def spi_netcdf_grid(rfile, wfile, vname, nspi):
from netcdf_tools import ncextractall
from netcdf_tools import ncwrite_climgrid
from netCDF4 import num2date
from netCDF4 import date2num
import numpy as np
# Import and extract the netcdf file, returns a dictionary
datdict = ncextractall(rfile)
# Read each variable
data = datdict[vname]
lon = datdict["lon"]
lat = datdict["lat"]
time = datdict["time"]
# convert missing numbers to NaN
miss = datdict[vname + "_missing_value"]
data = np.where(data == miss, np.nan, data)
# convert time units to actual date
time_u = datdict["time_units"]
if "time_calendar" in datdict.keys():
cal = datdict["time_calendar"]
time = num2date(time, units=time_u, calendar=cal)
else:
time = num2date(time, units=time_u)
trimmed = grid_tools.trim_time_jandec(data, time)
data = trimmed[0]
time = trimmed[1]
# Create an empty array to store the SPI data
spigrid = np.zeros(data.shape)
# Compute the SPI at all locations
for i in range(0, len(lat)):
for j in range(0, len(lon)):
tmpdata = data[:, i, j]
tmpdata = tmpdata.flatten()
tmpspi = spi(tmpdata, nspi)
tmpspi = tmpspi.flatten()
spigrid[:, i, j] = tmpspi
# convert missing numbers back to a float
spigrid = np.where(spigrid == np.nan, miss, spigrid)
# convert time back to original units
if "time_calendar" in datdict.keys():
cal = datdict["time_calendar"]
time = date2num(time, units=time_u, calendar=cal)
else:
time = date2num(time, units=time_u)
spidescrip = "The Standardised Precipitation Index (SPI) computed as per McKee et al. (1993)"
spilong_name = str(nspi) + "-month Standardised Precipitation Index"
spiname = "SPI" + str(nspi)
write = ncwrite_climgrid(
wfile, spigrid, spiname, spidescrip, spilong_name, miss, "standardised units", time, lon, lat, time_u
)
return spigrid, lon, lat, time
def nc_ipophase():
from netCDF4 import date2num
from netCDF4 import num2date
import numpy as np
from netcdf_tools import ncwrite_climgrid
from netcdf_tools import ncextractall
#Input file (this file goes from January 1870 to April 2016 as is)
file = '/Users/ailieg/Data/HadISST_sst.nc'
nc = ncextractall(file)
sst = nc['sst']
lon = nc['longitude']
nlon = lon.size
lat = nc['latitude']
nlat = lat.size
time = nc['time']
miss = nc['sst_missing_value']
units = nc['sst_units']
#convert time units to actual date
time_u = nc['time_units']
if 'time_calendar' in nc.keys():
cal = nc['time_calendar']
time = num2date(time,units = time_u, calendar=cal)
else: time = num2date(time,units = time_u)
#extract years and months from the datetime array
ntime = time.size
year = np.zeros(ntime)
month = np.zeros(ntime)
i = 0
while (i < ntime):
year[i] = time[i].year
month[i] = time[i].month
i=i+1
#Extract data from 1950 to 2015 only
sst = sst[(year > 1976) & (year < 2000), :,:]
time = time[(year > 1976) & (year < 2000)]
ntime = time.size
#Reshape the array to determine climatology over the whole period
nyear = ntime/12
sst = sst.reshape([nyear,12,nlat,nlon])
time = time.reshape([nyear, 12])
mid = int(nyear/2)
time = time[mid,:]
sst = np.mean(sst, axis=0)
#Write the output file
descrip = 'Monthly climatology of HadISST SSTs from IPO positive years computed as 1977-1999'
long_name = 'sst'
missing = miss
climunits = units
time = date2num(time,units = time_u, calendar=cal)
print(sst.shape,time.size,lon.size,lat.size)
filename = '/Users/ailieg/Data/IPO_ACCESS/HadISST_IPOpos_1977_1999.nc'
print("Writing netCDF file...")
ncw = ncwrite_climgrid(filename, sst, 'sst', descrip, long_name, missing, climunits, time, lon, lat, time_u, cal)
print("NetCDF file written")
return sst, lat, lon