def setUp(self):
self.clt = cdms2.open(cdat_info.get_sampledata_path() +
'/clt.nc')('clt')[0, ...]
self.tas = cdms2.open(cdat_info.get_sampledata_path() + \
'/tas_ecm_1979.nc')('tas')[0, ...]
if PLOT:
lllat = self.clt.getLatitude()[:].min()
urlat = self.clt.getLatitude()[:].max()
lllon = self.clt.getLongitude()[:].min()
urlon = self.clt.getLongitude()[:].max()
self.cmap = bm(llcrnrlat=lllat,
llcrnrlon=lllon,
urcrnrlat=urlat,
urcrnrlon=urlon,
resolution='i',
projection='cyl')
lllat = self.tas.getLatitude()[:].min()
urlat = self.tas.getLatitude()[:].max()
lllon = self.tas.getLongitude()[:].min()
urlon = self.tas.getLongitude()[:].max()
self.tmap = bm(llcrnrlat=lllat,
llcrnrlon=lllon,
urcrnrlat=urlat,
urcrnrlon=urlon,
resolution='i',
projection='cyl')
def setUp(self):
self.rootPe = 0
self.pe = MPI.COMM_WORLD.Get_rank()
self.clt = cdms2.open(sys.prefix + '/sample_data/clt.nc')('clt')[0, ...]
# self.tas = cdms2.open(sys.prefix + \
# 'tas_ccsr-95a_1979.01-1979.12.nc')('tas')[0, 0,...]
self.tas = cdms2.open(sys.prefix + \
'/sample_data/tas_ecm_1979.nc')('tas')[0, ...]
if PLOT:
lllat = self.clt.getLatitude()[:].min()
urlat = self.clt.getLatitude()[:].max()
lllon = self.clt.getLongitude()[:].min()
urlon = self.clt.getLongitude()[:].max()
self.cmap = bm(llcrnrlat = lllat, llcrnrlon = lllon,
urcrnrlat = urlat, urcrnrlon = urlon,
resolution = 'i', projection = 'cyl')
lllat = self.tas.getLatitude()[:].min()
urlat = self.tas.getLatitude()[:].max()
lllon = self.tas.getLongitude()[:].min()
urlon = self.tas.getLongitude()[:].max()
self.tmap = bm(llcrnrlat = lllat, llcrnrlon = lllon,
urcrnrlat = urlat, urcrnrlon = urlon,
resolution = 'i', projection = 'cyl')
def plotmap(self, domain = [0., 360., -90., 90.], res='c', stepp=2, scale=20):
latitudes = self.windspeed.latitudes.data
longitudes = self.windspeed.longitudes.data
m = bm(projection='cyl',llcrnrlat=latitudes.min(),urcrnrlat=latitudes.max(),\
llcrnrlon=longitudes.min(),urcrnrlon=longitudes.max(),\
lat_ts=0, resolution=res)
lons, lats = np.meshgrid(longitudes, latitudes)
cmap = palettable.colorbrewer.sequential.Oranges_9.mpl_colormap
f, ax = plt.subplots(figsize=(10,6))
m.ax = ax
x, y = m(lons, lats)
im = m.pcolormesh(lons, lats, self.windspeed.data, cmap=cmap)
cb = m.colorbar(im)
cb.set_label('wind speed (m/s)', fontsize=14)
Q = m.quiver(x[::stepp,::stepp], y[::stepp,::stepp], \
self.uanoms.data[::stepp,::stepp], self.vanoms.data[::stepp,::stepp], \
pivot='middle', scale=scale)
l,b,w,h = ax.get_position().bounds
qk = plt.quiverkey(Q, l+w-0.1, b-0.03, 5, "5 m/s", labelpos='E', fontproperties={'size':14}, coordinates='figure')
m.drawcoastlines()
return f
def _get_basemap(self):
"""
given the projection and the domain, returns
the dataset as well as the Basemap instance
"""
if self.domain:
self._get_domain(self.domain)
else:
self.dset_domain = self.analogs.dset
# get the lat and lons
latitudes = self.dset_domain['latitudes'].data
longitudes = self.dset_domain['longitudes'].data
if not(self.proj):
self.proj = 'cyl'
if self.proj in ['cyl', 'merc']:
"""
NOTE: using a Mercator projection won't work if either / both
the northermost / southermost latitudes are 90 / -90
if one wants (but why would you do that ?) to plot a global
domain in merc, one needs to pass:
domain = [0., 360., -89.9, 89.9]
to the `scalar_plot` class
Any sub-domain of a global domain will work fine
"""
self.m = bm(projection=self.proj,llcrnrlat=latitudes.min(),urcrnrlat=latitudes.max(),\
llcrnrlon=longitudes.min(),urcrnrlon=longitudes.max(),\
lat_ts=0, resolution=self.res)
if self.proj == 'moll':
self.m = bm(projection='moll',lon_0=180, resolution=self.res)
if self.proj in ['npstere','spstere']:
self.m = bm(projection=self.proj,boundinglat=0,lon_0=0, resolution=self.res, round=True)
return self
def setUp(self):
self.clt = cdms2.open(sys.prefix + '/sample_data/clt.nc')('clt')[0, ...]
