def lu_subplot(var, pos, res, title, hgt, par=True, mer=True):
ax = plt.subplot(pos)
ax.set_title(title, fontsize=10)
# Get the basemap object
bm = get_basemap(var, projection='lcc', resolution=res, ax=ax)
# Convert the lats and lons to x and y. Make sure you convert the lats and lons to
# numpy arrays via to_np, or basemap crashes with an undefined RuntimeError.
x, y = bm(to_np(lons), to_np(lats))
# Define gridlines
parallels = np.arange(-90,90,0.2)
meridians = np.arange(0,360,0.2)
# Add geographic outlines
if par == True:
bm.drawparallels(parallels,labels=[1,0,0,0],fontsize=8)
else:
bm.drawparallels(parallels,fontsize=8)
if mer == True:
bm.drawmeridians(meridians,labels=[0,0,0,1],fontsize=8)
else:
bm.drawmeridians(meridians,fontsize=8)
bm.drawrivers(linewidth=0.75,color='blue') #only for high resolution plots
bm.readshapefile('/home/kristofh/projects/shp_files/BEZIRKSGRENZEOGDPolygon', 'BEZIRKSGRENZEOGDPolygon', linewidth=0.6)
heights = np.arange(100, 800, 50)
categ = np.arange(1,34, 1)
# Draw the contours and filled contours
hgt_cont = bm.contour(x,y,to_np(hgt), levels=heights, colors="magenta", linewidths=0.4, alpha=1)
var_contf = bm.pcolormesh(x,y,to_np(var), cmap=get_cmap("viridis"), alpha=0.9)
cb_var = fig.colorbar(var_contf, ticks=categ[::3], ax=ax, shrink=0.7)
cb_var.ax.tick_params(labelsize=8)
return
def allmodelrunpercent(directories, hubheight):
#got the total points working. need to figure out if it is the right number
#
totalcount = 0
totalpercentup = np.zeros((19, 29))
for directory in directories:
os.chdir(directory)
files = gatherfiles('wrfout*')
for file in files:
ncfile = Dataset(file)
windmatrix = verticalwindinterpolation(ncfile, hubheight)
for i in range(0, len(totalpercentup)):
for k in range(0, len(totalpercentup[0])):
if (windmatrix[i, k] > 3.0) and (windmatrix[i, k] < 22.0):
totalpercentup[i, k] = totalpercentup[i, k] + 1
totalcount += 1
press = getvar(ncfile, 'pressure') #hPa
pressground = press[0, :, :]
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
bm.contourf(x,
y,
to_np((totalpercentup / totalcount) * 100),
cmap=get_cmap('jet'))
plt.colorbar(shrink=.62)
plt.title('Percent in threshold for ALL model runs')
os.chdir('/Users/twsee/Desktop/renewableoutput/')
plt.savefig('Percent in threshold for ALL model runs quick fix')
plt.close()
def var_diff_subplot(var, pos, res, title, tmin, trange, cmap=blrd, levels=levels, steps=0.25, par=True, mer=True):
ax = plt.subplot(pos)
ax.set_title(title, fontsize=16)
# Get the basemap object
bm = get_basemap(var, projection='lcc', resolution=res, ax=ax)
# Convert the lats and lons to x and y. Make sure you convert the lats and lons to
# numpy arrays via to_np, or basemap crashes with an undefined RuntimeError.
x, y = bm(to_np(lons), to_np(lats))
# Define gridlines
parallels = np.arange(-90,90,0.2)
meridians = np.arange(0,360,0.2)
# Add geographic outlines
if par == True:
bm.drawparallels(parallels,labels=[1,0,0,0],fontsize=12)
else:
bm.drawparallels(parallels,fontsize=12)
if mer == True:
bm.drawmeridians(meridians,labels=[0,0,0,1],fontsize=12)
else:
bm.drawmeridians(meridians,fontsize=12)
# bm.drawrivers(linewidth=0.75,color='blue') #only for high resolution plots
bm.readshapefile('/home/kristofh/projects/shp_files/BEZIRKSGRENZEOGDPolygon', 'BEZIRKSGRENZEOGDPolygon', linewidth=0.6)
# levels=np.arange(tmin, tmin+trange+steps, steps)
# heights = np.arange(100, 800, 50)
# Draw the contours and filled contours
# hgt_cont = bm.contour(x,y,to_np(hgt), levels=heights, colors="magenta", linewidths=0.4, alpha=1)
var_cont = bm.contour(x,y,to_np(var), colors="black", levels=levels, linewidths=0.4, alpha=0.5)
var_contf = bm.contourf(x,y,to_np(var), levels=levels, colors=cmap, extend='both', alpha=0.9)
cb_var = fig.colorbar(var_contf, ax=ax, ticks=levels, shrink=1.)
