Plot Documentation

Overview

The plot module provides visualization tools for creating publication-quality plots of aerosol data. It includes various plot types commonly used in aerosol science.

Basic Usage

from AeroViz import plot

# Create a time series plot
plot.time_series(data, 'BC')

# Create a scatter plot
plot.scatter(data, 'BC', 'PM2.5')

Available Plot Types

time_series()

Create a time series plot of specified variables.

plot.time_series(
    data,
    variables=['BC', 'PM2.5'],
    start='2024-01-01',
    end='2024-12-31',
    title='Time Series of BC and PM2.5'
)

scatter()

Create a scatter plot of two variables.

plot.scatter(
    data,
    x='BC',
    y='PM2.5',
    color='PM2.5',
    size='BC'
)

box()

Create a box plot of specified variables.

plot.box(
    data,
    variables=['BC', 'PM2.5'],
    by='month'
)

histogram()

Create a histogram of specified variables.

plot.histogram(
    data,
    variables=['BC'],
    bins=50
)

Parameters

Common Parameters

data (DataFrame): Input data
variables (list): Variables to plot
title (str): Plot title
figsize (tuple): Figure size
style (str): Plot style

Time Series Parameters

start (str): Start date
end (str): End date
freq (str): Time frequency

Scatter Parameters

x (str): X-axis variable
y (str): Y-axis variable
color (str): Color variable
size (str): Size variable

Examples

Time Series with Multiple Variables

plot.time_series(
    data,
    variables=['BC', 'PM2.5', 'PM10'],
    start='2024-01-01',
    end='2024-12-31',
    title='Aerosol Components Time Series'
)

Scatter Plot with Color and Size

plot.scatter(
    data,
    x='BC',
    y='PM2.5',
    color='PM10',
    size='PM1',
    title='BC vs PM2.5'
)

Box Plot by Month

plot.box(
    data,
    variables=['BC'],
    by='month',
    title='Monthly BC Distribution'
)

Notes

All plots are customizable with various parameters
Plots can be saved in multiple formats (PNG, PDF, etc.)
The plot module uses matplotlib and seaborn under the hood
Publication-quality settings are applied by default

WindRose and Conditional Bivariate Probability Function (CBPF)

WindRose

from AeroViz import plot, DataBase

df = DataBase()  # build default data, uers can use their own data

# wind rose
plot.meteorology.wind_rose(df, 'WS', 'WD', typ='bar')
plot.meteorology.wind_rose(df, 'WS', 'WD', 'PM2.5', typ='scatter')

plot.meteorology.CBPF(df, 'WS', 'WD', 'PM2.5')
plot.meteorology.CBPF(df, 'WS', 'WD', 'PM2.5', percentile=[75, 100])

Linear Regression

from AeroViz import plot, DataBase

df = DataBase()  # build default data, uers can use their own data

# regression
plot.linear_regression(df, x='PM25', y='Extinction')
plot.linear_regression(df, x='PM25', y=['Extinction', 'Scattering', 'Absorption'])

plot.multiple_linear_regression(df, x=['AS', 'AN', 'OM', 'EC', 'SS', 'Soil'], y=['Extinction'])
plot.multiple_linear_regression(df, x=['NO', 'NO2', 'CO', 'PM1'], y=['PM25'])

Timeseries

from AeroViz import plot, DataBase

df = DataBase()  # build default data, uers can use their own data

# timeseries
plot.timeseries.timeseries(df,
                           y=['Extinction', 'Scattering'],
                           color=[None, None],
                           style=['line', 'line'],
                           times=('2021-02-01', '2021-03-31'), ylim=[0, None], ylim2=[0, None], rolling=50,
                           inset_kws2=dict(bbox_to_anchor=(1.12, 0, 1.2, 1)))

plot.timeseries.timeseries(df, y='WS', color='WD', style='scatter', times=('2020-10-01', '2020-11-30'),
                           scatter_kws=dict(cmap='hsv'), cbar_kws=dict(ticks=[0, 90, 180, 270, 360]),
                           ylim=[0, None])

plot.timeseries.timeseries_template(df.loc['2021-02-01', '2021-03-31'])

Particle Size Distribution

[!IMPORTANT]\ The provided code of distribution suitable for SMPS and APS data in "dX/dlogdp" unit. It can be converted into surface area and volume distribution. At the same time, chemical composition data can also be used to calculate particle extinction through Mie theory.

PNSD

from pathlib import Path
from AeroViz import plot
from AeroViz.tools import DataBase

df = DataBase()  # build default data, uers can use their own data

PNSD = DataBase('DEFAULT_PNSD_DATA.csv')

plot.distribution.distribution.heatmap(PNSD, unit='Number')
plot.distribution.distribution.heatmap_tms(PNSD, unit='Number', freq='60d')

For some basic plot

Three_dimension	Correlation Matrix	Mutiply Linear Regression

Pie & Donut	Dounts	Scatter

PyMieScatt

Mie_Q	Mie_MEE