TSWC to BWC

This example demonstrates how to convert a time series wind climate (TSWC) object to a binned wind climate (BWC) object using the bwc_from_tswc function from the windkit library.

Prepare Sample Data

First, we need to prepare the sample data. This is done by creating a DataFrame with the wind speed and direction data.

import numpy as np
import pandas as pd
import windkit as wk

size = 8760  # 1 year of hourly data

df = pd.DataFrame(
    {
        "wspd_50": np.random.uniform(5, 15, size=size),  # Wind speed in m/s
        "wdir_50": np.random.uniform(0, 360, size=size),  # Wind direction in degrees
    },
    index=pd.date_range("2023-01-01", periods=size, freq="h"),
)
df.index.name = "time"  # Set the index name to 'time'
print(df.head())

                       wspd_50     wdir_50
time
2023-01-01 00:00:00   7.315907  240.071192
2023-01-01 01:00:00  12.355987  350.416001
2023-01-01 02:00:00  14.835689  118.958768
2023-01-01 03:00:00  10.257354  212.482614
2023-01-01 04:00:00   9.707300  311.425267

Create TSWC

tswc = wk.tswc_from_dataframe(
    df,
    west_east=0.0,
    south_north=0.0,
    crs=4326,
    height_to_columns={50: ["wspd_50", "wdir_50"]},
)
print(tswc)

<xarray.Dataset> Size: 210kB
Dimensions:         (time: 8760, height: 1, stacked_point: 1)
Coordinates:
  * time            (time) datetime64[us] 70kB 2023-01-01 ... 2023-12-31T23:0...
  * height          (height) int64 8B 50
    west_east       (stacked_point) float64 8B 0.0
    south_north     (stacked_point) float64 8B 0.0
    crs             int8 1B 0
Dimensions without coordinates: stacked_point
Data variables:
    wind_speed      (time, height, stacked_point) float64 70kB 7.316 ... 11.21
    wind_direction  (time, height, stacked_point) float64 70kB 240.1 ... 240.9
Attributes:
    history:          2026-01-29T09:29:35+00:00:\twindkit==2.0.1\twk.tswc_fro...
    Conventions:      CF-1.8
    Package name:     windkit
    Package version:  2.0.1
    Creation date:    2026-01-29T09:29:35+00:00
    Object type:      Time Series Wind Climate
    author:           Default User
    author_email:     default_email@example.com
    institution:      Default Institution

Convert TSWC to BWC

Now, we can convert the time series wind climate (TSWC) object to a binned wind climate (BWC) object

bwc = wk.bwc_from_tswc(tswc)
print(bwc)

bwc.wsfreq.sel(sector=270).plot()

<xarray.Dataset> Size: 4kB
Dimensions:       (height: 1, sector: 12, stacked_point: 1, wsbin: 30)
Coordinates:
  * height        (height) int64 8B 50
  * sector        (sector) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
    sector_ceil   (sector) float64 96B 15.0 45.0 75.0 ... 285.0 315.0 345.0
    sector_floor  (sector) float64 96B 345.0 15.0 45.0 ... 255.0 285.0 315.0
    south_north   (stacked_point) float64 8B 0.0
    west_east     (stacked_point) float64 8B 0.0
  * wsbin         (wsbin) float64 240B 0.5 1.5 2.5 3.5 ... 26.5 27.5 28.5 29.5
    wsceil        (wsbin) float64 240B 1.0 2.0 3.0 4.0 ... 27.0 28.0 29.0 30.0
    wsfloor       (wsbin) float64 240B 0.0 1.0 2.0 3.0 ... 26.0 27.0 28.0 29.0
    crs           int8 1B 0
Dimensions without coordinates: stacked_point
Data variables:
    wdfreq        (sector, height, stacked_point) float64 96B 0.08014 ... 0.0...
    wsfreq        (wsbin, sector, height, stacked_point) float64 3kB 0.0 ... 0.0
Attributes: (12/20)
    description:
    long_name:            Horizontal Wind Speed
    standard_name:        wind_speed
    units:                m s-1
    Conventions:          CF-1.8
    Package name:         windkit
    ...                   ...
    end_time:             2023-12-31 23:00:00
    interval:             0 days 01:00:00
    count_expected:       8760
    count:                8760
    count_missing:        0
    recovery_percentage:  100.0

[<matplotlib.lines.Line2D object at 0x792ba256d940>]