def test_PPSD_w_IRIS(self): # Bands to be used this is the upper and lower frequency band pairs fres = zip([0.1, 0.05], [0.2, 0.1]) file_dataANMO = os.path.join(self.path, 'IUANMO.seed') # Read in ANMO data for one day st = read(file_dataANMO) # Use a canned ANMO response which will stay static paz = {'gain': 86298.5, 'zeros': [0, 0], 'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004, -0.073199], 'sensitivity': 3.3554*10**9} # Make an empty PPSD and add the data ppsd = PPSD(st[0].stats, paz) ppsd.add(st) ppsd.calculate_histogram() # Get the 50th percentile from the PPSD (per, perval) = ppsd.get_percentile(percentile=50) # Read in the results obtained from a Mustang flat file file_dataIRIS = os.path.join(self.path, 'IRISpdfExample') freq, power, hits = np.genfromtxt(file_dataIRIS, comments='#', delimiter=',', unpack=True) # For each frequency pair we want to compare the mean of the bands for fre in fres: pervalGoodOBSPY = [] # Get the values for the bands from the PPSD perinv = 1 / per mask = (fre[0] < perinv) & (perinv < fre[1]) pervalGoodOBSPY = perval[mask] # Now we sort out all of the data from the IRIS flat file mask = (fre[0] < freq) & (freq < fre[1]) triples = list(zip(freq[mask], hits[mask], power[mask])) # We now have all of the frequency values of interest # We will get the distinct frequency values freqdistinct = sorted(list(set(freq[mask])), reverse=True) percenlist = [] # We will loop through the frequency values and compute a # 50th percentile for curfreq in freqdistinct: tempvalslist = [] for triple in triples: if np.isclose(curfreq, triple[0], atol=1e-3, rtol=0.0): tempvalslist += [int(triple[2])] * int(triple[1]) percenlist.append(np.percentile(tempvalslist, 50)) # Here is the actual test np.testing.assert_allclose(np.mean(pervalGoodOBSPY), np.mean(percenlist), rtol=0.0, atol=1.0)
def test_ppsd_w_iris(self): # Bands to be used this is the upper and lower frequency band pairs fres = zip([0.1, 0.05], [0.2, 0.1]) file_data_anmo = os.path.join(self.path, 'IUANMO.seed') # Read in ANMO data for one day st = read(file_data_anmo) # Use a canned ANMO response which will stay static paz = { 'gain': 86298.5, 'zeros': [0, 0], 'poles': [ -59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004, -0.073199 ], 'sensitivity': 3.3554 * 10**9 } # Make an empty PPSD and add the data # use highest frequency given by IRIS Mustang noise-pdf web service # (0.475683 Hz == 2.10224036 s) as center of first bin, so that we # end up with the same bins. ppsd = PPSD(st[0].stats, paz, period_limits=(2.10224036, 1400)) ppsd.add(st) ppsd.calculate_histogram() # Get the 50th percentile from the PPSD (per, perval) = ppsd.get_percentile(percentile=50) perinv = 1 / per # Read in the results obtained from a Mustang flat file file_data_iris = os.path.join(self.path, 'IRISpdfExample') data = np.genfromtxt(file_data_iris, comments='#', delimiter=',', dtype=[(native_str("freq"), np.float64), (native_str("power"), np.int32), (native_str("hits"), np.int32)]) freq = data["freq"] power = data["power"] hits = data["hits"] # cut data to same period range as in the ppsd we computed # (Mustang returns more long periods, probably due to some zero padding # or longer nfft in psd) num_periods = len(ppsd.period_bin_centers) freqdistinct = np.array(sorted(set(freq), reverse=True)[:num_periods]) # just make sure that we compare the same periods in the following # (as we access both frequency arrays by indices from now on) np.testing.assert_allclose(freqdistinct, 1 / ppsd.period_bin_centers, rtol=1e-4) # For each frequency pair we want to compare the mean of the bands for fre in fres: # determine which bins we want to compare mask = (fre[0] < perinv) & (perinv < fre[1]) # Get the values for the bands from the PPSD per_val_good_obspy = perval[mask] percenlist = [] # Now we sort out all of the data from the IRIS flat file # We will loop through the frequency values and compute a # 50th percentile for curfreq in freqdistinct[mask]: mask_ = curfreq == freq tempvalslist = np.repeat(power[mask_], hits[mask_]) percenlist.append(np.percentile(tempvalslist, 50)) # Here is the actual test np.testing.assert_allclose(np.mean(per_val_good_obspy), np.mean(percenlist), rtol=0.0, atol=1.2)
