Functions

bacteria.functions.bin_column(df_3d, column='F/V', bins=10, sort_by='Cell Cycle', print_bins=True, conf=0.95, method=<function mean>)

Perform the binning of one column.

Parameters

df_3dDataFrame

DataFrame with the 3D data

columnstr

DataFrame column to bin

binsint or list (optional)

Amount of bins to use, default = 10. This parameter can also receive a list/nd.array with the desired bins intervals. e.g., [0,500,1000,1500,2000,2500,3000, 3500,4000,4500].

sort_bystr (optional)

The parameter to bin the data (x axis), the default is ‘Cell Cycle’, but is also possible to bin by ‘Volume’ or ‘Time (fps)’ columns.

print_binsbool

If true will plot a report of bins

conffloat (optional)

confidence interval desired for bootstrap, default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

Returns

bins_meannd.array

array(2,bins) with bin value in the first column and the mean value in the second

cidict

array(2,bins) with inferior Confidence Interval in the first column and the upper one in the second

bacteria.functions.cells_length(df, limit_inf, limit_sup, ax=None)

Plot the cell’s lenght over time, filtering by size. The data will be filted based on the size of the cells (< mean + 1 std)

Parameters

dfDataFrame

DataFrame of 3D data

limit_infint

Start moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

limit_supint

End moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.cells_pos_shift(df_3d, pos)

Function to extract the Cell ID of all cells that were born after an moment in time.

Parameters

df_3dDataFrame

3D data

posint

Moment in time to be the start

Returns

cells_posnd.array

array with the Cell ID for cells that were born after the treshold

bacteria.functions.cells_pre_shift(df_3d, pre)

Function to extract the Cell ID of all cells that divided before an moment in time.

Parameters

df_3dDataFrame

3D data

preint

Moment in time to be the limit

Returns

cells_prend.array

array with the Cell ID for cells that divided before the treshold

bacteria.functions.ci_bootstrap(arr, conf=0.95, method=<function mean>, plot=False)

Calculate the confidence interval (CI) based on different methods.By default the bootstrap is done for 10000 times.

Parameters

arr: nd.array

data to calculate the CI

conffloat (optional)

confidence interval desired, default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

plotbool (optional)

If true plot the distribution based of the boostrap and the CI.

Returns

ci_lowfloat

Value for the lower CI (default 2.5%)

ci_highfloat

Value for the higher CI (default 97.5%)

bacteria.functions.column2d_mean(df, column='Vd-Vb', window=30, conf=0.95, method=<function mean>)

Estimate the column mean for all cells using the ‘pd.melt()’ method, sorting by time at division.

Parameters

dfDataFrame

DataFrame with the deravtive data alongside with the time and volume or the 2D df

columnstr

column to calculate the mean

windowint

value to use in ‘rolling.mean()’

conffloat (optional)

confidence interval desired for bootstrap, default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

Returns

timesnd.array

1D time vector with the uniques time points (fps)

meannd.array

1D time vector with the mean for each time point

cind.array

array(2,bins) with inferior Confidence Interval in the first column and the

bacteria.functions.column_mean(df, column, conf=0.95, method=<function mean>, smooth=False, factor=10, plot_hist=False)

Estimate the column mean for all cells using the ‘pd.melt()’ method, sorting by time.

Parameters

dfDataFrame

DataFrame with the deravtive data alongside with the time and volume or the 3D df

columnstr

column to calculate the mean

conffloat (optional)

confidence interval desired for bootstrap, default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

smoothbool

If true will smooth the column value and the CI

factorint

Factor to smooth the data, default = 10

plot_hist: bool (optional)

Plot a histogram with the data distribution

Returns

timend.array

1D time vector with the uniques time points (fps)

meannd.array

1D time vector with the mean for each time points

cind.array

array(2,bins) with inferior Confidence Interval in the first column and the

bacteria.functions.combined_filters(df_2d, df_3d, std=1, daughter=True, lentgh=True, plot=False)

Plot the mean fluo for the lineage for the entire experiment. Check also fluor_lineage(). The default filter is ‘stat0==2’ removing the data with grow rate = 0, values with NaN values and data with low points.

