/usr/lib/python2.7/dist-packages/pyqtgraph/flowchart/library/functions.py is in python-pyqtgraph 0.9.10-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 | import numpy as np
from ...metaarray import MetaArray
def downsample(data, n, axis=0, xvals='subsample'):
"""Downsample by averaging points together across axis.
If multiple axes are specified, runs once per axis.
If a metaArray is given, then the axis values can be either subsampled
or downsampled to match.
"""
ma = None
if (hasattr(data, 'implements') and data.implements('MetaArray')):
ma = data
data = data.view(np.ndarray)
if hasattr(axis, '__len__'):
if not hasattr(n, '__len__'):
n = [n]*len(axis)
for i in range(len(axis)):
data = downsample(data, n[i], axis[i])
return data
nPts = int(data.shape[axis] / n)
s = list(data.shape)
s[axis] = nPts
s.insert(axis+1, n)
sl = [slice(None)] * data.ndim
sl[axis] = slice(0, nPts*n)
d1 = data[tuple(sl)]
#print d1.shape, s
d1.shape = tuple(s)
d2 = d1.mean(axis+1)
if ma is None:
return d2
else:
info = ma.infoCopy()
if 'values' in info[axis]:
if xvals == 'subsample':
info[axis]['values'] = info[axis]['values'][::n][:nPts]
elif xvals == 'downsample':
info[axis]['values'] = downsample(info[axis]['values'], n)
return MetaArray(d2, info=info)
def applyFilter(data, b, a, padding=100, bidir=True):
"""Apply a linear filter with coefficients a, b. Optionally pad the data before filtering
and/or run the filter in both directions."""
try:
import scipy.signal
except ImportError:
raise Exception("applyFilter() requires the package scipy.signal.")
d1 = data.view(np.ndarray)
if padding > 0:
d1 = np.hstack([d1[:padding], d1, d1[-padding:]])
if bidir:
d1 = scipy.signal.lfilter(b, a, scipy.signal.lfilter(b, a, d1)[::-1])[::-1]
else:
d1 = scipy.signal.lfilter(b, a, d1)
if padding > 0:
d1 = d1[padding:-padding]
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d1, info=data.infoCopy())
else:
return d1
def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
"""return data passed through bessel filter"""
try:
import scipy.signal
except ImportError:
raise Exception("besselFilter() requires the package scipy.signal.")
if dt is None:
try:
tvals = data.xvals('Time')
dt = (tvals[-1]-tvals[0]) / (len(tvals)-1)
except:
dt = 1.0
b,a = scipy.signal.bessel(order, cutoff * dt, btype=btype)
return applyFilter(data, b, a, bidir=bidir)
#base = data.mean()
#d1 = scipy.signal.lfilter(b, a, data.view(ndarray)-base) + base
#if (hasattr(data, 'implements') and data.implements('MetaArray')):
#return MetaArray(d1, info=data.infoCopy())
#return d1
def butterworthFilter(data, wPass, wStop=None, gPass=2.0, gStop=20.0, order=1, dt=None, btype='low', bidir=True):
"""return data passed through bessel filter"""
try:
import scipy.signal
except ImportError:
raise Exception("butterworthFilter() requires the package scipy.signal.")
if dt is None:
try:
tvals = data.xvals('Time')
dt = (tvals[-1]-tvals[0]) / (len(tvals)-1)
except:
dt = 1.0
if wStop is None:
wStop = wPass * 2.0
ord, Wn = scipy.signal.buttord(wPass*dt*2., wStop*dt*2., gPass, gStop)
#print "butterworth ord %f Wn %f c %f sc %f" % (ord, Wn, cutoff, stopCutoff)
b,a = scipy.signal.butter(ord, Wn, btype=btype)
return applyFilter(data, b, a, bidir=bidir)
def rollingSum(data, n):
d1 = data.copy()
d1[1:] += d1[:-1] # integrate
d2 = np.empty(len(d1) - n + 1, dtype=data.dtype)
d2[0] = d1[n-1] # copy first point
d2[1:] = d1[n:] - d1[:-n] # subtract
return d2
def mode(data, bins=None):
"""Returns location max value from histogram."""
if bins is None:
bins = int(len(data)/10.)
if bins < 2:
bins = 2
y, x = np.histogram(data, bins=bins)
ind = np.argmax(y)
mode = 0.5 * (x[ind] + x[ind+1])
return mode
def modeFilter(data, window=500, step=None, bins=None):
"""Filter based on histogram-based mode function"""
d1 = data.view(np.ndarray)
vals = []
l2 = int(window/2.)
if step is None:
step = l2
i = 0
while True:
if i > len(data)-step:
break
vals.append(mode(d1[i:i+window], bins))
i += step
chunks = [np.linspace(vals[0], vals[0], l2)]
for i in range(len(vals)-1):
chunks.append(np.linspace(vals[i], vals[i+1], step))
remain = len(data) - step*(len(vals)-1) - l2
chunks.append(np.linspace(vals[-1], vals[-1], remain))
d2 = np.hstack(chunks)
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d2, info=data.infoCopy())
return d2
def denoise(data, radius=2, threshold=4):
"""Very simple noise removal function. Compares a point to surrounding points,
replaces with nearby values if the difference is too large."""
