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 contourPlotMeanVarOnMap(variable, startDate, endDate, \
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 = []
for station in stations:
vals = Util.loadDailyVariableRange(station, startDate, endDate, \
variable, castFloat=True)
data.append(np.mean(vals))
# 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 + " -- Mean " + startDate + " to " + endDate)
# display plot
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 plotMeanWindVectorsOnMap(startDate, endDate, showIt=True):
import numpy as np
import wUUtils as Util
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# 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 station locations (Toronto, Montreal, Detroit)
stations = Util.getStationList()
lon, lat = Util.getStationLonLat(stations)
# 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
# calculate mean wind at each station
windX = []
windY = []
for station in stations:
wX = Util.loadDailyVariableRange(station, startDate, endDate, \
'WindMeanX', castFloat=True)
windX.append(np.mean(wX))
wY = Util.loadDailyVariableRange(station, startDate, endDate, \
'WindMeanY', castFloat=True)
windY.append(np.mean(wY))
for istation in range(len(stations)):
stretch = 50000
dx, dy = stretch*windX[istation], stretch*windY[istation]
plt.arrow(xpt[istation],ypt[istation],dx,dy,color='r',width=12000,head_length=40000,head_width=40000)
plt.text(xpt[istation]+30000,ypt[istation]+20000,stations[istation], size='large')
plt.title("Time-mean Wind: " + startDate + " to " + endDate)
if showIt:
plt.show()
def plotAdvectionOnMap(targetStation, variable, date, \
width_fac = 16, height_fac = 12):
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import wUAdvection as Adv
import wUUtils as Util
# 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 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 station locations (Toronto, Montreal, Detroit)
stations = Util.getStationList()
lon, lat = Util.getStationLonLat(stations)
# 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 advection arrows between all other cities
# and the target station
for istation in range(len(stations)):
if stations[istation] != targetStation:
# print(targetStation, stations[istation], variable, date, date)
dD, uVec = Adv.dDeriv(targetStation, stations[istation], variable, \
date, nextDay(date))
stretch = 2500
dD = stretch*dD[0]
dx, dy = dD*uVec
plt.arrow(xpt[istation],ypt[istation],dx,dy,color='r',width=12000,head_length=40000,head_width=40000)
for istation in range(len(stations)):
plt.text(xpt[istation]+30000,ypt[istation]+20000,stations[istation])
plt.show()
def plotStationsOnMap(showIt=True):
import wUUtils as Util
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
m = Basemap(width=3200000,height=2500000,projection='lcc',
resolution='i',lat_1=45.,lat_0=43.6,lon_0=-81.)
# draw coastlines.
m.drawcoastlines(color='gray')
m.drawcountries(color='gray')
m.drawstates(color='gray')
# 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='lightblue')
# fill continents, set lake color same as ocean color.
m.fillcontinents(color='navajowhite',lake_color='lightblue')
# plot city locations
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
for icity, city in enumerate(cityName):
if city == 'KBUF':
xoff, yoff = 20000, -40000
elif city == 'CYOW':
xoff, yoff = -100000, 23000
else:
xoff, yoff = 20000, 20000
plt.text(xpt[icity]+xoff,ypt[icity]+yoff, \
city, \
fontweight='bold', \
fontsize='medium', \
color='navy')
if showIt:
plt.show()
def collectAllStats(stationList = None):
import wUUtils as Util
import wUnderground as wU
import os
if not os.path.exists('CSV_DATA'):
os.mkdir('CSV_DATA')
#stations = ['CYYZ', 'CYUL']
if stationList == None:
stations = Util.getStationList()
else:
stations = stationList
for station in stations:
print('Processing ' + station)
# open file for csv output
outfile = open('CSV_DATA/' + station + '.csv','w')
# calculate summary statistics
dates, data = wU.getJSONFolder(station)
TimeZone = getTimeZoneOffset(data)
