#!============================================================================
#! 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 '================================================'


#! 2. Cast it into floats
print '\n================================================'
print '#! 2. Cast it into floats'
print '================================================'


#! 3. Create an array of int ranging from 0-10
print '\n================================================'
print '#! 3. Create an array of int ranging from 0-10'
print '================================================'


#! 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 '================================================'

#! 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 '================================================'


#! 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 '================================================'


#! 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 '================================================'




#! 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 '================================================'


#! 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 '================================================'


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



#! 11. Remove all values smaller 15 and larger 26
print '\n================================================'
print '#! 11. Remove all values smaller 15 and larger 26'
print '================================================'



#! 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 '================================================'


#! 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 '================================================'




#! 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 '================================================'


#! 15. Save the  array and load it again
print '\n================================================'
print '#! 15. Save the  array and load it again'
print '================================================'