def AreaAvgOverTime(var, model, timeRange, latRange, lonRange):
avgOverTime = []
lat = []
lon = []
for i in range(timeRange[0], timeRange[1]):
print i
g = GetDataMap.getDataMap(latRange, lonRange, i, model, var)
if len(lat) == 0:
lat = g[0]
lon = g[1]
TimeAverage = GetDataMap.getTimeAvg(g[2])
if (len(avgOverTime) == 0):
for j in range(0, len(TimeAverage)):
row = []
for c in range(0, len(TimeAverage[0])):
row.append(TimeAverage[j][c])
avgOverTime.append(row)
else:
for j in range(0, len(TimeAverage)):
for c in range(0, len(TimeAverage[0])):
avgOverTime[j][c] += TimeAverage[j][c]
for i in range(0, len(avgOverTime)):
for j in range(0, len(avgOverTime[i])):
avgOverTime[i][j] /= len(range(timeRange[0], timeRange[1]))
return [lat, lon, avgOverTime]
def AreaAvgOverTime(var, model, timeRange, latRange, lonRange):
avgOverTime = []
lat = []
lon = []
for i in range(timeRange[0],timeRange[1]):
print i
g = GetDataMap.getDataMap(latRange, lonRange, i, model, var)
if len(lat) == 0:
lat = g[0]
lon = g[1]
TimeAverage = GetDataMap.getTimeAvg(g[2])
if(len(avgOverTime) == 0):
for j in range(0,len(TimeAverage)):
row = []
for c in range(0,len(TimeAverage[0])):
row.append(TimeAverage[j][c])
avgOverTime.append(row)
else:
for j in range(0,len(TimeAverage)):
for c in range(0,len(TimeAverage[0])):
avgOverTime[j][c] += TimeAverage[j][c]
for i in range(0, len(avgOverTime)):
for j in range(0, len(avgOverTime[i])):
avgOverTime[i][j] /= len(range(timeRange[0],timeRange[1]))
return [lat, lon, avgOverTime]
def AreaExtremeAvgOverTime(var, model, timeRange, latRange, lonRange):
extAvgOverTime = []
lat = []
lon = []
for i in range(timeRange[0], timeRange[1]):
print i
g = GetDataMap.getDataMap(latRange, lonRange, i, model, var)
if len(lat) == 0:
lat = g[0]
lon = g[1]
annualExt = FindExtremes.findAnnualMax(g[2])
if (len(extAvgOverTime) == 0):
for j in range(0, len(annualExt)):
row = []
for c in range(0, len(annualExt[0])):
row.append(annualExt[j][c])
extAvgOverTime.append(row)
else:
for j in range(0, len(annualExt)):
for c in range(0, len(annualExt[0])):
extAvgOverTime[j][c] += annualExt[j][c]
for i in range(0, len(extAvgOverTime)):
for j in range(0, len(extAvgOverTime[i])):
extAvgOverTime[i][j] /= len(range(timeRange[0], timeRange[1]))
return [lat, lon, extAvgOverTime]
def AreaExtremeAvgOverTime(var, model, timeRange, latRange, lonRange):
extAvgOverTime = []
lat = []
lon = []
for i in range(timeRange[0], timeRange[1]):
print i
g = GetDataMap.getDataMap(latRange, lonRange, i, model, var)
if len(lat) == 0:
lat = g[0]
lon = g[1]
annualExt = FindExtremes.findAnnualMax(g[2])
if(len(extAvgOverTime) == 0):
for j in range(0,len(annualExt)):
row = []
for c in range(0,len(annualExt[0])):
row.append(annualExt[j][c])
extAvgOverTime.append(row)
else:
for j in range(0,len(annualExt)):
for c in range(0,len(annualExt[0])):
extAvgOverTime[j][c] += annualExt[j][c]
for i in range(0, len(extAvgOverTime)):
for j in range(0, len(extAvgOverTime[i])):
extAvgOverTime[i][j] /= len(range(timeRange[0], timeRange[1]))
return [lat, lon, extAvgOverTime]
def AreaAvgOverTime(var, models, ensemble, monthes, timeRange, latRange, lonRange):
avgOverTime = []
lat = []
lon = []
for m in range(0, len(models)):
curModelAvg = []
for i in range(timeRange[0],timeRange[1]):
print i
g = GetDataMap.getDataMap(latRange, lonRange, i, models[m], ensemble, monthes, var)
if len(lat) == 0:
lat = g[0]
lon = g[1]
TimeAverage = GetDataMap.getTimeAvg(g[2])
if(len(curModelAvg) == 0):
for j in range(0,len(TimeAverage)):
row = []
for c in range(0,len(TimeAverage[0])):
row.append(TimeAverage[j][c])
curModelAvg.append(row)
else:
for j in range(0,len(TimeAverage)):
for c in range(0,len(TimeAverage[0])):
curModelAvg[j][c] += TimeAverage[j][c]
for i in range(0, len(curModelAvg)):
for j in range(0, len(curModelAvg[i])):
curModelAvg[i][j] /= len(range(timeRange[0],timeRange[1]))
if len(avgOverTime) == 0:
for i in range(0, len(curModelAvg)):
row = []
for j in range(0, len(curModelAvg[i])):
row.append(curModelAvg[i][j])
avgOverTime.append(row)
else:
for i in range(0, len(curModelAvg)):
for j in range(0, len(curModelAvg[i])):
avgOverTime[i][j] += curModelAvg[i][j]
for i in range(0, len(avgOverTime)):
for j in range(0, len(avgOverTime[i])):
avgOverTime[i][j] /= len(models)
return [lat, lon, avgOverTime]
import matplotlib.pyplot as plt
import numpy as np
import scipy
import LatLonIndex
import GetDataMap
upLat = 40
lowLat = 20
lftLon = 70
rgtLon = 100
latLonRange = LatLonIndex.findLatLonRange([lowLat, upLat], [lftLon, rgtLon], [])
latRange = latLonRange[0]
lonRange = latLonRange[1]
Data = GetDataMap.getDataMap(latRange,lonRange,2004,"tasmax")
print len(Data[2])
print len(Data[2][0])
Data_average = GetDataMap.getTimeAvg(Data[2])
tempDataLat = Data[0]
tempDataLon = Data[1]
tempDataData = Data_average[2]
flatTLat = np.array(tempDataLat)
flatTLon = np.array(tempDataLon)
flatTData = np.array(tempDataData)
m = Basemap(width=10000000/5,height=7000000/5,
resolution='l',projection='stere',\
lat_ts=40,lat_0=(upLat+lowLat)/2,lon_0=(lftLon+rgtLon)/2)
import numpy as np
import scipy
import LatLonIndex
import GetDataMap
upLat = 40
lowLat = 20
lftLon = 70
rgtLon = 100
latLonRange = LatLonIndex.findLatLonRange([lowLat, upLat], [lftLon, rgtLon],
[])
latRange = latLonRange[0]
lonRange = latLonRange[1]
Data = GetDataMap.getDataMap(latRange, lonRange, 2004, "tasmax")
print len(Data[2])
print len(Data[2][0])
Data_average = GetDataMap.getTimeAvg(Data[2])
tempDataLat = Data[0]
tempDataLon = Data[1]
tempDataData = Data_average[2]
flatTLat = np.array(tempDataLat)
flatTLon = np.array(tempDataLon)
flatTData = np.array(tempDataData)
m = Basemap(width=10000000/5,height=7000000/5,
resolution='l',projection='stere',\
lat_ts=40,lat_0=(upLat+lowLat)/2,lon_0=(lftLon+rgtLon)/2)
