def avg_time_series(nc_in, param_in, region, box_in, model_in, print_info):
'''
avg_time_series ....
Parameters
----------
nc_in : string
path to nc file to be analyzed
param : string
the parameter to be read from the nc files
region : string
Just the name of a region
box_in : numerical arrays
array of 4 numbers that indicates the long1, long2, lat1, lat2 of the
region
model_in : string
Just the name of the model
Returns
-------
'''
print(nc_in)
print(param_in)
print(region)
print(box_in)
print(model_in)
nc_in_filename = os.path.basename(nc_in) # ###
nc_fldmean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_fldmean_85.nc"
nc_ymean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_ymean_85.nc"
png_fldmean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_fldmean_85.png"
png_ymean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_ymean_85.png"
out_dir = nc_in.replace(nc_in_filename, 'avg_85')
check_and_create(out_dir)
nc_fldmean = nc_in.replace(nc_in_filename, 'avg_85/'+nc_fldmean)
nc_ymean = nc_in.replace(nc_in_filename, 'avg_85/'+nc_ymean)
png_fldmean = nc_in.replace(nc_in_filename, 'avg_85/'+png_fldmean)
png_ymean = nc_in.replace(nc_in_filename, 'avg_85/'+png_ymean)
print(nc_fldmean)
print(nc_ymean)
print("-"*80)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
data_in = Dataset(nc_in, mode='r') # file handler
if print_info:
ncdump(data_in, True)
print("-"*80)
data_in.close()
box = "-sellonlatbox,%d,%d,%d,%d" % (box_in[0], box_in[1], box_in[2], box_in[3])
# Create nc files for field mean and year mean.
if os.path.exists(nc_fldmean):
print("%s already exists", nc_fldmean)
else:
print("%s Create", nc_fldmean)
cdo.fldmean(input=box+" "+nc_in, output=nc_fldmean, options='-f nc', returnCdf=True)
if os.path.exists(nc_ymean):
print("%s already exists", nc_ymean)
else:
print("%s Create", nc_ymean)
cdo.yearmean(input=nc_fldmean, output=nc_ymean, options='-f nc', returnCdf=True)
# Create file handlers for field mean and year mean
data_fldmean = Dataset(nc_fldmean, mode='r')
data_ymean = Dataset(nc_ymean, mode='r')
# Check field mean is ok
if print_info:
ncdump(data_fldmean, True)
# Check year mean is ok
if print_info:
ncdump(data_ymean, True)
param_year = data_ymean.variables[param_in][:]
print("number of data points: %d" % len(param_year))
print("-"*80)
# plot_time_series (data_fldmeanmean, param_in, region, title_fldmean)
plot_time_series(data_ymean, param_in, region)
# plt.savefig(png_ymean, dpi=300)
data_fldmean.close()
data_ymean.close()
lat2 = 8.25
long1 = 284.0
long2 = 288.75
box = "-sellonlatbox,%d,%d,%d,%d" % (long1, long2, lat1, lat2)
data_spat_avg = cdo.fldmean(input=box + " " + nc_in,
options='-f nc',
returnCdf=True)
cdo.fldmean(input=box + " " + nc_in,
output=nc_spat_avg,
options='-f nc',
returnCdf=True)
#cdo -f nc -r -b 64 yearmean t2m_mon_2011-2015.nc t2m_mon_2011-2015_annual.nc
data_ymean = cdo.yearmean(input=nc_spat_avg, options='-f nc', returnCdf=True)
ncdump(data_ymean, True)
t2my = data_ymean.variables['t2m'][:] # shape is time, lat, lon as shown above
print(t2my)
print("-" * 70)
# write to a file
# cdo.fldmean(input="-sellonlatbox,-65,-80,-15,15 "+nc_in, output=nc_spat_avg \
# , options='-f nc', returnCdf=True)
# verify file is written correctly
# type: <class 'netCDF4._netCDF4.Dataset'>
# nc_fh = Dataset(nc_spat_avg, 'r') #filehandler
ncdump(data_spat_avg, True)
lats = data_spat_avg.variables['lat'][:] # extract/copy the data
lons = data_spat_avg.variables['lon'][:]
time = data_spat_avg.variables['time'][:]
http://dx.doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 ''' import datetime as dt # Python standard library datetime module import numpy as np from netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/ import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid from useful_functions import ncdump #here starts the program TK_SILENCE_DEPRECATION = 1 nc_f = '../nc_files/examples/air.sig995.2012.nc' # Your filename nc_fid = Dataset(nc_f, 'r') # Dataset is the class behavior to open the file # and create an instance of the ncCDF4 class nc_attrs, nc_dims, nc_vars = ncdump(nc_fid, True) # Extract data from NetCDF file lats = nc_fid.variables['lat'][:] # extract/copy the data lons = nc_fid.variables['lon'][:] time = nc_fid.variables['time'][:] air = nc_fid.variables['air'][:] # shape is time, lat, lon as shown above time_idx = 237 # some random day in 2012 # Python and the renalaysis are slightly off in time so this fixes that problem offset = dt.timedelta(hours=48) # List of all times in the file as datetime objects dt_time = [dt.date(1, 1, 1) + dt.timedelta(hours=t) - offset for t in time] cur_time = dt_time[time_idx] # Plot of global temperature on our random day fig = plt.figure()
def pr_time_series(nc_in, param_in, model_in, print_info=False):
'''
avg_time_series ....
