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!conda install scipy netcdf4 numpy matplotlib basemap basemap-data-hires --yes


    

    




Fetching package metadata ...........
Solving package specifications: .

# All requested packages already installed.
# packages in environment at /opt/conda:
#
basemap                   1.1.0                    py36_2    conda-forge
basemap-data-hires        1.1.0                         0    conda-forge
matplotlib                2.1.0                    py36_0    conda-forge
netcdf4                   1.3.0                    py36_0    conda-forge
numpy                     1.13.3          py36_blas_openblas_200  [blas_openblas]  conda-forge
scipy                     0.19.1          py36_blas_openblas_202  [blas_openblas]  conda-forge

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%matplotlib inline


    

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from netCDF4 import MFDataset, MFTime, num2date
import numpy as np
import matplotlib.pyplot as plt


    

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base = "http://data.ceda.ac.uk/neodc/casix/seawifs_plankton/data/monthly/PSC_monthly_{0}.nc"


    

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urls = [ base.format(yr) for yr in range(1998, 2007, 1)]


    

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rootgrp = MFDataset(urls, aggdim = 'time')


    

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MFtime = MFTime(rootgrp.variables['time'])
time = num2date(MFtime[:], 'days since 1998-01-01')


    

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lats = rootgrp['latitude'][:]
lons = rootgrp['longitude'][:]
lat_bnds, lon_bnds = [55.5, 57], [-3.5, 0]
lat_inds = np.where((lats > lat_bnds[0]) & (lats < lat_bnds[1]))[0]
lon_inds = np.where((lons > lon_bnds[0]) & (lons < lon_bnds[1]))[0]


    

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plankton = rootgrp.variables['phytoplankton_size_class']
roi = plankton[:, lat_inds, lon_inds]


    

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roi_mean = np.mean(roi, (1,2))
roi_sum = np.sum(roi, (1,2))


    

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fig = plt.figure(figsize=(8, 8), dpi= 100)
plt.plot_date(time, roi_sum)


    

    
Out[11]:





[<matplotlib.lines.Line2D at 0x7f9be7861978>]






    

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