def plotWindVectorsOnMap(date, showIt=True):
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import wUUtils as Util
# setup Lambert Conformal basemap.
m = Basemap(width=3200000,height=2500000,projection='lcc',
resolution='i',lat_1=45.,lat_0=43.6,lon_0=-80.)
# draw coastlines.
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents will be drawn on top.
m.drawmapboundary(fill_color='aqua')
# fill continents, set lake color same as ocean color.
m.fillcontinents(color='wheat',lake_color='aqua')
# get city locations (Toronto, Montreal, Detroit)
cityName = Util.getStationList()
lon, lat = Util.getStationLonLat(cityName)
# convert to map projection coords.
# Note that lon,lat can be scalars, lists or numpy arrays.
xpt,ypt = m(lon,lat)
m.plot(xpt,ypt,'bo') # plot a blue dot there
# compute wind vectors
windX = Util.loadDailyVariable(cityName, date, 'WindMeanX')
windY = Util.loadDailyVariable(cityName, date, 'WindMeanY')
for icity in range(len(cityName)):
stretch = 20000
dx, dy = stretch*windX[icity], stretch*windY[icity]
plt.arrow(xpt[icity],ypt[icity],dx,dy,color='r',width=12000,head_length=40000,head_width=40000)
plt.text(xpt[icity]+30000,ypt[icity]+20000,cityName[icity], size='large')
plt.title("Daily-mean wind: " + date)
if showIt:
plt.show()
def contourPlotVarOnMapFrame(variable, date, \
contours, npts = 20, \
figname='figure', frameNum=0, \
title='Data', units='units', \
width_fac = 16, height_fac = 12):
import wUUtils as Util
stations = Util.getStationList()
data = Util.loadDailyVariable(stations, date, variable)
contourPlotDataOnMapFrame(data, contours, npts, \
figname, frameNum, title, \
units, width_fac, height_fac)
def contourPlotVarOnMap(variable, date, npts = 20, ncntrs = 10, \
width_fac = 16, height_fac = 12):
import numpy as np
import wUUtils as Util
import matplotlib.pyplot as plt
import scipy.interpolate
from mpl_toolkits.basemap import Basemap
# open new figure window
plt.figure()
# setup Lambert Conformal basemap.
m = Basemap(width=width_fac*100000,height=height_fac*100000, \
projection='lcc', resolution='i', \
lat_1=45.,lat_0=43.6,lon_0=-82.)
# draw coastlines.
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents/data will be drawn on top.
m.drawmapboundary(fill_color='aqua')
# load data
stations = Util.getStationList()
lon, lat = Util.getStationLonLat(stations)
data = Util.loadDailyVariable(stations, date, variable)
# print(zip(stations,data))
# convert data to arrays:
x, y, z = np.array(lon), np.array(lat), np.array(data)
# map data points to projection coordinates
xmap, ymap = m(x,y)
# Set up a regular grid of interpolation points
xi, yi = np.linspace(x.min(), x.max(), npts), \
np.linspace(y.min(), y.max(), npts)
# map regular lon-lat grid to projection coordinates
xi, yi = m(*np.meshgrid(xi,yi))
# Interpolate data to projected regular grid
# function is one of 'linear', 'multiquadric', 'gaussian',
# 'inverse', 'cubic', 'quintic', 'thin_plate'
rbf = scipy.interpolate.Rbf(xmap, ymap, z, \
function='linear')
zi = rbf(xi, yi)
# draw filled contours
cs = m.contourf(xi,yi,zi,ncntrs,cmap=plt.cm.jet)
# plot circles at original (projected) data points
m.scatter(xmap,ymap,c=z)
# add colorbar.
cbar = m.colorbar(cs,location='bottom',pad="5%")
cbar.set_label(variable)
plt.title(variable + " -- " + date)
# display plot
plt.show()
