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 plot_roses(filename,
vdir,
mag,
nsector=16,
bins=10,
title=None,
legtitle=None,
dpi=150,
figsize=(10, 10),
tfont=17,
lfont=14):
"""
Plots the rose chart
from wind data and
saves it to png file.
"""
fig = plt.figure(figsize=figsize)
# [left, bottom, width, height] as a fraction of total figure size
right_rectangle = [0.05, 0.05, 0.85, 0.8]
ax = WindroseAxes(fig, right_rectangle)
fig.add_axes(ax)
# ax.bar(wind['dir'], wind['sp'], normed=True, opening=0.9, edgecolor='white', bins=np.logspace(-1,1.3, 10), nsector=16)
# ax.bar(wind['dir'], wind['sp'], normed=True, opening=0.9, edgecolor='white', bins=np.linspace(0,max(wind['sp']), 10), nsector=16)
ax.bar(vdir,
mag,
normed=True,
opening=0.9,
edgecolor='white',
bins=bins,
nsector=nsector)
if title:
ax.set_title("{}".format(title), position=(0.5, 1.1), fontsize=tfont)
cfont = max([8, lfont - 2])
ax.tick_params(axis='both', which='major', labelsize=cfont)
ax.set_legend()
if legtitle:
ax.legend(title='{}'.format(legtitle), loc=(0.0, 0.0))
#used to pretty up the printing around of wind occurent frequencies
tictic = ax.get_yticks()
ax.set_yticks(np.arange(0, tictic[-1], tictic[-1] / len(tictic)))
ax.yaxis.set_major_formatter(tkr.FormatStrFormatter('%2.0f'))
if isinstance(filename, list):
for item in filename:
fig.savefig(item, dpi=dpi)
else:
fig.savefig(filename, dpi=dpi)
plt.close()
return 0
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 all_sta_wind_rose():
fig = plt.figure(figsize=(16, 16))
rect = [[0.05, 0.55, 0.35, 0.35], [0.55, 0.55, 0.35, 0.35], [0.05, 0.05, 0.35, 0.35], [0.55, 0.05,0.35, 0.35]]
plot = 0
for station in station_dict.keys():
wa = WindroseAxes(fig, rect[plot])
fig.add_axes(wa)
ANN, DJF, MAM, JJA, SON = load_AWS(station)
# define data limits
max_mod = max(ANN['FF_10m'])
wa.set_title(station_dict[station], fontsize=28, color='dimgrey', pad=50)
wa.axes.spines['polar'].set_visible(False)
wa.tick_params(axis='both', which='both', labelsize=20, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
wa.bar(ANN['WD'], ANN['FF_10m'], bins=np.arange(0, 20, 4), cmap=plt.get_cmap('viridis'), normed=True, opening=0.8, edgecolor='white')
wa.set_yticks([5,10,15])
wa.set_yticklabels([ '', '10%', '15%'])
plot = plot + 1
lgd = wa.set_legend(bbox_to_anchor=(-0.45, 1.))
frame = lgd.get_frame()
frame.set_facecolor('white')
for ln in lgd.get_texts():
plt.setp(ln, color='dimgrey', fontsize=24)
lgd.get_frame().set_linewidth(0.0)
if host == 'bsl':
plt.savefig('/users/ellgil82/figures/Hindcast/Validation/wind_rose_all_stations_all_years.png')
plt.savefig('/users/ellgil82/figures/Hindcast/Validation/wind_rose_all_stations_all_years.eps')
elif host == 'jasmin':
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/wind_rose_all_stations_all_years.png')
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/wind_rose_all_stations_all_years.eps')
plt.show()
def wind_rose(station, AWS_var, model_var):
fig = plt.figure(figsize = (16,8))
rect = [0.05, 0.1, 0.45, 0.6]
wa = WindroseAxes(fig, rect)
fig.add_axes(wa)
# define data limits
max_mod = max(np.mean(vars_yr['FF_10m'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)))
max_obs = max(AWS_var['FF'])
wa.set_title('Observed', fontsize = 28, color = 'dimgrey', pad = 50)
wa.axes.spines['polar'].set_visible(False)
wa.tick_params(axis='both', which='both', labelsize=24, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
wa.bar(AWS_var['WD'], AWS_var['FF'], bins = np.arange(0, max(max_mod, max_obs),4), cmap = plt.get_cmap('viridis'), normed = True,opening=0.8, edgecolor='white')
wa.set_yticklabels([])
rect = [0.55, 0.1, 0.45, 0.6]
wa = WindroseAxes(fig, rect)
fig.add_axes(wa)
wa.set_title('Modelled', fontsize=28, color='dimgrey', pad = 50)
wa.bar(np.mean(vars_yr['WD'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)),
np.mean(vars_yr['FF_10m'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)),
