Wind Rose

The notebook is inspired by this plot.

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

wind_df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/pea_wind.csv")
records_count = wind_df.shape[0]

def is_float(x):
    try:
        float(x)
    except ValueError:
        return False
    return True


wind_df = wind_df[wind_df['sped'].apply(lambda x: is_float(x))]
wind_df = wind_df[wind_df['drct'].apply(lambda x: is_float(x))]

wind_df['sped'] = wind_df['sped'].astype(float)
wind_df['drct'] = wind_df['drct'].astype(float)

wind_df = wind_df[wind_df['sped'].apply(lambda x: x >= 2.0)]

wind_df.head()

	station	valid	drct	sped
0	PEA	2002-05-17 00:14	330.0	8.05
1	PEA	2002-05-17 00:20	330.0	8.05
2	PEA	2002-05-17 00:34	340.0	10.35
3	PEA	2002-05-17 00:40	340.0	10.35
4	PEA	2002-05-17 00:54	340.0	14.95

# Compute calm
calm = 100 - wind_df.shape[0] / records_count * 100

# Define the speed bins
bins = [2, 5, 7, 10, 15, 20, float('inf')]
bin_ids = list(range(6))

wind_df['speed_group'] = pd.cut(wind_df['sped'], bins=bins, labels=bin_ids, right=False)

# Group by 'drct' and 'speed_group', and count the occurrences
grouped_counts = wind_df.groupby(['drct', 'speed_group'], observed=False).size().reset_index(name='count')

# Calculate the total number of observations in the dataset
total_observations = wind_df.shape[0]

# Calculate the percentage of each speed group within each direction relative to the total number of observations
grouped_counts['percentage_of_total'] = (grouped_counts['count'] / total_observations) * 100

ggplot(grouped_counts) + \
    geom_bar(
        aes('drct', 'percentage_of_total', fill=as_discrete('speed_group', order=1)), 
        size=0, width=.8,
        stat='identity',
        tooltips=layer_tooltips().format('^y', '{.2g}%').format('^x', '{}°')
    ) + \
    geom_rect(
        # Visually align the width of the rectangle with the bars - widen it by 5 (half a bar width)
        xmin=5, xmax=365, 
        ymin=-1, ymax=0, fill='white', size=0
    ) + \
    geom_hline(yintercept=0, size=2) + \
    geom_text(x=180, y=-1, label=f'Calm\n{calm:.1f}%', hjust='middle', vjust='center', size='10') + \
    scale_fill_manual(
        name='Wind Speed (mph):', 
        values=['#002bff', '#03d3f8', '#7afe81', '#fde609', '#ff4404', '#780200'], 
        labels={
            0: '2 - 4.9', 
            1: '5 - 6.9', 
            2: '7 - 9.9', 
            3: '10 - 14.9', 
            4: '15 - 19.9', 
            5: '20+'
        },
    ) + \
    scale_y_continuous(
        breaks=[0, 1, 2, 3, 4, 5], # To not add automatically generated ticks for values outside of the data range
        format='{}%'
    ) + \
    scale_x_continuous(
        labels={
            45:  'NE', 
            90:  'E', 
            135: 'SE', 
            180: 'S', 
            225: 'SW',
            270: 'W',
            315: 'NW',
            360: 'N', 
        },
    ) + \
    labs(
        title="Wind rose for [PEA] PELLA",
        subtitle="Observations from 2002 to 2024",
        caption="Data is provided by the Iowa Environmental Mesonet of Iowa State University"
    ) + \
    ggsize(800, 800) + \
    theme_minimal2() + \
    theme(
        plot_title=element_text(size=24, face='bold'),
        plot_subtitle=element_text(size=18),
        panel_grid_minor_x=element_line(),
        panel_grid=element_line(color='#A0A0A0'),
        axis_ticks_y=element_line(),
        axis_text_x=element_text(size=18),
        axis_title=element_blank()
    ) + \
    coord_polar(
        ylim=[-1, None], # -1 is to make inner circle
        start=(3.14 * 2) / 36 / 2 # Divide by 2 (i.e. rotate by half a bar width) to make the N-S axis perpendicular
    )