Script example

This example use randoms values for wind speed and direction(ws and wd variables). In situation, these variables are loaded with reals values (1-D array), from a database or directly from a text file (see the “load” facility from the matplotlib.pylab interface for that).

from windrose import WindroseAxes
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import numpy as np

# Create wind speed and direction variables

ws = np.random.random(500) * 6
wd = np.random.random(500) * 360

A stacked histogram with normed (displayed in percent) results

ax = WindroseAxes.from_ax()
ax.bar(wd, ws, normed=True, opening=0.8, edgecolor='white')
ax.set_legend()
bar

Another stacked histogram representation, not normed, with bins limits

ax = WindroseAxes.from_ax()
ax.box(wd, ws, bins=np.arange(0, 8, 1))
ax.set_legend()
box

A windrose in filled representation, with a controled colormap

ax = WindroseAxes.from_ax()
ax.contourf(wd, ws, bins=np.arange(0, 8, 1), cmap=cm.hot)
ax.set_legend()
contourf

Same as above, but with contours over each filled region…

ax = WindroseAxes.from_ax()
ax.contourf(wd, ws, bins=np.arange(0, 8, 1), cmap=cm.hot)
ax.contour(wd, ws, bins=np.arange(0, 8, 1), colors='black')
ax.set_legend()
contourf-contour

…or without filled regions

ax = WindroseAxes.from_ax()
ax.contour(wd, ws, bins=np.arange(0, 8, 1), cmap=cm.hot, lw=3)
ax.set_legend()
contour

After that, you can have a look at the computed values used to plot the windrose with the ax._info dictionnary :

  • ax._info['bins'] : list of bins (limits) used for wind speeds. If not set in the call, bins will be set to 6 parts between wind speed min and max.
  • ax._info['dir'] : list of directions “bundaries” used to compute the distribution by wind direction sector. This can be set by the nsector parameter (see below).
  • ax._info['table'] : the resulting table of the computation. It’s a 2D histogram, where each line represents a wind speed class, and each column represents a wind direction class.

So, to know the frequency of each wind direction, for all wind speeds, do:

ax.bar(wd, ws, normed=True, nsector=16)
table = ax._info['table']
wd_freq = np.sum(table, axis=0)

and to have a graphical representation of this result :

direction = ax._info['dir']
wd_freq = np.sum(table, axis=0)
plt.bar(np.arange(16), wd_freq, align='center')
xlabels = ('N','','N-E','','E','','S-E','','S','','S-O','','O','','N-O','')
xticks=arange(16)
gca().set_xticks(xticks)
draw()
gca().set_xticklabels(xlabels)
draw()
histo\_WD

In addition of all the standard pyplot parameters, you can pass special parameters to control the windrose production. For the stacked histogram windrose, calling help(ax.bar) will give : bar(self, direction, var, **kwargs) method of windrose.WindroseAxes instance Plot a windrose in bar mode. For each var bins and for each sector, a colored bar will be draw on the axes.

Mandatory:

  • direction : 1D array - directions the wind blows from, North centred
  • var : 1D array - values of the variable to compute. Typically the wind speeds

Optional:

  • nsector : integer - number of sectors used to compute the windrose table. If not set, nsectors=16, then each sector will be 360/16=22.5°, and the resulting computed table will be aligned with the cardinals points.
  • bins : 1D array or integer - number of bins, or a sequence of bins variable. If not set, bins=6 between min(var) and max(var).
  • blowto : bool. If True, the windrose will be pi rotated, to show where the wind blow to (usefull for pollutant rose).
  • colors : string or tuple - one string color ('k' or 'black'), in this case all bins will be plotted in this color; a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified.
  • cmap : a cm Colormap instance from matplotlib.cm. - if cmap == None and colors == None, a default Colormap is used.
  • edgecolor : string - The string color each edge bar will be plotted. Default : no edgecolor
  • opening : float - between 0.0 and 1.0, to control the space between each sector (1.0 for no space)
  • mean_values : Bool - specify wind speed statistics with direction=specific mean wind speeds. If this flag is specified, var is expected to be an array of mean wind speeds corresponding to each entry in direction. These are used to generate a distribution of wind speeds assuming the distribution is Weibull with shape factor = 2.
  • weibull_factors : Bool - specify wind speed statistics with direction=specific weibull scale and shape factors. If this flag is specified, var is expected to be of the form [[7,2], …., [7.5,1.9]] where var[i][0] is the weibull scale factor and var[i][1] is the shape factor

probability density function (pdf) and fitting Weibull distribution

A probability density function can be plot using:

from windrose import WindAxes
ax = WindAxes.from_ax()
bins = np.arange(0, 6 + 1, 0.5)
bins = bins[1:]
ax, params = ax.pdf(ws, bins=bins)
pdf

