def noiseRemoval():
data = wave.load('A00269')
wave.plot(data, title="A00269")
data = wave.discardNoise(data)
wave.plot(data, title="A00269 - After noise removal")
level = 6
omission = ([1,2], True) # 5-40 hz
rebuilt = wave.decomp(data, 'sym5', level, omissions=omission)
wave.plot(rebuilt, title="A00269 - After wavelet decompisition")
data = wave.load('A00420')
wave.plot(data, title="A00420")
data = wave.discardNoise(data)
wave.plot(data, title="A00420 - After noise removal")
rebuilt = wave.decomp(data, 'sym5', level, omissions=omission)
wave.plot(rebuilt, title="A00420 - After wavelet decompisition")
data = wave.load('A00550')
wave.plot(data, title="A00550")
data = wave.discardNoise(data)
wave.plot(data, title="A00550 - After noise removal")
rebuilt = wave.decomp(data, 'sym5', level, omissions=omission)
wave.plot(rebuilt, title="A00420 - After wavelet decompisition")
def saveSignalFeatures():
"""
This function saves all the features for each signal into a giant dataframe
This is so we don't have to re-derive the peaks, intervals, etc. for each signal
Parameters
----------
None
Returns
-------
Saves dataframe as hardcoded_features.csv where each row is a filtered signal with the getFeatures() features
"""
records = wave.getRecords('All')[0]
returnMatrix = []
for i in records:
sig = Signal(i, wave.load(i))
features = getFeatures(sig)
returnMatrix.append(features)
df = pd.DataFrame(returnMatrix, )
df.to_csv('hardcoded_features.csv')
def write(self, data: ndarray) -> None:
with load(self.path, 'wb') as file:
file.setnchannels(self.channels)
file.setsampwidth(self.bitdepth // 8)
file.setframerate(self.samprate)
file.writeframesraw(data)
self.load()
def load(self) -> None:
with load(self.path, 'rb') as file:
self.channels, self.bitdepth, self.samprate, self.size, self.comptype, self.compname = file.getparams(
)
self.bitdepth *= 8
self.content = fromstring(file.readframes(self.size),
dtype=f'int{self.bitdepth}')
def getFeaturesNames():
records = wave.getRecords('All')[0]
returnMatrix = []
sig = Signal(records[0], wave.load(records[0]))
features_names = getFeatures(sig, names=True)
return features_names
record = records_train.ix[records_train['file'] == row['file']]
filename = record['file'].tolist()
answer = record['answer'].tolist()
new_validation.append([filename[0], answer[0]])
#print(len(records))
#print(records)
records = wave.getRecords('All')
wired_list = []
feat_list = []
for record in records:
# try:
data = wave.load(record)
print('running record: ' + record)
#sig = challenge.Signal(record, data)
noise_features = challenge.noise_feature_extract(data)
feat_list.append(noise_features)
print('the number of records in the feature list: ' + str(len(feat_list)))
# except:
# wired_list.append(record)
# print ('stupid one found: ' + record)
#
#
feat_list = np.array(feat_list)
#feat_list=[]
#for record in records:
## try:
cb_lrf.set_label("1/day")
cb_eig = common.add_wavelength_colorbar(fig,
eig_im,
ax=row_eig.tolist(),
pad=0.01)
cb_eig.set_label("Wavenumber (1/k)")
return fig
INPUT = sys.argv[1]
OUTPUT = sys.argv[2]
with open(INPUT) as f:
lrfs = wave.load(f)
if OUTPUT == 'figs/fig10.pdf':
items = ["Stable 1%", "Unstable 1%", "Unstable 10%", "Unstable 20%"]
elif OUTPUT == "figs/S2.pdf":
items = ["Stable", "Unstable"]
kwargs = {'eig_xlim': (-300, 300)}
else:
raise ValueError("Output not allowed.")
print(f"Plotting {items}")
print(f"Saving to {OUTPUT}")
couplers = {key: WaveCoupler.from_tom_data(lrfs[key]) for key in items}
logging.info("Computing the spectra")
plot_data = {
import matplotlib.pyplot as plt
import numpy as np
import wave
import common
import sys
with open(sys.argv[1]) as f:
S = wave.load(f)
maxstep = S['maxstep']
Input_reg = S['Input_reg']
YMAX = 45 # Upper limit for y-axis
themean = np.mean(maxstep, axis=0) / 48
thestd = np.std(maxstep, axis=0) / 48
fig, ax = plt.subplots(figsize=(3.5, 3.5 / 1.61), constrained_layout=True)
ax2 = ax.twinx()
# Shading goes in the back
ax.fill_between(100 * Input_reg,
themean - thestd,
themean + thestd,
color='silver')
ax.plot(100 * Input_reg, themean, color='k')
ax.scatter(100 * Input_reg, themean, color='k')
ax.set_ylabel('Time to actual\nprognostic failure (days)')
ax.set_xlabel('Regularization $\sigma$ (%)')
ax.set_xlim((-1, 26))
ax.set_ylim((0, YMAX))
def pointDetection():
records = wave.getRecords('N') # N O A ~
data = wave.load(records[0])
sig = Signal(records[0], data)
fig = plt.figure(figsize=(200, 6)) # I used figures to customize size
ax = fig.add_subplot(211)
ax.plot(sig.data)
ax.plot(*zip(*sig.Ppeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.Tpeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.RPeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.QPoints), marker='o', color='r', ls='')
ax.plot(*zip(*sig.SPoints), marker='o', color='r', ls='')
ax.axhline(sig.baseline)
ax.set_title(sig.name)
fig.savefig('/Users/samy/Downloads/{0}.png'.format(sig.name))
plt.show()
records = wave.getRecords('A') # N O A ~
data = wave.load(records[0])
sig = Signal(records[0], data)
fig = plt.figure(figsize=(200, 6)) # I used figures to customize size
ax = fig.add_subplot(211)
ax.plot(sig.data)
ax.plot(*zip(*sig.Ppeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.Tpeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.RPeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.QPoints), marker='o', color='r', ls='')
ax.plot(*zip(*sig.SPoints), marker='o', color='r', ls='')
ax.axhline(sig.baseline)
ax.set_title(sig.name)
fig.savefig('/Users/samy/Downloads/{0}.png'.format(sig.name))
plt.show()
records = wave.getRecords('O') # N O A ~
data = wave.load(records[0])
sig = Signal(records[0], data)
fig = plt.figure(figsize=(200, 6)) # I used figures to customize size
ax = fig.add_subplot(211)
ax.plot(sig.data)
ax.plot(*zip(*sig.Ppeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.Tpeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.RPeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.QPoints), marker='o', color='r', ls='')
ax.plot(*zip(*sig.SPoints), marker='o', color='r', ls='')
ax.axhline(sig.baseline)
ax.set_title(sig.name)
fig.savefig('/Users/samy/Downloads/{0}.png'.format(sig.name))
plt.show()
records = wave.getRecords('~') # N O A ~
data = wave.load(records[0])
sig = Signal(records[0], data)
fig = plt.figure(figsize=(200, 6)) # I used figures to customize size
ax = fig.add_subplot(211)
ax.plot(sig.data)
ax.plot(*zip(*sig.Ppeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.Tpeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.RPeaks), marker='o', color='r', ls='')
ax.plot(*zip(*sig.QPoints), marker='o', color='r', ls='')
ax.plot(*zip(*sig.SPoints), marker='o', color='r', ls='')
ax.axhline(sig.baseline)
ax.set_title(sig.name)
fig.savefig('/Users/samy/Downloads/{0}.png'.format(sig.name))
plt.show()