cb_var.ax.tick_params(labelsize=8)
return
def averagewindspeed(directories, hubheight):
total = 0
count = 0
for directory in directories:
os.chdir(directory)
files = gatherfiles('wrfout*')
for file in files:
ncfile = Dataset(file)
windmatrix = verticalwindinterpolation(ncfile, hubheight)
total = windmatrix + total
count += 1
average = total / count
press = getvar(ncfile, 'pressure') #hPa
pressground = press[0, :, :]
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
bm.contourf(x, y, to_np(average), cmap=get_cmap('jet'))
plt.colorbar(shrink=.62)
plt.title('Average Wind Speed at ' + str(hubheight) +
'm across all model runs')
os.chdir('/Users/twsee/Desktop/renewableoutput/')
plt.savefig('Average Wind Speed across all runs')
plt.close()
def var_subplot(var, pos, res, title, hgt, par=True, mer=True):
ax = plt.subplot(pos)
ax.set_title(title, fontsize=10)
# Get the basemap object
bm = get_basemap(var, projection='lcc', resolution=res, ax=ax)
# Convert the lats and lons to x and y. Make sure you convert the lats and lons to
# numpy arrays via to_np, or basemap crashes with an undefined RuntimeError.
x, y = bm(to_np(lons), to_np(lats))
# Define gridlines
parallels = np.arange(-90,90,0.2)
meridians = np.arange(0,360,0.2)
# Add geographic outlines
if par == True:
bm.drawparallels(parallels,labels=[1,0,0,0],fontsize=8)
else:
bm.drawparallels(parallels,fontsize=8)
if mer == True:
bm.drawmeridians(meridians,labels=[0,0,0,1],fontsize=8)
else:
bm.drawmeridians(meridians,fontsize=8)
bm.drawrivers(linewidth=0.75,color='blue') #only for high resolution plots
bm.readshapefile('/home/kristofh/projects/shp_files/BEZIRKSGRENZEOGDPolygon', 'BEZIRKSGRENZEOGDPolygon', linewidth=0.6)
# levels=np.arange(-1.0, 1.0+steps, steps)
# levels=np.insert(levels,0, np.arange((maxdiff_spr.min()*10).round()/10,-1,steps*6))
# levels=np.append(levels, np.arange(1,(maxdiff_spr.max()*10).round()/10,steps*6))
#
# contour_levels=np.arange(-1.0, 1.0+steps, steps*2)
# contour_levels=np.insert(contour_levels,0, (var.min()*10).round()/10)
# contour_levels=np.append(contour_levels, (var.max()*10).round()/10)
heights = np.arange(100, 800, 75)
# Draw the contours and filled contours
hgt_cont = bm.contour(x,y,to_np(hgt), levels=heights, colors="magenta", linewidths=0.4, alpha=1)
var_cont = bm.contour(x,y,to_np(var), colors="black", linewidths=0.4, alpha=0.5)
var_contf = bm.contourf(x,y,to_np(var), cmap=get_cmap("RdBu_r"), extend='both', alpha=0.9)
cb_var = fig.colorbar(var_contf, ax=ax, shrink=0.4)
cb_var.ax.tick_params(labelsize=8)
return
def percentinthreshold(files, hubheight):
totalcount = 0
dailycount = 0
date, hour = gettimeanddates(files[0])
totalpercentup = np.zeros((19, 29))
dailypercentup = np.zeros((19, 29))
for file in files:
ncfile = Dataset(file)
windmatrix = verticalwindinterpolation(ncfile, hubheight)
for i in range(0, len(dailypercentup)):
for k in range(0, len(dailypercentup[0])):
if (windmatrix[i, k] > 3.0) and (windmatrix[i, k] < 22.0):
dailypercentup[i, k] = dailypercentup[i, k] + 1
totalpercentup[i, k] = totalpercentup[i, k] + 1
if dailycount == 23:
dailycount = 0
dailypercentup = np.zeros((19, 29))
totalcount += 1
