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added flowchart node for removing periodic noise from waveform

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metaarray updates:
  - better handling of HDF5 files
  - fixed some isinstance problems that appear during reloads
This commit is contained in:
Luke Campagnola 2012-06-18 13:45:47 -04:00
parent e53c2165e6
commit a4963f93b7
11 changed files with 119 additions and 42 deletions
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2
examples/Flowchart.py
View File

@ -17,6 +17,7 @@ from pyqtgraph.flowchart import Flowchart
from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph as pg
import numpy as np
import pyqtgraph.metaarray as metaarray
app = QtGui.QApplication([])
@ -46,6 +47,7 @@ win.show()
data = np.random.normal(size=1000)
data[200:300] += 1
data += np.sin(np.linspace(0, 100, 1000))
data = metaarray.MetaArray(data, info=[{'name': 'Time', 'values': np.linspace(0, 1.0, len(data))}, {}])
fc.setInput(dataIn=data)

12
flowchart/library/Data.py
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@ -10,12 +10,6 @@ from pyqtgraph.graphicsItems.LinearRegionItem import LinearRegionItem
from . import functions
try:
import metaarray
HAVE_METAARRAY = True
except:
HAVE_METAARRAY = False
class ColumnSelectNode(Node):
"""Select named columns from a record array or MetaArray."""
nodeName = "ColumnSelect"
@ -31,7 +25,7 @@ class ColumnSelectNode(Node):
self.updateList(In)
out = {}
if HAVE_METAARRAY and isinstance(In, metaarray.MetaArray):
if hasattr(In, 'implements') and In.implements('MetaArray'):
for c in self.columns:
out[c] = In[self.axis:c]
elif isinstance(In, np.ndarray) and In.dtype.fields is not None:
@ -47,7 +41,7 @@ class ColumnSelectNode(Node):
return self.columnList
def updateList(self, data):
if HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
if hasattr(data, 'implements') and data.implements('MetaArray'):
cols = data.listColumns()
for ax in cols: ## find first axis with columns
if len(cols[ax]) > 0:
@ -161,7 +155,7 @@ class RegionSelectNode(CtrlNode):
if self.selected.isConnected():
if data is None:
sliced = None
elif isinstance(data, MetaArray):
elif (hasattr(data, 'implements') and data.implements('MetaArray')):
sliced = data[0:s['start']:s['stop']]
else:
mask = (data['time'] >= s['start']) * (data['time'] < s['stop'])

53
flowchart/library/Filters.py
View File

@ -145,7 +145,7 @@ class Derivative(CtrlNode):
nodeName = 'DerivativeFilter'
def processData(self, data):
if HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
if HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
info = data.infoCopy()
if 'values' in info[0]:
info[0]['values'] = info[0]['values'][:-1]
@ -198,3 +198,54 @@ class HistogramDetrend(CtrlNode):
class RemovePeriodic(CtrlNode):
nodeName = 'RemovePeriodic'
uiTemplate = [
#('windowSize', 'intSpin', {'value': 500, 'min': 10, 'max': 1000000, 'suffix': 'pts'}),
#('numBins', 'intSpin', {'value': 50, 'min': 3, 'max': 1000000})
('f0', 'spin', {'value': 60, 'suffix': 'Hz', 'siPrefix': True, 'min': 0, 'max': None}),
('harmonics', 'intSpin', {'value': 30, 'min': 0}),
]
def processData(self, data):
times = data.xvals('Time')
dt = times[1]-times[0]
data1 = data.asarray()
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
f0 = self.ctrls['f0'].value()
for i in xrange(1, self.ctrls['harmonics'].value()+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))
if ind1 > len(ft)/2.:
break
print "--->", f
print ind1, ind2, abs(ft[ind1-2:ind2+2])
print ft[ind1-2:ind2+2]
mag = (abs(ft[ind1-1]) + abs(ft[ind2+1])) * 0.5
print "new mag:", mag
for j in range(ind1, ind2+1):
phase = np.angle(ft[j])
re = mag * np.cos(phase)
im = mag * np.sin(phase)
ft[j] = re + im*1j
ft[len(ft)-j] = re - im*1j
print abs(ft[ind1-2:ind2+2])
print ft[ind1-2:ind2+2]
data2 = np.fft.ifft(ft).real
ma = metaarray.MetaArray(data2, info=data.infoCopy())
return ma

