mibiscreen.visualize API reference

mibiscreen module for data visualization.

ordination_plots

Ordination plot.

@author: Alraune Zech

ordination_plot(ordination_output, plot_loadings=True, plot_scores=True, rescale_loadings_scores=False, adjust_text=True, scale_focus='loadings', axis_ranges=False, save_fig=False, **kwargs)

Function creating ordination plot.

Based on ordination analysis providing ordination loadings and scores.

Input

ordination_output : Dictionary
    contains ordination results:
        - as numpy arrays; ordination loading and scores
        - names of the samples and the Environmental and Species variables
        - method : String (pca, cca, rda) The ordination method used in the analysis.
plot_loadings : Boolean; default is True
    flag to plot the ordination loadings
plot_scores : Boolean; default is True
    flag to plot the ordiantion scores
rescale_loadings_scores : Boolean; default is False
    flag to rescale loadings and scores to have a loading close to 1
adjust_text : Boolean, default is True
    flag to perform automized adjustment of text labes of loadings and scores to avoid overlap
scale_focus : String, default is "loadings"
    flag to specify if scaling focusses on either 'loadings' or 'scores' or 'none'.
axis_ranges : Boolean or list/array of 4 values, default is False,
    if array or list it gives fixed x and y axis dimensions [x_min, x_maxm y_min, y_max]
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
**kwargs: dict
    dictionary with plot settings (e.g. fonts, arrow specifics, etc)

Output

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/ordination_plots.py

def ordination_plot(ordination_output,
                    plot_loadings = True,
                    plot_scores = True,
                    rescale_loadings_scores = False,
                    adjust_text = True,
                    scale_focus = "loadings",
                    axis_ranges = False,
                    save_fig=False,
                    **kwargs,
                    ):
    """Function creating ordination plot.

    Based on ordination analysis providing ordination loadings and scores.

    Input
    -----
        ordination_output : Dictionary
            contains ordination results:
                - as numpy arrays; ordination loading and scores
                - names of the samples and the Environmental and Species variables
                - method : String (pca, cca, rda) The ordination method used in the analysis.
        plot_loadings : Boolean; default is True
            flag to plot the ordination loadings
        plot_scores : Boolean; default is True
            flag to plot the ordiantion scores
        rescale_loadings_scores : Boolean; default is False
            flag to rescale loadings and scores to have a loading close to 1
        adjust_text : Boolean, default is True
            flag to perform automized adjustment of text labes of loadings and scores to avoid overlap
        scale_focus : String, default is "loadings"
            flag to specify if scaling focusses on either 'loadings' or 'scores' or 'none'.
        axis_ranges : Boolean or list/array of 4 values, default is False,
            if array or list it gives fixed x and y axis dimensions [x_min, x_maxm y_min, y_max]
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        **kwargs: dict
            dictionary with plot settings (e.g. fonts, arrow specifics, etc)

    Output
    ------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.
    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    ### ---------------------------------------------------------------------------
    ### check on completeness of input ordination_output

    if not isinstance(ordination_output,dict):
        raise TypeError("Input data must be given as dictionary with standard output of ordination methods.")

    if "loadings_independent" not in ordination_output.keys():
        raise KeyError("Input dictionary does not contain data on loadings ('loadings_independent')")
    else:
        loadings_independent = ordination_output["loadings_independent"]
        names_independent = ordination_output["names_independent"]
        if len(loadings_independent) == 0:
            loadings_independent = np.array([[],[]]).T

    if "scores" not in ordination_output.keys():
        raise KeyError("Input dictionary does not contain data on scores ('scores')")
    else:
        scores = ordination_output["scores"]

    if "sample_index" not in ordination_output.keys():
        sample_index = np.arange(scores.shape[0])
    else:
        sample_index = ordination_output["sample_index"]

    if "loadings_dependent" in ordination_output.keys():
        loadings_dependent = ordination_output["loadings_dependent"]
        names_dependent = ordination_output["names_dependent"]
        if len(loadings_dependent) == 0:
            loadings_dependent = np.array([[],[]]).T
        loadings = np.append(loadings_independent, loadings_dependent, axis=0)
    else:
        loadings = loadings_independent

    ### ---------------------------------------------------------------------------
    ### Rescale ordination_output given plot specifics

    # Determing the largest values in the PCA scores.
    max_load = np.max(np.abs(loadings))
    max_score = np.max(np.abs(scores))

    if max_load > 1 or rescale_loadings_scores:
        # loadings = loadings / (max_load*1.05)
        loadings = loadings / (max_load)
    if max_score > 1 or rescale_loadings_scores:
        # scores = scores / (max_score*1.05)
        scores = scores / (max_score)

    if axis_ranges is not False:
        # Takes the given axis dimensions for both ordination axes
        x_lim_neg,x_lim_pos,y_lim_neg,y_lim_pos = axis_ranges
    else:
        # Adjusts axis dimensions for both ordination axes to ordination_output
        if plot_scores and plot_loadings:
            # When plotting both scores and loadings, scores or loadings are scaled
            # depending on the extent of the other. Depends on the input of Scale_focus.
            if scale_focus == "loadings":
                scores = scores * np.max(np.abs(loadings))
            elif scale_focus == "scores":
                loadings = loadings *  np.max(np.abs(scores))
            full_coords = np.append(loadings, scores, axis=0)
        elif plot_loadings:
            full_coords = loadings
        else:
            full_coords = scores

        x_lim_pos = 0.11*np.ceil(np.max(full_coords[:,0])*10)
        y_lim_pos = 0.11*np.ceil(np.max(full_coords[:,1])*10)
        x_lim_neg = 0.11*np.floor(np.min(full_coords[:,0])*10)
        y_lim_neg = 0.11*np.floor(np.min(full_coords[:,1])*10)

    ### ---------------------------------------------------------------------------
    ### Create Figure, finally!
    fig, ax = plt.subplots(figsize=settings['figsize'])
    texts = []

