def extract(self, **args):
area = args['area']
variable = args['variable']
inputLat = args['lat']
inputLon = args['lon']
# lat_min = regionConfig.regions[area][1]['llcrnrlat']
# lat_max = regionConfig.regions[area][1]['urcrnrlat']
# lon_min = regionConfig.regions[area][1]['llcrnrlon']
# lon_max = regionConfig.regions[area][1]['urcrnrlon']
#
# grid = self.get_grid(params=args,
# lonrange=(lon_min, lon_max),
# latrange=(lat_min, lat_max))
grid = self.get_grid(params=args)
#extract lat/lon and value
(lat, lon), (latIndex, lonIndex) = Extractor.getGridPoint(inputLat, inputLon, grid.lats, grid.lons,
grid.data)
value = grid.data[latIndex, lonIndex]
value = value * 1000
if value is ma.masked:
raise LandError()
#return extracted values
return (lat, lon), value
def extract(self, **args):
area = args['area']
variable = args['variable']
inputLat = args['lat']
inputLon = args['lon']
step = args['step']
# lat_min = regionConfig.regions[area][1]['llcrnrlat']
# lat_max = regionConfig.regions[area][1]['urcrnrlat']
# lon_min = regionConfig.regions[area][1]['llcrnrlon']
# lon_max = regionConfig.regions[area][1]['urcrnrlon']
latestFilePath = serverCfg['dataDir'][
'currents'] + 'daily/latest_HYCOM_currents.nc'
grid = currentforecastGrid(latestFilePath, latestFilePath, ('u', 'v'),
(-90, 90), (105, 295), (0, FORECAST_STEPS))
#extract lat/lon and value
step = int(step)
u = grid.data[0][step]
v = grid.data[1][step]
mag = np.sqrt(u**2 + v**2)
(lat, lon), (latIndex, lonIndex) = Extractor.getGridPoint(
inputLat, inputLon, grid.lats, grid.lons, mag)
value = mag[latIndex, lonIndex]
if value is ma.masked:
raise LandError()
#return extracted values
return (lat, lon), value
def extract(self, **args):
response = {}
area = args['area']
variable = args['variable']
period = args['period']
inputLat = args['lat']
inputLon = args['lon']
monthNoStr = args['date'].strftime('%m')
yearStr = args['date'].strftime('%Y')
dateStr = args['date'].strftime('%d')
step = int(args['step'])
if period == 'hourly':
try:
variable = self.VARIABLE_MAP[variable]
except KeyError:
response['error'] = "The variable %s is not found. " % variable
return response
fileName = 'ww3.glob_24m.' + yearStr + monthNoStr + '.nc4' #ww3.glob_24m.197901.nc4
filePath = os.path.join(serverCfg['dataDir']['ww3'], period, fileName)
lat_min = regionConfig.regions[area][1]['llcrnrlat']
lat_max = regionConfig.regions[area][1]['urcrnrlat']
lon_min = regionConfig.regions[area][1]['llcrnrlon']
lon_max = regionConfig.regions[area][1]['urcrnrlon']
# Get grid from the file
# grid = ww3Grid(filePath, filePath, variable, (lat_min, lat_max), (lon_min, lon_max), (0, FORECAST_STEPS), params= args)
grid = ww3Grid(filePath, filePath, variable, (-90, 90), (0, 360), (0, FORECAST_STEPS), params= args)
#extract lat/lon and value
(lat, lon), (latIndex, lonIndex) = Extractor.getGridPoint(inputLat, inputLon, grid.lats, grid.lons,
grid.data[step])
value = grid.data[step][latIndex, lonIndex]
if value is ma.masked:
raise LandError()
response['value'] = float(value)
else:
response['error'] = "Only hourly data is available. Please choose hourly period."
