def extractVerticalSection(nc,dataVar,vertSection,tIndex,coordVal):
""" Takes five arguments; the NetCDF dataset, the variable object, the
string reprenting verticle setion, the index along the time axis and the
index along the latitude/longitude. Using the arguments it extracts returns
the data, from a NetCDF file"""
import netcdfUtils
# Find the number of dimensions for the data variable
numDims = len(dataVar.dimensions)
# check number of dimensions and raise error if they are not 2,or 3,or 4
if numDims < 3 or numDims > 4:
raise ValueError("Cannot extract data from variable with %d dimensions" % numDims)
# Check if the longitude, latitude and vertical dimensions are present
lonVar = netcdfUtils.findLongitudeVar(nc, dataVar)
latVar = netcdfUtils.findLatitudeVar(nc, dataVar)
zVar=netcdfUtils.findVerticalVar(nc,dataVar)
#raise error if the longitude, latitude and vertical dimensions are abscent
if lonVar is None or latVar is None or zVar is None:
raise ValueError("Cannot extract map data if longitude, latitude and Vertical dimensios are abscent")
# change longitutude value from range (-180 to 180) into (0 to 360)
if vertSection=='NS':
#Map the slicing longitude from -180-to-180 to 0-360
if min(lonVar[:])>=0:
if coordVal<=0:
coordVal=coordVal+180
else:
coordVal=coordVal+180
#the dimensions present are time, vertical, latitude or longitude
if vertSection=='NS'and numDims == 4:
coordVar=netcdfUtils.findLongitudeVar(nc, dataVar)
coordIndex=utils.findNearestIndex(coordVar,coordVal)
return dataVar[tIndex,:,:,coordIndex]
# the dimensions present must be vertical, latitude or longitude
elif vertSection=='NS'and numDims == 3:
coordVar=netcdfUtils.findLongitudeVar(nc, dataVar)
coordIndex=utils.findNearestIndex(coordVar,coordVal)
return dataVar[:,:,coordIndex]
# the dimensions present are time, vertical, latitude & longitude in that order
if vertSection=='EW'and numDims == 4:
coordVar=netcdfUtils.findLatitudeVar(nc, dataVar)
coordIndex=utils.findNearestIndex(coordVar,coordVal)
return dataVar[tIndex,:,coordIndex,:]
# the dimensions present must be vertical, latitude & longitude in that order
elif vertSection=='EW'and numDims == 3:
coordVar=netcdfUtils.findLatitudeVar(nc, dataVar)
coordIndex=utils.findNearestIndex(coordVar,coordVal)
return dataVar[:,coordIndex,:]
def findNearestLonIndex(nc,dataVar,lonval):
""" Searches through longitude values for the target value, returning the index
of the longitude value that is closest numerically to the target value.
If more than one longitude value is equally close to the target, the index
of the first value will be returned. """
import utils
import netcdfUtils
# map the longitutude value from range (-180 to 180) onto (0 to 360) in the data file
coordval=netcdfUtils.findLongitudeVar(nc,dataVar)
if min(coordval[:])>=0:
#check if the latitude value is within the latitude range
if lonval<=0:
lonval=lonval+180
else:
lonval=lonval+180
# check if the Variable object representing the latitude axis exists in the netcdf file
if coordval is None:
raise ValueError("No Variable object for the longitude axis exists in the netcdf file")
#create an array of longitude values from where to search
lonvals=coordval[:]
#find the index of the given longitudevalue and check if it exists
lonIndex=utils.findNearestIndex(lonvals,lonval)
#if no index is found
if lonIndex is None:
raise ValueError("No index value found for the longitude value entered")
#longitude index value
return lonIndex
def plotMap(vfile,ufile,varnameV,varnameU, tIndex, zIndex):
""" TODO: add a docstring! """
lonValues=[]
ncV = netCDF4.Dataset(vfile)
ncU = netCDF4.Dataset(ufile)
dataVarV = ncV.variables[varnameV]
dataVarU = ncU.variables[varnameU]
# Extract the required data
dataV = extract.extractMapData(ncV, dataVarV, tIndex, zIndex)
dataU = extract.extractMapData(ncU, dataVarU, tIndex, zIndex)
# Find the longitude and latitude values
lonVar = netcdfUtils.findLongitudeVar(ncV, dataVarV)