2
flowchart/library/common.py
View File

@ -134,7 +134,7 @@ class CtrlNode(Node):
def metaArrayWrapper(fn):
def newFn(self, data, *args, **kargs):
if HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
if HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
d1 = fn(self, data.view(np.ndarray), *args, **kargs)
info = data.infoCopy()
if d1.shape != data.shape:

14
flowchart/library/functions.py
View File

@ -9,7 +9,7 @@ def downsample(data, n, axis=0, xvals='subsample'):
or downsampled to match.
"""
ma = None
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
ma = data
data = data.view(np.ndarray)
@ -60,7 +60,7 @@ def applyFilter(data, b, a, padding=100, bidir=True):
if padding > 0:
d1 = d1[padding:-padding]
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d1, info=data.infoCopy())
else:
return d1
@ -79,7 +79,7 @@ def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
return applyFilter(data, b, a, bidir=bidir)
#base = data.mean()
#d1 = scipy.signal.lfilter(b, a, data.view(ndarray)-base) + base
#if isinstance(data, MetaArray):
#if (hasattr(data, 'implements') and data.implements('MetaArray')):
#return MetaArray(d1, info=data.infoCopy())
#return d1
@ -142,7 +142,7 @@ def modeFilter(data, window=500, step=None, bins=None):
chunks.append(np.linspace(vals[-1], vals[-1], remain))
d2 = np.hstack(chunks)
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d2, info=data.infoCopy())
return d2
@ -169,7 +169,7 @@ def denoise(data, radius=2, threshold=4):
d6[:radius] = d1[:radius]
d6[-radius:] = d1[-radius:]
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d6, info=data.infoCopy())
return d6
@ -191,7 +191,7 @@ def adaptiveDetrend(data, x=None, threshold=3.0):
base = lr[1] + lr[0]*x
d4 = d - base
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d4, info=data.infoCopy())
return d4
@ -214,7 +214,7 @@ def histogramDetrend(data, window=500, bins=50, threshold=3.0):
base = np.linspace(v[0], v[1], len(data))
d3 = data.view(np.ndarray) - base
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
return MetaArray(d3, info=data.infoCopy())
return d3

2
graphicsItems/MultiPlotItem.py
View File

@ -26,7 +26,7 @@ class MultiPlotItem(GraphicsLayout.GraphicsLayout):
#self.layout.clear()
self.plots = []
if HAVE_METAARRAY and isinstance(data, MetaArray):
if HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
if data.ndim != 2:
raise Exception("MultiPlot currently only accepts 2D MetaArray.")
ic = data.infoCopy()

2
graphicsItems/PlotItem/PlotItem.py
View File

@ -887,7 +887,7 @@ class PlotItem(GraphicsWidget):
if params is None:
params = {}
#if HAVE_METAARRAY and isinstance(data, MetaArray):
#if HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
#curve = self._plotMetaArray(data, x=x, **kargs)
#elif isinstance(data, np.ndarray):
#curve = self._plotArray(data, x=x, **kargs)

12
imageview/ImageView.py
View File

@ -29,11 +29,11 @@ import pyqtgraph.debug as debug
from pyqtgraph.SignalProxy import SignalProxy
try:
import pyqtgraph.metaarray as metaarray
HAVE_METAARRAY = True
except:
HAVE_METAARRAY = False
#try:
#import pyqtgraph.metaarray as metaarray
#HAVE_METAARRAY = True
#except:
#HAVE_METAARRAY = False
class PlotROI(ROI):
@ -195,7 +195,7 @@ class ImageView(QtGui.QWidget):
"""
prof = debug.Profiler('ImageView.setImage', disabled=True)
if HAVE_METAARRAY and isinstance(img, metaarray.MetaArray):
if hasattr(img, 'implements') and img.implements('MetaArray'):
img = img.asarray()
if not isinstance(img, np.ndarray):