Parameters

df_2dDataFrame

DataFrame of 2D data

df_3dDataFrame

DataFrame of 3D data

stdint (optional)

Number of standard deviations desired to use as reference for the cell size filter

daughterbool (optional)

If True the daughter filter will be used. If True alongised with ‘lentgh’ the two filters will be used

lentghbool (optional)

If True the lentgh filter will be used. If True alongised with ‘daughter’ the two filters will be used

plotbool (optional)

Plot the histogram pre and pos filter the data

Returns

df_2dDataFramef

Filtered DataFrame of 2D data

df_3dDataFrame

Filtered DataFrame of 3D data

gc_filternd.array

Array with good cells ID after filter

bacteria.functions.concatenate_clist(path, fps=3, filters=True, save_mat=False, path2save=None, const=0.1067, direct=False)

Concatenate all c_list.mat from one experiment in DataFrames.

Parameters

pathstr

Experiment path

fpsint (optional)

Frames per second used in the experiment, usual 3

filtersbool (optional)

Filter data based on data2d[‘stat0’] = 2 and is.nan()

save_matbool (optional)

save the dataframes in .mat format.

path2savestr (optional)

If false, the data will be saved into the current directory else, should pass a path to save.

constfloat (optional)

parameter extracted from the microscope to transform pixel to micrometers

directbool

If you have all the clists in one folder.

Returns

data2Dpandas DataFrame

with 2D data

data3Dpandas DataFrame

with 3D data

gcnd.array

array with good cells ID

bacteria.functions.derivative(df_3d, column='Fluor1 sum', minus=1, plus=2, bar=True)

Calculate the derivatine of the fluor sum using a 3 slide window. Return a dataframe with the derivative, derivative normalized by volume, logrith

Parameters

df_3dDataFrame

DataFrame of 3D data

columnstr

column to calculate the derivative

minusint (optional)

value to change the window to the convolution to adapt the range for the loop (e.g, default = i in range(1,len(x)-2))

plusint (optional)

value to change the window to the convolution to adapt the range for the loop (e.g, default = i in range(1,len(x)-2))

Returns

df_derivativeDataFrame

DataFrame with the deravtives data alongside with the time and volume

dict_derivativedict

A dict with other dicts that contains the [volume,fluor,time,F/V and all derivatives] of each cell at the slide window (cell number = key).

bacteria.functions.derivative_binning(df_deriv, derivative_column='Derivative', bins=10, sort_by='Cell Cycle', print_bins=True, conf=0.95, method=<function mean>)

Perform the binning of the derivative of one column. Please check ‘derivative()’.

Parameters

df_derivDataFrame

DataFrame with the derivative data, output from ‘derivative()’

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume.

binsint or list (optional)

Amount of bins to use, default = 10. This parameter can also receive a list/nd.array with the desired bins intervals. e.g., [0,500,1000,1500,2000,2500,3000, 3500,4000,4500].

sort_bystr (optional)

The parameter to bin the data (x axis), the default is ‘Cell Cycle’, but is also possible to bin by ‘Volume’ or ‘Time (fps)’ columns.

print_binsbool

If true will plot a report of bins

conffloat (optional)

confidence interval desired for bootstrap,

default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

Returns

bins_meannd.array

array(2,bins) with bin value in the first

column and the mean value in the second

cidict

array(2,bins) with inferior Confidence

Interval in the first column and the upper one in the second

bacteria.functions.df_data2d(data, fps=3, const=0.1067, comp=False)

Create a pandas DataFrame of the 2D data using the headers pre-determined.

Parameters

data: clist.mat

Supersegger clist already loaded

fps: int (optional)

Frames per second used in the experiment, usual 3

constfloat (optional)

parameter extracted from the microscope to transform pixel to micrometers

compbool

Input data are the compiled clists (clist for all xy/fovs)

Returns

df: pandas DataFrame

bacteria.functions.df_data3d(data, fps=3, filters=True, const=0.1067, comp=False)

Create a pandas DataFrame of the 2D data using the headers pre-determined.

