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test_windrose.py
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test_windrose.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Run all unit tests (from project root directory)
# $ nosetests -s -v
# Run a test (from project root directory)
# $ nosetests tests.test_windrose:test_windrose_np_plot_and_pd_plot -s -v
import matplotlib
matplotlib.use('Agg') # noqa
# import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib import cm as cm
from windrose import WindroseAxes
from windrose import WindAxes
from windrose import WindAxesFactory
# from windrose import FIGSIZE_DEFAULT, DPI_DEFAULT
from windrose import wrbar, wrbox, wrcontour, wrcontourf, wrpdf, wrscatter
from windrose import plot_windrose
import numpy as np
import pandas as pd
# Create wind speed and direction variables
N = 500
ws = np.random.random(N) * 6
wd = np.random.random(N) * 360
df = pd.DataFrame({'speed': ws, 'direction': wd})
def test_windrose_np_mpl_oo():
bins = np.arange(0, 8, 1)
# windrose with scatter plot
ax = WindroseAxes.from_ax()
ax.scatter(wd, ws, alpha=0.2)
ax.set_legend()
plt.savefig('tests/output/oo/scatter.png')
# windrose like 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()
plt.savefig('tests/output/oo/bar.png')
# Another stacked histogram representation, not normed, with bins limits
ax = WindroseAxes.from_ax()
ax.box(wd, ws, bins=bins)
ax.set_legend()
plt.savefig('tests/output/oo/box.png')
# A windrose in filled representation, with a controled colormap
ax = WindroseAxes.from_ax()
ax.contourf(wd, ws, bins=bins, cmap=cm.hot)
ax.set_legend()
plt.savefig('tests/output/oo/contourf.png')
# Same as above, but with contours over each filled region...
ax = WindroseAxes.from_ax()
ax.contourf(wd, ws, bins=bins, cmap=cm.hot)
ax.contour(wd, ws, bins=bins, colors='black')
ax.set_legend()
plt.savefig('tests/output/oo/contourf-contour.png')
# ...or without filled regions
ax = WindroseAxes.from_ax()
ax.contour(wd, ws, bins=bins, cmap=cm.hot, lw=3)
ax.set_legend()
plt.savefig('tests/output/oo/contour.png')
# print ax._info
# plt.show()
ax = WindAxes.from_ax()
bins = bins[1:]
ax.pdf(ws, bins=bins)
plt.savefig('tests/output/oo/pdf.png')
def test_windrose_np_mpl_func():
bins = np.arange(0, 8, 1)
wrscatter(wd, ws, alpha=0.2)
plt.savefig('tests/output/func/scatter.png')
wrbar(wd, ws, normed=True, opening=0.8, edgecolor='white')
plt.savefig('tests/output/func/bar.png')
wrbox(wd, ws, bins=bins)
plt.savefig('tests/output/func/box.png')
wrcontourf(wd, ws, bins=bins, cmap=cm.hot)
plt.savefig('tests/output/func/contourf.png')
# ax = wrcontourf(wd, ws, bins=bin, cmap=cm.hot)
# wrcontour(wd, ws, bins=np.arange(0, 8, 1), colors='black')
# plt.savefig('tests/output/func/wrcontourf-contour.png')
wrcontour(wd, ws, bins=bins, cmap=cm.hot, lw=3)
plt.savefig('tests/output/func/contour.png')
wrpdf(ws, bins=bins)
plt.savefig('tests/output/func/pdf.png')
def test_windrose_pandas():
bins = np.arange(0.01, 8, 1)
kind = 'scatter'
plot_windrose(df, kind=kind, alpha=0.2)
plt.savefig('tests/output/df/%s.png' % kind)
kind = 'bar'
plot_windrose(df, kind=kind, normed=True, opening=0.8, edgecolor='white')
plt.savefig('tests/output/df/%s.png' % kind)
kind = 'box'
plot_windrose(df, kind=kind, bins=bins)
plt.savefig('tests/output/df/%s.png' % kind)
kind = 'contourf'
plot_windrose(df, kind=kind, bins=bins, cmap=cm.hot)
plt.savefig('tests/output/df/%s.png' % kind)
kind = 'contour'
plot_windrose(df, kind=kind, bins=bins, cmap=cm.hot, lw=3)
plt.savefig('tests/output/df/%s.png' % kind)
kind = 'pdf'
plot_windrose(df, kind=kind, bins=bins)
plt.savefig('tests/output/df/%s.png' % kind)
def test_windaxesfactory():
ax = WindAxesFactory.create('WindroseAxes')
ax.bar(wd, ws, normed=True, opening=0.8, edgecolor='white')
ax.set_legend()
plt.savefig('tests/output/oo/bar_from_factory.png')
ax = WindAxesFactory.create('WindAxes')
bins = np.arange(0, 8, 1)
bins = bins[1:]
ax.pdf(ws, bins=bins)
plt.savefig('tests/output/oo/pdf_from_factory.png')
def test_windrose_np_plot_and_pd_plot():
# bins = np.arange(0.01, 8, 1)
kind = 'scatter'
plot_windrose(df, kind=kind, alpha=0.2)
plt.savefig('tests/output/df/%s.png' % kind)
plot_windrose(wd, ws, kind=kind, alpha=0.2)
plt.savefig('tests/output/func/%s.png' % kind)
def test_windrose_pd_not_default_names():
# bins = np.arange(0.01, 8, 1)
kind = 'scatter'
df_not_default_names = pd.DataFrame({'wind_speed': ws, 'wind_direction': wd})
plot_windrose(df_not_default_names, kind=kind, alpha=0.2, var_name='wind_speed', direction_name='wind_direction')
# def test_plot_by():
# #df = pd.read_csv("samples/sample_wind_poitiers.csv", parse_dates=['Timestamp'])
# #df['Timestamp'] = pd.to_timestamp()
# #df = df.set_index('Timestamp')
# #by = 'year_month'
# #df[by] = df.index.map(lambda dt: (dt.year, dt.month))
# #df = df.reset_index()
# #df = df.set_index([by, 'Timestamp'])
# #print(df)