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 (y = column name or list of columns)
plot.timeseries(data, y='BC')

# Create a scatter plot (x / y are column names)
plot.scatter(data, x='BC', y='PM2.5')

All plot functions take a time-indexed DataFrame and return (fig, ax).

Available Plot Types

timeseries()

Create a time series plot of one or more columns.

plot.timeseries(
    data,
    y=['BC', 'PM2.5'],
)

scatter()

Create a scatter plot of two variables (c colors points, s sizes them).

plot.scatter(
    data,
    x='BC',
    y='PM2.5',
    c='PM2.5',
    s='BC',
)

box()

Box plot of y grouped by numeric x binned with x_bins (use integer edges of width >= 2). It does not accept a categorical/string x-axis.

import numpy as np

plot.box(
    data,
    x='WS',
    y='PM2.5',
    x_bins=np.arange(0, 11, 2),
)

diurnal_pattern()

Mean (+/- spread) by hour of day.

plot.diurnal_pattern(data, y='BC')

Parameters

Common Parameters

data / df (DataFrame): time-indexed input data
ax (Axes): existing axis to draw into (optional)
title (str): plot title

Time Series Parameters

y (str | list): column(s) on the primary axis
y2 (str | list): column(s) on a secondary axis
rolling (int | str): rolling-window smoothing

Scatter Parameters

x (str): X-axis column
y (str): Y-axis column
c (str): color column
s (str): size column

Examples

Time Series with Multiple Variables

plot.timeseries(
    data,
    y=['BC', 'PM2.5', 'PM10'],
)

Scatter Plot with Color and Size

plot.scatter(
    data,
    x='BC',
    y='PM2.5',
    c='PM10',
    s='PM1',
)

Box Plot by 2-month Bins

import numpy as np

data['month'] = data.index.month
plot.box(
    data,
    x='month',
    y='BC',
    x_bins=np.arange(0, 13, 2),
)

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