else:
dailycount += 1
totalcount += 1
press = getvar(ncfile, 'pressure') #hPa
pressground = press[0, :, :]
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
bm.contourf(x,
y,
to_np((totalpercentup / totalcount) * 100),
cmap=get_cmap('jet'))
plt.colorbar(shrink=.62)
plt.title('Energy Production Percentage for ' + date + ' Model Run')
plt.savefig('Energy-Production-Percentage-for-' + date + '-Model-Run')
plt.close()
def drawmap(ax,wrf_file,map_dir,Lat_min,Lat_max,Lon_min,Lon_max):
m = get_basemap(wrfin=wrf_file,llcrnrlat=Lat_min,urcrnrlat=Lat_max,\
llcrnrlon=Lon_min,urcrnrlon=Lon_max,resolution ='l',ax=ax)
CHNshp = map_dir+'CHN_adm_shp/CHN_adm1'
TWNshp = map_dir+'TWN_adm_shp/TWN_adm0'
m.readshapefile(CHNshp,'CHN',drawbounds = True)
m.readshapefile(TWNshp,'TWN',drawbounds = True)
parallels = np.arange(-90.,91.,1.)
meridians = np.arange(-180.,181.,2.)
m.drawparallels(parallels,labels=[1,0,0,1],linewidth=0.2,xoffset=0.2,fontsize=12,fontname='Arial')
m.drawmeridians(meridians,labels=[1,0,0,1],linewidth=0.2,yoffset=0.2,fontsize=12,fontname='Arial')
xminor_ticks = np.arange(Lon_min,Lon_max,1)
yminor_ticks = np.arange(Lat_min,Lat_max, 1)
ax.set_xticks(xminor_ticks, minor=True)
ax.set_yticks(yminor_ticks, minor=True)
for info, shape in zip(m.CHN_info, m.CHN):
if info['NAME_1'] =='Guangdong':
x, y = zip(*shape)
m.plot(x, y, marker = None, color= 'k',linewidth=1.5)
return m
def energyproductioninterpolated(files, hubheight):
"""
Creates hourly and daily energy production outputs for wind a turbine
output plots interpolated from the surface to hub height
Parameters
----------
arg1: list
A 1D glob list of the file outputs from WRF
arg2: integer
level of the wind turbine hub height
Returns
-------
None
"""
count = 0
dailyenergy = 0
for file in files:
date, hour = gettimeanddates(file)
ncfile = Dataset(file)
cbarticks = np.arange(0.0, 10.0, 0.5)
r = 52.0 #meters, rotorlength
Area = np.pi * r**2
cp = 0.4 #unitless
density = 1.23 #kg/m^3
press = getvar(ncfile, 'pressure') #hPa
pressground = press[0, :, :]
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
energyproduction = (
0.5 * density * Area *
verticalwindinterpolationenergy(ncfile, hubheight)**3 * cp) / 10**6
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
bm.contour(x,
y,
to_np(energyproduction),
cbarticks,
colors="black",
vmin=0,
vmax=10.0)
bm.contourf(x,
y,
to_np(energyproduction),
cbarticks,
cmap=get_cmap('jet'),
vmin=0,
vmax=10.0)
bm.barbs(x, y, uaground, vaground)
plt.colorbar(shrink=.62, ticks=cbarticks)
plt.title('Hourly Energy Production for ' + date + ' ' + hour +
'-Interpolated-to-' + str(hubheight) + 'm')
plt.savefig('Hourly-output-' + date + '-' + hour +
'-Interpolated-to-' + str(hubheight) + 'm')
plt.close()
if count == 23:
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
dailyenergy = dailyenergy / 24.0
bm.contour(x,
y,
to_np(dailyenergy),
cbarticks,
colors="black",
vmin=0,
vmax=10.0)
bm.contourf(x,
y,
to_np(dailyenergy),
cbarticks,
cmap=get_cmap('jet'),
vmin=0,
vmax=10.0)
plt.colorbar(shrink=.62, ticks=cbarticks)
plt.title('Dialy Average ' + yesterdaysdate + '-Interpolated-to-' +
str(hubheight) + 'm')
plt.savefig('Daily-Average-' + yesterdaysdate +
'-Interpolated-to-' + str(hubheight) + 'm')
plt.show()
plt.close()
dailyenergy = 0
count = 0
else:
dailyenergy = energyproduction + dailyenergy
count = count + 1
yesterdaysdate = date
plt.xticks(ind + width, stats_var)
plt.legend(loc='best')
plt.show()
fig3.savefig(outputdir + '%s_bar_chart.png' %stat_name, bbox_inches='tight')
'''
#######################################
########## BIAS MAPS ##########
#######################################
# Create 2D map with points representing stations
# (larger points = greater bias, blue = negative bias, red = positive bias)
# Get the basemap object
bm = wrf.get_basemap(t_basemap)
x, y = bm(x_list, y_list)
# Get x, y coordinates for each of 6 stations (KSEA,KPUW,KPDX,KLMT,KBOI,KMLP)
xlist_few = [-122.309,-117.11]
ylist_few = [47.449,46.744]
x_few, y_few = bm(xlist_few,ylist_few)
label_few = ['KSEA','KPUW']
# Make array with all variable names (for each model) in "stats_all" data frame
stat_var = np.unique(stats_all.index)
for sv in stat_var:
# Create a figure
fig4 = plt.figure(figsize=(12,9))
# pressure levels interpolation ###################################################################################################### # Extract the pressure, geopotential height, and wind variables # p = getvar(ncfile, "pressure") p = getvar(ncfile, "pressure") LWC = getvar(ncfile, "QCLOUD") #, meta=False) # Interpolate geopotential height LWC_750 = interplevel(LWC, p, 750) # Get the lat/lon coordinates lats, lons = latlon_coords(LWC_750) # Get the basemap object bm = get_basemap(LWC_750) # Create the figure fig = plt.figure(figsize=(12, 9)) ax = plt.axes() # Convert the lat/lon coordinates to x/y coordinates in the projection space x, y = bm(to_np(lons), to_np(lats)) # Add the 500 hPa geopotential height contours lower_level = to_np(LWC_750).min().min() upper_level = to_np(LWC_750).max().max() levels = np.arange(lower_level, upper_level, (upper_level - lower_level) / 10) contours = bm.contourf(x, y, to_np(LWC_750), levels=levels) plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
#get vavriable from netCDF file
var = getvar(ncfile, param, timeidx=idx)
title = var.description
title1 = var.name + ' [' + var.units + ']' + ' - ' + run
currtime = str(pd.to_datetime(time))
var_mean = var.mean(dim={'south_north','west_east'})
var_std = var.std(dim={'south_north','west_east'})
# Smooth the sea level pressure since it tends to be noisy near the mountains
# smooth_slp = smooth2d(slp, 3)
# Get the latitude and longitude points
lats, lons = latlon_coords(var)
# Get the basemap object
bm = get_basemap(var, resolution='h', projection='lcc')
# Define gridlines
parallels = np.arange(-90,90,0.2)
meridians = np.arange(0,360,0.2)
# Create a figure
fig = plt.figure(idx, figsize=(12,9))
# Add geographic outlines
bm.drawcoastlines(linewidth=1)
bm.drawstates(linewidth=1)
bm.drawcountries(linewidth=1)
bm.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
bm.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
bm.drawrivers(linewidth=0.75,color='blue') #only for high resolution plots
def plot_spatial_wrf_Tair_Wind(file_path,height,timeidx):