return response
def extract(self, **args):
area = args['area']
variable = args['variable']
inputLat = args['lat']
inputLon = args['lon']
step = args['step']
# lat_min = regionConfig.regions[area][1]['llcrnrlat']
# lat_max = regionConfig.regions[area][1]['urcrnrlat']
# lon_min = regionConfig.regions[area][1]['llcrnrlon']
# lon_max = regionConfig.regions[area][1]['urcrnrlon']
fileName = serverCfg['dataDir']['ww3forecast'] + 'ww3_????????_??.nc'
latestFilePath = max(glob.iglob(fileName), key=os.path.getctime)
# grid = ww3forecastGrid(latestFilePath, latestFilePath, variable,
# latrange=(lat_min, lat_max),
# lonrange=(lon_min, lon_max),
# depthrange=(0, FORECAST_STEPS))
grid = ww3forecastGrid(latestFilePath,
latestFilePath,
variable,
latrange=(-90, 90),
lonrange=(0, 360),
depthrange=(0, FORECAST_STEPS))
if variable == 'wnd_spd':
grid.data = grid.data * 1.94384449
#extract lat/lon and value
step = int(step)
(lat, lon), (latIndex, lonIndex) = Extractor.getGridPoint(
inputLat, inputLon, grid.lats, grid.lons, grid.data[step])
value = grid.data[step][latIndex, lonIndex]
if value is ma.masked:
raise LandError()
#return extracted values
return (lat, lon), value
lons,
variable[0],
strategy='exhaustive')
return (latidx, latidx + 1), (lonidx, lonidx + 1), (0, 0)
def load_data(self, variable):
return variable
def clip_data(self, variable, (lat_idx1, lat_idx2), (lon_idx1, lon_idx2),
(depth_idx1, depth_idx2)):
data = variable[:, lat_idx1, lon_idx1]
if data[0] is ma.masked:
raise LandError()
return data
class SeaLevelSurfacePlotter(SurfacePlotter):
DATASET = 'sealevel'
# PRODUCT_NAME = """
#IB not removed; seasonal not removed; global trend not removed; GIA removed
#Data from CSIRO CMAR"""
def __init__(self, variable):
super(SeaLevelSurfacePlotter, self).__init__()
if variable == "rec":
self.PRODUCT_NAME = """
def extract(**args):
area = args['area']
variable = args['variable']
inputLat = args['lat']
inputLon = np.mod(args['lon'], 360.0)
area = args['area']
period = args['period']
inputDate = args["date"]
date = inputDate.strftime('%Y%m%d')
yearStr = '%04i' % inputDate.year
monthStr = '%02i' % inputDate.month
yearMonthStr = yearStr + monthStr
# if variable == 'uv':
# variable = 'u'
# elif variable == 'uveta':
# variable = 'eta'
# lat_min = regionConfig.regions[area][1]['llcrnrlat']
# lat_max = regionConfig.regions[area][1]['urcrnrlat']
# lon_min = regionConfig.regions[area][1]['llcrnrlon']
# lon_max = regionConfig.regions[area][1]['urcrnrlon']
lat_min = -999.0
lat_max = 999.0
lon_min = -999.0
lon_max = 999.0
# Load surface data
if variable == 'uv':
if period == 'monthly':
input_data_file = os.path.join(server_config['dataDir']['bran'],
period, 'u',
'u_%s_%s.nc4' % (yearStr, monthStr))
lats, lons, zlevels, u = \
branPlotterNew.load_BRAN_data(input_data_file, 'u',
lat_min, lat_max,
lon_min, lon_max)
input_data_file = os.path.join(server_config['dataDir']['bran'],
period, 'v',
'v_%s_%s.nc4' % (yearStr, monthStr))
lats, lons, zlevels, v = \
branPlotterNew.load_BRAN_data(input_data_file, 'v',
lat_min, lat_max,
lon_min, lon_max)
else:
