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 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 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)

lon, lat = np.meshgrid(flatTLon[0,:], flatTLat[:,0])
x, y = m(lon,lat)
cs = m.pcolor(x,y,np.squeeze(flatTData), vmin=min(min(x) for x in flatTData), vmax=max(max(x) for x in flatTData))
Exemple #5
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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)

lon, lat = np.meshgrid(flatTLon[0, :], flatTLat[:, 0])
x, y = m(lon, lat)
cs = m.pcolor(x,