Parameters

dataclist.mat

Supersegger clist already loaded

fpsint (optional)

Frames per second used in the experiment, usual 3

filtersbool (optional)

Filter data based on data2d[‘stat0’] = 2 and non NaN’s

constfloat (optional)

parameter extracted from the microscope to transform pixel to micrometers

compbool

Input data are the compiled clists (clist for all xy/fovs)

Returns

dfpandas DataFrame

3D data in df

gcnd.array

array with good cells ID

bacteria.functions.diff_minima(df_3d, df_2d, derivative_column='Derivative/V', order=3, pre=None, pos=None, adjust=False)

Function to plot the distance between the minimas of the daughter and mother by the mothe volume. The ‘pre’ and ‘pos’ to be used, both must be different than ‘None’. It is possible to use a higher order together with adjust order for each cell, the higher the order, the higher the noise acceptance. Higher orders take longer and can reach 20 minutes to run. The value of the minimum used comes from the column ‘Smoothed Volume’, output of ‘lineage_derivative()’. Also, plot the histogram of the diff.

Parameters

df_3dDataFrame

DataFrame of 3D data

df_2dDataFrame

DataFrame of 2D data

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume. Default is ‘Derivative/V’.

orderint (optional)

The order of the polynomial used to fit the savgol_filter from scipy in ‘lineage_derivative()’ higher order is better to find more minimas, accept more noise. Default is 3.

preint (optional)

Save the data pre this moment in minutes

posint (optional)

Save the data pos this moment in minutes

adjustbool (optional)

If true it’s possible to pass a bigger order value and the algorithm will adjust for each cell if needed and the order used for each cell is stored in the dictionary vol_dict[‘order’][‘cell id’]. Default false

plotbool (optional)

If true will plot the result of the analysis, if ‘pre’ or ‘pos’ is None will plot just all the data.

Returns

vol_dictdict

Dictionary with the following keys ‘all’, ‘pre’, ‘pos’ with subkeys ‘diff’ and ‘minima’, ‘diff’ is the difference between daughter and mother volume at minima and ‘minima’ the minima volume of the mother. The key ‘order’ store the order used for each cell and the value of ‘pre’ and ‘pos’ for plot purpose.

bacteria.functions.fluor_lineage(df_2d, df_3d, limit_inf, limit_sup)

Calculate the fluor mean for every mother cell and daugther. The bins here were not made using pandas.

Parameters

df_2dDataFrame

DataFrame of 2D data

df_3dDataFrame

DataFrame of 3D data

limit_infint

Start moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

limit_sup: int

End moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

Returns

mean_mnd.array

vector with the mean value for every mother cell

bins_mnd.array

vector with bins used for mother cell’s (cell cycle)

mean_dnd.array

vector with the mean value for every daugther cell

bins_dnd.array

vector with bins used for daugthers cell’s (cell cycle)

cells_removedlist

list with cell id for each cell without daugther

bacteria.functions.lineage_corr_filter(df_3d, reverse_lineages, shift=None, threshold=0.96, column='Fluor1 mean', method=<function var>)

Filter the lineages based on the correlation between the longest lineage of a mother cell. Return the lineages that match the parameters.

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

shiftint (optional)

Value to use to extract the lineages that pass through this time point, usually used to get the shift moment. If shift is passed automatically the output will be filtered for these cells. Default is None.

thresholdfloat (optional)

Value to be used as threshold to determine if the lineage must be maintained. If the correlation between the daughter lineages is less than the threshold value, the lineage is maintained. Default ‘.96’.

columnstr (optional)

The column to extract the feature of the dataset. Default ‘Fluor1 mean’.

methodfunction (optional)

Method to use to extract feature of each cell, default its np.mean. Other options are np.std and stats.sem.

