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Size distribution

Heatmaps, per-scan curves and 3-D views of dN/dS/dV/dlogDp matrices, plus lognormal curve fitting. Input is the matrix RawDataReader('SMPS' | 'APS') returns (index = time, columns = diameters). Usage: Visualization.

AeroViz.plot.distribution

Functions:

plot_dist 

plot_dist(data: DataFrame | ndarray, data_std: DataFrame | None = None, std_scale: float | None = 1, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'] = 'Number', additional: Literal['Std', 'Enhancement', 'Error'] = None, fig: Figure | None = None, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot particle size distribution curves and optionally show enhancements.

Parameters:

Name Type Description Default
data dict or list

If dict, keys are labels and values are arrays of distribution values. If listed, it should contain three arrays for different curves.

 required
data_std dict

Dictionary containing standard deviation data for ambient extinction distribution.

 None
std_scale float

The width of standard deviation.

 1
unit (Number, Surface, Volume, Extinction)

Unit of measurement for the data.

 'Number'
additional (std, enhancement, error)

Whether to show enhancement curves.

 'std'
fig Figure

Matplotlib Figure object to use.

 None
ax AxesSubplot

Matplotlib AxesSubplot object to use. If not provided, a new subplot will be created.

 None
**kwargs dict

Additional keyword arguments.

 {}

Returns:

Name Type Description
ax AxesSubplot

Matplotlib AxesSubplot.

Examples:

>>> plot_dist(DataFrame(...), additional="Enhancement")

heatmap 

heatmap(data: DataFrame, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'Blues', colorbar: bool = False, magic_number: int = 11, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot a heatmap of particle size distribution.

Parameters:

Name Type Description Default
data DataFrame

The data containing particle size distribution values. Each column corresponds to a size bin, and each row corresponds to a different distribution.

 required
unit (Number, Surface, Volume, Extinction)

The unit of measurement for the data.

 'Number'
cmap str

The colormap to use for the heatmap.

 'Blues'
colorbar bool

Whether to show the colorbar.

 False
magic_number int

The number of bins to use for the histogram.

 11
ax Axes

The axes to plot the heatmap on. If not provided, a new subplot will be created.

 None
**kwargs

Additional keyword arguments to pass to matplotlib functions.

 {}

Returns:

Type Description
Axes

The Axes object containing the heatmap.

Examples:

>>> heatmap(DataFrame(...), unit='Number')
Notes

This function calculates a 2D histogram of the log-transformed particle sizes and the distribution values. It then plots the heatmap using a logarithmic color scale.

heatmap_tms 

heatmap_tms(data: DataFrame, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'jet', ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot the size distribution over time.

Parameters:

Name Type Description Default
data DataFrame

A DataFrame of particle concentrations to plot the heatmap.

 required
ax Axis

An axis object to plot on. If none is provided, one will be created.

 None
unit Literal['Number', 'Surface', 'Volume', 'Extinction']

default='Number'

 required
cmap colormap

The colormap to use. Can be anything other that 'jet'.

 'viridis'

Returns:

Name Type Description
ax Axis
Notes 
Do not dropna when using this code.

Examples:

Plot a SPMS + APS data:

>>> heatmap_tms(DataFrame(...), cmap='jet')

three_dimension 

three_dimension(data: DataFrame | ndarray, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'Blues', ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Create a 3D plot with data from a pandas DataFrame or numpy array.

Parameters:

Name Type Description Default
data DataFrame or ndarray

Input data containing the values to be plotted.

 required
unit (Number, Surface, Volume, Extinction)

Unit of measurement for the data.

 'Number'
cmap str

The colormap to use for the facecolors.

 'Blues'
ax AxesSubplot

Matplotlib AxesSubplot. If not provided, a new subplot will be created.

 None
**kwargs

Additional keyword arguments to customize the plot.

 {}

Returns:

Type Description
Axes

Matplotlib Axes object representing the 3D plot.
Notes
  • The function creates a 3D plot with data provided in a pandas DataFrame or numpy array.
  • The x-axis is logarithmically scaled, and ticks and labels are formatted accordingly.
  • Additional customization can be done using the **kwargs.
Example

three_dimension(DataFrame(...), unit='Number', cmap='Blues')

curve_fitting 

curve_fitting(dp: ndarray, dist: ndarray | Series | DataFrame, mode: int = None, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'] = None, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Fit a log-normal distribution to the given data and plot the result.

Parameters:

Name Type Description Default
dp array

Array of diameter values.

 required
dist array

Array of distribution values corresponding to each diameter.

 required
mode int

Number of log-normal distributions to fit. Defaults to None.

 None
**kwargs

Additional keyword arguments passed to the plot function.

 {}

Returns:

Type Description
None
Notes
  • The function fits a sum of log-normal distribution to the input data.
  • The number of distribution is determined by the 'mode' parameter.
  • Additional plotting customization can be done using the **kwargs.
Example

curve_fitting(dp, dist, mode=2, xlabel="Diameter (nm)", ylabel="Distribution")

Modules

distribution

Classes
Unit
Methods:
load_jsonfile classmethod 
load_jsonfile()

讀取 JSON 檔中數據并將其變成屬性

update_jsonfile classmethod 
update_jsonfile(key, value)

更新JSON檔

del_jsonfile classmethod 
del_jsonfile(key)

更新JSON檔

Functions:
plot_dist 
plot_dist(data: DataFrame | ndarray, data_std: DataFrame | None = None, std_scale: float | None = 1, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'] = 'Number', additional: Literal['Std', 'Enhancement', 'Error'] = None, fig: Figure | None = None, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot particle size distribution curves and optionally show enhancements.