bins = np.arange(0, max(max_mod, max_obs),4), cmap = plt.get_cmap('viridis'), normed = True, opening=0.8, edgecolor='white')
lgd = wa.set_legend( bbox_to_anchor=(-0.5, 0.9))
frame = lgd.get_frame()
frame.set_facecolor('white')
for ln in lgd.get_texts():
plt.setp(ln, color='dimgrey', fontsize = 18)
lgd.get_frame().set_linewidth(0.0)
wa.axes.spines['polar'].set_visible(False)
wa.set_yticklabels([])
wa.tick_params(axis='both', which='both', labelsize=24, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
plt.savefig('/users/ellgil82/figures/Hindcast/validation/wind_rose_' + station + '_' + year + '.png')
plt.savefig('/users/ellgil82/figures/Hindcast/validation/wind_rose_' + station + '_' + year + '.eps')
plt.show()
def animate_windrose_graph(i):
all_data_map=data_service.get_all_data_map()
is_active = data_service.get_active()
if is_active == "1_3":
data_service.update_all_data_map(data_service.get_start_date(), data_service.get_end_date())
ws = my_service.restore_lost_data(all_data_map['FF'])
wd = my_service.restore_lost_data(all_data_map['dd'])
ws_scale_1 = tab1_service.map_speed_to_scale_one(ws)
wd = tab1_service.map_compass_to_degrees(wd)
# hide axes
windrose_graph_fig.patch.set_visible(False)
windrose_graph_ax.axis('off')
windrose_graph_ax.axis('tight')
ws_frequency_map = tab1_service.map_ws_by_frequency(ws)
final_data = [list(ws_frequency_map.keys()), list(ws_frequency_map.values())]
table = windrose_graph_ax.table(cellText=final_data, loc='top', cellLoc='center',
rowLabels=[" м/с ", " % "], bbox=[0., 0., 0.23, .28])
windrose_graph_fig.subplots_adjust(bottom=0.6)
table.auto_set_font_size(False)
table.set_fontsize(7)
for (row, col), cell in table.get_celld().items():
if row == 0:
cell.set_text_props(fontproperties=FontProperties(weight='normal', size=7))
for key, cell in table.get_celld().items():
cell.set_linewidth(0.5)
# windrose_graph_fig.clear()
rect = [0.1, 0.1, 0.8, 0.8]
wa = WindroseAxes(windrose_graph_fig, rect)
windrose_graph_fig.add_axes(wa)
windrose_graph_ax.grid()
wa.bar(wd, ws_scale_1, normed=True, opening=0.8, edgecolor='white')
# wa.set_legend()
wa.set_legend(title="інтенсивність", loc="upper right")
from windrose import WindroseAxes
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import numpy as np
from windrose import WindroseAxes
from matplotlib import pyplot as plt
import numpy as np
kmh_to_ms = 0.277778
# Decembre 2020
ws = [
5.6 * kmh_to_ms, 6.8 * kmh_to_ms, 6.4 * kmh_to_ms, 4.5 * kmh_to_ms,
3.5 * kmh_to_ms, 5 * kmh_to_ms, 5.1 * kmh_to_ms
]
wd = [45, 45, 67.5, 67.5, 45, 45, 45]
# Février 2021
# ws = [5.60*kmh_to_ms, 2.90*kmh_to_ms, 2.90*kmh_to_ms, 3.40*kmh_to_ms, 5.00*kmh_to_ms, 6.30*kmh_to_ms, 3.70*kmh_to_ms]
# wd = [135, 45, 90, 90, 90, 112.5, 112.5]
fig = plt.figure()
rect = [0.125, 0.125, 0.75, 0.75]
# rect = [0.5, 0.5, 0.5, 0.5]
wa = WindroseAxes(fig, rect)
fig.add_axes(wa)
wa.bar(wd, ws, normed=True, opening=0.8, edgecolor='white')
wa.set_legend(bbox_to_anchor=(1, -0.08), title="WIND SPEED\n(m/s)")
plt.show()
mto[wsp].hist(bins=100)
ax1.set_xscale("log")
ax1.set_xlabel("wind speed (m/s)")
right_rectangle = [0.5, 0.1, 0.5, 0.75] # [left, bottom, width, height]
ax = WindroseAxes(fig, right_rectangle)
fig.add_axes(ax)
ax.bar(mto[wdir],
mto[wsp],
normed=True,
opening=0.8,
edgecolor='white',
bins=np.logspace(-1, 1, 10))
ax.set_title("annual", position=(0.5, 1.1))
ax.set_legend()
ax.legend(title="wind speed (m/s)", loc=(1.1, 0))
######################################################################
# The numbers around the radar plot indicate frequencies for the given
# direction. They look quite ugly, and would benefit from some formatting.
# which is not super trivial:
#
from windrose import WindroseAxes
fig = plt.figure(figsize=(10, 5))
left_rectangle = [
0, 0.1, 0.4, 0.75
] # [left, bottom, width, height] as a fraction of total figure size
