In [1]:

    

!conda install -y netcdf4


    

    




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

# All requested packages already installed.
# packages in environment at C:\Users\Reasearch\Anaconda3:
#
netcdf4                   1.2.4               np111py35_0  

In [2]:

    

from netCDF4 import Dataset, num2date, date2num
from numpy import * 
import matplotlib.pyplot as plt 
%matplotlib inline


    

In [3]:

    

from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets


    

In [4]:

    

x = linspace(0, 1, 100) # generates a hundred values between 0 and 1
f = 2
a = 3


    

In [5]:

    

plt.plot(x, sin(2*pi*x*f))


    

    
Out[5]:





[<matplotlib.lines.Line2D at 0x78c8400>]






    

In [6]:

    

def pltsin(f):
    plt.plot(x, sin(2*pi*x*f))


    

In [7]:

    

pltsin(0.5)


    

In [ ]:

    




    

In [8]:

    

interact(pltsin, f=(1, 10, 0.2), x = (1, 10, 0.2))


    

    
Out[8]:





<function __main__.pltsin>






    

In [9]:

    

def pltsina(f, a):
    plt.plot(x, a*sin(2*pi*x*f))
    plt.ylim(-10.5, 10.5)


    

In [10]:

    

interact(pltsina, f=(1, 10, 0.2), a = (1, 10, 0.2))


    

    
Out[10]:





<function __main__.pltsina>






    

In [11]:

    

f=Dataset ('ncep-data/air.sig995.2013.nc') # get individual data set out of the right folder


    

In [12]:

    

air = f.variables['air'] # get variable


    

In [13]:

    

plt.imshow(air[0,:,:]) # display first timestep


    

    
Out[13]:





<matplotlib.image.AxesImage at 0x8db2eb8>






    

In [14]:

    

# Create function to browse through the days 

def sh(time):
    plt.imshow(air[time,:,:])


    

In [15]:

    

# Now make it interactive

interact(sh, time=(0, 355, 1))


    

    
Out[15]:





<function __main__.sh>






    

In [16]:

    

# Browse variable 

def sh(time =0, var='air',  year = '2013'):
    f=Dataset('ncep-data/'+var+'.sig995.'+year+'.nc')
    vv=f.variables[var]
    plt.imshow(vv[time,:,:])


    

In [17]:

    

#Give a list of variables 

variabs =['air', 'uwnd', 'vwnd', 'rhum']
year = ['2013', '2014', '2015']


    

In [18]:

    

# Now interact with it 

interact(sh, time=(0, 355, 1), year = year, var=variabs)


    

    
Out[18]:





<function __main__.sh>






    

In [19]:

    

help(sh)


    

    




Help on function sh in module __main__:

sh(time=0, var='air', year='2013')

In [20]:

    

from mpl_toolkits.basemap import Basemap


    

In [21]:

    

# create north polar sterographic projection 

m=Basemap(projection='npstere', boundinglat=60, lon_0=0, resolution ='l')
m.fillcontinents(color='gray', lake_color='gray')
m.drawparallels(arange(-80.,81.,20.))
m.drawmeridians(arange(-180.,181.,20.))
m.drawmapboundary(fill_color='white')


    

    
Out[21]:





<matplotlib.patches.Rectangle at 0x90d4898>






    

In [22]:

    

# Set up some variables 
lon = f.variables['lon'][:]
lat = f.variables['lat'][:]
lon, lat = meshgrid(lon, lat)
x, y = m(lon, lat)


    

In [23]:

    

def sh(time =0, var='air',  year = '2013'):
    f=Dataset('ncep-data/'+var+'.sig995.'+year+'.nc')
    vv=f.variables[var]
    tt=f.variables['time']
    dd=num2date(tt[time], tt.units)
    m.fillcontinents(color='gray', lake_color='gray')
    m.drawparallels(arange(-80.,81.,20.))
    m.drawmeridians(arange(-180.,181.,20.))
    m.drawmapboundary(fill_color='white')
    cs = m.contourf(x, y, vv[time,:,:]-273.15)


    

In [24]:

    

interact(sh, year=year, time=(0,355,1), var=variabs)


    

    
Out[24]:





<function __main__.sh>






    

In [32]:

    

my_map = Basemap (projection='merc', lat_0=0, lon_0=30,
                 resolution='h', area_thresh=1000.0,
                 llcrnrlon=29, llcrnrlat=-1,
                 urcrnrlon=31, urcrnrlat=1)
# area threshold states how rivers etc look - scale, resolution sets resolution, llcrnlon etc sets box, 
# lat and lon decide where you look 
my_map.drawcoastlines()

my_map.drawcountries()
my_map.fillcontinents(color='coral')

my_map.drawmapboundary()

my_map.drawmeridians(arange(0,360,30))
my_map.drawparallels(arange(-90, 90, 30))

lon=30
lat=0

x,y=my_map(lon, lat)
my_map.plot(x, y, 'bo', markersize=7.2)
plt.show() # here the function that decides actually plots


    

In [26]:

    

# This just lets the output of the following code samples 
# display inline on this page, at an appropirate size
from pylab import rcParams


    

In [30]:

    

# Create a simple basemap 

my_map = Basemap (projection='ortho', lat_0=50, lon_0=0,
                 resolution='l', area_thresh=1000.0)

my_map.drawcoastlines()

my_map.drawcountries()
my_map.fillcontinents(color='red', lake_color='gray')


plt.show()


    

In [41]:

    

#Check the first few lats and longs

import csv

# Open the earthquake data file.
filename = '1.0_week.csv'

# Create empty lists for the latitudes and longitudes.
lats, lons, mags = [], [], []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        mags.append(float(row[4]))
        
# Display the first 5 lats and lons.
print('lats', lats[0:5])
print('lons', lons[0:5])
print('mags', mags[0:5])


    

    




lats [58.8194, 38.3672, 38.8388329, 32.8655, 61.2948]
lons [-153.8621, -118.9048, -122.8259964, -116.1966667, -149.1667]
mags [1.9, 1.5, 1.34, 1.1, 1.0]

In [87]:

    

### And now create a plot of these on a map projection

import csv

# Open the earthquake data file.
filename = '1.0_week.csv'

# Create empty lists for the latitudes and longitudes.
lats, lons, mags = [], [], []

# Read through the entire file, skip the first line,
#  and pull out just the lats and lons.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes and longitudes in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        mags.append(float(row[4]))
        
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
eq_map = Basemap(projection='robin', resolution = 'l', area_thresh = 1000.0,
              lat_0=52, lon_0=0)
eq_map.drawcoastlines()
eq_map.drawcountries()
eq_map.fillcontinents(color = 'coral')
eq_map.drawmapboundary()
eq_map.drawmeridians(np.arange(0, 360, 30))
eq_map.drawparallels(np.arange(-90, 90, 30))
 
min_marker_size = 1
for lon, lat, mags in zip(lons, lats, mags):
    x,y = eq_map(lon, lat) 
    msize = mags * min_marker_size  
    eq_map.plot(x, y, , markersize=msize)
    if mags >= 5.0 
        eqcolor = 'r'
    elif: mags >= 1.0 and <= 3.0  
        eqcolor = 'g'
    elif: <= 1.0
        eqcolor = 'y
    eq_map.plot(x, y, eqcolor, markersize=msize)
    
plt.show()


    

    




  File "<ipython-input-87-3f019e994e8c>", line 44
    eq_map.plot(x, y, , markersize=msize)
                      ^
SyntaxError: invalid syntax

In [65]:

    

x,y


    

    
Out[65]:





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In [ ]: