In [1]:

    

import os
from IPython.core.display import HTML
import netCDF4
import pandas as pd


    

In [2]:

    

%matplotlib inline
from datetime import datetime


    

In [3]:

    

url='http://geoport.whoi.edu/thredds/dodsC/usgs/data2/emontgomery/stellwagen/CF-1.6/HURRIRENE_BB/9141wh-a.nc'
nc = netCDF4.Dataset(url)
ncv = nc.variables
uvar = ncv['u_1205']
time_var = ncv['time']
jd = netCDF4.num2date(time_var[:],time_var.units)


    

In [4]:

    

shape(uvar)


    

    
Out[4]:





(8237, 33)

In [5]:

    

u=uvar[:,15]
ts = pd.Series(u,index=jd)
ts1h = pd.rolling_mean(ts, window=10, center=True, freq='1H')
df = pd.DataFrame(ts1h,columns=['u'])


    

In [6]:

    

shape(ts1h)


    

    
Out[6]:





(688,)

In [7]:

    

roll_l = pd.rolling_mean(df['u'], window=40, center=True, freq='1H')
df.plot()


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f94e4e68710>






    

In [8]:

    

import numpy as np
from oceans import lanc

window_size = 96+1+96
freq = 1./40
wt = lanc(window_size, freq)
df['low'] = np.convolve(wt, df['u'], mode='same')
df['high'] = df['u'] - df['low']


    

In [9]:

    

import pandas as pd

roll_l = pd.rolling_mean(df['u'], window=40, center=True, freq='1H')
roll_h = df['u'] - roll_l


    

In [10]:

    

import iris
from iris.pandas import as_cube

cube = as_cube(df['u'])

low = cube.rolling_window('index',
                        iris.analysis.SUM,
                        len(wt),
                        weights=wt)
t = low.coord('index')
t = t.units.num2date(t.points)

pad = np.zeros(window_size) * np.NaN
low = np.r_[pad, low.data, pad]

high = df['u'] - low


    

In [11]:

    

%matplotlib inline

import matplotlib.pyplot as plt

fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(15, 7),
                                    sharex=True, sharey=True)
x = df.index.to_pydatetime()

ax0.plot(x, df['u'], label='original')
ax0.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
           ncol=3, fancybox=True, shadow=True, numpoints=1)

ax1.plot(x, df['high'], label='lanc high')
ax1.plot(x, roll_h, label='pandas high')
ax1.plot(x, high, label='iris high')
ax1.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
           ncol=3, fancybox=True, shadow=True, numpoints=1)

ax2.plot(x, df['low'], label='lanc low')
ax2.plot(x, roll_l, label='pandas low')
ax2.plot(x, low, label='iris low')
ax2.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
           ncol=3, fancybox=True, shadow=True, numpoints=1)


    

    
Out[11]:





<matplotlib.legend.Legend at 0x7f94d73b7390>






    

In [11]:

    




    

In [11]: