gridlib.plot package

Submodules

gridlib.plot.heatmap module

Module with functions to plot event heatmap and state heatmap.

gridlib.plot.heatmap.event_spectrum_heatmap(fit_result_full: Dict[str, Dict[str, Union[ndarray, float]]], fit_results_resampled: List[Dict[str, Dict[str, Union[ndarray, float]]]], fit_key: str = 'grid', scale: str = 'log', threshold: float = 1e-05, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), cm_max: int = 20, cm_step: int = 2, add_legend: bool = True)[source]

Function plots the event spectrum heatmap for resampled data and plots the full data points as circles.

Parameters

fit_result_full (Dict[str, Dict[str, Union[np.ndarray, float]]]) –

A dictionary mapping keys (fitting procedure) to the corresponding fit results for the full data. For example:

{
    "grid": {
        "k": array([1.00000000e-03, 1.04737090e-03, ...]),
        "s": array([3.85818587e-17, 6.42847878e-18, ...]),
        "a": 0.010564217803906671,
        "loss": 0.004705659331508584,
    },
}

fit_results_resampled (List[Dict[str, Dict[str, Union[np.ndarray, float]]]]) –

A list consisting of dictionaries mapping keys (fitting procedure) to the corresponding fit results for the resampled data. For example:

[
    {
        "grid": {
            "k": array([1.00000000e-03, 1.04737090e-03, ...]),
            "s": array([3.85818587e-17, 6.42847878e-18, ...]),
            "a": 0.010564217803906671,
            "loss": 0.004705659331508584,
        },
    },
    ...
]

fit_key (str, optional) – The mapping key (fitting procedure) used to plot the resampling results from, by default “grid”.
scale ({"log", "linear"}, optional) – The scale of the x-axis. If scale is set to “log” than the x-axis will be logarithmic. If scale is set to “linear”, the x-axis will be linear, by default “log”.
threshold (float, optional) – Minimum weight value that is shown for full data spectrum, by default 10e-6.
xlim (Tuple[float, float], optional) – A tuple setting the x-axis limits. If the value is set to None, there are no limits, by default None.
ylim (Tuple[float, float], optional) – A tuple setting the y-axis limits. If the value is set to None, there are no limits, by default None.
figsize (Tuple[float, float], optional) – Width, height of the figure in inches, default is (6, 4).
cm_max (int, optional) – Maximum value of the colormap/colorbar, by default 20.
cm_step (int, optional) – Step size of the colormap, by default 2.
add_legend (bool, optional) – If True, a legend is added to the figure, by default True.

Returns

fig (matplotlib.figure.Figure) – The top level container for all the plot elements.
ax (matplotlib.axes.Axes) – A single matplotlib.axes.Axes object.

gridlib.plot.heatmap.state_spectrum_heatmap(fit_result_full: Dict[str, Union[ndarray, float]], fit_results_resampled: List[Dict[str, Union[ndarray, float]]], fit_key: str = 'grid', scale: str = 'log', threshold: float = 1e-05, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), cm_max: int = 20, cm_step: int = 2, add_legend: bool = True)[source]

Function plots the state spectrum heatmap for resampled data and plots the full data points as circles.

Parameters

fit_result_full (Dict[str, Dict[str, Union[np.ndarray, float]]]) –

A dictionary mapping keys (fitting procedure) to the corresponding fit results for the full data. For example:

{
    "grid": {
        "k": array([1.00000000e-03, 1.04737090e-03, ...]),
        "s": array([3.85818587e-17, 6.42847878e-18, ...]),
        "a": 0.010564217803906671,
        "loss": 0.004705659331508584,
    },
}

fit_results_resampled (List[Dict[str, Dict[str, Union[np.ndarray, float]]]]) –

A list consisting of dictionaries mapping keys (fitting procedure) to the corresponding fit results for the resampled data. For example:

[
    {
        "grid": {
            "k": array([1.00000000e-03, 1.04737090e-03, ...]),
            "s": array([3.85818587e-17, 6.42847878e-18, ...]),
            "a": 0.010564217803906671,
            "loss": 0.004705659331508584,
        },
    },
    ...
]

fit_key (str, optional) – The mapping key (fitting procedure) used to plot the resampling results from, by default “grid”.
scale ({"log", "linear"}, optional) – The scale of the x-axis. If scale is set to “log” than the x-axis will be logarithmic. If scale is set to “linear”, the x-axis will be linear, by default “log”.
threshold (float, optional) – Minimum weight value that is shown for full data spectrum, by default 10e-6.
xlim (Tuple[float, float], optional) – A tuple setting the x-axis limits. If the value is set to None, there are no limits, by default None.
ylim (Tuple[float, float], optional) – A tuple setting the y-axis limits. If the value is set to None, there are no limits, by default None.
figsize (Tuple[float, float], optional) – Width, height of the figure in inches, default is (6, 4).
cm_max (int, optional) – Maximum value of the colormap/colorbar, by default 20.
cm_step (int, optional) – Step size of the colormap, by default 2.
add_legend (bool, optional) – If True, a legend is added to the figure, by default True.