Returns

filtered_lineagelist

List with the Cell ID for each lineage that match the paramters (threshold and shift). Longest lineages for each mother cell and different from each other in case of more than one.

bacteria.functions.lineage_derivative(df_3d, reverse_lineage, column='Fluor1 sum', derivative_column='Derivative', std=2, window=None, order=5, perc=0.7, mode='nearest')

Plot a single lineages derivative. It’s possible to chose different columns to inspect. The function is already removing the last value of the reverse_lineage, because the last cell has no daughter and is removed by our filters. In this code the volume on the X axis are filtred using a standard window (lenght*perc) and summed with the last value of the previous cell and with the mean diff inside the cell. To create this volume array the data is filtered using the parameters ‘perc’ and ‘order’, in case of the order is biggter than the window the order will be reduced automatically. This manipulation isn’t used to normalize the derivative data. Also, the derivative data is filtered to remove the outliers based on the ‘std’ parameter.

Parameters

df_3dDataFrame

3D data

reverse_lineagelist

List with the lineage Cell ID’s, output from ‘lineages()’

columnstr (optional)

The df column to compute the derivative, default is ‘Fluor1 sum’.

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume. Default is ‘Derivative’.

stdint (optional)

Number of standard deviations to filter the data

windowint (optional)

The window value to smooth the graph using savgol_filter from scipy. Default 19 based on previous tests.

orderint (optional)

The order of the polynomial used to fit the savgol_filter from scipy. Default 5 based on previous tests. If the order is too large for one cell the order will be automatically decreased using ‘_adjust_order()’.

percfloat (optional)

Percentage of the length of each cell to estimate the window to smooth the data for the volume adjust. Default is .7, should be between .1 to 1.

modestr (optional)

This determines the type of extension to use for the padded signal to which the filter is applied check ‘scipy.signal.savgol_filter()’

Returns

df_lineageDataFrame

Data frame with the columns ‘Cell ID’, ‘Time (fps)’, ‘Volume’, ‘Derivative’, ‘Smoothed Volume’, ‘Smoothed Derivative’. The columns ‘Cell ID’, ‘Time (fps)’, ‘Volume’ are related and should be used to access the data relative for each cell. The columns ‘Smoothed Volume’ and ‘Smoothed Volume’ are the data filtered and concatenated of the entire lineage (these columns are not related with the ‘Cell ID). The column ‘Time (fps)’ is used to plot the time for each cell and for entire lineage.

peaksdict

Dictionary data with the indexes of local minima and local maxima related to ‘Smoothed Volume’,’Smoothed Derivative’ and ‘Time (fps)’.

order_dictdict

Dictionary with the order used in each cell

bacteria.functions.lineages(df_2d, nodes_min=5)

Extract the lineage for the entire dataset using AnyTree lib, data is organized using the node structure. The output is in dicts and anytree.node types. The last value of reverse lineage is the ID of a cell that we don’t have data, cells without daughters are removed by Stat0 == 2 filter.

Parameters

df_2dpd.DataFrame

2D data

nodes_minint

The minimum value of nodes in the lineage

Returns

reverse_lineagesdict

Key values are the cell id of cells with more than X nodes (nodes_min), and the data values is a list with the nodes to reach the key cell.

large_lineagesdict

Key values are the cell id of the root of the lineage, the data values are the all cells ids that are part of this lineage (mother and daughters).

large_nodesdict

Key values are the cell id of the root of the lineage, and the values are the Node with all the information about this lineage, structure in anytree.Node dataype

all_nodesdict

Key values are all the cell id’s in the dataset, and the values are the Node with all the information about this lineage, structure in anytree.Node dataype

bacteria.functions.lineages_pos_shift(df_3d, reverse_lineages, shift)

Filter the lineages based on time, used to extract lineages pre shift. Save the Cell ID of every lineage that the first cell was born after the shift.

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

shiftint

The time value of the shift

Returns

lineage_filterlist

List with the Cell ID used as a key in ‘reverse_lineages’ dict.

bacteria.functions.lineages_pre_shift(df_3d, reverse_lineages, shift)

Filter the lineages based on time, used to extract lineages pre shift. Save the Cell ID of every lineage that the first cell was born before the shift.

