Added metaarray to support flowchart

Prettied up flowchart example

examples/Flowchart.py

@@ -3,7 +3,7 @@ import initExample ## Add path to library (just for examples; you do not need th
 
 
 from pyqtgraph.flowchart import Flowchart
-from pyqtgraph.Qt import QtGui
+from pyqtgraph.Qt import QtGui, QtCore
 import pyqtgraph as pg
 import numpy as np
 

flowchart/library/functions.py

@@ -1,3 +1,7 @@
+import scipy
+import numpy as np
+from pyqtgraph.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.
@@ -7,7 +11,7 @@ def downsample(data, n, axis=0, xvals='subsample'):
     ma = None
     if isinstance(data, MetaArray):
         ma = data
-        data = data.view(ndarray)
+        data = data.view(np.ndarray)
         
     
     if hasattr(axis, '__len__'):
@@ -43,10 +47,10 @@ def downsample(data, n, axis=0, xvals='subsample'):
 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."""
-    d1 = data.view(ndarray)
+    d1 = data.view(np.ndarray)
     
     if padding > 0:
-        d1 = numpy.hstack([d1[:padding], d1, d1[-padding:]])
+        d1 = np.hstack([d1[:padding], d1, d1[-padding:]])
     
     if bidir:
         d1 = scipy.signal.lfilter(b, a, scipy.signal.lfilter(b, a, d1)[::-1])[::-1]
@@ -68,7 +72,7 @@ def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
             tvals = data.xvals('Time')
             dt = (tvals[-1]-tvals[0]) / (len(tvals)-1)
         except:
-            raise Exception('Must specify dt for this data.')
+            dt = 1.0
     
     b,a = scipy.signal.bessel(order, cutoff * dt, btype=btype) 
     
@@ -86,7 +90,7 @@ def butterworthFilter(data, wPass, wStop=None, gPass=2.0, gStop=20.0, order=1, d
             tvals = data.xvals('Time')
             dt = (tvals[-1]-tvals[0]) / (len(tvals)-1)
         except:
-            raise Exception('Must specify dt for this data.')
+            dt = 1.0
     
     if wStop is None:
         wStop = wPass * 2.0
@@ -148,7 +152,7 @@ def denoise(data, radius=2, threshold=4):
     
     
     r2 = radius * 2
-    d1 = data.view(ndarray)
+    d1 = data.view(np.ndarray)
     d2 = data[radius:] - data[:-radius] #a derivative
     #d3 = data[r2:] - data[:-r2]
     #d4 = d2 - d3
@@ -174,7 +178,7 @@ def adaptiveDetrend(data, x=None, threshold=3.0):
     if x is None:
         x = data.xvals(0)
     
-    d = data.view(ndarray)
+    d = data.view(np.ndarray)
     
     d2 = scipy.signal.detrend(d)
     

metaarray/__init__.py

@@ -0,0 +1 @@
+from MetaArray import *

metaarray/license.txt

@@ -0,0 +1,8 @@
+Copyright (c) 2010  Luke Campagnola    ('luke.campagnola@%s.com' % 'gmail')
+The MIT License
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

metaarray/readMeta.m

@@ -0,0 +1,86 @@
+function f = readMeta(file)
+info = hdf5info(file);
+f = readMetaRecursive(info.GroupHierarchy.Groups(1));
+end
+
+
+function f = readMetaRecursive(root)
+typ = 0;
+for i = 1:length(root.Attributes)
+    if strcmp(root.Attributes(i).Shortname, '_metaType_')
+        typ = root.Attributes(i).Value.Data;
+        break
+    end
+end
+if typ == 0
+    printf('group has no _metaType_')
+    typ = 'dict';
+end
+
+list = 0;
+if strcmp(typ, 'list') || strcmp(typ, 'tuple')
+    data = {};
+    list = 1;
+elseif strcmp(typ, 'dict')
+    data = struct();
+else
+    printf('Unrecognized meta type %s', typ);
+    data = struct();
+end
+
+for i = 1:length(root.Attributes)
+    name = root.Attributes(i).Shortname;
+    if strcmp(name, '_metaType_')
+        continue
+    end
+    val = root.Attributes(i).Value;
+    if isa(val, 'hdf5.h5string')
+        val = val.Data;
+    end
+    if list
+        ind = str2num(name)+1;
+        data{ind} = val;
+    else
+        data.(name) = val;
+    end
+end
+
+for i = 1:length(root.Datasets)
+    fullName = root.Datasets(i).Name;
+    name = stripName(fullName);
+    file = root.Datasets(i).Filename;
+    data2 = hdf5read(file, fullName);
+    if list
+        ind = str2num(name)+1;
+        data{ind} = data2;
+    else
+        data.(name) = data2;
+    end
+end
+
+for i = 1:length(root.Groups)
+    name = stripName(root.Groups(i).Name);
+    data2 = readMetaRecursive(root.Groups(i));
+    if list
+        ind = str2num(name)+1;
+        data{ind} = data2;
+    else
+        data.(name) = data2;
+    end
+end
+f = data;
+return;
+end
+
+
+function f = stripName(str)
+inds = strfind(str, '/');
+if isempty(inds)
+    f = str;
+else
+    f = str(inds(length(inds))+1:length(str));
+end
+end
+
+
+