mass.visualization

Contains function for visualizing simulation results.

This module contains the various functions for visualization of solutions returned in MassSolutions after simulation of models.

In general, it is recommended to pass an matplotlib.axes.Axes instance into the visualization function in order to guaruntee more control over the generated plot and ensure that the plot be placed onto a figure as expected. If an Axes is not passed into the function, then the currrent axes instance will be used, accessed via:

matplotlib.pyplot.gca()

All functions will return the axes instance utilzed in generating the plot.

The legend input format for the plotting function must be one of the following:

  1. An iterable of legend labels as strings (e.g. legend=["A", "B", ...]).

  2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location (e.g. legend="best" or legend=1).

  3. An iterable of the format (labels, loc) to set both the legend labels and location, where the labels and loc in the iterable follows the formats specified above in both 1 and 2, respectively. (e.g. legend=(["A", "B", ...], "best"))

Valid legend location strings and the integer corresponding to the legend location codes are the following:

Location String (str)

Location Code (int)

‘best’

0

‘upper right’

1

‘upper left’

2

‘lower left’

3

‘lower right’

4

‘right’

5

‘center left’

6

‘center right’

7

‘lower center’

8

‘upper center’

9

‘center’

10

‘upper right outside’

11

‘upper left outside’

12

‘lower left outside’

13

‘lower right outside’

14

‘right outside’

15

‘left outside’

16

‘lower outside’

17

‘upper outside’

18

Some other important things to note about the legend include the following:

  • Note that the last four “outside” locations are NOT matplotlib.legend location values, but rather they utilize the legend kwarg bbox_to_anchor to place the legend outside of the plot.

  • If only legend labels are provided (i.e. format specified above in 1), the default legend location specified in the matplotlib.rcsetup will be used.

  • If only the legend location is provided (i.e. format specified above in 2), the corresponding keys in mass_solution will be used as default legend labels.

  • If invalid input is provided (e.g. too many legend labels provided, invalid legend location), a warning will be issued and the default values will be used.

  • See the matplotlib.legend documentation for additional control over the plot legend.

The following are optional kwargs that can be passed to the functions of the visualiation module.

time_vector :

iterable of values to treat as time points for the solutions. If provided, the original solutions in the MassSolution input will be converted into interpolating functions and the solutions are recalculated based on the provided time_vector. If None then the current time values will be used.

Default is None.

plot_function :

str representing the plotting function to use. Accepted values are the following:

For all functions:

  • "plot" for a linear x-axis and a linear y-axis via Axes.plot()

  • "loglog" for a logarithmic x-axis and a logarithmic y-axis via Axes.loglog()

In addition, for functions in time_profiles and phase_portraits submodules only:

  • "semilogx” for a logarithmic x-axis and a linear y-axis via Axes.semilogx()

  • "semilogy" for a linear x-axis and a logarithmic y-axis via Axes.semilogy()

Default is "plot".

title :

Either a str to set as the title or a tuple of length 2 where the first value is the title string and the second value is a dict of font options. Arguments are passed to the corresponding setter method Axes.set_title().

Default is None.

xlabel :

Either a str to set as the xlabel or a tuple of length 2 where the first value is the xlabel string and the second value is a dict of font options. Arguments are passed to the corresponding setter method Axes.set_xlabel().

Default is None. Not valid for plot_tiled_phase_portraits()

ylabel :

Either a str to set as the ylabel or a tuple of length 2 where the first value is the ylabel string and the second value is a dict of font options. Arguments are passed to the corresponding setter method Axes.set_ylabel().

Default is None. Not valid for plot_tiled_phase_portraits()

xlim :

tuple of form (xmin, xmax) containing numerical values specifying the limits of the x-axis. Passed to the corresponding setter method Axes.set_xlim(). For plot_tiled_phase_portraits(), the limits will be applied to all tiles containing phase portrait plots.

Default is None.

ylim :

tuple of form (ymin, ymax) containing numerical values specifying the limits of the y-axis. Passed to the corresponding setter method Axes.set_ylim(). For plot_tiled_phase_portraits(), the limits will be applied to all tiles containing phase portrait plots.