Optimal parameters of Weibull distribution can be displayed using

print(params)
(1, 1.7042156870194352, 0, 7.0907180300605459)

Overlay of a map

This example illustrate how to set an windrose axe on top of any other axes. Specifically, overlaying a map is often usefull. It rely on matplotlib toolbox inset_axes utilities.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt

import windrose

ws = np.random.random(500) * 6
wd = np.random.random(500) * 360

minlon, maxlon, minlat, maxlat = (6.5, 7.0, 45.85, 46.05)

proj = ccrs.PlateCarree()
fig = plt.figure(figsize=(12, 6))
# Draw main ax on top of which we will add windroses
main_ax = fig.add_subplot(1, 1, 1, projection=proj)
main_ax.set_extent([minlon, maxlon, minlat, maxlat], crs=proj)
main_ax.gridlines(draw_labels=True)
main_ax.coastlines()

request = cimgt.OSM()
main_ax.add_image(request, 12)

# Coordinates of the station we were measuring windspeed
cham_lon, cham_lat = (6.8599, 45.9259)
passy_lon, passy_lat = (6.7, 45.9159)

# Inset axe it with a fixed size
wrax_cham = inset_axes(main_ax,
        width=1,                             # size in inches
        height=1,                            # size in inches
        loc='center',                        # center bbox at given position
        bbox_to_anchor=(cham_lon, cham_lat), # position of the axe
        bbox_transform=main_ax.transData,    # use data coordinate (not axe coordinate)
        axes_class=windrose.WindroseAxes,    # specify the class of the axe
        )

# Inset axe with size relative to main axe
height_deg = 0.1
wrax_passy = inset_axes(main_ax,
        width="100%",                        # size in % of bbox
        height="100%",                       # size in % of bbox
        #loc='center',  # don't know why, but this doesn't work.
        # specify the center lon and lat of the plot, and size in degree
        bbox_to_anchor=(passy_lon-height_deg/2, passy_lat-height_deg/2, height_deg, height_deg),
        bbox_transform=main_ax.transData,
        axes_class=windrose.WindroseAxes,
        )

wrax_cham.bar(wd, ws)
wrax_passy.bar(wd, ws)
for ax in [wrax_cham, wrax_passy]:
  ax.tick_params(labelleft=False, labelbottom=False)
map_overlay

Functional API

Instead of using object oriented approach like previously shown, some “shortcut” functions have been defined: wrbox, wrbar, wrcontour, wrcontourf, wrpdf. See unit tests.

Pandas support

windrose not only supports Numpy arrays. It also supports also Pandas DataFrame. plot_windrose function provides most of plotting features previously shown.

from windrose import plot_windrose
N = 500
ws = np.random.random(N) * 6
wd = np.random.random(N) * 360
df = pd.DataFrame({'speed': ws, 'direction': wd})
plot_windrose(df, kind='contour', bins=np.arange(0.01,8,1), cmap=cm.hot, lw=3)

Mandatory:

  • df: Pandas DataFrame with DateTimeIndex as index and at least 2 columns ('speed' and 'direction').

Optional:

  • kind : kind of plot (might be either, 'contour', 'contourf', 'bar', 'box', 'pdf')
  • var_name : name of var column name ; default value is VAR_DEFAULT='speed'
  • direction_name : name of direction column name ; default value is DIR_DEFAULT='direction'
  • clean_flag : cleanup data flag (remove data points with NaN, var=0) before plotting ; default value is True.

Subplots

subplots

Video export

A video of plots can be exported. A playlist of videos is available at https://www.youtube.com/playlist?list=PLE9hIvV5BUzsQ4EPBDnJucgmmZ85D_b-W

See:

|Video1| |Video2| |Video3|

Source code

This is just a sample for now. API for video need to be created.

Use:

$ python samples/example_animate.py --help

to display command line interface usage.