    # Plotting the ordination scores by iterating over every coordinate
    # in the scores array, if the Plot_scores parameter is set to true.
    if plot_scores:
        for i, (x, y) in enumerate(scores):
            plt.scatter(x, y,
                        color=settings['score_color'],
                        marker = settings['score_marker'],
                        s = settings['score_marker_size'],
                        facecolor=settings['score_facecolor'],
                        edgecolor=settings['score_edgecolor'],
                        zorder = 7,
                        )
            # Plotting the name of the scores and storing it in a list for the purpose of adjusting the position later
            tex = plt.text(x, y, sample_index[i], color='black', fontsize = settings['score_fontsize'],zorder = 9)
            texts.append(tex)

    if plot_loadings:
        # Plots independent (=environmental) and dependent (=species) variables
        # with different colours and text formatting.
        for i, (x, y) in enumerate(loadings_independent):
            plt.arrow(0, 0, x, y,
                      color = settings['arrow_color_independent'],
                      width = settings['arrow_width'],
                      head_length = settings['arrow_head_length'],
                      head_width = settings['arrow_head_width'],
                      zorder = 10,
                      )
            #Plotting the name of the loading
            tex = plt.text(x, y, names_independent[i],
                            color='black',
                            fontstyle = settings['fontstyle_independent'],
                            weight = settings['weight_independent'],
                            fontsize = settings['loading_fontsize'],
                            zorder = 11,
                            )
            texts.append(tex)
        if "loadings_dependent" in ordination_output.keys():
            for i, (x, y) in enumerate(loadings_dependent):
                plt.arrow(0, 0, x, y,
                          color=settings['arrow_color_dependent'],
                          width = settings['arrow_width'],
                          head_length = settings['arrow_head_length'],
                          head_width = settings['arrow_head_width'],
                          zorder = 8,
                          )
                tex = plt.text(x, y, names_dependent[i],
                                color='black',
                                fontstyle = settings['fontstyle_dependent'],
                                weight = settings['weight_dependent'],
                                fontsize = settings['loading_fontsize'],
                                zorder = 9,
                                )
                # and storing it in a list for the purpose of adjusting the position later
                texts.append(tex)

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics

    # Plotting lines that indicate the origin
    plt.plot([-1, 1], [0, 0], color='grey', linewidth=0.75, linestyle='--')
    plt.plot([0, 0], [-1, 1], color='grey', linewidth=0.75, linestyle='--')

    # Setting the x and y axis limits with the previously determined values
    plt.xlim(x_lim_neg, x_lim_pos)
    plt.ylim(y_lim_neg, y_lim_pos)
    plt.tick_params(axis="both",which="major",labelsize=settings['label_fontsize'])

    if ordination_output["method"]=='pca':
        percent_explained = ordination_output["percent_explained"]
        plt.xlabel('PC1 ({:.1f}%)'.format(percent_explained[0]), fontsize = settings['label_fontsize'])
        plt.ylabel('PC2 ({:.1f}%)'.format(percent_explained[1]), fontsize = settings['label_fontsize'])
    else:
        plt.xlabel('ordination axis 1', fontsize = settings['label_fontsize'])
        plt.ylabel('ordination axis 2', fontsize = settings['label_fontsize'])

    if adjust_text:
        try:
            from adjustText import adjust_text
            adjust_text(texts)
        except ImportError:
            print("WARNING: packages 'adjustText' not installed.")
            print(" For making text adjustment, install package 'adjustText'.")

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided

    if isinstance(settings['title'],str):
        plt.title(settings['title'],fontsize = settings['label_fontsize'])

    plt.tight_layout()
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Figure saved to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig, ax

screening_plots

Activity plot.

@author: alraune

activity_data_prep(data, verbose=False)

Preparing data required for activity plot.

Activity plot requires data analysis of

• contaminant concentrations
• metabolites counts
• NA screening

Functions checks if required quantities are provided as columns in DataFrame, extracts it from DataFrame and saves it to dictionary.

When data is provided as list of pd.Series/np.arrays/lists it checks data on compatibility and saves it to dictionary.

Input

data: list or pandas.DataFrame
    quantities required in plot:
        - total concentration of contaminants per sample
        - total count of metabolites per sample
        - traffic light on NA activity per sample
    if DataFrame, it contains the three required quantities with their standard names
    if list of arrays: the three quantities are given order above
    if list of pandas-Series, quantities given in standard names
verbose: Boolean, default True
    verbosity flag

Output

activity_data_dict: dict
    of np.arrays containing quantities required in activity plot:
    - total concentration of contaminants per sample
    - total count of metabolites per sample
    - traffic light on NA activity per sample

Source code in mibiscreen/visualize/screening_plots.py

def activity_data_prep(data,
                       verbose = False,
                       ):
    """Preparing data required for activity plot.

    Activity plot requires data analysis of:
        - contaminant concentrations
        - metabolites counts
        - NA screening

    Functions checks if required quantities are provided as columns in DataFrame,
    extracts it from DataFrame and saves it to dictionary.

    When data is provided as list of pd.Series/np.arrays/lists it checks data
    on compatibility and saves it to dictionary.

    Input
    ----------
        data: list or pandas.DataFrame
            quantities required in plot:
                - total concentration of contaminants per sample
                - total count of metabolites per sample
                - traffic light on NA activity per sample
            if DataFrame, it contains the three required quantities with their standard names
            if list of arrays: the three quantities are given order above
            if list of pandas-Series, quantities given in standard names
        verbose: Boolean, default True
            verbosity flag

    Output
    -------
        activity_data_dict: dict
            of np.arrays containing quantities required in activity plot:
            - total concentration of contaminants per sample
            - total count of metabolites per sample
            - traffic light on NA activity per sample
    """
    if isinstance(data, pd.DataFrame):
        ### check on correct data input format and extracting column names as list
        data_frame,cols= check_data_frame(data)

        if names.name_metabolites_count not in cols:
            raise ValueError("Count of metabolites not in DataFrame. Run 'total_metabolites_count()' first.")
        else:
            meta_count = data_frame[names.name_metabolites_count].values

        if names.name_total_contaminants not in cols:
            raise ValueError("Total concentration of contaminants not in DataFrame. \
                             Run 'total_contaminant_concentration()' first.")
        else:
            tot_cont = data_frame[names.name_total_contaminants].values

        if names.name_na_traffic_light not in cols:
            raise ValueError("Traffic light on NA activity per sample not in DataFrame. \
                             Run 'sample_NA_traffic()' first.")
        else:
            well_color = data_frame[names.name_na_traffic_light].values

    elif isinstance(data, list) and len(data)>=3:

        if len(data[0]) != len(data[1]) or len(data[0]) != len(data[2]):
           raise ValueError("Provided arrays/lists/series of data must have the same length.")

        if isinstance(data[0], (np.ndarray, list)) and isinstance(data[1], (np.ndarray, list)) \
            and isinstance(data[2], (np.ndarray, list)):
            tot_cont = data[0]
            meta_count = data[1]
            well_color = data[2]
            if verbose:
                print("List of arrays/lists interpreted as quantities for activity plot in the order:")
                print("  total concentration; metabolite count; NA traffic light color")

        elif isinstance(data[0], pd.Series) and isinstance(data[1], pd.Series) and isinstance(data[2], pd.Series):
            meta_count, tot_cont, well_color = False, False, False
            for series in data:
                if series.name == names.name_metabolites_count:
                    meta_count = series.values
                if series.name == names.name_total_contaminants:
                    tot_cont = series.values
                if series.name == names.name_na_traffic_light:
                    well_color = series.values
            if meta_count is False or tot_cont is False or well_color is False:
                raise ValueError("List of data frames does not contain all required quantities:\
                                 total concentration; metabolite count; NA traffic light color")
        else:
            raise ValueError("List elements in data must be lists, np.arrays or pd.series.")
    else:
        raise ValueError("Data needs to be DataFrame or list of at least three lists/np.arrays/pd.series.")

    return dict(
        meta_count = meta_count,
        tot_cont = tot_cont,
        well_color = well_color,
        )

activity_plot(activity_data_dict, xlabel='Concentration contaminants [$\\mu$g/L]', ylabel='Metabolite count', save_fig=False, title_text=False, **kwargs)

Creating activity plot.