lonVals = lonVar[:]
if (lonVals>180).all():
lonValues.append(lonVals-360)
lonValues.append(lonVals)
latVar = netcdfUtils.findLatitudeVar(ncV, dataVarV)
latVals = latVar[:]
#title = "Plot of %s" % netcdfUtils.getTitle(dataVarV)
#plotting.displayMapPlot(dataV, lonVals, latVals, title)
plotting.displayWindMapPlot(dataV,dataU, lonVals, latVals)
def extractMapData(nc, dataVar, tIndex, zIndex):
""" TODO: add a docstring! """
# Find the number of dimensions for the data variable
numDims = len(dataVar.dimensions)
if numDims < 2 or numDims > 4:
raise ValueError("Cannot extract data from variable with %d dimensions" % numDims)
# Check that longitude and latitude variables are present
lonVar = netcdfUtils.findLongitudeVar(nc, dataVar)
latVar = netcdfUtils.findLatitudeVar(nc, dataVar)
if lonVar is None or latVar is None:
raise ValueError("Cannot extract map data if longitude and latitude are not present")
# If this is a four-dimensional variable, assume that there are t,z,y,x axes, in this order
if numDims == 4:
return dataVar[tIndex,zIndex]
elif numDims == 3:
# We don't know if the first dimension is t or z
tVar = netcdfUtils.findTimeVar(nc, dataVar)
if tVar is None:
# We assume the first dimension is z
return dataVar[zIndex]
else:
# We assume the first dimension is t
return dataVar[tIndex]
else:
# numDims must be 2
return dataVar[:]
def plotTimeseries(filename,varIdentifier,lonVal,latVal,zVal):
"""Takes five aruguments i.e. a string representing location of NetCDF file,
variable identifier, the longitude and latitude values in degress as well as
the value of the vertical axis in the units used in the data file
(this is ignored if the data variable has no vertical dimension). Uses
the extractTimeseries() function in the extract module and plot using the
displayTimeseriesPlot() function in the plotting module."""
import netcdfUtils
import netCDF4
import plotting
#A netCDF4 dataset Object
nc=netCDF4.Dataset(filename)
# A varible object representing a data variable (e.g. sst)
dataVar=nc.variables[varIdentifier]
# Check that longitude,latitude, vertical and time variables are present
lonVar = netcdfUtils.findLongitudeVar(nc, dataVar)
latVar = netcdfUtils.findLatitudeVar(nc, dataVar)
timeVar=netcdfUtils.findTimeVar(nc,dataVar)
#check if the file has latitude, longitude and time dimensions
if lonVar is None or latVar is None or timeVar is None:
raise ValueError("Cannot extract map data if longitude, latitude & time are abscent")
#extract data as well as the vertical and longitude/latitude coordinates
data=extract.extractTimeseries(nc,dataVar,lonVal,latVal,zVal)
# get the title of the plot
title=netcdfUtils.getTitle(dataVar)
#plot the data against time
plotting.displayTimeseriesPlot(nc,dataVar,data[0],data[1],title)
return
#### Here are some test functions.
#### Simply run this script to run them.
#if __name__ == '__main__':
#plotMap("POLCOMS.nc", "POT", 0,0)
#plotMap("C:/Users/Developer202/Desktop/MWR/Data/Reanalysis/Surface/vwnd.mon.mean.nc","vwnd",800,0)
#plotMap("C:/Users/Developer202/Desktop/MWR/Data/Reanalysis/Surface/uwnd.mon.mean.nc","uwnd",800,0)
#plotMap("GlobModel_temp.nc", "ta", 0,5)
#plotMap("GlobModel_ozone.nc", "colo3", 0,5)
#plotMap("rfe2015_02_seas_anom.nc","rfe",0,5)
#plotMap("sst.day.mean.2014.v2.nc","sst",0,5)
#plotMap("FOAM_Natl.nc", "ssh", 0,0)
#plotMap("HadCEM.nc", "salinity", 0,10)
# The OSTIA dataset is large, so this test can be slow. Uncomment
# this line to run it
#plotMap("OSTIA.nc", "analysed_sst", 0,0)
def plotVerticalSection(filename,varIdentifier,vertSection,coordVal,tIndex):
"""Takes five aruguments i.e. a string representing location of NetCDF file,
variable identifier, string indicating whether vertical section is
North-South (NS) or East-West (EW), the value of latitude/longitude depending
on EW or NS, and the index along the time axis for which to get the data. Uses
the extractVerticalSection() function from the extract module to extract data.
