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Commit e9d756eb authored by syz's avatar syz
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removed legacy versions of filters

parent 098d5147
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pycroscopy/processing/fft.py
View file @ e9d756eb
......@@ -151,6 +151,7 @@ def are_compatible_filters(frequency_filters):
return False
return True
def build_composite_freq_filter(frequency_filters):
if not are_compatible_filters(frequency_filters):
raise ValueError('frequency filters must be a single or list of FrequencyFilter objects')
......@@ -405,221 +406,6 @@ class HarmonicPassFilter(FrequencyFilter):
this_parms.update(basic_parms)
return this_parms
def noiseBandFilter(num_pts, samp_rate, freqs, freq_widths, show_plots=False):
"""
Builds a filter that removes specified noise frequencies
Parameters
----------
num_pts : unsigned int
Number of points in the FFT signal
samp_rate : unsigned int
sampling rate
freqs : 1D array or list
Target frequencies as unsigned ints
freq_widths : 1D array or list
Width around the target frequency that should be set to 0\n
show_plots : bool
If True, plots will be displayed during calculation. Default False
Note
----
sampRate, freqs, freq_widths have same units - eg MHz
Returns
-------
noise_filter : 1D numpy array
Array of ones set to 0 at noise bands
"""
num_pts = abs(int(num_pts))
w_vec = 1
# Making code a little more robust with handling different inputs:
samp_rate = float(samp_rate)
freqs = np.array(freqs)
freq_widths = np.array(freq_widths)
if freqs.ndim != freq_widths.ndim:
warn('Error in noiseBandFilter: dimensionality of frequencies and frequency widths do not match!')
return 1
if freqs.shape != freq_widths.shape:
warn('Error in noiseBandFilter: shape of frequencies and frequency widths do not match!')
return 1
cent = int(round(0.5 * num_pts))
noise_filter = np.ones(num_pts, dtype=np.int16)
if show_plots:
w_vec = np.arange(-0.5 * samp_rate, 0.5 * samp_rate, samp_rate / num_pts)
fig, ax = plt.subplots(2, 1)
ax[0].plot(w_vec, noise_filter)
ax[0].set_yscale('log')
ax[0].axis('tight')
ax[0].set_xlabel('Freq')
ax[0].set_title('Before clean up')
# Setting noise freq bands to 0
for cur_freq, d_freq in zip(freqs, freq_widths):
ind = int(round(num_pts * (cur_freq / samp_rate)))
sz = int(round(cent * d_freq / samp_rate))
noise_filter[cent - ind - sz:cent - ind + sz + 1] = 0
noise_filter[cent + ind - sz:cent + ind + sz + 1] = 0
if show_plots:
ax[1].plot(w_vec, noise_filter)
ax[1].set_yscale('log')
ax[1].axis('tight')
ax[1].set_xlabel('Freq')
ax[1].set_title('After clean up')
plt.show()
return noise_filter
###############################################################################
def makeLPF(num_pts, samp_rate, f_cutoff, roll_off=0.05):
"""
Builds a low pass filter
Parameters
----------
num_pts : unsigned int
Points in the FFT. Assuming Signal in frequency space (ie - after FFT shifting)
samp_rate : unsigned integer
Sampling rate
f_cutoff : unsigned integer
Cutoff frequency for filter
roll_off : 0 < float < 1
Frequency band over which the filter rolls off. rol off = 0.05 on a
100 kHz low pass filter -> roll off from 95 kHz (1) to 100 kHz (0)
Returns
-------
LPF : 1D numpy array describing the low pass filter
"""
num_pts = abs(int(num_pts))
cent = int(round(0.5 * num_pts))
if f_cutoff >= 0.5 * samp_rate:
warn('Error in LPFClip --> LPF too high! Skipping')
return 1
# BW = 0.1; %MHz - Nothing beyond BW.
roll_off *= f_cutoff # MHz
sz = int(np.round(num_pts * (roll_off / samp_rate)))
ind = int(np.round(num_pts * (f_cutoff / samp_rate)))
lpf = np.zeros(num_pts, dtype=np.float32)
extent = 5.0
t2 = np.linspace(-extent / 2, extent / 2, num=sz)
smoothing = 0.5 * (1 + erf(t2))
lpf[cent - ind:cent - ind + sz] = smoothing
lpf[cent - ind + sz:cent + ind - sz + 1] = 1
lpf[cent + ind - sz + 1:cent + ind + 1] = 1 - smoothing
return lpf # return the filter itself so that it may be repeatedly used outside
###############################################################################
def harmonicsPassFilter(num_pts, samp_rate, first_freq, band_width, num_harm, do_plots=False):
"""
Builds a filter that only keeps N harmonics
Parameters
----------
num_pts : unsigned int
Number of points in the FFt signal
samp_rate : unsigned int
Sampling rate
first_freq : unsigned int
Frequency of the first harmonic
band_width : unsigned int
Frequency band around each harmonic that needs to be preserved
num_harm : unsigned int
Number of harmonics to preserve
do_plots : Boolean (optional)
Whether or not to generate plots. Not necessary after debugging
Note that the frequency values must all have the same units
Returns
-------
harm_filter : 1D numpy array
0s where the signal is to be rejected and 1s at harmonics
"""
num_pts = abs(int(num_pts))
harm_filter = np.ones(num_pts, dtype=np.int16)
cent = int(round(0.5 * num_pts))
w_vec = 1
if do_plots:
print(
'OnlyKeepHarmonics: samp_rate = %2.1e Hz, first harmonic = %3.2f Hz, %d harmonics w/- %3.2f Hz bands\n' % (
samp_rate, first_freq, num_harm, band_width))
w_vec = np.arange(-samp_rate / 2.0, samp_rate / 2.0, samp_rate / num_pts)
fig, ax = plt.subplots(figsize=(5, 5))
ax.plot(w_vec, harm_filter)
ax.set_title('Raw')
sz = int(round(cent * band_width / samp_rate))
# First harmonic
ind = int(round(num_pts * (first_freq / samp_rate)))
if ind >= num_pts:
warn('Invalid harmonic frequency')
return 1
harm_filter[max(cent - ind + sz + 1, 0):min(num_pts, cent + ind - sz)] = 0
if do_plots:
fig2, ax2 = plt.subplots(figsize=(5, 5))
ax2.plot(w_vec, harm_filter)
ax2.set_title('Step 1')
# Last harmonic
ind = int(round(num_pts * (num_harm * first_freq / samp_rate)))
harm_filter[:cent - ind - sz] = 0
harm_filter[cent + ind + sz + 1:] = 0
if do_plots:
fig3, ax3 = plt.subplots(figsize=(5, 5))
ax3.plot(w_vec, harm_filter)
ax3.set_title('Step 2')
if num_harm == 1:
return harm_filter
for harm_ind in range(1, num_harm):
ind = int(round(num_pts * (harm_ind * first_freq / samp_rate)))
ind2 = int(round(num_pts * ((harm_ind + 1) * first_freq / samp_rate)))
harm_filter[cent - ind2 + sz + 1:cent - ind - sz] = 0
harm_filter[cent + ind + sz + 1:cent + ind2 - sz] = 0
if do_plots:
fig4, ax4 = plt.subplots(figsize=(5, 5))
ax4.plot(w_vec, harm_filter)
ax4.set_title('Step %d' % (harm_ind + 2))
return harm_filter
# def removeNoiseHarmonics(F_AI_vec,samp_rate,noise_combs):
# """
# Removes specified noise frequencies from the signal
......
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