Activity plot shows scatter of total number of metabolites vs total concentration of contaminant per well with color coding of NA traffic lights: red/yellow/green corresponding to no natural attenuation going on (red), limited/unknown NA activity (yellow) or active natural attenuation (green)

Input

activity_data_dict: dict
    list or pandas.DataFrame
    quantities required in plot:
        - total concentration of contaminants per sample
        - total count of metabolites per sample
        - traffic light on NA activity per sample
    if DataFrame, it contains the three required quantities with their standard names
    if list of arrays: the three quantities are given order above
    if list of pandas-Series, quantities given in standard names
xlabel: str, default r"Concentration contaminants"
    x-axis label
ylabel: str, default "Metabolite count"
    y-axis label
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
title_text: str or False, default False
    text displayed as figure title,
    in case of False, no title will be displayed
**kwargs: dict
    dictionary with plot settings

Output

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/screening_plots.py

def activity_plot(
        activity_data_dict,
        xlabel = r"Concentration contaminants [$\mu$g/L]",
        ylabel = "Metabolite count",
        save_fig=False,
        title_text = False,
        **kwargs,
        ):
    """Creating activity plot.

    Activity plot shows scatter of total number of metabolites vs total concentration
    of contaminant per well with color coding of NA traffic lights: red/yellow/green
    corresponding to no natural attenuation going on (red), limited/unknown NA activity (yellow)
    or active natural attenuation (green)

    Input
    ----------
        activity_data_dict: dict
            list or pandas.DataFrame
            quantities required in plot:
                - total concentration of contaminants per sample
                - total count of metabolites per sample
                - traffic light on NA activity per sample
            if DataFrame, it contains the three required quantities with their standard names
            if list of arrays: the three quantities are given order above
            if list of pandas-Series, quantities given in standard names
        xlabel: str, default r"Concentration contaminants"
            x-axis label
        ylabel: str, default "Metabolite count"
            y-axis label
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        title_text: str or False, default False
            text displayed as figure title,
            in case of False, no title will be displayed
        **kwargs: dict
            dictionary with plot settings

    Output
    -------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    ### ---------------------------------------------------------------------------
    ### Handling of input data
    if not isinstance(activity_data_dict,dict):
        raise ValueError("Input data needs to be dictionary containing keywords for quantities:\
                          tot_cont, meta_count, well_color. Consider running first 'activity_data_prep()'")

    if len(activity_data_dict['tot_cont']) <= 1:
        raise ValueError("Too little data for activity plot. At least two values per quantity required.")

    ### ---------------------------------------------------------------------------
    ### Creating Figure
    fig, ax = plt.subplots(figsize=settings['figsize'])
    ax.scatter(activity_data_dict['tot_cont'],
               activity_data_dict['meta_count'],
               c=activity_data_dict['well_color'],
               zorder = 3,
               s = settings['markersize'],
               ec = settings['ec'],
               lw = settings['lw'],
               )

    ### generate legend labels
    if "green" in activity_data_dict['well_color']:
        ax.scatter([], [],
                   label="available",
                   c="green",
                   s = settings['markersize'],
                   ec = settings['ec'],
                   lw = settings['lw'],
                   )
    if "y" in activity_data_dict['well_color']:
        ax.scatter([], [],
                   label="unknown",
                   c="y",
                   s = settings['markersize'],
                   ec = settings['ec'],
                   lw = settings['lw'],
                   )
    if "red" in activity_data_dict['well_color']:
        ax.scatter([], [],
                   label="depleted",
                   c="red",
                   s = settings['markersize'],
                   ec = settings['ec'],
                   lw = settings['lw'],
                   )

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics
    ax.set_xlabel(xlabel,fontsize=settings['textsize'])
    ax.set_ylabel(ylabel, fontsize=settings['textsize'])
    ax.grid()
    ax.minorticks_on()
    ax.tick_params(axis="both", which="major", labelsize=settings['textsize'])
    ax.tick_params(axis="both", which="minor", labelsize=settings['textsize'])
    plt.legend(title = 'Electron acceptors:',loc =settings['loc'], fontsize=settings['textsize'] )
    if title_text:
        plt.title(title_text,fontsize = settings['textsize'])
    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig, ax

contaminants_bar(data_frame, list_contaminants, list_labels=False, sort=False, name_sample=False, xlabel='Samples', ylabel='Total concentration [$\\mu$g/l]', yscale='linear', title_text='Total concentration of contaminants per sample', xtick_autorotate=False, save_fig=False, **kwargs)

Creating a bar plot of contaminant concentrations (or counts) per sample.

A selected list on quantities (at least 2) from a data frame is displayed as overlaying bars showing concentrations of individual components and/or sums of different contaminants with one bar per sample. The plot can also be used to display total counts of a selected range of contaminants per sample.

Input

data_frame : pd.DataFrame
    Contaminant concentrations in [ug/l], i.e. microgram per liter
list_contaminants : list
    list of column names to select from data_frame
list_labels: list or False, default: False
    list of quantity names to be displayed in legend
    if False, the names in 'list_contaminants' will be used
sort: Boolean, default False
    weather to sort data and display concentrations bars
    in ascending order (based on values of first quantity in
    list_contaminants)
name_sample: Boolean, default False
    weather to display sample_nr on x-axis (True) or
    just number all samples starting from 1 (False)
xlabel: str, default 'Samples'
    x-axis label
ylabel: str, default 'Total concentration',
    y-axis label
yscale: str, default 'linear',
    scaling of y-axis, typically 'log' or 'linear'
title_text: str or False, default 'Total concentration of contaminants per sample'
    text displayed as figure title,
    in case of False, no title will be displayed
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/screening_plots.py

def contaminants_bar(data_frame,
                     list_contaminants,
                     list_labels = False,
                     sort = False,
                     name_sample = False,
                     xlabel = 'Samples',
                     ylabel = r'Total concentration [$\mu$g/l]',
                     yscale = 'linear',
                     title_text = 'Total concentration of contaminants per sample',
                     xtick_autorotate = False,
                     save_fig = False,
                     **kwargs,
                     ):
    """Creating a bar plot of contaminant concentrations (or counts) per sample.