######################################################
# Note that since I have updated Python, get_basemap cannot work
# for the new version. Thus the function cannot really work.
######################################################
from wrf import get_basemap
# Open the NetCDF file
ncfile = Dataset(file_path)
# Extract the pressure, geopotential height, and wind variables
p = getvar(ncfile, "pressure",timeidx=timeidx)
z = getvar(ncfile, "z", units="dm",timeidx=timeidx)
ua = getvar(ncfile, "ua", units="kt",timeidx=timeidx)
va = getvar(ncfile, "va", units="kt",timeidx=timeidx)
wspd = getvar(ncfile, "wspd_wdir", units="kts",timeidx=timeidx)[0,:]
# Interpolate geopotential height, u, and v winds to 500 hPa
ht_hgt = interplevel(z, p, height)
u_hgt = interplevel(ua, p, height)
v_hgt = interplevel(va, p, height)
wspd_hgt = interplevel(wspd, p, height)
# Get the lat/lon coordinates
lats, lons = latlon_coords(ht_hgt)
# Get the basemap object
bm = get_basemap(ht_hgt)
# Create the figure
fig = plt.figure(figsize=(12,9))
ax = plt.axes()
# Convert the lat/lon coordinates to x/y coordinates in the projection space
x, y = bm(to_np(lons), to_np(lats))
# Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.)
contours = bm.contour(x, y, to_np(ht_hgt), levels=levels, colors="black")
plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
# Add the wind speed contours
levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
wspd_contours = bm.contourf(x, y, to_np(wspd_hgt), levels=levels,
cmap=get_cmap("rainbow"))
plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05)
# Add the geographic boundaries
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
# Add the 500 hPa wind barbs, only plotting every 125th data point.
bm.barbs(x[::125,::125], y[::125,::125], to_np(u_hgt[::125, ::125]),
to_np(v_hgt[::125, ::125]), length=6)
plt.title("500 MB Height (dm), Wind Speed (kt), Barbs (kt)")
plt.show()
LWC = calculate_lwc_wrf(ncfile) LWC_at_req_level = LWC[requested_level_index] # The calculations below are for creating the map p = getvar(ncfile, "pressure") z = getvar(ncfile, "z", units="dm") # Interpolate geopotential height ht_for_map_creation = interplevel(z, p, requested_level) # Get the lat/lon coordinates lats, lons = latlon_coords(ht_for_map_creation) # Get the basemap object bm = get_basemap(ht_for_map_creation) # Create the figure fig = plt.figure(figsize=(12,9)) ax = plt.axes() # Convert the lat/lon coordinates to x/y coordinates in the projection space x, y = bm(to_np(lons), to_np(lats)) # Add the geographic boundaries bm.drawcoastlines(linewidth=0.25) bm.drawstates(linewidth=0.25) bm.drawcountries(linewidth=0.25) plt.title( str(requested_level) + "hPa LWC")
# Open the NetCDF file
ncfile = Dataset("%s/%s/wrfout_d01_%s" %
(options.exp, options.dir, options.datetime))
# Get the WRF variables
dbz = getvar(ncfile, _variable)
# Get the latitude and longitude points
lats, lons = latlon_coords(dbz)
# Create the figure that will have 3 subplots
fig = plt.figure(figsize=(10, 7))
ax_dbz = fig.add_subplot(1, 1, 1)
# Get the basemap object
bm = get_basemap(dbz)
# Convert the lat/lon points in to x/y points in the projection space
x, y = bm(to_np(lons), to_np(lats))
# Make the contour plot for dbz
contour_levels = [25, 45]
c1 = bm.contour(x,
y,
to_np(dbz),
levels=contour_levels,
colors="black",
zorder=3,
linewidths=1.0,
ax=ax_dbz)
try:
ncfile = Dataset(os.path.join(options.dir, "wrfinput_d01_ic"))
suffix = os.path.split(options.dir)[-1]
except:
print("\n NOPE --- cannot find a wrf netcdf file..... %s" %
os.path.join(options.dir, "wrfinput_d01_ic"))
sys.exit(-1)
# Get the WRF variables
var = getvar(ncfile, _variable)[0]
# Get the latitude and longitude points
lats, lons = latlon_coords(var)
# Get the basemap object
bm = get_basemap(var)
# Convert the lat/lon points in to x/y points in the projection space
x, y = bm(to_np(lons), to_np(lats))
# Make the contour plot for var, mask off min values
mask_var = np.ma.masked_less_equal(to_np(var), _var_min)
# If Clip,
if _clip_variable:
mask_var = np.clip(mask_var, _clevels.min(), _clevels.max())
c1_contour = bm.contour(x,
y,
mask_var,
levels=_llevels,
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2019-01-26_12_00_00")
# Get the sea level pressure
slp = getvar(ncfile, "slp")
# Smooth the sea level pressure since it tends to be noisy near the
# mountains
smooth_slp = smooth2d(slp, 3, cenweight=4)
# Get the latitude and longitude points
lats, lons = latlon_coords(slp)
# Get the basemap object
bm = get_basemap(slp)
# Create a figure
fig = plt.figure(figsize=(12,9))
