season-polar-plots 0.0.6
Create polar plots to display seasonal trends in time series data.
License: MIT
Requires Python: >=3.7

## __Seasonality polar plots__
~~~
pip install season-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) 
#
| Parameters  |   |
| ------ | ------ |
| 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 | (str) '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(mode = 'all', label=None ,rd_years = True, col = 'viridis_r', a = 1, psize = None, pmarker = None, nylabels = 10, off = 0, rlab_angle = 15, linreg = False, start_month = 1)
#
| Parameters  |   |
| ------ | ------ |
| label | (str) label for time series variable |
| mode | (str) 'all' uses 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 | (bool) as default, years are plotted in radius direction; rd_years=False plots variable in radius direction |
| col | (str) color gradient (default 'viridis_r') |
| a | (float) transparency alpha (0-1) |
| psize | (float) marker size for daily data points |
| pmarker | (MarkerStyle) marker style  for daily data points|
| nylabels | (int) number of (year) labels in radius direction |
| off | (int or float) off-set from circle center |
| rlab_angle | (float) angle of the radius axis labels |
| linreg | (bool) if True plots linear regression (day in year ~ year) in polar projection (only for daily extreme values) |
| start_month | (int) 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)
#
| Parameters  |   |
| ------ | ------ |
| mode | (str) '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)
#
| Parameters  |   |
| ------ | ------ |
| mode | (str) '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)
#
| Parameters  |   |
| ------ | ------ |
| mode | (str) 'all' to use all data points, 'min' or 'max' to filter on extreme values |
| plot | (bool) if True plots the data as circular histogram and fitted von-Mises distribution |
| print_par | (bool) if True prints the fitted von-Mises parameters kappa and mu |
| bins | (int) number of bins for histogram |
| col_hist | (str) color of the histogram |
| col_vm | (str) color of the fitted von Mises distribution |
| off | (float) off-set from circle center |
| a | (float) transparency alpha (0-1) |
| rwidth | (float) relative width of histogram bins |

__Returns__: Plot; kappa, mu, mu_nday