windrose: A Jupyter Notebook repository from sytlnrf

#windrose

A windrose, also known as a polar rose plot, is a special diagram for representing the distribution of meteorological datas, typically wind speeds by class and direction. This is a simple module for the matplotlib python library, which requires numpy for internal computation.

Original code forked from:

windrose 1.4 by Lionel Roubeyrie lionel.roubeyrie@gmail.com http://youarealegend.blogspot.fr/search/label/windrose

##Requirements:

matplotlib http://matplotlib.org/
numpy http://www.numpy.org/
and naturally python https://www.python.org/ :-P

Option libraries:

Pandas http://pandas.pydata.org/ (to feed plot functions easily)
Scipy http://www.scipy.org/ (to fit data with Weibull distribution)
ffmpeg https://www.ffmpeg.org/ (to output video)
click http://click.pocoo.org/ (for command line interface tools)

Install

A package is available and can be downloaded from PyPi and installed using:

$ pip install windrose

##Notebook example :

An IPython (Jupyter) notebook showing this package usage is available at:

http://nbviewer.ipython.org/github/scls19fr/windrose/blob/master/windrose_sample_random.ipynb

##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()

###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)

##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.

##Video export A video of plots can be exported. See:

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.

Development

You can help to develop this library.

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

$ 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/