Default is None.

xmargin :

float value greater than -0.5 to set as the padding for the x-axis data limits prior to autoscaling. Passed to the corresponding setter method Axes.set_xmargin(). For plot_tiled_phase_portraits(), the margins will be applied to all tiles containing phase portrait plots.

Default is 0.15 for plot_tiled_phase_portraits(), otherwise None.

ymargin :

float value greater than -0.5 to set as the padding for the y-axis data limits prior to autoscaling. Passed to the corresponding setter method Axes.set_ymargin(). For plot_tiled_phase_portraits(), the margins will be applied to all tiles containing phase portrait plots.

Default is 0.15 for plot_tiled_phase_portraits(), otherwise None.

color :

Value or iterable of values representing valid matplotlib.colors values to use as line colors. If a single color is provided, that color will be applied to all solutions being plotted. If an iterable of color values is provided, it must be equal to the number of solutions to be plotted. For plot_tiled_phase_portraits(), the colors will be applied to all tiles containing phase portrait plots.

Default is None. Ignored if the kwarg prop_cycler is also provided.

linestyle :

Value or iterable of values representing valid matplotlib linestyles. If a single linestyle is provided, that linestyle will be applied to all solutions being plotted. If an iterable is provided, it must be equal to the number of solutions to be plotted. For plot_tiled_phase_portraits(), the linestyles will be applied to all tiles containing phase portrait plots.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg prop_cycler is also provided.

linewidth :

float value representing the linewidth (in points) to set.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg prop_cycler is also provided.

marker :

Value or iterable of values representing valud matplotlib marker values to use as line markers. If a single marker is provided, that marker will be applied to all solutions being plotted. If an iterable is provided, it must be equal to the number of solutions to be plotted. For plot_tiled_phase_portraits(), the markers will be applied to all tiles containing phase portrait plots.

For functions in comparision, default is "o", otherwise default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg prop_cycler is also provided.

markersize :

float value representing the size of the marker (in points) to set. For plot_tiled_phase_portraits(), the markersizes will be applied to all tiles containing phase portrait plots. Ignored if the kwarg prop_cycler is also provided.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg prop_cycler is also provided.

grid :

Either a bool or a tuple of form (which, axis) where the values for which and axis are one of the following:

  • which arguement must be "major", "minor", or "both"

  • axis argument must be "x", "y", or "both"

If grid=False then grid lines will be removed. If grid=True, then grid lines will be created based on the default values in matplotlib.rcsetup unless overridden by grid_color, grid_linestyle, or grid_linewidth kwargs. Passed to the setter method Axes.grid(). For plot_tiled_phase_portraits(), the grid lines will be applied to all tiles containing phase portrait plots.

Default is None.

grid_color :

Value representing a valid matplotlib.colors value to use as the color of the grid lines. For plot_tiled_phase_portraits(), the color of the grid lines will be applied to all tiles containing phase portrait plots.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg grid is None.

grid_linestyle :

Value representing a valid matplotlib value to use as the linestyles of the grid lines. For plot_tiled_phase_portraits(), the linestyle of the grid lines will be applied to all tiles containing phase portrait plots.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg grid is None.

grid_linewidth :

float value representing the grid linewidth (in points) to set.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg grid is None.

legend_ncol :

int indicating the number of columns to use in the legend. If None the the following formula is applied:

ncols = int(ceil(sqrt(N_total) / 3))

where N_total is equal to the total number of solution lines.

Default is None. Not valid for plot_tiled_phase_portraits()

deviation :

bool indicating whether to plot the deviation from the initial value for the observable variables.

Default is False.

deviation_zero_centered :

bool indicating whether to center deviations around zero for for the observable variables.

