-12.70000 64.30000 165.3000    
   4.900000 64.00000 166.6000    
   23.90000 64.90000 168.7000    
//////////////
lines:

And that's it! Couldn't be easier, right? One little thing to take care of is that 


import numpy as np

dtype = np.dtype('B')
try:
    with open("c:\temp\Binary_File.jpg", "rb") as f:
        numpy_data = np.fromfile(f,dtype)
    print(numpy_data)
except IOError:
    print('Error While Opening the file!')    

/////////////////

with open(file_path, 'rb') as file_obj:
    file_obj.seek(seek_to_position)
    data_ro = np.frombuffer(file_obj.read(total_num_bytes), dtype=your_dtype_here)
    data_rw = data_ro.copy() #without copy(), the result is read-only

///////////////////////

import numpy as np
>>> import cv2
>>> xbash = np.fromfile('/bin/bash', dtype='uint8')
>>> xbash.shape
(1086744,)
>>> cv2.imwrite('bash1.png', xbash[:10000].reshape(100,100))
//////////

# Plot 2D histogram using pcolor
fig2 = plt.figure()
plt.pcolormesh(xedges,yedges,Hmasked)
plt.xlabel('x')
plt.ylabel('y')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
//////////////////////

dt=np.transpose(np.genfromtxt(ft))
lon=dt[0,:]
lat=dt[1,:]
temp=dt[2,:]

import csv
import numpy as np
import matplotlib.pyplot as plt

# Make plot 1
fig1, ax1 = plt.subplots(1)
#plt.fill_between(dist2shore,bathy,1000,color='k')
plt.scatter(dist2shore,depth,s=20,c=temp,marker='o', edgecolor='none')
plt.ylim((-0.5,max(depth)+5))
ax1.set_ylim(ax1.get_ylim()[::-1])
ax1.set_xlim(min(dist2shore),max(dist2shore))
cbar = plt.colorbar(orientation='horizontal', extend='both')
cbar.ax.set_xlabel('Temperature ($^\circ$C)')
plt.title('OTN Glider transect')
plt.ylabel('Depth (m)')
plt.xlabel('Distance form shore (km)')
plt.show()
 
# Save figure (without 'white' borders)
plt.savefig('glider_Dist2Shore.png', bbox_inches='tight')
////////////////////////
 

# For original tutorial, see (bottom of page): This tutorial came from 
here (bottom of page): http://polar.ncep.noaa.gov/global/examples/usingpython.shtml
 
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import netCDF4
 
plt.figure()
 
nc = '/data/SSTlobster/RTOFS/rtofs_glo_3dz_n048_daily_3ztio.nc'
 
# In this example we will extract the surface temperature field from the model.
# Remember that indexing in Python starts at zero.
file = netCDF4.Dataset(nc)
lat  = file.variables['Latitude'][:]
lon  = file.variables['Longitude'][:]
data = file.variables['temperature'][0,0,:,:]
file.close()
 
#There is a quirk to the global NetCDF files that isn't in the NOMADS data, namely that there are junk values of 
longitude (lon>500) in the rightmost column of the longitude array (they are ignored by the model itself). 
So we have to work around them a little with NaN substitution.
lon = np.where(np.greater_equal(lon,500),np.nan,lon)
 
#Plot the field using Basemap. Start with setting the map projection using the limits of the lat/lon data itself
m=Basemap(projection='mill',lat_ts=10, \
      llcrnrlon=np.nanmin(lon),urcrnrlon=np.nanmax(lon), \
      llcrnrlat=lat.min(),urcrnrlat=lat.max(), \
      resolution='c')
 
#Convert the lat/lon values to x/y projections.
x, y = m(lon,lat)
 
#Plot the field using the fast pcolormesh routine and set the colormap to jet.
cs = m.pcolormesh(x,y,data,shading='flat', \
      cmap=plt.cm.jet)
 
#Add a coastline and axis values.
m.drawcoastlines()
m.fillcontinents()
m.drawmapboundary()
m.drawparallels(np.arange(-90.,120.,30.), \
      labels=[1,0,0,0])
m.drawmeridians(np.arange(-180.,180.,60.), \
      labels=[0,0,0,1])
 
#Add a colorbar and title, and then show the plot.
colorbar(cs)
plt.title('Global RTOFS SST from NetCDF')
plt.show()