58
metaarray/MetaArray.py
View File

@ -4,7 +4,7 @@ MetaArray.py - Class encapsulating ndarray with meta data
Copyright 2010 Luke Campagnola
Distributed under MIT/X11 license. See license.txt for more infomation.
MetaArray is an extension of ndarray which allows storage of per-axis meta data
MetaArray is an array class based on numpy.ndarray that allows storage of per-axis meta data
such as axis values, names, units, column names, etc. It also enables several
new methods for slicing and indexing the array based on this meta data.
More info at http://www.scipy.org/Cookbook/MetaArray
@ -126,7 +126,7 @@ class MetaArray(object):
raise Exception("File read failed: %s" % file)
else:
self._info = info
if isinstance(data, MetaArray):
if (hasattr(data, 'implements') and data.implements('MetaArray')):
self._info = data._info
self._data = data.asarray()
elif isinstance(data, tuple): ## create empty array with specified shape
@ -172,6 +172,13 @@ class MetaArray(object):
info[i]['cols'] = list(info[i]['cols'])
if len(info[i]['cols']) != self.shape[i]:
raise Exception('Length of column list for axis %d does not match data. (given %d, but should be %d)' % (i, len(info[i]['cols']), self.shape[i]))
def implements(self, name=None):
## Rather than isinstance(obj, MetaArray) use object.implements('MetaArray')
if name is None:
return ['MetaArray']
else:
return name == 'MetaArray'
#def __array_finalize__(self,obj):
### array_finalize is called every time a MetaArray is created
@ -670,7 +677,18 @@ class MetaArray(object):
#### File I/O Routines
def readFile(self, filename, **kwargs):
"""Load the data and meta info stored in *filename*"""
"""Load the data and meta info stored in *filename*
Different arguments are allowed depending on the type of file.
For HDF5 files:
*writable* (bool) if True, then any modifications to data in the array will be stored to disk.
*readAllData* (bool) if True, then all data in the array is immediately read from disk
and the file is closed (this is the default for files < 500MB). Otherwise, the file will
be left open and data will be read only as requested (this is
the default for files >= 500MB).
"""
## decide which read function to use
fd = open(filename, 'rb')
magic = fd.read(8)
@ -833,27 +851,39 @@ class MetaArray(object):
#raise Exception() ## stress-testing
#return subarr
def _readHDF5(self, fileName, close=False, writable=False):
def _readHDF5(self, fileName, readAllData=None, writable=False, **kargs):
if 'close' in kargs and readAllData is None: ## for backward compatibility
readAllData = kargs['close']
if not HAVE_HDF5:
raise Exception("The file '%s' is HDF5-formatted, but the HDF5 library (h5py) was not found." % fileName)
f = h5py.File(fileName, 'r')
if readAllData is True and writable is True:
raise Exception("Incompatible arguments: readAllData=True and writable=True")
## by default, readAllData=True for files < 500MB
if readAllData is None:
size = os.stat(fileName).st_size
readAllData = (size < 500e6)
if writable is True:
mode = 'r+'
else:
mode = 'r'
f = h5py.File(fileName, mode)
ver = f.attrs['MetaArray']
if ver > MetaArray.version:
print("Warning: This file was written with MetaArray version %s, but you are using version %s. (Will attempt to read anyway)" % (str(ver), str(MetaArray.version)))
meta = MetaArray.readHDF5Meta(f['info'])
self._info = meta
if close:
self._data = f['data'][:]
f.close()
else:
if writable or not readAllData: ## read all data, convert to ndarray, close file
self._data = f['data']
self._openFile = f
#meta = H5MetaList(f['info'])
#subarr = arr.view(subtype)
#subarr._info = meta
#self._data = arr
#return subarr
else:
self._data = f['data'][:]
f.close()
@staticmethod
def mapHDF5Array(data, writable=False):

2
widgets/DataTreeWidget.py
View File

@ -50,7 +50,7 @@ class DataTreeWidget(QtGui.QTreeWidget):
if isinstance(data, types.TracebackType): ## convert traceback to a list of strings
data = list(map(str.strip, traceback.format_list(traceback.extract_tb(data))))
elif HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
elif HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
data = {
'data': data.view(np.ndarray),
'meta': data.infoCopy()

2
widgets/TableWidget.py
View File

@ -94,7 +94,7 @@ class TableWidget(QtGui.QTableWidget):
return lambda d: d.__iter__(), None
elif isinstance(data, dict):
return lambda d: iter(d.values()), list(map(str, data.keys()))
elif HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
elif HAVE_METAARRAY and (hasattr(data, 'implements') and data.implements('MetaArray')):
if data.axisHasColumns(0):
header = [str(data.columnName(0, i)) for i in range(data.shape[0])]
elif data.axisHasValues(0):