Default is False. Ignored if the kwarg deviation=False.

deviation_normalization :

str indicating how to normalize the plotted deviation values. Can be one of the following:

  • ‘initial’ to normalize the solution by dividing by the initial value of the solution variable

  • ‘range’ to normalize the solution through dividing by the range of solution values (maximum value - minimum value)

Default is initial. Ignored if the kwarg deviation=False.

annotate_time_points :

Either the string "endpoints" or an iterable containing the numerical values for the time points of interest to be annotated by plotting points for the solutions at the given time points. If annotate_time_points="endpoints" then only the initial and final time points will be utilized. If None then no time points will be annotated. For plot_tiled_phase_portraits(), the time points will be applied to all tiles containing phase portrait plots.

Default is None.

annotate_time_points_color :

Value or iterable of values representing valid matplotlib.colors values to use as time point colors. If a single color is provided, that color will be applied to all time points being plotted. If an iterable of color values is provided, it must be equal to the number of time points to be plotted. For plot_tiled_phase_portraits(), the colors will be applied to all tiles containing phase portrait plots.

Default is None.

annotate_time_points_marker :

Value or iterable of values representing valud matplotlib marker values to use as time point markers. If a single marker is provided, that marker will be applied to all solutions being plotted. If an iterable is provided, it must be equal to the number of time points to be plotted. For plot_tiled_phase_portraits(), the markers will be applied to all tiles containing phase portrait plots.

Default is None.

annotate_time_points_markersize :

float value representing the size of the marker (in points) to set. For plot_tiled_phase_portraits(), the markersizes will be applied to all tiles containing phase portrait plots.

Default is None to use default value in matplotlib.rcsetup.

annotate_time_points_labels :

bool indicating whether to annotate the time points with their labels on the plot itself.

Default is False.

annotate_time_points_legend :

A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the location to use for the legend of annotated time points. Cannot be the same location value as the plot’s legend location. If None, no legend is created.

Default is None.

prop_cycle :

A valid matplotlib.rcsetup.cycler() instance to use in the plot. If provided, then the color, linestyle, linewidth, marker, and markersize kwargs are ignored.

Default is None.

mean_line_alpha :

float indicating the alpha (opacity) value to use for the line representing the mean solution.

Default is 1.0. Only valid for ensemble visualization functions.

interval_fill_alpha :

float indicating the alpha (opacity) value to use in shading the interval.

Default is 0.5. Only valid for ensemble visualization functions.

interval_border_alpha :

float indicating the alpha (opacity) value for border lines of the interval.

Default is 0.5. Only valid for ensemble visualization functions.

CI_distribution :

Either "t" to calculate the confidence interval using a t-distribution or "z" to calculate the confidence interval using a z-distribution.

Default is "t". Only valid for ensemble visualization functions.

tile_ticks_on :

bool indicating whether to leave tick marks on tiles containing phase portraits.

Default is False. Only valid for plot_tiled_phase_portraits().

tile_xlabel_fontdict :

Font properties to set using a dict. Applied to all tile ylabels.

Default is None. Only valid for plot_tiled_phase_portraits().

tile_ylabel_fontdict :

Font properties to set using a dict. Applied to all tile ylabels.

Default is None. Only valid for plot_tiled_phase_portraits().

data_tile_fontsize :

Valid fontsize value representing the fontsize to be used for the data values on tiles displaying additional data.

Default is large. Only valid for plot_tiled_phase_portraits(). Ignored if no additional data is provided (i.e. additional_data=None).

data_tile_color :

Value representing a valid matplotlib.colors value to use as the facecolor of the tiles displaying additional data.

Default is None to utilize the color "lightgray". Only valid for plot_tiled_phase_portraits(). Ignored if no additional data is provided (i.e. additional_data=None).

diag_tile_color :

Value representing a valid matplotlib.colors value to use as the facecolor of the tiles on the diagonal.

Default is None to utilize the color "black". Only valid for plot_tiled_phase_portraits().

empty_tile_color :

Value representing a valid matplotlib.colors value to use as the facecolor of the empty tiles.