input_data_file = os.path.join(server_config['dataDir']['bran'],
'averages', period, 'u',
BRAN_VERSION + '_%smthavg_%s_%s.nc4' % (monthInt, months[0].strftime('%Y%m'), months[-1].strftime('%Y%m')))
lats, lons, zlevels, u = \
branPlotterNew.load_BRAN_data(input_data_file, 'u',
lat_min, lat_max,
lon_min, lon_max)
input_data_file = os.path.join(server_config['dataDir']['bran'],
'averages', period, 'v',
BRAN_VERSION + '_%smthavg_%s_%s.nc4' % (monthInt, months[0].strftime('%Y%m'), months[-1].strftime('%Y%m')))
lats, lons, zlevels, v = \
branPlotterNew.load_BRAN_data(input_data_file, 'v',
lat_min, lat_max,
lon_min, lon_max)
data = np.sqrt(u**2 + v**2)
else:
if period == 'monthly':
input_data_file = os.path.join(server_config['dataDir']['bran'],
period, variable,
'%s_%s_%s.nc4' % (variable,
yearStr,
monthStr))
else:
monthInt = int(''.join(i for i in period if i.isdigit()))
months = dateRange.getMonths(date, monthInt)
input_data_file = os.path.join(server_config['dataDir']['bran'],
'averages', period, variable,
BRAN_VERSION + '_%smthavg_%s_%s.nc4' % (monthInt, months[0].strftime('%Y%m'), months[-1].strftime('%Y%m')))
lats, lons, zlevels, data = \
branPlotterNew.load_BRAN_data(input_data_file, variable,
lat_min, lat_max,
lon_min, lon_max)
#extract lat/lon and value
(lat, lon), (latIndex, lonIndex) = Extractor.getGridPoint(inputLat, inputLon, lats, lons,
data)
value = data[latIndex, lonIndex]
if value is ma.masked:
raise LandError()
#return extracted values
return (lat, lon), value
def RosePlot(opath, wdir, units, lat, lon, ilat, ilon, xstr, title, var, binwd):
'''Plots a windrose of angular values in wdir, with annotations
Arguments:
wdir -- an array of angles in the range (0,360). (NumPy Array)
units -- measurements units for wdir variable. (float)
lat -- latitude of point. (float)
lon -- longitude of point. (float)
xstr -- x-axis label. (string)
title -- plot title. (string)
var -- the variable that is measured by wdir. (string)
binwd -- width of histogram bins. (float)
Returns:
rosefig -- An annotated windrose of wdir values.
'''
#set number of bins and max bin value.
N,wdnbin,wdmax = 8,8,2*np.pi
degree = ur'\u00b0'
if wdir[0] == -999.0:
raise LandError()
#flip directions as WWIII direction are given in meteorological convention.
if var == "Dm":
wdir = conv.dirflip(wdir)
wdir = conv.dirshift(wdir)
else:
wdir = conv.dirshift(wdir)
#make a basic histogram of wave directions over the range (-22.5,337.5).
try:
whist = np.histogram(wdir, wdnbin, (-22.5,337.5), density=False)
except TypeError:
# support older numpy
whist = np.histogram(wdir, wdnbin, (-22.5,337.5), normed=False)
#calculate mean bearing
meanb = meanbearing(wdir)
#force square figure and square axes looks better for polar
rosefig = figure(figsize=(10,7.5))
ax = rosefig.add_axes([0.075, 0.1, 0.6, 0.725], polar=True)
#plot radial gridlines at seperations of 45 degrees
plt.thetagrids((0,45,90,135,180,225,270,315),('N', '45' + degree, 'E', '135' + degree, 'S', '215' + degree, 'W', '315' + degree),frac = 1.1)
#rotate axis to zero at North and set direction of increasing angle clockwise.