# Add geographic outlines
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
# Convert the lats and lons to x and y. Make sure you convert the lats and
# lons to numpy arrays via to_np, or basemap crashes with an undefined
# RuntimeError.
x, y = bm(to_np(lons), to_np(lats))
# Draw the contours and filled contours
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from wrf import to_np, getvar, get_basemap, latlon_coords
import sys
import numpy as np
import landuse_colormap as lu_cm
geo_em_file = Dataset(sys.argv[1])
lis_input_file = Dataset(sys.argv[2])
var = getvar(geo_em_file, 'LU_INDEX', timeidx=0)
lats, lons = latlon_coords(var)
bm = get_basemap(var, resolution='h')
x, y = bm(to_np(lons), to_np(lats))
lu_index = lis_input_file['LANDCOVER'][:]
lu_index_proc = lu_index * np.reshape(np.arange(1,
len(lu_index) + 1, 1),
(len(lu_index), 1, 1))
lu_index_proc = np.sum(lu_index_proc, axis=0)
# Get land cover schemes
scheme = lis_input_file.LANDCOVER_SCHEME
if scheme == 'IGBPNCEP':
cm, labels = lu_cm.LU_MODIS21()
elif scheme == 'USGS':
cm, labels = lu_cm.LU_USGS24()
elif scheme == 'UMD':
cm, labels = lu_cm.LU_UMD()
else:
#
# plt.title("Visibility (FSL method)",fontsize=12,color='k')
# fileName=main+'ARW/wrf_output/'+date+'/plots/wrfout_d02_'
# plt.savefig(fileName+'visibility_fsl_'+file_date_list[ii]+'.png',dpi=100)
vis=vis_avg ;
for ii in range(0,vis.shape[0]):
fig=plt.figure(figsize=(8,8),dpi=100); ax = fig.add_axes([0.1,0.1,0.8,0.8])
#lats, lons = wrf.latlon_coords(vis)
lons=np.array(wrf.to_np(lon.data[0,:,:])) ; lats=np.array(wrf.to_np(lat.data[0,:,:]))
#lons=lons[0,:]; lats=lats[:,0]
#lons,lats=np.meshgrid(lons,lats)
#m =Basemap(projection='mill',llcrnrlat=lats.min(),urcrnrlat=lats.max(),llcrnrlon=lons.min(),urcrnrlon=lons.max(),resolution='l') #wrf.get_basemap(vis)
m = wrf.get_basemap(tmp) ; x, y = m(wrf.to_np(lons), wrf.to_np(lats))
Z=maskoceans(lons,lats,wrf.to_np(vis)[ii,:,:],inlands=False)#, resolution='c', grid=2.5)
#Z=wrf.to_np(vis)[ii,:,:]
#nice_cmap=plt.get_cmap('RdYlGn_r') ;
clevs=[0,1,2,3,4,5,6,7,8,9,10]
#['white 0 ','lime 1','limegreen 2','greenyellow 3','yellow 4','gold 5','orange 6','indianred 7',
#'firebrick 8', 'darkred 9','lightskyblue 10','deepskyblue 11','royalblue 12 ','blue 13']
mymap = mcolors.ListedColormap(['white','ghostwhite','floralwhite','greenyellow','yellow','gold','orange','indianred','firebrick', \
'darkred','lightskyblue','deepskyblue','royalblue','blue'])
nice_cmap= plt.get_cmap(mymap)
#colors = nice_cmap([0,1, 2, 3, 4, 5,6,7,8,9,10,11,12,13])
colors = nice_cmap([13,11,12,9,8,7,6,4,3,2,0,1])
cmap, norm = mcolors.from_levels_and_colors(clevs, colors, extend='both')
def energyproduction(files, level):
"""
Creates hourly and daily energy production outputs for wind a turbine
output plots
Parameters
----------
arg1: list
A 1D glob list of the file outputs from WRF
arg2: integer
level of the WRF module to run the evaluation on, typicall 0-2
for low levels of the atmosphere.