Default is None to utilize the color "white". Only valid for plot_tiled_phase_portraits().

xy_line :

Whether to plot a line with equation y=x. If xy_line=True, then the line will be created based on the default values in matplotlib.rcsetup unless overridden by xy_linecolor, xy_linestyle, or xy_linewidth kwargs.

Ignored if the kwarg xy_line=False. Only valid for functions in the comparison submodule.

xy_linecolor :

Value representing a valid matplotlib.colors value to use as the color of the y=x line.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg xy_line=False. Only valid for functions in the comparison submodule.

xy_linestyle :

Value representing a valid matplotlib value to use as the style of the y=x line.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg xy_line=False. Only valid for functions in the comparison submodule.

xy_linewidth :

float value representing the linewidth (in points) to set for the y=x line.

Default is None to use default value in matplotlib.rcsetup. Ignored if the kwarg xy_line=False. Only valid for functions in the comparison submodule.

xy_legend :

str indicating where to place a legend for the y=x line.

Default is None. Ignored if the kwarg xy_line=False. Only valid for functions in the comparison submodule.

Submodules

Package Contents

Functions

plot_comparison(x, y[, compare, observable, ax, legend])

Plot values of two objects for comparision.

plot_ensemble_phase_portrait(mass_solution_list, x, y)

Plot a phase portrait for an ensemble of class:~.MassSolution objects.

plot_phase_portrait(mass_solution, x, y[, ax, legend])

Plot phase portraits of solutions in a given MassSolution.

plot_tiled_phase_portraits(mass_solution[, ...])

Plot phase portraits of solutions in a given MassSolution.

plot_ensemble_time_profile(mass_solution_list, observable)

Plot time profiles for an ensemble of class:~.MassSolution objects.

plot_time_profile(mass_solution[, observable, ax, legend])

Plot time profiles of solutions in a given MassSolution.
mass.visualization.plot_comparison(x, y, compare=None, observable=None, ax=None, legend=None, **kwargs)[source]

Plot values of two objects for comparision.

This function can take two MassModel, ConcSolution, cobra.Solution, or pandas.Series objects and plot them against one another in a calibration plot.

Accepted kwargs are passed onto various matplotlib methods utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

  • If a pandas.Series, the index must correspond to the identifier of the assoicated object. (e.g. a metabolite identifier for compare="concentrations", or a reaction identifier for compare="Keqs")

Parameters

  • x (MassModel, ConcSolution, Solution, Series) – The object to access for x-axis values.

  • y (MassModel, ConcSolution, Solution, Series) – The object to access for y-axis values.

  • compare (str) –

    The values to be compared. Must be one of the following:

    Not required if both x and y are pandas.Series.

  • observable (iterable) – An iterable containing string identifiers of mass objects or the objects themselves corresponding to the object or index where the value is located.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • legend (iterable, str, int) –

    There are three possible input formats for the legend:

    1. An iterable of legend labels as strings.

    2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location.

    3. An iterable of the format (labels, loc) to set both the legend labels and location, where labels and loc follows the labels specified in 1 and 2.

    See the visualization documentation for more information about legend and valid legend locations.

  • **kwargs –

    • plot_function

    • title

    • xlabel

    • ylabel

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • marker

    • markersize

    • legend_ncol

    • xy_line

    • xy_linecolor

    • xy_linewidth

    • xy_linestyle

    • xy_legend

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes

mass.visualization.plot_ensemble_phase_portrait(mass_solution_list, x, y, ax=None, legend=None, **kwargs)[source]

Plot a phase portrait for an ensemble of class:~.MassSolution objects.

The plotted lines represent the mean for the values of a particular solution specified in observable.

Accepted kwargs are passed onto various matplotlib methods utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

Parameters

  • mass_solution_list (iterable) – An iterable of MassSolution objects containing the time-dependent solutions to be plotted.

  • x (mass object or its string identifier) – The string identifier of a mass object or the object itself that corresponds to the key for the desired solution in the MassSolution for the x-axis of the phase portrait.

