season-polar-plots 0.0.2

Create polar plots to display seasonal trends in time series data.

Seasonality polar plots

This is a package to create polar plots for displaying seasonal trends in time series data. Requires

• matplotlib (3.5.1)
• numpy (1.19.2)
• pandas (1.3.5)
• seaborn (0.11.2)
• scipy (1.6.0)

1. Read data into SeasonData class:

from season_polar_plots import SeasonData

SeasonData(data, year_start, year_end, t_res = 'daily')

Arguments
data	pandas Series or single column DataFrame with datetime index
year_start	(int) start year of period to be analyzed
year_end	(int) end year of period to be analyzed
t_res	'daily' or 'monthly': temporal resolution. Monthly values can be aggregated from daily values if 'monthly' is chosen (see sp_plot() and get_mgrid() function)

2. Plot function:

self.sp_plot(label, mode = 'all', rd_years = True, col = 'viridis_r', a = 1, psize = None, pmarker = None, nylabels = 10, off = 0, rlab_angle = 15, linreg = False, start_month = 1)

Arguments
label	label for time series variable
mode	'all' to select all data points; daily resolution: 'min' or 'max' filter time series on annual extreme values; monthly resolution: 'sum', 'mean' / 'min', 'max' aggregate / filter data for each month
rd_years	as default, years are plotted in radius direction; rd_years=False plots variable in radius direction
col	color gradient (default 'viridis_r')
a	transparency alpha (0-1)
psize	marker size for daily data points
pmarker	marker style for daily data points
nylabels	number of (year) labels in radius direction
off	off-set from circle center
rlab_angle	angle of the radius axis labels
linreg	linreg=True plots linear regression (day in year ~ year) in polar projection (only for daily extreme values)
start_month	start month for linear regression

Returns: Plot; if linreg=True prints R² and p-values for slope and intercept

Obtain annual extreme values from daily time series:

self.get_ev( mode)

Arguments
mode	'min' or 'max': filter time series on annual extreme values

Returns: DataFrame (containing nr of day in year of extreme values, extreme values)

Obtain aggregated / filtered monthly values:

self.get_mgrid(mode)

Arguments
mode	'all' if data is already in monthly resolution; 'sum', 'mean' / 'min', 'max' aggregate / filter data for each month

Returns: DataFrame (containing monthly data)

3. Von-Mises distribution (ML fit with scipy):

self.von_mises(mode, plot = True, print_par = True, bins = 15, col_hist = 'b', col_vm ='r', off = 0.1, a = 1, rwidth = 0.8)

Arguments
mode	'all' to use all data points, 'min' or 'max' to filter on extreme values
plot	if True plots the data as circular histogram and fitted von-Mises distribution
print_par	if True prints the fitted von-Mises parameters kappa and mu
bins	number of bins for histogram
col_hist	color of the histogram
col_vm	color of the fitted von Mises distribution
off	off-set from circle center
a	transparency alpha (0-1)
rwidth	relative width of histogram bins

Returns: Plot; kappa, mu, mu_nday