self.tas = cdms2.open(sys.prefix + \
'/sample_data/tas_ecm_1979.nc')('tas')[0, ...]
if PLOT:
lllat = self.clt.getLatitude()[:].min()
urlat = self.clt.getLatitude()[:].max()
lllon = self.clt.getLongitude()[:].min()
urlon = self.clt.getLongitude()[:].max()
self.cmap = bm(llcrnrlat = lllat, llcrnrlon = lllon,
urcrnrlat = urlat, urcrnrlon = urlon,
resolution = 'i', projection = 'cyl')
lllat = self.tas.getLatitude()[:].min()
urlat = self.tas.getLatitude()[:].max()
lllon = self.tas.getLongitude()[:].min()
urlon = self.tas.getLongitude()[:].max()
self.tmap = bm(llcrnrlat = lllat, llcrnrlon = lllon,
urcrnrlat = urlat, urcrnrlon = urlon,
resolution = 'i', projection = 'cyl')
def plotmap(self,
domain=[0., 360., -90., 90.],
res='c',
stepp=2,
scale=20):
latitudes = self.windspeed.latitudes.data
longitudes = self.windspeed.longitudes.data
m = bm(projection='cyl',llcrnrlat=latitudes.min(),urcrnrlat=latitudes.max(),\
llcrnrlon=longitudes.min(),urcrnrlon=longitudes.max(),\
lat_ts=0, resolution=res)
lons, lats = np.meshgrid(longitudes, latitudes)
cmap = palettable.colorbrewer.sequential.Oranges_9.mpl_colormap
f, ax = plt.subplots(figsize=(10, 6))
m.ax = ax
x, y = m(lons, lats)
im = m.pcolormesh(lons, lats, self.windspeed.data, cmap=cmap)
cb = m.colorbar(im)
cb.set_label('wind speed (m/s)', fontsize=14)
Q = m.quiver(x[::stepp,::stepp], y[::stepp,::stepp], \
self.uanoms.data[::stepp,::stepp], self.vanoms.data[::stepp,::stepp], \
pivot='middle', scale=scale)
l, b, w, h = ax.get_position().bounds
qk = plt.quiverkey(Q,
l + w - 0.1,
b - 0.03,
5,
"5 m/s",
labelpos='E',
fontproperties={'size': 14},
coordinates='figure')
m.drawcoastlines()
return f
left_lon = boxlon[0]
right_lon = boxlon[-1]
if 0 in boxlon[1:-2]: # if we cross the gml
left_lon = boxlon[0] - 360
num_subplots = 2 * num_eofs
for subplot in range(num_subplots):
if subplot <= num_eofs - 1: #the F maps
# Generate the clevs
x = round(np.max(np.abs(eof_grid[subplot, :, :])), 2)
clev = np.linspace(-x, x, 21)
plt.subplot(2, num_eofs, subplot + 1)
plt.title(round(varfrac[subplot], 3))
m = bm(projection='cea',
llcrnrlat=southern_lat,
urcrnrlat=northern_lat,
llcrnrlon=left_lon,
urcrnrlon=right_lon,
resolution='c')
m.drawcoastlines()
m.drawmapboundary(fill_color='0.3')
cs = m.contourf(bmlon,
bmlat,
eof_grid[subplot, :, :],
clev,
shading='flat',
latlon=True)
cbar = m.colorbar(cs, location='right', pad="5%")
else: # the amplitude time series
atx = np.arange(0, len(dates), 3)
labx = np.array(dates)[atx]
plt.subplot(2, num_eofs, subplot + 1)
corr_array[i,j,0] = corr(hold[i,j,:,0,0], hold[i,j,:,1,0])
corr_array[i,j,1] = corr(hold[i,j,:,0,1], hold[i,j,:,1,1])
#r, pval = regress(diffv1_master[:,i,j], diffv2_master[:,i,j])
corr_array[:,:,2] = corr_array[:,:,1] - corr_array[:,:,0]
llcrnlat, urcrnlat, llcrnlon, urcrnrlon = [lats[0], lats[-1], lons[0], lons[-1]]
if 0 in lons[1:-2]: # if we cross the gml
llcrnlon = lons[0]-360
# Make the map of the corr array
fig = plt.figure()
plt.subplot(3,1,1)
bmlon, bmlat = np.meshgrid(lons, lats)
m = bm(projection = 'cea', llcrnrlat=llcrnlat,urcrnrlat=urcrnlat, llcrnrlon=llcrnlon,urcrnrlon=urcrnrlon,resolution='c')