  • y (mass object or its string identifier) – The string identifier of a mass object or the object itself that corresponds to the key for the desired solution in the MassSolution for the y-axis of the phase portrait.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • legend (iterable, str, int) –

    There are three possible input formats for the legend:

    1. An iterable of legend labels as strings.

    2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location.

    3. An iterable of the format (labels, loc) to set both the legend labels and location, where labels and loc follows the format specified in 1 and 2.

    See the visualization documentation for more information about legend and valid legend locations.

  • **kwargs –

    • time_vector

    • plot_function

    • title

    • xlabel

    • ylabel

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • linestyle

    • linewidth

    • marker

    • markersize

    • legend_ncol

    • annotate_time_points

    • annotate_time_points_color

    • annotate_time_points_marker

    • annotate_time_points_markersize

    • annotate_time_points_labels

    • annotate_time_points_legend

    • deviation

    • deviation_zero_centered

    • deviation_normalization

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes

mass.visualization.plot_phase_portrait(mass_solution, x, y, ax=None, legend=None, **kwargs)[source]

Plot phase portraits of solutions in a given MassSolution.

Accepted kwargs are passed onto various matplotlib methods utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

Parameters

  • mass_solution (MassSolution) – The MassSolution containing the time-dependent solutions to be plotted.

  • x (mass object or its string identifier) – The string identifier of a mass object or the object itself that corresponds to the key for the desired solution in the MassSolution for the x-axis of the phase portrait.

  • y (mass object or its string identifier) – The string identifier of a mass object or the object itself that corresponds to the key for the desired solution in the MassSolution for the y-axis of the phase portrait.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • legend (iterable, str, int) –

    There are three possible input formats for the legend:

    1. An iterable of legend labels as strings.

    2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location.

    3. An iterable of the format (labels, loc) to set both the legend labels and location, where labels and loc follows the format specified in 1 and 2.

    See the visualization documentation for more information about legend and valid legend locations.

  • **kwargs –

    • time_vector

    • plot_function

    • title

    • xlabel

    • ylabel

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • linestyle

    • linewidth

    • marker

    • markersize

    • legend_ncol

    • annotate_time_points

    • annotate_time_points_color

    • annotate_time_points_marker

    • annotate_time_points_markersize

    • annotate_time_points_labels

    • annotate_time_points_legend

    • deviation

    • deviation_zero_centered

    • deviation_normalization

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes

mass.visualization.plot_tiled_phase_portraits(mass_solution, observable=None, ax=None, plot_tile_placement='all', additional_data=None, **kwargs)[source]

Plot phase portraits of solutions in a given MassSolution.

Accepted kwargs are passed onto various matplotlib methods in utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

  • To prevent any changes to the original MassSolution, a copy of the MassSolution will be created and used.

  • i and j represent the number of rows and columns, respectively.

Parameters

  • mass_solution (MassSolution) – The MassSolution containing the time-dependent solutions to be plotted.

  • observable (iterable) – An iterable containing string identifiers of the mass objects or the objects themselves that correspond to the keys for the desired solutions in the MassSolution.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • plot_tile_placement (str) –

    A string representing the location to place the tiles containing phase portrait plots. Must be one of the following:

    • "lower" to place plot tiles on the lower left triangular section (i < j) on the figure tiles.

    • "upper" to place plot tiles on the upper right triangular section (i > j) on the figure tiles.

    • all to place plot tiles on the lower left triangular section (i < j) AND on the upper right triangular section (i > j) on the figure tiles.

  • additional_data (array_like, None) – A matrix of shape (N, N) where N_obs is the number of observables provided, or the number of keys in the MassSolution if observable=None. The value at (i, j) of the matrix must correspond to the empty tile that the data should be displayed on. All other values are ignored. If None then no data will be displayed and tiles will be left empty.