    A selected list on quantities (at least 2) from a data frame is displayed as
    overlaying bars showing concentrations of individual components and/or sums
    of different contaminants with one bar per sample.
    The plot can also be used to display total counts of a selected range of
    contaminants per sample.

    Input
    -----
        data_frame : pd.DataFrame
            Contaminant concentrations in [ug/l], i.e. microgram per liter
        list_contaminants : list
            list of column names to select from data_frame
        list_labels: list or False, default: False
            list of quantity names to be displayed in legend
            if False, the names in 'list_contaminants' will be used
        sort: Boolean, default False
            weather to sort data and display concentrations bars
            in ascending order (based on values of first quantity in
            list_contaminants)
        name_sample: Boolean, default False
            weather to display sample_nr on x-axis (True) or
            just number all samples starting from 1 (False)
        xlabel: str, default 'Samples'
            x-axis label
        ylabel: str, default 'Total concentration',
            y-axis label
        yscale: str, default 'linear',
            scaling of y-axis, typically 'log' or 'linear'
        title_text: str or False, default 'Total concentration of contaminants per sample'
            text displayed as figure title,
            in case of False, no title will be displayed
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    ### Plotting data check

    data,cols= check_data_frame(data_frame,inplace = False)

    ### sorting out which columns in data to use for summation of electrons available
    quantities,_ = determine_quantities(cols,
                                        name_list = list_contaminants,
                                        )

    ### Plotting data preparation
    if sort:
        sort_args = np.argsort(data_frame[list_contaminants[0]].values)
    else:
        sort_args = np.arange(len(data_frame[list_contaminants[0]].values))

    if name_sample is False:
        n_bars = np.arange(len(data_frame[list_contaminants[0]].values))
    else:
        if names.name_sample not in cols:
            raise ValueError("No {} provided in data_frame.".format(names.name_sample))
        else:
            n_bars = data_frame[names.name_sample].values[sort_args]

    if list_labels is False:
        list_labels = list_contaminants
    else:
        if not isinstance(list_labels, list):
            raise ValueError("list_labels need to contain a list of string label names.")
        if len(list_labels)!=len(list_contaminants):
            raise ValueError("List of label names must be of same length of list of selected quantities.")

    ### plotting actual data
    fig, ax = plt.subplots(figsize=settings['figsize'])

    for i,cont_group in enumerate(list_contaminants):
        plt.bar(n_bars,data_frame[cont_group].values[sort_args],label=list_labels[i])

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics
    plt.xlabel(xlabel,fontsize = settings['textsize'])
    plt.ylabel(ylabel,fontsize = settings['textsize'])
    plt.yscale(yscale)
    plt.legend(loc =settings['loc'],fontsize = settings['textsize'])
    if title_text:
        plt.title(title_text,fontsize = settings['textsize'])
    if xtick_autorotate:
        fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right', which='major')

    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig, ax

electron_balance_bar(electron_balance_bar_dict, sample_nr=False, sort=False, xlabel='Samples', ylabel='Electron capacity/needed [mmol e-/l]', yscale='linear', title_text='Electron balance per sample', xtick_autorotate=False, save_fig=False, **kwargs)

Creating a bar plot for electron balance per sample.

Displayed are bars of electron concentrations for: - “total_reductors” - ‘total_oxidators_BTEXIIN’ - ‘total_oxidators_BTEX’

The plot shows the three quantities as overlapping bars with the smallest in front and the largest to the back, indicating if an excess of or need for electrons is present at the sample location, including quantitative differences.

Operates on dictionary containing electron concentration values extracted from data frame, color and sample selection. The dictionary can be prepared by running the function ‘electron_balance_bar_data_prep()’ on the data frame.

Input

electron_balance_bar_dict : dict
    dictionary with plot relevant specifics
sort: Boolean, default False
    weather to sort data and display concentrations bars
    in ascending order (based on values of first quantity in
    list_contaminants)
sample_nr: Boolean, default False
    weather to display sample_nr on x-axis (True) or
    just number all samples starting from 1 (False)
xlabel: str, default 'Samples'
    x-axis label
ylabel: str, default r'Electron capacity/needed [mmol e-/l]'
    y-axis label
yscale: str, default 'linear',
    scaling of y-axis, typically 'log' or 'linear'
title_text: str or False, default 'Total concentration of contaminants per sample'
    text displayed as figure title,
    in case of False, no title will be displayed
xtick_autorotate: Boolean, default 'False'
    sample names at x-axis are rotated by 45 degrees
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/screening_plots.py

def electron_balance_bar(electron_balance_bar_dict,
                         sample_nr = False,
                         sort = False,
                         xlabel = 'Samples',
                         ylabel = r'Electron capacity/needed [mmol e-/l]',
                         yscale = 'linear',
                         title_text = 'Electron balance per sample',
                         xtick_autorotate = False,
                         save_fig = False,
                         **kwargs,
                         ):
    """Creating a bar plot for electron balance per sample.

    Displayed are bars of electron concentrations for:
    - "total_reductors"
    - 'total_oxidators_BTEXIIN'
    - 'total_oxidators_BTEX'

    The plot shows the three quantities as overlapping bars with the smallest in
    front and the largest to the back, indicating if an excess of or need for
    electrons is present at the sample location, including quantitative differences.

    Operates on dictionary containing electron concentration values extracted
    from data frame, color and sample selection. The dictionary can be prepared
    by running the function 'electron_balance_bar_data_prep()' on the data frame.