m.drawcoastlines()
m.drawmapboundary(fill_color='0.3')
clevs = np.linspace(-1, 1, 11)
cs = m.contourf(bmlon, bmlat, corr_array[:,:,1], clevs, shading = 'flat', latlon = True, cmap=plt.cm.RdBu_r)
cbar = m.colorbar(cs, location='right', pad="5%")
cbar.set_label("correlation-coefficient", fontsize = 8)
plt.title("test")
plt.subplot(3,1,2)
m = bm(projection = 'cea', llcrnrlat=llcrnlat,urcrnrlat=urcrnlat, llcrnrlon=llcrnlon,urcrnrlon=urcrnrlon,resolution='c')
m.drawcoastlines()
m.drawmapboundary(fill_color='0.3')
clevs = np.linspace(-1, 1, 11)
cs = m.contourf(bmlon, bmlat, corr_array[:,:,0], clevs, shading = 'flat', latlon = True, cmap=plt.cm.RdBu_r)
cbar = m.colorbar(cs, location='right', pad="5%")
pass
dset = xr.concat(l, dim='time')
if ndays != 1:
dset = dset.mean('time')
dseta = dseta.mean('time')
lat = dset.lat
lon = dset.lon
if os.path.exists( "/Users/nicolasf/operational/Vanuatu/code/basemap_pickle_SST.pickle" ):
m = pickle.load( open( "/Users/nicolasf/operational/Vanuatu/code/basemap_pickle_SST.pickle", "rb" ) )
else:
m = bm(projection='cyl',llcrnrlat=lat.data.min(),urcrnrlat=lat.data.max(), llcrnrlon=lon.data.min(),urcrnrlon=lon.data.max(), lat_ts=0,resolution='f')
pickle.dump( m, open( "/Users/nicolasf/operational/Vanuatu/code/basemap_pickle_SST.pickle", "wb" ) )
lons, lats = np.meshgrid(lon, lat)
jet = plt.get_cmap('jet')
f = plot_field_contourf(m, dset['CRW_SST'], lats, lons, 22.5, 30, 0.25, grid=True, title='CRW SST, last {} days to {:%Y-%m-%d}\nData made available by NOAA Coral Reef Watch'.format(ndays, last_available), cmap=jet)
f.savefig('/Users/nicolasf/operational/Vanuatu/figures/CRW_SST_last_{}days.png'.format(ndays), dpi=200)
f = plot_field_contourf(m, dseta['CRW_SSTANOMALY'], lats, lons, -1.5, 1.5, 0.05, grid=True, title='CRW SST anomaly, last {} days to {:%Y-%m-%d}\nData made available by NOAA Coral Reef Watch'.format(ndays, last_available), cmap=cm.balance)
f.savefig('/Users/nicolasf/operational/Vanuatu/figures/CRW_SST_anomaly_last_{}days.png'.format(ndays), dpi=200)
cities['Lae'] = (-6.73333, 147)
cities['Alotau'] = (-10.316667, 150.433333)
cities['Daru'] = (-9.083333, 143.2)
cities['Madang'] = (-5.216667, 145.8)
cities['Wewak'] = (-3.55, 143.633333)
# ### original resolution of the dataset
res = 0.25
# ### factor by which the resolution will be increased
interp_factor = 10
# ### set up the map
m = bm(projection='cyl', llcrnrlon=domain['lonmin']+0.25,\
llcrnrlat=domain['latmin']+0.25,\
urcrnrlon=domain['lonmax']-0.25,\
urcrnrlat=domain['latmax']-0.25, resolution='f')
# get the date of today (UTC)
today = datetime.utcnow()
lag = 2
trmm_date = today - timedelta(days=lag)
print("processing up to {:%Y-%m-%d}\n".format(trmm_date))
# loads the virtual stations file
Virtual_Stations = pd.read_excel('../data/PNG_stations_subset.xls')
for ndays in [30, 60, 90]:
# ## Apply the PCM to the dataset:
# In[72]:
get_ipython().run_cell_magic(u'time', u'', u'# Normalize data:\nXn = scaler.transform(X) \n\n# Reduce the dataset (compute the y):\nXr = reducer.transform(Xn) # Here we compute: np.dot(Xn - reducer.mean_, np.transpose(reducer.components_))\n\n# Classify the dataset:\nLABELS = gmm.predict(Xr) # [Np,1]\nPOST = gmm.predict_proba(Xr) # [Np,Nc]')