Parameters:

Name Type Description Default
data dict or list

If dict, keys are labels and values are arrays of distribution values. If listed, it should contain three arrays for different curves.

 required
data_std dict

Dictionary containing standard deviation data for ambient extinction distribution.

 None
std_scale float

The width of standard deviation.

 1
unit (Number, Surface, Volume, Extinction)

Unit of measurement for the data.

 'Number'
additional (std, enhancement, error)

Whether to show enhancement curves.

 'std'
fig Figure

Matplotlib Figure object to use.

 None
ax AxesSubplot

Matplotlib AxesSubplot object to use. If not provided, a new subplot will be created.

 None
**kwargs dict

Additional keyword arguments.

 {}

Returns:

Name Type Description
ax AxesSubplot

Matplotlib AxesSubplot.

Examples:

>>> plot_dist(DataFrame(...), additional="Enhancement")
heatmap 
heatmap(data: DataFrame, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'Blues', colorbar: bool = False, magic_number: int = 11, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot a heatmap of particle size distribution.

Parameters:

Name Type Description Default
data DataFrame

The data containing particle size distribution values. Each column corresponds to a size bin, and each row corresponds to a different distribution.

 required
unit (Number, Surface, Volume, Extinction)

The unit of measurement for the data.

 'Number'
cmap str

The colormap to use for the heatmap.

 'Blues'
colorbar bool

Whether to show the colorbar.

 False
magic_number int

The number of bins to use for the histogram.

 11
ax Axes

The axes to plot the heatmap on. If not provided, a new subplot will be created.

 None
**kwargs

Additional keyword arguments to pass to matplotlib functions.

 {}

Returns:

Type Description
Axes

The Axes object containing the heatmap.

Examples:

>>> heatmap(DataFrame(...), unit='Number')
Notes

This function calculates a 2D histogram of the log-transformed particle sizes and the distribution values. It then plots the heatmap using a logarithmic color scale.

heatmap_tms 
heatmap_tms(data: DataFrame, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'jet', ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Plot the size distribution over time.

Parameters:

Name Type Description Default
data DataFrame

A DataFrame of particle concentrations to plot the heatmap.

 required
ax Axis

An axis object to plot on. If none is provided, one will be created.

 None
unit Literal['Number', 'Surface', 'Volume', 'Extinction']

default='Number'

 required
cmap colormap

The colormap to use. Can be anything other that 'jet'.

 'viridis'

Returns:

Name Type Description
ax Axis
Notes 
Do not dropna when using this code.

Examples:

Plot a SPMS + APS data:

>>> heatmap_tms(DataFrame(...), cmap='jet')
three_dimension 
three_dimension(data: DataFrame | ndarray, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'], cmap: str = 'Blues', ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Create a 3D plot with data from a pandas DataFrame or numpy array.

Parameters:

Name Type Description Default
data DataFrame or ndarray

Input data containing the values to be plotted.

 required
unit (Number, Surface, Volume, Extinction)

Unit of measurement for the data.

 'Number'
cmap str

The colormap to use for the facecolors.

 'Blues'
ax AxesSubplot

Matplotlib AxesSubplot. If not provided, a new subplot will be created.

 None
**kwargs

Additional keyword arguments to customize the plot.

 {}

Returns:

Type Description
Axes

Matplotlib Axes object representing the 3D plot.
Notes
  • The function creates a 3D plot with data provided in a pandas DataFrame or numpy array.
  • The x-axis is logarithmically scaled, and ticks and labels are formatted accordingly.
  • Additional customization can be done using the **kwargs.
Example

three_dimension(DataFrame(...), unit='Number', cmap='Blues')

curve_fitting 
curve_fitting(dp: ndarray, dist: ndarray | Series | DataFrame, mode: int = None, unit: Literal['Number', 'Surface', 'Volume', 'Extinction'] = None, ax: Axes | None = None, **kwargs) -> tuple[Figure, Axes]

Fit a log-normal distribution to the given data and plot the result.

Parameters:

Name Type Description Default
dp array

Array of diameter values.

 required
dist array

Array of distribution values corresponding to each diameter.

 required
mode int

Number of log-normal distributions to fit. Defaults to None.

 None
**kwargs

Additional keyword arguments passed to the plot function.

 {}

Returns:

Type Description
None
Notes
  • The function fits a sum of log-normal distribution to the input data.
  • The number of distribution is determined by the 'mode' parameter.
  • Additional plotting customization can be done using the **kwargs.
Example

curve_fitting(dp, dist, mode=2, xlabel="Diameter (nm)", ylabel="Distribution")

ls_mode 
ls_mode(**kwargs) -> tuple[Figure, Axes]

Plot log-normal mass size distribution for small mode, large mode, and sea salt particles.

Parameters:

Name Type Description Default
**kwargs dict

Additional keyword arguments.

 {}

Examples:

Example : Plot log-normal mass size distribution with default settings

>>> ls_mode()
lognorm_dist 
lognorm_dist(**kwargs) -> tuple[Figure, Axes]

Plot various particle size distribution to illustrate log-normal distribution and transformations.

Parameters:

Name Type Description Default
**kwargs dict

Additional keyword arguments.

 {}

Examples:

Example : Plot default particle size distribution

>>> lognorm_dist()