NUFEB Simulation Analysis

Get Simulation Data

class nufeb_tools.utils.get_data(directory=None, id=None, test=None, timestep=10)[source]

Bases: object

Collect results for analysis.

NUFEB simulation data class to collect results for analysis

test

Set test = True to get example data from the Github repository

Type

bool

directory

Path to the directory containing NUFEB simulation data.

Type

str

timestep

Length of simulation timestep in seconds

Type

int

SucRatio

Relative cyanobacterial sucrose secretion level, 0-100

Type

int

timepoints

List of timepoints in the simulation

Type

List(str)

dims

Size of the simulation boundaries in micrometers

Type

List(str)

numsteps

Number of timepoints

Type

int

biomass

Pandas Dataframe containing the biomass vs time data from biomass.csv

Type

pandas.DataFrame

ntypes

Pandas Dataframe containing the cell number vs time data from ntypes.csv

Type

pandas.DataFrame

avg_con

Pandas Dataframe containing the average nutrient concentrations vs time data from avg_concentration.csv

Type

pandas.DataFrame

positions

Pandas Dataframe containing the single cell biomass over time of all cell ids present at the timepoint

Type

pandas.DataFrame

get_local_data()[source]

Collect NUFEB simulation data from a local directory.

convert_units_avg_con()[source]

Convert the object attribute avg_con, which contains the average nutrient concentration, units to hours and mM.

convert_units_biomass()[source]

Convert the object attribute biomass units to hours and femtograms.

calc_biomass()[source]
collect_positions(h5)[source]

Extract the x, y, z position of each cell during the simulation.

Parameters

timepoint (int) – The simulation timestep to get the position data from.

Returns

Dataframe containing Timestep, ID, type, radius, x, y, z columns

Return type

pandas.DataFrame

get_neighbor_distance(id, timepoint)[source]

Get the nearest neighbor cell distances

Parameters

  • id (int) – The ID of the reference cell

  • timepoint (int) – The timepoint to check the neighbor distances from

Returns

Dataframe containing ID, type, Distance

Return type

pandas.DataFrame

get_neighbors(timestep)[source]

Get the nearest neighbor cell distances

Parameters

timestep (int) – The timepoint to check the neighbor distances from

Returns

Pandas dataframe containing pairwise neighbor distances

Return type

pd.DataFrame

get_mothers__old()[source]

Assign mother cells based on initial cells in the simulation.

Returns

Dataframe containing ID, type, position, radius, and mother_cell

Return type

pandas.DataFrame

get_mothers()[source]

Assign mother cells based on initial cells in the simulation.

Returns

Dataframe containing Timestep, ID, type, position, radius, biomass, total biomass, and mother_cell

Return type

pandas.DataFrame

count_colony_area(timestep)[source]

Count the 2d area in pixel dimensions of each colony at a given timestep.

Parameters

timestep (int) – Timestep to count

get_colony_areas()[source]

Count colony areas for all timesteps

get_nutrient_grid(h5)[source]
get_local_con(timestep, cellID)[source]

Get the local nutrient concentration of a cell

Parameters

  • timestep (int) – The timestep at which to check the concentration

  • cellID (int) – The cell identification number

Returns

The concentration of the specified nutrient within the cell’s grid

Return type

Nutrient Concentration (float)

get_fitness(timestep, cellID)[source]

Get the fitness of an individual cell based on the relative Monod growth rate at a given timestep

Parameters

  • timestep (int) – The timestep at which to check the concentration

  • cellID (int) – The cell identification number

Returns

The Monod growth rate (1/s)

Return type

float

collect_fitness()[source]

Spatial Analysis

nufeb_tools.spatial.fitness_metrics(obj)[source]

Function to calculate colony-level fitness metrics.

Mother cell: Cell ID which seeded the colony

Type: Cell type - cyanobacteria are type 1 and E. coli are type 2

Voronoi area: Area of species-specific Voronoi Tesselation at the beginning of the simulation

IPTG: Sucrose induction level

total biomass: Biomass of each colony at the end of the simulation (fg)

Nearest 1: Distance to nearest cyanobacteria colony

Nearest 2: Distance to nearest E. coli colony

Nearest Neighbor: Distance to nearest colony

IC1: Average distance to nearest cyanobacteria colony

IC2: Average distance to nearest E. coli colony

IC: Average distance to nearest colony

Relative Neighbor Dist 1: Distance to nearest cyanobacteria colony divided by IC1

Relative Neighbor Dist 2: Distance to nearest E. coli colony divided by IC2

Relative Neighbor Dist: Distance to nearest colony divided by IC

Z1: Relative neighbor distance 1 divided by sqrt(D_sucrose/mu_cyano)

Z2: Relative neighbor distance 2 divided by sqrt(D_sucrose/mu_ecw)

Z1_2: Relative neighbor distance 1 divided by sqrt(D_sucrose/mu_ecw)

Z2_1: Relative neighbor distance 2 divided by sqrt(D_sucrose/mu_cyano)

LogNearest 1: log(Nearest 1)

LogNearest 2: log(Nearest 2)

LogNearest: log(Nearest Neighbor)

Inv1: Inverse sum of neighbor distance 1

Inv2: Inverse sum of neighbor distance 2

Log Inv1: Log squared inverse sum of neighbor distance 1

Log Inv2: Log squared inverse sum of neighbor distance 2

Colony Area: 2D area of colony at the end of the simulation

Parameters

obj (nufeb_tools.utils.get_data) – Data object collected with nufeb_tools.utils.get_data

Returns

Dataframe containing colony number (mother cell ID), cell type, total biomass, colony area, Voronoi area, nearest neighbor, mean neighbor distance, etc.

Return type

pandas.DataFrame

from nufeb_tools import utils, spatial
x = utils.get_data(directory = None,test=True)
metrics = spatial(x)
metrics.head()
Metrics

mother_cell

type

Voronoi Area

sucRatio

total biomass

Nearest 1

Nearest 2

Nearest Neighbor

IC1

IC2

IC

Relative Neighbor Dist 1

Relative Neighbor Dist 2

Relative Neighbor Dist

Z1

Z2

Z1_2

Z2_1

LogNearest 1

LogNearest 2

LogNearest

Inv1

Inv2

Log Inv1

Log Inv2

Colony Area

0

17

1

1.3721664010759044e-10

56

3274.0540292160968

1.2218809270955987e-05

1.5250550809724869e-05

1.2218809270955987e-05

8.35779934739048e-06

7.427405876587581e-06

5.678714310045285e-06

1.4619648980650646

2.0532809251473845

2.151685857719887

525.1659178645756

367.9636654080846

261.9953051730434

737.578011015187

-11.312534049970473

-11.090894936892633

-11.312534049970473

81841.03522893938

65571.40213993627

22.625068099940947

22.181789873785267

2860.0

1

9

1

3.199399919849626e-09

56

2366.4452758679004

9.12914563362859e-06

4.715400301140931e-06

4.715400301140931e-06

8.35779934739048e-06

7.427405876587581e-06

5.678714310045285e-06

1.092290596385153

0.6348650362577677

0.8303640654715958

392.3717966037814

113.77267616903244

195.7468394188747

228.05573507798357

-11.6040384456519

-12.264676745982944

-12.264676745982944

109539.27564879112

212071.07268454845

23.2080768913038

24.52935349196589

1826.0

2

18

1

0.0

56

4237.0059685613915

1.7105525423090635e-05

2.763856002037732e-06

2.763856002037732e-06

8.35779934739048e-06

7.427405876587581e-06

5.678714310045285e-06

2.046654234219133

0.3721159241815324

0.4867045340084544

735.1975761440449

66.68602320392425

366.77611164784554

133.6711990373123

-10.97610902249168

-12.798883751086846

-12.798883751086846

58460.64211802033

361813.35035643005

21.95221804498336

25.597767502173692

3618.0

3

11

1

9.405959110956312e-09

56

3532.419993283879

2.4033751267748442e-05

5.813811142443484e-06

5.813811142443484e-06

8.35779934739048e-06

7.427405876587581e-06

5.678714310045285e-06

2.8756075934333594

0.78275123765211

1.0237900385584147

1032.973570858309

140.2750159423561

515.330901580897

281.1793037748489

-10.636051412711192

-12.055274239595036

-12.055274239595036

41608.153003643994

172004.2112650585

21.272102825422383

24.110548479190072

2623.0

4

8

1

1.0844410415183224e-09

56

4543.177970151615

1.648329154022339e-05

6.4671554798071755e-06

6.4671554798071755e-06

8.35779934739048e-06

7.427405876587581e-06

5.678714310045285e-06

1.9722047461416858

0.8707152385724235

1.1388414924073902

708.4538877063212

156.03883851830264

353.4341932648232

312.7776652290108

-11.013163324043719

-11.948774193758952

-11.948774193758952

60667.494569258066

154627.48701842187

22.026326648087437

23.897548387517904

3200.0

Plotting

Average Nutrient Concentration

nufeb_tools.plot.average_nutrients(df, nutrient, ax=None, legend=None, **kwargs)[source]

