def main():
loc = 'fl0'
weatherDir = '/data1/ancillary_data/fl0/eol/'
weatherFileTag = 'v2'
iyear = 2010
fyear = 2010
wdir, wspeed, temp, rh, dtw = weatherout(loc, weatherDir, weatherFileTag,
iyear, fyear)
hours = []
for k in dtw:
hours.append(k.hour)
hours = np.asarray(hours)
inds = np.where((wspeed <= 10.0) & (hours > 8) & (hours < 18))[0]
fig = plt.figure(figsize=(8, 8), dpi=80, facecolor='w', edgecolor='w')
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect, axisbg='w')
fig.add_axes(ax)
ax.bar(wdir[inds],
wspeed[inds],
normed=True,
opening=0.9,
edgecolor='white')
ax.set_legend()
#fig2 = plt.figure(figsize=(8, 8), dpi=80, facecolor='w', edgecolor='w')
ax2 = WindAxes.from_ax()
bins = np.arange(0, 10, 0.5)
bins = bins[1:]
ax2, params = ax2.pdf(wspeed[inds], bins=bins)
ax3 = WindAxes.from_ax()
bins = np.arange(0, 360, 15)
bins = bins[1:]
ax3, params = ax3.pdf(wdir[inds], bins=bins)
# fig2, ax2 = plt.subplots(figsize=(8,6))
# ax2.scatter(wdir, wspeed, facecolors='red', edgecolors='black', s=35)
# ax2.grid(True)
plt.show(block=False)
pdfsav = PdfPages('/data/iortega/results/fl0/windrose.pdf')
pdfsav.savefig(fig, dpi=200)
pdfsav.close()
user_input = raw_input('Press any key to exit >>> ')
sys.exit()
def main():
loc = 'fl0'
weatherDir = '/data1/ancillary_data/fl0/eol/'
weatherFileTag = 'v2'
iyear = 2010
fyear = 2016
wdir, wspeed, temp, rh, dtw = weatherout(loc, weatherDir, weatherFileTag, iyear, fyear )
hours = []
for k in dtw:
hours.append(k.hour)
hours = np.asarray(hours)
inds = np.where( (wspeed <= 10.0) & (hours > 8) & (hours < 18) )[0]
fig = plt.figure(figsize=(8, 8), dpi=80, facecolor='w', edgecolor='w')
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindroseAxes(fig, rect, axisbg='w')
fig.add_axes(ax)
ax.bar(wdir[inds], wspeed[inds], normed=True, opening=0.9, edgecolor='white')
ax.set_legend()
#fig2 = plt.figure(figsize=(8, 8), dpi=80, facecolor='w', edgecolor='w')
ax2 = WindAxes.from_ax()
bins = np.arange(0, 10 , 0.5)
bins = bins[1:]
ax2, params = ax2.pdf(wspeed[inds], bins=bins)
ax3 = WindAxes.from_ax()
bins = np.arange(0, 360, 15)
bins = bins[1:]
ax3, params = ax3.pdf(wdir[inds], bins=bins)
# fig2, ax2 = plt.subplots(figsize=(8,6))
# ax2.scatter(wdir, wspeed, facecolors='red', edgecolors='black', s=35)
# ax2.grid(True)
plt.show(block=False)
pdfsav = PdfPages('/data/iortega/results/fl0/windrose.pdf')
pdfsav.savefig(fig,dpi=200)
pdfsav.close()
user_input = raw_input('Press any key to exit >>> ')
sys.exit()
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")
plt.close()
# 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")
plt.close()
# 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")
plt.close()
# 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")
plt.close()
# 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")
plt.close()
# ...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")
plt.close()
# print ax._info
# plt.show()
ax = WindAxes.from_ax()
bins = bins[1:]
ax.pdf(ws, bins=bins)
plt.savefig("tests/output/oo/pdf.png")
plt.close()
def plot_weibull(ws,diro,file_Merge):
'''画weibull分布图'''
ws.values[ws.values==0]=np.nan
ws=ws.dropna()
binn = np.arange(0.0, 30.0, 1)
ax2, params = wrpdf(ws, bins=binn, Nx=100, bar_color='darkgreen', \
plot_color='royalblue', Nbins=26, \
ax = WindAxes.from_ax(fig = plt.figure(figsize=(6,4))))
ax2.text(15, 0.1,"A = %.2f, K = %.2f" % (params[3],params[1]),fontsize=12)
ax2.set_title(file_Merge[6:10]+'_'+ws.name[3:])
plt.savefig(diro+'\\'+file_Merge[6:10]+'_'+ws.name[3:]+'Weibull.png',bbox_inches='tight',dpi=96)
plt.show()
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 main():
# Create wind speed and direction variables
N = 500
ws = np.random.random(N) * 6
wd = np.random.random(N) * 360
ax = WindroseAxes.from_ax()
ax.scatter(wd, ws, alpha=0.2)
ax.set_legend()
# 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()
# Another stacked histogram representation, not normed, with bins limits
ax = WindroseAxes.from_ax()
bins = np.arange(0, 8, 1)
ax.box(wd, ws, bins=bins)
ax.set_legend()
# 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()
# 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()
# ...or without filled regions
ax = WindroseAxes.from_ax()
ax.contour(wd, ws, bins=bins, cmap=cm.hot, lw=3)
ax.set_legend()
# print ax._info
# plt.show()
ax = WindAxes.from_ax()
bins = np.arange(0, 6 + 1, 0.5)
bins = bins[1:]
ax.pdf(ws, bins=bins)
plt.show()
def main():
# Create wind speed and direction variables
N = 500
ws = np.random.random(N) * 6
wd = np.random.random(N) * 360
ax = WindroseAxes.from_ax()
ax.scatter(wd, ws, alpha=0.2)
ax.set_legend()
# 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()
# Another stacked histogram representation, not normed, with bins limits
ax = WindroseAxes.from_ax()
bins = np.arange(0, 8, 1)
ax.box(wd, ws, bins=bins)
ax.set_legend()
# 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()
# 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()
# ...or without filled regions
ax = WindroseAxes.from_ax()
ax.contour(wd, ws, bins=bins, cmap=cm.hot, lw=3)
ax.set_legend()
# print ax._info
# plt.show()
ax = WindAxes.from_ax()
bins = np.arange(0, 6 + 1, 0.5)
bins = bins[1:]
ax.pdf(ws, bins=bins)
plt.show()
#Another stacked histogram representation, not normed, with bins limits ax = WindroseAxes.from_ax() bins = np.arange(0, 8, 1) ax.box(wd, ws, bins=bins) ax.set_legend() #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() #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() #...or without filled regions ax = WindroseAxes.from_ax() ax.contour(wd, ws, bins=bins, cmap=cm.hot, lw=3) ax.set_legend() ##print ax._info #plt.show() ax = WindAxes.from_ax() bins = np.arange(0, 6 + 1, 0.5) bins = bins[1:] ax.pdf(ws, bins=bins) plt.show()