  • **kwargs –

    • time_vector

    • plot_function

    • title

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • linestyle

    • linewidth

    • marker

    • markersize

    • annotate_time_points

    • annotate_time_points_color

    • annotate_time_points_marker

    • annotate_time_points_markersize

    • annotate_time_points_legend

    • annotate_time_points_zorder

    • tile_ticks_on

    • tile_xlabel_fontdict

    • tile_ylabel_fontdict

    • data_tile_fontsize

    • data_tile_color

    • diag_tile_color

    • empty_tile_color

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes

mass.visualization.plot_ensemble_time_profile(mass_solution_list, observable, ax=None, legend=None, interval_type=None, **kwargs)[source]

Plot time profiles for an ensemble of class:~.MassSolution objects.

The plotted lines represent the mean for the values of a particular solution specified in observable.

Accepted kwargs are passed onto various matplotlib methods utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

Parameters

  • mass_solution_list (iterable) – An iterable of MassSolution objects containing the time-dependent solutions to be plotted.

  • observable (iterable) – An iterable containing string identifiers of the mass objects or the objects themselves that correspond to the keys for the desired solutions in the MassSolution.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • legend (iterable, str, int) –

    There are three possible input formats for the legend:

    1. An iterable of legend labels as strings.

    2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location.

    3. An iterable of the format (labels, loc) to set both the legend labels and location, where labels and loc follows the labels specified in 1 and 2.

    See the visualization documentation for more information about legend and valid legend locations.

  • interval_type (str, None) –

    The type of interval to display with the plotted mean of the solution. Can be one of the following:

    • "range": Interval shading occurs from the minimum to the maximum value for each time point.

    • "CI=": Interval shading occurs for a confidence interval. (e.g. confidence interval of 95% is specified as "CI=95.0".)

    • None to prevent interval shading from occurring.

    Default is None

  • **kwargs –

    • time_vector

    • plot_function

    • title

    • xlabel

    • ylabel

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • linestyle

    • linewidth

    • marker

    • markersize

    • legend_ncol

    • annotate_time_points

    • annotate_time_points_color

    • annotate_time_points_marker

    • annotate_time_points_markersize

    • annotate_time_points_labels

    • annotate_time_points_legend

    • deviation

    • deviation_zero_centered

    • deviation_normalization

    • mean_line_alpha

    • interval_fill_alpha

    • interval_border_alpha

    • CI_distribution

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes

mass.visualization.plot_time_profile(mass_solution, observable=None, ax=None, legend=None, **kwargs)[source]

Plot time profiles of solutions in a given MassSolution.

Accepted kwargs are passed onto various matplotlib methods utilized in the function. See the visualization module documentation for more detailed information about the possible kwargs.

Notes

Parameters

  • mass_solution (MassSolution) – The MassSolution containing the time-dependent solutions to be plotted.

  • observable (iterable, None) – An iterable containing string identifiers of the mass objects or the objects themselves that correspond to the keys for the desired solutions in the MassSolution. If None then all solutions are plotted.

  • ax (matplotlib.axes.Axes, None) – An Axes instance to plot the data on. If None then the current axes instance is used.

  • legend (iterable, str, int) –

    There are three possible input formats for the legend:

    1. An iterable of legend labels as strings.

    2. A str representing the location of the legend, or an int between 0 and 14 (inclusive) corresponding to the legend location.

    3. An iterable of the format (labels, loc) to set both the legend labels and location, where labels and loc follows the labels specified in 1 and 2.

    See the visualization documentation for more information about legend and valid legend locations.

  • **kwargs –

    • time_vector

    • plot_function

    • title

    • xlabel

    • ylabel

    • xlim

    • ylim

    • grid

    • grid_color

    • grid_linestyle

    • grid_linewidth

    • prop_cycle

    • color

    • linestyle

    • linewidth

    • marker

    • markersize

    • legend_ncol

    • annotate_time_points

    • annotate_time_points_color

    • annotate_time_points_marker

    • annotate_time_points_markersize

    • annotate_time_points_labels

    • annotate_time_points_legend

    • annotate_time_points_zorder

    • deviation

    • deviation_zero_centered

    • deviation_normalization

    See visualization documentation for more information on optional kwargs.

Returns

ax – The Axes instance containing the newly created plot.

Return type

matplotlib.axes.Axes