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

shiftint

The time value of the shift

Returns

lineage_filterlist

List with the Cell ID used as a key in ‘reverse_lineages’ dict.

bacteria.functions.lineages_shift(df_3d, reverse_lineages, shift)

Filter the lineages based on time, used to extract lineages that pass through a time point (up shift or down shift). Save the Cell ID of every lineage that the first cell was born before the shift and the last was born after the shift.

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

shiftint

The time value of the shift

Returns

lineage_filterlist

List with the Cell ID used as a key in ‘reverse_lineages’ dict.

bacteria.functions.longer_lineages(df_3d, reverse_lineages, column='Fluor1 mean', method=<function var>)

Filter the lineages based on length and return the variance (default or other method - np.mean, np.std, stats.sem and others) for one feature (as default ‘Fluor1 mean’, but can be used any column of df_3d). Return a dictionary with the root cell as a key, each root cell have dictionaries with the longest lineages and use the last cell ID as key. The values in the dict is the method applied into the column choosed. This function is used as support for other functions.

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

columnstr (optional)

The column to estimate the

methodfunction (optional)

Method to use to extract feature of each cell, default its np.mean. Other options are np.std and stats.sem.

Returns

root_dictdict

Dict with the mother Cell ID used as a key and inside each mother cell another dict with the longest daughters, the key of this dict is the last cell of the lineage.

lineage_arraynd.array

Array with all lineages in arrays. Used in plot functions.

len_dictdict

Dict with the mother Cell ID used as a key and inside the lenght of the longest lineage.

bacteria.functions.plot_2d_data(df_2d)

Plot The Growth Rate, Cell Age and Long axis (L) death by ‘Birth Time + Age’.

Parameters

df_2dDataFrame

DataFrame of 2D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

shiftint

The time value of the shift

Returns

None

bacteria.functions.plot_bins(arr, ci, column='Fluor1 sum', derivative_column='Derivative', sort_by='Cell Cycle', cmap='rainbow', line='k', ax=None)

Plot the bins that are organized into arrays.

Parameters

arrnd.array

2D array containing bins and the data to plot

cind.array

2D array containing the inferior and superior confidence interval values

columnstr (optional)

The feature used to calculate the derivative, yaxis label

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume.

sort_bystr (optional)

The parameter used to bin the data. Default is ‘Cell Cycle’, xaxis legend.

cmapstr (optional)

The desired colormap to plot the interval between bins. Default is ‘rainbow’, to remove this pass ‘None’. Check matplotlib colormaps for all options.

linestr (optional)

The color of the line, default is black. Can be adjusted accordingly to the color map.

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_cell_lineage(df_2d, reverse_lineage, x_axis, y_axis, z_axis, c_axis, colormap='rainbow', ax=None)

Plot the cell size lineage. It’s possible to pick different varibales for each axis. The function is already removing the last value of the reverse_lineage, because the last cell has no daughter and is removed by our filters.

Parameters

df_2dDataFrame

2D data

reverse_lineagelist

list with the Cell ID’s to plot, check ‘lineages()’

x_axisstr

Data to plot in the X axis, use the df_2d terms.

y_axisstr

Data to plot in the Y axis, use the df_2d terms.

z_axisstr

Data to use as Z axis, use the df_2d terms. In this case the Z is the size of each scatter point. It’s intrensting to use cell size as parameter, at division or at birth.

c_axisstr

Data to use in the color of each saccater point and in the color bar, use the df_2d terms. It’s intresting to use fluorescence data.

colormapstr (optional)

Colormap to use. Check the matplotlib documentation. Default = rainbow.

axnd.array (optional)

Pass the ax for subplot.

Returns

None

bacteria.functions.plot_cell_size_lineage(df_2d, reverse_lineage, ax=None)

Plot the cell size lineage. X-axis is the Cell ID and the Y-axis is the Cell Size (‘Long axis (L) death’) in px, the size of the circle is proportionald to the cell size.

Parameters

df_2dDataFrame

2D data

reverse_lineagelist

list with the Cell ID’s to plot, check ‘lineages()’

axnd.array (optional)

Pass the ax for subplot.