plt.rgrids((0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9), angle=0)
plt.ylim(0.0,1.0)
try:
ax.set_theta_direction(-1)
ax.set_theta_zero_location('N')
except:
# hack around older matplotlib
pass
#lines, labels = plt.rgrids()
#set Nmax
Nmax = 1
#initialize prob and perc arrays
prob = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
perc = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
#variable to normalize bins so that they sum to 1 (probability)
normalizer = 1/float(len(wdir))
#normalize histogram to calculate probabilities and percentages
for i in range(0,N):
prob[i] = float(whist[0][i])*normalizer
perc[i] = round(100*prob[i],2)
units = '%'
theta = np.linspace(-2*np.pi/(2*N), 2*np.pi-2*np.pi/(2*N), N, endpoint=False)
#some basic settings for graphics
width = 2*np.pi/N
length = np.size(wdir)
#rounded mean bearing for labelling purposes
meanbr = round((meanb*180)/(np.pi),1)
#set up bar chart properties
bars = ax.bar(theta, prob, width, bottom=0.0)
my_cmap = hc.decile_rose()
#plot a line to indicate mean bearing
ax.arrow(0, 0, meanb, 1.0, edgecolor = 'r', facecolor = 'r', lw='3')
#plot bar chart of histogram in theta space
for r,bar in zip(prob, bars):
bar.set_facecolor(my_cmap(r))
bar.set_alpha(0.5)
lpack.rosepack(title)
formilat,formilon = NESWformat(ilat,ilon)
formlat,formlon = NESWformat(lat,lon)
#various annotations to plot
plt.figtext(0.76, 0.82,'Probabilities:', fontsize = 10, weight = 550)
plt.figtext(0.76, 0.575,'Location & Plot Data:', fontsize=10, weight = 550)
plt.figtext(0.76, 0.51, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10)
plt.figtext(0.76, 0.46, 'Grid Lat/Lon:\n %s %s' % (formlat, formlon), fontsize=10)
plt.figtext(0.76, 0.425, 'Data points: %s' % length, fontsize=10)
plt.figtext(0.76, 0.4, 'Bins: %s' % int(N), fontsize=10)
plt.figtext(0.76, 0.375, 'Bin Width: %s %s' % (binwd, degree), fontsize=10)
plt.figtext(0.76, 0.275, 'Mean Bearing: %s%s %s' % (meanbr, degree,'T'), color='r', fontsize=10)
#display wave direction percentages for 8 primary compass points in the windrose
plt.figtext(0.76, 0.325, 'Directional Statistics:', fontsize=10, weight=550)
plt.figtext(0.76, 0.25, 'North: %s %s' % (perc[0], units), fontsize=10)
plt.figtext(0.76, 0.225, 'North East: %s %s' % (perc[1], units), fontsize=10)
plt.figtext(0.76, 0.2, 'East: %s %s' % (perc[2], units), fontsize=10)
plt.figtext(0.76, 0.175, 'South East: %s %s' % (perc[3], units), fontsize=10)
plt.figtext(0.76, 0.15, 'South: %s %s' % (perc[4], units), fontsize=10)
plt.figtext(0.76, 0.125, 'South West: %s %s' % (perc[5], units), fontsize=10)
plt.figtext(0.76, 0.1, 'West: %s %s' % (perc[6], units), fontsize=10)
plt.figtext(0.76, 0.075, 'North West: %s %s' % (perc[7], units), fontsize=10)
#Bureau of Meteorology Copyright
plt.figtext(0.58, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10)
plt.figtext(0.02, 0.02, getCopyright(), fontsize=8)
#define image name
imgname = opath + '.png'
#write image file
plt.savefig(imgname)
plt.close()
pngcrush(imgname)
return
def HistPlot(opath, wheight, units, lat, lon, ilat, ilon, xstr, title, var, binwd):
'''Returns a normalized, annotated histogram for values of wheight
Arguments:
wheight -- an array of positive doubles. (NumPy Array)
units -- units associated with wheight. (string)
lat -- latitude of the point. (float)
lon -- longitude of the point. (float)
xstr -- x-axis label. (string)
title -- title of the plot. (string)
var -- variable associated with the values in wheight. (string)
binwd -- width of histogram bins. (float)
Returns:
histfig -- An annotated histogram of wheight values.
'''
#calculate the values of some pertinent statistical quantities
wavg = np.average(wheight)
wavgr = round(wavg,2)
maxwave = round(np.max(wheight),2)
minwave = round(np.min(wheight),2)
imaxwave = int(maxwave)+ 1
iminwave = int(minwave)- 1
Nmax = imaxwave
Nmin = iminwave
length = np.size(wheight)
debug = False
#calculate range of data
#binthresh = maxwave - minwave
#Error message if selected coordinates are out of range or on land
if maxwave == -999.0:
raise LandError()
#alter bin width depending on range of data
#if binthresh < 10:
# binwd = 0.1
#elif binthresh > 10:
# binwd = 0.2
#else:
# print "Error"
#bins per unit and total number of bins
binperunit = float(1/binwd)
binnum = (Nmax - Nmin)*binperunit
initial = round(0.001,3)
final = round(Nmax+0.001,3)
#histogram of selected variable
try:
whist,wbins = np.histogram(wheight, Nmax * binperunit,
(initial, final), density=True)
except TypeError:
# support older numpy
whist,wbins = np.histogram(wheight, Nmax * binperunit,
(initial, final), normed=True)
#print wbins
#calculate max and mins of histogram
maxy = np.max(whist*binwd)
maxx = np.max(wheight)
minx = np.min(wheight)
#do some basic statistics on histogram, std. deviation and quartiles.
