plot.md

AeroViz.plot Usage

WindRose and Conditional Bivariate Probability Function (CBPF)

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.

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