Returns
-------
None
"""
count = 0
dailyenergy = 0
uaaverage = 0
vaaverage = 0
for file in files:
date, hour = gettimeanddates(file)
ncfile = Dataset(file)
cbarticks = np.arange(0.0, 10.0, 0.5)
r = 52.0 #meters, rotorlength
Area = np.pi * r**2
cp = 0.4 #unitless
density = 1.23 #kg/m^3
press = getvar(ncfile, 'pressure') #hPa
T = getvar(ncfile, 'T') #K
ua = getvar(ncfile, 'ua') #m/s
va = getvar(ncfile, 'va') #m/s
pressground, uaground, vaground, Tground = press[level, :, :], ua[
level, :, :], va[level, :, :], T[level, :, :]
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
totalwind = np.sqrt(uaground**2 + vaground**2)
#set total wind so that if below 3.0 or above 22.0 wind is 0 meaning prod is zero
wind = totalwind.to_pandas()
windmatrix = wind.as_matrix()
for i in range(0, len(windmatrix)):
for k in range(0, len(windmatrix[0])):
if windmatrix[i, k] < 3.0:
windmatrix[i, k] = 0.0
elif windmatrix[i, k] > 22.0:
windmatrix[i, k] = 0.0
energyproduction = (0.5 * density * Area * windmatrix**3 *
cp) / 10**6 #megawatts output
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
bm.contour(x,
y,
to_np(energyproduction),
cbarticks,
colors="black",
vmin=0,
vmax=10.0)
bm.contourf(x,
y,
to_np(energyproduction),
cbarticks,
cmap=get_cmap('jet'),
vmin=0,
vmax=10.0)
bm.barbs(x, y, uaground, vaground)
plt.colorbar(shrink=.62, ticks=cbarticks)
plt.title('Hourly Energy Production for ' + date + ' ' + hour)
plt.savefig('Hourly-output-' + date + '-' + hour)
plt.close()
if count == 23:
bm = get_basemap(pressground)
fig = plt.figure(figsize=(12, 9))
lat, lon = latlon_coords(pressground)
x, y = bm(to_np(lon), to_np(lat))
bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
dailyenergy = dailyenergy / 24.0
uaaverage = uaaverage / 24.0
vaaverage = vaaverage / 24.0
bm.contour(x,
y,
to_np(dailyenergy),
cbarticks,
colors="black",
vmin=0,
vmax=10.0)
bm.contourf(x,
y,
to_np(dailyenergy),
cbarticks,
cmap=get_cmap('jet'),
vmin=0,
vmax=10.0)
bm.barbs(x, y, uaaverage, vaaverage)
plt.colorbar(shrink=.62, ticks=cbarticks)
plt.title('Dialy Average ' + yesterdaysdate)
plt.savefig('Daily-Average-' + yesterdaysdate)
plt.show()
plt.close()
dailyenergy = 0
count = 0
uaaverage = 0
vaaverage = 0
else:
uaaverage = uaground + uaaverage
vaaverage = vaground + vaaverage
dailyenergy = energyproduction + dailyenergy
count = count + 1
yesterdaysdate = date
panelnr = 0
panel_titles = ('00h UTC', '06h UTC', '12h UTC', '18h UTC')
i = t
for row in range(2):
for col in range(2):
# Interpolate geopotential height, u, and v winds to 500 hPa
ht_500 = interplevel(z, p, 500)
u_500 = interplevel(ua, p, 500)
v_500 = interplevel(va, p, 500)
wspd_500 = interplevel(wspd, p, 500)
# Get the lat/lon coordinates
lats, lons = latlon_coords(ht_500)
# Get the basemap object
bm = get_basemap(ht_500)
# Create the figure
#fig = plt.figure(figsize=(12,9))
#ax = plt.axes()
# Convert the lat/lon coordinates to x/y coordinates in the projection space
x, y = bm(to_np(lons), to_np(lats))
#norm = BoundaryNorm(levels, ncolors=custom_cmap.N, clip=True)
#h = bm.pcolormesh(lons,lats,wspd[panelnr+i])#, cmap=custom_cmap, latlon=True, vmin=vmin, vmax=vmax, norm=norm)
ax[row, col].set_title(panel_titles[panelnr], fontsize=11)
# Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.)
contours = bm.contour(x,