# ## Time for figures
# In[73]:
# Plot LABELS map:
lon = dsub['LONGITUDE'].values
lat = dsub['LATITUDE'].values
mp0 = bm(projection='cyl', llcrnrlat=-1,urcrnrlat=71, llcrnrlon=-91,urcrnrlon=1, lat_ts=0,resolution='c')
fig, axes = plt.subplots(nrows=1,ncols=1,figsize=(5,5), dpi=160, facecolor='w', edgecolor='k')
axes = np.reshape(axes,(1,1))
for ie in range(1):
mp = mp0
mp.ax = axes[0,ie]
plt.jet()
sc = mp.scatter(lon,lat,1,LABELS)
mp.drawmeridians(np.arange(0, 360, 10), labels=[0,0,0,1], color='0.8', linewidth=0.5, fontsize=8)
mp.drawparallels(np.arange(-80, 80, 5), labels=[1,0,0,0], color='0.8', linewidth=0.5, fontsize=8)
mp.drawlsmask(land_color='0.8', ocean_color='w')
# mp.drawcoastlines()
mp.colorbar(sc,ax=axes[0,ie])
# ax.set_title(tit)
axes[0,ie].set_title("LABELS (K=%i)"%(K))
plt.tight_layout()
def plot(self):
"""
basemap plot of a scalar quantity
"""
if self.domain:
self.get_domain(self.domain)
else:
self.dset_domain = self.compos.dset
# get the lat and lons
latitudes = self.dset_domain['latitudes'].data
longitudes = self.dset_domain['longitudes'].data
# get the data (composite anomalies)
mat = self.dset_domain['composite_anomalies'].data
pvalues = self.dset_domain['pvalues'].data
if not(self.proj):
self.proj = 'cyl'
if self.proj in ['merc', 'cyl']:
m = bm(projection=self.proj,llcrnrlat=latitudes.min(),urcrnrlat=latitudes.max(),\
llcrnrlon=longitudes.min(),urcrnrlon=longitudes.max(),\
lat_ts=0, resolution=self.res)
if self.proj == 'moll':
m = bm(projection='moll',lon_0=180, resolution=res)
if self.proj in ['npstere','spstere']:
m = bm(projection=self.proj,boundinglat=0,lon_0=0, resolution=self.res, round=True)
# we add cyclic longitude
mat, lon = addcyclic(mat, longitudes)
pvalues, lon = addcyclic(pvalues, longitudes)
longitudes = lon
# m = pickle.load( open( "basemap.pickle", "rb" ) )
# meshgrid lon and lat for plotting with basemap
lons, lats = np.meshgrid(longitudes, latitudes)
# ravel and removes nans for calculation of intervals etc
calc_data = self.compos.dset['composite_anomalies'].data
calc_data = np.ravel(calc_data[np.isfinite(calc_data)])
# the following is borrowed from xray
# see: plot.py in xray/xray/plot
if self.vmin is None:
self.vmin = np.percentile(calc_data, self.robust_percentile) if self.robust else calc_data.min()
if self.vmax is None:
self.vmax = np.percentile(calc_data, 100 - self.robust_percentile) if self.robust else calc_data.max()
del(calc_data)
# Simple heuristics for whether these data should have a divergent map
divergent = ((self.vmin < 0) and (self.vmax > 0)) or self.center is not None
# Now set center to 0 so math below makes sense
if self.center is None:
self.center = 0
# A divergent map should be symmetric around the center value
if divergent:
vlim = max(abs(self.vmin - self.center), abs(self.vmax - self.center))
self.vmin, self.vmax = -vlim, vlim
# Now add in the centering value and set the limits
self.vmin += self.center
