(data[0][:])+np.array(data[1][:]), label=r'${\itE. coli}$', alpha=0.75, width=0.2)
plt.ylim(0,1)
plt.xlim(0,1)
plt.grid(False)
plt.ylabel(r'Relative Abundance', fontsize=18)
# plt.savefig('Fig3B_2.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 5C
Time average biovolume
"""
plt.figure(figsize=(4,3))
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig5C_1')
df_wt = pd.read_excel('SI_Data.xlsx', sheet_name='Fig5C_0')
df = df.sort_values(by='Time', ascending=True)
df_wt = df_wt.sort_values(by='Time', ascending=True)
times = df['Time']
times= np.arange(0,372,24)
def time_average(df, subject, ranger = 72):
time_averaged_m = []
time_averaged_std = []
for t2 in times:
df2 = df.drop(df[(df.Time > t2+ranger)|(df.Time < t2-ranger)].index, inplace = False)
time_averaged_m.append(np.mean(df2[subject]))
time_averaged_std.append(np.std(df2[subject], ddof=1))
return((time_averaged_m, time_averaged_std))
pa14_mean, pa14_std = time_average(df_wt, 'PA_WT')
pa14_mean_del, pa14_std_del = time_average(df, 'PA_bqsS')
c_pa='#E2E604'
c_ec = '#9813DE'
c_ent = '#008F8F'
plt.scatter(df_wt['Time'],df_wt['PA_WT'], color='black', alpha=0.25)
plt.plot(times, pa14_mean, 'black', linestyle='--')
plt.fill_between(times, np.asarray(pa14_mean) - np.asarray(pa14_std), np.asarray(pa14_mean) + np.asarray(pa14_std), color = 'black', alpha=0.15)
plt.scatter(df['Time'],df['PA_bqsS'], color='green', alpha=0.25, marker='D')
plt.plot(times, pa14_mean_del, 'green', linestyle='--')
plt.fill_between(times, np.asarray(pa14_mean_del) - np.asarray(pa14_std_del), np.asarray(pa14_mean_del) + np.asarray(pa14_std_del), color = 'green', alpha=0.15)
plt.xlim(0,360)
plt.xticks(fontsize=15)
plt.ylim(-50000,700000)
plt.yticks(fontsize=15)
plt.grid(False)
# plt.savefig('Fig3C.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 5D
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig5D').T.reset_index()
new_header = df.iloc[0]
df = df[1:]
df.columns = new_header
df.drop(columns=['Index'])
my_pal = {'Pa_mutant':'#E2E604', 'Pa_wt':'#E2E604',
'Ec_mutant':'#9813DE', 'Ec_wt':'#9813DE',
'Ef_mutant':'#008F8F', 'Ef_wt':'#008F8F'}
#loop for running one sided test
for inp in list(df.columns)[1:]:
arr = df[inp].dropna()
print(inp)
print(stats.wilcoxon(arr, alternative='less', zero_method = 'zsplit'))
sns.boxplot(df, palette =my_pal)
sns.stripplot(df, color='black')
plt.xticks(rotation=45)
plt.hlines(y=0, xmin=-0.5, xmax=5.5, linewidth=2, color='r', linestyle='--')
plt.ylabel('Growth of Invaders', fontsize=16)
plt.grid(False)
# plt.savefig('Fig3D.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 6A
"""
to_plot = []
to_plot2=[]
plot_dict_pre = {r'WT':[216041.71467526804, 376725.7601454683, 285949.8442536336,309289.94785010454, 367276.81454676663, 415122.3440446011],
'$\Delta bqsS$':[397859.0728222483, 341144.0556416029, 521094.96936566086, 613590.5673621687, 668634.2710488527, 501324.46009506844, 473167.6972423181]}
plt.figure(figsize=(3,4))
plt.boxplot(plot_dict_pre.values(), widths=0.3)
plt.xticks(ticks=[1,2], labels=plot_dict_pre.keys(), fontsize=20)
plt.scatter(np.ones(len(plot_dict_pre['WT'])), plot_dict_pre['WT'], alpha=0.75, color='black', s=60)
plt.scatter(np.ones(len(plot_dict_pre['$\Delta bqsS$']))*2, plot_dict_pre['$\Delta bqsS$'], marker='D', alpha=0.75, color='green', s=60)
plt.yticks(ticks=np.arange(200000, 800000, 100000), labels=np.arange(2,8,1), fontsize=20)
plt.yticks(fontsize=20)
plt.ylabel(r'Volume ($um^3$)', fontsize=20)
plt.xlabel(r'$P. aeruginosa$ Strain', fontsize = 20)
plt.title('Parent Chamber', fontsize = 20)
plt.grid(False)
# plt.savefig('Fig4A.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict_pre['WT'], plot_dict_pre['$\Delta bqsS$']))
#%%
"""
Figure 6B
"""
plot_dict_post = {r'WT':[42167.54187893376, 138331.075653775, 197788.37685636582, 55864.185694406304, 129865.69168123294,8784.902380816098],
'$\Delta bqsS$':[29323.739031003217, 97789.70671581058, 18374.87530246589, 10775.614615360977, 1439.9204144279065, 3611.4793872326204, 4521.823017211099]}