q1 = round(sci.stats.scoreatpercentile(wheight,25),1)
q3 = round(sci.stats.scoreatpercentile(wheight,75),1)
x=linspace(0,maxx+0.5,Nmax*binperunit)
#set up figure
histfig=plt.figure(figsize = (10,7.5))
histax=histfig.add_axes([0.1,0.1,0.675,0.75])
plt.xlim(0,maxx+0.5)
plt.ylim(0,maxy+0.01)
whistnorm = whist*binwd
#np.hist(wheight,100,(0,Nmax),color='b',normed=True, histtype = 'stepfille)
#set up bar chart properties
bars=plt.bar(wbins[:-1],whistnorm,binwd,0)
if debug == True:
FILE = open('/home/jsmith/Test/verification/WW3' + '_' + str(lat) + '_' + str(lon) + '_' + var + '_array.txt', "w")
FILE.writelines(list( "%s \n" % i for i in whist))
FILE.close()
my_cmap = hc.quartile_colors(wheight,wavg,Nmax,binwd)
#bar chart of histogram
for r,bar in zip(wbins[:-1], bars):
bar.set_facecolor(my_cmap(r/Nmax))
bar.set_alpha(0.5)
#specify plot properties and plot guassian kde and average line
plt.xlim(minx-0.5,maxx+0.5)
plt.ylim(0,maxy+0.01)
#histax.plot(x, approximate_pdf(x), color = 'black', linewidth=3, alpha=1)
histax.axvline(wavg, color='r', lw='3')
#choose which legend to display based on variable
if var == 'Hs':
lpack.heightpack(title,wavg)
#plt.figtext(0.79,0.175, 'Rogue Wave Height: %s %s' % (2*wavgr,units), fontsize = 10, color = 'm')
elif var == 'Tm':
lpack.timepack(title)
formilat, formilon = NESWformat(ilat,ilon)
formlat, formlon = NESWformat(lat,lon)
#specify graph grid properties
xticks = [10,20,30,40,50,60,70,80,90,100]
plt.xticks = xticks, [('%s' % str(x/10))]
plt.grid(True)
#x,y axis labels
plt.xlabel('%s (%s)' % (xstr,units), fontsize=12)
plt.ylabel('Frequency', fontsize=12)
#various annotations for graphics
plt.figtext(0.79, 0.775, 'Distribution:', fontsize=10, weight=550)
plt.figtext(0.79, 0.615, 'Location & Plot Data:',fontsize = 10, weight = 550)
plt.figtext(0.79, 0.55, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10)
plt.figtext(0.79, 0.50, 'Grid Lat/Lon:\n %s %s' % (formlat,formlon), fontsize=10)
plt.figtext(0.79, 0.465, 'Data points: %s' % length ,fontsize=10)
plt.figtext(0.79, 0.44, 'Bins: %s' % int(binnum), fontsize=10)
plt.figtext(0.79, 0.415, 'Bin Width: %s %s' % (binwd,units), fontsize=10)
plt.figtext(0.79, 0.365, 'Statistical Information:', fontsize=10, weight=550)
plt.figtext(0.79, 0.265, 'Mean: %s %s' % (round(wavgr,1), units), color='r', fontsize=10)
plt.figtext(0.79, 0.315, 'Max: %s %s' % (round(maxwave,1), units), fontsize=10)
plt.figtext(0.79, 0.215, 'Min: %s %s' % (round(minwave,1), units), fontsize=10)
plt.figtext(0.79, 0.24,'25th Percentile: %s %s' % (round(q1,1),units), fontsize=10)
plt.figtext(0.79, 0.29,'75th Percentile: %s %s' % (round(q3,1),units), fontsize=10)
#Bureau of Meteorology Copyright
plt.figtext(0.68, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10)
plt.figtext(0.02, 0.02, getCopyright(), fontsize=8)
#define image name
imgname = opath + '.png'
#write image file
plt.savefig(imgname)
plt.close()
pngcrush(imgname)
return