Returns

fig (matplotlib.figure.Figure) – The top level container for all the plot elements.
ax (matplotlib.axes.Axes) – A single matplotlib.axes.Axes object.

gridlib.plot.spectrum module

Module with functions to plot event spectrum and state spectrum.

gridlib.plot.spectrum.event_spectrum(fit_values, scale: str = 'log', threshold: float = 0.0, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), color=None, add_legend: bool = True)[source]

Function plots the event spectrum of different fit values in one figure.

Parameters

key_to_k_and_weight (Dict[str, Dict[str, np.ndarray]]) –

The decay rates and weights wrapped in the following structure:

{

“label”: {
“k”: np.ndarray with the decay rates, “weight”: np.ndarray with the respective weights

}

}
scale ({"log", "linear"}, optional) – The scale on the x-axis. The default value is “log”.
threshold (float, optional) – The weight threshold for plotting lines, by default 0.0.
xlim (Tuple[float, float], optional) – A tuple of the x-axis limits, by default None.
ylim (Tuple[float, float], optional) – A tuple of the y-axis limits, by default None.
figsize (Tuple[float, float], optional) – A tuple of the figure size, by default (6, 4).
color (color or sequence of colors, optional) – The color or colors to use for the plotting. The standard gridlib colors are used if the value is set to None. The value is by default None.
add_legend (bool, optional) – Indicates whether the legend needs to be plotted, by default True.

Returns

fig (matplotlib.Figure) – TODO: link this to matplotlib.Figure documentation https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure
ax (matplotlib.axes.Axes) – TODO: link to the matplotlib.axes.Axes documentation https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes

gridlib.plot.spectrum.state_spectrum(fit_values, scale: str = 'log', threshold: float = 0.0, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), color=None, add_legend: bool = True)[source]

Function plots the state spectrum of different fit values in one figure.

Parameters

key_to_k_and_weight (Dict[str, Dict[str, np.ndarray]]) –

The decay rates and weights wrapped in the following structure:

{

“label”: {
“k”: np.ndarray with the decay rates, “weight”: np.ndarray with the respective weights

}

}
scale ({"log", "linear"}, optional) – The scale on the x-axis. The default value is “log”.
threshold (float, optional) – The weight threshold for plotting lines, by default 0.0.
xlim (Tuple[float, float], optional) – A tuple of the x-axis limits, by default None.
ylim (Tuple[float, float], optional) – A tuple of the y-axis limits, by default None.
figsize (Tuple[float, float], optional) – A tuple of the figure size, by default (6, 4).
color (color or sequence of colors, optional) – The color or colors to use for the plotting. The standard gridlib colors are used if the value is set to None. The value is by default None.
add_legend (bool, optional) – Indicates whether the legend needs to be plotted, by default True.

Returns

fig (matplotlib.Figure) – TODO: link this to matplotlib.Figure documentation https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure
ax (matplotlib.axes.Axes) – TODO: link to the matplotlib.axes.Axes documentation https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes

gridlib.plot.survival_function module

Module with functions to plot survival functions.

gridlib.plot.survival_function.data_sf(data: Dict[str, Dict[str, ndarray]], process_data_flag: bool = True, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), kwargs_plot: Optional[Dict] = None, kwargs_text: Optional[Dict] = None)[source]: Function plots a single data dictionary

gridlib.plot.survival_function.data_sf_multiple(data: Dict[str, Dict[str, ndarray]], process_data_flag: bool = True, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), kwargs_plot: Optional[Dict] = None, kwargs_text: Optional[Dict] = None)[source]: Function plots a single or multiple data dictionaries

gridlib.plot.survival_function.data_sf_vs_grid(data: Dict[str, Dict[str, ndarray]], fit_values_grid: Dict[str, Dict[str, ndarray]], process_data_flag: bool = True, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), kwargs_plot: Optional[Dict] = None, kwargs_text: Optional[Dict] = None)[source]

Function plots the survival function of the true data and the GRID curves.

Parameters

data (Dict[str, Dict[str, np.ndarray]]) –
Survival function data for every time-lapse condition with the following data structure:

{

f”{t_tl}”: {
“time”: np.ndarray with the time points, “value”: np.ndarray with the survival function values,

}

}
xlim (Tuple[float, float] = None, ylim: Tuple[float, float] = None, path_save: str or pathlib.Path) – Path designates the place where the figure should be saved if a value is set. The figure is not saved if the values is set to None. (default None)

Return type

None

gridlib.plot.survival_function.data_sf_vs_multi_exp(data: Dict[str, Dict[str, ndarray]], fit_values_multi_exp: Dict[str, Dict[str, ndarray]], process_data_flag: bool = True, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (6, 4), kwargs_plot: Optional[Dict] = None, kwargs_text: Optional[Dict] = None) → Tuple[source]

Function plots the survival function of the true data and the multi-exponential curves.

Parameters

data (Dict[str, Dict[str, np.ndarray]]) –
Data of the survival function from real data with the following data structure: {

f”{t_tl}”: {
“time”: np.ndarray with all the time values, “value”: np.ndarray with all the survival function values corresponding to the

respective time value

}

}
data_multi_exp (Dict[str, Dict[str, np.ndarray]]) –
Survival function data for time-lapse conditions with the following data structure:

{

f”{t_tl}”: {
“time”: np.ndarray with the time points, “value”: np.ndarray with the survival function values,

}

gridlib.plot package

Submodules

gridlib.plot.heatmap module

gridlib.plot.spectrum module

gridlib.plot.survival_function module

Module contents