    Input
    -----
        electron_balance_bar_dict : dict
            dictionary with plot relevant specifics
        sort: Boolean, default False
            weather to sort data and display concentrations bars
            in ascending order (based on values of first quantity in
            list_contaminants)
        sample_nr: Boolean, default False
            weather to display sample_nr on x-axis (True) or
            just number all samples starting from 1 (False)
        xlabel: str, default 'Samples'
            x-axis label
        ylabel: str, default r'Electron capacity/needed [mmol e-/l]'
            y-axis label
        yscale: str, default 'linear',
            scaling of y-axis, typically 'log' or 'linear'
        title_text: str or False, default 'Total concentration of contaminants per sample'
            text displayed as figure title,
            in case of False, no title will be displayed
        xtick_autorotate: Boolean, default 'False'
            sample names at x-axis are rotated by 45 degrees
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    if sort:
        sort_args = np.argsort(electron_balance_bar_dict['EA capacity']['height'])
    else:
        sort_args = np.arange(len(electron_balance_bar_dict['EA capacity']['height']))

    if sample_nr:
        n_bars = electron_balance_bar_dict['EA capacity']['sample_nr'][sort_args]
    else:
        n_bars = np.arange(1,len(electron_balance_bar_dict['EA capacity']['height'])+1)

    fig, ax = plt.subplots(figsize=settings['figsize'])

    for key, value in electron_balance_bar_dict.items():
        # print(f"{key}: {value}")
        if key != 'EA capacity minor':
            plt.bar(n_bars,value['height'][sort_args],color = value['color'],label = key)
        else:
            plt.bar(n_bars,value['height'][sort_args],color = value['color'],zorder = 1)

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics
    plt.xlabel(xlabel,fontsize = settings['textsize'])
    plt.ylabel(ylabel,fontsize = settings['textsize'])
    plt.yscale(yscale)
    plt.legend(loc =settings['loc'],fontsize = settings['textsize'])
    if title_text:
        plt.title(title_text,fontsize = settings['textsize'])
    if xtick_autorotate:
        fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right', which='major')


    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig, ax

electron_balance_bar_data_prep(data_frame, color_order=['C1', 'C0', 'C2'], list_samples=False)

Preparing dictionary from data_frame for electron balance plot.

Requires data_frame to contain the following quantities

• “total_reductors”
• ‘total_oxidators_BTEXIIN’
• ‘total_oxidators_BTEX’

Input

data_frame : pd.DataFrame
    Contaminant concentrations in [ug/l], i.e. microgram per liter
color_order: list of colors, default = ['C1','C0','C2']
    colors to be used in the plot for the three quantities
list_samples: list or False, default: False
    if False, bars for all samples are displayed
    if list, bars are only displayed for selected samples (given by indexs)

---

electron_balance_bar_dict : dict
    dictionary with plot relevant specifics

Source code in mibiscreen/visualize/screening_plots.py

def electron_balance_bar_data_prep(data_frame,
                                   color_order = ['C1','C0','C2'],
                                   list_samples = False,
                                   ):
    """Preparing dictionary from data_frame for electron balance plot.

    Requires data_frame to contain the following quantities:
        - "total_reductors"
        - 'total_oxidators_BTEXIIN'
        - 'total_oxidators_BTEX'

    Input
    -----
        data_frame : pd.DataFrame
            Contaminant concentrations in [ug/l], i.e. microgram per liter
        color_order: list of colors, default = ['C1','C0','C2']
            colors to be used in the plot for the three quantities
        list_samples: list or False, default: False
            if False, bars for all samples are displayed
            if list, bars are only displayed for selected samples (given by indexs)

    Returns:
    --------
        electron_balance_bar_dict : dict
            dictionary with plot relevant specifics
    """
    electron_balance_bar_dict = dict()

    electron_balance_bar_dict['EA capacity'] = dict(
        height = data_frame[names.name_total_reductors].values,
        color = color_order[0],
        sample_nr = data_frame[names.name_sample],
    )
    electron_balance_bar_dict['BTEXIIN'] = dict(
        height = data_frame[names.name_total_oxidators_BTEXIIN].values,
        color = color_order[1],
    )
    electron_balance_bar_dict['BTEX'] = dict(
        # height = data_frame[names.name_total_oxidators_BTEX],
        height = data_frame[names.name_total_oxidators_BTEX].values,
        color = color_order[2],
    )

    electron_balance_bar_dict['EA capacity minor'] = dict(
        height = data_frame[names.name_total_reductors].where(data_frame[names.name_e_balance] < 1, 0).values,
        color = color_order[0],
    )

    if list_samples:
        for key, value in electron_balance_bar_dict.items():
            value['height'] = value['height'][list_samples]
        val = electron_balance_bar_dict['EA capacity']['sample_nr'][list_samples]
        electron_balance_bar_dict['EA capacity']['sample_nr'] = val

    return electron_balance_bar_dict

threshold_ratio_bar(data_threshold_ratios, list_contaminants=False, list_labels=False, list_samples=False, nrows=1, ncols=False, unity_line=False, list_sort=False, list_colors=False, sharex=False, sharey=False, xlabel='ratio to threshold concentration $C/C_\\mathrm{threshold}$', ylabel=False, xscale=False, title_text=False, save_fig=False, **kwargs)

Horizontal bar plots showing relative threshold exceedance of contaminants per sample.

Creates a figure with subfigures for each (selected) sample displaying the relative exceedance of contaminant concentrations are ratio of concentration to exceedance value (with <1 being lower then exceedance value).

The required input data frame can be create with the function: ‘thresholds_for_intervention_ratio()’ using the recommended keywords: - include = False - keep_setting_data = False

Input

data_threshold_ratios: pd.Data_frame
    each column contains a relative contaminant concentration
    each rows contains a sample

list_contaminants : list
    list of column names to select from data_frame
list_labels: list or False, default: False
    list of quantity names to be displayed in legend
    if False, the names in 'list_contaminants' will be used
list_samples: list or False, default: False
    if False, bars for all samples are displayed
    if list, bars are only displayed for selected samples (given by indexs)
nrows, ncolsint, default: 1
    Number of rows/columns of the subplot grid.
    Number of total subfigures need to fit to number of selected samples.
unity_line: Boolean, default False
    weather to include unity line (i.e. solid black line at 1 indicating
    equality of contaminant concentration and exceedance threshold value)
list_sort: list or False, default False
    list representing order of display of quantities
list_colors: list or False, default False
    list of colors to use for individual bars
    in case of False the standard color order in python is used
sharex, sharey: bool or {'none', 'all', 'row', 'col'}, default: False
    Controls sharing of properties among x (sharex) or y (sharey) axes
    See matplotlib.pyplot.subplots() for more details
xlabel: str, default r'ratio to threshold concentration'
    x-axis label
ylabel: str, default False
    y-axis label
xscale: str or False, default 'False',
    scaling of y-axis, when False --> 'linear', typical other option: 'log'
title_text: str or False, default 'False'
    text displayed as figure title,
    in case of False, no title will be displayed
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/screening_plots.py

def threshold_ratio_bar(data_threshold_ratios,
                        list_contaminants = False,
                        list_labels = False,
                        list_samples = False,
                        nrows=1,
                        ncols=False,
                        unity_line = False,
                        list_sort = False,
                        list_colors = False,
                        sharex=False,
                        sharey=False,
                        xlabel = r'ratio to threshold concentration $C/C_\mathrm{threshold}$',
                        ylabel = False,
                        xscale = False,
                        title_text = False,
                        save_fig = False,
                        **kwargs,
                        ):
    """Horizontal bar plots showing relative threshold exceedance of contaminants per sample.