Returns

None

bacteria.functions.plot_column2d_mean(times, mean, ci, column='Vd-Vb', color='green', ax=None)

Plot the colunm mean for the entire experiment. Check also ‘column_mean()’.

Parameters

timend.array

1D time vector with the uniques time points (fps) output of ‘column_mean()’

meannd.array

1D time vector with the mean growth rate for each time point output of ‘column_mean()’

cind.array

array(2,bins) with inferior Confidence Interval in the first column and the upper one in the second output of ‘column_mean()’

columnstr

DataFrame column to estimate the column_mean, default is ‘Volume’

color: str (optional)

Color to plot the growth rate.

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_column_mean(times, mean, ci, column, color='Orange', ax=None)

Plot the colunm mean for the entire experiment. Check also ‘column_mean()’.

Parameters

timend.array

1D time vector with the uniques time points (fps) output of ‘column_mean()’

meannd.array

1D time vector with the mean growth rate for each time point output of ‘column_mean()’

cind.array

array(2,bins) with inferior Confidence Interval in the first column and the upper one in the second output of ‘column_mean()’

columnstr

DataFrame column to estimate the column_mean, default is ‘Volume’

color: str (optional)

Color to plot the growth rate.

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_corr_lineage(df_3d, reverse_lineages, mother_cell, column='Fluor1 mean', derivative_column='Derivative', method=<function var>, derivative_plot=False)

Function to produce three plots. The first one is the correlation between the lineages from the same mother cell (and same size), the second one present the variance of the column for each cell inside the lineages easy to see if the lineages are differents, and the third graph present the dV/dt, dF/dt, Volume and Fluor1 mean of the lineage, and if the ‘derivative_plot = True’, will plot the lineage oscilations using ‘plot_lineage_derivative()’

Parameters

df_3dDataFrame

DataFrame of 3D data

reverse_lineagedict

Dict with the lineage Cell ID’s as key, output from ‘lineages()’

mother_cellint

Mother cell (first cell of the lineage), to plot the lineage

columnstr (optional)

The column to extract the feature of the dataset. Default ‘Fluor1 mean’.

methodfunction (optional)

Method to use to extract feature of each cell, default its np.mean. Other options are np.std and stats.sem.

derivative_plotbool (optional)

If true beside plot the dV/dt, dF/dt, Volume and Fluor1 mean, will plot the lineage oscilations using ‘plot_lineage_derivative()’

Returns

None

bacteria.functions.plot_derivative_mean(times, mean, ci, column='Fluor1 sum', derivative_column='Derivative', color='Orange', ax=None)

Plot the derivative mean for the entire experiment. Check also derivative() and column_mean().

Parameters

timend.array

1D time vector with the uniques time points (fps) output of ‘derivative()’

meannd.array

1D time vector with the mean derivative for each time point output of ‘derivative()’

cind.array

array(2,bins) with inferior Confidence Interval in the first column and the upper one in the second output of ‘derivative()’

columnstr (optional)

The feature used to calculate the derivative, yaxis label

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume.

color: str (optional)

Color to plot the growth rate.

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_diff_minima(vol_dict, key=None, color=None, color_pre='black', color_pos='red', ax=None)

Function to plot the difference between the minimas. The function will plot the ‘pre’ and ‘pos’ moments automatically if present in the dictionary. Also, plot the histogram of the diff. It’s possible to choose the key to plot (‘all’,’pre’ or ‘pos’), if you pass just the key will plot the cloud and the histogram, if you pass the ‘key’ and the ‘ax’ will return just the cloud plot. If the ‘pre’ os ‘pos’ are less than 2, the function will plot just the all data vector, to plot those moments you should pass the ‘key’.

Parameters

vol_dictdict

Dict with the values of the diff minima, output of ‘diff_minima()’.

keystr (optional)

key used to plot just one moment, ‘all’, ‘pre’ or ‘pos’

colorstr (optional)

color to plot the cloud and the histogram of ‘all’ data

color_prestr (optional)

color to plot the cloud and the histogram of ‘pre’ data, default ‘black’

color_posstr (optional)

color to plot the cloud and the histogram of ‘pos’ data, default ‘red’

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_distance_minima(vol_dict, key, color=None, ax=None)

Function to plot the distance between the minimas. The function will plot the volume minima by the volume at birth. It’s necessary to pass the ‘key’ perform the cloud plot (‘pre’,’pos’,’all’).