r2 = radius * 2
d1 = data.view(np.ndarray)
d2 = d1[radius:] - d1[:-radius] #a derivative
#d3 = data[r2:] - data[:-r2]
#d4 = d2 - d3
stdev = d2.std()
#print "denoise: stdev of derivative:", stdev
mask1 = d2 > stdev*threshold #where derivative is large and positive
mask2 = d2 < -stdev*threshold #where derivative is large and negative
maskpos = mask1[:-radius] * mask2[radius:] #both need to be true
maskneg = mask1[radius:] * mask2[:-radius]
mask = maskpos + maskneg
d5 = np.where(mask, d1[:-r2], d1[radius:-radius]) #where both are true replace the value with the value from 2 points before
d6 = np.empty(d1.shape, dtype=d1.dtype) #add points back to the ends
d6[radius:-radius] = d5
d6[:radius] = d1[:radius]
d6[-radius:] = d1[-radius:]
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d6, info=data.infoCopy())
return d6
def adaptiveDetrend(data, x=None, threshold=3.0):
"""Return the signal with baseline removed. Discards outliers from baseline measurement."""
try:
import scipy.signal
except ImportError:
raise Exception("adaptiveDetrend() requires the package scipy.signal.")
if x is None:
x = data.xvals(0)
d = data.view(np.ndarray)
d2 = scipy.signal.detrend(d)
stdev = d2.std()
mask = abs(d2) < stdev*threshold
#d3 = where(mask, 0, d2)
#d4 = d2 - lowPass(d3, cutoffs[1], dt=dt)
lr = scipy.stats.linregress(x[mask], d[mask])
base = lr[1] + lr[0]*x
d4 = d - base
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d4, info=data.infoCopy())
return d4
def histogramDetrend(data, window=500, bins=50, threshold=3.0, offsetOnly=False):
"""Linear detrend. Works by finding the most common value at the beginning and end of a trace, excluding outliers.
If offsetOnly is True, then only the offset from the beginning of the trace is subtracted.
"""
d1 = data.view(np.ndarray)
d2 = [d1[:window], d1[-window:]]
v = [0, 0]
for i in [0, 1]:
d3 = d2[i]
stdev = d3.std()
mask = abs(d3-np.median(d3)) < stdev*threshold
d4 = d3[mask]
y, x = np.histogram(d4, bins=bins)
ind = np.argmax(y)
v[i] = 0.5 * (x[ind] + x[ind+1])
if offsetOnly:
d3 = data.view(np.ndarray) - v[0]
else:
base = np.linspace(v[0], v[1], len(data))
d3 = data.view(np.ndarray) - base
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d3, info=data.infoCopy())
return d3
def concatenateColumns(data):
"""Returns a single record array with columns taken from the elements in data.
data should be a list of elements, which can be either record arrays or tuples (name, type, data)
"""
## first determine dtype
dtype = []
names = set()
maxLen = 0
for element in data:
if isinstance(element, np.ndarray):
## use existing columns
for i in range(len(element.dtype)):
name = element.dtype.names[i]
dtype.append((name, element.dtype[i]))
maxLen = max(maxLen, len(element))
else:
name, type, d = element
if type is None:
type = suggestDType(d)
dtype.append((name, type))
if isinstance(d, list) or isinstance(d, np.ndarray):
maxLen = max(maxLen, len(d))
if name in names:
raise Exception('Name "%s" repeated' % name)
names.add(name)
## create empty array
out = np.empty(maxLen, dtype)
## fill columns
for element in data:
if isinstance(element, np.ndarray):
for i in range(len(element.dtype)):
name = element.dtype.names[i]
try:
out[name] = element[name]
except:
print("Column:", name)
print("Input shape:", element.shape, element.dtype)
print("Output shape:", out.shape, out.dtype)
raise
else:
name, type, d = element
out[name] = d
return out
def suggestDType(x):
"""Return a suitable dtype for x"""
if isinstance(x, list) or isinstance(x, tuple):
if len(x) == 0:
raise Exception('can not determine dtype for empty list')
x = x[0]
if hasattr(x, 'dtype'):
return x.dtype
elif isinstance(x, float):
return float
elif isinstance(x, int):
return int
#elif isinstance(x, basestring): ## don't try to guess correct string length; use object instead.
#return '<U%d' % len(x)
else:
return object
def removePeriodic(data, f0=60.0, dt=None, harmonics=10, samples=4):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
data1 = data.asarray()
if dt is None:
times = data.xvals('Time')
dt = times[1]-times[0]
else:
data1 = data
if dt is None:
raise Exception('Must specify dt for this data')
ft = np.fft.fft(data1)
## determine frequencies in fft data
df = 1.0 / (len(data1) * dt)
freqs = np.linspace(0.0, (len(ft)-1) * df, len(ft))
## flatten spikes at f0 and harmonics
for i in xrange(1, harmonics + 2):
f = f0 * i # target frequency
## determine index range to check for this frequency
ind1 = int(np.floor(f / df))
ind2 = int(np.ceil(f / df)) + (samples-1)
if ind1 > len(ft)/2.:
break
mag = (abs(ft[ind1-1]) + abs(ft[ind2+1])) * 0.5
for j in range(ind1, ind2+1):
phase = np.angle(ft[j]) ## Must preserve the phase of each point, otherwise any transients in the trace might lead to large artifacts.
re = mag * np.cos(phase)
im = mag * np.sin(phase)
ft[j] = re + im*1j
ft[len(ft)-j] = re - im*1j
data2 = np.fft.ifft(ft).real
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return metaarray.MetaArray(data2, info=data.infoCopy())
else:
return data2
|