#!============================================================================
#! NumPy
#!============================================================================
import numpy

#! 1. Construct an array from the list: [1,2,3]
print '================================================'
print '#! 1. Construct an array from the list: [1,2,3]'
print '================================================'
a = numpy.array([1,2,3])
print a

#! 2. Cast it into floats
print '\n================================================'
print '#! 2. Cast it into floats'
print '================================================'
b = a.astype(float)
print b

#! 3. Create an array of int ranging from 0-10
print '\n================================================'
print '#! 3. Create an array of int ranging from 0-10'
print '================================================'
a = numpy.arange(10,dtype=int)
print a

#! 4. Create an array containing 7 evenly spaced numbers between 0 and 23
print '\n================================================'
print '#! 4. Create an array containing 7 evenly spaced numbers between 0 and 23'
print '================================================'
a = numpy.linspace(0,23,7)

#! 5. Create an array with shape (2,5,5,2) containing only the number 5
print '\n================================================'
print '#! 5. Create an array with shape (2,5,5,2) containing only the number 5'
print '================================================'
a = numpy.ones((2,5,5,2))*5.0

#! 6. Reshape the resulting array from shape (2,5,5,2) to only 2D
print '\n================================================'
print '#! 6. Reshape the resulting array from shape (2,5,5,2) to only 2D'
print '================================================'
c = a.reshape((10,10))

#! 7. Starting from a=numpy.arange(10) and b=numpy.arange(5)
# create the array [0,1,2,3,4,4,3,2,1,0]
print '\n================================================'
print '#! 7. Starting from a=numpy.arange(10) and b=numpy.arange(5) # create the array [0,1,2,3,4,4,3,2,1,0]'
print '================================================'
a=numpy.arange(10)
b=numpy.arange(5)
a[5:]=b[5::-1]
print a



#! 8. Calculate the mean, std, var, max, min,  of an array with shape (15,15) containing floats drawn from a normal
#distribution with mean 21 and sigma 4.5 (use numpy.random.normal())
print '\n================================================'
print '#! 8. Calculate the mean, std, var, max, min,  of an array with shape (15,15) containing floats drawn from a normal distribution with mean '
print '================================================'
a = numpy.random.normal(loc=21.,scale=4.5,size=(15,15))
print a.mean()
print a.std()
print a.var()
print a.max()
print a.min()

#! 9. Do the same but not on all elements but only on the 1nd dimension
print '================================================'
print '#! 9. Do the same but not on all elements but only on the 1nd dimension'
print '================================================'
a.mean(1)
a.std(1)
a.var(1)
a.max(1)
a.min(1)

#! 10. Transform the array such that it is only 1d
print '\n================================================'
print '#! 10. Transform the array such that it is only 1d'
print '================================================'
a = a.flatten()


#! 11. Remove all values smaller 15 and larger 26
print '\n================================================'
print '#! 11. Remove all values smaller 15 and larger 26'
print '================================================'
b = a[(a>15)&(a<26)]


#! 12. Clip all values smaller 15 and larger 26 to NaN (hint:clip )
print '\n================================================'
print '#! 12. Clip all values smaller 15 and larger 26 to NaN (hint:clip )'
print '================================================'
b = a.clip(15,26)
b[b==15] = numpy.NaN
b[b==26] = numpy.NaN
print b

#! 13. Calculate the sum of the resulting array. It should not be NaN! (hint:nansum)
print '\n================================================'
print '#! 13. Calculate the sum of the resulting array. It should not be NaN! (hint:nansum)'
print '================================================'
print numpy.nansum(b)



#! 14. Convert the NaN's to 0.0 (hint: nan_to_sum)
print '\n================================================'
print '#! 14. Convert the NaN\'s to 0.0 (hint: nan_to_sum)'
print '================================================'
c = numpy.nan_to_num(b)

#! 15. Save the  array and load it again
print '\n================================================'
print '#! 15. Save the  array and load it again'
print '================================================'
numpy.savetxt('lastarray.gz',c)
c_loaded = numpy.loadtxt('lastarray.gz')