    Creates a figure with subfigures for each (selected) sample displaying the
    relative exceedance of contaminant concentrations are ratio of concentration
    to exceedance value (with <1 being lower then exceedance value).

    The required input data frame can be create with the function:
    'thresholds_for_intervention_ratio()'
    using the recommended keywords:
        - include = False
        - keep_setting_data = False

    Input
    -----
        data_threshold_ratios: pd.Data_frame
            each column contains a relative contaminant concentration
            each rows contains a sample

        list_contaminants : list
            list of column names to select from data_frame
        list_labels: list or False, default: False
            list of quantity names to be displayed in legend
            if False, the names in 'list_contaminants' will be used
        list_samples: list or False, default: False
            if False, bars for all samples are displayed
            if list, bars are only displayed for selected samples (given by indexs)
        nrows, ncolsint, default: 1
            Number of rows/columns of the subplot grid.
            Number of total subfigures need to fit to number of selected samples.
        unity_line: Boolean, default False
            weather to include unity line (i.e. solid black line at 1 indicating
            equality of contaminant concentration and exceedance threshold value)
        list_sort: list or False, default False
            list representing order of display of quantities
        list_colors: list or False, default False
            list of colors to use for individual bars
            in case of False the standard color order in python is used
        sharex, sharey: bool or {'none', 'all', 'row', 'col'}, default: False
            Controls sharing of properties among x (sharex) or y (sharey) axes
            See matplotlib.pyplot.subplots() for more details
        xlabel: str, default r'ratio to threshold concentration'
            x-axis label
        ylabel: str, default False
            y-axis label
        xscale: str or False, default 'False',
            scaling of y-axis, when False --> 'linear', typical other option: 'log'
        title_text: str or False, default 'False'
            text displayed as figure title,
            in case of False, no title will be displayed
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    ### data prepration
    if list_contaminants:
        data_threshold_ratios = data_threshold_ratios[list_contaminants]
    else:
        list_contaminants = data_threshold_ratios.columns.to_list()

    if list_labels:
        if len(list_labels)<len(list_contaminants):
            raise ValueError("Number of label names too short.")
    else:
        list_labels = list_contaminants

    if list_samples:
        data_threshold_ratios = data_threshold_ratios.iloc[list_samples]
    else:
        list_samples = data_threshold_ratios.index.values

    if ncols and nrows:
        if len(list_samples) != nrows * ncols:
            raise ValueError("Number of subplots does not match selection of samples\n \
                             check keywords 'nrows' and 'ncols'.")
    elif ncols:
        nrows = int(np.ceil(len(list_samples)/ncols))
    elif nrows:
        ncols = int(np.ceil(len(list_samples)/nrows))
    else:
        ncols = len(list_samples)
        nrows = 1

    if list_colors:
        if len(list_colors)<len(list_samples):
            raise ValueError("Number of colors too short.")
    else:
        list_colors = ['C{}'.format(i) for i in range(len(list_contaminants))]

    if list_sort:
        if len(list_sort) != len(list_contaminants):
            raise ValueError("Lenght of list for resorting does not match number of quantities.")
        list_contaminants = [list_contaminants[i] for i in list_sort]
        data_threshold_ratios = data_threshold_ratios.iloc[:,list_sort]


    ### creating figure
    fig, ax = plt.subplots(figsize=settings['figsize'],
                            nrows=nrows,
                            ncols=ncols,
                            sharex=sharex,
                            sharey=sharey,
                            )
    if len(list_samples)>1:
        axs = ax.flatten()

    for i in range(len(list_samples)):

        if len(list_samples)>1:
            axi = axs[i]
        else:
            axi = ax

        # plt.bar(n_bars,value['height'][sort_args],color = value['color'],zorder = 1)
        axi.barh(list_labels,data_threshold_ratios.iloc[i,:],color = list_colors[i])
        if unity_line:
            axi.plot([1,1],[-0.5,len(list_labels)-.5],'k--')

        ### ---------------------------------------------------------------------------
        ### Adapt plot optics

        axi.set_xlabel(xlabel,fontsize=settings['textsize'])
        if ylabel:
            axi.set_ylabel(ylabel, fontsize=settings['textsize'])
        if xscale:
            axi.set_xscale(xscale)

        axi.grid(settings['grid'])
        axi.tick_params(axis="both", which="major", labelsize=settings['textsize'])

    if title_text:
        plt.title(title_text,fontsize = settings['textsize'])
    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig, ax

stable_isotope_plots

Linear regression plots for stable isotope analysis in mibiscreen.

@author: Alraune Zech

Keeling_plot(concentration, delta, coefficients, relative_abundance=None, save_fig=False, **kwargs)

Creating a Keeling plot.

A Keeling plot is an approach to identify the isotopic composition of a contaminating source from measured concentrations and isotopic composition (delta) of a target species in the mix of the source and a pool. It is based on the linear relationship of the concentration and the delta-value which are measured over time or across a spatial interval.

The plot shows the inverse concentration data against the delta-values along the linear regression line. For gaining the regression coefficients perform a linear fitting or run

Keeling_regression() [in the module analysis]

The parameter of interest, the delta (or relative_abundance, respectively) of the source quantity is the intercept of linear fit with the y-axis, or in other words, the absolute value of the linear fit function.

Input

c_mix : np.array, pd.dataframe
    total molecular mass/molar concentration of target substance
    at different locations (at a time) or at different times (at one location)
delta_mix : np.array, pd.dataframe (same length as c_mix)
    relative isotope ratio (delta-value) of target substance
relative_abundance : None or np.array, pd.dataframe (same length as c_mix), default None
    if not None it replaces delta_mix in the inverse estimation and plotting
    relative abundance of target substance
coefficients : tuple of lenght 2
    containing coefficients of the linear fit
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string. =
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/stable_isotope_plots.py

def Keeling_plot(concentration,
                 delta,
                 coefficients,
                 relative_abundance = None,
                 save_fig = False,
                 **kwargs,
                 ):
    """Creating a Keeling plot.

    A Keeling plot is an approach to identify the isotopic composition of a
    contaminating source from measured concentrations and isotopic composition
    (delta) of a target species in the mix of the source and a pool. It is based
    on the linear relationship of the concentration and the delta-value
    which are measured over time or across a spatial interval.

    The plot shows the inverse concentration data against the delta-values
    along the linear regression line. For gaining the regression coefficients
    perform a linear fitting or run

        Keeling_regression() [in the module analysis]

    The parameter of interest, the delta (or relative_abundance, respectively)
    of the source quantity is the intercept of linear fit with the y-axis,
    or in other words, the absolute value of the linear fit function.