Parameters

vol_dictdict

Dict with the values of the diff minima, output of ‘diff_minima()’.

keystr (optional)

key used to plot just one moment, ‘all’, ‘pre’ or ‘por’

colorstr (optional)

Cloud color

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_distance_minima_lineage(df_3d, df_2d, lineages_list, column='Fluor1 sum', derivative_column='Derivative', order=5, ax=None)

Function to plot the distance between the minimas for an specifics lineages. Here we are calculating the minima inside the lineage, so it might be better to just use the diff_minima() function. Change the diff_minima() function to return the minimum along with the Cells ID and use that data here. This may be better for the smooth (TODO) tuning issue

Parameters

df_3dDataFrame

DataFrame of 3D data

df_2dDataFrame

DataFrame of 2D data

lineages_listlist

List with the lineages to plot, its better to use the output of ‘lineage_corr_filter()’

derivative_columnstr (optional)

Derivative column to bin. Columns: ‘Derivative’, ‘Derivative/V’ is the derivative normalized by volume, ‘Log Derivative’ logarithm of the derivative, ‘Log Derivative/V’ logarithm of the derivative normalized by volume. Default is ‘Derivative’.

orderint (optional)

The order of the polynomial used to fit the savgol_filter from scipy in ‘lineage_derivative()’ higher order is better to find more minimas.

axnd.array (optional)

Pass the ax for subplot.

Returns

None

bacteria.functions.plot_fluor_lineage(df_2d, df_3d, limit_inf, limit_sup, conf=0.95, method=<function mean>, ax=None)

Plot the mean fluo for the lineage for the entire experiment. Check also fluor_lineage().

Parameters

df_2dDataFrame

DataFrame of 2D data

df_3dDataFrame

DataFrame of 3D data

limit_infint

Start moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

limit_sup: int

End moment in time (fps) of the stationary phase to filter the data (use growth_rate() to see it)

conffloat (optional)

confidence interval desired for bootstrap, default - 0.95

methodfunction (optional)

Method to use as basis for the boostrap, default its np.mean. Other options are np.std and stats.sem.

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_fluor_lineage_single_cell(df_2d, df_3d, cell_id, ax=None)

Plot the cell’s fluo in the cell cycle from the lineage, mother and 1 or 2 daughter’s. If just plot the mother data it’s because this cell has no daughter’s, one can check this on the dataframe.

Parameters

df_2dDataFrame

DataFrame of 2D data

df_3dDataFrame

DataFrame of 3D data

cell_id: int

Cell to plot the fluo

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.plot_large_lineages(df_2d, lineage_list, return_plot=False)

Plot the large lineage data without prune the data. This plot is made using graphviz, the variable can be used to save the plot in pdf.

Parameters

df_2dDataFrame

2D data

lineage_listlist

list with the Cell ID’s to plot

return_plotbool (optional)

If true return the graphviz variable.

Returns

None

bacteria.functions.plot_lineage_derivative(df_lineage, peaks, column='Fluor1 sum', derivative_column='Derivative/V', x_axis='Smoothed Volume', ax=None)

Plot a single lineages derivative with the local minima and local maxmia. The input parameters are the output of ‘lineage_derivative()’. This function can receive both volume (‘Smoothed Volume’) and time (‘Time (fps)’) as the X axis.

Parameters

df_lineageDataFrame

DataFrame output of ‘lineage_derivative()’ with all data necesseary to plot the lineages.

peaksdict

Dictionary data with the indexes of local minima and local maxima related to ‘Smoothed Volume’,’Smoothed Derivative’ and ‘Time (fps)’. Output of ‘lineage_derivative()’

x_axisstr (optional)

Column to use as X axis in the plot. Should be ‘Smoothed Volume’ (default) or ‘Time (fps)’.

axnd.array (optional)

Pass the ax for subplot.