plt.figure(figsize=(3,4))
plt.boxplot(plot_dict_post.values(), widths=0.3)
plt.xticks(ticks=[1,2], labels=plot_dict_pre.keys(), fontsize=20)
plt.scatter(np.ones(len(plot_dict_post['WT'])), plot_dict_post['WT'], alpha=0.75, color='black', s=60)
plt.scatter(np.ones(len(plot_dict_post['$\Delta bqsS$']))*2, plot_dict_post['$\Delta bqsS$'], marker='D', alpha=0.75, color='green', s=60)
plt.yticks(ticks=np.arange(0, 250000, 50000), labels=np.arange(0,2.5,0.5), fontsize=20)
plt.ylabel(r'Volume ($um^3$)', fontsize=20)
plt.xlabel(r'$P. aeruginosa$ Strain', fontsize = 20)
plt.title('Progeny Chamber', fontsize = 20)
plt.grid(False)
# plt.savefig('Fig4B.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict_post['WT'], plot_dict_post['$\Delta bqsS$']))
#%%
"""
Figure 6C
"""
plot_dict_post = {'WT':np.log10([1e6,1.2e6,1.6e5,0.2e6,1.2e6,0.4e5,0.6e6,0.6e5]),
'$\Delta bqsS$':np.log10([3.2e5,0.4e5,0.4e5,0.4e5,0.4e5,0.2e5,0.8e5,0.4e5])
}
plt.figure(figsize=(3,4))
plt.boxplot(plot_dict_post.values(), widths=0.3)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[0]])), plot_dict_post[list(plot_dict_post.keys())[0]], alpha=0.75, color='black', s=60)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[1]]))*2, plot_dict_post[list(plot_dict_post.keys())[1]], alpha=0.75, marker='D', color='green', s=60)
plt.xticks(ticks=[1,2], labels=plot_dict_post.keys(), fontsize=20)
plt.yticks(np.arange(4,7.5,0.75), fontsize=20)
plt.ylabel('log$_{10}$ CFUs / ($\mu$l$\cdot$hr)', fontsize=20)
plt.xlabel('$P. aeruginosa$ strain', fontsize=20)
plt.title('Dispersal Rate', fontsize = 20)
plt.grid(False)
# plt.savefig('Fig4C.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
print(stats.mannwhitneyu(plot_dict_post['WT'], plot_dict_post['$\Delta bqsS$']))
#%%
"""
Figure 6E
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig6E')
my_pal = {'WT_Pre':'black', 'WT_Post':'black', 'bqsS_Pre':'green', 'bqsS_Post':'green'}
#this one is reported in paper
#colonized chamber
print(stats.mannwhitneyu(df['WT_Post'].dropna(), df['bqsS_Post']))
plot_dict_post = {'WT':df['WT_Post'].dropna(),
'$\Delta bqsS$':df['bqsS_Post'].dropna()
}
plt.figure(figsize=(3,4))
plt.boxplot(plot_dict_post.values(), widths=0.3)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[0]])), plot_dict_post[list(plot_dict_post.keys())[0]], alpha=0.75, color='black', s=60)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[1]]))*2, plot_dict_post[list(plot_dict_post.keys())[1]], alpha=0.75, marker='D', color='green', s=60)
plt.xticks(ticks=[1,2], labels=plot_dict_post.keys(), fontsize=20)
plt.yticks(np.arange(0,.9,.2), fontsize=20)
plt.xlabel('$P. aeruginosa$ strain', fontsize=20)
plt.ylabel('Biofilm Joint Local Density', fontsize=20)
plt.grid(False)
# plt.savefig('Fig4E.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()
#%%
"""
Figure 6F
"""
df = pd.read_excel('SI_Data.xlsx', sheet_name='Fig6F')
my_pal = {'WT_Pre':'black', 'WT_Post':'black', 'bqsS_Pre':'green', 'bqsS_Post':'green'}
#this one is reported in paper
#colonized chamber
print(stats.mannwhitneyu(df['WT_Post'].dropna(), df['bqsS_Post']))
plot_dict_post = {'WT':df['WT_Post'].dropna(),
'$\Delta bqsS$':df['bqsS_Post'].dropna()
}
plt.figure(figsize=(3,4))
plt.boxplot(plot_dict_post.values(), widths=0.3)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[0]])), plot_dict_post[list(plot_dict_post.keys())[0]], alpha=0.75, color='black', s=60)
plt.scatter(np.ones(len(plot_dict_post[list(plot_dict_post.keys())[1]]))*2, plot_dict_post[list(plot_dict_post.keys())[1]], alpha=0.75, marker='D', color='green', s=60)
plt.xticks(ticks=[1,2], labels=plot_dict_post.keys(), fontsize=20)
plt.yticks(np.arange(3,19,3), fontsize=20)
plt.ylabel('Biofilm Height', fontsize=20)
plt.xlabel('$P. aeruginosa$ strain', fontsize=20)
plt.grid(False)
# plt.savefig('Fig4D.svg', dpi=300, facecolor='w', edgecolor='b',
# orientation='portrait', format='svg',
# transparent=False, bbox_inches='tight', pad_inches=.05, metadata=None)
plt.show()