windrose 1.6

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()

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()

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()

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()

###…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()

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()

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)

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)

Optimal parameters of Weibull distribution can be displayed using

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

Issues

You can submit issues using https://github.com/scls19fr/windrose/issues

Clone

You can clone repository to try to fix issues yourself using:

$ git clone https://github.com/scls19fr/windrose.git

Run unit tests

Run all unit tests

$ nosetests -s -v

Run a given test

$ nosetests tests.test_windrose:test_plot_by -s -v

Install development version

$ python setup.py install

or

$ sudo pip install git+https://github.com/scls19fr/windrose.git

Collaborating

• Fork repository

• Create a branch which fix a given issue

• Submit pull requests

https://help.github.com/categories/collaborating/