    Input
    -----
        c_mix : np.array, pd.dataframe
            total molecular mass/molar concentration of target substance
            at different locations (at a time) or at different times (at one location)
        delta_mix : np.array, pd.dataframe (same length as c_mix)
            relative isotope ratio (delta-value) of target substance
        relative_abundance : None or np.array, pd.dataframe (same length as c_mix), default None
            if not None it replaces delta_mix in the inverse estimation and plotting
            relative abundance of target substance
        coefficients : tuple of lenght 2
            containing coefficients of the linear fit
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string. =
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    if relative_abundance is not None:
        y = relative_abundance
        text = 'x'
    else:
        y = delta
        text = r"\delta"

    x = 1/concentration

    ### ---------------------------------------------------------------------------
    ### create plot
    fig, ax = plt.subplots(figsize=settings['figsize'])
    ax.scatter(x,y, marker=settings['marker'], zorder = 3,label = 'data')

    ### ---------------------------------------------------------------------------
    ### plot linear regression trend line

    polynomial = np.poly1d(coefficients)
    trendline_x = np.linspace(min(0,np.min(x)),np.max(x), 100)
    trendline_y = polynomial(trendline_x)

    ax.plot(trendline_x, trendline_y, color= settings['fit_color'], label='linear fit')
    ax.text(0.5, 0.1,
            r"${}_{{source}} = {:.3f}$".format(text,coefficients[1]),
             bbox=dict(boxstyle="round", facecolor='w'),#,alpha=0.5)
             transform=ax.transAxes,
             fontsize=settings['fontsize'])
    ax.scatter(0,coefficients[1],
               c = settings['intercept_color'],
               zorder = 3,
               label = r'intercept: ${}_{{source}}$'.format(text),
               )

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics

    ax.set_xlabel('Inverse concentration $1/c$',fontsize=settings['fontsize'])
    ax.set_ylabel('${}$'.format(text),fontsize=settings['fontsize'])
    ax.grid(True,zorder = 0)
    ax.set_xlim([0-x[-1]*0.05, x[-1]*1.05])
    ax.legend(loc =settings['loc'], fontsize=settings['fontsize'])
    if isinstance(settings['title'],str):
        ax.set_title(settings['title'],fontsize = settings['fontsize'])
    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig,ax

Lambda_plot(delta_C, delta_H, coefficients, save_fig=False, **kwargs)

Creating a Lambda plot.

A Lambda plot shows the δ13C versus δ2H signatures of a chemical compound. Relative changes in the carbon and hydrogen isotope ratios can indicate the occurrence of specific enzymatic degradation reactions. The relative changes are indicated by the lambda-H/C value which is the slope of the linear regression of hydrogen versus carbon isotope signatures. For gaining the regression coefficients perform a linear fitting or run

 Lambda_regression() [in the module analysis]

Lambda-values linking to specific enzymatic reactions

To be added!

Details provided in Vogt et al. [2016, 2020].

References

C. Vogt, C. Dorer, F. Musat, and H. H. Richnow. Multi-element isotope fractionation concepts to characterize the biodegradation of hydrocarbons - from enzymes to the environment. Current Opinion in Biotechnology, 41:90–98, 2016. C. Vogt, F. Musat, and H.-H. Richnow. Compound-Specific Isotope Analysis for Studying the Biological Degradation of Hydrocarbons. In Anaerobic Utilization of Hydrocarbons, Oils, and Lipids, pages 285-321. Springer Nature Switzerland, 2020.

A. Fischer, I. Herklotz, S. Herrmann, M. Thullner, S. A. Weelink, A. J. Stams, M. Schl ̈omann, H.-H. Richnow, and C. Vogt. Combined Carbon and Hydrogen Isotope Fractionation Investigations for Elucidating Benzene Biodegradation Pathways. Environmental Science and Technology, 42:4356–4363, 2008.

S. Kuemmel, F.-A. Herbst, A. Bahr, M. Arcia Duarte, D. H. Pieper, N. Jehmlich, J. Seifert, M. Von Bergen, P. Bombach, H. H. Richnow, and C. Vogt. Anaerobic naphthalene degradation by sulfate-reducing Desulfobacteraceae from various anoxic aquifers. FEMS Microbiology Ecology, 91(3), 2015.

Input

delta_C : np.array, pd.series
    relative isotope ratio (delta-value) of carbon of target molecule
delta_H : np.array, pd.series (same length as delta_C)
    relative isotope ratio (delta-value) of hydrogen of target molecule
coefficients : tuple of lenght 2
    containing coefficients of the linear fit
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string.
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/stable_isotope_plots.py

def Lambda_plot(delta_C,
                delta_H,
                coefficients,
                save_fig = False,
                **kwargs,
                ):
    """Creating a Lambda plot.

    A Lambda plot shows the δ13C versus δ2H signatures of a chemical compound.
    Relative changes in the carbon and hydrogen isotope ratios can indicate the
    occurrence of specific enzymatic degradation reactions. The relative changes
    are indicated by the lambda-H/C value which is the slope of the linear
    regression of hydrogen versus carbon isotope signatures. For gaining the
    regression coefficients perform a linear fitting or run

         Lambda_regression() [in the module analysis]

    Lambda-values linking to specific enzymatic reactions:
        To be added!

    Details provided in Vogt et al. [2016, 2020].

    References:
        C. Vogt, C. Dorer, F. Musat, and H. H. Richnow. Multi-element isotope
        fractionation concepts to characterize the biodegradation of hydrocarbons
        - from enzymes to the environment. Current Opinion in Biotechnology,
        41:90–98, 2016.
        C. Vogt, F. Musat, and H.-H. Richnow. Compound-Specific Isotope Analysis
        for Studying the Biological Degradation of Hydrocarbons. In Anaerobic
        Utilization of Hydrocarbons, Oils, and Lipids, pages 285-321.
        Springer Nature Switzerland, 2020.

        A. Fischer, I. Herklotz, S. Herrmann, M. Thullner, S. A. Weelink,
        A. J. Stams, M. Schl ̈omann, H.-H. Richnow, and C. Vogt. Combined Carbon
        and Hydrogen Isotope Fractionation Investigations for Elucidating
        Benzene Biodegradation Pathways. Environmental Science and Technology,
        42:4356–4363, 2008.

        S. Kuemmel, F.-A. Herbst, A. Bahr, M. Arcia Duarte, D. H. Pieper,
        N. Jehmlich, J. Seifert, M. Von Bergen, P. Bombach, H. H. Richnow,
        and C. Vogt. Anaerobic naphthalene degradation by sulfate-reducing
        Desulfobacteraceae from various anoxic aquifers.
        FEMS Microbiology Ecology, 91(3), 2015.