TempMean = dailyMean(data,'TemperatureC')
TempMin, TempMinTime = dailyMax(data,'TemperatureC',-1)
TempMax, TempMaxTime = dailyMax(data,'TemperatureC',1)
TotalPrecip = dailySum(data,'Precipitationmm')
VisibilityMean = dailyMean(data,'VisibilityKm')
PressMean = dailyMean(data, 'Sea Level PressurehPa')
PressMin, PressMinTime = dailyMax(data,'Sea Level PressurehPa',-1)
PressMax, PressMaxTime = dailyMax(data,'Sea Level PressurehPa',1)
HumidityMean = dailyMean(data,'Humidity')
WindMaxSpd, WindMaxDir, WindMaxTime = dailyMaxWind(data)
addWindVectors(data)
WindMeanX = dailyMean(data,'xSpeed')
WindMeanY = dailyMean(data,'ySpeed')
headString = 'date, TimeZone, TempMean, TempMin, TempMinTime, ' \
+ 'TempMax, TempMaxTime, TotalPrecip, VisibilityMean, ' \
+ 'PressMean, PressMin, PressMinTime, ' \
+ 'PressMax, PressMaxTime, HumidityMean, ' \
+ 'WindMaxSpd, WindMaxDir, WindMaxTime, ' \
+ 'WindMeanX, WindMeanY'
# print(headString)
outfile.write(headString + '\n')
for ii, dd in enumerate(dates):
dataString = dd.isoformat() + ', '
dataString += unicode(TimeZone[ii]) + ', '
dataString += unicode('%.2f' % TempMean[ii]) + ', '
dataString += unicode(TempMin[ii]) + ', '
dataString += unicode(TempMinTime[ii]) + ', '
dataString += unicode(TempMax[ii]) + ', '
dataString += unicode(TempMaxTime[ii]) + ', '
dataString += unicode(TotalPrecip[ii]) + ', '
dataString += unicode('%.1f' % VisibilityMean[ii]) + ', '
dataString += unicode('%.0f' % PressMean[ii]) + ', '
dataString += unicode(PressMin[ii]) + ', '
dataString += unicode(PressMinTime[ii]) + ', '
dataString += unicode(PressMax[ii]) + ', '
dataString += unicode(PressMaxTime[ii]) + ', '
dataString += unicode('%.0f' % HumidityMean[ii]) + ', '
dataString += unicode(WindMaxSpd[ii]) + ', '
dataString += unicode(WindMaxDir[ii]) + ', '
dataString += unicode(WindMaxTime[ii]) + ', '
dataString += unicode('%.2f' % WindMeanX[ii]) + ', '
dataString += unicode('%.2f' % WindMeanY[ii])
# write daily summary to csv file
outfile.write(dataString + '\n')
# print(dataString)
outfile.close()
def VariableWithInset(data, insetData, \
contours, npts = 20, \
figname='figure', frameNum=0, \
title='Data', units='units', \
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
fig = plt.figure()
# get station locations
lon, lat = Util.getStationLonLat(Util.getStationList())
# compute centre of lon/lat set
lon0, lat0 = 0.5*(np.min(lon)+np.max(lon)), \
0.5*(np.min(lat)+np.max(lat))
# print('centre at: ', lon0, lat0)
# 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=lat0,lon_0=lon0)
# 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)
# convert data to arrays:
x, y, z = np.array(lon), np.array(lat), np.array(data)
# print('\nmain axes:')
# print(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,levels=contours,cmap=plt.cm.jet)
# add colorbar.
cbar = m.colorbar(cs,location='bottom',pad="5%")
cbar.set_label(units)
plt.title(title)
# create insets
for iax in range(len(insetData)):
corner = 0.65-0.22*iax
ax1 = plt.axes([0.7, corner, 0.2, 0.2])
m1 = Basemap(width=width_fac*100000,height=height_fac*100000, \
projection='lcc', resolution='i', \
lat_1=45.,lat_0=lat0,lon_0=lon0)
# draw coastlines.
m1.drawcoastlines()
m1.drawcountries()
m1.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.
m1.drawmapboundary(fill_color='aqua')
# convert data to arrays:
x, y, z = np.array(lon), np.array(lat), np.array(insetData[iax])
# print('\niax ' + str(iax) + ':')
# print(insetData[iax])
# map data points to projection coordinates
xmap, ymap = m1(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 = m1(*np.meshgrid(xi,yi))
# Interpolate data to projected regular grid
# function is one of 'linear', 'multiquadric', 'gaussian',
# 'inverse', 'cubic', 'quintic', 'thin_plate'
rbf1 = scipy.interpolate.Rbf(xmap, ymap, z, \
function='linear')
zi = rbf1(xi, yi)
# draw filled contours
cs1 = m1.contourf(xi,yi,zi,levels=contours,cmap=plt.cm.jet)
# save plot
filename = '%s_frame_%03d.png' % (figname, frameNum)
# print('saving file: ' + filename)
plt.savefig(filename, bbox_inches='tight')
plt.close()