def test_ppsd_w_iris(self): # Bands to be used this is the upper and lower frequency band pairs fres = zip([0.1, 0.05], [0.2, 0.1]) file_data_anmo = os.path.join(self.path, 'IUANMO.seed') # Read in ANMO data for one day st = read(file_data_anmo) # Use a canned ANMO response which will stay static paz = {'gain': 86298.5, 'zeros': [0, 0], 'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004, -0.073199], 'sensitivity': 3.3554 * 10 ** 9} # Make an empty PPSD and add the data # use highest frequency given by IRIS Mustang noise-pdf web service # (0.475683 Hz == 2.10224036 s) as center of first bin, so that we # end up with the same bins. ppsd = PPSD(st[0].stats, paz, period_limits=(2.10224036, 1400)) ppsd.add(st) ppsd.calculate_histogram() # Get the 50th percentile from the PPSD (per, perval) = ppsd.get_percentile(percentile=50) perinv = 1 / per # Read in the results obtained from a Mustang flat file file_data_iris = os.path.join(self.path, 'IRISpdfExample') data = np.genfromtxt( file_data_iris, comments='#', delimiter=',', dtype=[(native_str("freq"), np.float64), (native_str("power"), np.int32), (native_str("hits"), np.int32)]) freq = data["freq"] power = data["power"] hits = data["hits"] # cut data to same period range as in the ppsd we computed # (Mustang returns more long periods, probably due to some zero padding # or longer nfft in psd) num_periods = len(ppsd.period_bin_centers) freqdistinct = np.array(sorted(set(freq), reverse=True)[:num_periods]) # just make sure that we compare the same periods in the following # (as we access both frequency arrays by indices from now on) np.testing.assert_allclose(freqdistinct, 1 / ppsd.period_bin_centers, rtol=1e-4) # For each frequency pair we want to compare the mean of the bands for fre in fres: # determine which bins we want to compare mask = (fre[0] < perinv) & (perinv < fre[1]) # Get the values for the bands from the PPSD per_val_good_obspy = perval[mask] percenlist = [] # Now we sort out all of the data from the IRIS flat file # We will loop through the frequency values and compute a # 50th percentile for curfreq in freqdistinct[mask]: mask_ = curfreq == freq tempvalslist = np.repeat(power[mask_], hits[mask_]) percenlist.append(np.percentile(tempvalslist, 50)) # Here is the actual test np.testing.assert_allclose(np.mean(per_val_good_obspy), np.mean(percenlist), rtol=0.0, atol=1.2)
def test_PPSD_w_IRIS(self): # Bands to be used this is the upper and lower frequency band pairs fres = zip([0.1, 0.05], [0.2, 0.1]) file_dataANMO = os.path.join(self.path, 'IUANMO.seed') # Read in ANMO data for one day st = read(file_dataANMO) # Use a canned ANMO response which will stay static paz = { 'gain': 86298.5, 'zeros': [0, 0], 'poles': [ -59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004, -0.073199 ], 'sensitivity': 3.3554 * 10**9 } # Make an empty PPSD and add the data ppsd = PPSD(st[0].stats, paz) ppsd.add(st) # Get the 50th percentile from the PPSD (per, perval) = ppsd.get_percentile(percentile=50) # Read in the results obtained from a Mustang flat file file_dataIRIS = os.path.join(self.path, 'IRISpdfExample') freq, power, hits = np.genfromtxt(file_dataIRIS, comments='#', delimiter=',', unpack=True) # For each frequency pair we want to compare the mean of the bands for fre in fres: pervalGoodOBSPY = [] # Get the values for the bands from the PPSD perinv = 1 / per mask = (fre[0] < perinv) & (perinv < fre[1]) pervalGoodOBSPY = perval[mask] # Now we sort out all of the data from the IRIS flat file mask = (fre[0] < freq) & (freq < fre[1]) triples = list(zip(freq[mask], hits[mask], power[mask])) # We now have all of the frequency values of interest # We will get the distinct frequency values freqdistinct = sorted(list(set(freq[mask])), reverse=True) percenlist = [] # We will loop through the frequency values and compute a # 50th percentile for curfreq in freqdistinct: tempvalslist = [] for triple in triples: if np.isclose(curfreq, triple[0], atol=1e-3, rtol=0.0): tempvalslist += [int(triple[2])] * int(triple[1]) percenlist.append(np.percentile(tempvalslist, 50)) # Here is the actual test np.testing.assert_allclose(np.mean(pervalGoodOBSPY), np.mean(percenlist), rtol=0.0, atol=1.0)