aidanheerdegen

from netCDF4 import *
from numpy import *
from scipy.stats import binned_statistic

# Overturning in rho z coords

def rhozmoc (data_file,grid_file,minval=1002,maxval=1030,inc=0.1):

    print 'Reading file ',grid_file
    input1 = Dataset(grid_file, 'r')

aidanheerdegen

from netCDF4 import *
from numpy import *
from scipy.stats import binned_statistic

# Can't use multiprocessing.dummy as binned_statistic doesn't release the GIL
import multiprocessing

def binsum(binarray, valarray, bins):

    summed, bin_edges, bin_number = binned_statistic(binarray, valarray, sum, bins, (min(bins),max(bins)) )

aidanheerdegen

module quart

  implicit none
  type quaternion
     ! q(4) = (w, x, y, z) where w is the scalar part
     real :: q(4)
     ! Flags to tell us if we want to keep the quaternion unitised, and
     ! also if the quaternion represents an improper rotation (rotation + inversion)
     logical :: unit
     logical :: improper

aidanheerdegen

import numpy as np
from netCDF4 import Dataset
import sys

import xarray
from xarray.ufuncs import *
from glob import glob
import os
import string

aidanheerdegen

! Open an iced restart file, report times. Write new iced restart with modified time header
! single processor (not parallelized)

! intel compiler:
! ifort edit_time.f90 -o edit_time -i4 -r8 -convert big_endian -assume byterecl

program edit_time

  implicit none

aidanheerdegen

#!/bin/bash
# A cross-node version of gnu parallel
#
#    node-parallel [command [arguments]] < list_of_arguments
#    node-parallel [command [arguments]] ( ::: arguments | :::: argfile(s) )*
#
# The command can use gnu parallel replace tags (e.g. '{}' is replaced with an
# argument), see 'man parallel' for full details. 
# Note that unlike gnu parallel a '{}' will not be automatically added if not 
# present, it must be manually added.

aidanheerdegen

#!/usr/bin/env python

import netCDF4 as nc
import sys
import numpy as np
import itertools as it

name_var = sys.argv[1]
orig     = sys.argv[2]
new      = sys.argv[3]

aidanheerdegen

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mpc
import matplotlib as mpl
import cartopy.crs as ccrs
import cartopy.feature as cpf
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

# set the size limits

aidanheerdegen

import xarray
from scipy.stats import norm
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

list_indices = ['tasmax']
indices = list_indices[0]

data = xarray.open_dataset('/g/data3/w97/dc8106/AMZ_def_EXPs/test/tasmax_sc_001GPsc_E0_test.nc', chunks={'time':1000})

aidanheerdegen