Parameters
----------
nc_in : string
path to nc file to be analyzed
param : string
the parameter to be read from the nc files
region : string
Just the name of a region
box_in : numerical arrays
array of 4 numbers that indicates the long1, long2, lat1, lat2 of the
region
model_in : string
Just the name of the model
Returns
-------
'''
print(nc_in)
print(param_in)
print(model_in)
nc_in_filename = os.path.basename(nc_in) # ###
nc_pmax = os.path.splitext(nc_in_filename)[0]+"_pmax.nc"
nc_pmax_fldmax = os.path.splitext(nc_in_filename)[0]+"_pmax_fldmax.nc"
png_pmax = os.path.splitext(nc_in_filename)[0]+"_pmax.png"
png_pmax_fldmax = os.path.splitext(nc_in_filename)[0]+"_pmax_fldmax.png"
out_dir = nc_in.replace(nc_in_filename, 'pmax')
check_and_create(out_dir)
nc_pmax = nc_in.replace(nc_in_filename, 'pmax/'+nc_pmax)
nc_pmax_fldmax = nc_in.replace(nc_in_filename, 'pmax/'+nc_pmax_fldmax)
png_pmax = nc_in.replace(nc_in_filename, 'pmax/'+png_pmax)
png_pmax_fldmax = nc_in.replace(nc_in_filename, 'pmax/'+png_pmax_fldmax)
print(nc_pmax_fldmax)
print("-"*80)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
data_in = Dataset(nc_in, mode='r') # file handler
if print_info:
ncdump(data_in, True)
print("-"*80)
data_in.close()
# box = "-sellonlatbox,%d,%d,%d,%d" % (box_in[0], box_in[1], box_in[2], box_in[3])
# Create nc files for precipitation max for year.
if os.path.exists(nc_pmax):
print("%s already exists", nc_pmax)
else:
print("%s Create", nc_pmax)
# cdo.yearmax(input=box+" "+nc_in, output=nc_pmax, options='-f nc', returnCdf=True)
cdo.yearmax(input=nc_in, output=nc_pmax, options='-f nc', returnCdf=True)
if os.path.exists(nc_pmax_fldmax):
print("%s already exists", nc_pmax_fldmax)
else:
print("%s Create", nc_pmax_fldmax)
cdo.fldmax(input=nc_pmax, output=nc_pmax_fldmax, options='-f nc', returnCdf=True)
# Create file handlers for field mean and year mean
data_pmax = Dataset(nc_pmax, mode='r')
data_fld_max = Dataset(nc_pmax_fldmax, mode='r')
# Check pmax and fldmax are ok
if print_info:
ncdump(data_pmax, True)
if print_info:
ncdump(data_fld_max, True)
# Check pmax temporal is ok
if print_info:
ncdump(data_pmax, True)
param_year = data_pmax.variables[param_in][:]
print("number of data points: %d" % len(param_year))
print("-"*80)
title_pmax = ('Precipitation max for ' + nc_in_filename)
plot_time_series(data_fld_max, param_in, title_pmax)
# plt.savefig(png_pmax_fldmax)
plt.savefig('../'+region+'_'+param+'.png', dpi=300) # ../ queda guardado en el directorio anterior en donde esta
data_pmax.close()
data_fld_max.close()
plt.show()
# type: <class 'netCDF4._netCDF4.Dataset'>
# nc_data = cdo.fldmean(input="-sellonlatbox,-65,-80,-15,15 "+nc_in \
# , options='-f nc', returnCdf=True)
# write to a file
#box values for avg (from insumos5 example):
lat1 = 3.75
lat2 = 8.25
long1 = 284.0
long2 = 288.75
box = "-sellonlatbox,%d,%d,%d,%d" % (long1, long2, lat1, lat2)
cdo.fldmean(input=box+" "+nc_in, output=nc_out \
, options='-f nc', returnCdf=True)
# verify file is written correctly
# type: <class 'netCDF4._netCDF4.Dataset'>
nc_fh = Dataset(nc_out, 'r') #filehandler
ncdump(nc_fh, True)
nc_fh.close()
# add_offset: 293.69328912771044
# scale_factor: 0.00038463520406703495
ds = xr.open_dataset(nc_out) # NetCDF or OPeNDAP URL
#ds is of type <class 'xarray.core.dataset.Dataset'>
# select a variable to plot
ds['t2m'].plot()
plt.show()
TK_SILENCE_DEPRECATION = 1
nc_file = "pr_Amon_MPI-ESM-P_historical_r1i1p1_185001-200512"
nc_dir = "../nc_files/cmip5_converted/MPI-ESM-P/pr"
cmip5_std_dir = "cmip5/HadGEM2AO/pr/"
nc_in = nc_dir+cmip5_std_dir+nc_file # Your filename
nc_spat_avg = os.path.splitext(nc_in)[0]+"_avg_spat.nc"
nc_spat_avg2 = os.path.splitext(nc_in)[0]+"_avg_spat2.nc"
# Initialize CDO
cdo = Cdo()
cdo.degub = True
fh_orig = Dataset(nc_in, mode='r') # file handler
ncdump(fh_orig, True)
print("-"*80)
# put vars into numpy arrays
lons = fh_orig.variables['lon'][:]
lats = fh_orig.variables['lat'][:]
# print(type(lons))<class 'numpy.ma.core.MaskedArray'>
# print(type(lats))
# From http://www.bamboodream.sakura.ne.jp/hiroblog/?page_id=552
# instead of writing to file, return the data directly
# type: <class 'netCDF4._netCDF4.Dataset'>
# box values for avg (from insumos5 example):
# Region 1 (Colombia)
lat1 = -3