# template file for Lab #6, Task #3
import numpy
import matplotlib.pyplot as p
import lab6
from lab6_1 import freq_res_usr
from lab6_2 import zfreqs_to_usr

# return appropriately scaled unit-sample response
# for a low-pass filter with specified cutoff frequency
def low_pass(cutoff_freq):
    pass # your code here

# return appropriately scaled unit-sample response
# for a high-pass filter with specified cutoff frequency
def high_pass(cutoff_freq):
    pass # your code here

if __name__ == '__main__':
    # A low pass filter, cut-off of 0.6pi
    lp_cutoff = 0.6 * numpy.pi
    usr_lp = low_pass(lp_cutoff)

    # Plot the response of the result
    omega_lp, mag_hejw_lp = freq_res_usr(usr_lp)
    lab6.plot_freq_response(omega_lp,mag_hejw_lp,
         'low-pass filter @ $\Omega=%3.2g$' % lp_cutoff)

    # Determine the performance ratios for lowpass
    quality_lp = \
      lab6.filter_quality((lp_cutoff+0.1*numpy.pi,numpy.pi),
                          (0,lp_cutoff-0.1*numpy.pi),
                          omega_lp,mag_hejw_lp)
    print 'quality ratio for low-pass filter=',quality_lp

    # A high pass filter, cut-off of 0.4pi
    hp_cutoff = 0.4 * numpy.pi
    usr_hp = high_pass(hp_cutoff)

    # Plot the response of the result
    omega_hp, mag_hejw_hp = freq_res_usr(usr_hp)
    lab6.plot_freq_response(omega_hp,mag_hejw_hp,
          'high-pass filter @ $\Omega=%3.2g$' % hp_cutoff)

    quality_hp = \
      lab6.filter_quality((0,hp_cutoff-0.1*numpy.pi),
                          (hp_cutoff+0.1*numpy.pi,numpy.pi),
                          omega_hp,mag_hejw_hp)
    print 'quality ratio for high-pass filter=',quality_hp

    p.show()