    Input
    -----
        delta_C : np.array, pd.series
            relative isotope ratio (delta-value) of carbon of target molecule
        delta_H : np.array, pd.series (same length as delta_C)
            relative isotope ratio (delta-value) of hydrogen of target molecule
        coefficients : tuple of lenght 2
            containing coefficients of the linear fit
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string.
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    fig, ax = plt.subplots(figsize=settings['figsize'])
    ax.scatter(delta_C, delta_H, marker=settings['marker'],
               color = settings['marker_color'],
               edgecolors = settings['marker_edgecolors'],
               zorder = 3,label= 'data')

    ### ---------------------------------------------------------------------------
    ### plot linear regression trend line

    polynomial = np.poly1d(coefficients)
    trendline_x = np.linspace(np.min(delta_C), np.max(delta_C), 100)
    trendline_y = polynomial(trendline_x)
    ax.plot(trendline_x, trendline_y, color= settings['fit_color'], label='linear fit')
    ax.text(0.4, 0.1,
             r"$\Lambda = {:.2f}$".format(coefficients[0]),
             bbox=dict(boxstyle="round", facecolor='w'),#,alpha=0.5),
             transform=ax.transAxes,
             fontsize=settings['fontsize'])
    ### ---------------------------------------------------------------------------
    ### Adapt plot optics

    ax.grid(True,zorder = 0)
    ax.set_xlabel(r'$\delta^{{13}}$C')
    ax.set_ylabel(r'$\delta^2$H')
    ax.legend(loc =settings['loc'], fontsize=settings['fontsize'])
    if isinstance(settings['title'],str):
        ax.set_title(settings['title'],fontsize = settings['fontsize'])
    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig,ax

Rayleigh_fractionation_plot(concentration, delta, coefficients, save_fig=False, **kwargs)

Creating a Rayleigh fractionation plot.

Rayleigh fractionation is a common application to characterize the removal of a substance from a finite pool using stable isotopes. It is based on the change in the isotopic composition of the pool due to different kinetics of the change in lighter and heavier isotopes.

We follow the most simple approach assuming that the substance removal follows first-order kinetics, where the rate coefficients for the lighter and heavier isotopes of the substance differ due to kinetic isotope fractionation effects. The isotopic composition of the remaining substance in the pool will change over time, leading to the so-called Rayleigh fractionation.

The plot shows the log-transformed concentration data against the delta-values along the linear regression line. For gaining the regression coefficients perform a linear fitting or run

Rayleigh_fractionation() [in the module analysis]

The parameter of interest, the kinetic fractionation factor (epsilon or alpha -1) of the removal process is the slope of the the linear trend line.

Input

concentration : np.array, pd.series
    total molecular mass/molar concentration of target substance
    at different locations (at a time) or at different times (at one location)
delta : np.array, pd.series (same length as concentration)
    relative isotope ratio (delta-value) of target substance
coefficients : tuple of lenght 2
    containing coefficients of the linear fit
save_fig: Boolean or string, optional, default is False.
    Flag to save figure to file with name provided as string. =
**kwargs: dict
    dictionary with plot settings

---

fig : Figure object
    Figure object of created activity plot.
ax :  Axes object
    Axes object of created activity plot.

Source code in mibiscreen/visualize/stable_isotope_plots.py

def Rayleigh_fractionation_plot(concentration,
                                delta,
                                coefficients,
                                save_fig = False,
                                **kwargs,
                                ):
    """Creating a Rayleigh fractionation plot.

    Rayleigh fractionation is a common application to characterize the removal
    of a substance from a finite pool using stable isotopes. It is based on the
    change in the isotopic composition of the pool due to different kinetics of
    the change in lighter and heavier isotopes.

    We follow the most simple approach assuming that the substance removal follows
    first-order kinetics, where the rate coefficients for the lighter and heavier
    isotopes of the substance differ due to kinetic isotope fractionation effects.
    The isotopic composition of the remaining substance in the pool will change
    over time, leading to the so-called Rayleigh fractionation.

    The plot shows the log-transformed concentration data against the delta-values
    along the linear regression line. For gaining the regression coefficients
    perform a linear fitting or run

        Rayleigh_fractionation() [in the module analysis]

    The parameter of interest, the kinetic fractionation factor (epsilon or alpha -1)
    of the removal process is the slope of the the linear trend line.

    Input
    -----
        concentration : np.array, pd.series
            total molecular mass/molar concentration of target substance
            at different locations (at a time) or at different times (at one location)
        delta : np.array, pd.series (same length as concentration)
            relative isotope ratio (delta-value) of target substance
        coefficients : tuple of lenght 2
            containing coefficients of the linear fit
        save_fig: Boolean or string, optional, default is False.
            Flag to save figure to file with name provided as string. =
        **kwargs: dict
            dictionary with plot settings

    Returns:
    --------
        fig : Figure object
            Figure object of created activity plot.
        ax :  Axes object
            Axes object of created activity plot.

    """
    settings = copy.copy(DEF_settings)
    settings.update(**kwargs)

    x = np.log(concentration)
    ### ---------------------------------------------------------------------------
    ### create plot
    fig, ax = plt.subplots(figsize=settings['figsize'])
    ax.scatter(x,delta, marker=settings['marker'], zorder = 3,label = 'data')

    ### ---------------------------------------------------------------------------
    ### plot linear regression trend line

    polynomial = np.poly1d(coefficients)
    trendline_x = np.linspace(np.min(x), np.max(x), 100)
    trendline_y = polynomial(trendline_x)
    ax.plot(trendline_x, trendline_y, color= settings['fit_color'], label='linear fit')
    ax.text(0.1, 0.1,
             r"$\epsilon = 1-\alpha = {:.3f}$".format(coefficients[0]),
             bbox=dict(boxstyle="round", facecolor='w'),#,alpha=0.5),
             transform=ax.transAxes,
             fontsize=settings['fontsize'])

    ### ---------------------------------------------------------------------------
    ### Adapt plot optics

    ax.set_xlabel(r'log-concentration $\ln c$',fontsize=settings['fontsize'])
    ax.set_ylabel(r'$\delta$',fontsize=settings['fontsize'])
    ax.grid(True,zorder = 0)
    ax.legend(loc =settings['loc'], fontsize=settings['fontsize'])
    if isinstance(settings['title'],str):
        ax.set_title(settings['title'],fontsize = settings['fontsize'])
    fig.tight_layout()

    ### ---------------------------------------------------------------------------
    ### Save figure to file if file path provided
    if save_fig is not False:
        try:
            plt.savefig(save_fig,dpi = settings['dpi'])
            print("Save Figure to file:\n", save_fig)
        except OSError:
            print("WARNING: Figure could not be saved. Check provided file path and name: {}".format(save_fig))

    return fig,ax