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   6.017143  214.886927
2023-01-01 01:00:00  13.566500  261.612819
2023-01-01 02:00:00   8.023053   90.301051
2023-01-01 03:00:00   7.832558  272.449948
2023-01-01 04:00:00  10.263807  239.604732

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 6.017 ... 12.95
    wind_direction  (time, height, stacked_point) float64 70kB 214.9 ... 124.6
Attributes:
    history:          2026-09-29T09:45:31+00:00:\twindkit==2.2.0\twk.tswc_fro...
    Conventions:      CF-1.8
    Package name:     windkit
    Package version:  2.2.0
    Creation date:    2026-09-29T09:45:31+00:00
    Object type:      Time Series Wind Climate

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:       (wsbin: 30, sector: 12, height: 1, stacked_point: 1)
Coordinates:
  * 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
  * 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
  * 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:
    wsfreq        (wsbin, sector, height, stacked_point) float64 3kB 0.0 ... 0.0
    wdfreq        (sector, height, stacked_point) float64 96B 0.08116 ... 0.0...
Attributes: (12/14)
    history:              2026-09-29T09:45:31+00:00:\twindkit==2.2.0\twk.tswc...
    Conventions:          CF-1.8
    Package name:         windkit
    Package version:      2.2.0
    Creation date:        2026-09-29T09:45:31+00:00
    Object type:          Binned Wind Climate
    ...                   ...
    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 0x70f636a60980>]