self.vmax += self.center
# Choose default colormaps if not provided
if self.cmap is None:
if divergent:
# get the colormap defined in the dset_dict
self.cmap = eval(self.compos.dset_dict['plot']['cmap'])
else:
# get the default matplotlib colormap
self.cmap = plt.get_cmap()
# ===============================================================
# plots
r, c = mat.shape
f, ax = plt.subplots(figsize=(8,8*(c/r)), dpi=200)
# the basemap instance gets attached to the created axes
m.ax = ax
# pcolormesh
im = m.pcolormesh(lons, lats, ma.masked_invalid(mat), \
vmin=self.vmin, vmax=self.vmax, cmap=self.cmap, latlon=True)
# if test is defined, one contours the p-values for that level
if self.test:
#P = ma.where(self.dset['pvalues'].data <0.1 , 1, np.nan)
#m.contourf(lon, lat, P, colors='0.99', \
# hatches=['...'], latlon=True, zorder=2, alpha=0.2)
m.contour(lons, lats, pvalues, [self.test], latlon=True, \
colors='#8C001A', linewidths=1.5)
# sets the colorbar
cb = m.colorbar(im)
[l.set_fontsize(14) for l in cb.ax.yaxis.get_ticklabels()]
cb.set_label(self.compos.dset_dict['units'],fontsize=14)
# draw the coastlines
m.drawcoastlines()
# if one is plotting SST data, fill the continents
if self.compos.variable in ['sst','SST']:
m.fillcontinents('0.8', lake_color='0.8')
# if grid, plots the lat / lon lines on the map
if self.grid:
# if it's hires ('f' or 'h'), every 10 degrees
if self.res in ['h','f']:
m.drawmeridians(np.arange(0, 360, 5), labels=[0,0,0,1], fontsize=14)
m.drawparallels(np.arange(-80, 80+5, 5), labels=[1,0,0,0], fontsize=14)
# if not hires, plots lines every 40 degrees
else:
m.drawmeridians(np.arange(0, 360, 60), labels=[0,0,0,1], fontsize=14)
m.drawparallels(np.arange(-80, 80+10, 40), labels=[1,0,0,0], fontsize=14)
if not(self.border):
ax.axis('off')
# set the title from the description in the dataset + variable JSON entry
ax.set_title(self.compos.dset_dict['description'], fontsize=14)
return f
self.dset_domain.close()
plt.close(f)
http://nbviewer.jupyter.org/github/nicolasfauchereau/metocean/blob/master/notebooks/xray.ipynb
Author: Mrugen Anil Deshmukh (mad5js)
Date Created: 1 April 2016
"""
import xray
import numpy as np
from mpl_toolkits.basemap import Basemap as bm
from matplotlib import pyplot as plt
import math
# This piece of code has been used directly from the tutorial at -
# http://nbviewer.jupyter.org/github/nicolasfauchereau/metocean/blob/master/notebooks/xray.ipynb
m = bm(projection='cyl',llcrnrlat=-90,urcrnrlat=90,\
llcrnrlon=0,urcrnrlon=360,\
lat_ts=0,resolution='c')
# The following function has been borrowed from the given tutorial as well
def plot_field(X, lat, lon, vmin, vmax, step, cmap=plt.get_cmap('jet'), ax=False, title=False, grid=False):
if not ax:
f, ax = plt.subplots(figsize=(10, 10))
m.ax = ax
im = m.contourf(lons, lats, X, np.arange(vmin, vmax+step, step), latlon=True, cmap=cmap, extend='both', ax=ax)
m.drawcoastlines()
if grid:
m.drawmeridians(np.arange(0, 360, 60), labels=[0,0,0,1])
m.drawparallels([-90, 0, 90], labels=[1,0,0,0])
m.colorbar(im)
if title:
ax.set_title(title)