Returns

None

bacteria.functions.plot_lineage_ratio(df_lineage, x_axis='Smoothed Volume', ax=None)

Plot a single lineages F/V ratio. The input parameters are the output of ‘lineage_derivative()’. This function can receive both volume (‘Smoothed Volume’) and time (‘Time (fps)’) as the X axis.

Parameters

df_lineageDataFrame

DataFrame output of ‘lineage_derivative()’ with all data necesseary to plot the lineages.

x_axisstr (optional)

Column to use as X axis in the plot. Should be ‘Smoothed Volume’ (default) or ‘Time (fps)’.

axnd.array (optional)

Pass the ax for subplot.

Returns

None

bacteria.functions.plot_lineages_check(df_2d, reverse_lineages, y_axis, colormap='rainbow')

Plot all lineages using the length as x-axis. It’s possible to chose different columns to inspect. The function is already removing the last value of the reverse_lineage, because the last cell has no daughter and is removed by our filters.

Parameters

df_2dDataFrame

2D data

reverse_lineagesdict

Dict with all lineages, output from ‘lineages()’

y_axisstr

Data to plot in the Y axis, use the df_2d terms.

colormapstr (optional)

Colormap to use. Check the matplotlib documentation. Default = rainbow.

Returns

None

bacteria.functions.plot_reverse_lineages(df_2d, lineage_list, column='Long axis (L) death', label='Cell Size ', for_ref=2, return_plot=False)

Plot the reverse lineage data with one column value. This plot remove the last value, as by the filter we dont have the data from cells without daughters. This plot is made using graphviz, the variable can be used to save the plot in pdf.

Parameters

df_2dDataFrame

2D data

lineage_listlist

list with the Cell ID’s to plot

columnstr (optional)

column value to plot alongside with the ID’s

labelstr (optional)

Label to use alongside with the column value, as usual the column name is to big.

for_refint (optional)

The limit of the for range (in range - for_ref)

return_plotbool (optional)

If true return the graphviz variable.

Returns

None

bacteria.functions.plot_total_fluo_volume(df_3d, reverse_lineage, ax=None)

Plot the total fluorescence by the volume.

Parameters

df_3dDataFrame

3D data

reverse_lineagelist

List with the lineage Cell ID’s, output from ‘lineages()’

axnd.array

axis to plot

Returns

None

bacteria.functions.roll_mean_plot2D(ax, df, y, x='Cell birth time', window=50, fsize=15)

A helper function to make a roll mean plot for 2D data

Parameters

axAxes

The axes to draw to

dfpandas DataFrame

df from 2D data

ystr

The y data

xstr (optional)

The x data, using ‘Cell birth time’

windowint (optional)

Window value to roll mean, using 50

fsize = int (optional)

Plot font size

Returns

outlist

list of artists added

bacteria.functions.roll_mean_plot3D(ax, df, y, id, x='Time (fps)', window=1, fsize=15)

A helper function to make a roll mean plot for 3D data

Parameters

axAxes

The axes to draw to

dfpandas DataFrame

df from 2D data

ystr

The y data

idint

Cell ID for plot (check good cells using ‘good_cells()’)

xstr (optional)

The x data, using ‘Cell birth time’

windowint (optional)

Window value to roll mean, using 1

fsize = int (optional)

Plot font size

Returns

outlist

list of artists added

bacteria.functions.scatter_linear_regression(df, x='Area birth', y='Area death', ax=None)

Scatter plot with linear regression.

Parameters

dfDataFrame

DataFrame of 2D data

xstr (optional)

X column for the Linear Regression model, usual ‘Area birth’

y: str (optional)

Y column for the Linear Regression model, usual ‘Area death’

axnd.array (optional)

the ax position to plot

Returns

None

bacteria.functions.smooth(data, factor=10)

1-D smooth filter

Parameters

datand.array

The file location in drive or HDD

factorint

The factor to convolve and smooth the graph. The greater the value, the greater the noise attenuation. Default 10.

Example

smooth(data,10)

See Also

Developed by Mike X Cohen.