This is a function to plot the nutrient concentration over time

Parameters

  • df (pandas.DataFrame) – Pandas Dataframe containing nutrient data

  • nutrient (str) – Name of the nutrient to plot, e.g., 'Sucrose'

  • ax – Axis on which to make the plot

  • legend (bool) – Include legend in the plot

  • **kwargs – Additional arguments to pass to plt.plot

_images/average_nutrients.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
import seaborn as sns
x = utils.get_data(directory = None,test=True)
f, ax = plt.subplots()
sns.set_context('talk')
sns.set_style('white')
plot.average_nutrients(x.avg_con,'Sucrose',color='Green',legend=True)

Single Cell Growth

nufeb_tools.plot.biomass_time(df, id=None, ax=None, legend=None, **kwargs)[source]

This is a function to plot the cell biomass over time

Parameters

  • df (pandas.DataFrame) – Pandas Dataframe containing biomass data

  • ax – Axis on which to make the plot

  • legend (bool) – Include legend in the plot

  • **kwargs – Additional arguments to pass to plt.plot

_images/biomass_vs_time.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
import seaborn as sns
f, ax = plt.subplots()
sns.set_context('talk')
sns.set_style('white')
x = utils.get_data(directory = None,test=True)
plot.biomass_time(x.positions)
f.tight_layout()

Single Cell Growth Rate

nufeb_tools.plot.growth_rate_div(df, **kwargs)[source]

Plot a heatmap of the single cell growth rates relative to each division

Parameters

  • df (pandas.DataFrame) – Pandas Dataframe containing biomass data

  • **kwargs – Additional arguments to pass to plt.plot

nufeb_tools.plot.growth_rate_time(df, period=3)[source]

Plot a heatmap of the single cell growth rates over time

Parameters

  • df (pandas.DataFrame) – Pandas Dataframe containing biomass data

  • period (int) – Number of timesteps to average growth rate calculation over

  • **kwargs – Additional arguments to pass to plt.plot

Returns

matplotlib.figure.Figure

_images/growth_rate_div.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
x = utils.get_data(directory = None,test=True)
plot.growth_rate_div(x.positions)
_images/growth_rate_time.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
x = utils.get_data(directory = None,test=True)
plot.growth_rate_time(x.positions)

Overall Cell Growth

nufeb_tools.plot.overall_growth(df, ax=None, **kwargs)[source]

This is a function to generate growth curve plots

Parameters

  • df (pandas.DataFrame) – Pandas Dataframe containing biomass data over time

  • ax (plt.ax) – Axis to plot data on

  • **kwargs –

_images/total_biomass_vs_time.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
sns.set_context('talk')
f, ax = plt.subplots()
x = utils.get_data(directory = None,test=True)
plot.overall_growth(x.biomass,ax=ax)
f.tight_layout()

Whole Colony Plotting

nufeb_tools.plot.colony(obj, time, colors=None, colony=None, ax=None, by=None, img=array([], dtype=float64), fitness=None, overlay=None, **kwargs)[source]

Plot bacterial colonies at a specific timepoint

Parameters

  • obj (nufeb_tools.utils.get_data) – Object containing cell locations

  • time (int) – Simulation timestep to plot

  • colors (dict, optional) – Dictionary of colors to plot each colony. Defaults to random.

  • colony (int, optional) – Plot a specific colony. Defaults to None.

  • ax (matplotlib.pyplot.axes, optional) – Axis to plot on. Defaults to None.

  • by (str, optional) – Plot by species. Defaults to None.

  • img (np.array, optional) – Image array to overlay colonies onto.

  • fitness (pandas.DataFrame,optional) – Takes a dataframe containing spatial metrics data as an input or if bool, will calculate the metrics internally. Defaults to None.

  • overlay (bool, optional) – If True, plot a specific colony on top of the others.

_images/colonies.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
x = utils.get_data(directory=r'E:\sucrose\runs\Run_50_50_1.00e+00_1_2022-01-11_48525')
f,ax = plt.subplots()
plot.colony(x,35000,colors,ax=ax)
plt.show()

Plot colonies over time by species:

_images/Colonies_over_time.png 
from nufeb_tools import utils, plot
import matplotlib.pyplot as plt
x = utils.get_data(directory=r'E:\sucrose\runs\Run_50_50_1.00e+00_1_2022-01-11_48525')
f, axes = plt.subplots(ncols=3,figsize=(15,5))
for ax, time in zip(axes,[100,20000,25900]):
    plot.plot_colony(x,time,by='Species',ax=ax)
plt.show()