Plots using the displayVertSectionPlot() function from the plotting module."""
import netCDF4
import netcdfUtils
import plotting
nc=netCDF4.Dataset(filename) #A netCDF4 dataset Object
dataVar=nc.variables[varIdentifier] # A varible object representing a data variable ('lat' or 'latitude')
# Check that longitude,latitude, vertical and time variables are present
lonVar = netcdfUtils.findLongitudeVar(nc, dataVar)
latVar = netcdfUtils.findLatitudeVar(nc, dataVar)
zVar=netcdfUtils.findVerticalVar(nc,dataVar)
#check if string that indicate vertical section is EW or NS else raise error
if vertSection!='NS'and vertSection!='EW':
raise ValueError("Only NS or EW can be used to indicate Vertical section")
# if the vertical section identifier is North-South get latitudes
if vertSection=='NS':
#check if the slicing longitude is within the range of values
if coordVal<-180 or coordVal>180:
raise ValueError("The Longitude values range from -180(180W) to 180(180E)")
#get the latitude values for the x axis
coordValues=latVar[:]
# if the vertical section identifier is East-West get longitudes
elif vertSection=='EW':
#check if the slicing latitude is within the range of values
if coordVal<-90 or coordVal>90:
raise ValueError("The Latitude values range from -90(90S) to +90(90N)")
#get the longitude values for the x axis
coordValues=lonVar[:]
#check if the latitude, longitude and vertical dimesions are present in file
if lonVar is None or latVar is None :
raise ValueError("Cannot extract map data if longitude and latitude are not present")
if zVar is None:
raise ValueError("Cannot extract map data if Vertical dimension is not present")
#extract data as well as the vertical and longitude/latitude coordinates
data=extract.extractVerticalSection(nc,dataVar,vertSection,tIndex,coordVal)
#get the title of the plot
title=netcdfUtils.getTitle(dataVar)
#plot the exrated data using the displayVertSectionPlot() from plotting module
plotting.displayVertSectionPlot(data,zVar,coordValues,title,vertSection,coordVal)
return
def extractTimeseries(nc,dataVar,lonVal,latVal,zVal):
""" Takes five arguments; the NetCDF dataset, the variable object, the
longitude value, the latitude value and the vertical value and use them to
extracts data and time values at the point described. It returns the data of
the and variable represented by the variable object the time values for
each point described by the longitude, latitude and vertical values in the
NetCDF file"""
# Find the number of dimensions for the data variable
numDims = len(dataVar.dimensions)
# Check if the number of dimensions and raise error if they are not 3 or 4
if numDims < 3 or numDims > 4:
raise ValueError("Cannot extract data from variable with %d dimensions" % numDims)
# Check if longitude, latitude and vertical dimensions are present
lonVar = netcdfUtils.findLongitudeVar(nc, dataVar)
latVar = netcdfUtils.findLatitudeVar(nc, dataVar)
timeVar=netcdfUtils.findTimeVar(nc,dataVar)
zVar=netcdfUtils.findVerticalVar(nc,dataVar)
timeVal=timeVar[:]
# check if the longitude, latitude or the time dimension is abscent and raise error
if lonVar is None or latVar is None or timeVar is None:
raise ValueError("Cannot extract map data if longitude, latitude and time dimensios are abscent")
# map the longitutude value from range (-180 to 180) onto (0 to 360) in the data file
#lonVal=lonVal+180
if min(lonVar[:])>=0:
if lonVal<0:
lonVal=lonVal+180
# if the dimensions are 4 i.e. time,vertical,latitude and longitude
if numDims == 4:
#get the vertical,longitude and latitude indices
lonIndex=utils.findNearestIndex(lonVar,lonVal)
latIndex=utils.findNearestIndex(latVar,latVal)
zIndex=utils.findNearestIndex(zVar,zVal)
return dataVar[:,zIndex,latIndex,lonIndex],timeVal
# if the dimensions are 3 ie time, latitude and longitude
elif numDims == 3:
#get the longitude and latitude indices
lonIndex=utils.findNearestIndex(lonVar,lonVal)
latIndex=utils.findNearestIndex(latVar,latVal)
return dataVar[:,latIndex,lonIndex],timeVal
